R: Difference between revisions
Line 5,455: | Line 5,455: | ||
https://stackoverflow.com/a/10801902 | https://stackoverflow.com/a/10801902 | ||
* '''lapply''' applies a function '''over a list'''. So there will be several function calls. | * '''lapply''' applies a function '''over a list'''. So there will be several function calls. | ||
* '''do.call''' calls a function with '''a list of arguments''' (... argument) such as [https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/c c()] or [https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/cbind rbind()/cbind()] or [https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/sum sum] or [https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/Extract "["] or paste. So there is only one function call. | * '''do.call''' calls a function with '''a list of arguments''' (... argument) such as [https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/c c()] or [https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/cbind rbind()/cbind()] or [https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/sum sum] or | ||
[https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/order order] or [https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/Extract "["] or paste. So there is only one function call. | |||
: <syntaxhighlight lang='rsplus'> | : <syntaxhighlight lang='rsplus'> | ||
> X <- list(1:3,4:6,7:9) | > X <- list(1:3,4:6,7:9) |
Revision as of 20:29, 2 August 2019
Install and upgrade R
Online Editor
We can run R on web browsers without installing it on local machines (similar to [/ideone.com Ideone.com] for C++. It does not require an account either (cf RStudio).
rdrr.io
It can produce graphics too. The package I am testing (cobs) is available too.
rstudio.cloud
RDocumentation
The interactive engine is based on DataCamp Light
For example, tbl_df function from dplyr package.
The website DataCamp allows to run library() on the Script window. After that, we can use the packages on R Console.
Here is a list of (common) R packages that users can use on the web.
The packages on RDocumentation may be outdated. For example, the current stringr on CRAN is v1.2.0 (2/18/2017) but RDocumentation has v1.1.0 (8/19/2016).
Web Applications
See also CRAN Task View: Web Technologies and Services
TexLive
TexLive can be installed by 2 ways
- sudo apt install texlive It includes tlmgr utility for package manager.
- Official website
texlive-latex-extra
https://packages.debian.org/sid/texlive-latex-extra
For example, framed and titling packages are included.
tlmgr - TeX Live package manager
https://www.tug.org/texlive/tlmgr.html
TinyTex
https://github.com/yihui/tinytex
Rmarkdown: create HTML5 web, slides and more
HTTP protocol
- http://en.wikipedia.org/wiki/File:Http_request_telnet_ubuntu.png
- Query string
- How to capture http header? Use curl -i en.wikipedia.org.
- Web Inspector. Build-in in Chrome. Right click on any page and choose 'Inspect Element'.
- Web server
- Simple TCP/IP web server
- HTTP Made Really Easy
- Illustrated Guide to HTTP
- nweb: a tiny, safe Web server with 200 lines
- Tiny HTTPd
An HTTP server is conceptually simple:
- Open port 80 for listening
- When contact is made, gather a little information (get mainly - you can ignore the rest for now)
- Translate the request into a file request
- Open the file and spit it back at the client
It gets more difficult depending on how much of HTTP you want to support - POST is a little more complicated, scripts, handling multiple requests, etc.
Example in R
> co <- socketConnection(port=8080, server=TRUE, blocking=TRUE) > # Now open a web browser and type http://localhost:8080/index.html > readLines(co,1) [1] "GET /index.html HTTP/1.1" > readLines(co,1) [1] "Host: localhost:8080" > readLines(co,1) [1] "User-Agent: Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:23.0) Gecko/20100101 Firefox/23.0" > readLines(co,1) [1] "Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8" > readLines(co,1) [1] "Accept-Language: en-US,en;q=0.5" > readLines(co,1) [1] "Accept-Encoding: gzip, deflate" > readLines(co,1) [1] "Connection: keep-alive" > readLines(co,1) [1] ""
Example in C (Very simple http server written in C, 187 lines)
Create a simple hello world html page and save it as <index.html> in the current directory (/home/brb/Downloads/)
Launch the server program (assume we have done gcc http_server.c -o http_server)
$ ./http_server -p 50002 Server started at port no. 50002 with root directory as /home/brb/Downloads
Secondly open a browser and type http://localhost:50002/index.html. The server will respond
GET /index.html HTTP/1.1 Host: localhost:50002 User-Agent: Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:23.0) Gecko/20100101 Firefox/23.0 Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8 Accept-Language: en-US,en;q=0.5 Accept-Encoding: gzip, deflate Connection: keep-alive file: /home/brb/Downloads/index.html GET /favicon.ico HTTP/1.1 Host: localhost:50002 User-Agent: Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:23.0) Gecko/20100101 Firefox/23.0 Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8 Accept-Language: en-US,en;q=0.5 Accept-Encoding: gzip, deflate Connection: keep-alive file: /home/brb/Downloads/favicon.ico GET /favicon.ico HTTP/1.1 Host: localhost:50003 User-Agent: Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:23.0) Gecko/20100101 Firefox/23.0 Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8 Accept-Language: en-US,en;q=0.5 Accept-Encoding: gzip, deflate Connection: keep-alive file: /home/brb/Downloads/favicon.ico
The browser will show the page from <index.html> in server.
The only bad thing is the code does not close the port. For example, if I have use Ctrl+C to close the program and try to re-launch with the same port, it will complain socket() or bind(): Address already in use.
Another Example in C (55 lines)
http://mwaidyanatha.blogspot.com/2011/05/writing-simple-web-server-in-c.html
The response is embedded in the C code.
If we test the server program by opening a browser and type "http://localhost:15000/", the server received the follwing 7 lines
GET / HTTP/1.1 Host: localhost:15000 User-Agent: Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:23.0) Gecko/20100101 Firefox/23.0 Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8 Accept-Language: en-US,en;q=0.5 Accept-Encoding: gzip, deflate Connection: keep-alive
If we include a non-executable file's name in the url, we will be able to download that file. Try "http://localhost:15000/client.c".
If we use telnet program to test, wee need to type anything we want
$ telnet localhost 15000 Trying 127.0.0.1... Connected to localhost. Escape character is '^]'. ThisCanBeAnything <=== This is what I typed in the client and it is also shown on server HTTP/1.1 200 OK <=== From here is what I got from server Content-length: 37Content-Type: text/html HTML_DATA_HERE_AS_YOU_MENTIONED_ABOVE <=== The html tags are not passed from server, interesting! Connection closed by foreign host. $
See also more examples under C page.
Others
- http://rosettacode.org/wiki/Hello_world/ (Different languages)
- http://kperisetla.blogspot.com/2012/07/simple-http-web-server-in-c.html (Windows web server)
- http://css.dzone.com/articles/web-server-c (handling HTTP GET request, handling content types(txt, html, jpg, zip. rar, pdf, php etc.), sending proper HTTP error codes, serving the files from a web root, change in web root in a config file, zero copy optimization using sendfile method and php file handling.)
- https://github.com/gtungatkar/Simple-HTTP-server
- https://github.com/davidmoreno/onion
shiny
See Shiny.
plumber: Turning your R code into a RESTful Web API
- https://github.com/trestletech/plumber
- https://www.rstudio.com/resources/videos/plumber-turning-your-r-code-into-an-api/
- RStudio 1.2 Preview: Plumber Integration
- Using docker to deploy an R plumber API
Docker
- There are two major Docker images. They include gcc, gfortran, .... So it can be used to install Rcpp package for example.
- Official which supports version tags. The official Docker image's dockerfile still points to rocker/r-base.
- rocker project which only has the latest tag
- Using Docker as a Personal Productivity Tool – Running Command Line Apps Bundled in Docker Containers
- Dockerized RStudio server from Duke University. 110 containers were set up on a cloud server (4 cores, 28GB RAM, 400GB disk). Each container has its own port number. Each student is mapped to a single container. https://github.com/mccahill/docker-rstudio
- RStudio in the cloud with Amazon Lightsail and docker
- Mark McCahill (RStudio + Docker)
- BiocImageBuilder
- Why Use Docker with R? A DevOps Perspective
- Running your R script in Docker. Goal: containerizing an R script to eventually execute it automatically each time the container is started, without any user interaction. An enhanced version of the instruction is at this page.
httpuv
http and WebSocket library.
See also the servr package which can start an HTTP server in R to serve static files, or dynamic documents that can be converted to HTML files (e.g., R Markdown) under a given directory.
RApache
gWidgetsWWW
- http://www.jstatsoft.org/v49/i10/paper
- gWidgetsWWW2 gWidgetsWWW based on Rook
- Compare shiny with gWidgetsWWW2.rapache
Rook
Since R 2.13, the internal web server was exposed.
Tutorual from useR2012 and Jeffrey Horner
Here is another one from http://www.rinfinance.com.
Rook is also supported by [rApache too. See http://rapache.net/manual.html.
Google group. https://groups.google.com/forum/?fromgroups#!forum/rrook
Advantage
- the web applications are created on desktop, whether it is Windows, Mac or Linux.
- No Apache is needed.
- create multiple applications at the same time. This complements the limit of rApache.
4 lines of code example.
library(Rook) s <- Rhttpd$new() s$start(quiet=TRUE) s$print() s$browse(1) # OR s$browse("RookTest")
Notice that after s$browse() command, the cursor will return to R because the command just a shortcut to open the web page http://127.0.0.1:10215/custom/RookTest.
We can add Rook application to the server; see ?Rhttpd.
s$add( app=system.file('exampleApps/helloworld.R',package='Rook'),name='hello' ) s$add( app=system.file('exampleApps/helloworldref.R',package='Rook'),name='helloref' ) s$add( app=system.file('exampleApps/summary.R',package='Rook'),name='summary' ) s$print() #Server started on 127.0.0.1:10221 #[1] RookTest http://127.0.0.1:10221/custom/RookTest #[2] helloref http://127.0.0.1:10221/custom/helloref #[3] summary http://127.0.0.1:10221/custom/summary #[4] hello http://127.0.0.1:10221/custom/hello # Stops the server but doesn't uninstall the app ## Not run: s$stop() ## End(Not run) s$remove(all=TRUE) rm(s)
For example, the interface and the source code of summary app are given below
app <- function(env) { req <- Rook::Request$new(env) res <- Rook::Response$new() res$write('Choose a CSV file:\n') res$write('<form method="POST" enctype="multipart/form-data">\n') res$write('<input type="file" name="data">\n') res$write('<input type="submit" name="Upload">\n</form>\n<br>') if (!is.null(req$POST())){ data <- req$POST()[['data']] res$write("<h3>Summary of Data</h3>"); res$write("<pre>") res$write(paste(capture.output(summary(read.csv(data$tempfile,stringsAsFactors=FALSE)),file=NULL),collapse='\n')) res$write("</pre>") res$write("<h3>First few lines (head())</h3>"); res$write("<pre>") res$write(paste(capture.output(head(read.csv(data$tempfile,stringsAsFactors=FALSE)),file=NULL),collapse='\n')) res$write("</pre>") } res$finish() }
More example:
- http://lamages.blogspot.com/2012/08/rook-rocks-example-with-googlevis.html
- Self-organizing map
- Deploy Rook apps with rApache. First one and two.
- A Simple Prediction Web Service Using the New fiery Package
sumo
Sumo is a fully-functional web application template that exposes an authenticated user's R session within java server pages. See the paper http://journal.r-project.org/archive/2012-1/RJournal_2012-1_Bergsma+Smith.pdf.
Stockplot
FastRWeb
http://cran.r-project.org/web/packages/FastRWeb/index.html
WebDriver
'WebDriver' Client for 'PhantomJS'
https://github.com/rstudio/webdriver
Rwui
CGHWithR and WebDevelopR
CGHwithR is still working with old version of R although it is removed from CRAN. Its successor is WebDevelopR. Its The vignette (year 2013) provides a review of several available methods.
manipulate from RStudio
This is not a web application. But the manipulate package can be used to create interactive plot within R(Studio) environment easily. Its source is available at here.
Mathematica also has manipulate function for plotting; see here.
RCloud
RCloud is an environment for collaboratively creating and sharing data analysis scripts. RCloud lets you mix analysis code in R, HTML5, Markdown, Python, and others. Much like Sage, iPython notebooks and Mathematica, RCloud provides a notebook interface that lets you easily record a session and annotate it with text, equations, and supporting images.
See also the Talk in UseR 2014.
cloudyr and flyio - Input Output Files in R from Cloud or Local
https://blog.socialcops.com/inside-sc/announcements/flyio-r-package-interact-data-cloud/ Announcing flyio, an R Package to Interact with Data in the Cloud]
Dropbox access
rdrop2 package
Web page scraping
http://www.slideshare.net/schamber/web-data-from-r#btnNext
xml2 package
rvest package depends on xml2.
purrr: Functional Programming Tools
- https://purrr.tidyverse.org/
- purrr cookbook
- Functional programming (cf Object-Oriented Programming)
- Getting started with the purrr package in R, especially the map() function.
- Purr yourself into a math genius
- Write & Read Multiple Excel files with purrr
rvest
On Ubuntu, we need to install two packages first!
sudo apt-get install libcurl4-openssl-dev # OR libcurl4-gnutls-dev sudo apt-get install libxml2-dev
- https://github.com/hadley/rvest
- Visualizing obesity across United States by using data from Wikipedia
- rvest tutorial: scraping the web using R
- https://renkun.me/pipeR-tutorial/Examples/rvest.html
- http://zevross.com/blog/2015/05/19/scrape-website-data-with-the-new-r-package-rvest/
- Google scholar scraping with rvest package
Animate
- Animating Changes in Football Kits using R: rvest, tidyverse, xml2, purrr & magick
- Animated Directional Chord Diagrams tweenr & magick
- x-mas tRees with gganimate, ggplot, plotly and friends
- Create animation in R: learn by examples (gganimate)
V8: Embedded JavaScript Engine for R
R⁶ — General (Attys) Distributions: V8, rvest, ggbeeswarm, hrbrthemes and tidyverse packages are used.
pubmed.mineR
Text mining of PubMed Abstracts (http://www.ncbi.nlm.nih.gov/pubmed). The algorithms are designed for two formats (text and XML) from PubMed.
R code for scraping the P-values from pubmed, calculating the Science-wise False Discovery Rate, et al (Jeff Leek)
These R packages import sports, weather, stock data and more
- https://www.computerworld.com/article/3109890/data-analytics/these-r-packages-import-sports-weather-stock-data-and-more.html
- https://github.com/ALShum/rwunderground
- Accessing APIs from R (and a little R programming). A personal key is required. 500 times per day for a free account.
- https://cran.r-project.org/web/packages/rnoaa/index.html. A personal API key (token) is required. 10,000 requests per day
http://ram-n.github.io/weatherData/(not working)
Diving Into Dynamic Website Content with splashr
https://rud.is/b/2017/02/09/diving-into-dynamic-website-content-with-splashr/
Send email
mailR
Easiest. Require rJava package (not trivial to install, see rJava). mailR is an interface to Apache Commons Email to send emails from within R. See also send bulk email
Before we use the mailR package, we have followed here to have Allow less secure apps: 'ON' ; or you might get an error Error: EmailException (Java): Sending the email to the following server failed : smtp.gmail.com:465. Once we turn on this option, we may get an email for the notification of this change. Note that the recipient can be other than a gmail.
> send.mail(from = "[email protected]", to = c("[email protected]", "Recipient 2 <[email protected]>"), replyTo = c("Reply to someone else <[email protected]>") subject = "Subject of the email", body = "Body of the email", smtp = list(host.name = "smtp.gmail.com", port = 465, user.name = "gmail_username", passwd = "password", ssl = TRUE), attach.files ="./myattachment.txt", authenticate = TRUE, send = TRUE) [1] "Java-Object{org.apache.commons.mail.SimpleEmail@7791a895}"
MailR SMTP Setup (Gmail, Outlook, Yahoo) | STARTTLS
gmailr
More complicated. gmailr provides access the Google's gmail.com RESTful API. Vignette and an example on here. Note that it does not use a password; it uses a json file for oauth authentication downloaded from https://console.cloud.google.com/. See also https://github.com/jimhester/gmailr/issues/1.
library(gmailr) gmail_auth('mysecret.json', scope = 'compose') test_email <- mime() %>% to("[email protected]") %>% from("[email protected]") %>% subject("This is a subject") %>% html_body("<html><body>I wish <b>this</b> was bold</body></html>") send_message(test_email)
sendmailR
sendmailR provides a simple SMTP client. It is not clear how to use the package (i.e. where to enter the password).
emayili
GEO (Gene Expression Omnibus)
See this internal link.
Interactive html output
sendplot
RIGHT
The supported plot types include scatterplot, barplot, box plot, line plot and pie plot.
In addition to tooltip boxes, the package can create a table showing all information about selected nodes.
r2d3
r2d3 - R Interface to D3 Visualizations
d3Network
- http://christophergandrud.github.io/d3Network/ (old)
- https://christophergandrud.github.io/networkD3/ (new)
library(d3Network) Source <- c("A", "A", "A", "A", "B", "B", "C", "C", "D") Target <- c("B", "C", "D", "J", "E", "F", "G", "H", "I") NetworkData <- data.frame(Source, Target) d3SimpleNetwork(NetworkData, height = 800, width = 1024, file="tmp.html")
htmlwidgets for R
Embed widgets in R Markdown documents and Shiny web applications.
- Official website http://www.htmlwidgets.org/.
- How to write a useful htmlwidgets in R: tips and walk-through a real example
networkD3
This is a port of Christopher Gandrud's d3Network package to the htmlwidgets framework.
scatterD3
scatterD3 is an HTML R widget for interactive scatter plots visualization. It is based on the htmlwidgets R package and on the d3.js javascript library.
dygraphs
rthreejs - Create interactive 3D scatter plots, network plots, and globes
d3heatmap
See R
svgPanZoom
This 'htmlwidget' provides pan and zoom interactivity to R graphics, including 'base', 'lattice', and 'ggplot2'. The interactivity is provided through the 'svg-pan-zoom.js' library.
DT: An R interface to the DataTables library
plotly
- Power curves and ggplot2.
- TIME SERIES CHARTS BY THE ECONOMIST IN R USING PLOTLY & FIVE INTERACTIVE R VISUALIZATIONS WITH D3, GGPLOT2, & RSTUDIO
- Filled chord diagram
- DASHBOARDS IN R WITH SHINY & PLOTLY
- Plotly Graphs in Shiny,
- How to plot basic charts with plotly
- How to add Trend Lines in R Using Plotly
- Introduction to Interactive Graphics in R with plotly
Amazon
Download product information and reviews from Amazon.com
sudo apt-get install libxml2-dev sudo apt-get install libcurl4-openssl-dev
and in R
install.packages("devtools") install.packages("XML") install.packages("pbapply") install.packages("dplyr") devtools::install_github("56north/Rmazon") product_info <- Rmazon::get_product_info("1593273843") reviews <- Rmazon::get_reviews("1593273843") reviews[1,6] # only show partial characters from the 1st review nchar(reviews[1,6]) as.character(reviews[1,6]) # show the complete text from the 1st review reviews <- Rmazon::get_reviews("B07BNGJXGS") # Fetching 30 reviews of 'BOOX Note Ereader,Android 6.0 32 GB 10.3" Dual Touch HD Display' # |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02s reviews # A tibble: 30 x 6 reviewRating reviewDate reviewFormat Verified_Purcha… reviewHeadline <dbl> <chr> <lgl> <lgl> <chr> 1 4 May 23, 2… NA TRUE Good for PDF … 2 3 May 8, 20… NA FALSE The reading s… 3 5 May 17, 2… NA TRUE E-reader and … 4 3 May 24, 2… NA TRUE Good hardware… 5 3 June 21, … NA TRUE Poor QC 6 5 August 5,… NA TRUE Excellent for… 7 5 May 31, 2… NA TRUE Especially li… 8 5 July 4, 2… NA TRUE Android 6 rea… 9 4 July 15, … NA TRUE Remember the … 10 4 June 9, 2… NA TRUE Overall fanta… # ... with 20 more rows, and 1 more variable: reviewText <chr> reviews[1, 6] # 6-th column is the review text
gutenbergr
OCR
- Tesseract package: High Quality OCR in R, How to do Optical Character Recognition (OCR) of non-English documents in R using Tesseract?
- https://cran.r-project.org/web/packages/abbyyR/index.html
Wikipedia
WikipediR: R's MediaWiki API client library
Creating local repository for CRAN and Bioconductor
Parallel Computing
See R parallel.
Cloud Computing
Install R on Amazon EC2
http://randyzwitch.com/r-amazon-ec2/
Bioconductor on Amazon EC2
http://www.bioconductor.org/help/bioconductor-cloud-ami/
Big Data Analysis
- CRAN Task View: High-Performance and Parallel Computing with R
- R for big data in one picture
- Handling large data sets in R
- Big Data Analytics with R by Simon Walkowiak
- pbdR
- https://en.wikipedia.org/wiki/Programming_with_Big_Data_in_R
- Programming with Big Data in R - pbdR George Ostrouchov and Mike Matheson Oak Ridge National Laboratory
bigmemory, biganalytics, bigtabulate
ff, ffbase
- tapply does not work. Using tapply, ave functions for ff vectors in R
- Popularity bigdata / large data packages in R and ffbase useR presentation
- ffbase: statistical functions for large datasets in useR 2013
- ffbase package
biglm
data.table
See data.table.
Reproducible Research
http://cran.r-project.org/web/views/ReproducibleResearch.html
Reproducible Environments
https://rviews.rstudio.com/2019/04/22/reproducible-environments/
Some lessons in R coding
- don't use rand() and srand() in c. The result is platform dependent. My experience is Ubuntu/Debian/CentOS give the same result but they are different from macOS and Windows. Use Rcpp package and R's random number generator instead.
- don't use list.files() directly. The result is platform dependent even different Linux OS. An extra sorting helps!
Useful R packages
Rcpp
http://cran.r-project.org/web/packages/Rcpp/index.html. See more here.
RInside : embed R in C++ code
- http://dirk.eddelbuettel.com/code/rinside.html
- http://dirk.eddelbuettel.com/papers/rfinance2010_rcpp_rinside_tutorial_handout.pdf
Ubuntu
With RInside, R can be embedded in a graphical application. For example, $HOME/R/x86_64-pc-linux-gnu-library/3.0/RInside/examples/qt directory includes source code of a Qt application to show a kernel density plot with various options like kernel functions, bandwidth and an R command text box to generate the random data. See my demo on Youtube. I have tested this qtdensity example successfully using Qt 4.8.5.
- Follow the instruction cairoDevice to install required libraries for cairoDevice package and then cairoDevice itself.
- Install Qt. Check 'qmake' command becomes available by typing 'whereis qmake' or 'which qmake' in terminal.
- Open Qt Creator from Ubuntu start menu/Launcher. Open the project file $HOME/R/x86_64-pc-linux-gnu-library/3.0/RInside/examples/qt/qtdensity.pro in Qt Creator.
- Under Qt Creator, hit 'Ctrl + R' or the big green triangle button on the lower-left corner to build/run the project. If everything works well, you shall see the interactive program qtdensity appears on your desktop.
With RInside + Wt web toolkit installed, we can also create a web application. To demonstrate the example in examples/wt directory, we can do
cd ~/R/x86_64-pc-linux-gnu-library/3.0/RInside/examples/wt make sudo ./wtdensity --docroot . --http-address localhost --http-port 8080
Then we can go to the browser's address bar and type http://localhost:8080 to see how it works (a screenshot is in here).
Windows 7
To make RInside works on Windows OS, try the following
- Make sure R is installed under C:\ instead of C:\Program Files if we don't want to get an error like g++.exe: error: Files/R/R-3.0.1/library/RInside/include: No such file or directory.
- Install RTools
- Instal RInside package from source (the binary version will give an error )
- Create a DOS batch file containing necessary paths in PATH environment variable
@echo off set PATH=C:\Rtools\bin;c:\Rtools\gcc-4.6.3\bin;%PATH% set PATH=C:\R\R-3.0.1\bin\i386;%PATH% set PKG_LIBS=`Rscript -e "Rcpp:::LdFlags()"` set PKG_CPPFLAGS=`Rscript -e "Rcpp:::CxxFlags()"` set R_HOME=C:\R\R-3.0.1 echo Setting environment for using R cmd
In the Windows command prompt, run
cd C:\R\R-3.0.1\library\RInside\examples\standard make -f Makefile.win
Now we can test by running any of executable files that make generates. For example, rinside_sample0.
rinside_sample0
As for the Qt application qdensity program, we need to make sure the same version of MinGW was used in building RInside/Rcpp and Qt. See some discussions in
- http://stackoverflow.com/questions/12280707/using-rinside-with-qt-in-windows
- http://www.mail-archive.com/[email protected]/msg04377.html
So the Qt and Wt web tool applications on Windows may or may not be possible.
GUI
Qt and R
- http://cran.r-project.org/web/packages/qtbase/index.html QtDesigner is such a tool, and its output is compatible with the qtbase R package
- http://qtinterfaces.r-forge.r-project.org
tkrplot
On Ubuntu, we need to install tk packages, such as by
sudo apt-get install tk-dev
reticulate - Interface to 'Python'
- https://cran.r-project.org/web/packages/reticulate/index.html, Github
- Using Python in R markdown
- Importing Python modules and call its functions directly from R — import() function
- Sourcing Python scripts — source_python() function
- Python REPL — The repl_python() function creates an interactive Python console within R.
- Install Python packages https://rstudio.github.io/reticulate/articles/python_packages.html
- Better to have anaconda3 installed. 2.26G space is required on macOS.
- Direct running py_install("pandas") would ask me to upgrade virtualenv
- Running virtualenv_create("r-reticulate") and then py_install("pandas") works
- reticulate: R interface to Python JJ Allaire
- Cheat sheet
- R or Python? Why not both? Using Anaconda Python within R with {reticulate}
- Run Python from R
- R and Python: Using reticulate to get the best of both worlds. Note
- RStudio v1.2 preview release includes support for using reticulate to execute Python chunks within R Notebooks
- Error from my execution: ValueError: 'RBF' is not in list
- The reticulate package solves the hardest problem in data science: people
- reticulate, virtualenv, and Python in Linux
- Bugs
- Pass Python objects to R: Works. Or use py_run_string()
- Cannot pass R variables to Python: use source_python()
- R vs Python for data science by Norm Matloff.
- Test python and markdown files
def add_three(x): z = x + 3 return z
--- title: "R Notebook" output: html_notebook --- ```{r} library(reticulate) py_discover_config() x <- 5 source_python("test.py") y <- add_three(x) print(y) ``` Pass R variables to Python. Works ```{python} a = 7 print(r.x) ``` Pass python variables to R. Works. ```{r} py$a py_run_string("y = 10"); py$y ```
Hadoop (eg ~100 terabytes)
See also HighPerformanceComputing
- RHadoop
- Hive
- MapReduce. Introduction by Linux Journal.
- http://www.techspritz.com/category/tutorials/hadoopmapredcue/ Single node or multinode cluster setup using Ubuntu with VirtualBox (Excellent)
- Running Hadoop on Ubuntu Linux (Single-Node Cluster)
- Ubuntu 12.04 http://www.youtube.com/watch?v=WN2tJk_oL6E and instruction
- Linux Mint http://blog.hackedexistence.com/installing-hadoop-single-node-on-linux-mint
- http://www.r-bloggers.com/search/hadoop
RHadoop
- RDataMining.com based on Mac.
- Ubuntu 12.04 - Crishantha.com, nikhilshah123sh.blogspot.com.Bighadoop.wordpress contains an example.
- RapReduce in R by RevolutionAnalytics with a few examples.
- https://twitter.com/hashtag/rhadoop
- Bigd8ta.com based on Ubuntu 14.04.
Snowdoop: an alternative to MapReduce algorithm
- http://matloff.wordpress.com/2014/11/26/how-about-a-snowdoop-package/
- http://matloff.wordpress.com/2014/12/26/snowdooppartools-update/comment-page-1/#comment-665
XML
On Ubuntu, we need to install libxml2-dev before we can install XML package.
sudo apt-get update sudo apt-get install libxml2-dev
On CentOS,
yum -y install libxml2 libxml2-devel
XML
- http://giventhedata.blogspot.com/2012/06/r-and-web-for-beginners-part-ii-xml-in.html. It gave an example of extracting the XML-values from each XML-tag for all nodes and save them in a data frame using xmlSApply().
