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                           +------ Model ---------+
                           +------ Model ---------+
</syntaxhighlight>
</syntaxhighlight>
== Cheat sheet ==
The cheat sheets are downloaded from [https://www.rstudio.com/resources/cheatsheets/ RStudio]
* [http://taichimd.us/pdf/data-transformation.pdf Data Transformation with dply]
* [http://taichimd.us/pdf/data-import.pdf Data Import]
* [http://taichimd.us/pdf/data-import-cheatsheet.pdf Data Import with readr, tibble, and tidyr] (not in RStudio anymore?)
== Books ==
[https://christianb.gumroad.com/l/tidyverse-booster?layout=profile Going from Beginner to Advanced in the Tidyverse]
== Online ==
* [https://github.com/matloff/TidyverseSkeptic TidyverseSkeptic] by Norm Matloff
* [https://github.com/matloff/TidyverseSkeptic TidyverseSkeptic] by Norm Matloff
* [http://r4ds.had.co.nz/ R for Data Science] and [http://tidyverse.org/ tidyverse] package (it is a collection of '''ggplot2, tibble, tidyr, readr, purrr''', '''dplyr''', '''stringr''' & '''forcats''' 8 packages).  
* [http://r4ds.had.co.nz/ R for Data Science] and [http://tidyverse.org/ tidyverse] package (it is a collection of '''ggplot2, tibble, tidyr, readr, purrr''', '''dplyr''', '''stringr''' & '''forcats''' 8 packages).  
** tidyverse, among others, was used at [http://juliasilge.com/blog/Mining-CRAN-DESCRIPTION/ 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.
** tidyverse, among others, was used at [http://juliasilge.com/blog/Mining-CRAN-DESCRIPTION/ 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.
** [http://brettklamer.com/diversions/statistical/compile-r-for-data-science-to-a-pdf/ Compile R for Data Science to a PDF]
** [http://brettklamer.com/diversions/statistical/compile-r-for-data-science-to-a-pdf/ Compile R for Data Science to a PDF]
* [https://style.tidyverse.org/ The tidyverse style guide] by Hadley Wickham
* [https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf Data Wrangling with dplyr and tidyr Cheat Sheet]
* [https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf Data Wrangling with dplyr and tidyr Cheat Sheet]
* [https://hbctraining.github.io/Intro-to-R/lessons/07_intro_tidyverse.html Data Wrangling with Tidyverse] from the Harvard Chan School of Public Health.  
* [https://hbctraining.github.io/Intro-to-R/lessons/07_intro_tidyverse.html Data Wrangling with Tidyverse] from the Harvard Chan School of Public Health.  
* [http://datascienceplus.com/best-packages-for-data-manipulation-in-r/ 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.
* [http://datascienceplus.com/best-packages-for-data-manipulation-in-r/ 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.
* [https://www.datanovia.com/en/courses/data-manipulation-in-r/ DATA MANIPULATION IN R] by Alboukadel Kassambara
* [https://www.datanovia.com/en/courses/data-manipulation-in-r/ DATA MANIPULATION IN R] by Alboukadel Kassambara
** subset data frame columns: pull() [''return a vector''], select() [''return data frame''], select_if(), other helper functions
** subset data frame columns: '''pull'''() [''return a vector''], '''select'''() [''return data frame''], select_if(), other helper functions
** subset (filter) data frame rows: slice(), filter(), filter_all(), filter_if(), filter_at(), sample_n(), top_n()
** 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()
** identify and remove duplicate rows: duplicated(), unique(), distinct(). [https://dplyr.tidyverse.org/reference/distinct.html distinct()] will keep only distinct variables if variables are specified (cf [https://dplyr.tidyverse.org/reference/select.html select()] which keeps all rows0.
** ordering rows: arrange(), desc()
** ordering rows: arrange(), desc()  
*** cf stats::reorder() to change a factor variable's order based on another variable. So the output is still a vector. It is useful in creating multiple boxplots. On the other hand, arrange() is to change the row order of a data frame and its input is a data frame.
*** desc() can be used in arrange() [see ?desc] and reorder() [see [[Ggplot2#Ordered_barplot_and_facet|ordered barplot]] ] too.
*** desc(x) is just doing the negative operation -x.
** renaming and adding columns: rename()
** renaming and adding columns: rename()
** 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://www.dataenq.com/2020/08/data-manipulation-in-r-using-data-frame.html Data manipulation in r using data frames - an extensive article of basics]
** [https://www.dataenq.com/2020/08/Data-manipulation-r-data-frames-aggregation-sorting.html Data manipulation in r using data frames - an extensive article of basics part2 - aggregation and sorting]
* [http://www.deeplytrivial.com/p/the-to-z-of-tidyverse.html The A to Z of tidyverse] from Deeply Trivial
* [https://github.com/SISBID Summer Institute in Statistics for Big Data (SISBID)], [http://www.biostat.washington.edu/suminst/sisbid2020/modules SISBID 2020 Modules]
* [https://finnstats.com/index.php/2021/04/02/tidyverse-in-r/ Complete tutorial]


== Cheat sheet ==
== Animation to explain ==
The cheat sheets are downloaded from [https://www.rstudio.com/resources/cheatsheets/ RStudio]
* [https://github.com/gadenbuie/tidyexplain tidyexplain] - Tidy Animated Verbs
* [http://taichimd.us/pdf/data-transformation.pdf Data Transformation with dply]
* [https://tidydatatutor.com/ Tidy Data Tutor helps you visualize data analysis pipelines]
* [http://taichimd.us/pdf/data-import.pdf Data Import]
 
* [http://taichimd.us/pdf/data-import-cheatsheet.pdf Data Import with readr, tibble, and tidyr] (not in RStudio anymore?)
== Base-R and Tidyverse ==
* [https://matloff.wordpress.com/2022/08/24/base-r-and-tidyverse-code-side-by-side/ Base-R and Tidyverse Code, Side-by-Side]
 
== tidyverse vs python panda ==
[https://www.r-bloggers.com/2024/02/why-pandas-feels-clunky-when-coming-from-r/ Why pandas feels clunky when coming from R]


= Examples =
= Examples =
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| [[:File:Ad public.svg]]
| [[:File:Ad public.svg]]
|}
|}
== palmerpenguins data ==
[https://www.r-bloggers.com/2023/11/introduction-to-data-manipulation-in-r-with-dplyr/ Introduction to data manipulation in R with {dplyr}]
== glm() and ggplot2(), mtcars ==
<syntaxhighlight lang='rsplus'>
data(mtcars)
# Fit a Poisson regression model to predict "mpg" based on "wt"
model <- mtcars %>%
  select(mpg, wt) %>%
  mutate(wt = as.numeric(wt)) %>%
  glm(mpg ~ wt, family = poisson(link = "log"), data = .)
# Print the summary of the model
summary(model)
# Make predictions on new data
new_data <- data.frame(wt = c(2.5, 3.0, 3.5))
predictions <- predict(model, new_data, type = "response")
print(predictions)
# Visualize the results with ggplot2
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  stat_smooth(method = "glm", formula = "y ~ x",
              method.args = list(family = poisson(link = "log")),
              se = FALSE, color = "red") +
  labs(x = "Weight", y = "Miles per gallon")
</syntaxhighlight>


== Opioid prescribing habits in texas ==
== Opioid prescribing habits in texas ==
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* It can read multiple sheets (27 sheets) at a time and merge them by rows.
* It can read multiple sheets (27 sheets) at a time and merge them by rows.
* [https://dplyr.tidyverse.org/reference/case_when.html case_when()]: A general vectorised if
<ul>
<li>[https://dplyr.tidyverse.org/reference/case_when.html case_when()]: A general vectorised if. This function allows you to vectorise multiple if_else() statements. [https://www.sharpsightlabs.com/blog/case-when-r/ How to use the R case_when function].
<pre>
case_when(
  condition_1 ~ result_1,
  condition_2 ~ result_2,
  ...
  condition_n ~ result_n,
  .default = default_result
)
</pre>
<pre>
x %>% mutate(group = case_when(
  PredScore > quantile(PredScore, .5) ~ 'High',
  PredScore < quantile(PredScore, .5) ~ 'Low',
  TRUE ~ NA_character_
))
</pre>
</li>
</ul>
* fill()
* fill()
* [https://dplyr.tidyverse.org/reference/bind.html bind_rows()]
* [https://dplyr.tidyverse.org/reference/bind.html bind_rows()]. [https://seandavi.github.io/TargetOsteoAnalysis/articles/multivariate_survival.html Another example]. [https://finnstats.com/index.php/2022/07/27/error-in-rbinddeparse-level-numbers-of-columns-of-arguments-do-not-match/ Error in rbind(deparse.level, …) : numbers of columns of arguments do not match].
* full_join(), left_join(), right_join(), inner_join(). See the exercises from [https://sw23993.wordpress.com/2017/07/10/useful-dplyr-functions-wexamples/ Useful dplyr functions (with examples)]. Suppose df1=50x3, df2=45x3 with 25 overlaps. Then left_join=50x5, right_join=45x5, inner_join=25x5, full_join=70x5.
* full_join(), left_join(), right_join(), inner_join(). See the exercises from [https://sw23993.wordpress.com/2017/07/10/useful-dplyr-functions-wexamples/ Useful dplyr functions (with examples)]. Suppose df1=50x3, df2=45x3 with 25 overlaps. Then left_join=50x5, right_join=45x5, inner_join=25x5, full_join=70x5.
* gather()
* [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/gather gather()]
* [https://tidyr.tidyverse.org/reference/replace_na.html replace_na()]
* [https://tidyr.tidyverse.org/reference/replace_na.html replace_na()]
* [https://www.rdocumentation.org/packages/stringr/versions/1.4.0/topics/case str_to_title()]
* [https://www.rdocumentation.org/packages/stringr/versions/1.4.0/topics/case str_to_title()]
* [https://dplyr.tidyverse.org/reference/tally.html count()]
* [https://dplyr.tidyverse.org/reference/count.html count()], [https://datasciencetut.com/count-observations-by-group-in-r/ Count Observations by Group in R]
* [https://dplyr.tidyverse.org/reference/top_n.html top_n()]
* [https://datasciencetut.com/how-to-count-distinct-values-in-r/ How to Count Distinct Values in R] [https://dplyr.tidyverse.org/reference/n_distinct.html n_distinct()], [https://datasciencetut.com/filtering-for-unique-values-in-r/ Filtering for Unique Values in R- Using the dplyr]
<ul>
<li>
[https://dplyr.tidyverse.org/reference/top_n.html top_n()]. [https://stackoverflow.com/a/27766224 weight parameter]. '''top_n(n=5, wt=x)''' won't order rows by weight in the output actually. [https://dplyr.tidyverse.org/reference/slice.html? slice_max(order_by = x, n = 5)] does it.
<pre>
set.seed(1)
d <- data.frame(
  x  = runif(90),
  grp = gl(3, 30)
)
 
> d %>% group_by(grp) %>% top_n(5, wt=x)
# A tibble: 15 x 2
# Groups:  grp [3]
      x grp 
  <dbl> <fct>
1 0.908 1   
2 0.898 1   
...
15 0.961 3
 
> d %>% group_by(grp) %>% slice_max(order_by = x, n = 5)
# A tibble: 15 x 2
# Groups:  grp [3]
      x grp 
  <dbl> <fct>
1 0.992 1   
2 0.945 1   
...
15 0.864 3
</pre>
</li>
</ul>
* [https://www.rdocumentation.org/packages/knitr/versions/1.25/topics/kable kable()]
* [https://www.rdocumentation.org/packages/knitr/versions/1.25/topics/kable kable()]
== Tidying the Freedom Index ==
https://pacha.dev/blog/2023/06/05/freedom-index/index.html
tidyverse
* gsub()
* read_excel()
* filter()
* pivot_longer()
* case_when()
* fill()
* group_by(), mutate(), row_number(), ungroup()
* pivot_wider()
* drop_na()
* ungroup(), distinct()
* left_join()
ggplot2
* geom_line()
* facet_wrap()
* theme_minimal()
* theme()
* labs()


== Useful dplyr functions (with examples) ==
== Useful dplyr functions (with examples) ==
https://sw23993.wordpress.com/2017/07/10/useful-dplyr-functions-wexamples/
* https://sw23993.wordpress.com/2017/07/10/useful-dplyr-functions-wexamples/
* [https://tomaztsql.wordpress.com/2022/07/14/eight-r-tidyverse-tips-for-everyday-data-engineering/ Eight R Tidyverse tips for everyday data engineering]
* [https://datacornering.com/my-top-10-favorite-dplyr-tips-and-tricks/ My top 10 favorite dplyr tips and tricks]
** Rename columns by using the dplyr select function
** Calculate in row context with dplyr
** Rearrange columns quickly with dplyr everything
** Drop unnecessary columns with dplyr
** Use dplyr count or add_count instead of group_by and summarize
** Replace nested ifelse with dplyr case_when function
** Execute calculations across columns conditionally with dplyr
** Filter by calculation of grouped data inside filter function
** Get top and bottom values by each group with dplyr
** Reflow your dplyr code


== Supervised machine learning case studies in R ==
== Supervised machine learning case studies in R ==
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* [https://macro.cepremap.fr/article/2019-11/fipu-EA-data/ Automating update of a fiscal database for the Euro Area]
* [https://macro.cepremap.fr/article/2019-11/fipu-EA-data/ Automating update of a fiscal database for the Euro Area]
** readxl::read_excel()
** readxl::read_excel()
** [https://dplyr.tidyverse.org/reference/mutate.html transmute()], as.Date()
** [https://dplyr.tidyverse.org/reference/mutate.html transmute()] (transmute() adds new variables and drops any existing ones), as.Date()
** filter(), is.na()
** filter(), is.na()
** na.omit(), first()
** na.omit(), first()
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** tibble(), [https://dplyr.tidyverse.org/reference/join.html left_join()]
** tibble(), [https://dplyr.tidyverse.org/reference/join.html left_join()]
* [http://skranz.github.io//r/2019/12/17/EU_Cars_CO2.html Exploring eu wide data on new car registrations and co2 efficiency] (data is available)
* [http://skranz.github.io//r/2019/12/17/EU_Cars_CO2.html Exploring eu wide data on new car registrations and co2 efficiency] (data is available)
== Calculating change from baseline ==
[https://thomasadventure.blog/posts/calculating-change-from-baseline-in-r/ group_by() + mutate() + ungroup()]. We can accomplish the task by using split() + lapply() + do.call().
<pre>
trial_data_chg <- trial_data %>%
  arrange(USUBJID, AVISITN) %>%
  group_by(USUBJID) %>%
  mutate(CHG = AVAL - AVAL[1L]) %>%
  ungroup()
# If the baseline is missing
trial_data_chg2 <- trial_data %>%
  group_by(USUBJID) %>%
  mutate(
    CHG = if (any(AVISIT == "Baseline")) AVAL - AVAL[AVISIT == "Baseline"] else NA
  ) %>%
  ungroup()
</pre>
== Split data and fitting models to subsets ==
https://twitter.com/romain_francois/status/1226967548144635907?s=20
<pre>
library(dplyr)
iris %>%
  group_by(Species) %>%
  summarise(broom::tidy(lm(Petal.Length ~ Sepal.Length))
</pre>
== Show all possible group combinations ==
* [https://statisticaloddsandends.wordpress.com/2020/07/23/tidyrcomplete-to-show-all-possible-combinations-of-variables/ tidyr::complete() to show all possible combinations of variables]
* [https://tidyr.tidyverse.org/reference/complete.html complete()]
== Ten Tremendous Tricks in the Tidyverse ==
https://youtu.be/NDHSBUN_rVU (video).
* count(),
* add_count(),
* summarize() w/ a list column,
* fct_reorder() + geom_col() + coord_flip(),
* fct_lump(),
* scale_x/y_log10(),
* crossing(),
* separate(),
* extract().
== Gapminder dataset ==
[https://appsilon.com/r-dplyr-gapminder/ Hands-on R and dplyr – Analyzing the Gapminder Dataset]


