Tidyverse: Difference between revisions
(Created page with "= Tidyverse = * [https://www.tidyverse.org/ Tidyverse] Homepage * [https://www.rstudio.com/resources/webinars/pipelines-for-data-analysis-in-r/ Pipelines for data analysis in...") |
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** compute and add new variables to a data frame: mutate(), transmutate() | ** compute and add new variables to a data frame: mutate(), transmutate() | ||
** computing summary statistics (pay to view) | ** computing summary statistics (pay to view) | ||
* [https://resources.rstudio.com/webinars/a-gentle-introduction-to-tidy-statistics-in-r A Gentle Introduction to Tidy Statistics in R] by Thomas Mock on RStudio webinar. Good coverage with step-by-step explanation. | |||
= Install on Ubuntu = | = Install on Ubuntu = |
Revision as of 15:18, 23 August 2019
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)
- A Gentle Introduction to Tidy Statistics in R by Thomas Mock on RStudio webinar. Good coverage with step-by-step explanation.
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