- http://www.quantumforest.com/2011/10/reading-html-pages-in-r-for-text-processing/
- https://tonybreyal.wordpress.com/2011/11/18/htmltotext-extracting-text-from-html-via-xpath/
- https://www.tutorialspoint.com/r/r_xml_files.htm
- https://www.datacamp.com/community/tutorials/r-data-import-tutorial#xml
- Extracting data from XML PubMed and Zillow are used to illustrate. xmlTreeParse(), xmlRoot(), xmlName() and xmlSApply().
- https://yihui.name/en/2010/10/grabbing-tables-in-webpages-using-the-xml-package/
library(XML) # Read and parse HTML file doc.html = htmlTreeParse('http://apiolaza.net/babel.html', useInternal = TRUE) # Extract all the paragraphs (HTML tag is p, starting at # the root of the document). Unlist flattens the list to # create a character vector. doc.text = unlist(xpathApply(doc.html, '//p', xmlValue)) # Replace all by spaces doc.text = gsub('\n', ' ', doc.text) # Join all the elements of the character vector into a single # character string, separated by spaces doc.text = paste(doc.text, collapse = ' ')
This post http://stackoverflow.com/questions/25315381/using-xpathsapply-to-scrape-xml-attributes-in-r can be used to monitor new releases from github.com.
> library(RCurl) # getURL() > library(XML) # htmlParse and xpathSApply > xData <- getURL("https://github.com/alexdobin/STAR/releases") > doc = htmlParse(xData) > plain.text <- xpathSApply(doc, "//span[@class='css-truncate-target']", xmlValue) # I look at the source code and search 2.5.3a and find the tag as # <span class="css-truncate-target">2.5.3a</span> > plain.text [1] "2.5.3a" "2.5.2b" "2.5.2a" "2.5.1b" "2.5.1a" [6] "2.5.0c" "2.5.0b" "STAR_2.5.0a" "STAR_2.4.2a" "STAR_2.4.1d" > > # try bwa > > xData <- getURL("https://github.com/lh3/bwa/releases") > doc = htmlParse(xData) > xpathSApply(doc, "//span[@class='css-truncate-target']", xmlValue) [1] "v0.7.15" "v0.7.13" > # try picard > xData <- getURL("https://github.com/broadinstitute/picard/releases") > doc = htmlParse(xData) > xpathSApply(doc, "//span[@class='css-truncate-target']", xmlValue) [1] "2.9.1" "2.9.0" "2.8.3" "2.8.2" "2.8.1" "2.8.0" "2.7.2" "2.7.1" "2.7.0" [10] "2.6.0"
This method can be used to monitor new tags/releases from some projects like Cura, BWA, Picard, STAR. But for some projects like sratools the class attribute in the span element ("css-truncate-target") can be different (such as "tag-name").
xmlview
RCurl
On Ubuntu, we need to install the packages (the first one is for XML package that RCurl suggests)
# Test on Ubuntu 14.04 sudo apt-get install libxml2-dev sudo apt-get install libcurl4-openssl-dev
Scrape google scholar results
No google ID is required
Seems not work
Error in data.frame(footer = xpathLVApply(doc, xpath.base, "/font/span[@class='gs_fl']", : arguments imply differing number of rows: 2, 0
devtools
devtools package depends on Curl. It actually depends on some system files. If we just need to install a package, consider the remotes package which was suggested by the BiocManager package.
# Ubuntu 14.04 sudo apt-get install libcurl4-openssl-dev # Ubuntu 16.04 sudo apt-get install build-essential libcurl4-gnutls-dev libxml2-dev libssl-dev
Lazy-load database XXX is corrupt. internal error -3. It often happens when you use install_github to install a package that's currently loaded; try restarting R and running the app again.
httr
httr imports curl, jsonlite, mime, openssl and R6 packages.
When I tried to install httr package, I got an error and some message:
Configuration failed because openssl was not found. Try installing: * deb: libssl-dev (Debian, Ubuntu, etc) * rpm: openssl-devel (Fedora, CentOS, RHEL) * csw: libssl_dev (Solaris) * brew: openssl (Mac OSX) If openssl is already installed, check that 'pkg-config' is in your PATH and PKG_CONFIG_PATH contains a openssl.pc file. If pkg-config is unavailable you can set INCLUDE_DIR and LIB_DIR manually via: R CMD INSTALL --configure-vars='INCLUDE_DIR=... LIB_DIR=...' -------------------------------------------------------------------- ERROR: configuration failed for package ‘openssl’
It turns out after I run sudo apt-get install libssl-dev in the terminal (Debian), it would go smoothly with installing httr package. Nice httr!
Real example: see this post. Unfortunately I did not get a table result; I only get an html file (R 3.2.5, httr 1.1.0 on Ubuntu and Debian).
Since httr package was used in many other packages, take a look at how others use it. For example, aRxiv package.
curl
curl is independent of RCurl package.
- http://cran.r-project.org/web/packages/curl/vignettes/intro.html
- https://www.opencpu.org/posts/curl-release-0-8/
library(curl) h <- new_handle() handle_setform(h, name="aaa", email="bbb" ) req <- curl_fetch_memory("http://localhost/d/phpmyql3_scripts/ch02/form2.html", handle = h) rawToChar(req$content)
rOpenSci packages
rOpenSci contains packages that allow access to data repositories through the R statistical programming environment
remotes
Download and install R packages stored in 'GitHub', 'BitBucket', or plain 'subversion' or 'git' repositories. This package is a lightweight replacement of the 'install_*' functions in 'devtools'. Also remotes does not require any extra OS level library (at least on Ubuntu 16.04).
Example:
# https://github.com/henrikbengtsson/matrixstats remotes::install_github('HenrikBengtsson/matrixStats@develop')
DirichletMultinomial
On Ubuntu, we do
sudo apt-get install libgsl0-dev
Create GUI
gWidgets
GenOrd: Generate ordinal and discrete variables with given correlation matrix and marginal distributions
rjson
http://heuristically.wordpress.com/2013/05/20/geolocate-ip-addresses-in-r/
RJSONIO
Accessing Bitcoin Data with R
http://blog.revolutionanalytics.com/2015/11/accessing-bitcoin-data-with-r.html
Plot IP on google map
- http://thebiobucket.blogspot.com/2011/12/some-fun-with-googlevis-plotting-blog.html#more (RCurl, RJONIO, plyr, googleVis)
- http://devblog.icans-gmbh.com/using-the-maxmind-geoip-api-with-r/ (RCurl, RJONIO, maps)
- http://cran.r-project.org/web/packages/geoPlot/index.html (geoPlot package (deprecated as 8/12/2013))
- http://archive09.linux.com/feature/135384 (Not R) ApacheMap
- http://batchgeo.com/features/geolocation-ip-lookup/ (Not R) (Enter a spreadsheet of adress, city, zip or a column of IPs and it will show the location on google map)
- http://code.google.com/p/apachegeomap/
The following example is modified from the first of above list.
require(RJSONIO) # fromJSON require(RCurl) # getURL temp = getURL("https://gist.github.com/arraytools/6743826/raw/23c8b0bc4b8f0d1bfe1c2fad985ca2e091aeb916/ip.txt", ssl.verifypeer = FALSE) ip <- read.table(textConnection(temp), as.is=TRUE) names(ip) <- "IP" nr = nrow(ip) Lon <- as.numeric(rep(NA, nr)) Lat <- Lon Coords <- data.frame(Lon, Lat) ip2coordinates <- function(ip) { api <- "http://freegeoip.net/json/" get.ips <- getURL(paste(api, URLencode(ip), sep="")) # result <- ldply(fromJSON(get.ips), data.frame) result <- data.frame(fromJSON(get.ips)) names(result)[1] <- "ip.address" return(result) } for (i in 1:nr){ cat(i, "\n") try( Coords[i, 1:2] <- ip2coordinates(ip$IP[i])[c("longitude", "latitude")] ) } # append to log-file: logfile <- data.frame(ip, Lat = Coords$Lat, Long = Coords$Lon, LatLong = paste(round(Coords$Lat, 1), round(Coords$Lon, 1), sep = ":")) log_gmap <- logfile[!is.na(logfile$Lat), ] require(googleVis) # gvisMap gmap <- gvisMap(log_gmap, "LatLong", options = list(showTip = TRUE, enableScrollWheel = TRUE, mapType = 'hybrid', useMapTypeControl = TRUE, width = 1024, height = 800)) plot(gmap)
The plot.gvis() method in googleVis packages also teaches the startDynamicHelp() function in the tools package, which was used to launch a http server. See Jeffrey Horner's note about deploying Rook App.
Map
leaflet
- rstudio.github.io/leaflet/#installation-and-use
- https://metvurst.wordpress.com/2015/07/24/mapview-basic-interactive-viewing-of-spatial-data-in-r-6/
choroplethr
- http://blog.revolutionanalytics.com/2014/01/easy-data-maps-with-r-the-choroplethr-package-.html
- http://www.arilamstein.com/blog/2015/06/25/learn-to-map-census-data-in-r/
- http://www.arilamstein.com/blog/2015/09/10/user-question-how-to-add-a-state-border-to-a-zip-code-map/
ggplot2
How to make maps with Census data in R
googleVis
See an example from RJSONIO above.
googleAuthR
Create R functions that interact with OAuth2 Google APIs easily, with auto-refresh and Shiny compatibility.
gtrendsR - Google Trends
- Download and plot Google Trends data with R
- Analyzing Google Trends Data in R
- microarray analysis from 2004-04-01
- ngs next generation sequencing from 2004-04-01
- dna sequencing from 2004-01-01.
- rna sequencing from 2004-01-01. It can be seen RNA sequencing >> DNA sequencing.
- Python vs R – Who Is Really Ahead in Data Science, Machine Learning? and The Incredible Growth of Python by David Robinson
quantmod
Maintaining a database of price files in R. It consists of 3 steps.
- Initial data downloading
- Update existing data
- Create a batch file
caret
- http://topepo.github.io/caret/index.html & https://github.com/topepo/caret/
- https://www.r-project.org/conferences/useR-2013/Tutorials/kuhn/user_caret_2up.pdf
- https://github.com/cran/caret source code mirrored on github
- Cheatsheet https://www.rstudio.com/resources/cheatsheets/
Tool for connecting Excel with R
- https://bert-toolkit.com/
- BERT: a newcomer in the R Excel connection
- http://blog.revolutionanalytics.com/2018/08/how-to-use-r-with-excel.html
Read/Write Excel files package
- http://www.milanor.net/blog/?p=779
- flipAPI. One useful feature of DownloadXLSX, which is not supported by the readxl package, is that it can read Excel files directly from the URL.
- xlsx: depends on Java
- openxlsx: not depend on Java. Depend on zip application. On Windows, it seems to be OK without installing Rtools. But it can not read xls file; it works on xlsx file.
- It can't be used to open .xls or .xlm files.
- When I try the package to read an xlsx file, I got a warning: No data found on worksheet. 6/28/2018
- Use R to write multiple tables to a single Excel file
- readxl: it does not depend on anything although it can only read but not write Excel files.
- It is part of tidyverse package. The readxl website provides several articles for more examples.
- readxl webinar.
- One advantage of read_excel (as with read_csv in the readr package) is that the data imports into an easy to print object with three attributes a tbl_df, a tbl and a data.frame.
- For writing to Excel formats, use writexl or openxlsx package.
- writexl: zero dependency xlsx writer for R
library(readxl) read_excel(path, sheet = NULL, range = NULL, col_names = TRUE, col_types = NULL, na = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = readxl_progress(), .name_repair = "unique") # Example read_excel(path, range = cell_cols("c:cx"), col_types = "numeric")
For the Chromosome column, integer values becomes strings (but converted to double, so 5 becomes 5.000000) or NA (empty on sheets).
> head(read_excel("~/Downloads/BRCA.xls", 4)[ , -9], 3) UniqueID (Double-click) CloneID UGCluster 1 HK1A1 21652 Hs.445981 2 HK1A2 22012 Hs.119177 3 HK1A4 22293 Hs.501376 Name Symbol EntrezID 1 Catenin (cadherin-associated protein), alpha 1, 102kDa CTNNA1 1495 2 ADP-ribosylation factor 3 ARF3 377 3 Uroporphyrinogen III synthase UROS 7390 Chromosome Cytoband ChimericClusterIDs Filter 1 5.000000 5q31.2 <NA> 1 2 12.000000 12q13 <NA> 1 3 <NA> 10q25.2-q26.3 <NA> 1
The hidden worksheets become visible (Not sure what are those first rows mean in the output).
> excel_sheets("~/Downloads/BRCA.xls") DEFINEDNAME: 21 00 00 01 0b 00 00 00 02 00 00 00 00 00 00 0d 3b 01 00 00 00 9a 0c 00 00 1a 00 DEFINEDNAME: 21 00 00 01 0b 00 00 00 04 00 00 00 00 00 00 0d 3b 03 00 00 00 9b 0c 00 00 0a 00 DEFINEDNAME: 21 00 00 01 0b 00 00 00 03 00 00 00 00 00 00 0d 3b 02 00 00 00 9a 0c 00 00 06 00 [1] "Experiment descriptors" "Filtered log ratio" "Gene identifiers" [4] "Gene annotations" "CollateInfo" "GeneSubsets" [7] "GeneSubsetsTemp"
The Chinese character works too.
> read_excel("~/Downloads/testChinese.xlsx", 1) 中文 B C 1 a b c 2 1 2 3
To read all worksheets we need a convenient function
read_excel_allsheets <- function(filename) { sheets <- readxl::excel_sheets(filename) sheets <- sheets[-1] # Skip sheet 1 x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X, col_types = "numeric")) names(x) <- sheets x } dcfile <- "table0.77_dC_biospear.xlsx" dc <- read_excel_allsheets(dcfile) # Each component (eg dc[[1]]) is a tibble.
readr (it is not designed to read Excel files)
Compared to base equivalents like read.csv(), readr is much faster and gives more convenient output: it never converts strings to factors, can parse date/times, and it doesn’t munge the column names.
1.0.0 released.
The read_csv() function from the readr package is as fast as fread() function from data.table package. For files beyond 100MB in size fread() and read_csv() can be expected to be around 5 times faster than read.csv(). See 5.3 of Efficient R Programming book.
Note that fread() can read-n a selection of the columns.
Colors
- scales package. This is used in ggplot2 package.
- colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes.
- How to expand color palette with ggplot and RColorBrewer
- palette_explorer() function from the tmaptools package. See selecting color palettes with shiny.
- Cookbook for R
- Sequential, diverging and qualitative colour scales/palettes from colorbrewer.org: scale_colour_brewer(), scale_fill_brewer(), ...
- http://colorbrewer2.org/
- It seems there is no choice of getting only 2 colors no matter which set name we can use
- To see the set names used in brewer.pal, see
- RColorBrewer::display.brewer.all()
- For example, Set1 from http://colorbrewer2.org/
- To list all R color names, colors().
- Color Chart (include Hex and RGB) & Using Color in R from http://research.stowers.org
- Code to generate rectangles with colored background https://www.r-graph-gallery.com/42-colors-names/
- convert hex value to color names
library(plotrix) sapply(rainbow(4), color.id) sapply(RColorBrewer::brewer.pal(4, "Set1"), color.id)
- hsv() function. Matrix-style screensaver in R
Below is an example using the option scale_fill_brewer(palette = "Paired"). See the source code at gist. Note that only 'set1' and 'set3' palettes in qualitative scheme can support up to 12 classes.
According to the information from the colorbrew website, qualitative schemes do not imply magnitude differences between legend classes, and hues are used to create the primary visual differences between classes.
ggplot2
See ggplot2
Data Manipulation & Tidyverse
Import | | readr, readxl | haven, DBI, httr +----- Visualize ------+ | | ggplot2, ggvis | | | | Tidy ------------- Transform tibble dplyr Model tidyr | broom +------ Model ---------+
- TidyverseSkeptic by Norm Matloff
- R for Data Science and tidyverse package (it is a collection of ggplot2, tibble, tidyr, readr, purrr & dplyr packages).
- tidyverse, among others, was used at Mining CRAN DESCRIPTION Files (tbl_df(), %>%, summarise(), count(), mutate(), arrange(), unite(), ggplot(), filter(), select(), ...). Note that there is a problem to reproduce the result. I need to run cran <- cran[, -14] to remove the MD5sum column.
- Compile R for Data Science to a PDF
- Data Wrangling with dplyr and tidyr Cheat Sheet
- Data Wrangling with Tidyverse from the Harvard Chan School of Public Health.
- Best packages for data manipulation in R. It demonstrates to perform the same tasks using data.table and dplyr packages. data.table is faster and it may be a go-to package when performance and memory are the constraints.
- DATA MANIPULATION IN R by Alboukadel Kassambara
- subset data frame columns: pull(), select(), select_if(), other helper functions
- subset (filter) data frame rows: slice(), filter(), filter_all(), filter_if(), filter_at(), sample_n(), top_n()
- identify and remove duplicate rows: duplicated(), unique(), distinct()
- ordering rows: arrange(), desc()
- renaming and adding columns: rename()
- compute and add new variables to a data frame: mutate(), transmutate()
- computing summary statistics (pay to view)
Install on Ubuntu
How to install Tidyverse on Ubuntu 16.04 and 17.04
# Ubuntu >= 18.04. However, I get unmet dependencies errors on R 3.5.3. # r-cran-curl : Depends: r-api-3.4 sudo apt-get install r-cran-curl r-cran-openssl r-cran-xml2 # Works fine on Ubuntu 16.04, 18.04 sudo apt install libcurl4-openssl-dev libssl-dev libxml2-dev
80 R packages will be installed after tidyverse has been installed.
Install on Raspberry Pi/(ARM based) Chromebook
In additional to the requirements of installing on Ubuntu, I got an error when it is installing a dependent package fs: undefined symbol: pthread_atfork. The fs package version is 1.2.6. The solution is to add one line in fs/src/Makevars file and then install the "fs" package using the source on the local machine.
5 most useful data manipulation functions
- subset() for making subsets of data (natch)
- merge() for combining data sets in a smart and easy way
- melt()-reshape2 package for converting from wide to long data formats. See an example here where we want to combine multiple columns of values into 1 column. melt() is replaced by gather().
- dcast()-reshape2 package for converting from long to wide data formats (or just use tapply()), and for making summary tables
- ddply()-plyr package for doing split-apply-combine operations, which covers a huge swath of the most tricky data operations
data.table
Fast aggregation of large data (e.g. 100GB in RAM or just several GB size file), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread).
Some resources:
- https://www.rdocumentation.org/packages/data.table/versions/1.12.0
- R Packages: dplyr vs data.table
- Cheat sheet from RStudio
- Reading large data tables in R. fread(FILENAME)
- Note that 'x[, 2] always return 2. If you want to do the thing you want, use x[, 2, with=FALSE] or x[, V2] where V2 is the header name. See the FAQ #1 in data.table.
- Understanding data.table Rolling Joins
- Intro to The data.table Package
- Subsetting rows and/or columns
- Alternative to using tapply(), aggregate(), table() to summarize data
- Similarities to SQL, DT[i, j, by]
- R : data.table (with 50 examples) from ListenData
- Describe Data
- Selecting or Keeping Columns
- Rename Variables
- Subsetting Rows / Filtering
- Faster Data Manipulation with Indexing
- Performance Comparison
- Sorting Data
- Adding Columns (Calculation on rows)
- How to write Sub Queries (like SQL)
- Summarize or Aggregate Columns
- GROUP BY (Within Group Calculation)
- Remove Duplicates
- Extract values within a group
- SQL's RANK OVER PARTITION
- Cumulative SUM by GROUP
- Lag and Lead
- Between and LIKE Operator
- Merging / Joins
- Convert a data.table to data.frame
- R Tutorial: data.table from dezyre.com
- Syntax: DT[where, select|update|do, by]
- Keys and setkey()
- Fast grouping using j and by: DT[,sum(v),by=x]
- Fast ordered joins: X[Y,roll=TRUE]
- In the Introduction to data.table vignette, the data.table::order() function is SLOWER than base::order() from my Odroid xu4 (running Ubuntu 14.04.4 trusty on uSD)
odt = data.table(col=sample(1e7)) (t1 <- system.time(ans1 <- odt[base::order(col)])) ## uses order from base R # user system elapsed # 2.730 0.210 2.947 (t2 <- system.time(ans2 <- odt[order(col)])) ## uses data.table's order # user system elapsed # 2.830 0.215 3.052 (identical(ans1, ans2)) # [1] TRUE
- Boost Your Data Munging with R
- rbindlist(). One problem, it uses too much memory. In fact, when I try to analyze R package downloads, the command "dat <- rbindlist(logs)" uses up my 64GB memory (OS becomes unresponsive).
OpenMP enabled compiler for Mac. This instruction works on my Mac El Capitan (10.11.6) when I need to upgrade the data.table version from 1.11.4 to 1.11.6.
Question: how to make use multicore with data.table package?
reshape & reshape2 (superceded by tidyr package)
- Data Shape Transformation With Reshape()
- Use acast() function in reshape2 package. It will convert data.frame used for analysis to a table-like data.frame good for display.
- http://lamages.blogspot.com/2013/10/creating-matrix-from-long-dataframe.html
tidyr and benchmark
An evolution of reshape2. It's designed specifically for data tidying (not general reshaping or aggregating) and works well with dplyr data pipelines.
- vignette("tidy-data") & Cheat sheet
- Main functions
- Reshape data: gather() & spread(). These two will be deprecated
- Split cells: separate() & unite()
- Handle missing: drop_na() & fill() & replace_na()
- Other functions
- tidyr::separate() function. If a cell contains many elements separated by ",", we can use this function to create more columns. The opposite function is unite().
- tidyr::separate_rows(). If a cell contains many elements separated by ",", we can use this function to create one more row. See the cheat sheet link above.
- http://blog.rstudio.org/2014/07/22/introducing-tidyr/
- http://rpubs.com/seandavi/GEOMetadbSurvey2014
- http://timelyportfolio.github.io/rCharts_factor_analytics/factors_with_new_R.html
- tidyr vs reshape2
- A tidyr Tutorial from U of Virginia
- Benchmarking cast in R from long data frame to wide matrix
Make wide tables long with gather() (see 6.3.1 of Efficient R Programming)
library(tidyr) library(efficient) data(pew) # wide table dim(pew) # 18 x 10, (religion, '<$10k', '$10--20k', '$20--30k', ..., '>150k') pewt <- gather(data = pew, key = Income, value = Count, -religion) dim(pew) # 162 x 3, (religion, Income, Count) args(gather) # function(data, key, value, ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE)
where the three arguments of gather() requires:
- data: a data frame in which column names will become row vaues
- key: the name of the categorical variable into which the column names in the original datasets are converted.
- value: the name of cell value columns
In this example, the 'religion' column will not be included (-religion).
dplyr, plyr packages
- plyr package suffered from being slow in some cases. dplyr addresses this by porting much of the computation to C++. Another additional feature is the ability to work with data stored directly in an external database. The benefits of doing this are the data can be managed natively in a relational database, queries can be conducted on that database, and only the results of query returned.
- Essential functions: 3 rows functions, 3 column functions and 1 mixed function.
select, mutate, rename +------------------+ filter + + arrange + + group_by + + drop_na + + ungroup + summarise + +------------------+
- These functions works on data frames and tibble objects. Note stats package also has a filter() function for time series data. If we have not loaded the dplyr package, the filter() function below will give an error (count() also is from dplyr).
iris %>% filter(Species == "setosa") %>% count() head(iris %>% filter(Species == "setosa") %>% arrange(Sepal.Length))
- dplyr tutorial from PH525x series (Biomedical Data Science by Rafael Irizarry and Michael Love). For select() function, some additional options to select columns based on a specific criteria include
- start_with()/ ends_with() = Select columns that start/end with a character string
- contains() = Select columns that contain a character string
- matches() = Select columns that match a regular expression
- one_of() = Select columns names that are from a group of names
- Data Transformation in the book R for Data Science. Five key functions in the dplyr package:
- Filter rows: filter()
- Arrange rows: arrange()
- Select columns: select()
- Add new variables: mutate()
- Grouped summaries: group_by() & summarise()
# filter jan1 <- filter(flights, month == 1, day == 1) filter(flights, month == 11 | month == 12) filter(flights, arr_delay <= 120, dep_delay <= 120) df <- tibble(x = c(1, NA, 3)) filter(df, x > 1) filter(df, is.na(x) | x > 1) # arrange arrange(flights, year, month, day) arrange(flights, desc(arr_delay)) # select select(flights, year, month, day) select(flights, year:day) select(flights, -(year:day)) # mutate flights_sml <- select(flights, year:day, ends_with("delay"), distance, air_time ) mutate(flights_sml, gain = arr_delay - dep_delay, speed = distance / air_time * 60 ) # if you only want to keep the new variables transmute(flights, gain = arr_delay - dep_delay, hours = air_time / 60, gain_per_hour = gain / hours ) # summarise() by_day <- group_by(flights, year, month, day) summarise(by_day, delay = mean(dep_delay, na.rm = TRUE)) # pipe. Note summarise() can return more than 1 variable. delays <- flights %>% group_by(dest) %>% summarise( count = n(), dist = mean(distance, na.rm = TRUE), delay = mean(arr_delay, na.rm = TRUE) ) %>% filter(count > 20, dest != "HNL") flights %>% group_by(year, month, day) %>% summarise(mean = mean(dep_delay, na.rm = TRUE))
- Videos
- Hands-on dplyr tutorial for faster data manipulation in R by Data School. At time 17:00, it compares the %>% operator, with() and aggregate() for finding group mean.
- https://youtu.be/aywFompr1F4 (shorter video) by Roger Peng
- https://youtu.be/8SGif63VW6E by Hadley Wickham
- Tidy eval: Programming with dplyr, tidyr, and ggplot2. Bang bang "!!" operator was introduced for use in a function call.
- Efficient R Programming
- Data wrangling: Transformation from R-exercises.
- Express Intro to dplyr by rollingyours.
- the dot.
- stringr and plyr A data.frame is pretty much a list of vectors, so we use plyr to apply over the list and stringr to search and replace in the vectors.
- https://randomjohn.github.io/r-maps-with-census-data/ dplyr and stringr are used
- 5 interesting subtle insights from TED videos data analysis in R
- What is tidy eval and why should I care?
stringr
- https://www.rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf
- stringr Cheat sheet (2 pages, this will immediately download the pdf file)
- Efficient data carpentry → Regular expressions from Efficient R programming book by Gillespie & Lovelace.
magrittr
- Vignettes
- magrittr and wrapr Pipes in R, an Examination. Instead of nested statements, it is using pipe operator %>%. So the code is easier to read. Impressive!
x %>% f # f(x) x %>% f(y) # f(x, y) x %>% f(arg=y) # f(x, arg=y) x %>% f(z, .) # f(z, x) x %>% f(y) %>% g(z) # g(f(x, y), z) x %>% select(which(colSums(!is.na(.))>0)) # remove columns with all missing data x %>% select(which(colSums(!is.na(.))>0)) %>% filter((rowSums(!is.na(.))>0)) # remove all-NA columns _and_ rows
suppressPackageStartupMessages(library("dplyr")) starwars %>% filter(., height > 200) %>% select(., height, mass) %>% head(.) # instead of starwars %>% filter(height > 200) %>% select(height, mass) %>% head
iris$Species iris[["Species"]] iris %>% `[[`("Species") iris %>% `[[`(5) iris %>% subset(select = "Species")
- Split-apply-combine: group + summarize + sort/arrange + top n. The following example is from Efficient R programming.
data(wb_ineq, package = "efficient") wb_ineq %>% filter(grepl("g", Country)) %>% group_by(Year) %>% summarise(gini = mean(gini, na.rm = TRUE)) %>% arrange(desc(gini)) %>% top_n(n = 5)
- Writing Pipe-friendly Functions
- http://rud.is/b/2015/02/04/a-step-to-the-right-in-r-assignments/
- http://rpubs.com/tjmahr/pipelines_2015
- http://danielmarcelino.com/i-loved-this-crosstable/
- http://moderndata.plot.ly/using-the-pipe-operator-in-r-with-plotly/
- RMSE
f <- function(x) { (y - x) %>% '^'(2) %>% sum %>% '/'(length(x)) %>% sqrt %>% round(2) }
# Examples from R for Data Science-Import, Tidy, Transform, Visualize, and Model diamonds <- ggplot2::diamonds diamonds2 <- diamonds %>% dplyr::mutate(price_per_carat = price / carat) pryr::object_size(diamonds) pryr::object_size(diamonds2) pryr::object_size(diamonds, diamonds2) rnorm(100) %>% matrix(ncol = 2) %>% plot() %>% str() rnorm(100) %>% matrix(ncol = 2) %T>% plot() %>% str() # 'tee' pipe # %T>% works like %>% except that it returns the lefthand side (rnorm(100) %>% matrix(ncol = 2)) # instead of the righthand side. # If a function does not have a data frame based api, you can use %$%. # It explodes out the variables in a data frame. mtcars %$% cor(disp, mpg) # For assignment, magrittr provides the %<>% operator mtcars <- mtcars %>% transform(cyl = cyl * 2) # can be simplified by mtcars %<>% transform(cyl = cyl * 2)
Upsides of using magrittr: no need to create intermediate objects, code is easy to read.