= Install on Ubuntu =
= Install on Ubuntu =
[https://stackoverflow.com/a/46983233 How to install Tidyverse on Ubuntu 16.04 and 17.04]
* [https://stackoverflow.com/a/46983233 How to install Tidyverse on Ubuntu 16.04 and 17.04]
* [http://dirk.eddelbuettel.com/blog/2020/06/21/#007_cran_r_binaries R and CRAN Binaries for Ubuntu]
<syntaxhighlight lang='bash'>
<syntaxhighlight lang='bash'>
sudo apt install r-cran-tidyverse
# Ubuntu >= 18.04. However, I get unmet dependencies errors on R 3.5.3.
# Ubuntu >= 18.04. However, I get unmet dependencies errors on R 3.5.3.
# r-cran-curl : Depends: r-api-3.4
# r-cran-curl : Depends: r-api-3.4
sudo apt-get install r-cran-curl r-cran-openssl r-cran-xml2
sudo apt-get install r-cran-curl r-cran-openssl r-cran-xml2


# Works fine on Ubuntu 16.04, 18.04
# Works fine on Ubuntu 16.04, 18.04, 20.04
sudo apt install libcurl4-openssl-dev libssl-dev libxml2-dev
sudo apt install libcurl4-openssl-dev libssl-dev libxml2-dev
</syntaxhighlight>
</syntaxhighlight>
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= Miscellaneous examples using tibble or dplyr packages =
= Miscellaneous examples using tibble or dplyr packages =
== Print all columns or rows ==
[https://tibble.tidyverse.org/reference/formatting.html ?print.tbl_df]
* print(x, width = Inf) # all columns
* print(x, n = Inf)    # all rows
== Move a column to rownames ==
== Move a column to rownames ==
?tibble::column_to_rownames  
?tibble::column_to_rownames  
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<pre>
<pre>
tibble::rownames_to_column(trees, "newVar")
tibble::rownames_to_column(trees, "newVar")
# Still a data frame
</pre>
Old way [https://dplyr.tidyverse.org/reference/add_rownames.html add_rownames()]
<pre>
data.frame(x=1:5, y=2:6) %>% magrittr::set_rownames(letters[1:5]) %>% add_rownames("newvar")
# tibble object
</pre>
== Remove rows or columns only containing NAs ==
[https://twitter.com/patilindrajeets/status/1462917447359598594 Surgically removing specific rows or columns that only contains `NA`s]
<pre>
library(dplyr)
df <- tibble(x = c(NA, NA, NA),
            y = c(2, 3, NA),
            z = c(NA, 5, NA) )
# removing columns where all elements are NA
df %>% select(where(~ !all(is.na(.x))))
# removing rows where all elements are NA
df %>% filter(if_any(.fns = ~ !is.na(.x)))
</pre>
</pre>


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</pre>
</pre>


== Drop a variable ==
== Drop/remove a variable/column ==
<pre>
<pre>
select(df, -x)  
select(df, -x) # 'x' is the name of the variable
</pre>
</pre>
== Drop a level ==
group_by() has a .drop argument so you can also group by factor levels that don't appear in the data. See [https://twitter.com/sharlagelfand/status/1275122060969287680 this example].


== Remove rownames ==
== Remove rownames ==
tibble [https://tibble.tidyverse.org/reference/rownames.html has_rownames(), rownames_to_column(), column_to_rownames()]
<pre>
has_rownames(mtcars)
#> [1] TRUE
# Remove row names
remove_rownames(mtcars) %>% has_rownames()
#> [1] FALSE
</pre>
<pre>
<pre>
> tibble::has_rownames(trees)
> tibble::has_rownames(trees)
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</pre>
</pre>


= [https://cran.r-project.org/web/packages/data.table/index.html data.table] =
== relocate: change column order ==
[https://dplyr.tidyverse.org/reference/relocate.html relocate()]
<pre>
# Move Petal.Width column to appear next to Sepal.Width
iris %>% relocate(Petal.Width, .after = Sepal.Width) %>% head()
 
# Move Petal.Width to the last column
iris %>% relocate(Petal.Width, .after = last_col()) %>% head()
</pre>
 
== pull: extract a single column ==
[https://dplyr.tidyverse.org/reference/pull.html pull()]
 
<syntaxhighlight lang="rsplus>
x <- iris %>% filter(Species == 'setosa') %>% select(Sepal.Length) %>% pull()
# x <- iris %>% filter(Species == 'setosa') %>% pull(Sepal.Length)
# x <- iris %>% filter(Species == 'setosa') %>% .$Sepal.Length
y <- iris %>% filter(Species == 'virginica') %>% select(Sepal.Length) %>% pull()
t.test(x, y)
</syntaxhighlight>
 
== Convert Multiple Columns to Numeric ==
[https://datasciencetut.com/convert-multiple-columns-to-numeric-in-r/ Convert Multiple Columns to Numeric in R]. '''mutate_at()''', '''mutate_if()'''
 
== select(): extract multiple columns ==
 
== select(): drop columns ==
[https://www.r-bloggers.com/2024/04/simplifying-data-manipulation-how-to-drop-columns-from-data-frames-in-r/ Simplifying Data Manipulation: How to Drop Columns from Data Frames in R]
 
== slice(): select rows by index ==
[https://dplyr.tidyverse.org/reference/slice.html ?slice]
<pre>
mtcars %>% slice_max(mpg, n = 1)
#                mpg cyl disp hp drat    wt qsec vs am gear carb
# Toyota Corolla 33.9  4 71.1 65 4.22 1.835 19.9  1  1    4    1
 
mtcars %>% slice(which.max(mpg))
#                mpg cyl disp hp drat    wt qsec vs am gear carb
# Toyota Corolla 33.9  4 71.1 65 4.22 1.835 19.9  1  1    4    1
</pre>
 
== Reorder columns ==
* [https://dplyr.tidyverse.org/reference/select.html select()]
* [https://dplyr.tidyverse.org/reference/relocate.html relocate()]
 
== reorder() ==
<pre>
iris %>% ggplot(aes(x=Species, y = Sepal.Width)) +
        geom_boxplot() +
        xlab=("Species")
 
# reorder the boxplot based on the Species' median
iris %>% ggplot(aes(x=reorder(Species, Sepal.Width, FUN = median),
                    y=Sepal.Width)) +
        geom_boxplot() +
        xlab=("Species")
</pre>
 
== fct_reorder() ==
[https://www.eoda.de/en/wissen/blog/10-tidyverse-functions-that-might-save-your-day/ 10 Tidyverse functions that might save your day]
 
== Standardize variables ==
[https://datasciencetut.com/how-to-standardize-data-in-r/ How to Standardize Data in R?]
 
== Anonymous functions ==
* See [[R#anonymous_function|R]] page
* [https://campus.datacamp.com/courses/intermediate-r/chapter-4-the-apply-family?ex=4 lapply and anonymous functions]
 
== Transformation on multiple columns ==
* [https://datasciencetut.com/how-to-apply-a-transformation-to-multiple-columns-in-r/ How to apply a transformation to multiple columns in R?]
** '''df %>% mutate(across(c(col1, col2), function(x) x*2))'''
** '''df %>% summarise(across(c(col1, col2), mean, na.rm=TRUE))
* select() vs '''across()'''
** the across() and select() functions are both used to manipulate columns in a data frame
** The select() function is used to select columns from a data frame.
** The across() function is used to apply a function to multiple columns in a data frame. It’s often used inside other functions like '''mutate()''' or '''summarize()'''.
:<syntaxhighlight lang='rsplus'>
data.frame(
  x = c(1, 2, 3),
  y = c(4, 5, 6)
) %>%
mutate(across(everything(), ~ .x * 2)) # purrr-style lambda
#  x  y
#1 2  8
#2 4 10
#3 6 12
</syntaxhighlight>
* [https://twitter.com/ChBurkhart/status/1655559927715463169?s=20 Quick tidyverse tip: How to make your summary statistics more human readable with pivot_wider]
 
= Reading and writing data =
[https://www.danielecook.com/speeding-up-reading-and-writing-in-r/ Speeding up Reading and Writing in R]
 
== [https://cran.r-project.org/web/packages/data.table/index.html 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).
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).


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* [https://github.com/chuvanan/rdatatable-cookbook cookbook]
* [https://github.com/chuvanan/rdatatable-cookbook cookbook]
* [https://www.waldrn.com/dplyr-vs-data-table/ R Packages: dplyr vs data.table]
* [https://www.waldrn.com/dplyr-vs-data-table/ R Packages: dplyr vs data.table]
* [https://martinctc.github.io/blog/comparing-common-operations-in-dplyr-and-data.table/ Comparing Common Operations in dplyr and data.table]
* [https://github.com/rstudio/cheatsheets/raw/master/datatable.pdf Cheat sheet] from [https://www.rstudio.com/resources/cheatsheets/ RStudio]
* [https://github.com/rstudio/cheatsheets/raw/master/datatable.pdf Cheat sheet] from [https://www.rstudio.com/resources/cheatsheets/ RStudio]
* [https://www.r-bloggers.com/importing-data-into-r-part-two/ Reading large data tables in R]. fread(FILENAME)
* [https://www.r-bloggers.com/importing-data-into-r-part-two/ Reading large data tables in R]. fread(FILENAME)
Line 381: Line 704:
* [https://www.rdocumentation.org/packages/data.table/versions/1.12.0/topics/rbindlist 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).
* [https://www.rdocumentation.org/packages/data.table/versions/1.12.0/topics/rbindlist 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).
* [https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread Convenience features of fread]
* [https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread Convenience features of fread]
* [https://www.infoworld.com/article/3575086/the-ultimate-r-datatable-cheat-sheet.html?s=09 The ultimate R data.table cheat sheet] from infoworld


[https://github.com/Rdatatable/data.table/wiki/Installation#openmp-enabled-compiler-for-mac 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.
[https://github.com/Rdatatable/data.table/wiki/Installation#openmp-enabled-compiler-for-mac 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.
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Question: how to make use multicore with data.table package?
Question: how to make use multicore with data.table package?


== dtplyr ==
=== dtplyr ===
https://www.tidyverse.org/blog/2019/11/dtplyr-1-0-0/
https://www.tidyverse.org/blog/2019/11/dtplyr-1-0-0/


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== Pivot ==
== Pivot ==
* [https://www.r-bloggers.com/using-r-from-gather-to-pivot/ From gather to pivot]
<ul>
<li>tidyr package. [https://tidyr.tidyverse.org/articles/pivot.html pivot vignette],
[https://tidyr.tidyverse.org/reference/pivot_wider.html pivot_wider()]
<pre>
R> d2 <- tibble(o=rep(LETTERS[1:2], each=3), n=rep(letters[1:3], 2), v=1:6); d2
# A tibble: 6 × 3
  o    n        v
  <chr> <chr> <int>
1 A    a        1
2 A    b        2
3 A    c        3
4 B    a        4
5 B    b        5
6 B    c        6
R> d1 <- d2%>% pivot_wider(names_from=n, values_from=v); d1
# A tibble: 2 × 4
  o        a    b    c
  <chr> <int> <int> <int>
1 A        1    2    3
2 B        4    5    6
</pre>
[https://tidyr.tidyverse.org/reference/pivot_longer.html pivot_longer()]
<pre>
R> d1 %>% pivot_longer(!o, names_to = 'n', values_to = 'v')
# Pivot all columns except 'o' column
# A tibble: 6 × 3
  o    n        v
  <chr> <chr> <int>
1 A    a        1
2 A    b        2
3 A    c        3
4 B    a        4
5 B    b        5
6 B    c        6
</pre>
<ul>
<li>In addition to the '''names_from''' and '''values_from''' columns, the data must have other columns </li>
<li>For each (combination) of unique value from other columns, the values from '''names_from''' variable must be unique</li>
</ul>
</li>
<li>Conversion from gather() to pivot_longer()
<pre>
gather(df, key=KeyName, value = valueName, col1, col2, ...) # No quotes around KeyName and valueName
 
pivot_longer(df, cols, names_to = "keyName", values_to = "valueName")
  # cols can be everything()
  # cols can be numerical numbers or column names
</pre>
</li>
</ul>
* [https://www.r-bloggers.com/using-r-from-gather-to-pivot/ From gather to pivot]. [https://tidyr.tidyverse.org/reference/pivot_longer.html pivot_longer()]/pivot_wider()
* [https://blog.methodsconsultants.com/posts/data-pivoting-with-tidyr/ Data Pivoting with tidyr]
* [https://blog.methodsconsultants.com/posts/data-pivoting-with-tidyr/ Data Pivoting with tidyr]
<ul>
<li>[https://stemangiola.github.io/bioc_2020_tidytranscriptomics/articles/tidytranscriptomics.html A Tidy Transcriptomics introduction to RNA-Seq analyses]
<pre>
data %>% pivot_longer(cols = c("counts", "counts_scaled"), names_to = "source", values_to = "abundance")
</pre>
</li>
<li>[https://onunicornsandgenes.blog/2020/05/17/using-r-setting-a-colour-scheme-in-ggplot2/ Using R: setting a colour scheme in ggplot2]. Note the new (default) column names '''value''' and '''name''' after the function '''pivot_longer(data, cols)'''.
<pre>
set(1)
dat1 <- data.frame(y=rnorm(10), x1=rnorm(10), x2=rnorm(10))
dat2 <- pivot_longer(dat1, -y)
head(dat2, 2)
# A tibble: 2 x 3
      y name  value
  <dbl> <chr>  <dbl>
1 -1.28 x1    0.717
2 -1.28 x2    -0.320


== unnest() ==
dat3 <- pivot_wider(dat2)
* [https://tidyr.tidyverse.org/reference/unnest.html help]
range(dat1 - dat3)
* [https://stackoverflow.com/a/38021139 annotate boxplot in ggplot2]
</pre>
</li>
</ul>
* [https://thatdatatho.com/2020/03/28/tidyrs-pivot_longer-and-pivot_wider-examples-tidytuesday-challenge/ pivot_longer()’s Advantage Over gather()]
* [https://datascienceplus.com/how-to-carry-column-metadata-in-pivot_longer/ How to carry column metadata in pivot_longer]
* [https://datawookie.dev/blog/2021/10/working-with-really-wide-data/ Working with Really Wide Data]
* [https://towardsdev.com/data-reshaping-with-r-from-wide-to-long-and-back-7a5eb674d73e Data Reshaping with R: From Wide to Long (and back)]


== Benchmark ==
== Benchmark ==
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* Main functions
* Main functions
** Reshape data: [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/gather gather()] & [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/spread spread()]. [https://tidyr.tidyverse.org/dev/articles/pivot.html These two will be deprecated]
** Reshape data: [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/gather gather()] & [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/spread spread()]. [https://tidyr.tidyverse.org/dev/articles/pivot.html These two will be deprecated]
** Split cells: [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/separate separate()] & [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/unite unite()]
** Break apart or combine columns/Split cells: [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/separate separate()] & [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/unite unite()]
** Handle missing: [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/drop_na drop_na()] & fill() & replace_na()
** Handle missing: [https://www.rdocumentation.org/packages/tidyr/versions/0.8.3/topics/drop_na drop_na()] & fill() & replace_na()
* Other functions
* Other functions
Line 445: Line 842:
= dplyr, plyr packages =
= 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.
* 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.
* [https://twitter.com/kearneymw/status/1476538812406788101?s=20 It's amazing the things one can do in base R (without installing or loading any other #rstats packages)]
* Essential functions: 3 rows functions, 3 column functions and 1 mixed function.
* Essential functions: 3 rows functions, 3 column functions and 1 mixed function.
: <syntaxhighlight lang='rsplus'>
: <syntaxhighlight lang='rsplus'>
Line 462: Line 860:
</syntaxhighlight>
</syntaxhighlight>
* [http://genomicsclass.github.io/book/pages/dplyr_tutorial.html 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
* [http://genomicsclass.github.io/book/pages/dplyr_tutorial.html 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
** [https://tidyselect.r-lib.org/reference/starts_with.html starts_with()]/ ends_with() = Select columns that start/end with a character string
** contains() = Select columns that contain a character string
** contains() = Select columns that contain a character string
** matches() = Select columns that match a regular expression
** matches() = Select columns that match a regular expression
** one_of() = Select columns names that are from a group of names
** one_of() = Select columns names that are from a group of names
* [http://r4ds.had.co.nz/transform.html Data Transformation] in the book '''R for Data Science'''. Five key functions in the '''dplyr''' package:
* [http://r4ds.had.co.nz/transform.html Data Transformation] in the book '''R for Data Science'''. Five key functions in the '''dplyr''' package:
** Filter rows: filter()
** Filter rows: '''filter()'''. [https://stackoverflow.com/a/39882777 filter is faster than subset() for very large records]. But [https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/subset subset()] can both subset rows and select columns.
** Arrange rows: arrange()
** Arrange rows: '''arrange()'''
** Select columns: select()
** Select columns: '''select()'''. Or use '''$''' or '''<nowiki>[[Number]]</nowiki>''' or '''<nowiki>[[NAME]]</nowiki>'''.
** Add new variables: mutate()
** Add new variables: '''mutate()'''
** Grouped summaries: group_by() & summarise()
** Grouped summaries: '''group_by()''' & '''summarise()'''
: <syntaxhighlight lang='rsplus'>
: <syntaxhighlight lang='rsplus'>
# filter
# filter
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   group_by(year, month, day) %>%  
   group_by(year, month, day) %>%  
   summarise(mean = mean(dep_delay, na.rm = TRUE))
   summarise(mean = mean(dep_delay, na.rm = TRUE))
</syntaxhighlight>
* Another example
:<syntaxhighlight lang='r'>
data <- data.frame(
  name = c("Alice", "Bob", "Charlie", "David", "Eve"),
  age = c(25, 30, 35, 40, 45),
  gender = c("F", "M", "M", "M", "F"),
  score1 = c(80, 85, 90, 95, 100),
  score2 = c(75, 80, 85, 90, 95)
)
# Example usage of dplyr functions
result <- data %>%
  filter(gender == "M") %>%                # Keep only rows where gender is "M"
  select(name, age, score1) %>%            # Select specific columns
  mutate(score_diff = score1 - score2) %>% # Calculate a new column based on existing columns
  arrange(desc(age)) %>%                  # Arrange rows in descending order of age
  #group_by(gender) %>%                    # Group the data by gender
  summarize(mean_score1 = mean(score1))    # Calculate the mean of score1 for each group
</syntaxhighlight>
</syntaxhighlight>
* [https://csgillespie.github.io/efficientR/data-carpentry.html#dplyr Efficient R Programming]
* [https://csgillespie.github.io/efficientR/data-carpentry.html#dplyr Efficient R Programming]
* [http://www.r-exercises.com/2017/07/19/data-wrangling-transforming-23/ Data wrangling: Transformation] from R-exercises.
* [http://www.r-exercises.com/2017/07/19/data-wrangling-transforming-23/ Data wrangling: Transformation] from R-exercises.
* [https://rollingyours.wordpress.com/2016/06/29/express-intro-to-dplyr/ Express Intro to dplyr] by rollingyours.
* [https://rollingyours.wordpress.com/2016/06/29/express-intro-to-dplyr/ Express Intro to dplyr] by rollingyours.
* [https://martinsbioblogg.wordpress.com/2017/05/21/using-r-when-using-do-in-dplyr-dont-forget-the-dot/ the dot].
<ul>
<li>[https://martinsbioblogg.wordpress.com/2017/05/21/using-r-when-using-do-in-dplyr-dont-forget-the-dot/ the dot].
<pre>
matrix(rnorm(12),4, 3) %>% .[1:2, 1:2]
</pre>
</li>
</ul>
* [http://martinsbioblogg.wordpress.com/2013/03/24/using-r-reading-tables-that-need-a-little-cleaning/ 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.
* [http://martinsbioblogg.wordpress.com/2013/03/24/using-r-reading-tables-that-need-a-little-cleaning/ 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
* https://randomjohn.github.io/r-maps-with-census-data/ dplyr and stringr are used
* [https://datascienceplus.com/5-interesting-subtle-insights-from-ted-videos-data-analysis-in-r/ 5 interesting subtle insights from TED videos data analysis in R]
* [https://datascienceplus.com/5-interesting-subtle-insights-from-ted-videos-data-analysis-in-r/ 5 interesting subtle insights from TED videos data analysis in R]
* [https://www.mango-solutions.com/blog/what-is-tidy-eval-and-why-should-i-care What is tidy eval and why should I care?]
* [https://www.mango-solutions.com/blog/what-is-tidy-eval-and-why-should-i-care What is tidy eval and why should I care?]
* [https://towardsdatascience.com/what-you-need-to-know-about-the-new-dplyr-1-0-0-7eaaaf6d78ac The Seven Key Things You Need To Know About dplyr 1.0.0]
== select() for columns ==
[https://www.quantargo.com/courses/course-r-introduction/03-dplyr/02-select-columns-data-frame/recipe Select columns from a data frame]
<pre>
select(my_data_frame, column_one, column_two, ...)
select(my_data_frame, new_column_name = current_column, ...)
select(my_data_frame, column_start:column_end)
select(my_data_frame, index_one, index_two, ...)
select(my_data_frame, index_start:index_end)
</pre>
=== select() + everything() ===
If we want one particular column (say the dependent variable y) to appear first or last in the dataset. We can use the everything().
<pre>
iris %>% select(Species, everything()) %>% head()
iris %>% select(-Species, everything()) %>% head() # put Species to the last col
</pre>
=== .$Name ===
Extract a column using piping. The '''.''' represents the data frame that is being piped in, and $Name extracts the ‘Name’ column.
<pre>
mtcars %>% .$mpg  # A vector
mtcars %>% select(mpg) # A list
</pre>
== filter() for rows ==
<pre>
mtcars %>% filter(mpg>10)
identical(mtcars %>% filter(mpg>10), subset(mtcars, mpg>10))
# [1] TRUE
</pre>
=== filter by date ===
[https://datasciencetut.com/what-is-the-best-way-to-filter-by-date-in-r/ What Is the Best Way to Filter by Date in R?]
== arrange (reorder) ==
<ul>
<li>Arrange values by a Single Variable:
<pre>
# Create a sample data frame
students <- data.frame(
  Name = c("Ali", "Boby", "Charlie", "Davdas"),
  Score = c(85, 92, 78, 95)
)
# Arrange by Score in ascending order
arrange(students, Score)
#      Name Score
# 1 Charlie    78
# 2    Ali    85
# 3    Boby    92
# 4  Davdas    95
</pre>
<li>Arrange values by Multiple Variables:
This is like the "sort" function in Excel.
<pre>
# Create a sample data frame
transactions <- data.frame(
  Date = c("2024-04-01", "2024-04-01", "2024-04-02", "2024-04-03"),
  Amount = c(100, 150, 200, 75)
)
# Arrange by Date in ascending order, then by Amount in descending order
arrange(transactions, Date, desc(Amount))
#        Date Amount
# 1 2024-04-01    150
# 2 2024-04-01    100
# 3 2024-04-02    200
# 4 2024-04-03    75
</pre>
<li>Arrange values with Missing Values:
<pre>
# Create a sample data frame with missing values
data <- data.frame(
  ID = c(1, 2, NA, 4),
  Value = c(20, NA, 15, 30)
)
# Arrange by Value in ascending order, placing missing values first
arrange(data, desc(is.na(Value)), Value)
#  ID Value
# 1  2    NA
# 2 NA    15
# 3  1    20
# 4  4    30
</pre>
</ul>
=== arrange and match ===
How to do the following in pipe ''' A <- A[match(id.ref, A$id), ]'''
[https://stackoverflow.com/a/52216391 How to sort rows of a data frame based on a vector using dplyr pipe], [https://stackoverflow.com/a/59730594 Order data frame rows according to vector with specific order]
<ul>
<li>Data
<syntaxhighlight lang='r'>
library(dplyr)
# Create a sample dataframe 'A'
set.seed(1); A <- data.frame(
    id = sample(letters[1:5]),
    value = 1:5
    )
print(A)
  id value
1  a    1
2  d    2
3  c    3
4  e    4
5  b    5
# Create a reference vector 'id.ref'
id.ref <- c("e", "d", "c", "b", "a")
</syntaxhighlight>
<syntaxhighlight lang='r'>
# Goal:
A[match(id.ref, A$id),]
  id value
4  e    4
2  d    2
3  c    3
5  b    5
1  a    1
</syntaxhighlight>
<li>Method 1 (best): no match() is needed. Brilliant!
<syntaxhighlight lang='r'>
A %>% arrange(factor(id, levels=id.ref))
  id value
1  e    4
2  d    2
3  c    3
4  b    5
5  a    1
# detail:
factor(A$id, levels=id.ref)
[1] a d c e b
Levels: e d c b a
</syntaxhighlight>
<li>Method 2: complicated
<syntaxhighlight lang='r'>
A %>%
    mutate(id.match = match(id, id.ref)) %>%
    arrange(id.match) %>%
    select(-id.match)
  id value
1  e    4
2  d    2
3  c    3
4  b    5
5  a    1
# detail:
A %>%
    mutate(id.match = match(id, id.ref))
  id value id.match
1  a    1        5
2  d    2        2
3  c    3        3
4  e    4        1
5  b    5        4
</syntaxhighlight>
<li>Method 3: a simplified version of Method 2, but it needs match()
<syntaxhighlight lang='r'>
A %>% arrange(match(id, id.ref))
  id value
1  e    4
2  d    2
3  c    3
4  b    5
5  a    1
</syntaxhighlight>
</ul>
== group_by() ==
* [https://dplyr.tidyverse.org/reference/group_by.html ?group_by] and ungroup(),
* [https://dplyr.tidyverse.org/articles/grouping.html Grouped data]
* Is ungroup() recommended after every group_by()? Always ungroup() when you’ve finished with your calculations. See [https://bookdown.org/yih_huynh/Guide-to-R-Book/groupby.html#ungrouping here] or [https://community.rstudio.com/t/is-ungroup-recommended-after-every-group-by/5296 this].
* You might want to use ungroup() if you want to perform further calculations or manipulations on the data that don’t depend on the grouping. For example, after ungrouping the data, you could add new columns or filter rows without being restricted by the grouping.
<pre>
                  +-- mutate() + ungroup()
x -- group_by() --|
                  +-- summarise() # reduce the dimension, no way to get back
</pre>
=== Subset rows by group ===
[https://datasciencetut.com/subset-rows-based-on-their-integer-locations/ Subset rows based on their integer locations-slice in R]
=== Rank by Group ===
[https://datasciencetut.com/how-to-rank-by-group-in-r/ How to Rank by Group in R?]
<pre>
df %>% arrange(team, points) %>%
    group_by(team) %>%
    mutate(rank = rank(points))
</pre>
=== group_by() + summarise(), arrange(desc()) ===
[https://r4ds.had.co.nz/transform.html Data transformation] from R for Data Science
[https://www.guru99.com/r-aggregate-function.html#3 Function in summarise()]
* group_by(var1) %>% summarise(varY = mean(var2)) %>% ggplot(aes(x = varX, y = varY, fill = varF)) + geom_bar(stat = "identity") + theme_classic()
* summarise(newvar = sum(var1) / sum(var2))
* arrange(desc(var1, var2))
* Distinct number of observation: '''n_distinct()'''
*  Count the number of rows: '''n()'''
* nth observation of the group: '''nth()'''
* First observation of the group: '''first()'''
* Last observation of the group: '''last()'''
=== group_by() + summarise() + across() ===
* [https://twitter.com/ChBurkhart/status/1647243881095000069?s=20 Get a summarize from multiple columns without explicitly specifying the column names]
* [https://dplyr.tidyverse.org/reference/across.html ?across]
=== group_by() + nest(), mutate(, map()), unnest(), list-columns ===
[https://www.rdocumentation.org/packages/tidyr/versions/1.3.0/topics/nest nest(data=)] is a function in the tidyr package in R that allows you to create nested data frames, where '''one column contains another data frame or list'''. This is useful when you want to perform analysis or visualization on each group separately. '''PS:''' it seems group_by() is not needed.
:<syntaxhighlight lang='rsplus'>
histogram <- gss_cat |>
  nest(data = -marital) |>  # OR nest(.by = marital). 6x2 tibble. Col1=marital, col2=data.
  mutate(
    histogram = pmap(
      .l = list(marital, data),
      .f = \(marital, data) {
        ggplot(data, aes(x = tvhours)) +
          geom_histogram(binwidth = 1) +
          labs(
            title = marital
          )
      }
    )
  )
histogram$histogram[[1]]
</syntaxhighlight>
[https://r4ds.had.co.nz/many-models.html Many models]  from R for Data Science
<ul>
<li>[https://tidyr.tidyverse.org/reference/nest.html ?unnest],  vignette('rectangle'),  vignette('nest') & vignette('pivot')
<syntaxhighlight lang='rsplus'>
tibble(x = 1:2, y = list(1:4, 2:3)) %>% unnest(y) %>% group_by(x) %>% nest()
# returns to tibble(x = 1:2, y = list(1:4, 2:3)) with 'groups' information
</syntaxhighlight>
</li>
<li>[https://stackoverflow.com/a/38021139 annotate boxplot in ggplot2] </li>
<li>[https://towardsdatascience.com/coding-in-r-nest-and-map-your-way-to-efficient-code-4e44ba58ee4a Coding in R: Nest and map your way to efficient code]
<pre>
      group_by() + nest()    mutate(, map())  unnest()
data  -------------------->  --------------->  ------->
</pre>
<syntaxhighlight lang='rsplus'>
install.packages('gapminder'); library(gapminder)
gapminder_nest <- gapminder %>%
  group_by(country) %>%
  nest()  # country, data
          # each row of 'data' is a tibble
gapminder_nest$data[[1]]  # tibble 57 x 8
gapminder_nest <- gapminder_nest %>%
          mutate(pop_mean = map(.x = data, ~mean(.x$pop, na.rm = T)))
                                    # country, data, pop_mean
gapminder_nest %>% unnest(pop_mean) # country, data, pop_mean
gapminder_plot <- gapminder_nest %>%
  unnest(pop_mean) %>%
  select(country, pop_mean) %>%
  ungroup() %>%
  top_n(pop_mean, n = -10) %>%
  mutate(pop_mean = pop_mean/10^3)
gapminder_plot %>%
  ggplot(aes(x = reorder(country, pop_mean), y = pop_mean)) +
  geom_point(colour = "#FF6699", size = 5) +
  geom_segment(aes(xend = country, yend = 0), colour = "#FF6699") +
  geom_text(aes(label = round(pop_mean, 0)), hjust = -1) +
  theme_minimal() +
  labs(title = "Countries with smallest mean population from 1960 to 2016",
      subtitle = "(thousands)",
      x = "",
      y = "") +
  theme(legend.position = "none",
        axis.text.x = element_blank(),
        plot.title = element_text(size = 14, face = "bold"),
        panel.grid.major.y = element_blank()) +
  coord_flip() +
  scale_y_continuous()
</syntaxhighlight>
</li>
<li>[https://www.tidymodels.org/learn/statistics/tidy-analysis/ Tidy analysis] from tidymodels </li>
<li>[https://community.rstudio.com/t/is-nest-mutate-map-unnest-really-the-best-alternative-to-dplyr-do/11009 Is nest() + mutate() + map() + unnest() really the best alternative to dplyr::do()] </li>
</ul>
== across() ==
<ul>
<li>[https://dplyr.tidyverse.org/reference/across.html ?across]. Applying a function or operation to multiple columns in a data frame simultaneously.
<pre>
across(.cols, .fns, ..., .names = NULL, .unpack = FALSE)
gdf <-
  tibble(g = c(1, 1, 2, 3), v1 = 10:13, v2 = 20:23) %>%
  group_by(g)
gdf %>% mutate(across(v1:v2, ~ .x + rnorm(1)))
#>      g    v1    v2
#>  <dbl> <dbl> <dbl>
#> 1    1  10.3  20.7
#> 2    1  11.3  21.7
#> 3    2  11.2  22.6
#> 4    3  13.5  22.7
</pre>
<li>[https://www.infoworld.com/article/3537612/dplyr-across-first-look-at-a-new-tidyverse-function.html dplyr across: First look at a new Tidyverse function].
* [https://dplyr.tidyverse.org/reference/across.html Apply a function (or functions) across multiple columns]. across(), if_any(), if_all().
* [https://tidyselect.r-lib.org/reference/starts_with.html Select variables that match a pattern]. starts_with(), ends_with(), contains(), matches(), num_range().
* [https://twitter.com/romain_francois/status/1350078666554933249/photo/2 data %>% group_by(Var1) %>% summarise(across(contains("SomeKey"), mean, na.rm = TRUE))]
<syntaxhighlight lang='rsplus'>
ny <- filter(cases, State == "NY") %>%
  select(County = `County Name`, starts_with(c("3", "4")))
daily_totals <- ny %>%
  summarize(
    across(starts_with("4"), sum)
  )
median_and_max <- list(
  med = ~median(.x, na.rm = TRUE),
  max = ~max(.x, na.rm = TRUE)
)
april_median_and_max <- ny %>%
  summarize(
    across(starts_with("4"), median_and_max)
  )
</pre>
<pre>
# across(.cols = everything(), .fns = NULL, ..., .names = NULL)
# Rounding the columns Sepal.Length and Sepal.Width
iris %>%
  as_tibble() %>%
  mutate(across(c(Sepal.Length, Sepal.Width), round))
iris %>% summarise(across(contains("Sepal"), ~mean(.x, na.rm = TRUE)))
# filter rows
iris %>% filter(if_any(ends_with("Width"), ~ . > 4))
iris %>% select(starts_with("Sepal"))
iris %>% select(starts_with(c("Petal", "Sepal")))
iris %>% select(contains("Sepal"))
</syntaxhighlight>
</ul>
== ave() - Adding a column of means by group to original data ==
* [https://stackoverflow.com/a/7976250 Adding a column of means by group to original data],
* [https://stackoverflow.com/a/6057297 ave(, FUN) for any function instead of average]
== mutate vs tapply ==
[https://matloff.wordpress.com/2022/08/06/base-r-is-alive-and-well/ Base-R is alive and well]
== mutate + replace() or ifelse() ==
<ul>
<li>mutate() is similar to [https://stat.ethz.ch/R-manual/R-devel/library/base/html/with.html base::within()] </li>
<li>[https://stackoverflow.com/a/28013895 Change value of variable with dplyr]
<pre>
mtcars %>%
    mutate(mpg=replace(mpg, cyl==4, NA)) %>%
    as.data.frame()
# VS
mtcars$mpg[mtcars$cyl == 4] <- NA
</pre>
</li>
<li>[https://stackoverflow.com/a/35610521 using ifelse()] </li>
<li>[https://stackoverflow.com/a/61602568 using case_when()] </li>
<li>[https://dplyr.tidyverse.org/reference/mutate_all.html Mutate multiple columns] </li>
<li>[https://www.bioinfoblog.com/entry/tidydata/advancedmutate Apply the mutate function to multiple columns at once | mutate_at / mutate_all / mutate_if]
<pre>
mutate_at(data, .vars = vars(starts_with("Petal")), .funs = ~ . * 2) %>% head()
mutate_at(data, .vars = vars(starts_with("Petal")), `*`, 2) %>% head()
</pre>
</li>
<li>[https://dplyr.tidyverse.org/reference/recode.html recode()]
<pre>
char_vec <- sample(c("a", "b", "c"), 10, replace = TRUE)
recode(char_vec, a = "Apple", b = "Banana", .default = NA_character_)
</pre>
</li>
</ul>
== Hash table ==
<ul>
<li>[https://stackoverflow.com/a/7659297 Create new column based on 4 values in another column]. The trick is to create a named vector; like a [https://www.geeksforgeeks.org/python-dictionary/# Dictionary in Python].
Here is my example:
<syntaxhighlight lang='rsplus'>
hashtable <- data.frame(value=1:4, key=c("B", "C", "A", "D"))
input <- c("A", "B", "C", "D", "B", "B", "A", "A") # input to be matched with keys,
                                                  # this could be very long
# Trick: convert the hash table into a named vector
htb <- hashtable$value; names(htb) <- hashtable$key
# return the values according to the names
out <- htb[input]; out
A B C D B B A A
3 1 2 4 1 1 3 3
</syntaxhighlight>
We can implement using Python by creating a variable of [https://www.w3schools.com/python/python_dictionaries.asp dictionary type/structure].
<syntaxhighlight lang='python'>
hashtable = {'B': 1, 'C': 2, 'A': 3, 'D': 4}
input = ['A', 'B', 'C', 'D', 'B', 'B', 'A', 'A']
out = [hashtable[key] for key in input]
</syntaxhighlight>
Or using C
<syntaxhighlight lang='c'>
#include <stdio.h>
int main() {
    int hashtable[4] = {3, 1, 2, 4};
    char input[] = {'A', 'B', 'C', 'D', 'B', 'B', 'A', 'A'};
    int out[sizeof(input)/sizeof(input[0])];
    for (int i = 0; i < sizeof(input)/sizeof(input[0]); i++) {
        out[i] = hashtable[input[i] - 'A'];
    }
    for (int i = 0; i < sizeof(out)/sizeof(out[0]); i++) {
        printf("%d ", out[i]);
    }
    printf("\n");
    return 0;
}
</syntaxhighlight>
<li>[https://cran.r-project.org/web/packages/hash/index.html hash] package
<li>[https://cran.r-project.org/web/packages/digest/ digest] package
</ul>
== inner_join, left_join, full_join ==
* [https://dplyr.tidyverse.org/reference/mutate-joins.html Mutating joins]
* [https://statisticsglobe.com/r-dplyr-join-inner-left-right-full-semi-anti Join Data Frames with the R dplyr Package (9 Examples)]
* [https://www.datasciencemadesimple.com/join-in-r-merge-in-r/ Join in r: how to join (merge) data frames (inner, outer, left, right) in R]
* [https://www.guru99.com/r-dplyr-tutorial.html Dplyr Tutorial: Merge and Join Data in R with Examples]
== plyr::rbind.fill() ==
* https://www.rdocumentation.org/packages/plyr/versions/1.8.6/topics/rbind.fill
* [https://f1000research.com/articles/5-1542 An example usage]