When not to use the pipe
- your pipes are longer than (say) 10 steps
- you have multiple inputs or outputs
- Functions that use the current environment: assign(), get(), load()
- Functions that use lazy evaluation: tryCatch(), try()
outer()
Genomic sequence
- chartr
> yourSeq <- "AAAACCCGGGTTTNNN" > chartr("ACGT", "TGCA", yourSeq) [1] "TTTTGGGCCCAAANNN"
broom
broom: Convert Statistical Analysis Objects into Tidy Tibbles
lobstr package - dig into the internal representation and structure of R objects
Data Science
See Data science page
microbenchmark & rbenchmark
- microbenchmark
- rbenchmark (not updated since 2012)
jpeg
If we want to create the image on this wiki left hand side panel, we can use the jpeg package to read an existing plot and then edit and save it.
We can also use the jpeg package to import and manipulate a jpg image. See Fun with Heatmaps and Plotly.
Cairo
See White strips problem in png() or tiff().
cairoDevice
PS. Not sure the advantage of functions in this package compared to R's functions (eg. Cairo_svg() vs svg()).
For ubuntu OS, we need to install 2 libraries and 1 R package RGtk2.
sudo apt-get install libgtk2.0-dev libcairo2-dev
On Windows OS, we may got the error: unable to load shared object 'C:/Program Files/R/R-3.0.2/library/cairoDevice/libs/x64/cairoDevice.dll' . We need to follow the instruction in here.
igraph
creating directed networks with igraph
Identifying dependencies of R functions and scripts
https://stackoverflow.com/questions/8761857/identifying-dependencies-of-r-functions-and-scripts
library(mvbutils) foodweb(where = "package:batr") foodweb( find.funs("package:batr"), prune="survRiskPredict", lwd=2) foodweb( find.funs("package:batr"), prune="classPredict", lwd=2)
iterators
Iterator is useful over for-loop if the data is already a collection. It can be used to iterate over a vector, data frame, matrix, file
Iterator can be combined to use with foreach package http://www.exegetic.biz/blog/2013/11/iterators-in-r/ has more elaboration.
Colors
- http://www.bauer.uh.edu/parks/truecolor.htm Interactive RGB, Alpha and Color Picker
- http://deanattali.com/blog/colourpicker-package/ Not sure what it is doing
- How to Choose the Best Colors For Your Data Charts
- How to expand color palette with ggplot and RColorBrewer
- Color names in R
colortools
Tools that allow users generate color schemes and palettes
colourpicker
A Colour Picker Tool for Shiny and for Selecting Colours in Plots
inlmisc
GetTolColors(). Lots of examples.
rex
Friendly Regular Expressions
formatR
The best strategy to avoid failure is to put comments in complete lines or after complete R expressions.
See also this discussion on stackoverflow talks about R code reformatting.
library(formatR) tidy_source("Input.R", file = "output.R", width.cutoff=70) tidy_source("clipboard") # default width is getOption("width") which is 127 in my case.
Some issues
- Comments appearing at the beginning of a line within a long complete statement. This will break tidy_source().
cat("abcd", # This is my comment "defg")
will result in
> tidy_source("clipboard") Error in base::parse(text = code, srcfile = NULL) : 3:1: unexpected string constant 2: invisible(".BeGiN_TiDy_IdEnTiFiEr_HaHaHa# This is my comment.HaHaHa_EnD_TiDy_IdEnTiFiEr") 3: "defg" ^
- Comments appearing at the end of a line within a long complete statement won't break tidy_source() but tidy_source() cannot re-locate/tidy the comma sign.
cat("abcd" ,"defg" # This is my comment ,"ghij")
will become
cat("abcd", "defg" # This is my comment , "ghij")
Still bad!!
- Comments appearing at the end of a line within a long complete statement breaks tidy_source() function. For example,
cat("</p>", "<HR SIZE=5 WIDTH=\"100%\" NOSHADE>", ifelse(codeSurv == 0,"<h3><a name='Genes'><b><u>Genes which are differentially expressed among classes:</u></b></a></h3>", #4/9/09 "<h3><a name='Genes'><b><u>Genes significantly associated with survival:</u></b></a></h3>"), file=ExternalFileName, sep="\n", append=T)
will result in
> tidy_source("clipboard", width.cutoff=70) Error in base::parse(text = code, srcfile = NULL) : 3:129: unexpected SPECIAL 2: "<HR SIZE=5 WIDTH=\"100%\" NOSHADE>" , 3: ifelse ( codeSurv == 0 , "<h3><a name='Genes'><b><u>Genes which are differentially expressed among classes:</u></b></a></h3>" , %InLiNe_IdEnTiFiEr%
- width.cutoff parameter is not always working. For example, there is no any change for the following snippet though I hope it will move the cat() to the next line.
if (codePF & !GlobalTest & !DoExactPermTest) cat(paste("Multivariate Permutations test was computed based on", NumPermutations, "random permutations"), "<BR>", " ", file = ExternalFileName, sep = "\n", append = T)
- It merges lines though I don't always want to do that. For example
cat("abcd" ,"defg" ,"ghij")
will become
cat("abcd", "defg", "ghij")
Download papers
biorxivr
Search and Download Papers from the bioRxiv Preprint Server
aRxiv
Interface to the arXiv API
pdftools
- http://ropensci.org/blog/2016/03/01/pdftools-and-jeroen
- http://r-posts.com/how-to-extract-data-from-a-pdf-file-with-r/
- https://ropensci.org/technotes/2018/12/14/pdftools-20/
aside: set it aside
An RStudio addin to run long R commands aside your current session.
Teaching
- smovie: Some Movies to Illustrate Concepts in Statistics
Organize R research project
- Organizing R Research Projects: CPAT, A Case Study
- Project-oriented workflow. It suggests the here package. Don't use setwd() and rm(list = ls()).
- In RStudio, if we try to send a few lines of code and one of the line contains setwd(), it will give a message: The working directory was changed to XXX inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
- No update for the here package. Consider usethis package (Automate project and package setup).
- drake project
How to save (and load) datasets in R (.Rdata vs .Rds file)
How to save (and load) datasets in R: An overview
Text to speech
Text-to-Speech with the googleLanguageR package
Weather data
- prism package
- Weatherbase
logR
https://github.com/jangorecki/logR
Progress bar
https://github.com/r-lib/progress#readme
Configurable Progress bars, they may include percentage, elapsed time, and/or the estimated completion time. They work in terminals, in 'Emacs' 'ESS', 'RStudio', 'Windows' 'Rgui' and the 'macOS'.
cron
Different ways of using R
Extending R by John M. Chambers (2016)
10 things R can do that might surprise you
https://simplystatistics.org/2019/03/13/10-things-r-can-do-that-might-surprise-you/
R call C/C++
Mainly talks about .C() and .Call().
Note that scalars and arrays must be passed using pointers. So if we want to access a function not exported from a package, we may need to modify the function to make the arguments as pointers.
- R-Extension manual of course.
- Compiled Code chapter from 'R Packages' by Hadley Wickham
- http://faculty.washington.edu/kenrice/sisg-adv/sisg-07.pdf
- http://www.stat.berkeley.edu/scf/paciorek-cppWorkshop.pdf (Very useful)
- http://www.stat.harvard.edu/ccr2005/
- http://mazamascience.com/WorkingWithData/?p=1099
NAMESPACE file & useDynLib
- https://cran.r-project.org/doc/manuals/r-release/R-exts.html#useDynLib
- We don't need to include double quotes around the C/Fortran subroutines in .C() or .Fortran()
- digest package example: NAMESPACE and R functions using .Call().
- stats example: NAMESPACE
(From Writing R Extensions manual) Loading is most often done automatically based on the useDynLib() declaration in the NAMESPACE file, but may be done explicitly via a call to library.dynam(). This has the form
library.dynam("libname", package, lib.loc)
library.dynam.unload()
- https://stat.ethz.ch/R-manual/R-devel/library/base/html/library.dynam.html
- http://r-pkgs.had.co.nz/src.html. The library.dynam.unload() function should be placed in .onUnload() function. This function can be saved in any R files.
- digest package example zzz.R
gcc
Coping with varying `gcc` versions and capabilities in R packages
SEXP
Some examples from packages
- sva package has one C code function
R call Fortran
- R call Fortran 90
- The Need for Speed Part 1: Building an R Package with Fortran (or C) (Very detailed)
Embedding R
- See Writing for R Extensions Manual Chapter 8.
- Talk by Simon Urbanek in UseR 2004.
- Technical report by Friedrich Leisch in 2007.
- https://stat.ethz.ch/pipermail/r-help/attachments/20110729/b7d86ed7/attachment.pl
An very simple example (do not return from shell) from Writing R Extensions manual
The command-line R front-end, R_HOME/bin/exec/R, is one such example. Its source code is in file <src/main/Rmain.c>.
This example can be run by
R_HOME/bin/R CMD R_HOME/bin/exec/R
Note:
- R_HOME/bin/exec/R is the R binary. However, it couldn't be launched directly unless R_HOME and LD_LIBRARY_PATH are set up. Again, this is explained in Writing R Extension manual.
- R_HOME/bin/R is a shell-script front-end where users can invoke it. It sets up the environment for the executable. It can be copied to /usr/local/bin/R. When we run R_HOME/bin/R, it actually runs R_HOME/bin/R CMD R_HOME/bin/exec/R (see line 259 of R_HOME/bin/R as in R 3.0.2) so we know the important role of R_HOME/bin/exec/R.
More examples of embedding can be found in tests/Embedding directory. Read <index.html> for more information about these test examples.
An example from Bioconductor workshop
- What is covered in this section is different from Create and use a standalone Rmath library.
- Use eval() function. See R-Ext 8.1 and 8.2 and 5.11.
- http://stackoverflow.com/questions/2463437/r-from-c-simplest-possible-helloworld (obtained from searching R_tryEval on google)
- http://stackoverflow.com/questions/7457635/calling-r-function-from-c
Example: Create <embed.c> file
#include <Rembedded.h> #include <Rdefines.h> static void doSplinesExample(); int main(int argc, char *argv[]) { Rf_initEmbeddedR(argc, argv); doSplinesExample(); Rf_endEmbeddedR(0); return 0; } static void doSplinesExample() { SEXP e, result; int errorOccurred; // create and evaluate 'library(splines)' PROTECT(e = lang2(install("library"), mkString("splines"))); R_tryEval(e, R_GlobalEnv, &errorOccurred); if (errorOccurred) { // handle error } UNPROTECT(1); // 'options(FALSE)' ... PROTECT(e = lang2(install("options"), ScalarLogical(0))); // ... modified to 'options(example.ask=FALSE)' (this is obscure) SET_TAG(CDR(e), install("example.ask")); R_tryEval(e, R_GlobalEnv, NULL); UNPROTECT(1); // 'example("ns")' PROTECT(e = lang2(install("example"), mkString("ns"))); R_tryEval(e, R_GlobalEnv, &errorOccurred); UNPROTECT(1); }
Then build the executable. Note that I don't need to create R_HOME variable.
cd tar xzvf cd R-3.0.1 ./configure --enable-R-shlib make cd tests/Embedding make ~/R-3.0.1/bin/R CMD ./Rtest nano embed.c # Using a single line will give an error and cannot not show the real problem. # ../../bin/R CMD gcc -I../../include -L../../lib -lR embed.c # A better way is to run compile and link separately gcc -I../../include -c embed.c gcc -o embed embed.o -L../../lib -lR -lRblas ../../bin/R CMD ./embed
Note that if we want to call the executable file ./embed directly, we shall set up R environment by specifying R_HOME variable and including the directories used in linking R in LD_LIBRARY_PATH. This is based on the inform provided by Writing R Extensions.
export R_HOME=/home/brb/Downloads/R-3.0.2 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/brb/Downloads/R-3.0.2/lib ./embed # No need to include R CMD in front.
Question: Create a data frame in C? Answer: Use data.frame() via an eval() call from C. Or see the code is stats/src/model.c, as part of model.frame.default. Or using Rcpp as here.
Reference http://bioconductor.org/help/course-materials/2012/Seattle-Oct-2012/AdvancedR.pdf
Create a Simple Socket Server in R
This example is coming from this paper.
Create an R function
simpleServer <- function(port=6543) { sock <- socketConnection ( port=port , server=TRUE) on.exit(close( sock )) cat("\nWelcome to R!\nR>" ,file=sock ) while(( line <- readLines ( sock , n=1)) != "quit") { cat(paste("socket >" , line , "\n")) out<- capture.output (try(eval(parse(text=line )))) writeLines ( out , con=sock ) cat("\nR> " ,file =sock ) } }
Then run simpleServer(). Open another terminal and try to communicate with the server
$ telnet localhost 6543 Trying 127.0.0.1... Connected to localhost. Escape character is '^]'. Welcome to R! R> summary(iris[, 3:5]) Petal.Length Petal.Width Species Min. :1.000 Min. :0.100 setosa :50 1st Qu.:1.600 1st Qu.:0.300 versicolor:50 Median :4.350 Median :1.300 virginica :50 Mean :3.758 Mean :1.199 3rd Qu.:5.100 3rd Qu.:1.800 Max. :6.900 Max. :2.500 R> quit Connection closed by foreign host.
Rserve
Note the way of launching Rserve is like the way we launch C program when R was embedded in C. See Example from Bioconductor workshop.
See my Rserve page.
(Commercial) StatconnDcom
R.NET
rJava
- A primer in using Java from R - part 1
- Note rJava is needed by xlsx package.
Terminal
# jdk 7 sudo apt-get install openjdk-7-* update-alternatives --config java # oracle jdk 8 sudo add-apt-repository -y ppa:webupd8team/java sudo apt-get update echo debconf shared/accepted-oracle-license-v1-1 select true | sudo debconf-set-selections echo debconf shared/accepted-oracle-license-v1-1 seen true | sudo debconf-set-selections sudo apt-get -y install openjdk-8-jdk
and then run the following (thanks to http://stackoverflow.com/questions/12872699/error-unable-to-load-installed-packages-just-now) to fix an error: libjvm.so: cannot open shared object file: No such file or directory.
- Create the file /etc/ld.so.conf.d/java.conf with the following entries:
/usr/lib/jvm/java-8-oracle/jre/lib/amd64 /usr/lib/jvm/java-8-oracle/jre/lib/amd64/server
- And then run sudo ldconfig
Now go back to R
install.packages("rJava")
Done!
If above does not work, a simple way is by (under Ubuntu) running
sudo apt-get install r-cran-rjava
which will create new package 'default-jre' (under /usr/lib/jvm) and 'default-jre-headless'.
RCaller
RApache
littler
Provides hash-bang (#!) capability for R
FAQs:
- Difference between Rscript and littler
- Whats the difference between Rscript and R CMD BATCH
- Why (or when) is Rscript (or littler) better than R CMD BATCH?
root@ed5f80320266:/# ls -l /usr/bin/{r,R*} # R 3.5.2 docker container -rwxr-xr-x 1 root root 82632 Jan 26 18:26 /usr/bin/r # binary, can be used for 'shebang' lines, r --help # Example: r --verbose -e "date()" -rwxr-xr-x 1 root root 8722 Dec 20 11:35 /usr/bin/R # text, R --help # Example: R -q -e "date()" -rwxr-xr-x 1 root root 14552 Dec 20 11:35 /usr/bin/Rscript # binary, can be used for 'shebang' lines, Rscript --help # It won't show the startup message when it is used in the command line. # Example: Rscript -e "date()"
We can install littler using two ways.
- install.packages("littler"). This will install the latest version but the binary 'r' program is only available under the package/bin directory (eg ~/R/x86_64-pc-linux-gnu-library/3.4/littler/bin/r). You need to create a soft link in order to access it globally.
- sudo apt install littler. This will install 'r' globally; however, the installed version may be old.
After the installation, vignette contains several examples. The off-line vignette has a table of contents. Nice! The web version of examples does not have the TOC.
r was not meant to run interactively like R. See man r.
RInside: Embed R in C++
See RInside
(From RInside documentation) The RInside package makes it easier to embed R in your C++ applications. There is no code you would execute directly from the R environment. Rather, you write C++ programs that embed R which is illustrated by some the included examples.
The included examples are armadillo, eigen, mpi, qt, standard, threads and wt.
To run 'make' when we don't have a global R, we should modify the file <Makefile>. Also if we just want to create one executable file, we can do, for example, 'make rinside_sample1'.
To run any executable program, we need to specify LD_LIBRARY_PATH variable, something like
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/brb/Downloads/R-3.0.2/lib
The real build process looks like (check <Makefile> for completeness)
g++ -I/home/brb/Downloads/R-3.0.2/include \ -I/home/brb/Downloads/R-3.0.2/library/Rcpp/include \ -I/home/brb/Downloads/R-3.0.2/library/RInside/include -g -O2 -Wall \ -I/usr/local/include \ rinside_sample0.cpp \ -L/home/brb/Downloads/R-3.0.2/lib -lR -lRblas -lRlapack \ -L/home/brb/Downloads/R-3.0.2/library/Rcpp/lib -lRcpp \ -Wl,-rpath,/home/brb/Downloads/R-3.0.2/library/Rcpp/lib \ -L/home/brb/Downloads/R-3.0.2/library/RInside/lib -lRInside \ -Wl,-rpath,/home/brb/Downloads/R-3.0.2/library/RInside/lib \ -o rinside_sample0
Hello World example of embedding R in C++.
#include <RInside.h> // for the embedded R via RInside int main(int argc, char *argv[]) { RInside R(argc, argv); // create an embedded R instance R["txt"] = "Hello, world!\n"; // assign a char* (string) to 'txt' R.parseEvalQ("cat(txt)"); // eval the init string, ignoring any returns exit(0); }
The above can be compared to the Hello world example in Qt.
#include <QApplication.h> #include <QPushButton.h> int main( int argc, char **argv ) { QApplication app( argc, argv ); QPushButton hello( "Hello world!", 0 ); hello.resize( 100, 30 ); app.setMainWidget( &hello ); hello.show(); return app.exec(); }
RFortran
RFortran is an open source project with the following aim:
To provide an easy to use Fortran software library that enables Fortran programs to transfer data and commands to and from R.
It works only on Windows platform with Microsoft Visual Studio installed:(
Call R from other languages
C
Error: “not resolved from current namespace” error, when calling C routines from R
Solution: add getNativeSymbolInfo() around your C/Fortran symbols. Search Google:r dyn.load not resolved from current namespace
JRI
ryp2
http://rpy.sourceforge.net/rpy2.html
Create a standalone Rmath library
R has many math and statistical functions. We can easily use these functions in our C/C++/Fortran. The definite guide of doing this is on Chapter 9 "The standalone Rmath library" of R-admin manual.
Here is my experience based on R 3.0.2 on Windows OS.
Create a static library <libRmath.a> and a dynamic library <Rmath.dll>
Suppose we have downloaded R source code and build R from its source. See Build_R_from_its_source. Then the following 2 lines will generate files <libRmath.a> and <Rmath.dll> under C:\R\R-3.0.2\src\nmath\standalone directory.
cd C:\R\R-3.0.2\src\nmath\standalone make -f Makefile.win
Use Rmath library in our code
set CPLUS_INCLUDE_PATH=C:\R\R-3.0.2\src\include set LIBRARY_PATH=C:\R\R-3.0.2\src\nmath\standalone # It is not LD_LIBRARY_PATH in above. # Created <RmathEx1.cpp> from the book "Statistical Computing in C++ and R" web site # http://math.la.asu.edu/~eubank/CandR/ch4Code.cpp # It is OK to save the cpp file under any directory. # Force to link against the static library <libRmath.a> g++ RmathEx1.cpp -lRmath -lm -o RmathEx1.exe # OR g++ RmathEx1.cpp -Wl,-Bstatic -lRmath -lm -o RmathEx1.exe # Force to link against dynamic library <Rmath.dll> g++ RmathEx1.cpp Rmath.dll -lm -o RmathEx1Dll.exe
Test the executable program. Note that the executable program RmathEx1.exe can be transferred to and run in another computer without R installed. Isn't it cool!
c:\R>RmathEx1 Enter a argument for the normal cdf: 1 Enter a argument for the chi-squared cdf: 1 Prob(Z <= 1) = 0.841345 Prob(Chi^2 <= 1)= 0.682689
Below is the cpp program <RmathEx1.cpp>.
//RmathEx1.cpp #define MATHLIB_STANDALONE #include <iostream> #include "Rmath.h" using std::cout; using std::cin; using std::endl; int main() { double x1, x2; cout << "Enter a argument for the normal cdf:" << endl; cin >> x1; cout << "Enter a argument for the chi-squared cdf:" << endl; cin >> x2; cout << "Prob(Z <= " << x1 << ") = " << pnorm(x1, 0, 1, 1, 0) << endl; cout << "Prob(Chi^2 <= " << x2 << ")= " << pchisq(x2, 1, 1, 0) << endl; return 0; }
Calling R.dll directly
See Chapter 8.2.2 of R Extensions. This is related to embedding R under Windows. The file <R.dll> on Windows is like <libR.so> on Linux.
Create HTML report
ReportingTools (Jason Hackney) from Bioconductor.
htmlTable package
The htmlTable package is intended for generating tables using HTML formatting. This format is compatible with Markdown when used for HTML-output. The most basic table can easily be created by just passing a matrix or a data.frame to the htmlTable-function.
- http://cran.r-project.org/web/packages/htmlTable/vignettes/general.html
- http://gforge.se/2014/01/fast-track-publishing-using-knitr-part-iv/
formattable
- https://github.com/renkun-ken/formattable
- http://www.magesblog.com/2016/01/formatting-table-output-in-r.html
- Make Beautiful Tables with the Formattable Package
htmltab package
This package is NOT used to CREATE html report but EXTRACT html table.
ztable package
Makes zebra-striped tables (tables with alternating row colors) in LaTeX and HTML formats easily from a data.frame, matrix, lm, aov, anova, glm or coxph objects.
Create academic report
reports package in CRAN and in github repository. The youtube video gives an overview of the package.
Create pdf and epub files
# Idea: # knitr pdflatex # rnw -------> tex ----------> pdf library(knitr) knit("example.rnw") # create example.tex file
- A very simple example <002-minimal.Rnw> from yihui.name works fine on linux.
git clone https://github.com/yihui/knitr-examples.git
- <knitr-minimal.Rnw>. I have no problem to create pdf file on Windows but still cannot generate pdf on Linux from tex file. Some people suggested to run sudo apt-get install texlive-fonts-recommended to install missing fonts. It works!
To see a real example, check out DESeq2 package (inst/doc subdirectory). In addition to DESeq2, I also need to install DESeq, BiocStyle, airway, vsn, gplots, and pasilla packages from Bioconductor. Note that, it is best to use sudo/admin account to install packages.
Or starts with markdown file. Download the example <001-minimal.Rmd> and remove the last line of getting png file from internet.
# Idea: # knitr pandoc # rmd -------> md ----------> pdf git clone https://github.com/yihui/knitr-examples.git cd knitr-examples R -e "library(knitr); knit('001-minimal.Rmd')" pandoc 001-minimal.md -o 001-minimal.pdf # require pdflatex to be installed !!
To create an epub file (not success yet on Windows OS, missing figures on Linux OS)
# Idea: # knitr pandoc # rnw -------> tex ----------> markdown or epub library(knitr) knit("DESeq2.Rnw") # create DESeq2.tex system("pandoc -f latex -t markdown -o DESeq2.md DESeq2.tex")
## Windows OS, epub cannot be built pandoc: Error: "source" (line 41, column 7): unexpected "k" expecting "{document}" ## Linux OS, epub missing figures and R codes. ## First install texlive base and extra packages ## sudo apt-get install texlive-latex-base texlive-latex-extra pandoc: Could not find media `figure/SchwederSpjotvoll-1', skipping... pandoc: Could not find media `figure/sortedP-1', skipping... pandoc: Could not find media `figure/figHeatmap2c-1', skipping... pandoc: Could not find media `figure/figHeatmap2b-1', skipping... pandoc: Could not find media `figure/figHeatmap2a-1', skipping... pandoc: Could not find media `figure/plotCountsAdv-1', skipping... pandoc: Could not find media `figure/plotCounts-1', skipping... pandoc: Could not find media `figure/MA-1', skipping... pandoc: Could not find media `figure/MANoPrior-1', skipping...
The problems are at least
- figures need to be generated under the same directory as the source code
- figures cannot be in the format of pdf (DESeq2 generates both pdf and png files format)
- missing R codes
Convert tex to epub
kable() for tables
Create Tables In LaTeX, HTML, Markdown And ReStructuredText
- https://rmarkdown.rstudio.com/lesson-7.html
- https://stackoverflow.com/questions/20942466/creating-good-kable-output-in-rstudio
- http://kbroman.org/knitr_knutshell/pages/figs_tables.html
- https://blogs.reed.edu/ed-tech/2015/10/creating-nice-tables-using-r-markdown/
- kableExtra package
Create Word report
knitr + pandoc
- http://www.r-statistics.com/2013/03/write-ms-word-document-using-r-with-as-little-overhead-as-possible/
- http://www.carlboettiger.info/2012/04/07/writing-reproducibly-in-the-open-with-knitr.html
- http://rmarkdown.rstudio.com/articles_docx.html
It is better to create rmd file in RStudio. Rstudio provides a template for rmd file and it also provides a quick reference to R markdown language.
# Idea: # knitr pandoc # rmd -------> md --------> docx library(knitr) knit2html("example.rmd") #Create md and html files
and then
FILE <- "example" system(paste0("pandoc -o ", FILE, ".docx ", FILE, ".md"))
Note. For example reason, if I play around the above 2 commands for several times, the knit2html() does not work well. However, if I click 'Knit HTML' button on the RStudio, it then works again.