== Videos ==
== Videos ==
* [https://education.rstudio.com/trainers/ RStudio Instructor Training and Certification]
* [https://youtu.be/jWjqLW-u3hc 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/jWjqLW-u3hc 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/aywFompr1F4 (shorter video) by Roger Peng
Line 542: Line 1,414:
** [https://juliasilge.com/blog/tuition-resampling/ Preprocessing and resampling using #tidytuesday college data]
** [https://juliasilge.com/blog/tuition-resampling/ Preprocessing and resampling using #tidytuesday college data]
** [https://juliasilge.com/blog/beer-production/ Bootstrap resampling with #tidytuesday beer production data]
** [https://juliasilge.com/blog/beer-production/ Bootstrap resampling with #tidytuesday beer production data]
* [https://www.infoworld.com/article/3411819/do-more-with-r-video-tutorials.html “Do More with R” video tutorials]
* [https://www.infoworld.com/article/3411819/do-more-with-r-video-tutorials.html “Do More with R” video tutorials] by Sharon Machlis
* [https://www.lynda.com/R-tutorials/Learning-R-Tidyverse/586672-2.html Learning the R Tidyverse] from lynda.com
* [https://www.lynda.com/R-tutorials/Learning-R-Tidyverse/586672-2.html Learning the R Tidyverse] from lynda.com
* [https://www.youtube.com/watch?v=AuQOy06Dlr8 What's new in the tidyverse?] by Professor Mine Çetinkaya-Rundel
== dbplyr ==
* https://dbplyr.tidyverse.org/articles/dbplyr.html
* [https://dbplyr.tidyverse.org/reference/translate_sql.html translate_sql()] Translate an R expression to sql. [https://twitter.com/rfunctionaday/status/1452127344093708295 Some examples].


= stringr =
= stringr =
* https://www.rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf
<ul>
* [https://github.com/rstudio/cheatsheets/raw/master/strings.pdf stringr Cheat sheet] (2 pages, this will immediately download the pdf file)
<li>stringr is part of the tidyverse but is not a core package. You need to load it separately.
* [https://csgillespie.github.io/efficientR/data-carpentry.html#regular-expressions Efficient data carpentry &#8594; Regular expressions] from Efficient R programming book by Gillespie & Lovelace.
<li>[http://gastonsanchez.com/blog/resources/how-to/2013/09/22/Handling-and-Processing-Strings-in-R.html Handling Strings with R](ebook) by Gaston Sanchez.
<li>https://www.rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf
<li>[https://github.com/rstudio/cheatsheets/raw/master/strings.pdf stringr Cheat sheet] (2 pages, this will immediately download the pdf file)
* Detect Matches: '''str_detect()''', str_which(), str_count(), str_locate()
* Subset: '''str_sub()''', str_subset(), str_extract(), str_match()
* Manage Lengths: str_length(), str_pad(), str_trunc(), '''str_trim()'''
* Mutate Strings: '''str_sub()''', '''str_replace()''', str_replace_all(), '''str_remove()'''
** Case Conversion: str_to_lower(), str_to_upper(), str_to_title()
* Joint and Split: str_c(), str_dup(), str_split_fixed(), str_glue(), str_glue_date()
<li>[https://csgillespie.github.io/efficientR/data-carpentry.html#regular-expressions Efficient data carpentry &#8594; Regular expressions] from Efficient R programming book by Gillespie & Lovelace.
<li>Common functions:
{| class="wikitable"
|-
! `stringr` Function !! Description !! Base R Equivalent
|-
| `str_length()` || Returns the number of characters in each element of a character vector. || `nchar()`
|-
| `str_sub()` || Extracts substrings from a character vector. || `substr()`
|-
| `str_trim()` || Removes leading and trailing whitespace from strings. || `trimws()`
|-
| `str_split()` || Splits a string into pieces based on a delimiter. || `strsplit()`
|-
| `str_replace()` || Replaces occurrences of a pattern in a string with another string. || `gsub()`
|-
| `str_detect()` || Detects whether a pattern is present in each element of a character vector. || `grepl()`
|-
| `str_subset()` || Returns the elements of a character vector that contain a pattern. || `grep()`
|-
| `str_count()` || Counts the number of occurrences of a pattern in each element of a character vector. || `gregexpr()` and `lengths()`
|}
</ul>
 
== str_replace() ==
[https://datasciencetut.com/how-to-replace-string-in-column-in-r/ Replace a string in a column]: [https://dplyr.tidyverse.org/reference/across.html dplyr::across()] & str_replace()
<pre>
df <- data.frame(country=c('India', 'USA', 'CHINA', 'Algeria'),
                position=c('1', '1', '2', '3'),
                points=c(22, 25, 29, 13))
 
df %>%
  mutate(across('country', str_replace, 'India', 'Albania'))
 
df %>%
  mutate(across('country', str_replace, 'A|I', ''))
</pre>
 
== split ==
[https://statisticsglobe.com/split-data-frame-variable-into-multiple-columns-in-r Split Data Frame Variable into Multiple Columns in R (3 Examples)]
 
Three ways:
* base::strsplit(x, CHAR)
* [https://stringr.tidyverse.org/reference/str_split.html stringr::str_split_fixed(x, CHAR, 2)]
* [https://tidyr.tidyverse.org/reference/separate.html tidyr::separate(x, c("NewVar1", "NewVar2"), CHAR)]
<pre>
x <- c("a-1", "b-2", "c-3")


= [https://github.com/smbache/magrittr magrittr] =
stringr::str_split_fixed(x, "-", 2)
#      [,1] [,2]
# [1,] "a"  "1"
# [2,] "b"  "2"
# [3,] "c"  "3"
 
tidyr::separate(data.frame(x), x, c('x1', 'x2'), "-")
  # The first argument must be a data frame
  # The 2nd argument is the column names
#  x1 x2
# 1  a  1
# 2  b  2
# 3  c  3
</pre>
 
= [https://github.com/smbache/magrittr magrittr]: pipe =
* [https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html Vignettes]
* [https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html Vignettes]
** [https://www.tidyverse.org/blog/2020/08/magrittr-2-0/?s=09 magrittr 2.0 is coming soon], [https://www.tidyverse.org/blog/2020/11/magrittr-2-0-is-here/?s=09 magrittr 2.0 is here!]
* [https://thomasadventure.blog/posts/how-does-the-pipe-operator-actually-work/ How does the pipe operator actually work?]
* [http://www.win-vector.com/blog/2018/04/magrittr-and-wrapr-pipes-in-r-an-examination/ 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!
* [http://www.win-vector.com/blog/2018/04/magrittr-and-wrapr-pipes-in-r-an-examination/ 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!
: <syntaxhighlight lang='rsplus'>
: <syntaxhighlight lang='rsplus'>
Line 642: Line 1,591:
mtcars %<>% transform(cyl = cyl * 2)
mtcars %<>% transform(cyl = cyl * 2)
</syntaxhighlight>
</syntaxhighlight>
* [https://data-and-the-world.onrender.com/posts/magrittr-pipes The Four Pipes of magrittr] and lambda functions.


Upsides of using magrittr: no need to create intermediate objects, code is easy to read.
Upsides of using magrittr: no need to create intermediate objects, code is easy to read.
Line 650: Line 1,600:
* Functions that use the current environment: assign(), get(), load()
* Functions that use the current environment: assign(), get(), load()
* Functions that use lazy evaluation: tryCatch(), try()
* Functions that use lazy evaluation: tryCatch(), try()
== Dollar sign .$ ==
<ul>
<li>[http://thatdatatho.com/2019/03/13/tutorial-about-magrittrs-pipe-operator-and-placeholders/ A Short Tutorial about Magrittr’s Pipe Operator and Placeholders], [https://uc-r.github.io/pipe Simplify Your Code with %>%]
{{Pre}}
gapminder %>% dplyr::filter(continent == "Asia") %>%
  {stats::cor(.$lifeExp, .$gdpPercap)}
gapminder %>% dplyr::filter(continent == "Asia") %$%
  {stats::cor(lifeExp, gdpPercap)}
gapminder %>%
  dplyr::mutate(continent = ifelse(.$continent == "Americas", "Western Hemisphere", .$continent))
</pre>
</li>
<li>Another example [https://cran.r-project.org/web/packages/msigdbr/vignettes/msigdbr-intro.html Introduction to the msigdbr package]
<pre>
m_list  = m_df %>% split(x = .$gene_symbol, f = .$gs_name)
m_list2 = m_df %$% split(x = gene_symbol, f = gs_name)
all.equal(m_list, m_list2)
# [1] TRUE
</pre>
</li>
<li>[https://stackoverflow.com/a/48130912 Use $ dollar sign at end of of an R magrittr pipeline to return a vector]
<pre>
DF %>% filter(y > 0) %>% .$y
</pre>
</li>
</ul>
== %$% ==
Expose the names in lhs to the rhs expression. This is useful when functions do not have a built-in data argument.
<pre>
lhs %$% rhs
# lhs: A list, environment, or a data.frame.
# rhs: An expression where the names in lhs is available.
iris %>%
  subset(Sepal.Length > mean(Sepal.Length)) %$%
  cor(Sepal.Length, Sepal.Width)
</pre>
== set_rownames() and set_colnames() ==
https://stackoverflow.com/a/56613460, https://www.rdocumentation.org/packages/magrittr/versions/1.5/topics/extract
<pre>
data.frame(x=1:5, y=2:6) %>% magrittr::set_rownames(letters[1:5])
cbind(1:5, 2:6) %>% magrittr::set_colnames(letters[1:2])
</pre>
== match() ==
<syntaxhighlight lang='r'>
a <- 1:3
id <- letters[1:3]
set.seed(1234); id.ref <- sample(id)
id # [1] "b" "c" "a"
a[match(id.ref, b)] # [1] 2 3 1
id.ref %>% match(b) %>% `[`(a, .) # Same, but complicated
</syntaxhighlight>
== dtrackr ==
[https://terminological.github.io/dtrackr/ dtrackr]: Track your Data Pipelines


= purrr: : Functional Programming Tools =
= purrr: : Functional Programming Tools =
''While there is nothing fundamentally wrong with the base R apply functions, the syntax is somewhat inconsistent across the different apply functions, and the expected type of the object they return is often ambiguous (at least it is for sapply…).'' See [http://www.rebeccabarter.com/blog/2019-08-19_purrr/ Learn to purrr].
* https://purrr.tidyverse.org/
* https://purrr.tidyverse.org/
* Chap 21 [https://r4ds.had.co.nz/iteration.html Iteration] from '''R for Data Science''' book
* [https://github.com/rstudio/cheatsheets/raw/master/purrr.pdf cheatsheet]
* [http://colinfay.me/purrr-cookbook/ purrr cookbook]
* [http://colinfay.me/purrr-cookbook/ purrr cookbook]
* [https://en.wikipedia.org/wiki/Higher-order_function Higher-order function]
* [https://pythonbasics.org/decorators/ Python Decorator/metaprogramming]
* [https://www.r-bloggers.com/2020/11/iterating-over-the-lines-of-a-data-frame-with-purrr/ Iterating over the lines of a data.frame with purrr]
* Functional programming (cf Object-Oriented Programming)
* Functional programming (cf Object-Oriented Programming)
** [http://www.youtube.com/watch?v=vLmaZxegahk Functional programming for beginners]
** [http://www.youtube.com/watch?v=vLmaZxegahk Functional programming for beginners]
** [https://www.makeuseof.com/tag/functional-programming-languages/ 5 Functional Programming Languages You Should Know]
** [https://www.makeuseof.com/tag/functional-programming-languages/ 5 Functional Programming Languages You Should Know]
* [http://data.library.virginia.edu/getting-started-with-the-purrr-package-in-r/ Getting started with the purrr package in R], especially the [https://www.rdocumentation.org/packages/purrr/versions/0.2.5/topics/map map()] function.
<ul>
<li>[https://stackoverflow.com/a/56651232 What does the tilde mean in this context of R code], [https://stackoverflow.com/a/44834671 What is meaning of first tilde in purrr::map] </li>
<li>[http://data.library.virginia.edu/getting-started-with-the-purrr-package-in-r/ Getting started with the purrr package in R], especially the [https://www.rdocumentation.org/packages/purrr/versions/0.2.5/topics/map map()] and '''map_df()''' functions.
<syntaxhighlight lang='rsplus'>
library(purrr)
# map() is a replacement of lapply()
# lapply(dat, function(x) mean(x$Open))
map(dat, function(x)mean(x$Open)) 
 
# map allows us to bypass the function function.
# Using a tilda (~) in place of function and a dot (.) in place of x
map(dat, ~mean(.$Open))
 
# map allows you to specify the structure of your output.
map_dbl(dat, ~mean(.$Open))
 
# map2() is a replacement of mapply()
# mapply(function(x,y)plot(x$Close, type = "l", main = y), x = dat, y = stocks)
map2(dat, stocks, ~plot(.x$Close, type="l", main = .y))
</syntaxhighlight>
</li>
</ul>
* map_dfr() function from [https://youtu.be/bzUmK0Y07ck?t=646 "The Joy of Functional Programming (for Data Science)" with Hadley Wickham]. It can be used to replace a loop.  
* map_dfr() function from [https://youtu.be/bzUmK0Y07ck?t=646 "The Joy of Functional Programming (for Data Science)" with Hadley Wickham]. It can be used to replace a loop.  
:<syntaxhighlight lang='rsplus'>
:<syntaxhighlight lang='rsplus'>
Line 668: Line 1,707:
* [http://staff.math.su.se/hoehle/blog/2019/01/04/mathgenius.html Purr yourself into a math genius]
* [http://staff.math.su.se/hoehle/blog/2019/01/04/mathgenius.html Purr yourself into a math genius]
* [https://martinctc.github.io/blog/vignette-write-and-read-multiple-excel-files-with-purrr/ Write & Read Multiple Excel files with purrr]
* [https://martinctc.github.io/blog/vignette-write-and-read-multiple-excel-files-with-purrr/ Write & Read Multiple Excel files with purrr]
* [https://aosmith.rbind.io/2020/08/31/handling-errors/ Handling errors using purrr's possibly() and safely()]
* [https://www.business-science.io/code-tools/2020/10/08/automate-plots.html How to Automate Exploratory Analysis Plots]
* [https://www.infoworld.com/article/3601124/error-handling-in-r-with-purrrs-possibly.amp.html Easy error handling in R with purrr’s possibly]
<ul>
<li>[http://www.rebeccabarter.com/blog/2019-08-19_purrr/ Learn to purrr]. Lots of good information like tilde-dot is a shorthand for functions.
<syntaxhighlight lang='rsplus'>
function(x) {
  x + 10
}
# is the same as
~{.x + 10}
map_dbl(c(1, 4, 7), ~{.x + 10})
</syntaxhighlight>
</li>
<li>[https://aosmith.rbind.io/2018/06/05/a-closer-look-at-replicate-and-purrr/ A closer look at replicate() and purrr::map() for simulations]
<syntaxhighlight lang='rsplus'>
twogroup_fun = function(nrep = 10, b0 = 5, b1 = -2, sigma = 2) {
    ngroup = 2
    group = rep( c("group1", "group2"), each = nrep)
    eps = rnorm(ngroup*nrep, 0, sigma)
    growth = b0 + b1*(group == "group2") + eps
    growthfit = lm(growth ~ group)
    growthfit
}
sim_lm = replicate(5, twogroup_fun(), simplify = FALSE )
str(sim_lm, max.level = 1)
map_dbl(sim_lm, ~summary(.x)$r.squared)
# Same as function(x) {} style
map_dbl(sim_lm, function(x) summary(x)$r.squared)
# Same as sapply()
sapply(sim_lm, function(x) summary(x)$r.squared)
map_dfr(sim_lm, broom::tidy, .id = "model")
</syntaxhighlight>
</li>
<li>[http://adv-r.had.co.nz/Functional-programming.html Functional programming] from Advanced R.</li>
<li>[https://dcl-prog.stanford.edu/ Functional Programming] : Sara Altman, Bill Behrman, Hadley Wickham</li>
<li>[https://www.brodrigues.co/blog/2022-05-26-safer_programs/ Some learnings from functional programming you can use to write safer programs] </li>
</ul>
== map() and map_dbl() ==
<Ul>
<Li>[https://www.spsanderson.com/steveondata/posts/2023-03-26/index.html Mastering the map() Function in R]
<li>An example from https://purrr.tidyverse.org/
<syntaxhighlight lang='rsplus'>
mtcars |>
    split(mtcars$cyl) |>  # from base R
    map(\(df) lm(mpg ~ wt, data = df)) |>
    map(summary) |> map_dbl("r.squared")
#        4        6        8
# 0.5086326 0.4645102 0.4229655
</syntaxhighlight>
<li>Solution by base R lapply() and sapply(). See the article [https://purrr.tidyverse.org/articles/base.html purrr <-> base R]
<syntaxhighlight lang='rsplus'>
mtcars |>
    split(mtcars$cyl) |>
    lapply(function(df) lm(mpg ~ wt, data = df)) |>
    lapply(summary) |>
    sapply(function(x) x$r.squared)
#        4        6        8
# 0.5086326 0.4645102 0.4229655
</syntaxhighlight>
</ul>
== tilde ==
* The '''lambda syntax''' and tilde notation provided by purrr allow you to write concise and readable anonymous functions in R.
:<syntaxhighlight lang='rsplus'>
x <- 1:3
map_dbl(x, ~ .x^2)  # [1] 1 4 9
</syntaxhighlight>
:The notation '''~ .x^2''' is equivalent to writing '''function(.x) .x^2 ''' or '''function(z) z^2'''  or '''\(y) y^2'''
:<syntaxhighlight lang='rsplus'>
x <- list(a = 1:3, b = 4:6)
y <- list(a = 10, b = 100)
map2_dbl(x, y, ~ sum(.x * .y))
#  a    b
#  60 1500
</syntaxhighlight>
* https://dplyr.tidyverse.org/reference/funs.html
* [https://stackoverflow.com/a/14976479 Use of ~ (tilde) in R programming Language] (Hint: creating a formula object)
* [https://stackoverflow.com/a/44834671 What is meaning of first tilde in purrr::map] & the blog [https://www.itcodar.com/r/what-is-meaning-of-first-tilde-in-purrr-map.html What Is Meaning of First Tilde in Purrr::Map]
* [https://stackoverflow.com/a/68249687 Meaning of tilde and dot notation in dplyr]
* [https://www.rebeccabarter.com/blog/2019-08-19_purrr Learn to purrr] 2019
* [https://stackoverflow.com/q/58845722 dplyr piping data - difference between `.` and `.x`]
* [https://stackoverflow.com/a/62488532 Use of Tilde (~) and period (.) in R]
== .x  symbol ==
<ul>
<li>It is used with functions like purrr::map. In the context of an '''anonymous function''', '''.x''' is a '''placeholder''' for the first argument of the function.
* For a single argument function, you can use .. For example, ~ . + 2 is equivalent to function(.) {. + 2}.
* For a two argument function, you can use .x and .y. For example, ~ .x + .y is equivalent to function(.x, .y) {.x + .y}.
* For more arguments, you can use ..1, ..2, ..3, etc
<pre>
# Create a vector
vec <- c(1, 2, 3)
# Use purrr::map with an anonymous function
result <- purrr::map(vec, ~ .x * 2)
# Print the result
print(result)
[[1]]
[1] 2
[[2]]
[1] 4
[[3]]
[1] 6
</pre>
<li>[https://stackoverflow.com/a/56532176 dplyr piping data - difference between `.` and `.x`]
<li>[https://community.rstudio.com/t/function-argument-naming-conventions-x-vs-x/7764/2 Function argument naming conventions (`.x` vs `x`)]. Se [https://purrr.tidyverse.org/reference/map.html purrr::map]
</ul>
== negate() ==
[https://stackoverflow.com/a/48431135 How to select non-numeric columns using dplyr::select_if]
<syntaxhighlight lang='rsplus'>
library(tidyverse)
iris %>% select_if(negate(is.numeric))
</syntaxhighlight>
== pmap() ==
[https://purrr.tidyverse.org/reference/pmap.html ?pmap] - Map over multiple input simultaneously (in "parallel")
<pre>
# Create two lists with multiple elements
list1 <- list(1, 2, 3)
list2 <- list(10, 20, 30)
# Define a function to add the elements of each list
my_func <- function(x, y) {
  x + y
}
# Use pmap to apply the function to each element of the lists in parallel
result <- pmap(list(list1, list2), my_func); result
[[1]]
[1] 11
[[2]]
[1] 22
[[3]]
[1] 33
</pre>
A more practical example when we want to run analysis or visualization on each element of some group/class variable. nest() + pmap().
<syntaxhighlight lang='rsplus'>
# Create a data frame
df <- mpg %>%
  filter(manufacturer %in% c("audi", "volkswagen")) %>%
  select(manufacturer, year, cty)
# Nest the data by manufacturer
df_nested <- df %>%
  nest(data = -manufacturer)
# Create a function that takes a data frame and creates a ggplot object
my_plot_func <- function(data, manuf) {
    ggplot(data, aes(x = year, y = cty)) +
        geom_point() +
        ggtitle(manuf)
}
# Use pmap to apply the function to each element of the list-column in df_nested
df_nested_plot <- df_nested %>%
    mutate(plot = pmap(list(data, manufacturer), my_plot_func))
df_nested_plot[[1]]
</syntaxhighlight>
Another example: fitting regressions for data in each group
<syntaxhighlight lang='rsplus'>
library(tidyverse)
# create example data
data <- tibble(
  x = rnorm(100),
  y = rnorm(100),
  group = sample(c("A", "B", "C"), 100, replace = TRUE)
)
# create a nested dataframe
nested_data <- data %>%
  nest(data = -group)
# define a function that runs linear regression on each dataset
lm_func <- function(data) {
  lm(y ~ x, data = data)
}
# apply lm_func() to each row of the nested dataframe
results <- nested_data %>%
  mutate(model = pmap(list(data), lm_func))
</syntaxhighlight>
== reduce ==
[https://www.r-bloggers.com/2023/07/reducing-my-for-loop-usage-with-purrrreduce/ Reducing my for loop usage with purrr::reduce()]
== filter, subset data ==
[https://jcarroll.com.au/2023/08/30/four-filters-for-functional-programming-friends/ Four Filters for Functional (Programming) Friends]
== purrr vs base R ==
https://purrr.tidyverse.org/dev/articles/base.html