Another way is
library(pander) name = "demo" knit(paste0(name, ".Rmd"), encoding = "utf-8") Pandoc.brew(file = paste0(name, ".md"), output = paste0(-name, "docx"), convert = "docx")
Note that once we have used knitr command to create a md file, we can use pandoc shell command to convert it to different formats:
- A pdf file: pandoc -s report.md -t latex -o report.pdf
- A html file: pandoc -s report.md -o report.html (with the -c flag html files can be added easily)
- Openoffice: pandoc report.md -o report.odt
- Word docx: pandoc report.md -o report.docx
We can also create the epub file for reading on Kobo ereader. For example, download this file and save it as example.Rmd. I need to remove the line containing the link to http://i.imgur.com/RVNmr.jpg since it creates an error when I run pandoc (not sure if it is the pandoc version I have is too old). Now we just run these 2 lines to get the epub file. Amazing!
knit("example.Rmd") pandoc("example.md", format="epub")
PS. If we don't remove the link, we will get an error message (pandoc 1.10.1 on Windows 7)
> pandoc("Rmd_to_Epub.md", format="epub") executing pandoc -f markdown -t epub -o Rmd_to_Epub.epub "Rmd_to_Epub.utf8md" pandoc.exe: .\.\http://i.imgur.com/RVNmr.jpg: openBinaryFile: invalid argument (Invalid argument) Error in (function (input, format, ext, cfg) : conversion failed In addition: Warning message: running command 'pandoc -f markdown -t epub -o Rmd_to_Epub.epub "Rmd_to_Epub.utf8md"' had status 1
pander
Try pandoc[1] with a minimal reproducible example, you might give a try to my "pander" package [2] too:
library(pander) Pandoc.brew(system.file('examples/minimal.brew', package='pander'), output = tempfile(), convert = 'docx')
Where the content of the "minimal.brew" file is something you might have got used to with Sweave - although it's using "brew" syntax instead. See the examples of pander [3] for more details. Please note that pandoc should be installed first, which is pretty easy on Windows.
- http://johnmacfarlane.net/pandoc/
- http://rapporter.github.com/pander/
- http://rapporter.github.com/pander/#examples
R2wd
Use R2wd package. However, only 32-bit R is allowed and sometimes it can not produce all 'table's.
> library(R2wd) > wdGet() Loading required package: rcom Loading required package: rscproxy rcom requires a current version of statconnDCOM installed. To install statconnDCOM type installstatconnDCOM() This will download and install the current version of statconnDCOM You will need a working Internet connection because installation needs to download a file. Error in if (wdapp[["Documents"]][["Count"]] == 0) wdapp[["Documents"]]$Add() : argument is of length zero
The solution is to launch 32-bit R instead of 64-bit R since statconnDCOM does not support 64-bit R.
Convert from pdf to word
The best rendering of advanced tables is done by converting from pdf to Word. See http://biostat.mc.vanderbilt.edu/wiki/Main/SweaveConvert
rtf
Use rtf package for Rich Text Format (RTF) Output.
xtable
Package xtable will produce html output.
print(xtable(X), type="html")
If you save the file and then open it with Word, you will get serviceable results. I've had better luck copying the output from xtable and pasting it into Excel.
ReporteRs
Microsoft Word, Microsoft Powerpoint and HTML documents generation from R. The source code is hosted on https://github.com/davidgohel/ReporteRs
PDF manipulation
R Graphs Gallery
- Romain François
- R Graph Catalog written using R + Shiny. The source code is available on Github.
- Forest plot. See the packages rmeta and forestplot. The forest plot can be used to plot the quantities like relative risk (with 95% CI) in survival data.
COM client or server
Client
RDCOMClient where excel.link depends on it.
Server
Use R under proxy
http://support.rstudio.org/help/kb/faq/configuring-r-to-use-an-http-proxy
RStudio
- Github
- Installing RStudio (1.0.44) on Ubuntu will not install Java even the source code contains 37.5% Java??
- Preview
rstudio.cloud
Launch RStudio
Create .Rproj file
If you have an existing package that doesn't have an .Rproj file, you can use devtools::use_rstudio("path/to/package") to add it.
With an RStudio project file, you can
- Restore .RData into workspace at startup
- Save workspace to .RData on exit
- Always save history (even if no saving .RData)
- etc
package search
https://github.com/RhoInc/CRANsearcher
Git
Visual Studio
R and Python support now built in to Visual Studio 2017
List files using regular expression
- Extension
list.files(pattern = "\\.txt$")
where the dot (.) is a metacharacter. It is used to refer to any character.
- Start with
list.files(pattern = "^Something")
Using Sys.glob()"' as
> Sys.glob("~/Downloads/*.txt") [1] "/home/brb/Downloads/ip.txt" "/home/brb/Downloads/valgrind.txt"
Hidden tool: rsync in Rtools
c:\Rtools\bin>rsync -avz "/cygdrive/c/users/limingc/Downloads/a.exe" "/cygdrive/c/users/limingc/Documents/" sending incremental file list a.exe sent 323142 bytes received 31 bytes 646346.00 bytes/sec total size is 1198416 speedup is 3.71 c:\Rtools\bin>
And rsync works best when we need to sync folder.
c:\Rtools\bin>rsync -avz "/cygdrive/c/users/limingc/Downloads/binary" "/cygdrive/c/users/limingc/Documents/" sending incremental file list binary/ binary/Eula.txt binary/cherrytree.lnk binary/depends64.chm binary/depends64.dll binary/depends64.exe binary/mtputty.exe binary/procexp.chm binary/procexp.exe binary/pscp.exe binary/putty.exe binary/sqlite3.exe binary/wget.exe sent 4115294 bytes received 244 bytes 1175868.00 bytes/sec total size is 8036311 speedup is 1.95 c:\Rtools\bin>rm c:\users\limingc\Documents\binary\procexp.exe cygwin warning: MS-DOS style path detected: c:\users\limingc\Documents\binary\procexp.exe Preferred POSIX equivalent is: /cygdrive/c/users/limingc/Documents/binary/procexp.exe CYGWIN environment variable option "nodosfilewarning" turns off this warning. Consult the user's guide for more details about POSIX paths: http://cygwin.com/cygwin-ug-net/using.html#using-pathnames c:\Rtools\bin>rsync -avz "/cygdrive/c/users/limingc/Downloads/binary" "/cygdrive/c/users/limingc/Documents/" sending incremental file list binary/ binary/procexp.exe sent 1767277 bytes received 35 bytes 3534624.00 bytes/sec total size is 8036311 speedup is 4.55 c:\Rtools\bin>
Unforunately, if the destination is a network drive, I could get a permission denied (13) error. See also http://superuser.com/questions/69620/rsync-file-permissions-on-windows
Install rgdal package (geospatial Data) on ubuntu
Terminal
sudo apt-get install libgdal1-dev libproj-dev # https://stackoverflow.com/a/44389304 sudo apt-get install libgdal1i # Ubuntu 16.04 https://stackoverflow.com/a/12143411
R
install.packages("rgdal")
Install sf package
I got the following error even I have installed some libraries.
checking GDAL version >= 2.0.1... no configure: error: sf is not compatible with GDAL versions below 2.0.1
Then I follow the instruction here
sudo apt remove libgdal-dev sudo apt remove libproj-dev sudo apt remove gdal-bin sudo add-apt-repository ppa:ubuntugis/ubuntugis-stable sudo apt update sudo apt-cache policy libgdal-dev # Make sure a version >= 2.0 appears sudo apt install libgdal-dev
Set up Emacs on Windows
Edit the file C:\Program Files\GNU Emacs 23.2\site-lisp\site-start.el with something like
(setq-default inferior-R-program-name "c:/program files/r/r-2.15.2/bin/i386/rterm.exe")
Database
RSQLite
- https://cran.r-project.org/web/packages/RSQLite/vignettes/RSQLite.html
- https://github.com/rstats-db/RSQLite
Creating a new database:
library(DBI) mydb <- dbConnect(RSQLite::SQLite(), "my-db.sqlite") dbDisconnect(mydb) unlink("my-db.sqlite") # temporary database mydb <- dbConnect(RSQLite::SQLite(), "") dbDisconnect(mydb)
Loading data:
mydb <- dbConnect(RSQLite::SQLite(), "") dbWriteTable(mydb, "mtcars", mtcars) dbWriteTable(mydb, "iris", iris) dbListTables(mydb) dbListFields(con, "mtcars") dbReadTable(con, "mtcars")
Queries:
dbGetQuery(mydb, 'SELECT * FROM mtcars LIMIT 5') dbGetQuery(mydb, 'SELECT * FROM iris WHERE "Sepal.Length" < 4.6') dbGetQuery(mydb, 'SELECT * FROM iris WHERE "Sepal.Length" < :x', params = list(x = 4.6)) res <- dbSendQuery(con, "SELECT * FROM mtcars WHERE cyl = 4") dbFetch(res)
Batched queries:
dbClearResult(rs) rs <- dbSendQuery(mydb, 'SELECT * FROM mtcars') while (!dbHasCompleted(rs)) { df <- dbFetch(rs, n = 10) print(nrow(df)) } dbClearResult(rs)
Multiple parameterised queries:
rs <- dbSendQuery(mydb, 'SELECT * FROM iris WHERE "Sepal.Length" = :x') dbBind(rs, param = list(x = seq(4, 4.4, by = 0.1))) nrow(dbFetch(rs)) #> [1] 4 dbClearResult(rs)
Statements:
dbExecute(mydb, 'DELETE FROM iris WHERE "Sepal.Length" < 4') #> [1] 0 rs <- dbSendStatement(mydb, 'DELETE FROM iris WHERE "Sepal.Length" < :x') dbBind(rs, param = list(x = 4.5)) dbGetRowsAffected(rs) #> [1] 4 dbClearResult(rs)
sqldf
Manipulate R data frames using SQL. Depends on RSQLite. A use of gsub, reshape2 and sqldf with healthcare data
RPostgreSQL
RMySQL
- http://datascienceplus.com/bringing-the-powers-of-sql-into-r/
- See here about the installation of the required package (libmysqlclient-dev) in Ubuntu.
MongoDB
- http://www.r-bloggers.com/r-and-mongodb/
- http://watson.nci.nih.gov/~sdavis/blog/rmongodb-using-R-with-mongo/
odbc
RODBC
DBI
dbplyr
- To use databases with dplyr, you need to first install dbplyr
- https://db.rstudio.com/dplyr/
- Five commonly used backends: RMySQL, RPostgreSQ, RSQLite, ODBC, bigrquery.
- http://www.datacarpentry.org/R-ecology-lesson/05-r-and-databases.html
Create a new SQLite database:
surveys <- read.csv("data/surveys.csv") plots <- read.csv("data/plots.csv") my_db_file <- "portal-database.sqlite" my_db <- src_sqlite(my_db_file, create = TRUE) copy_to(my_db, surveys) copy_to(my_db, plots) my_db
Connect to a database:
download.file(url = "https://ndownloader.figshare.com/files/2292171", destfile = "portal_mammals.sqlite", mode = "wb") library(dbplyr) library(dplyr) mammals <- src_sqlite("portal_mammals.sqlite")
Querying the database with the SQL syntax:
tbl(mammals, sql("SELECT year, species_id, plot_id FROM surveys"))
Querying the database with the dplyr syntax:
surveys <- tbl(mammals, "surveys") surveys %>% select(year, species_id, plot_id) head(surveys, n = 10) show_query(head(surveys, n = 10)) # show which SQL commands are actually sent to the database
Simple database queries:
surveys %>% filter(weight < 5) %>% select(species_id, sex, weight)
Laziness (instruct R to stop being lazy):
data_subset <- surveys %>% filter(weight < 5) %>% select(species_id, sex, weight) %>% collect()
Complex database queries:
plots <- tbl(mammals, "plots") plots # # The plot_id column features in the plots table surveys # The plot_id column also features in the surveys table # Join databases method 1 plots %>% filter(plot_id == 1) %>% inner_join(surveys) %>% collect()
NoSQL
nodbi: the NoSQL Database Connector
Github
R source
https://github.com/wch/r-source/ Daily update, interesting, should be visited every day. Clicking 1000+ commits to look at daily changes.
If we are interested in a certain branch (say 3.2), look for R-3-2-branch.
R packages (only) source (metacran)
- https://github.com/cran/ by Gábor Csárdi, the author of igraph software.
Bioconductor packages source
Announcement, https://github.com/Bioconductor-mirror
Send local repository to Github in R by using reports package
http://www.youtube.com/watch?v=WdOI_-aZV0Y
My collection
- https://github.com/arraytools
- https://gist.github.com/4383351 heatmap using leukemia data
- https://gist.github.com/4382774 heatmap using sequential data
- https://gist.github.com/4484270 biocLite
How to download
Clone ~ Download.
- Command line
git clone https://gist.github.com/4484270.git
This will create a subdirectory called '4484270' with all cloned files there.
- Within R
library(devtools) source_gist("4484270")
or First download the json file from
https://api.github.com/users/MYUSERLOGIN/gists
and then
library(RJSONIO) x <- fromJSON("~/Downloads/gists.json") setwd("~/Downloads/") gist.id <- lapply(x, "[[", "id") lapply(gist.id, function(x){ cmd <- paste0("git clone https://gist.github.com/", x, ".git") system(cmd) })
Jekyll
An Easy Start with Jekyll, for R-Bloggers
Connect R with Arduino
- http://lamages.blogspot.com/2012/10/connecting-real-world-to-r-with-arduino.html
- http://jean-robert.github.io/2012/11/11/thermometer-R-using-Arduino-Java.html
- http://bio7.org/?p=2049
- http://www.rforge.net/Arduino/svn.html
Android App
- R Instructor $4.84
- Statistical Distribution (Not R related app)
- Data-driven Introspection of my Android Mobile usage in R
Common plots tips
Grouped boxplots
- Box Plots of Two-Way Layout
- Step by step to create a grouped boxplots
- 'at' parameter in boxplot() to change the equal spaced boxplots
- embed par(mar=) in boxplot()
- mtext(line=) to solve the problem the xlab overlapped with labels.
- ggplot2 approach (Hint: facet_grid is used)
Weather Time Line
The plot looks similar to a boxplot though it is not. See a screenshot on Android by Sam Ruston.
Horizontal bar plot
library(ggplot2) dtf <- data.frame(x = c("ETB", "PMA", "PER", "KON", "TRA", "DDR", "BUM", "MAT", "HED", "EXP"), y = c(.02, .11, -.01, -.03, -.03, .02, .1, -.01, -.02, 0.06)) ggplot(dtf, aes(x, y)) + geom_bar(stat = "identity", aes(fill = x), show.legend = FALSE) + coord_flip() + xlab("") + ylab("Fold Change")
Include bar values in a barplot
- https://stats.stackexchange.com/questions/3879/how-to-put-values-over-bars-in-barplot-in-r.
- barplot(), text() and axis() functions. The data can be from a table() object.
- How to label a barplot bar with positive and negative bars with ggplot2
Use text().
Or use geom_text() if we are using the ggplot2 package. See an example here or this.
For stacked barplot, see this post.
Grouped barplots
- https://www.r-graph-gallery.com/barplot/, https://www.r-graph-gallery.com/48-grouped-barplot-with-ggplot2/ (simpliest, no error bars)
library(ggplot2) # mydata <- data.frame(OUTGRP, INGRP, value) ggplot(mydata, aes(fill=INGRP, y=value, x=OUTGRP)) + geom_bar(position="dodge", stat="identity")
- https://datascienceplus.com/building-barplots-with-error-bars/. The error bars define 2 se (95% interval) for the black-and-white version and 1 se (68% interval) for ggplots. Be careful.
> 1 - 2*(1-pnorm(1)) [1] 0.6826895 > 1 - 2*(1-pnorm(1.96)) [1] 0.9500042
- two bars in one factor (stack). The data can be a 2-dim matrix with numerical values.
- two bars in one factor, Drawing multiple barplots on a graph in R (next to each other)
- Three variables barplots
- More alternatives (not done by R)
Math expression
- ?plotmath
- https://stackoverflow.com/questions/4973898/combining-paste-and-expression-functions-in-plot-labels
- https://andyphilips.github.io/blog/2017/08/16/mathematical-symbols-in-r-plots.html
- Use expression() function
- Don't need the backslash; use eta instead of \eta. eta will be recognized as a special keyword in expression()
- Use parentheses instead of curly braces; use hat(eta) instead of hat{eta}
- Summary: use expression(hat(eta)) instead of expression(\hat{\eta})
- [] means subscript, while ^ means superscript
- Spacing can be done with ~.
- Mix math symbols and text using paste()
- Using substitute() and paste() if we need to substitute text (this part is advanced)
# Expressions plot(x,y, xlab = expression(hat(x)[t]), ylab = expression(phi^{rho + a}), main = "Pure Expressions") # Expressions with Spacing # '~' is to add space and '*' is to squish characters together plot(1:10, xlab= expression(Delta * 'C')) plot(x,y, xlab = expression(hat(x)[t] ~ z ~ w), ylab = expression(phi^{rho + a} * z * w), main = "Pure Expressions with Spacing") # Expressions with Text plot(x,y, xlab = expression(paste("Text here ", hat(x), " here ", z^rho, " and here")), ylab = expression(paste("Here is some text of ", phi^{rho})), main = "Expressions with Text") # Substituting Expressions plot(x,y, xlab = substitute(paste("Here is ", pi, " = ", p), list(p = py)), ylab = substitute(paste("e is = ", e ), list(e = ee)), main = "Substituted Expressions")
Impose a line to a scatter plot
- abline + lsfit # least squares
plot(cars) abline(lsfit(cars[, 1], cars[, 2])) # OR abline(lm(cars[,2] ~ cars[,1]))
- abline + line # robust line fitting
plot(cars) (z <- line(cars)) abline(coef(z), col = 'green')
- lines
plot(cars) fit <- lm(cars[,2] ~ cars[,1]) lines(cars[,1], fitted(fit), col="blue") lines(stats::lowess(cars), col='red')
Rotating x axis labels for barplot
https://stackoverflow.com/questions/10286473/rotating-x-axis-labels-in-r-for-barplot
barplot(mytable,main="Car makes",ylab="Freqency",xlab="make",las=2)
Set R plots x axis to show at y=0
https://stackoverflow.com/questions/3422203/set-r-plots-x-axis-to-show-at-y-0
plot(1:10, rnorm(10), ylim=c(0,10), yaxs="i")
Different colors of axis labels in barplot
See Vary colors of axis labels in R based on another variable
Method 1: Append labels for the 2nd, 3rd, ... color gradually because 'col.axis' argument cannot accept more than one color.
tN <- table(Ni <- stats::rpois(100, lambda = 5)) r <- barplot(tN, col = rainbow(20)) axis(1, 1, LETTERS[1], col.axis="red", col="red") axis(1, 2, LETTERS[2], col.axis="blue", col = "blue")
Method 2: text() which can accept multiple colors in 'col' parameter but we need to find out the (x, y) by ourselves.
barplot(tN, col = rainbow(20), axisnames = F) text(4:6, par("usr")[3]-2 , LETTERS[4:6], col=c("black","red","blue"), xpd=TRUE)
Use text() to draw labels on X/Y-axis including rotation
- adj = 1 means top/rigth alignment. The default is to center the text.
- par("usr") gives the extremes of the user coordinates of the plotting region of the form c(x1, x2, y1, y2).
- par("usr") is determined *after* a plot has been created
- Example of using the "usr" parameter
- https://datascienceplus.com/building-barplots-with-error-bars/
par(mar = c(5, 6, 4, 5) + 0.1) plot(..., xaxt = "n") # "n" suppresses plotting of the axis; need mtext() and axis() to supplement text(x = barCenters, y = par("usr")[3] - 1, srt = 45, adj = 1, labels = myData$names, xpd = TRUE)
Vertically stacked plots with the same x axis
Increase/decrease legend font size
https://stackoverflow.com/a/36842578
plot(rnorm(100)) op <- par(cex=2) legend("topleft", legend = 1:4, col=1:4, pch=1) par(op)
Superimpose a density plot or any curves
Use lines().
Example 1
plot(cars, main = "Stopping Distance versus Speed") lines(stats::lowess(cars))
Example 2
require(survival) n = 10000 beta1 = 2; beta2 = -1 lambdaT = 1 # baseline hazard lambdaC = 2 # hazard of censoring set.seed(1234) x1 = rnorm(n,0) x2 = rnorm(n,0) # true event time T = rweibull(n, shape=1, scale=lambdaT*exp(-beta1*x1-beta2*x2)) C <- rweibull(n, shape=1, scale=lambdaC) time = pmin(T,C) status <- 1*(T <= C) status2 <- 1-status plot(survfit(Surv(time, status2) ~ 1), ylab="Survival probability", main = 'Exponential censoring time') xseq <- seq(.1, max(time), length =100) func <- function(x) 1-pweibull(x, shape = 1, scale = lambdaC) lines(xseq, func(xseq), col = 'red') # survival function of Weibull
Custom scales
Using custom scales with the 'scales' package
Time series
Time series stock price plot
- http://blog.revolutionanalytics.com/2015/08/plotting-time-series-in-r.html (ggplot2, xts, dygraphs)
- Visualize your Portfolio’s Performance and Generate a Nice Report with R
- https://timelyportfolio.github.io/rCharts_time_series/history.html
library(quantmod) getSymbols("AAPL") getSymbols("IBM") # similar to AAPL getSymbols("CSCO") # much smaller than AAPL, IBM getSymbols("DJI") # Dow Jones, huge chart_Series(Cl(AAPL), TA="add_TA(Cl(IBM), col='blue', on=1); add_TA(Cl(CSCO), col = 'green', on=1)", col='orange', subset = '2017::2017-08') tail(Cl(DJI))
Timeline plot
Circular plot
- http://freakonometrics.hypotheses.org/20667 which uses https://cran.r-project.org/web/packages/circlize/ circlize] package.
- https://www.biostars.org/p/17728/
- RCircos package from CRAN.
- OmicCircos from Bioconductor.
Word cloud
- Text mining and word cloud fundamentals in R : 5 simple steps you should know
- 7 Alternatives to Word Clouds for Visualizing Long Lists of Data
- Data + Art STEAM Project: Initial Results
World map
Visualising SSH attacks with R (rworldmap and rgeolocate packages)
Diagram/flowchart/Directed acyclic diagrams (DAGs)
DiagrammeR
diagram
Functions for Visualising Simple Graphs (Networks), Plotting Flow Diagrams
DAGitty (browser-based and R package)
dagR
Venn Diagram
- limma http://www.ats.ucla.edu/stat/r/faq/venn.htm - only black and white?
- VennDiagram - input has to be the numbers instead of the original vector?
- http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual#TOC-Venn-Diagrams and the R code or the Bioc package systemPipeR
# systemPipeR package method library(systemPipeR) setlist <- list(A=sample(letters, 18), B=sample(letters, 16), C=sample(letters, 20), D=sample(letters, 22), E=sample(letters, 18)) OLlist <- overLapper(setlist[1:3], type="vennsets") vennPlot(list(OLlist)) # R script source method source("http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/overLapper.R") setlist <- list(A=sample(letters, 18), B=sample(letters, 16), C=sample(letters, 20), D=sample(letters, 22), E=sample(letters, 18)) # or (obtained by dput(setlist)) setlist <- structure(list(A = c("o", "h", "u", "p", "i", "s", "a", "w", "b", "z", "n", "c", "k", "j", "y", "m", "t", "q"), B = c("h", "r", "x", "y", "b", "t", "d", "o", "m", "q", "g", "v", "c", "u", "f", "z"), C = c("b", "e", "t", "u", "s", "j", "o", "k", "d", "l", "g", "i", "w", "n", "p", "a", "y", "x", "m", "z"), D = c("f", "g", "b", "k", "j", "m", "e", "q", "i", "d", "o", "l", "c", "t", "x", "r", "s", "u", "w", "a", "z", "n"), E = c("u", "w", "o", "k", "n", "h", "p", "z", "l", "m", "r", "d", "q", "s", "x", "b", "v", "t"), F = c("o", "j", "r", "c", "l", "l", "u", "b", "f", "d", "u", "m", "y", "t", "y", "s", "a", "g", "t", "m", "x", "m" )), .Names = c("A", "B", "C", "D", "E", "F")) OLlist <- overLapper(setlist[1:3], type="vennsets") counts <- list(sapply(OLlist$Venn_List, length)) vennPlot(counts=counts)
Bump chart/Metro map
https://dominikkoch.github.io/Bump-Chart/
Amazing plots
New R logo 2/11/2016
- http://rud.is/b/2016/02/11/plot-the-new-svg-r-logo-with-ggplot2/
- https://www.stat.auckland.ac.nz/~paul/Reports/Rlogo/Rlogo.html
library(sp) library(maptools) library(ggplot2) library(ggthemes) # rgeos requires the installation of GEOS from http://trac.osgeo.org/geos/ system("curl http://download.osgeo.org/geos/geos-3.5.0.tar.bz2 | tar jx") system("cd geos-3.5.0; ./configure; make; sudo make install") library(rgeos) r_wkt_gist_file <- "https://gist.githubusercontent.com/hrbrmstr/07d0ccf14c2ff109f55a/raw/db274a39b8f024468f8550d7aeaabb83c576f7ef/rlogo.wkt" if (!file.exists("rlogo.wkt")) download.file(r_wkt_gist_file, "rlogo.wkt") rlogo <- readWKT(paste0(readLines("rlogo.wkt", warn=FALSE))) # rgeos rlogo_shp <- SpatialPolygonsDataFrame(rlogo, data.frame(poly=c("halo", "r"))) # sp rlogo_poly <- fortify(rlogo_shp, region="poly") # ggplot2 ggplot(rlogo_poly) + geom_polygon(aes(x=long, y=lat, group=id, fill=id)) + scale_fill_manual(values=c(halo="#b8babf", r="#1e63b5")) + coord_equal() + theme_map() + theme(legend.position="none")
3D plot
Using persp function to create the following plot. Code in github.
Christmas tree
http://wiekvoet.blogspot.com/2014/12/merry-christmas.html. Code in github.
Happy Thanksgiving
Happy Valentine's Day
- Geom❤️ 2017
- Happy Valentines day by Nerds 2019
treemap
Voronoi diagram
- https://www.stat.auckland.ac.nz/~paul/Reports/VoronoiTreemap/voronoiTreeMap.html
- http://letstalkdata.com/2014/05/creating-voronoi-diagrams-with-ggplot/
Silent Night
The code in github.
The Travelling Salesman Portrait
https://fronkonstin.com/2018/04/04/the-travelling-salesman-portrait/
Moon phase calendar
https://chichacha.netlify.com/2018/05/26/making-calendar-with-ggplot-moon-phase-calendar/
Calendar heatmap
https://stackoverflow.com/questions/26171068/add-dates-to-calendar-heat-map-r
Chaos
Rcpp, Camarón de la Isla and the Beauty of Maths
Google Analytics
GAR package
http://www.analyticsforfun.com/2015/10/query-your-google-analytics-data-with.html
Linear Programming
http://www.r-bloggers.com/modeling-and-solving-linear-programming-with-r-free-book/
Linear Algebra
- Elegant linear algebra in R with the Matrix package. Matrix package is used.
- Linear Algebra for Machine Learning and Deep Learning in R. MASS library is used.
Amazon Alexa
R and Singularity
https://www.rstudio.com/rviews/2017/03/29/r-and-singularity/
Teach kids about R with Minecraft
http://blog.revolutionanalytics.com/2017/06/teach-kids-about-r-with-minecraft.html
Secure API keys
Securely store API keys in R scripts with the "secret" package
Hide a password: keyring package
- https://cran.r-project.org/web/packages/keyring/index.html
- How to hide a password in R with the Keyring package
Vision and image recognition
- https://www.stoltzmaniac.com/google-vision-api-in-r-rooglevision/ Google vision API IN R] – RoogleVision
- Computer Vision Algorithms for R users and https://github.com/bnosac/image
Turn pictures into coloring pages
https://gist.github.com/jeroen/53a5f721cf81de2acba82ea47d0b19d0
Numerical optimization
- uniroot: One Dimensional Root (Zero) Finding. This is used in simulating survival data for predefined censoring rate
- optimize: One Dimensional Optimization
- optim: General-purpose optimization based on Nelder–Mead, quasi-Newton and conjugate-gradient algorithms.
- constrOptim: Linearly Constrained Optimization
- nlm: Non-Linear Minimization
- nls: Nonlinear Least Squares
R packages
Tricks
Getting help
- http://stackoverflow.com/questions/tagged/r and R page contains resources.
- https://stat.ethz.ch/pipermail/r-help/
- https://stat.ethz.ch/pipermail/r-devel/
Better Coder/coding, best practices
- http://www.mango-solutions.com/wp/2015/10/10-top-tips-for-becoming-a-better-coder/
- Writing Good R Code and Writing Well
- R Code – Best practices
- What best practices do you use for programming in R?