= forcats =
= forcats =
Line 685: Line 1,927:


= broom =
= broom =
[https://cran.r-project.org/web/packages/broom/index.html broom]: Convert Statistical Analysis Objects into Tidy Tibbles
<ul>
<li>[https://cran.r-project.org/web/packages/broom/index.html broom]: Convert Statistical Analysis Objects into Tidy Tibbles
<li>Especially the tidy() function.
{{Pre}}
R> str(survfit(Surv(time, status) ~ x, data = aml))
List of 17
$ n        : int [1:2] 11 12
$ time    : num [1:20] 9 13 18 23 28 31 34 45 48 161 ...
$ n.risk  : num [1:20] 11 10 8 7 6 5 4 3 2 1 ...
$ n.event  : num [1:20] 1 1 1 1 0 1 1 0 1 0 ...
...
 
R> tidy(survfit(Surv(time, status) ~ x, data = aml))
# A tibble: 20 x 9
    time n.risk n.event n.censor estimate std.error conf.high conf.low strata       
  <dbl>  <dbl>  <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl> <chr>         
1    9    11      1        0  0.909    0.0953    1      0.754  x=Maintained 
2    13    10      1        1  0.818    0.142      1      0.619  x=Maintained 
...
18    33      3      1        0  0.194    0.627      0.664  0.0569 x=Nonmaintained
19    43      2      1        0  0.0972    0.945      0.620  0.0153 x=Nonmaintained
20    45      1      1        0  0      Inf        NA      NA      x=Nonmaintained
</pre>
<li>[https://www.frontiersin.org/files/Articles/746571/fonc-11-746571-HTML-r1/image_m/fonc-11-746571-t002.jpg Tables from journal papers]
<li>Multiple univariate models
<pre>
library(tidyverse)
library(broom)
 
mtcars %>%
  select(-mpg) %>%
  names() %>%
  map_dfr(~ tidy(lm(as.formula(paste("mpg ~", .x)), data = mtcars)))
# A tibble: 20 × 5
#  term        estimate std.error statistic  p.value
#  <chr>          <dbl>    <dbl>    <dbl>    <dbl>
# 1 (Intercept)  37.9      2.07      18.3  8.37e-18
# 2 cyl          -2.88    0.322      -8.92  6.11e-10
# 3 (Intercept)  29.6      1.23      24.1  3.58e-21
# 4 disp        -0.0412  0.00471    -8.75  9.38e-10
</pre>
<li>Multivariate model
<pre>
lm(mpg ~ ., data = mtcars) |> tidy()
# A tibble: 11 × 5
#  term        estimate std.error statistic p.value
#  <chr>          <dbl>    <dbl>    <dbl>  <dbl>
# 1 (Intercept)  12.3      18.7        0.657  0.518
# 2 cyl          -0.111    1.05      -0.107  0.916
# 3 disp          0.0133    0.0179    0.747  0.463
</pre>
</ul>


= lobstr package - dig into the internal representation and structure of R objects =
= lobstr package - dig into the internal representation and structure of R objects =
Line 691: Line 1,984:


= Other packages =
= Other packages =
== Great R packages for data import, wrangling, and visualization ==
[https://www.computerworld.com/article/2921176/great-r-packages-for-data-import-wrangling-visualization.html Great R packages for data import, wrangling, and visualization]
== cli package ==
* https://cli.r-lib.org/
* [https://www.r-bloggers.com/2023/11/cliff-notes-about-the-cli-package/ Cliff notes about the cli package]
== tidytext ==
https://juliasilge.shinyapps.io/learntidytext/
== tidytuesdayR ==
* https://github.com/rfordatascience/tidytuesday
* https://cran.r-project.org/web/packages/tidytuesdayR/index.html, [https://github.com/thebioengineer/tidytuesdayR Github]
<pre>
install.packages("tidytuesdayR")
library("tidytuesdayR")
tt_datasets(2020)  # get the exact day of the data we want to load
coffee_ratings <- tt_load("2020-07-07")
print(coffee_ratings)  #  readme(coffee_ratings)
</pre>
== janitor ==
* [https://www.exploringdata.org/post/how-to-clean-data-janitor-package/ How to Clean Data: {janitor} Package]
* [https://sfirke.github.io/janitor/articles/janitor.html Overview of janitor functions]
* [https://rdrr.io/cran/janitor/man/clean_names.html clean_names()]
== funneljoin ==
== funneljoin ==
* https://github.com/robinsones/funneljoin
* https://github.com/robinsones/funneljoin
* [https://hookedondata.org/introducing-the-funneljoin-package/ Introducing the funneljoin package]
* [https://hookedondata.org/introducing-the-funneljoin-package/ Introducing the funneljoin package]
* https://www.slideshare.net/secret/Ba52FYuH2FoWE
* https://www.slideshare.net/secret/Ba52FYuH2FoWE

Revision as of 08:57, 7 May 2024

Tidyverse

   Import
     |
     | readr, readxl
     | haven, DBI, httr   +----- Visualize ------+
     |                    |    ggplot2, ggvis    |
     |                    |                      |
   Tidy ------------- Transform 
   tibble               dplyr                   Model 
   tidyr                  |                    broom
                          +------ Model ---------+

Cheat sheet

The cheat sheets are downloaded from RStudio

Books

Going from Beginner to Advanced in the Tidyverse

Online

Animation to explain

Base-R and Tidyverse

tidyverse vs python panda

Why pandas feels clunky when coming from R

Examples

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. See part 1 & part 2 about the data and markdown document. All documents are available in github repository.

Task R code Graph
Load the libraries
library(tidyverse)
library(readxl)
library(broom)
library(knitr)
Read Excel file
raw_df <- readxl::read_xlsx("ad_treatment.xlsx")

dplyr::glimpse(raw_df)
Check distribution
g2 <- ggplot(raw_df, aes(x = age)) +
  geom_density(fill = "blue")
g2
raw_df %>% summarize(min = min(age),
                     max = max(age))
File:Check dist.svg
Data cleaning
raw_df %>% 
  summarize(na_count = sum(is.na(mmse)))
Experimental variables

levels

# check Ns and levels for our variables
table(raw_df$drug_treatment, raw_df$health_status)
table(raw_df$drug_treatment, raw_df$health_status, raw_df$sex)

# tidy way of looking at variables
raw_df %>% 
  group_by(drug_treatment, health_status, sex) %>% 
  count()
Visual Exploratory

Data Analysis

ggplot(data = raw_df, # add the data
       aes(x = drug_treatment, y = mmse, # set x, y coordinates
           color = drug_treatment)) +    # color by treatment
  geom_boxplot() +
  facet_grid(~health_status)
File:Onefacet.svg
Summary Statistics
raw_df %>% 
  glimpse()
sum_df <- raw_df %>% 
            mutate(
              sex = factor(sex, 
                  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"))
              ) %>% 
            group_by(sex, health_status, drug_treatment # group by categorical variables
              ) %>%  
            summarize(
              mmse_mean = mean(mmse),      # calc mean
              mmse_se = sd(mmse)/sqrt(n()) # calc standard error
              ) %>%  
            ungroup() # ungrouping variable is a good habit to prevent errors

kable(sum_df)

write.csv(sum_df, "adx37_sum_stats.csv")
Plotting summary

statistics

g <- ggplot(data = sum_df, # add the data
       aes(x = drug_treatment,  #set x, y coordinates
           y = mmse_mean,
           group = drug_treatment,  # group by treatment
           color = drug_treatment)) +    # color by treatment
  geom_point(size = 3) + # set size of the dots
  facet_grid(sex~health_status) # create facets by sex and status
g
File:Twofacets.svg
ANOVA
# set up the statistics df
stats_df <- raw_df %>% # start with data
   mutate(drug_treatment = factor(drug_treatment, levels = c("Placebo", "Low dose", "High Dose")),
         sex = factor(sex, labels = c("Male", "Female")),
         health_status = factor(health_status, levels = c("Healthy", "Alzheimer's")))

glimpse(stats_df)
# this gives main effects AND interactions
ad_aov <- aov(mmse ~ sex * drug_treatment * health_status, 
        data = stats_df)

summary(ad_aov)


# this extracts ANOVA output into a nice tidy dataframe
tidy_ad_aov <- tidy(ad_aov)
# which we can save to Excel
write.csv(tidy_ad_aov, "ad_aov.csv")
Post-hocs
# pairwise t.tests
ad_pairwise <- pairwise.t.test(stats_df$mmse,
                               stats_df$sex:stats_df$drug_treatment:stats_df$health_status, 
                               p.adj = "none")
# look at the posthoc p.values in a tidy dataframe
kable(head(tidy(ad_pairwise)))


# call and tidy the tukey posthoc
tidy_ad_tukey <- tidy(
                      TukeyHSD(ad_aov, 
                              which = 'sex:drug_treatment:health_status'))
Publication plot
sig_df <- tribble(
  ~drug_treatment, ~ health_status, ~sex, ~mmse_mean,
  "Low dose", "Alzheimer's", "Male", 17,
  "High Dose", "Alzheimer's", "Male", 25,
  "Low dose", "Alzheimer's", "Female", 18, 
  "High Dose", "Alzheimer's", "Female", 24
  )

sig_df <- sig_df %>% 
  mutate(drug_treatment = factor(drug_treatment, levels = c("Placebo", "Low dose", "High Dose")),
         sex = factor(sex, levels = c("Male", "Female")),
         health_status = factor(health_status, levels = c("Healthy", "Alzheimer's")))
sig_df
# plot of cognitive function health and drug treatment
g1 <- ggplot(data = sum_df, 
       aes(x = drug_treatment, y = mmse_mean, fill = drug_treatment,  
           group = drug_treatment)) +
  geom_errorbar(aes(ymin = mmse_mean - mmse_se, 
                    ymax = mmse_mean + mmse_se), width = 0.5) +
  geom_bar(color = "black", stat = "identity", width = 0.7) +
  
  facet_grid(sex~health_status) +
  theme_bw() +
  scale_fill_manual(values = c("white", "grey", "black")) +
  theme(legend.position = "NULL",
        legend.title = element_blank(),
        axis.title = element_text(size = 20),
        legend.background = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        axis.text = element_text(size = 12)) +
  geom_text(data = sig_df, label = "*", size = 8) +
  labs(x = "\nDrug Treatment", 
       y = "Cognitive Function (MMSE)\n",
       caption = "\nFigure 1. Effect of novel drug treatment AD-x37 on cognitive function in 
                    healthy and demented elderly adults. 
                  \nn = 100/treatment group (total n = 600), * indicates significance 
                    at p < 0.001")
g1

# save the graph!
ggsave("ad_publication_graph.png", g1, height = 7, width = 8, units = "in")
File:Ad public.svg

palmerpenguins data

Introduction to data manipulation in R with {dplyr}

glm() and ggplot2(), mtcars

data(mtcars)

# Fit a Poisson regression model to predict "mpg" based on "wt"
model <- mtcars %>% 
  select(mpg, wt) %>% 
  mutate(wt = as.numeric(wt)) %>% 
  glm(mpg ~ wt, family = poisson(link = "log"), data = .)

# Print the summary of the model
summary(model)

# Make predictions on new data
new_data <- data.frame(wt = c(2.5, 3.0, 3.5))
predictions <- predict(model, new_data, type = "response")
print(predictions)

# Visualize the results with ggplot2
ggplot(data = mtcars, aes(x = wt, y = mpg)) + 
  geom_point() +
  stat_smooth(method = "glm", formula = "y ~ x", 
              method.args = list(family = poisson(link = "log")), 
              se = FALSE, color = "red") +
  labs(x = "Weight", y = "Miles per gallon")

Opioid prescribing habits in texas

https://juliasilge.com/blog/texas-opioids/.

  • It can read multiple sheets (27 sheets) at a time and merge them by rows.
  • case_when(): A general vectorised if. This function allows you to vectorise multiple if_else() statements. How to use the R case_when function.
    case_when(
      condition_1 ~ result_1,
      condition_2 ~ result_2,
      ...
      condition_n ~ result_n,
      .default = default_result
    )
    
    x %>% mutate(group = case_when(
      PredScore > quantile(PredScore, .5) ~ 'High',
      PredScore < quantile(PredScore, .5) ~ 'Low',
      TRUE ~ NA_character_
    ))
    
  • top_n(). weight parameter. top_n(n=5, wt=x) won't order rows by weight in the output actually. slice_max(order_by = x, n = 5) does it.
    set.seed(1)
    d <- data.frame(
      x   = runif(90),
      grp = gl(3, 30)
    ) 
    
    > d %>% group_by(grp) %>% top_n(5, wt=x)
    # A tibble: 15 x 2
    # Groups:   grp [3]
           x grp  
       <dbl> <fct>
     1 0.908 1    
     2 0.898 1    
    ...
    15 0.961 3 
    
    > d %>% group_by(grp) %>% slice_max(order_by = x, n = 5)
    # A tibble: 15 x 2
    # Groups:   grp [3]
           x grp  
       <dbl> <fct>
     1 0.992 1    
     2 0.945 1    
    ...
    15 0.864 3 
    

Tidying the Freedom Index

https://pacha.dev/blog/2023/06/05/freedom-index/index.html

tidyverse

  • gsub()
  • read_excel()
  • filter()
  • pivot_longer()
  • case_when()
  • fill()
  • group_by(), mutate(), row_number(), ungroup()
  • pivot_wider()
  • drop_na()
  • ungroup(), distinct()
  • left_join()

ggplot2

  • geom_line()
  • facet_wrap()
  • theme_minimal()
  • theme()
  • labs()

Useful dplyr functions (with examples)

Supervised machine learning case studies in R

Supervised machine learning case studies in R - A Free, Interactive Course Using Tidy Tools.

Time series data

Calculating change from baseline

group_by() + mutate() + ungroup(). We can accomplish the task by using split() + lapply() + do.call().

trial_data_chg <- trial_data %>%
  arrange(USUBJID, AVISITN) %>%
  group_by(USUBJID) %>%
  mutate(CHG = AVAL - AVAL[1L]) %>%
  ungroup()

# If the baseline is missing
trial_data_chg2 <- trial_data %>%
  group_by(USUBJID) %>%
  mutate(
    CHG = if (any(AVISIT == "Baseline")) AVAL - AVAL[AVISIT == "Baseline"] else NA
  ) %>%
  ungroup()

Split data and fitting models to subsets

https://twitter.com/romain_francois/status/1226967548144635907?s=20

library(dplyr)
iris %>% 
  group_by(Species) %>%
  summarise(broom::tidy(lm(Petal.Length ~ Sepal.Length))

Show all possible group combinations

Ten Tremendous Tricks in the Tidyverse

https://youtu.be/NDHSBUN_rVU (video).

  • count(),
  • add_count(),
  • summarize() w/ a list column,
  • fct_reorder() + geom_col() + coord_flip(),
  • fct_lump(),
  • scale_x/y_log10(),
  • crossing(),
  • separate(),
  • extract().

Gapminder dataset

Hands-on R and dplyr – Analyzing the Gapminder Dataset

Install on Ubuntu

sudo apt install r-cran-tidyverse

# 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, 20.04
sudo apt install libcurl4-openssl-dev libssl-dev libxml2-dev

80 R packages will be installed after tidyverse has been installed.

For RStudio server docker version (Debian 10), I also need to install zlib1g-dev

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

Miscellaneous examples using tibble or dplyr packages

Print all columns or rows

?print.tbl_df

  • print(x, width = Inf) # all columns
  • print(x, n = Inf) # all rows

Move a column to rownames

?tibble::column_to_rownames

# It assumes the input data frame has no row names; otherwise we will get
# Error: `df` must be a data frame without row names in `column_to_rownames()`
# 
tibble::column_to_rownames(data.frame(x=letters[1:5], y = rnorm(5)), "x")

Move rownames to a variable

https://tibble.tidyverse.org/reference/rownames.html

tibble::rownames_to_column(trees, "newVar")
# Still a data frame

Old way add_rownames()

data.frame(x=1:5, y=2:6) %>% magrittr::set_rownames(letters[1:5]) %>% add_rownames("newvar")
# tibble object

Remove rows or columns only containing NAs

Surgically removing specific rows or columns that only contains `NA`s

library(dplyr)
df <- tibble(x = c(NA, NA, NA),
             y = c(2, 3, NA),
             z = c(NA, 5, NA) )

# removing columns where all elements are NA
df %>% select(where(~ !all(is.na(.x))))

# removing rows where all elements are NA
df %>% filter(if_any(.fns = ~ !is.na(.x)))

Rename variables

dplyr::rename(os, newName = oldName)

Drop/remove a variable/column

select(df, -x) # 'x' is the name of the variable 

Drop a level

group_by() has a .drop argument so you can also group by factor levels that don't appear in the data. See this example.

Remove rownames

tibble has_rownames(), rownames_to_column(), column_to_rownames()

has_rownames(mtcars)
#> [1] TRUE

# Remove row names
remove_rownames(mtcars) %>% has_rownames()
#> [1] FALSE
> tibble::has_rownames(trees)
[1] FALSE
> rownames(trees)
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14" "15"
[16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
[31] "31"
> rownames(trees) <- NULL
> rownames(trees)
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14" "15"
[16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
[31] "31"

relocate: change column order

relocate()

# Move Petal.Width column to appear next to Sepal.Width
iris %>% relocate(Petal.Width, .after = Sepal.Width) %>% head() 

# Move Petal.Width to the last column
iris %>% relocate(Petal.Width, .after = last_col()) %>% head()

pull: extract a single column

pull()

x <- iris %>% filter(Species == 'setosa') %>% select(Sepal.Length) %>% pull()
# x <- iris %>% filter(Species == 'setosa') %>% pull(Sepal.Length)
# x <- iris %>% filter(Species == 'setosa') %>% .$Sepal.Length
y <- iris %>% filter(Species == 'virginica') %>% select(Sepal.Length) %>% pull()
t.test(x, y)

Convert Multiple Columns to Numeric

Convert Multiple Columns to Numeric in R. mutate_at(), mutate_if()

select(): extract multiple columns

select(): drop columns

Simplifying Data Manipulation: How to Drop Columns from Data Frames in R

slice(): select rows by index

?slice

mtcars %>% slice_max(mpg, n = 1)
#                 mpg cyl disp hp drat    wt qsec vs am gear carb
# Toyota Corolla 33.9   4 71.1 65 4.22 1.835 19.9  1  1    4    1

mtcars %>% slice(which.max(mpg))
#                 mpg cyl disp hp drat    wt qsec vs am gear carb
# Toyota Corolla 33.9   4 71.1 65 4.22 1.835 19.9  1  1    4    1

Reorder columns

reorder()

iris %>% ggplot(aes(x=Species, y = Sepal.Width)) + 
         geom_boxplot() +
         xlab=("Species")

# reorder the boxplot based on the Species' median
iris %>% ggplot(aes(x=reorder(Species, Sepal.Width, FUN = median),
                    y=Sepal.Width)) + 
         geom_boxplot() +
         xlab=("Species")

fct_reorder()

10 Tidyverse functions that might save your day

Standardize variables

How to Standardize Data in R?

Anonymous functions

Transformation on multiple columns

  • How to apply a transformation to multiple columns in R?
    • df %>% mutate(across(c(col1, col2), function(x) x*2))
    • df %>% summarise(across(c(col1, col2), mean, na.rm=TRUE))
  • select() vs across()
    • the across() and select() functions are both used to manipulate columns in a data frame
    • The select() function is used to select columns from a data frame.
    • The across() function is used to apply a function to multiple columns in a data frame. It’s often used inside other functions like mutate() or summarize().
data.frame(
  x = c(1, 2, 3),
  y = c(4, 5, 6)
) %>% 
mutate(across(everything(), ~ .x * 2)) # purrr-style lambda
#  x  y
#1 2  8
#2 4 10
#3 6 12

Reading and writing data

Speeding up Reading and Writing in R

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).

Note: data.table has its own ways (cf base R and dplyr) to subset columns.

Some resources:

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?

dtplyr

https://www.tidyverse.org/blog/2019/11/dtplyr-1-0-0/

reshape & reshape2 (superceded by tidyr package)

tidyr

Missing values

Handling Missing Values in R using tidyr

Pivot

  • tidyr package. pivot vignette, pivot_wider()
    R> d2 <- tibble(o=rep(LETTERS[1:2], each=3), n=rep(letters[1:3], 2), v=1:6); d2
    # A tibble: 6 × 3
      o     n         v
      <chr> <chr> <int>
    1 A     a         1
    2 A     b         2
    3 A     c         3
    4 B     a         4
    5 B     b         5
    6 B     c         6
    R> d1 <- d2%>% pivot_wider(names_from=n, values_from=v); d1
    # A tibble: 2 × 4
      o         a     b     c
      <chr> <int> <int> <int>
    1 A         1     2     3
    2 B         4     5     6
    

    pivot_longer()

    R> d1 %>% pivot_longer(!o, names_to = 'n', values_to = 'v')
    # Pivot all columns except 'o' column
    # A tibble: 6 × 3
      o     n         v
      <chr> <chr> <int>
    1 A     a         1
    2 A     b         2
    3 A     c         3
    4 B     a         4
    5 B     b         5
    6 B     c         6
    
    • In addition to the names_from and values_from columns, the data must have other columns
    • For each (combination) of unique value from other columns, the values from names_from variable must be unique
  • Conversion from gather() to pivot_longer()
    gather(df, key=KeyName, value = valueName, col1, col2, ...) # No quotes around KeyName and valueName
    
    pivot_longer(df, cols, names_to = "keyName", values_to = "valueName") 
      # cols can be everything()
      # cols can be numerical numbers or column names
    
  • A Tidy Transcriptomics introduction to RNA-Seq analyses
    data %>% pivot_longer(cols = c("counts", "counts_scaled"), names_to = "source", values_to = "abundance")
    
  • Using R: setting a colour scheme in ggplot2. Note the new (default) column names value and name after the function pivot_longer(data, cols).
    set(1)
    dat1 <- data.frame(y=rnorm(10), x1=rnorm(10), x2=rnorm(10))
    dat2 <- pivot_longer(dat1, -y)
    head(dat2, 2)
    # A tibble: 2 x 3
          y name   value
      <dbl> <chr>  <dbl>
    1 -1.28 x1     0.717
    2 -1.28 x2    -0.320
    
    dat3 <- pivot_wider(dat2)
    range(dat1 - dat3)
    

Benchmark

An evolution of reshape2. It's designed specifically for data tidying (not general reshaping or aggregating) and works well with dplyr data pipelines.

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 values. If the data is a matrix, use %>% as.data.frame() beforehand.
  • 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.
  • It's amazing the things one can do in base R (without installing or loading any other #rstats packages)
  • Essential functions: 3 rows functions, 3 column functions and 1 mixed function.
           select, mutate, rename, recode
            +------------------+
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
    • starts_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
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))
  • Another example
data <- data.frame(
  name = c("Alice", "Bob", "Charlie", "David", "Eve"),
  age = c(25, 30, 35, 40, 45),
  gender = c("F", "M", "M", "M", "F"),
  score1 = c(80, 85, 90, 95, 100),
  score2 = c(75, 80, 85, 90, 95)
)

# Example usage of dplyr functions
result <- data %>%
  filter(gender == "M") %>%                # Keep only rows where gender is "M"
  select(name, age, score1) %>%            # Select specific columns
  mutate(score_diff = score1 - score2) %>% # Calculate a new column based on existing columns
  arrange(desc(age)) %>%                   # Arrange rows in descending order of age
  #group_by(gender) %>%                    # Group the data by gender
  summarize(mean_score1 = mean(score1))    # Calculate the mean of score1 for each group
  • the dot.
    matrix(rnorm(12),4, 3) %>% .[1:2, 1:2]
    

select() for columns

Select columns from a data frame

select(my_data_frame, column_one, column_two, ...)
select(my_data_frame, new_column_name = current_column, ...)
select(my_data_frame, column_start:column_end)
select(my_data_frame, index_one, index_two, ...)
select(my_data_frame, index_start:index_end)

select() + everything()

If we want one particular column (say the dependent variable y) to appear first or last in the dataset. We can use the everything().

iris %>% select(Species, everything()) %>% head()
iris %>% select(-Species, everything()) %>% head() # put Species to the last col

.$Name

Extract a column using piping. The . represents the data frame that is being piped in, and $Name extracts the ‘Name’ column.

mtcars %>% .$mpg  # A vector

mtcars %>% select(mpg) # A list

filter() for rows

mtcars %>% filter(mpg>10)

identical(mtcars %>% filter(mpg>10), subset(mtcars, mpg>10))
# [1] TRUE

filter by date

What Is the Best Way to Filter by Date in R?

arrange (reorder)

  • Arrange values by a Single Variable:
    # Create a sample data frame
    students <- data.frame(
      Name = c("Ali", "Boby", "Charlie", "Davdas"),
      Score = c(85, 92, 78, 95)
    )
    
    # Arrange by Score in ascending order
    arrange(students, Score)
    #      Name Score
    # 1 Charlie    78
    # 2     Ali    85
    # 3    Boby    92
    # 4  Davdas    95
    
  • Arrange values by Multiple Variables: This is like the "sort" function in Excel.
    # Create a sample data frame
    transactions <- data.frame(
      Date = c("2024-04-01", "2024-04-01", "2024-04-02", "2024-04-03"),
      Amount = c(100, 150, 200, 75)
    )
    
    # Arrange by Date in ascending order, then by Amount in descending order
    arrange(transactions, Date, desc(Amount))
    #         Date Amount
    # 1 2024-04-01    150
    # 2 2024-04-01    100
    # 3 2024-04-02    200
    # 4 2024-04-03     75
    
  • Arrange values with Missing Values:
    # Create a sample data frame with missing values
    data <- data.frame(
      ID = c(1, 2, NA, 4),
      Value = c(20, NA, 15, 30)
    )
    
    # Arrange by Value in ascending order, placing missing values first
    arrange(data, desc(is.na(Value)), Value)
    #   ID Value
    # 1  2    NA
    # 2 NA    15
    # 3  1    20
    # 4  4    30
    

arrange and match

How to do the following in pipe A <- A[match(id.ref, A$id), ]

How to sort rows of a data frame based on a vector using dplyr pipe, Order data frame rows according to vector with specific order

  • Data
    library(dplyr)
    
    # Create a sample dataframe 'A'
    set.seed(1); A <- data.frame(
         id = sample(letters[1:5]),
         value = 1:5
         )
    print(A)
      id value
    1  a     1
    2  d     2
    3  c     3
    4  e     4
    5  b     5
    