E-notation
6.022E23 (or 6.022e23) is equivalent to 6.022×10^23
Getting user's home directory
See What are HOME and working directories?
# Windows normalizePath("~") # "C:\\Users\\brb\\Documents" Sys.getenv("R_USER") # "C:/Users/brb/Documents" Sys.getenv("HOME") # "C:/Users/brb/Documents" # Mac normalizePath("~") # [1] "/Users/brb" Sys.getenv("R_USER") # [1] "" Sys.getenv("HOME") # "/Users/brb" # Linux normalizePath("~") # [1] "/home/brb" Sys.getenv("R_USER") # [1] "" Sys.getenv("HOME") # [1] "/home/brb"
Rprofile.site, Renviron.site (all platforms) and Rconsole (Windows only)
- https://cran.r-project.org/doc/manuals/r-release/R-admin.html (Rprofile.site). Put R statements.
- https://cran.r-project.org/doc/manuals/r-release/R-exts.html (Renviron.site). Define environment variables.
- https://cran.r-project.org/doc/manuals/r-release/R-intro.html (Rprofile.site, Renviron.site, Rconsole (Windows only))
- How to store and use webservice keys and authentication details
- Use your .Rprofile to give you important notifications
If we like to install R packages to a personal directory, follow this. Just add the line
R_LIBS_SITE=F:/R/library
to the file R_HOME/etc/x64/Renviron.site.
Note that on Windows OS, R/etc contains
$ ls -l /c/Progra~1/r/r-3.2.0/etc total 142 -rw-r--r-- 1 Administ 1043 Jun 20 2013 Rcmd_environ -rw-r--r-- 1 Administ 1924 Mar 17 2010 Rconsole -rw-r--r-- 1 Administ 943 Oct 3 2011 Rdevga -rw-r--r-- 1 Administ 589 May 20 2013 Rprofile.site -rw-r--r-- 1 Administ 251894 Jan 17 2015 curl-ca-bundle.crt drwxr-xr-x 1 Administ 0 Jun 8 10:30 i386 -rw-r--r-- 1 Administ 1160 Dec 31 2014 repositories -rw-r--r-- 1 Administ 30188 Mar 17 2010 rgb.txt drwxr-xr-x 3 Administ 0 Jun 8 10:30 x64 $ ls /c/Progra~1/r/r-3.2.0/etc/i386 Makeconf $ cat /c/Progra~1/r/r-3.2.0/etc/Rconsole # Optional parameters for the console and the pager # The system-wide copy is in R_HOME/etc. # A user copy can be installed in `R_USER'. ## Style # This can be `yes' (for MDI) or `no' (for SDI). MDI = yes # MDI = no # the next two are only relevant for MDI toolbar = yes statusbar = no ## Font. # Please use only fixed width font. # If font=FixedFont the system fixed font is used; in this case # points and style are ignored. If font begins with "TT ", only # True Type fonts are searched for. font = TT Courier New points = 10 style = normal # Style can be normal, bold, italic # Dimensions (in characters) of the console. rows = 25 columns = 80 # Dimensions (in characters) of the internal pager. pgrows = 25 pgcolumns = 80 # should options(width=) be set to the console width? setwidthonresize = yes # memory limits for the console scrolling buffer, in chars and lines # NB: bufbytes is in bytes for R < 2.7.0, chars thereafter. bufbytes = 250000 buflines = 8000 # Initial position of the console (pixels, relative to the workspace for MDI) # xconsole = 0 # yconsole = 0 # Dimension of MDI frame in pixels # Format (w*h+xorg+yorg) or use -ve w and h for offsets from right bottom # This will come up maximized if w==0 # MDIsize = 0*0+0+0 # MDIsize = 1000*800+100+0 # MDIsize = -50*-50+50+50 # 50 pixels space all round # The internal pager can displays help in a single window # or in multiple windows (one for each topic) # pagerstyle can be set to `singlewindow' or `multiplewindows' pagerstyle = multiplewindows ## Colours for console and pager(s) # (see rwxxxx/etc/rgb.txt for the known colours). background = White normaltext = NavyBlue usertext = Red highlight = DarkRed ## Initial position of the graphics window ## (pixels, <0 values from opposite edge) xgraphics = -25 ygraphics = 0 ## Language for messages language = ## Default setting for console buffering: 'yes' or 'no' buffered = yes
and on Linux
brb@brb-T3500:~$ whereis R R: /usr/bin/R /etc/R /usr/lib/R /usr/bin/X11/R /usr/local/lib/R /usr/share/R /usr/share/man/man1/R.1.gz brb@brb-T3500:~$ ls /usr/lib/R bin COPYING etc lib library modules site-library SVN-REVISION brb@brb-T3500:~$ ls /usr/lib/R/etc javaconf ldpaths Makeconf Renviron Renviron.orig Renviron.site Renviron.ucf repositories Rprofile.site brb@brb-T3500:~$ ls /usr/local/lib/R site-library
and
brb@brb-T3500:~$ cat /usr/lib/R/etc/Rprofile.site ## Emacs please make this -*- R -*- ## empty Rprofile.site for R on Debian ## ## Copyright (C) 2008 Dirk Eddelbuettel and GPL'ed ## ## see help(Startup) for documentation on ~/.Rprofile and Rprofile.site # ## Example of .Rprofile # options(width=65, digits=5) # options(show.signif.stars=FALSE) # setHook(packageEvent("grDevices", "onLoad"), # function(...) grDevices::ps.options(horizontal=FALSE)) # set.seed(1234) # .First <- function() cat("\n Welcome to R!\n\n") # .Last <- function() cat("\n Goodbye!\n\n") # ## Example of Rprofile.site # local({ # # add MASS to the default packages, set a CRAN mirror # old <- getOption("defaultPackages"); r <- getOption("repos") # r["CRAN"] <- "http://my.local.cran" # options(defaultPackages = c(old, "MASS"), repos = r) #}) brb@brb-T3500:~$ cat /usr/lib/R/etc/Renviron.site ## Emacs please make this -*- R -*- ## empty Renviron.site for R on Debian ## ## Copyright (C) 2008 Dirk Eddelbuettel and GPL'ed ## ## see help(Startup) for documentation on ~/.Renviron and Renviron.site # ## Example ~/.Renviron on Unix # R_LIBS=~/R/library # PAGER=/usr/local/bin/less # ## Example .Renviron on Windows # R_LIBS=C:/R/library # MY_TCLTK="c:/Program Files/Tcl/bin" # ## Example of setting R_DEFAULT_PACKAGES (from R CMD check) # R_DEFAULT_PACKAGES='utils,grDevices,graphics,stats' # # this loads the packages in the order given, so they appear on # # the search path in reverse order. brb@brb-T3500:~$
What is the best place to save Rconsole on Windows platform
Put/create the file <Rconsole> under C:/Users/USERNAME/Documents folder so no matter how R was upgraded/downgraded, it always find my preference.
My preferred settings:
- Font: Consolas (it will be shown as "TT Consolas" in Rconsole)
- Size: 12
- background: black
- normaltext: white
- usertext: GreenYellow or orange (close to RStudio's Cobalt theme) or sienna1 or SpringGreen or tan1 or yellow
and others (default options)
- pagebg: white
- pagetext: navy
- highlight: DarkRed
- dataeditbg: white
- dataedittext: navy (View() function)
- dataedituser: red
- editorbg: white (edit() function)
- editortext: black
Saving and loading history automatically: .Rprofile & local()
- http://stat.ethz.ch/R-manual/R-patched/library/utils/html/savehistory.html
- .Rprofile will automatically be loaded when R has started from that directory
- Don't do things in your .Rprofile that affect how R code runs, such as loading a package like dplyr or ggplot or setting an option such as stringsAsFactors = FALSE. See Project-oriented workflow.
- .Rprofile has been created/used by the packrat package to restore a packrat environment. See the packrat/init.R file and R packages → packrat.
- Customizing Startup from R in Action, Fun with .Rprofile and customizing R startup
- You can also place a .Rprofile file in any directory that you are going to run R from or in the user home directory.
- At startup, R will source the Rprofile.site file. It will then look for a .Rprofile file to source in the current working directory. If it doesn't find it, it will look for one in the user's home directory.
options(continue=" ") # default is "+ " options(editor="nano") # default is "vi" on Linux # options(htmlhelp=TRUE) local((r <- getOption("repos") r["CRAN"] <- "http://cran.rstudio.com" options(repos = r))) .First <- function(){ # library(Hmisc) cat("\nWelcome at", date(), "\n") } .Last <- function(){ cat("\nGoodbye at ", date(), "\n") }
- https://stackoverflow.com/questions/16734937/saving-and-loading-history-automatically
- The history file will always be read from the $HOME directory and the history file will be overwritten by a new session. These two problems can be solved if we define R_HISTFILE system variable.
- local() function can be used in .Rprofile file to set up the environment even no new variables will be created (change repository, install packages, load libraries, source R files, run system() function, file/directory I/O, etc)
Linux or Mac
In ~/.profile or ~/.bashrc I put:
export R_HISTFILE=~/.Rhistory
In ~/.Rprofile I put:
if (interactive()) { if (.Platform$OS.type == "unix") .First <- function() try(utils::loadhistory("~/.Rhistory")) .Last <- function() try(savehistory(file.path(Sys.getenv("HOME"), ".Rhistory"))) }
Windows
If you launch R by clicking its icon from Windows Desktop, the R starts in C:\User\$USER\Documents directory. So we can create a new file .Rprofile in this directory.
if (interactive()) { .Last <- function() try(savehistory(file.path(Sys.getenv("HOME"), ".Rhistory"))) }
R release versions
rversions: Query the main 'R' 'SVN' repository to find the released versions & dates.
Detect number of running R instances in Windows
- http://stackoverflow.com/questions/15935931/detect-number-of-running-r-instances-in-windows-within-r
C:\Program Files\R>tasklist /FI "IMAGENAME eq Rscript.exe" INFO: No tasks are running which match the specified criteria. C:\Program Files\R>tasklist /FI "IMAGENAME eq Rgui.exe" Image Name PID Session Name Session# Mem Usage ============================================================================ Rgui.exe 1096 Console 1 44,712 K C:\Program Files\R>tasklist /FI "IMAGENAME eq Rserve.exe" Image Name PID Session Name Session# Mem Usage ============================================================================ Rserve.exe 6108 Console 1 381,796 K
In R, we can use
> system('tasklist /FI "IMAGENAME eq Rgui.exe" ', intern = TRUE) [1] "" [2] "Image Name PID Session Name Session# Mem Usage" [3] "============================================================================" [4] "Rgui.exe 1096 Console 1 44,804 K" > length(system('tasklist /FI "IMAGENAME eq Rgui.exe" ', intern = TRUE))-3
Editor
http://en.wikipedia.org/wiki/R_(programming_language)#Editors_and_IDEs
- Emacs + ESS. The ESS is useful in the case I want to tidy R code (the tidy_source() function in the formatR package sometimes gives errors; eg when I tested it on an R file like <GetComparisonResults.R> from BRB-ArrayTools v4.4 stable).
- Rstudio - editor/R terminal/R graphics/file browser/package manager. The new version (0.98) also provides a new feature for debugging step-by-step. See also RStudio Tricks
- geany - I like the feature that it shows defined functions on the side panel even for R code. RStudio can also do this (see the bottom of the code panel).
- Rgedit which includes a feature of splitting screen into two panes and run R in the bottom panel. See here.
- Komodo IDE with browser preview http://www.youtube.com/watch?v=wv89OOw9roI at 4:06 and http://docs.activestate.com/komodo/4.4/editor.html
GUI for Data Analysis
Rcmdr
http://cran.r-project.org/web/packages/Rcmdr/index.html
Deducer
http://cran.r-project.org/web/packages/Deducer/index.html
jamovi
Scope
See
- Assignments within functions in the An Introduction to R manual.
- source() does not work like C's preprocessor where statements in header files will be literally inserted into the code. It does not work when you define a variable in a function but want to use it outside the function (even through source())
## foo.R ## cat(ArrayTools, "\n") ## End of foo.R # 1. Error predict <- function() { ArrayTools <- "C:/Program Files" # or through load() function source("foo.R") # or through a function call; foo() } predict() # Object ArrayTools not found # 2. OK. Make the variable global predict <- function() { ArrayTools <<- "C:/Program Files' source("foo.R") } predict() ArrayTools # 3. OK. Create a global variable ArrayTools <- "C:/Program Files" predict <- function() { source("foo.R") } predict()
Note that any ordinary assignments done within the function are local and temporary and are lost after exit from the function.
Example 1.
> ttt <- data.frame(type=letters[1:5], JpnTest=rep("999", 5), stringsAsFactors = F) > ttt type JpnTest 1 a 999 2 b 999 3 c 999 4 d 999 5 e 999 > jpntest <- function() { ttt$JpnTest[1] ="N5"; print(ttt)} > jpntest() type JpnTest 1 a N5 2 b 999 3 c 999 4 d 999 5 e 999 > ttt type JpnTest 1 a 999 2 b 999 3 c 999 4 d 999 5 e 999
Example 2. How can we set global variables inside a function? The answer is to use the "<<-" operator or assign(, , envir = .GlobalEnv) function.
Other resource: Advanced R by Hadley Wickham.
Example 3. Writing functions in R, keeping scoping in mind
New environment
http://adv-r.had.co.nz/Environments.html
Run the same function on a bunch of R objects
mye = new.env() load(<filename>, mye) for(n in names(mye)) n = as_tibble(mye[[n]])
View all objects present in a package, ls()
https://stackoverflow.com/a/30392688. In the case of an R package created by Rcpp.package.skeleton("mypackage"), we will get
> devtools::load_all("mypackage") > search() [1] ".GlobalEnv" "devtools_shims" "package:mypackage" [4] "package:stats" "package:graphics" "package:grDevices" [7] "package:utils" "package:datasets" "package:methods" [10] "Autoloads" "package:base" > ls("package:mypackage") [1] "_mypackage_rcpp_hello_world" "evalCpp" "library.dynam.unload" [4] "rcpp_hello_world" "system.file"
Note that the first argument of ls() (or detach()) is used to specify the environment. It can be
- an integer (the position in the ‘search’ list);
- the character string name of an element in the search list;
- an explicit ‘environment’ (including using ‘sys.frame’ to access the currently active function calls).
Speedup R code
- Strategies to speedup R code from DataScience+
Profiler
(Video) Understand Code Performance with the profiler
&& vs &
See https://www.rdocumentation.org/packages/base/versions/3.5.1/topics/Logic.
The shorter form performs elementwise comparisons in much the same way as arithmetic operators. The longer form evaluates left to right examining only the first element of each vector.
stopifnot(): function argument sanity check
Vectorization
- Vectorization (Mathematics) from wikipedia
- Array programming from wikipedia
- Single instruction, multiple data (SIMD) from wikipedia
- What is vectorization stackoverflow
- http://www.noamross.net/blog/2014/4/16/vectorization-in-r--why.html
- https://github.com/vsbuffalo/devnotes/wiki/R-and-Vectorization
- Why Vectorize? statcompute.wordpress.com
- Beware of Vectorize from Jim Hester
- matrixStats: Functions that Apply to Rows and Columns of Matrices (and to Vectors). E.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Benchmark reports.
sapply vs vectorization
Speed test: sapply vs vectorization
lapply vs for loop
- lapply vs for loop - Performance R
- https://code-examples.net/en/q/286e03a
- R: are *apply loops faster than for loops?
split() and sapply()
split() can be used to split a vector, columns or rows. See How to split a data frame?
- Split rows of a data frame/matrix
split(mtcars,mtcars$cyl)
- Split columns of a data frame/matrix.
ma <- cbind(x = 1:10, y = (-4:5)^2, z = 11:20) split(ma, cbind(rep(1,10), rep(2, 10), rep(1,10))) # $`1` # [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # # $`2` # [1] 16 9 4 1 0 1 4 9 16 25
- split() + sapply() to merge columns. See below 'Mean of duplicated columns' for more detail.
- split() + sapply() to split a vector. See nsFilter() function which can remove duplicated probesets/rows using unique Entrez Gene IDs (genefilter package). The source code of nsFilter() and findLargest().
tSsp = split.default(testStat, lls) # testStat is a vector of numerics including probeset IDs as names # lls is a vector of entrez IDs (same length as testStat) # tSSp is a list of the same length as unique elements of lls. sapply(tSsp, function(x) names(which.max(x))) # return a vector of probset IDs of length of unique entrez IDs
- And here is another example from the bigmemory vignette,
planeindices <- split(1:nrow(x), x[,'TailNum']) planeStart <- sapply(planeindices, function(i) birthmonth(x[i, c('Year','Month'), drop=FALSE]))
Mean of duplicated columns: rowMeans
- Reduce columns of a matrix by a function in R
x <- matrix(1:60, nr=10); x[1, 2:3] <- NA colnames(x) <- c("A","A", "b", "b", "b", "c"); x res <- sapply(split(1:ncol(x), colnames(x)), function(i) rowMeans(x[, i, drop=F], na.rm = TRUE)) res # vapply() is safter than sapply(). # The 3rd arg in vapply() is a template of the return value. res2 <- vapply(split(1:ncol(x), colnames(x)), function(i) rowMeans(x[, i, drop=F], na.rm = TRUE), rep(0, nrow(x)))
- colSums, rowSums, colMeans, rowMeans (no group variable). These functions are equivalent to use of ‘apply’ with ‘FUN = mean’ or ‘FUN = sum’ with appropriate margins, but are a lot faster.
rowMeans(x, na.rm=T) # [1] 31 27 28 29 30 31 32 33 34 35 apply(x, 1, mean, na.rm=T) # [1] 31 27 28 29 30 31 32 33 34 35
- matrixStats: Functions that Apply to Rows and Columns of Matrices (and to Vectors)
Mean of duplicated rows: colMeans and rowsum
- colMeans(x, na.rm = FALSE, dims = 1)
x <- matrix(1:60, nr=10); x[1, 2:3] <- NA; x rownames(x) <- c(rep("a", 2), rep("b", 3), rep("c", 4), "d") res <- sapply(split(1:nrow(x), rownames(x)), function(i) colMeans(x[i, , drop=F], na.rm = TRUE)) res <- t(res) # transpose is needed since sapply() will form the resulting matrix by columns
- rowsum(x, group, reorder = TRUE, …)
x <- matrix(runif(100), ncol = 5) # 20 x 5 group <- sample(1:8, 20, TRUE) (xsum <- rowsum(x, group)) # 8 x 5
- How to calculate mean/median per group in a dataframe in r where doBy and dplyr are recommended.
- matrixStats: Functions that Apply to Rows and Columns of Matrices (and to Vectors)
- doBy package
- use ave() and unique()
- data.table package
- plyr package
- aggregate() function. Too slow! http://slowkow.com/2015/01/28/data-table-aggregate/. Don't use aggregate post.
> attach(mtcars) dim(mtcars) [1] 32 11 > head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 > with(mtcars, table(cyl, vs)) vs cyl 0 1 4 1 10 6 3 4 8 14 0 > aggdata <-aggregate(mtcars, by=list(cyl,vs), FUN=mean, na.rm=TRUE) > print(aggdata) Group.1 Group.2 mpg cyl disp hp drat wt qsec vs 1 4 0 26.00000 4 120.30 91.0000 4.430000 2.140000 16.70000 0 2 6 0 20.56667 6 155.00 131.6667 3.806667 2.755000 16.32667 0 3 8 0 15.10000 8 353.10 209.2143 3.229286 3.999214 16.77214 0 4 4 1 26.73000 4 103.62 81.8000 4.035000 2.300300 19.38100 1 5 6 1 19.12500 6 204.55 115.2500 3.420000 3.388750 19.21500 1 am gear carb 1 1.0000000 5.000000 2.000000 2 1.0000000 4.333333 4.666667 3 0.1428571 3.285714 3.500000 4 0.7000000 4.000000 1.500000 5 0.0000000 3.500000 2.500000 > detach(mtcars) # Another example: select rows with a minimum value from a certain column (yval in this case) > mydf <- read.table(header=T, text=' id xval yval A 1 1 A -2 2 B 3 3 B 4 4 C 5 5 ') > x = mydf$xval > y = mydf$yval > aggregate(mydf[, c(2,3)], by=list(id=mydf$id), FUN=function(x) x[which.min(y)]) id xval yval 1 A 1 1 2 B 3 3 3 C 5 5
Apply family
Vectorize, aggregate, apply, by, eapply, lapply, mapply, rapply, replicate, scale, sapply, split, tapply, and vapply.
The following list gives a hierarchical relationship among these functions.
- apply(X, MARGIN, FUN, ...) – Apply a Functions Over Array Margins
- lapply(X, FUN, ...) – Apply a Function over a List (including a data frame) or Vector X.
- sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE) – Apply a Function over a List or Vector
- replicate(n, expr, simplify = "array")
- mapply(FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE) – Multivariate version of sapply
- Vectorize(FUN, vectorize.args = arg.names, SIMPLIFY = TRUE, USE.NAMES = TRUE) - Vectorize a Scalar Function
- Map(FUN, ...) A wrapper to mapply with SIMPLIFY = FALSE, so it is guaranteed to return a list.
- vapply(X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE) – similar to sapply, but has a pre-specified type of return value
- rapply(object, f, classes = "ANY", deflt = NULL, how = c("unlist", "replace", "list"), ...) – A recursive version of lapply
- sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE) – Apply a Function over a List or Vector
- tapply(V, INDEX, FUN = NULL, ..., default = NA, simplify = TRUE) – Apply a Function Over a "Ragged" Array. V is typically a vector where split() will be applied. INDEX is a list of one or more factors.
- aggregate(D, by, FUN, ..., simplify = TRUE, drop = TRUE) - Apply a function to each columns of subset data frame split by factors. FUN (such as mean(), weighted.mean(), sum()) is a simple function applied to a vector. D is typically a data frame.
- by(D, INDICES, FUN, ..., simplify = TRUE) - Apply a Function to each subset data frame split by factors. FUN (such as summary(), lm()) is applied to a data frame. D is typically a data frame.
- eapply(env, FUN, ..., all.names = FALSE, USE.NAMES = TRUE) – Apply a Function over values in an environment
Difference between apply vs sapply vs lapply vs tapply?
- apply - When you want to apply a function to the rows or columns or both of a matrix and output is a one-dimensional if only row or column is selected else it is a 2D-matrix
- lapply - When you want to apply a function to each element of a list in turn and get a list back.
- sapply - When you want to apply a function to each element of a list in turn, but you want a vector back, rather than a list.
- tapply - When you want to apply a function to subsets of a vector and the subsets are defined by some other vector, usually a factor.
Some short examples:
- stern.nyu.edu.
- Apply Function in R – apply vs lapply vs sapply vs mapply vs tapply vs rapply vs vapply from datasciencemadesimple.com.
- How to use which one (apply family) when?
Apply vs for loop
Note that, apply's performance is not always better than a for loop. See
- http://tolstoy.newcastle.edu.au/R/help/06/05/27255.html (answered by Brian Ripley)
- https://stat.ethz.ch/pipermail/r-help/2014-October/422455.html (has one example)
- R: are *apply loops faster than for loops?
- Why loops are slow in R
- Why is `unlist(lapply)` faster than `sapply`?
Progress bar
What is the cost of a progress bar in R?
The package 'pbapply' creates a text-mode progress bar - it works on any platforms. On Windows platform, check out this post. It uses winProgressBar() and setWinProgressBar() functions.
lapply and its friends Map(), Reduce(), Filter() from the base package for manipulating lists
- Examples of using lapply() + split() on a data frame. See rollingyours.wordpress.com.
- mapply() documentation. Use mapply() to merge lists.
- Map() and Reduce() in functional programming
- Map(), Reduce(), and Filter() from Advanced R by Hadley
- If you have two or more lists (or data frames) that you need to process in parallel, use Map(). One good example is to compute the weighted.mean() function that requires two input objects. Map() is similar to mapply() function and is more concise than lapply(). Advanced R has a comment that Map() is better than mapply().
# Syntax: Map(f, ...) xs <- replicate(5, runif(10), simplify = FALSE) ws <- replicate(5, rpois(10, 5) + 1, simplify = FALSE) Map(weighted.mean, xs, ws) # instead of a more clumsy way lapply(seq_along(xs), function(i) { weighted.mean(xs[[i]], ws[[i]]) })
- Reduce() reduces a vector, x, to a single value by recursively calling a function, f, two arguments at a time. A good example of using Reduce() function is to read a list of matrix files and merge them. See How to combine multiple matrix frames into one using R?
# Syntax: Reduce(f, x, ...) > m1 <- data.frame(id=letters[1:4], val=1:4) > m2 <- data.frame(id=letters[2:6], val=2:6) > merge(m1, m2, "id", all = T) id val.x val.y 1 a 1 NA 2 b 2 2 3 c 3 3 4 d 4 4 5 e NA 5 6 f NA 6 > m <- list(m1, m2) > Reduce(function(x,y) merge(x,y, "id",all=T), m) id val.x val.y 1 a 1 NA 2 b 2 2 3 c 3 3 4 d 4 4 5 e NA 5 6 f NA 6
- If you have two or more lists (or data frames) that you need to process in parallel, use Map(). One good example is to compute the weighted.mean() function that requires two input objects. Map() is similar to mapply() function and is more concise than lapply(). Advanced R has a comment that Map() is better than mapply().
- Playing Map() and Reduce() in R – Subsetting - using parallel and future packages. Union Multiple Data.Frames with Different Column Names
sapply & vapply
- This discusses why vapply is safer and faster than sapply.
- Vector output: sapply and vapply from Advanced R (Hadley Wickham).
- THOSE “OTHER” APPLY FUNCTIONS…. rapply(), vapply() and eapply() are covered.
- Speed test: sapply vs. vectorization
See parallel::parSapply() for a parallel version of sapply(1:n, function(x)). We can this technique to speed up this example.
rapply - recursive version of lapply
- http://4dpiecharts.com/tag/recursive/
- Search in R source code. Mainly r-source/src/library/stats/R/dendrogram.R.
replicate
https://www.datacamp.com/community/tutorials/tutorial-on-loops-in-r
> replicate(5, rnorm(3)) [,1] [,2] [,3] [,4] [,5] [1,] 0.2509130 -0.3526600 -0.3170790 1.064816 -0.53708856 [2,] 0.5222548 1.5343319 0.6120194 -1.811913 -1.09352459 [3,] -1.9905533 -0.8902026 -0.5489822 1.308273 0.08773477
See parSapply() for a parallel version of replicate().
Vectorize
- Vectorize(FUN, vectorize.args = arg.names, SIMPLIFY = TRUE, USE.NAMES = TRUE): creates a function wrapper that vectorizes a scalar function. Its value is a list or vector or array. It calls mapply().
> rep(1:4, 4:1) [1] 1 1 1 1 2 2 2 3 3 4 > vrep <- Vectorize(rep.int) > vrep(1:4, 4:1) [[1]] [1] 1 1 1 1 [[2]] [1] 2 2 2 [[3]] [1] 3 3 [[4]] [1] 4
- Vectorizing functions in R is easy
> rweibull(1, 1, c(1, 2)) # no error but not sure what it gives? [1] 2.17123 > Vectorize("rweibull")(n=1, shape = 1, scale = c(1, 2)) [1] 1.6491761 0.9610109
- https://blogs.msdn.microsoft.com/gpalem/2013/03/28/make-vectorize-your-friend-in-r/
myfunc <- function(a, b) a*b myfunc(1, 2) # 2 myfunc(3, 5) # 15 myfunc(c(1,3), c(2,5)) # 2 15 Vectorize(myfunc)(c(1,3), c(2,5)) # 2 15 myfunc2 <- function(a, b) if (length(a) == 1) a * b else NA myfunc2(1, 2) # 2 myfunc2(3, 5) # 15 myfunc2(c(1,3), c(2,5)) # NA Vectorize(myfunc2)(c(1, 3), c(2, 5)) # 2 15 Vectorize(myfunc2)(c(1, 3, 6), c(2, 5)) # 2 15 12 # parameter will be re-used
plyr and dplyr packages
Practical Data Science for Stats - a PeerJ Collection
The Split-Apply-Combine Strategy for Data Analysis (plyr package) in J. Stat Software.
A quick introduction to plyr with a summary of apply functions in R and compare them with functions in plyr package.
- plyr has a common syntax -- easier to remember
- plyr requires less code since it takes care of the input and output format
- plyr can easily be run in parallel -- faster
Tutorials
- Introduction to dplyr from http://dplyr.tidyverse.org/.
- A video of dplyr package can be found on vimeo.
- Hands-on dplyr tutorial for faster data manipulation in R from dataschool.io.
Examples of using dplyr:
- Medicines under evaluation
- CBI GEO Metadata Survey
- Logistic Regression by Page Piccinini. mutate(), inner_join() and %>%.
- DESeq2 post analysis select(), gather(), arrange() and %>%.
tibble
Tibbles are data frames, but slightly tweaked to work better in the tidyverse.
Tibble objects
- it does not have row names (cf data frame),
- it never changes the type of the inputs (e.g. it never converts strings to factors!),
- it never changes the names of variables
Tibbles Vignette
> data(pew, package = "efficient") > dim(pew) [1] 18 10 > class(pew) # tibble is also a data frame!! [1] "tbl_df" "tbl" "data.frame" > tidyr::gather(pew, key=Income, value = Count, -religion) # make wide tables long # A tibble: 162 x 3 religion Income Count <chr> <chr> <int> 1 Agnostic <$10k 27 2 Atheist <$10k 12 ... > mean(tidyr::gather(pew, key=Income, value = Count, -religion)[, 3]) [1] NA Warning message: In mean.default(tidyr::gather(pew, key = Income, value = Count, : argument is not numeric or logical: returning NA > mean(tidyr::gather(pew, key=Income, value = Count, -religion)[[3]]) [1] 181.6975
If we try to do a match on some column of a tibble object, we will get zero matches. The issue is we cannot use an index to get a tibble column.
Subsetting: to extract a column from a tibble object, use [[ or $ or dplyr::pull(). Select Data Frame Columns in R.
TibbleObject$VarName # OR TibbleObject[["VarName"]] # OR pull(TibbleObject, VarName) # won't be a tibble object anymore dplyr::select(TibbleObject, -c(VarName1, VarName2)) # still a tibble object # OR dplyr::select(TibbleObject, 2:5) #
llply()
llply is equivalent to lapply except that it will preserve labels and can display a progress bar. This is handy if we want to do a crazy thing.
LLID2GOIDs <- lapply(rLLID, function(x) get("org.Hs.egGO")[[x]])
where rLLID is a list of entrez ID. For example,
get("org.Hs.egGO")[["6772"]]
returns a list of 49 GOs.
ddply()
http://lamages.blogspot.com/2012/06/transforming-subsets-of-data-in-r-with.html
ldply()
An R Script to Automatically download PubMed Citation Counts By Year of Publication
Using R's set.seed() to set seeds for use in C/C++ (including Rcpp)
http://rorynolan.rbind.io/2018/09/30/rcsetseed/
get_seed()
See the same blog
get_seed <- function() { sample.int(.Machine$integer.max, 1) }
Note: .Machine$integer.max = 2147483647 = 2^31 - 1.
.Machine and the largest integer, double
See ?.Machine.
Linux/Mac 32-bit Windows 64-bit Windows double.eps 2.220446e-16 2.220446e-16 2.220446e-16 double.neg.eps 1.110223e-16 1.110223e-16 1.110223e-16 double.xmin 2.225074e-308 2.225074e-308 2.225074e-308 double.xmax 1.797693e+308 1.797693e+308 1.797693e+308 double.base 2.000000e+00 2.000000e+00 2.000000e+00 double.digits 5.300000e+01 5.300000e+01 5.300000e+01 double.rounding 5.000000e+00 5.000000e+00 5.000000e+00 double.guard 0.000000e+00 0.000000e+00 0.000000e+00 double.ulp.digits -5.200000e+01 -5.200000e+01 -5.200000e+01 double.neg.ulp.digits -5.300000e+01 -5.300000e+01 -5.300000e+01 double.exponent 1.100000e+01 1.100000e+01 1.100000e+01 double.min.exp -1.022000e+03 -1.022000e+03 -1.022000e+03 double.max.exp 1.024000e+03 1.024000e+03 1.024000e+03 integer.max 2.147484e+09 2.147484e+09 2.147484e+09 sizeof.long 8.000000e+00 4.000000e+00 4.000000e+00 sizeof.longlong 8.000000e+00 8.000000e+00 8.000000e+00 sizeof.longdouble 1.600000e+01 1.200000e+01 1.600000e+01 sizeof.pointer 8.000000e+00 4.000000e+00 8.000000e+00
How to select a seed for simulation or randomization
How to select a seed for simulation or randomization
set.seed() allow alphanumeric seeds
https://stackoverflow.com/a/10913336
set.seed(), for loop and saving random seeds
http://r.789695.n4.nabble.com/set-seed-and-for-loop-td3585857.html. This question is legitimate when we want to debug on a certain iteration.
set.seed(1001) data <- vector("list", 30) seeds <- vector("list", 30) for(i in 1:30) { seeds[[i]] <- .Random.seed data[[i]] <- runif(5) } .Random.seed <- seeds[[23]] # restore data.23 <- runif(5) data.23 data[[23]]
- Duncan Murdoch: This works in this example, but wouldn't work with all RNGs, because some of them save state outside of .Random.seed. See ?.Random.seed for details.
- Uwe Ligges's comment: set.seed() actually generates a seed. See ?set.seed that points us to .Random.seed (and relevant references!) which contains the actual current seed.
- Petr Savicky's comment is also useful in the situation when it is not difficult to re-generate the data.
sample() inaccurate on very large populations, fixed in R 3.6.0
- The default method for generating from a discrete uniform distribution (used in ‘sample()’, for instance) has been changed. In prior versions, the probability of generating each integer could vary from equal by up to 0.04% (or possibly more if generating more than a million different integers). See also What's new in R 3.6.0 by David Smith.
# R 3.5.3 set.seed(123) m <- (2/5)*2^32 m > 2^31 # [1] FALSE log10(m) # [1] 9.23502 x <- sample(m, 1000000, replace = TRUE) table(x %% 2) # 0 1 # 400070 599930
- Fast sampling support in dqrng
- Differences of the output of sample()
# R 3.5.3 # docker run --net=host -it --rm r-base:3.5.3 > set.seed(1234) > sample(5) [1] 1 3 2 4 5 # R 3.6.0 # docker run --net=host -it --rm r-base:3.6.0 > set.seed(1234) > sample(5) [1] 4 5 2 3 1 > RNGkind(sample.kind = "Rounding") Warning message: In RNGkind(sample.kind = "Rounding") : non-uniform 'Rounding' sampler used > set.seed(1234) > sample(5) [1] 1 3 2 4 5
Regular Expression
See here.
Read rrd file
- https://en.wikipedia.org/wiki/RRDtool
- http://oss.oetiker.ch/rrdtool/
- https://github.com/pldimitrov/Rrd
- http://plamendimitrov.net/blog/2014/08/09/r-package-for-working-with-rrd-files/
file, connection
- cat() and scan() (read data into a vector or list from the console or file)
- read() and write()
- read.table() and write.table()
out = file('tmp.txt', 'w') writeLines("abcd", out) writeLines("eeeeee", out) close(out) readLines('tmp.txt') unlink('tmp.txt') args(writeLines) # function (text, con = stdout(), sep = "\n", useBytes = FALSE)
Clipboard (?connections), textConnection(), pipe()
- On Windows, we can use readClipboard() and writeClipboard().
source("clipboard") read.table("clipboard")
- reading/writing clipboard on macOS. Use textConnection() function:
x <- read.delim(textConnection("<USE_KEYBOARD_TO_PASTE_FROM_CLIPBOARD>")) # Or on Mac x <- read.delim(pipe("pbpaste")) # safely ignore the warning: incomplete final line found by readTableHeader on 'pbpaste'
An example is to copy data from this post. In this case we need to use read.table() instead of read.delim(). - Write to clipboard on mac. Note: pbcopy and pbpaste are macOS terminal commands. See pbcopy & pbpaste: Manipulating the Clipboard from the Command Line.
- pbcopy: takes standard input and places it in the clipboard buffer
- pbpaste: takes data from the clipboard buffer and writes it to the standard output
clip <- pipe("pbcopy", "w") write.table(apply(x, 1, mean), file = clip, row.names=F, col.names=F) close(clip)
- On Linux, we need to install "xclip". See R Copy from Clipboard in Ubuntu Linux. It seems to work.
# sudo apt-get install xclip read.table(pipe("xclip -selection clipboard -o",open="r"))
read/manipulate binary data
- x <- readBin(fn, raw(), file.info(fn)$size)
- rawToChar(x[1:16])
- See Biostrings C API
String Manipulation
- ebook by Gaston Sanchez.
- A guide to working with character data in R (6/22/2018)
- Chapter 7 of the book 'Data Manipulation with R' by Phil Spector.
- Chapter 7 of the book 'R Cookbook' by Paul Teetor.
- Chapter 2 of the book 'Using R for Data Management, Statistical Analysis and Graphics' by Horton and Kleinman.
- http://www.endmemo.com/program/R/deparse.php. It includes lots of examples for each R function it lists.
- Four ways to reverse a string in R
HTTPs connection
HTTPS connection becomes default in R 3.2.2. See
- http://blog.rstudio.org/2015/08/17/secure-https-connections-for-r/
- http://blog.revolutionanalytics.com/2015/08/good-advice-for-security-with-r.html
R 3.3.2 patched The internal methods of ‘download.file()’ and ‘url()’ now report if they are unable to follow the redirection of a ‘http://’ URL to a ‘https://’ URL (rather than failing silently)
setInternet2
There was a bug in ftp downloading in R 3.2.2 (r69053) Windows though it is fixed now in R 3.2 patch.
Read the discussion reported on 8/8/2015. The error only happened on ftp not http connection. The final solution is explained in this post. The following demonstrated the original problem.
url <- paste0("ftp://ftp.ncbi.nlm.nih.gov/genomes/ASSEMBLY_REPORTS/All/", "GCF_000001405.13.assembly.txt") f1 <- tempfile() download.file(url, f1)
It seems the bug was fixed in R 3.2-branch. See 8/16/2015 patch r69089 where a new argument INTERNET_FLAG_PASSIVE was added to InternetOpenUrl() function of wininet library. This article and this post explain differences of active and passive FTP.
The following R command will show the exact svn revision for the R you are currently using.
R.Version()$"svn rev"
If setInternet2(T), then https protocol is supported in download.file().
When setInternet(T) is enabled by default, download.file() does not work for ftp protocol (this is used in getGEO() function of the GEOquery package). If I use setInternet(F), download.file() works again for ftp protocol.
The setInternet2() function is defined in R> src> library> utils > R > windows > sysutils.R.
R up to 3.2.2
setInternet2 <- function(use = TRUE) .Internal(useInternet2(use))
See also
- <src/include/Internal.h> (declare do_setInternet2()),
- <src/main/names.c> (show do_setInternet2() in C)
- <src/main/internet.c> (define do_setInternet2() in C).
Note that: setInternet2(T) becomes default in R 3.2.2. To revert to the previous default use setInternet2(FALSE). See the <doc/NEWS.pdf> file. If we use setInternet2(F), then it solves the bug of getGEO() error. But it disables the https file download using the download.file() function. In R < 3.2.2, it is also possible to download from https by setIneternet2(T).
R 3.3.0
setInternet2 <- function(use = TRUE) { if(!is.na(use)) stop("use != NA is defunct") NA }
Note that setInternet2.Rd says As from \R 3.3.0 it changes nothing, and only \code{use = NA} is accepted. Also NEWS.Rd says setInternet2() has no effect and will be removed in due course.
File operation
- list.files()
- file.info()
- dir.create()
- file.create()
- file.copy()
read/download/source a file from internet
Simple text file http
retail <- read.csv("http://robjhyndman.com/data/ausretail.csv",header=FALSE)
Zip file and url() function
con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb')) source(con) close(con)
Here url() function is like file(), gzfile(), bzfile(), xzfile(), unz(), pipe(), fifo(), socketConnection(). They are used to create connections. By default, the connection is not opened (except for ‘socketConnection’), but may be opened by setting a non-empty value of argument ‘open’. See ?url.
Another example of using url() is
load(url("http:/www.example.com/example.RData"))
downloader package
This package provides a wrapper for the download.file function, making it possible to download files over https on Windows, Mac OS X, and other Unix-like platforms. The RCurl package provides this functionality (and much more) but can be difficult to install because it must be compiled with external dependencies. This package has no external dependencies, so it is much easier to install.
Google drive file based on https using RCurl package
require(RCurl) myCsv <- getURL("https://docs.google.com/spreadsheet/pub?hl=en_US&hl=en_US&key=0AkuuKBh0jM2TdGppUFFxcEdoUklCQlJhM2kweGpoUUE&single=true&gid=0&output=csv") read.csv(textConnection(myCsv))
Google sheet file using googlesheets package
Reading data from google sheets into R
Github files https using RCurl package
- http://support.rstudio.org/help/kb/faq/configuring-r-to-use-an-http-proxy
- http://tonybreyal.wordpress.com/2011/11/24/source_https-sourcing-an-r-script-from-github/
x = getURL("https://gist.github.com/arraytools/6671098/raw/c4cb0ca6fe78054da8dbe253a05f7046270d5693/GeneIDs.txt", ssl.verifypeer = FALSE) read.table(text=x)
- gistr package
summarytools: create summary tables for vectors and data frames
https://github.com/dcomtois/summarytools. R Package for quickly and neatly summarizing vectors and data frames.
Create publication tables using tables package
See p13 for example at here
R's tables packages is the best solution. For example,
> library(tables) > tabular( (Species + 1) ~ (n=1) + Format(digits=2)* + (Sepal.Length + Sepal.Width)*(mean + sd), data=iris ) Sepal.Length Sepal.Width Species n mean sd mean sd setosa 50 5.01 0.35 3.43 0.38 versicolor 50 5.94 0.52 2.77 0.31 virginica 50 6.59 0.64 2.97 0.32 All 150 5.84 0.83 3.06 0.44 > str(iris) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... </pre> and <pre> # This example shows some of the less common options > Sex <- factor(sample(c("Male", "Female"), 100, rep=TRUE)) > Status <- factor(sample(c("low", "medium", "high"), 100, rep=TRUE)) > z <- rnorm(100)+5 > fmt <- function(x) { s <- format(x, digits=2) even <- ((1:length(s)) %% 2) == 0 s[even] <- sprintf("(%s)", s[even]) s } > tabular( Justify(c)*Heading()*z*Sex*Heading(Statistic)*Format(fmt())*(mean+sd) ~ Status ) Status Sex Statistic high low medium Female mean 4.88 4.96 5.17 sd (1.20) (0.82) (1.35) Male mean 4.45 4.31 5.05 sd (1.01) (0.93) (0.75)
ClinReport: Statistical Reporting in Clinical Trials
https://cran.r-project.org/web/packages/ClinReport/index.html
Append figures to PDF files
How to append a plot to an existing pdf file. Hint: use the recordPlot() function.
Extracting tables from PDFs
- extracting Tables from PDFs in R using Tabulizer. This needs the rJava package. Linux works fine. Some issue came out on my macOS 10.12 Sierra. Library not loaded: /Library/Java/JavaVirtualMachines/jdk-9.jdk/Contents/Home/lib/server/libjvm.dylib. Referenced from: /Users/XXXXXXX/Library/R/3.5/library/rJava/libs/rJava.so.
- pdftools - Text Extraction, Rendering and Converting of PDF Documents. pdf_text() and pdf_data() functions.
library(pdftools) pdf_file <- "https://github.com/ropensci/tabulizer/raw/master/inst/examples/data.pdf" txt <- pdf_text(pdf_file) # length = number of pages # Suppose the table we are interested in is on page 1 cat(txt[1]) # Good but not in a data frame format pdf_data(pdf_file)[[1]] # data frame/tibble format
However, it seems it does not work on Table S6. Tabulizer package is better at this case. - How To Convert PDF To Text On Linux (GUI And Command Line). It works when I tested my PDF file.
sudo apt install poppler-utils pdftotext -layout input.pdf output.txt pdftotext -layout -f 3 -l 4 input.pdf output.txt # from page 3 to 4.
Create flat tables in R console using ftable()
> ftable(Titanic, row.vars = 1:3) Survived No Yes Class Sex Age 1st Male Child 0 5 Adult 118 57 Female Child 0 1 Adult 4 140 2nd Male Child 0 11 Adult 154 14 Female Child 0 13 Adult 13 80 3rd Male Child 35 13 Adult 387 75 Female Child 17 14 Adult 89 76 Crew Male Child 0 0 Adult 670 192 Female Child 0 0 Adult 3 20 > ftable(Titanic, row.vars = 1:2, col.vars = "Survived") Survived No Yes Class Sex 1st Male 118 62 Female 4 141 2nd Male 154 25 Female 13 93 3rd Male 422 88 Female 106 90 Crew Male 670 192 Female 3 20 > ftable(Titanic, row.vars = 2:1, col.vars = "Survived") Survived No Yes Sex Class Male 1st 118 62 2nd 154 25 3rd 422 88 Crew 670 192 Female 1st 4 141 2nd 13 93 3rd 106 90 Crew 3 20 > str(Titanic) table [1:4, 1:2, 1:2, 1:2] 0 0 35 0 0 0 17 0 118 154 ... - attr(*, "dimnames")=List of 4 ..$ Class : chr [1:4] "1st" "2nd" "3rd" "Crew" ..$ Sex : chr [1:2] "Male" "Female" ..$ Age : chr [1:2] "Child" "Adult" ..$ Survived: chr [1:2] "No" "Yes" > x <- ftable(mtcars[c("cyl", "vs", "am", "gear")]) > x gear 3 4 5 cyl vs am 4 0 0 0 0 0 1 0 0 1 1 0 1 2 0 1 0 6 1 6 0 0 0 0 0 1 0 2 1 1 0 2 2 0 1 0 0 0 8 0 0 12 0 0 1 0 0 2 1 0 0 0 0 1 0 0 0 > ftable(x, row.vars = c(2, 4)) cyl 4 6 8 am 0 1 0 1 0 1 vs gear 0 3 0 0 0 0 12 0 4 0 0 0 2 0 0 5 0 1 0 1 0 2 1 3 1 0 2 0 0 0 4 2 6 2 0 0 0 5 0 1 0 0 0 0 > > ## Start with expressions, use table()'s "dnn" to change labels > ftable(mtcars$cyl, mtcars$vs, mtcars$am, mtcars$gear, row.vars = c(2, 4), dnn = c("Cylinders", "V/S", "Transmission", "Gears")) Cylinders 4 6 8 Transmission 0 1 0 1 0 1 V/S Gears 0 3 0 0 0 0 12 0 4 0 0 0 2 0 0 5 0 1 0 1 0 2 1 3 1 0 2 0 0 0 4 2 6 2 0 0 0 5 0 1 0 0 0 0
addmargins
Puts Arbitrary Margins On Multidimensional Tables Or Arrays
tracemem, data type, copy
How to avoid copying a long vector
Tell if the current R is running in 32-bit or 64-bit mode
8 * .Machine$sizeof.pointer
where sizeof.pointer returns the number of *bytes* in a C SEXP type and '8' means number of bits per byte.
32- and 64-bit
See R-admin.html.
- For speed you may want to use a 32-bit build, but to handle large datasets a 64-bit build.
- Even on 64-bit builds of R there are limits on the size of R objects, some of which stem from the use of 32-bit integers (especially in FORTRAN code). For example, the dimensionas of an array are limited to 2^31 -1.
- Since R 2.15.0, it is possible to select '64-bit Files' from the standard installer even on a 32-bit version of Windows (2012/3/30).
Handling length 2^31 and more in R 3.0.0
From R News for 3.0.0 release:
There is a subtle change in behaviour for numeric index values 2^31 and larger. These never used to be legitimate and so were treated as NA, sometimes with a warning. They are now legal for long vectors so there is no longer a warning, and x[2^31] <- y will now extend the vector on a 64-bit platform and give an error on a 32-bit one.
In R 2.15.2, if I try to assign a vector of length 2^31, I will get an error
> x <- seq(1, 2^31) Error in from:to : result would be too long a vector
However, for R 3.0.0 (tested on my 64-bit Ubuntu with 16GB RAM. The R was compiled by myself):
> system.time(x <- seq(1,2^31)) user system elapsed 8.604 11.060 120.815 > length(x) [1] 2147483648 > length(x)/2^20 [1] 2048 > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 183823 9.9 407500 21.8 350000 18.7 Vcells 2147764406 16386.2 2368247221 18068.3 2148247383 16389.9 >
Note:
- 2^31 length is about 2 Giga length. It takes about 16 GB (2^31*8/2^20 MB) memory.
- On Windows, it is almost impossible to work with 2^31 length of data if the memory is less than 16 GB because virtual disk on Windows does not work well. For example, when I tested on my 12 GB Windows 7, the whole Windows system freezes for several minutes before I force to power off the machine.
- My slide in http://goo.gl/g7sGX shows the screenshots of running the above command on my Ubuntu and RHEL machines. As you can see the linux is pretty good at handling large (> system RAM) data. That said, as long as your linux system is 64-bit, you can possibly work on large data without too much pain.
- For large dataset, it makes sense to use database or specially crafted packages like bigmemory or ff or bigstatsr.
NA in index
- Question: what is seq(1, 3)[c(1, 2, NA)]?
Answer: It will reserve the element with NA in indexing and return the value NA for it.
- Question: What is TRUE & NA?
Answer: NA
- Question: What is FALSE & NA?
Answer: FALSE
- Question: c("A", "B", NA) != "" ?
Answer: TRUE TRUE NA
- Question: which(c("A", "B", NA) != "") ?
Answer: 1 2
- Question: c(1, 2, NA) != "" & !is.na(c(1, 2, NA)) ?
Answer: TRUE TRUE FALSE
- Question: c("A", "B", NA) != "" & !is.na(c("A", "B", NA)) ?
Answer: TRUE TRUE FALSE
Conclusion: In order to exclude empty or NA for numerical or character data type, we can use which() or a convenience function keep.complete(x) <- function(x) x != "" & !is.na(x). This will guarantee return logical values and not contain NAs.
Don't just use x != "" OR !is.na(x).
Constant and 'L'
Add 'L' after a constant. For example,
for(i in 1L:n) { } if (max.lines > 0L) { } label <- paste0(n-i+1L, ": ") n <- length(x); if(n == 0L) { }
Factor
labels argument
We can specify the factor levels and new labels using the factor() function.
sex <- factor(sex, levels = c("0", "1"), labels = c("Male", "Female")) drug_treatment <- factor(drug_treatment, levels = c("Placebo", "Low dose", "High dose")) health_status <- factor(health_status, levels = c("Healthy", "Alzheimer's"))
Create a factor from a continuous variable: cut()
facVar <- cut(contVar, c(-3, 0, 2, 4), labels = c("low", "medium", "high"))
library(tidyverse) tibble(age_yrs = c(0, 4, 10, 15, 24, 55), age_cat = case_when( age_yrs < 2 ~ "baby", age_yrs < 13 ~ "kid", age_yrs < 20 ~ "teen", TRUE ~ "adult") )
Data frame
stringsAsFactors = FALSE
http://www.win-vector.com/blog/2018/03/r-tip-use-stringsasfactors-false/
Convert data frame factor columns to characters
Convert data.frame columns from factors to characters
# Method 1: bob <- data.frame(lapply(bob, as.character), stringsAsFactors=FALSE) # Method 2: bob[] <- lapply(bob, as.character)
data.frame to vector
> a= matrix(1:6, 2,3) > rownames(a) <- c("a", "b") > colnames(a) <- c("x", "y", "z") > a x y z a 1 3 5 b 2 4 6 > unlist(data.frame(a)) x1 x2 y1 y2 z1 z2 1 2 3 4 5 6
merge
How to perform merges (joins) on two or more data frames with base R, tidyverse and data.table
matrix vs data.frame
ip1 <- installed.packages()[,c(1,3:4)] # class(ip1) = 'matrix' unique(ip1$Priority) # Error in ip1$Priority : $ operator is invalid for atomic vectors unique(ip1[, "Priority"]) # OK ip2 <- as.data.frame(installed.packages()[,c(1,3:4)], stringsAsFactors = FALSE) # matrix -> data.frame unique(ip2$Priority) # OK
The length of a matrix and a data frame is different.
> length(matrix(1:6, 3, 2)) [1] 6 > length(data.frame(matrix(1:6, 3, 2))) [1] 2 > x[1] X1 1 1 2 2 3 3 4 4 5 5 6 6 > x[[1]] [1] 1 2 3 4 5 6
So the length of a data frame is the number of columns. When we use sapply() function on a data frame, it will apply to each column of the data frame.
Convert a matrix (not data frame) of characters to numeric
Just change the mode of the object
tmp <- cbind(a=c("0.12", "0.34"), b =c("0.567", "0.890")); tmp a b 1 0.12 0.567 2 0.34 0.890 > is.data.frame(tmp) # FALSE > is.matrix(tmp) # TRUE > sum(tmp) Error in sum(tmp) : invalid 'type' (character) of argument > mode(tmp) # "character" > mode(tmp) <- "numeric" > sum(tmp) [1] 1.917
Select Data Frame Columns in R
This is part of series of DATA MANIPULATION IN R from datanovia.com
- pull(): Extract column values as a vector. The column of interest can be specified either by name or by index.
- select(): Extract one or multiple columns as a data table. It can be also used to remove columns from the data frame.
- select_if(): Select columns based on a particular condition. One can use this function to, for example, select columns if they are numeric.
- Helper functions - starts_with(), ends_with(), contains(), matches(), one_of(): Select columns/variables based on their names
Creating data frame using structure() function
Creating data frame using structure() function in R
Warning: row names were found from a short variable and have been discarded
https://stackoverflow.com/a/23534617
My example:
str(trainData) # 'data.frame': 503 obs. of 500 variables: # $ bm001: num 0.429 1 -0.5 1.415 -1.899 ... # $ bm002: num 0.0568 1 0.5 0.3556 -1.16 ... # ... trainData[1:3, 1:3] # bm001 bm002 bm003 # 1 0.4289449 0.05676296 1.657966 # 2 1.0000000 1.00000000 1.000000 # 3 -0.5000000 0.50000000 0.500000 o <- data.frame(time = trainData[1, ], status = trainData[2, ], treat = trainData[3, ], t(TData)) # Warning message: # In data.frame(time = trainData[1, ], status = trainData[2, ], treat = trainData[3, : # row names were found from a short variable and have been discarded
matrix (column-major order) multiply a vector
> matrix(1:6, 3,2) [,1] [,2] [1,] 1 4 [2,] 2 5 [3,] 3 6 > matrix(1:6, 3,2) * c(1,2,3) [,1] [,2] [1,] 1 4 [2,] 4 10 [3,] 9 18 > matrix(1:6, 3,2) * c(1,2,3,4) [,1] [,2] [1,] 1 16 [2,] 4 5 [3,] 9 12
Print a vector by suppressing names
Use unname.
format.pval
> args(format.pval) function (pv, digits = max(1L, getOption("digits") - 2L), eps = .Machine$double.eps, na.form = "NA", ...) > format.pval(c(stats::runif(5), pi^-100, NA)) [1] "0.19571" "0.46793" "0.71696" "0.93200" "0.74485" "< 2e-16" "NA" > format.pval(c(0.1, 0.0001, 1e-27)) [1] "1e-01" "1e-04" "<2e-16"
Customize R: options()
Change the default R repository
Edit global Rprofile file. On *NIX platforms, it's located in /usr/lib/R/library/base/R/Rprofile although local .Rprofile settings take precedence.
For example, I can specify the R mirror I like by creating a single line .Rprofile file under my home directory. Another good choice of repository is cloud.r-project.org.
local({ r = getOption("repos") r["CRAN"] = "https://cran.rstudio.com/" options(repos = r) }) options(continue = " ") message("Hi MC, loading ~/.Rprofile") if (interactive()) { .Last <- function() try(savehistory("~/.Rhistory")) }
Change the default web browser
When I run help.start() function in LXLE, it cannot find its default web browser (seamonkey).
> help.start() If the browser launched by 'xdg-open' is already running, it is *not* restarted, and you must switch to its window. Otherwise, be patient ... > /usr/bin/xdg-open: 461: /usr/bin/xdg-open: x-www-browser: not found /usr/bin/xdg-open: 461: /usr/bin/xdg-open: firefox: not found /usr/bin/xdg-open: 461: /usr/bin/xdg-open: mozilla: not found /usr/bin/xdg-open: 461: /usr/bin/xdg-open: epiphany: not found /usr/bin/xdg-open: 461: /usr/bin/xdg-open: konqueror: not found /usr/bin/xdg-open: 461: /usr/bin/xdg-open: chromium-browser: not found /usr/bin/xdg-open: 461: /usr/bin/xdg-open: google-chrome: not found /usr/bin/xdg-open: 461: /usr/bin/xdg-open: links2: not found /usr/bin/xdg-open: 461: /usr/bin/xdg-open: links: not found /usr/bin/xdg-open: 461: /usr/bin/xdg-open: lynx: not found /usr/bin/xdg-open: 461: /usr/bin/xdg-open: w3m: not found xdg-open: no method available for opening 'http://127.0.0.1:27919/doc/html/index.html'
The solution is to put
options(browser='seamonkey')
in the .Rprofile of your home directory. If the browser is not in the global PATH, we need to put the full path above.
For one-time only purpose, we can use the browser option in help.start() function:
> help.start(browser="seamonkey") If the browser launched by 'seamonkey' is already running, it is *not* restarted, and you must switch to its window. Otherwise, be patient ...
We can work made a change (or create the file) ~/.Renviron or etc/Renviron. See
- Changing default browser in options().
- https://stat.ethz.ch/R-manual/R-devel/library/utils/html/browseURL.html
Change the default editor
On my Linux and mac, the default editor is "vi". To change it to "nano",
options(editor = "nano")
Change prompt and remove '+' sign
See https://stackoverflow.com/a/1448823.
options(prompt="R> ", continue=" ")
digits
- Controlling number of decimal digits in print output in R
- ?print.default
- Formatting Decimal places in R, round(). format() where nsmall controls the minimum number of digits to the right of the decimal point
- The default digits 7 may be too small. The acceptable range is 1-22. See the following examples
In R,
> options()$digits # Default [1] 7 > 100000.07 + .04 [1] 100000.1 > options(digits = 16) > 100000.07 + .04 [1] 100000.11
In Python,
>>> 100000.07 + .04 100000.11
Disable scientific notation in printing: options(scipen)
> numer = 29707; denom = 93874 > c(numer/denom, numer, denom) [1] 3.164561e-01 2.970700e+04 9.387400e+04 # Method 1. Without changing the global option > format(c(numer/denom, numer, denom), scientific=FALSE) [1] " 0.3164561" "29707.0000000" "93874.0000000" # Method 2. Change the global option > options(scipen=999) > numer/denom [1] 0.3164561 > c(numer/denom, numer, denom) [1] 0.3164561 29707.0000000 93874.0000000 > c(4/5, numer, denom) [1] 0.8 29707.0 93874.0
Suppress warnings
Use options(). If warn is negative all warnings are ignored. If warn is zero (the default) warnings are stored until the top--level function returns.
op <- options("warn") options(warn = -1) .... options(op) # OR warnLevel <- options()$warn options(warn = -1) ... options(warn = warnLevel)
Converts warnings into errors
options(warn=2)
sprintf
Format number as fixed width, with leading zeros
- https://stackoverflow.com/questions/8266915/format-number-as-fixed-width-with-leading-zeros
- https://stackoverflow.com/questions/14409084/pad-with-leading-zeros-to-common-width?rq=1
# sprintf() a <- seq(1,101,25) sprintf("name_%03d", a) [1] "name_001" "name_026" "name_051" "name_076" "name_101" # formatC() paste("name", formatC(a, width=3, flag="0"), sep="_") [1] "name_001" "name_026" "name_051" "name_076" "name_101"
sprintf does not print
Use cat() or print() outside sprintf(). sprintf() do not print in a non interactive mode.
cat(sprintf('%5.2f\t%i\n',1.234, l234))
Creating publication quality graphs in R
HDF5 : Hierarchical Data Format
HDF5 is an open binary file format for storing and managing large, complex datasets. The file format was developed by the HDF Group, and is widely used in scientific computing.
- https://en.wikipedia.org/wiki/Hierarchical_Data_Format
- HDF5 tutorial and others
- rhdf5 package
- rhdf5 is used by ARCHS4 where you can download R program that will download hdf5 file storing expression and metadata such as gene ID, sample/GSM ID, tissues, et al.
> h5ls(destination_file) group name otype dclass dim 0 / data H5I_GROUP 1 /data expression H5I_DATASET INTEGER 35238 x 65429 2 / info H5I_GROUP 3 /info author H5I_DATASET STRING 1 4 /info contact H5I_DATASET STRING 1 5 /info creation-date H5I_DATASET STRING 1 6 /info lab H5I_DATASET STRING 1 7 /info version H5I_DATASET STRING 1 8 / meta H5I_GROUP 9 /meta Sample_channel_count H5I_DATASET STRING 65429 10 /meta Sample_characteristics_ch1 H5I_DATASET STRING 65429 11 /meta Sample_contact_address H5I_DATASET STRING 65429 12 /meta Sample_contact_city H5I_DATASET STRING 65429 13 /meta Sample_contact_country H5I_DATASET STRING 65429 14 /meta Sample_contact_department H5I_DATASET STRING 65429 15 /meta Sample_contact_email H5I_DATASET STRING 65429 16 /meta Sample_contact_institute H5I_DATASET STRING 65429 17 /meta Sample_contact_laboratory H5I_DATASET STRING 65429 18 /meta Sample_contact_name H5I_DATASET STRING 65429 19 /meta Sample_contact_phone H5I_DATASET STRING 65429 20 /meta Sample_contact_zip-postal_code H5I_DATASET STRING 65429 21 /meta Sample_data_processing H5I_DATASET STRING 65429 22 /meta Sample_data_row_count H5I_DATASET STRING 65429 23 /meta Sample_description H5I_DATASET STRING 65429 24 /meta Sample_extract_protocol_ch1 H5I_DATASET STRING 65429 25 /meta Sample_geo_accession H5I_DATASET STRING 65429 26 /meta Sample_instrument_model H5I_DATASET STRING 65429 27 /meta Sample_last_update_date H5I_DATASET STRING 65429 28 /meta Sample_library_selection H5I_DATASET STRING 65429 29 /meta Sample_library_source H5I_DATASET STRING 65429 30 /meta Sample_library_strategy H5I_DATASET STRING 65429 31 /meta Sample_molecule_ch1 H5I_DATASET STRING 65429 32 /meta Sample_organism_ch1 H5I_DATASET STRING 65429 33 /meta Sample_platform_id H5I_DATASET STRING 65429 34 /meta Sample_relation H5I_DATASET STRING 65429 35 /meta Sample_series_id H5I_DATASET STRING 65429 36 /meta Sample_source_name_ch1 H5I_DATASET STRING 65429 37 /meta Sample_status H5I_DATASET STRING 65429 38 /meta Sample_submission_date H5I_DATASET STRING 65429 39 /meta Sample_supplementary_file_1 H5I_DATASET STRING 65429 40 /meta Sample_supplementary_file_2 H5I_DATASET STRING 65429 41 /meta Sample_taxid_ch1 H5I_DATASET STRING 65429 42 /meta Sample_title H5I_DATASET STRING 65429 43 /meta Sample_type H5I_DATASET STRING 65429 44 /meta genes H5I_DATASET STRING 35238
Formats for writing/saving and sharing data
Efficiently Saving and Sharing Data in R
Write unix format files on Windows and vice versa
https://stat.ethz.ch/pipermail/r-devel/2012-April/063931.html
with() and within() functions
within() is similar to with() except it is used to create new columns and merge them with the original data sets. See youtube video.
closePr <- with(mariokart, totalPr - shipPr) head(closePr, 20) mk <- within(mariokart, { closePr <- totalPr - shipPr }) head(mk) # new column closePr mk <- mariokart aggregate(. ~ wheels + cond, mk, mean) # create mean according to each level of (wheels, cond) aggregate(totalPr ~ wheels + cond, mk, mean) tapply(mk$totalPr, mk[, c("wheels", "cond")], mean)
stem(): stem-and-leaf plot, bar chart on terminals
- https://en.wikipedia.org/wiki/Stem-and-leaf_display
- https://stackoverflow.com/questions/14736556/ascii-plotting-functions-for-r
- txtplot package
Graphical Parameters, Axes and Text, Combining Plots
15 Questions All R Users Have About Plots
See http://blog.datacamp.com/15-questions-about-r-plots/. This is a tremendous post. It covers the built-in plot() function and ggplot() from ggplot2 package.
- How To Draw An Empty R Plot? plot.new()
- How To Set The Axis Labels And Title Of The R Plots?
- How To Add And Change The Spacing Of The Tick Marks Of Your R Plot? axis()
- How To Create Two Different X- or Y-axes? par(new=TRUE), axis(), mtext()
- How To Add Or Change The R Plot’s Legend? legend()
- How To Draw A Grid In Your R Plot? grid()
- How To Draw A Plot With A PNG As Background? rasterImage() from the png package
- How To Adjust The Size Of Points In An R Plot? cex argument
- How To Fit A Smooth Curve To Your R Data? loess() and lines()
- How To Add Error Bars In An R Plot? arrows()
- How To Save A Plot As An Image On Disc
- How To Plot Two R Plots Next To Each Other? par(mfrow), gridBase package, lattice package
- How To Plot Multiple Lines Or Points? plot(), lines()
- How To Fix The Aspect Ratio For Your R Plots? asp parameter
- What Is The Function Of hjust And vjust In ggplot2?
jitter function
- https://www.rdocumentation.org/packages/base/versions/3.5.2/topics/jitter
- The jitter R Function 3 Example Codes (Basic Application & Boxplot Visualization)
- What does the “jitter” function do in R?
- How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
Scatterplot with the "rug" function
require(stats) # both 'density' and its default method with(faithful, { plot(density(eruptions, bw = 0.15)) rug(eruptions) rug(jitter(eruptions, amount = 0.01), side = 3, col = "light blue") })
See also the stripchart() function which produces one dimensional scatter plots (or dot plots) of the given data.
Identify/Locate Points in a Scatter Plot
Draw a single plot with two different y-axes
Draw Color Palette
SVG
Embed svg in html
svglite
https://blog.rstudio.org/2016/11/14/svglite-1-2-0/
pdf -> svg
Using Inkscape. See this post.
read.table
clipboard
source("clipboard") read.table("clipboard")
inline text
mydf <- read.table(header=T, text=' cond yval A 2 B 2.5 C 1.6 ')
http(s) connection
temp = getURL("https://gist.github.com/arraytools/6743826/raw/23c8b0bc4b8f0d1bfe1c2fad985ca2e091aeb916/ip.txt", ssl.verifypeer = FALSE) ip <- read.table(textConnection(temp), as.is=TRUE)
read only specific columns
Use 'colClasses' option in read.table, read.delim, .... For example, the following example reads only the 3rd column of the text file and also changes its data type from a data frame to a vector. Note that we have include double quotes around NULL.
x <- read.table("var_annot.vcf", colClasses = c(rep("NULL", 2), "character", rep("NULL", 7)), skip=62, header=T, stringsAsFactors = FALSE)[, 1] # system.time(x <- read.delim("Methylation450k.txt", colClasses = c("character", "numeric", rep("NULL", 188)), stringsAsFactors = FALSE))
To know the number of columns, we might want to read the first row first.
library(magrittr) scan("var_annot.vcf", sep="\t", what="character", skip=62, nlines=1, quiet=TRUE) %>% length()
Another method is to use pipe(), cut or awk. See ways to read only selected columns from a file into R
Serialization
If we want to pass an R object to C (use recv() function), we can use writeBin() to output the stream size and then use serialize() function to output the stream to a file. See the post on R mailing list.
> a <- list(1,2,3) > a_serial <- serialize(a, NULL) > a_length <- length(a_serial) > a_length [1] 70 > writeBin(as.integer(a_length), connection, endian="big") > serialize(a, connection)
In C++ process, I receive one int variable first to get the length, and then read <length> bytes from the connection.
socketConnection
See ?socketconnection.
Simple example
from the socketConnection's manual.
Open one R session
con1 <- socketConnection(port = 22131, server = TRUE) # wait until a connection from some client writeLines(LETTERS, con1) close(con1)
Open another R session (client)
con2 <- socketConnection(Sys.info()["nodename"], port = 22131) # as non-blocking, may need to loop for input readLines(con2) while(isIncomplete(con2)) { Sys.sleep(1) z <- readLines(con2) if(length(z)) print(z) } close(con2)
Use nc in client
The client does not have to be the R. We can use telnet, nc, etc. See the post here. For example, on the client machine, we can issue
nc localhost 22131 [ENTER]
Then the client will wait and show anything written from the server machine. The connection from nc will be terminated once close(con1) is given.
If I use the command
nc -v -w 2 localhost -z 22130-22135
then the connection will be established for a short time which means the cursor on the server machine will be returned. If we issue the above nc command again on the client machine it will show the connection to the port 22131 is refused. PS. "-w" switch denotes the number of seconds of the timeout for connects and final net reads.
Some post I don't have a chance to read. http://digitheadslabnotebook.blogspot.com/2010/09/how-to-send-http-put-request-from-r.html
Use curl command in client
On the server,
con1 <- socketConnection(port = 8080, server = TRUE)
On the client,
curl --trace-ascii debugdump.txt http://localhost:8080/
Then go to the server,
while(nchar(x <- readLines(con1, 1)) > 0) cat(x, "\n") close(con1) # return cursor in the client machine
Use telnet command in client
On the server,
con1 <- socketConnection(port = 8080, server = TRUE)
On the client,
sudo apt-get install telnet telnet localhost 8080 abcdefg hijklmn qestst
Go to the server,
readLines(con1, 1) readLines(con1, 1) readLines(con1, 1) close(con1) # return cursor in the client machine
Some tutorial about using telnet on http request. And this is a summary of using telnet.
Subsetting
Subset assignment of R Language Definition and Manipulation of functions.
The result of the command x[3:5] <- 13:15 is as if the following had been executed
`*tmp*` <- x x <- "[<-"(`*tmp*`, 3:5, value=13:15) rm(`*tmp*`)
Avoid Coercing Indices To Doubles
as.formula()
? as.formula xnam <- paste("x", 1:25, sep="") fmla <- as.formula(paste("y ~ ", paste(xnam, collapse= "+")))
outcome <- "mpg" variables <- c("cyl", "disp", "hp", "carb") # Method 1. The 'Call' portion of the model is reported as “formula = f” # our modeling effort, # fully parameterized! f <- as.formula( paste(outcome, paste(variables, collapse = " + "), sep = " ~ ")) print(f) # mpg ~ cyl + disp + hp + carb model <- lm(f, data = mtcars) print(model) # Call: # lm(formula = f, data = mtcars) # # Coefficients: # (Intercept) cyl disp hp carb # 34.021595 -1.048523 -0.026906 0.009349 -0.926863 # Method 2. eval() + bquote() + ".()" format(terms(model)) # or model$terms # [1] "mpg ~ cyl + disp + hp + carb" # The new line of code model <- eval(bquote( lm(.(f), data = mtcars) )) print(model) # Call: # lm(formula = mpg ~ cyl + disp + hp + carb, data = mtcars) # # Coefficients: # (Intercept) cyl disp hp carb # 34.021595 -1.048523 -0.026906 0.009349 -0.926863 # Note if we skip ".()" operator > eval(bquote( lm(f, data = mtcars) )) Call: lm(formula = f, data = mtcars) Coefficients: (Intercept) cyl disp hp carb 34.021595 -1.048523 -0.026906 0.009349 -0.926863
S3 and S4 methods
- How S4 works in R https://www.rdocumentation.org/packages/methods/versions/3.5.1/topics/Methods_Details
- Software for Data Analysis: Programming with R by John Chambers
- Programming with Data: A Guide to the S Language by John Chambers
- https://www.rmetrics.org/files/Meielisalp2009/Presentations/Chalabi1.pdf
- https://www.stat.auckland.ac.nz/S-Workshop/Gentleman/S4Objects.pdf
- packS4: Toy Example of S4 Package
- http://www.cyclismo.org/tutorial/R/s4Classes.html
- https://www.coursera.org/lecture/bioconductor/r-s4-methods-C4dNr
- https://www.bioconductor.org/help/course-materials/2013/UnderstandingRBioc2013/
- A (Not So) Short Introduction to S4
- http://adv-r.had.co.nz/S4.html
To get the source code of S4 methods, we can use showMethod(), getMethod() and showMethod(). For example
library(qrqc) showMethods("gcPlot") getMethod("gcPlot", "FASTQSummary") # get an error showMethods("gcPlot", "FASTQSummary") # good.
- Debug a S4 function
> library(genefilter) # Bioconductor > showMethods("nsFilter") Function: nsFilter (package genefilter) eset="ExpressionSet" > debug(nsFilter, signature="ExpressionSet")
- getClassDef() in S4 (Bioconductor course).
library(IRanges) ir <- IRanges(start=c(10, 20, 30), width=5) ir class(ir) ## [1] "IRanges" ## attr(,"package") ## [1] "IRanges" getClassDef(class(ir)) ## Class "IRanges" [package "IRanges"] ## ## Slots: ## ## Name: start width NAMES elementType ## Class: integer integer characterORNULL character ## ## Name: elementMetadata metadata ## Class: DataTableORNULL list ## ## Extends: ## Class "Ranges", directly ## Class "IntegerList", by class "Ranges", distance 2 ## Class "RangesORmissing", by class "Ranges", distance 2 ## Class "AtomicList", by class "Ranges", distance 3 ## Class "List", by class "Ranges", distance 4 ## Class "Vector", by class "Ranges", distance 5 ## Class "Annotated", by class "Ranges", distance 6 ## ## Known Subclasses: "NormalIRanges"
See what methods work on an object
see what methods work on an object, e.g. a GRanges object:
methods(class="GRanges")
Or if you have an object, x:
methods(class=class(x))
View S3 function definition: double colon '::' and triple colon ':::' operators
?":::"
- pkg::name returns the value of the exported variable name in namespace pkg
- pkg:::name returns the value of the internal variable name
base::"+" stats:::coef.default
Read the source code (include Fortran/C, S3 and S4 methods)
- lookup package
- Read the source
mcols() and DataFrame() from Bioc S4Vectors package
- mcols: Get or set the metadata columns.
- colData: SummarizedExperiment instances from GenomicRanges
- DataFrame: The DataFrame class extends the DataTable virtual class and supports the storage of any type of object (with length and [ methods) as columns.
For example, in Shrinkage of logarithmic fold changes vignette of the DESeq2paper package
> mcols(ddsNoPrior[genes, ]) DataFrame with 2 rows and 21 columns baseMean baseVar allZero dispGeneEst dispFit dispersion dispIter dispOutlier dispMAP <numeric> <numeric> <logical> <numeric> <numeric> <numeric> <numeric> <logical> <numeric> 1 163.5750 8904.607 FALSE 0.06263141 0.03862798 0.0577712 7 FALSE 0.0577712 2 175.3883 59643.515 FALSE 2.25306109 0.03807917 2.2530611 12 TRUE 1.6011440 Intercept strain_DBA.2J_vs_C57BL.6J SE_Intercept SE_strain_DBA.2J_vs_C57BL.6J WaldStatistic_Intercept <numeric> <numeric> <numeric> <numeric> <numeric> 1 6.210188 1.735829 0.1229354 0.1636645 50.515872 2 6.234880 1.823173 0.6870629 0.9481865 9.074686 WaldStatistic_strain_DBA.2J_vs_C57BL.6J WaldPvalue_Intercept WaldPvalue_strain_DBA.2J_vs_C57BL.6J <numeric> <numeric> <numeric> 1 10.60602 0.000000e+00 2.793908e-26 2 1.92280 1.140054e-19 5.450522e-02 betaConv betaIter deviance maxCooks <logical> <numeric> <numeric> <numeric> 1 TRUE 3 210.4045 0.2648753 2 TRUE 9 243.7455 0.3248949
findInterval()
Related functions are cuts() and split(). See also
order(), rank() and sort()
If we want to find the indices of the first 25 genes with the smallest p-values, we can use order(pval)[1:25].
> x = sample(10) > x [1] 4 3 10 7 5 8 6 1 9 2 > order(x) [1] 8 10 2 1 5 7 4 6 9 3 > rank(x) [1] 4 3 10 7 5 8 6 1 9 2 > rank(10*x) [1] 4 3 10 7 5 8 6 1 9 2 > x[order(x)] [1] 1 2 3 4 5 6 7 8 9 10 > sort(x) [1] 1 2 3 4 5 6 7 8 9 10
do.call
do.call constructs and executes a function call from a name or a function and a list of arguments to be passed to it.
Below are some examples from the help.
- Usage
do.call(what, args, quote = FALSE, envir = parent.frame()) # what: either a function or a non-empty character string naming the function to be called. # args: a list of arguments to the function call. The names attribute of args gives the argument names. # quote: a logical value indicating whether to quote the arguments. # envir: an environment within which to evaluate the call. This will be most useful # if what is a character string and the arguments are symbols or quoted expressions.
- do.call() is similar to lapply() but not the same.
> do.call("complex", list(imag = 1:3)) [1] 0+1i 0+2i 0+3i > lapply(list(imag = 1:3), complex) $imag [1] 0+0i > complex(imag=1:3) [1] 0+1i 0+2i 0+3i
> tmp <- expand.grid(letters[1:2], 1:3, c("+", "-")) > length(tmp) [1] 3 > tmp[1:4,] Var1 Var2 Var3 1 a 1 + 2 b 1 + 3 a 2 + 4 b 2 + > c(tmp, sep = "") $Var1 [1] a b a b a b a b a b a b Levels: a b $Var2 [1] 1 1 2 2 3 3 1 1 2 2 3 3 $Var3 [1] + + + + + + - - - - - - Levels: + - $sep [1] "" > do.call("paste", c(tmp, sep = "")) [1] "a1+" "b1+" "a2+" "b2+" "a3+" "b3+" "a1-" "b1-" "a2-" "b2-" "a3-" [12] "b3-"
- do.call() is doing what mapply() does but do.call() uses a list instead of multiple arguments. So do.call() more close to base::Map() function.
> mapply(paste, tmp[1], tmp[2], tmp[3], sep = "") Var1 [1,] "a1+" [2,] "b1+" [3,] "a2+" [4,] "b2+" [5,] "a3+" [6,] "b3+" [7,] "a1-" [8,] "b1-" [9,] "a2-" [10,] "b2-" [11,] "a3-" [12,] "b3-" # It does not work if we do not explicitly specify the arguments in mapply() > mapply(paste, tmp, sep = "") Var1 Var2 Var3 [1,] "a" "1" "+" [2,] "b" "1" "+" [3,] "a" "2" "+" [4,] "b" "2" "+" [5,] "a" "3" "+" [6,] "b" "3" "+" [7,] "a" "1" "-" [8,] "b" "1" "-" [9,] "a" "2" "-" [10,] "b" "2" "-" [11,] "a" "3" "-" [12,] "b" "3" "-"
- environment and quote arguments.
> A <- 2 > f <- function(x) print(x^2) > env <- new.env() > assign("A", 10, envir = env) > assign("f", f, envir = env) > f <- function(x) print(x) > f(A) [1] 2 > do.call("f", list(A)) [1] 2 > do.call("f", list(A), envir = env) [1] 4 > do.call(f, list(A), envir = env) [1] 2 # Why? > eval(call("f", A)) [1] 2 > eval(call("f", quote(A))) [1] 2 > eval(call("f", A), envir = env) [1] 4 > eval(call("f", quote(A)), envir = env) [1] 100
- Good use case; see Get all Parameters as List
> foo <- function(a=1, b=2, ...) { list(arg=do.call(c, as.list(match.call())[-1])) } > foo() $arg NULL > foo(a=1) $arg a 1 > foo(a=1, b=2, c=3) $arg a b c 1 2 3
do.call vs lapply
https://stackoverflow.com/a/10801883
- lapply returns a list of the same length as X, each element of which is the result of applying FUN to the corresponding element of X.
- do.call constructs and executes a function call from a name or a function and a list of arguments to be passed to it. It is widely used, for example, to assemble lists into simpler structures (often with rbind or cbind).
- Map applies a function to the corresponding elements of given vectors... Map is a simple wrapper to mapply which does not attempt to simplify the result, similar to Common Lisp's mapcar (with arguments being recycled, however). Future versions may allow some control of the result type.
> lapply(iris, class) # same as Map(class, iris) $Sepal.Length [1] "numeric" $Sepal.Width [1] "numeric" $Petal.Length [1] "numeric" $Petal.Width [1] "numeric" $Species [1] "factor" > x <- lapply(iris, class) > do.call(c, x) Sepal.Length Sepal.Width Petal.Length Petal.Width Species "numeric" "numeric" "numeric" "numeric" "factor"
https://stackoverflow.com/a/10801902
- lapply applies a function over a list. So there will be several function calls.
- do.call calls a function with a list of arguments (... argument) such as c() or rbind()/cbind() or sum or
order or "[" or paste. So there is only one function call.
> X <- list(1:3,4:6,7:9) > lapply(X,mean) [[1]] [1] 2 [[2]] [1] 5 [[3]] [1] 8 > do.call(sum, X) [1] 45 > sum(c(1,2,3), c(4,5,6), c(7,8,9)) [1] 45 > do.call(mean, X) # Error > do.call(rbind,X) [,1] [,2] [,3] [1,] 1 2 3 [2,] 4 5 6 [3,] 7 8 9 > lapply(X,rbind) [[1]] [,1] [,2] [,3] [1,] 1 2 3 [[2]] [,1] [,2] [,3] [1,] 4 5 6 [[3]] [,1] [,2] [,3] [1,] 7 8 9 > mapply(mean, X, trim=c(0,0.5,0.1)) [1] 2 5 8 > mapply(mean, X) [1] 2 5 8
Below is a good example to show the difference of lapply() and do.call() - Generating Random Strings.
> set.seed(1) > x <- replicate(2, sample(LETTERS, 4), FALSE) > x [[1]] [1] "Y" "D" "G" "A" [[2]] [1] "B" "W" "K" "N" > lapply(x, paste0) [[1]] [1] "Y" "D" "G" "A" [[2]] [1] "B" "W" "K" "N" > lapply(x, paste0, collapse= '') [[1]] [1] "YDGA" [[2]] [1] "BWKN" > do.call(paste0, x) [1] "YB" "DW" "GK" "AN"
do.call + rbind + lapply
Lots of examples. See for example this one for creating a data frame from a vector.
x <- readLines(textConnection("---CLUSTER 1 --- 3 4 5 6 ---CLUSTER 2 --- 9 10 8 11")) # create a list of where the 'clusters' are clust <- c(grep("CLUSTER", x), length(x) + 1L) # get size of each cluster clustSize <- diff(clust) - 1L # get cluster number clustNum <- gsub("[^0-9]+", "", x[grep("CLUSTER", x)]) result <- do.call(rbind, lapply(seq(length(clustNum)), function(.cl){ cbind(Object = x[seq(clust[.cl] + 1L, length = clustSize[.cl])] , Cluster = .cl ) })) result Object Cluster [1,] "3" "1" [2,] "4" "1" [3,] "5" "1" [4,] "6" "1" [5,] "9" "2" [6,] "10" "2" [7,] "8" "2" [8,] "11" "2"
A 2nd example is to sort a data frame by using do.call(order, list()).
Another example is to reproduce aggregate(). aggregate() = do.call() + by().
attach(mtcars) do.call(rbind, by(mtcars, list(cyl, vs), colMeans)) # the above approach give the same result as the following # except it does not have an extra Group.x columns aggregate(mtcars, list(cyl, vs), FUN=mean)
How to get examples from help file
See this post. Method 1:
example(acf, give.lines=TRUE)
Method 2:
Rd <- utils:::.getHelpFile(?acf) tools::Rd2ex(Rd)
"[" and "[[" with the sapply() function
Suppose we want to extract string from the id like "ABC-123-XYZ" before the first hyphen.
sapply(strsplit("ABC-123-XYZ", "-"), "[", 1)
is the same as
sapply(strsplit("ABC-123-XYZ", "-"), function(x) x[1])
Dealing with date
d1 = date() class(d1) # "character" d2 = Sys.Date() class(d2) # "Date" format(d2, "%a %b %d") library(lubridate); ymd("20140108") # "2014-01-08 UTC" mdy("08/04/2013") # "2013-08-04 UTC" dmy("03-04-2013") # "2013-04-03 UTC" ymd_hms("2011-08-03 10:15:03") # "2011-08-03 10:15:03 UTC" ymd_hms("2011-08-03 10:15:03", tz="Pacific/Auckland") # "2011-08-03 10:15:03 NZST" ?Sys.timezone x = dmy(c("1jan2013", "2jan2013", "31mar2013", "30jul2013")) wday(x[1]) # 3 wday(x[1], label=TRUE) # Tues
- http://www.r-statistics.com/2012/03/do-more-with-dates-and-times-in-r-with-lubridate-1-1-0/
- http://cran.r-project.org/web/packages/lubridate/vignettes/lubridate.html
- http://rpubs.com/seandavi/GEOMetadbSurvey2014
- We want our dates and times as class "Date" or the class "POSIXct", "POSIXlt". For more information type ?POSIXlt.
Nonstandard evaluation and deparse/substitute
- Vignette from the lazyeval package. It is needed in three cases
- Labelling: turn an argument into a label
- Formulas
- Dot-dot-dot
- substitute(expr, env) - capture expression.
- substitute() is often paired with deparse() to create informative labels for data sets and plots.
- Use 'substitute' to include the variable's name in a plot title, e.g.: var <- "abc"; hist(var,main=substitute(paste("Dist of ", var))) will show the title "Dist of var" instead of "Dist of abc" in the title.
- quote(expr) - similar to substitute() but do nothing?? noquote - print character strings without quotes
- eval(expr, envir), evalq(expr, envir) - eval evaluates its first argument in the current scope before passing it to the evaluator: evalq avoids this.
- deparse(expr) - turns unevaluated expressions into character strings. For example,
> deparse(args(lm)) [1] "function (formula, data, subset, weights, na.action, method = \"qr\", " [2] " model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE, " [3] " contrasts = NULL, offset, ...) " [4] "NULL" > deparse(args(lm), width=20) [1] "function (formula, data, " " subset, weights, " [3] " na.action, method = \"qr\", " " model = TRUE, x = FALSE, " [5] " y = FALSE, qr = TRUE, " " singular.ok = TRUE, " [7] " contrasts = NULL, " " offset, ...) " [9] "NULL"
- parse(text) - returns the parsed but unevaluated expressions in a list. See Create a Simple Socket Server in R for the application of eval(parse(text)). Be cautious!
Following is another example. Assume we have a bunch of functions (f1, f2, ...; each function implements a different algorithm) with same input arguments format (eg a1, a2). We like to run these function on the same data (to compare their performance).
f1 <- function(x) x+1; f2 <- function(x) x+2; f3 <- function(x) x+3 f1(1:3) f2(1:3) f3(1:3) # Or myfun <- function(f, a) { eval(parse(text = f))(a) } myfun("f1", 1:3) myfun("f2", 1:3) myfun("f3", 1:3) # Or with lapply method <- c("f1", "f2", "f3") res <- lapply(method, function(M) { Mres <- eval(parse(text = M))(1:3) return(Mres) }) names(res) <- method
The ‘…’ argument
See Section 10.4 of An Introduction to R. Especially, the expression list(...) evaluates all such arguments and returns them in a named list
Lazy evaluation in R functions arguments
- http://adv-r.had.co.nz/Functions.html
- https://stat.ethz.ch/pipermail/r-devel/2015-February/070688.html
R function arguments are lazy — they’re only evaluated if they’re actually used.
- Example 1. By default, R function arguments are lazy.
f <- function(x) { 999 } f(stop("This is an error!")) #> [1] 999
- Example 2. If you want to ensure that an argument is evaluated you can use force().
add <- function(x) { force(x) function(y) x + y } adders2 <- lapply(1:10, add) adders2[[1]](10) #> [1] 11 adders2[[10]](10) #> [1] 20
- Example 3. Default arguments are evaluated inside the function.
f <- function(x = ls()) { a <- 1 x } # ls() evaluated inside f: f() # [1] "a" "x" # ls() evaluated in global environment: f(ls()) # [1] "add" "adders" "f"
- Example 4. Laziness is useful in if statements — the second statement below will be evaluated only if the first is true.
x <- NULL if (!is.null(x) && x > 0) { }
Backtick sign, infix/prefix/postfix operators
The backtick sign ` (not the single quote) refers to functions or variables that have otherwise reserved or illegal names; e.g. '&&', '+', '(', 'for', 'if', etc. See some examples in this note.
infix operator.
1 + 2 # infix + 1 2 # prefix 1 2 + # postfix
List data type
Calling a function given a list of arguments
> args <- list(c(1:10, NA, NA), na.rm = TRUE) > do.call(mean, args) [1] 5.5 > mean(c(1:10, NA, NA), na.rm = TRUE) [1] 5.5
Error handling and exceptions, tryCatch(), stop(), warning() and message()
- http://adv-r.had.co.nz/Exceptions-Debugging.html
- try() allows execution to continue even after an error has occurred. You can suppress the message with try(..., silent = TRUE).
out <- try({ a <- 1 b <- "x" a + b }) elements <- list(1:10, c(-1, 10), c(T, F), letters) results <- lapply(elements, log) is.error <- function(x) inherits(x, "try-error") succeeded <- !sapply(results, is.error)
- tryCatch(): With tryCatch() you map conditions to handlers (like switch()), named functions that are called with the condition as an input. Note that try() is a simplified version of tryCatch().
tryCatch(expr, ..., finally) show_condition <- function(code) { tryCatch(code, error = function(c) "error", warning = function(c) "warning", message = function(c) "message" ) } show_condition(stop("!")) #> [1] "error" show_condition(warning("?!")) #> [1] "warning" show_condition(message("?")) #> [1] "message" show_condition(10) #> [1] 10
Below is another snippet from available.packages() function,
z <- tryCatch(download.file(....), error = identity) if (!inherits(z, "error")) STATEMENTS
Using list type
Avoid if-else or switch
?plot.stepfun.
y0 <- c(1,2,4,3) sfun0 <- stepfun(1:3, y0, f = 0) sfun.2 <- stepfun(1:3, y0, f = .2) sfun1 <- stepfun(1:3, y0, right = TRUE) tt <- seq(0, 3, by = 0.1) op <- par(mfrow = c(2,2)) plot(sfun0); plot(sfun0, xval = tt, add = TRUE, col.hor = "bisque") plot(sfun.2);plot(sfun.2, xval = tt, add = TRUE, col = "orange") # all colors plot(sfun1);lines(sfun1, xval = tt, col.hor = "coral") ##-- This is revealing : plot(sfun0, verticals = FALSE, main = "stepfun(x, y0, f=f) for f = 0, .2, 1") for(i in 1:3) lines(list(sfun0, sfun.2, stepfun(1:3, y0, f = 1))[[i]], col = i) legend(2.5, 1.9, paste("f =", c(0, 0.2, 1)), col = 1:3, lty = 1, y.intersp = 1) par(op)
Open a new Window device
X11() or dev.new()
par()
?par
text size and font on main, lab & axis
- Graphical Parameters from statmethods.net.
- Overlaying Data Summaries in Dotplots
Examples:
- cex.main=0.9
- cex.lab=0.8
- font.lab=2
- cex.axis=0.8
- font.axis=2
- col.axis="grey50"
layout
http://datascienceplus.com/adding-text-to-r-plot/
reset the settings
op <- par(mfrow=c(2,1), mar = c(5,7,4,2) + 0.1) .... par(op) # mfrow=c(1,1), mar = c(5,4,4,2) + .1
mtext (margin text) vs title
- https://datascienceplus.com/adding-text-to-r-plot/
- https://datascienceplus.com/mastering-r-plot-part-2-axis/
mgp (axis label locations)
- The margin line (in ‘mex’ units) for the axis title, axis labels and axis line. Note that ‘mgp[1]’ affects ‘title’ whereas ‘mgp[2:3]’ affect ‘axis’. The default is ‘c(3, 1, 0)’. If we like to make the axis labels closer to an axis, we can use mgp=c(2.3, 1, 0) for example.
- http://rfunction.com/archives/1302 mgp – A numeric vector of length 3, which sets the axis label locations relative to the edge of the inner plot window. The first value represents the location the labels (i.e. xlab and ylab in plot), the second the tick-mark labels, and third the tick marks. The default is c(3, 1, 0).
pch
- Full circle: pch=16
lty (line type)
las (label style)
0: The default, parallel to the axis
1: Always horizontal
2: Perpendicular to the axis
3: Always vertical
oma (outer margin), common title for two plots
The following trick is useful when we want to draw multiple plots with a common title.
par(mfrow=c(1,2),oma = c(0, 0, 2, 0)) # oma=c(0, 0, 0, 0) by default plot(1:10, main="Plot 1") plot(1:100, main="Plot 2") mtext("Title for Two Plots", outer = TRUE, cex = 1.5) # outer=FALSE by default
Mastering R plot – Part 3: Outer margins mtext() & par(xpd).
Non-standard fonts in postscript and pdf graphics
https://cran.r-project.org/doc/Rnews/Rnews_2006-2.pdf#page=41
NULL, NA, NaN, Inf
https://tomaztsql.wordpress.com/2018/07/04/r-null-values-null-na-nan-inf/
save() vs saveRDS()
- saveRDS() can only save one R object while save() does not have this constraint.
- saveRDS() doesn’t save the both the object and its name it just saves a representation of the object. As a result, the saved object can be loaded into a named object within R that is different from the name it had when originally serialized. See this post.
x <- 5 saveRDS(x, "myfile.rds") x2 <- readRDS("myfile.rds") identical(mod, mod2, ignore.environment = TRUE)
==, all.equal(), identical()
- ==: exact match
- all.equal: compare R objects x and y testing ‘near equality’
- identical: The safe and reliable way to test two objects for being exactly equal.
x <- 1.0; y <- 0.99999999999 all.equal(x, y) # [1] TRUE identical(x, y) # [1] FALSE
See also the testhat package.
testhat
tinytest
tinytest: Lightweight but Feature Complete Unit Testing Framework
Numerical Pitfall
Numerical pitfalls in computing variance
.1 - .3/3 ## [1] 0.00000000000000001388
Sys.getpid()
This can be used to monitor R process memory usage or stop the R process. See this post.
How to write R codes
- Code smells and feels from R Consortium
- write simple conditions,
- handle class properly,
- return and exit early,
- polymorphism,
- switch(),
- case_when(),
- %||%.
How to debug an R code
Using assign() in functions
For example, insert the following line to your function
assign(envir=globalenv(), "GlobalVar", localvar)
Debug lapply()/sapply()
- https://stackoverflow.com/questions/1395622/debugging-lapply-sapply-calls
- https://stat.ethz.ch/R-manual/R-devel/library/utils/html/recover.html. Use options(error=NULL) to turn it off.
Debugging with RStudio
- https://www.rstudio.com/resources/videos/debugging-techniques-in-rstudio/
- https://github.com/ajmcoqui/debuggingRStudio/blob/master/RStudio_Debugging_Cheatsheet.pdf
- https://support.rstudio.com/hc/en-us/articles/205612627-Debugging-with-RStudio
Debug R source code
Build R with debug information
- R -> Build R from its source on Windows
- http://www.stats.uwo.ca/faculty/murdoch/software/debuggingR/ (defunct)
- http://www.stats.uwo.ca/faculty/murdoch/software/debuggingR/gdb.shtml (defunct)
- Build R with debug information (see the discussion here). Cf output messages from running ./configure and make using the default options.
$ ./configure --help $ ./configure --enable-R-shlib --with-valgrind-instrumentation=2 \ --with-system-valgrind-headers \ CFLAGS='-g -O0 -fPIC' \ FFLAGS='-g -O0 -fPIC' \ CXXFLAGS='-g -O0 -fPIC' \ FCFLAGS='-g -O0 -fPIC' $ make -j4 $ sudo make install
- My note of debugging cor() function
- Using gdb to debug R packages with native code (Video) The steps to debug is given below.
# Make sure to create a file <src/Makevars> with something like: CFLAGS=-ggdb -O0 # Or more generally # CFLAGS=-Wall -Wextra -pedantic -O0 -ggdb # CXXFLAGS=-Wall -Wextra -pedantic -O0 -ggdb # FFLAGS=-Wall -Wextra -pedantic -O0 -ggdb $ tree nidemo $ R CMD INSTALL nidemo $ cat bug.R $ R -f bug.R $ R -d gdb (gdb) r > library(nidemo) > Ctrl+C (gdb) b nid_buggy_freq (gdb) c # continue > buggy_freq("nidemo/DESCRIPTION") # stop at breakpoint 1 (gdb) list (gdb) n # step through (gdb) # press RETURN a few times until you see the bug (gdb) d 1 # delete the first break point (gdb) b Rf_error # R's C entry point for the error function (gdb) c > buggy_freq("nidemo/DESCRIPTION") (gdb) bt 5 # last 5 stack frames (gdb) frame 2 (gdb) list (gdb) p freq_data (gdb) p ans (gdb) call Rf_PrintValues(ans) (gdb) call Rf_PrintValues(fname) (gdb) q # Edit buggy.c $ R CMD INSTALL nidemo # re-install the package $ R -f bug.R $ R -d gdb (gdb) run > source("bug.R") # error happened (gdb) bt 5 # show the last 5 frames (gdb) frame 2 (gdb) list (gdb) frame 1 (gdb) list (gdb) p file (gdb) p fh (gdb) q # Edit buggy.c $ R CMD INSTALL nidemo $ R -f bug.R
- Compiled code from "R packages" by Hadley Wickham
- Debugging C/C++ code from Bioconductor (case study)
- Same idea for the Rcpp situation. See What are productive ways to debug Rcpp compiled code loaded in R (on OS X Mavericks)?
.Call
- Writing R Extensions manual.
- R’s C interface from Advanced R by Hadley Wickham
Registering native routines
https://cran.rstudio.com/doc/manuals/r-release/R-exts.html#Registering-native-routines
Pay attention to the prefix argument .fixes (eg .fixes = "C_") in useDynLib() function in the NAMESPACE file.
Example of debugging cor() function
Note that R's cor() function called a C function cor().
stats::cor .... .Call(C_cor, x, y, na.method, method == "kendall")
A step-by-step screenshot of debugging using the GNU debugger gdb can be found on my Github repository https://github.com/arraytools/r-debug.
Locale bug (grep did not handle UTF-8 properly PR#16264)
https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16264
Path length in dir.create() (PR#17206)
https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=17206 (Windows only)
install.package() error, R_LIBS_USER is empty in R 3.4.1 & .libPaths()
- https://support.rstudio.com/hc/en-us/community/posts/115008369408-Since-update-to-R-3-4-1-R-LIBS-USER-is-empty and http://r.789695.n4.nabble.com/R-LIBS-USER-on-Ubuntu-16-04-td4740935.html. Modify /etc/R/Renviron (if you have a sudo right) by uncomment out line 43.
R_LIBS_USER=${R_LIBS_USER-'~/R/x86_64-pc-linux-gnu-library/3.4'}
- https://stackoverflow.com/questions/44873972/default-r-personal-library-location-is-null. Modify $HOME/.Renviron by adding a line
R_LIBS_USER="${HOME}/R/${R_PLATFORM}-library/3.4"
- http://stat.ethz.ch/R-manual/R-devel/library/base/html/libPaths.html. Play with .libPaths()
On Mac & R 3.4.0 (it's fine)
> Sys.getenv("R_LIBS_USER") [1] "~/Library/R/3.4/library" > .libPaths() [1] "/Library/Frameworks/R.framework/Versions/3.4/Resources/library"
On Linux & R 3.3.1 (ARM)
> Sys.getenv("R_LIBS_USER") [1] "~/R/armv7l-unknown-linux-gnueabihf-library/3.3" > .libPaths() [1] "/home/$USER/R/armv7l-unknown-linux-gnueabihf-library/3.3" [2] "/usr/local/lib/R/library"
On Linux & R 3.4.1 (*Problematic*)
> Sys.getenv("R_LIBS_USER") [1] "" > .libPaths() [1] "/usr/local/lib/R/site-library" "/usr/lib/R/site-library" [3] "/usr/lib/R/library"
I need to specify the lib parameter when I use the install.packages command.
> install.packages("devtools", "~/R/x86_64-pc-linux-gnu-library/3.4") > library(devtools) Error in library(devtools) : there is no package called 'devtools' # Specify lib.loc parameter will not help with the dependency package > library(devtools, lib.loc = "~/R/x86_64-pc-linux-gnu-library/3.4") Error: package or namespace load failed for 'devtools': .onLoad failed in loadNamespace() for 'devtools', details: call: loadNamespace(name) error: there is no package called 'withr' # A solution is to redefine .libPaths > .libPaths(c("~/R/x86_64-pc-linux-gnu-library/3.4", .libPaths())) > library(devtools) # Works
A better solution is to specify R_LIBS_USER in ~/.Renviron file or ~/.bash_profile; see ?Startup.
Using external data from within another package
https://logfc.wordpress.com/2017/03/02/using-external-data-from-within-another-package/
How to run R scripts from the command line
https://support.rstudio.com/hc/en-us/articles/218012917-How-to-run-R-scripts-from-the-command-line
How to exit a sourced R script
- How to exit a sourced R script
- Problem using the source-function within R-functions The best way to handle the generic sort of problem you are describing is to take those source'd files, and rewrite their content as functions to be called from your other functions.
Decimal point & decimal comma
Countries using Arabic numerals with decimal comma (Austria, Belgium, Brazil France, Germany, Netherlands, Norway, South Africa, Spain, Sweden, ...) https://en.wikipedia.org/wiki/Decimal_mark
setting seed locally (not globally) in R
https://stackoverflow.com/questions/14324096/setting-seed-locally-not-globally-in-r
R's internal C API
https://github.com/hadley/r-internals
cleancall package for C resource cleanup
Resource Cleanup in C and the R API
Random numbers: multivariate normal
Why MASS::mvrnorm() gives different result on Mac and Linux/Windows?
The reason could be the covariance matrix decomposition - and that may be due to the LAPACK/BLAS libraries. See
- https://stackoverflow.com/questions/11567613/different-random-number-generation-between-os
- https://stats.stackexchange.com/questions/149321/generating-and-working-with-random-vectors-in-r
- Cholesky versus eigendecomposition for drawing samples from a multivariate normal distribution
set.seed(1234) junk <- biospear::simdata(n=500, p=500, q.main = 10, q.inter = 10, prob.tt = .5, m0=1, alpha.tt= -.5, beta.main= -.5, beta.inter= -.5, b.corr = .7, b.corr.by=25, wei.shape = 1, recr=3, fu=2, timefactor=1) ## Method 1: MASS::mvrnorm() ## This is simdata() has used. It gives different numbers on different OS. ## library(MASS) set.seed(1234) m0 <-1 n <- 500 prob.tt <- .5 p <- 500 b.corr.by <- 25 b.corr <- .7 data <- data.frame(treat = rbinom(n, 1, prob.tt) - 0.5) n.blocks <- p%/%b.corr.by covMat <- diag(n.blocks) %x% matrix(b.corr^abs(matrix(1:b.corr.by, b.corr.by, b.corr.by, byrow = TRUE) - matrix(1:b.corr.by, b.corr.by, b.corr.by)), b.corr.by, b.corr.by) diag(covMat) <- 1 data <- cbind(data, mvrnorm(n, rep(0, p), Sigma = covMat)) range(data) # Mac: -4.963827 4.133723 # Linux/Windows: -4.327635 4.408097 packageVersion("MASS") # Mac: [1] ‘7.3.49’ # Linux: [1] ‘7.3.49’ # Windows: [1] ‘7.3.47’ R.version$version.string # Mac: [1] "R version 3.4.3 (2017-11-30)" # Linux: [1] "R version 3.4.4 (2018-03-15)" # Windows: [1] "R version 3.4.3 (2017-11-30)" ## Method 2: mvtnorm::rmvnorm() library(mvtnorm) set.seed(1234) sigma <- matrix(c(4,2,2,3), ncol=2) x <- rmvnorm(n=n, rep(0, p), sigma=covMat) range(x) # Mac: [1] -4.482566 4.459236 # Linux: [1] -4.482566 4.459236 ## Method 3: mvnfast::rmvn() set.seed(1234) x <- mvnfast::rmvn(n, rep(0, p), covMat) range(x) # Mac: [1] -4.323585 4.355666 # Linux: [1] -4.323585 4.355666 library(microbenchmark) library(MASS) library(mvtnorm) library(mvnfast) microbenchmark(v1 <- rmvnorm(n=n, rep(0, p), sigma=covMat, "eigen"), v2 <- rmvnorm(n=n, rep(0, p), sigma=covMat, "svd"), v3 <- rmvnorm(n=n, rep(0, p), sigma=covMat, "chol"), v4 <- rmvn(n, rep(0, p), covMat), v5 <- mvrnorm(n, rep(0, p), Sigma = covMat)) Unit: milliseconds expr min lq v1 <- rmvnorm(n = n, rep(0, p), sigma = covMat, "eigen") 296.55374 300.81089 v2 <- rmvnorm(n = n, rep(0, p), sigma = covMat, "svd") 461.81867 466.98806 v3 <- rmvnorm(n = n, rep(0, p), sigma = covMat, "chol") 118.33759 120.01829 v4 <- rmvn(n, rep(0, p), covMat) 66.64675 69.89383 v5 <- mvrnorm(n, rep(0, p), Sigma = covMat) 291.19826 294.88038 mean median uq max neval cld 306.72485 301.99339 304.46662 335.6137 100 d 478.58536 470.44085 493.89041 571.7990 100 e 125.85427 121.26185 122.21361 151.1658 100 b 71.67996 70.52985 70.92923 100.2622 100 a 301.88144 296.76028 299.50839 346.7049 100 c
A little more investigation shows the eigen values differ a little bit on macOS and Linux.
set.seed(1234); x <- mvrnorm(n, rep(0, p), Sigma = covMat) debug(mvrnorm) # eS --- macOS # eS2 -- Linux Browse[2]> range(abs(eS$values - eS2$values)) # [1] 0.000000e+00 1.776357e-15 Browse[2]> var(as.vector(eS$vectors)) [1] 0.002000006 Browse[2]> var(as.vector(eS2$vectors)) [1] 0.001999987 Browse[2]> all.equal(eS$values, eS2$values) [1] TRUE Browse[2]> which(eS$values != eS2$values) [1] 6 7 8 9 10 11 12 13 14 20 22 23 24 25 26 27 28 29 ... [451] 494 495 496 497 499 500 Browse[2]> range(abs(eS$vectors - eS2$vectors)) [1] 0.0000000 0.5636919
rle() running length encoding
- https://en.wikipedia.org/wiki/Run-length_encoding
- How to Find Consecutive Repeats in R
- R Function of the Day: rle
- Creating nice tables using R Markdown
- https://rosettacode.org/wiki/Run-length_encoding
- R's base::rle() function
- R's Rle class from S4Vectors package which was used in for example IRanges/GRanges/GenomicRanges package
citation()
citation() citation("MASS") toBibtex(citation())
Monitor memory usage
- Windows: memory.size(max=TRUE)
- Linux
- RStudio: htop -p PID where PID is the process ID of /usr/lib/rstudio/bin/rsession, not /usr/lib/rstudio/bin/rstudio. This is obtained by running x <- rnorm(2*1e8). The object size can be obtained through print(object.size(x), units = "auto"). Note that 1e8*8/2^20 = 762.9395.
- R: htop -p PID where PID is the process ID of /usr/lib/R/bin/exec/R. Alternatively, use htop -p `pgrep -f /usr/lib/R/bin/exec/R`
- To find the peak memory usage grep VmPeak /proc/$PID/status
References:
- How to monitor CPU/memory usage of a single process?. htop -p $PID is recommended. It only shows the percentage of memory usage.
- Peak memory usage of a linux/unix process grep VmPeak /proc/$PID/status is recommended.
- How can I see the memory usage of a Linux process? pmap $PID | tail -n 1 is recommended. It shows the memory usage in absolute value (eg 1722376K).
Resource
Books
- A list of recommended books http://blog.revolutionanalytics.com/2015/11/r-recommended-reading.html
- Learning R programming by reading books: A book list
- Modern Applied Statistics with S by William N. Venables and Brian D. Ripley
- Seamless R and C++ Integration with Rcpp by Dirk Eddelbuettel
- Advanced R by Hadley Wickham 2014
- http://brettklamer.com/diversions/statistical/compile-hadleys-advanced-r-programming-to-a-pdf/ Compile Hadley's Advanced R to a PDF
- Functional programming and unit testing for data munging with R by Bruno Rodrigues
- R Cookbook by Paul Teetor
- Machine Learning with R by Brett Lantz
- R for Everyone by Jared P. Lander
- The Art of R Programming by Norman Matloff
- Applied Predictive Modeling by Max Kuhn
- R in Action by Robert Kabacoff
- The R Book by Michael J. Crawley
- Regression Modeling Strategies, with Applications to Linear Models, Survival Analysis and Logistic Regression by Frank E. Harrell
- Data Manipulation with R by Phil Spector
- DATA MANIPULATION IN R by ALBOUKADEL KASSAMBARA
- Review of Efficient R Programming
- R packages: Organize, Test, Document, and Share Your Code by Hadley Wicklam 2015
- Text Mining with R: A Tidy Approach and a blog
- Efficient R programming by Colin Gillespie and Robin Lovelace. It works to re-create the html version of the book if we follow their simple instruction in the Appendix. Note that pdf version has advantages of expected output (mathematical notations, tables) over the epub version.
# R 3.4.1 .libPaths(c("~/R/x86_64-pc-linux-gnu-library/3.4", .libPaths())) setwd("/tmp/efficientR/") bookdown::render_book("index.Rmd", output_format = "bookdown::pdf_book") # generated pdf file is located _book/_main.pdf bookdown::render_book("index.Rmd", output_format = "bookdown::epub_book") # generated epub file is located _book/_main.epub. # This cannot be done in RStudio ("parse_dt" not resolved from current namespace (lubridate)) # but it is OK to run in an R terminal
- Learning statistics with R: A tutorial for psychology students and other beginners by Danielle Navarro
Webinar
useR!
- http://blog.revolutionanalytics.com/2017/07/revisiting-user2017.html
- UseR 2018 workshop and tutorials
- useR! 2019, tutorial
R consortium
https://www.youtube.com/channel/UC_R5smHVXRYGhZYDJsnXTwg/featured
Blogs, Tips, Socials, Communities
- Google: revolutionanalytics In case you missed it
- Why R is hard to learn by Bob Musenchen.
- My 15 practical tips for a bioinformatician
- The R community is one of R's best features
- Bioinformatics Training at the Harvard Chan Bioinformatics Core
Bug Tracking System
https://bugs.r-project.org/bugzilla3/ and Search existing bug reports. Remember to select 'All' in the Status drop-down list.
Use sessionInfo().