    # Create a reference vector 'id.ref'
    id.ref <- c("e", "d", "c", "b", "a")
    # Goal:
    A[match(id.ref, A$id),]
      id value
    4  e     4
    2  d     2
    3  c     3
    5  b     5
    1  a     1
  • Method 1 (best): no match() is needed. Brilliant!
    A %>% arrange(factor(id, levels=id.ref))
      id value
    1  e     4
    2  d     2
    3  c     3
    4  b     5
    5  a     1
    # detail:
    factor(A$id, levels=id.ref)
    [1] a d c e b
    Levels: e d c b a
  • Method 2: complicated
    A %>%
         mutate(id.match = match(id, id.ref)) %>%
         arrange(id.match) %>%
         select(-id.match)
      id value
    1  e     4
    2  d     2
    3  c     3
    4  b     5
    5  a     1
    # detail:
    A %>%
         mutate(id.match = match(id, id.ref)) 
      id value id.match
    1  a     1        5
    2  d     2        2
    3  c     3        3
    4  e     4        1
    5  b     5        4
  • Method 3: a simplified version of Method 2, but it needs match()
    A %>% arrange(match(id, id.ref))
      id value
    1  e     4
    2  d     2
    3  c     3
    4  b     5
    5  a     1

group_by()

  • ?group_by and ungroup(),
  • Grouped data
  • Is ungroup() recommended after every group_by()? Always ungroup() when you’ve finished with your calculations. See here or this.
  • You might want to use ungroup() if you want to perform further calculations or manipulations on the data that don’t depend on the grouping. For example, after ungrouping the data, you could add new columns or filter rows without being restricted by the grouping.
                  +-- mutate() + ungroup() 
x -- group_by() --|
                  +-- summarise() # reduce the dimension, no way to get back

Subset rows by group

Subset rows based on their integer locations-slice in R

Rank by Group

How to Rank by Group in R?

df %>% arrange(team, points) %>%
    group_by(team) %>%
    mutate(rank = rank(points))

group_by() + summarise(), arrange(desc())

Data transformation from R for Data Science

Function in summarise()

  • group_by(var1) %>% summarise(varY = mean(var2)) %>% ggplot(aes(x = varX, y = varY, fill = varF)) + geom_bar(stat = "identity") + theme_classic()
  • summarise(newvar = sum(var1) / sum(var2))
  • arrange(desc(var1, var2))
  • Distinct number of observation: n_distinct()
  • Count the number of rows: n()
  • nth observation of the group: nth()
  • First observation of the group: first()
  • Last observation of the group: last()

group_by() + summarise() + across()

group_by() + nest(), mutate(, map()), unnest(), list-columns

nest(data=) is a function in the tidyr package in R that allows you to create nested data frames, where one column contains another data frame or list. This is useful when you want to perform analysis or visualization on each group separately. PS: it seems group_by() is not needed.

histogram <- gss_cat |> 
  nest(data = -marital) |>  # OR nest(.by = marital). 6x2 tibble. Col1=marital, col2=data.
  mutate(
    histogram = pmap(
      .l = list(marital, data),
      .f = \(marital, data) {
        ggplot(data, aes(x = tvhours)) +
          geom_histogram(binwidth = 1) +
          labs(
            title = marital
          )
      }
    )
  )
histogram$histogram[[1]]

Many models from R for Data Science

  • ?unnest, vignette('rectangle'), vignette('nest') & vignette('pivot')
    tibble(x = 1:2, y = list(1:4, 2:3)) %>% unnest(y) %>% group_by(x) %>% nest()
    # returns to tibble(x = 1:2, y = list(1:4, 2:3)) with 'groups' information
  • annotate boxplot in ggplot2
  • Coding in R: Nest and map your way to efficient code
          group_by() + nest()    mutate(, map())   unnest()
    data  -------------------->  --------------->  ------->
    
    install.packages('gapminder'); library(gapminder)
    
    gapminder_nest <- gapminder %>% 
      group_by(country) %>% 
      nest()  # country, data
              # each row of 'data' is a tibble
    
    gapminder_nest$data[[1]]  # tibble 57 x 8
    
    gapminder_nest <- gapminder_nest %>%
              mutate(pop_mean = map(.x = data, ~mean(.x$pop, na.rm = T)))
                                        # country, data, pop_mean
    
    gapminder_nest %>% unnest(pop_mean) # country, data, pop_mean
    
    gapminder_plot <- gapminder_nest %>% 
      unnest(pop_mean) %>% 
      select(country, pop_mean) %>% 
      ungroup() %>% 
      top_n(pop_mean, n = -10) %>% 
      mutate(pop_mean = pop_mean/10^3)
    gapminder_plot %>% 
      ggplot(aes(x = reorder(country, pop_mean), y = pop_mean)) +
      geom_point(colour = "#FF6699", size = 5) +
      geom_segment(aes(xend = country, yend = 0), colour = "#FF6699") +
      geom_text(aes(label = round(pop_mean, 0)), hjust = -1) +
      theme_minimal() +
      labs(title = "Countries with smallest mean population from 1960 to 2016",
           subtitle = "(thousands)",
           x = "",
           y = "") +
      theme(legend.position = "none",
            axis.text.x = element_blank(),
            plot.title = element_text(size = 14, face = "bold"),
            panel.grid.major.y = element_blank()) +
      coord_flip() +
      scale_y_continuous()
  • Tidy analysis from tidymodels
  • Is nest() + mutate() + map() + unnest() really the best alternative to dplyr::do()

across()

  • ?across. Applying a function or operation to multiple columns in a data frame simultaneously.
    across(.cols, .fns, ..., .names = NULL, .unpack = FALSE)
    gdf <-
      tibble(g = c(1, 1, 2, 3), v1 = 10:13, v2 = 20:23) %>%
      group_by(g)
    gdf %>% mutate(across(v1:v2, ~ .x + rnorm(1)))
    #>       g    v1    v2
    #>   <dbl> <dbl> <dbl>
    #> 1     1  10.3  20.7
    #> 2     1  11.3  21.7
    #> 3     2  11.2  22.6
    #> 4     3  13.5  22.7
    
  • dplyr across: First look at a new Tidyverse function.
    ny <- filter(cases, State == "NY") %>%
      select(County = `County Name`, starts_with(c("3", "4")))
    
    daily_totals <- ny %>%
      summarize(
        across(starts_with("4"), sum)
      )
    
    median_and_max <- list(
      med = ~median(.x, na.rm = TRUE),
      max = ~max(.x, na.rm = TRUE)
    )
    
    april_median_and_max <- ny %>%
      summarize(
        across(starts_with("4"), median_and_max)
      )
    </pre>
    <pre>
    # across(.cols = everything(), .fns = NULL, ..., .names = NULL)
    
    # Rounding the columns Sepal.Length and Sepal.Width
    iris %>%
      as_tibble() %>%
      mutate(across(c(Sepal.Length, Sepal.Width), round))
    
    iris %>% summarise(across(contains("Sepal"), ~mean(.x, na.rm = TRUE)))
    
    # filter rows
    iris %>% filter(if_any(ends_with("Width"), ~ . > 4))
    
    iris %>% select(starts_with("Sepal"))
    
    iris %>% select(starts_with(c("Petal", "Sepal")))
    
    iris %>% select(contains("Sepal"))

ave() - Adding a column of means by group to original data

mutate vs tapply

Base-R is alive and well

mutate + replace() or ifelse()

Hash table

  • Create new column based on 4 values in another column. The trick is to create a named vector; like a Dictionary in Python. Here is my example:
    hashtable <- data.frame(value=1:4, key=c("B", "C", "A", "D"))
    input <- c("A", "B", "C", "D", "B", "B", "A", "A") # input to be matched with keys, 
                                                       # this could be very long
    # Trick: convert the hash table into a named vector
    htb <- hashtable$value; names(htb) <- hashtable$key
    
    # return the values according to the names
    out <- htb[input]; out
    A B C D B B A A
    3 1 2 4 1 1 3 3

    We can implement using Python by creating a variable of dictionary type/structure.

    hashtable = {'B': 1, 'C': 2, 'A': 3, 'D': 4}
    input = ['A', 'B', 'C', 'D', 'B', 'B', 'A', 'A']
    out = [hashtable[key] for key in input]

    Or using C

    #include <stdio.h>
    
    int main() {
        int hashtable[4] = {3, 1, 2, 4};
        char input[] = {'A', 'B', 'C', 'D', 'B', 'B', 'A', 'A'};
        int out[sizeof(input)/sizeof(input[0])];
    
        for (int i = 0; i < sizeof(input)/sizeof(input[0]); i++) {
            out[i] = hashtable[input[i] - 'A'];
        }
    
        for (int i = 0; i < sizeof(out)/sizeof(out[0]); i++) {
            printf("%d ", out[i]);
        }
        printf("\n");
    
        return 0;
    }
  • hash package
  • digest package

inner_join, left_join, full_join

plyr::rbind.fill()

Videos

dbplyr

stringr

  • stringr is part of the tidyverse but is not a core package. You need to load it separately.
  • Handling Strings with R(ebook) by Gaston Sanchez.
  • https://www.rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf
  • stringr Cheat sheet (2 pages, this will immediately download the pdf file)
    • Detect Matches: str_detect(), str_which(), str_count(), str_locate()
    • Subset: str_sub(), str_subset(), str_extract(), str_match()
    • Manage Lengths: str_length(), str_pad(), str_trunc(), str_trim()
    • Mutate Strings: str_sub(), str_replace(), str_replace_all(), str_remove()
      • Case Conversion: str_to_lower(), str_to_upper(), str_to_title()
    • Joint and Split: str_c(), str_dup(), str_split_fixed(), str_glue(), str_glue_date()
  • Efficient data carpentry → Regular expressions from Efficient R programming book by Gillespie & Lovelace.
  • Common functions:
    `stringr` Function Description Base R Equivalent
    `str_length()` Returns the number of characters in each element of a character vector. `nchar()`
    `str_sub()` Extracts substrings from a character vector. `substr()`
    `str_trim()` Removes leading and trailing whitespace from strings. `trimws()`
    `str_split()` Splits a string into pieces based on a delimiter. `strsplit()`
    `str_replace()` Replaces occurrences of a pattern in a string with another string. `gsub()`
    `str_detect()` Detects whether a pattern is present in each element of a character vector. `grepl()`
    `str_subset()` Returns the elements of a character vector that contain a pattern. `grep()`
    `str_count()` Counts the number of occurrences of a pattern in each element of a character vector. `gregexpr()` and `lengths()`

str_replace()

Replace a string in a column: dplyr::across() & str_replace()

df <- data.frame(country=c('India', 'USA', 'CHINA', 'Algeria'),
                 position=c('1', '1', '2', '3'),
                 points=c(22, 25, 29, 13))

df %>%
  mutate(across('country', str_replace, 'India', 'Albania'))

df %>%
  mutate(across('country', str_replace, 'A|I', ''))

split

Split Data Frame Variable into Multiple Columns in R (3 Examples)

Three ways:

x <- c("a-1", "b-2", "c-3")

stringr::str_split_fixed(x, "-", 2)
#      [,1] [,2]
# [1,] "a"  "1" 
# [2,] "b"  "2" 
# [3,] "c"  "3" 

tidyr::separate(data.frame(x), x, c('x1', 'x2'), "-")
   # The first argument must be a data frame
   # The 2nd argument is the column names
#   x1 x2
# 1  a  1
# 2  b  2
# 3  c  3

magrittr: pipe

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)
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()

Dollar sign .$

%$%

Expose the names in lhs to the rhs expression. This is useful when functions do not have a built-in data argument.

lhs %$% rhs
# lhs:	A list, environment, or a data.frame.
# rhs: An expression where the names in lhs is available.

iris %>%
  subset(Sepal.Length > mean(Sepal.Length)) %$%
  cor(Sepal.Length, Sepal.Width)

set_rownames() and set_colnames()

https://stackoverflow.com/a/56613460, https://www.rdocumentation.org/packages/magrittr/versions/1.5/topics/extract

data.frame(x=1:5, y=2:6) %>% magrittr::set_rownames(letters[1:5])

cbind(1:5, 2:6) %>% magrittr::set_colnames(letters[1:2])

match()

a <- 1:3
id <- letters[1:3]
set.seed(1234); id.ref <- sample(id) 
id # [1] "b" "c" "a"

a[match(id.ref, b)] # [1] 2 3 1
id.ref %>% match(b) %>% `[`(a, .) # Same, but complicated

dtrackr

dtrackr: Track your Data Pipelines

purrr: : Functional Programming Tools

While there is nothing fundamentally wrong with the base R apply functions, the syntax is somewhat inconsistent across the different apply functions, and the expected type of the object they return is often ambiguous (at least it is for sapply…). See Learn to purrr.

  • What does the tilde mean in this context of R code, What is meaning of first tilde in purrr::map
  • Getting started with the purrr package in R, especially the map() and map_df() functions.
    library(purrr)
    # map() is a replacement of lapply()
    # lapply(dat, function(x) mean(x$Open))
    map(dat, function(x)mean(x$Open))  
    
    # map allows us to bypass the function function. 
    # Using a tilda (~) in place of function and a dot (.) in place of x
    map(dat, ~mean(.$Open))
    
    # map allows you to specify the structure of your output.
    map_dbl(dat, ~mean(.$Open))
    
    # map2() is a replacement of mapply()
    # mapply(function(x,y)plot(x$Close, type = "l", main = y), x = dat, y = stocks)
    map2(dat, stocks, ~plot(.x$Close, type="l", main = .y))
data <- map(paths, read.csv)
data <- map_dfr(paths, read.csv, id = "path")

out1 <- mtcars %>% map_dbl(mean, na.rm = TRUE)
out2 <- mtcars %>% map_dbl(median, na.rm = TRUE)

map() and map_dbl()

  • Mastering the map() Function in R
  • An example from https://purrr.tidyverse.org/
    mtcars |> 
         split(mtcars$cyl) |>  # from base R
         map(\(df) lm(mpg ~ wt, data = df)) |> 
         map(summary) |> map_dbl("r.squared")
    #         4         6         8 
    # 0.5086326 0.4645102 0.4229655
  • Solution by base R lapply() and sapply(). See the article purrr <-> base R
    mtcars |>
         split(mtcars$cyl) |>
         lapply(function(df) lm(mpg ~ wt, data = df)) |>
         lapply(summary) |>
         sapply(function(x) x$r.squared)
    #         4         6         8 
    # 0.5086326 0.4645102 0.4229655

tilde

  • The lambda syntax and tilde notation provided by purrr allow you to write concise and readable anonymous functions in R.
x <- 1:3
map_dbl(x, ~ .x^2)  # [1] 1 4 9
The notation ~ .x^2 is equivalent to writing function(.x) .x^2 or function(z) z^2 or \(y) y^2
x <- list(a = 1:3, b = 4:6)
y <- list(a = 10, b = 100)
map2_dbl(x, y, ~ sum(.x * .y))
#   a    b 
#  60 1500

.x symbol

  • It is used with functions like purrr::map. In the context of an anonymous function, .x is a placeholder for the first argument of the function.
    • For a single argument function, you can use .. For example, ~ . + 2 is equivalent to function(.) {. + 2}.
    • For a two argument function, you can use .x and .y. For example, ~ .x + .y is equivalent to function(.x, .y) {.x + .y}.
    • For more arguments, you can use ..1, ..2, ..3, etc
    # Create a vector
    vec <- c(1, 2, 3)
    
    # Use purrr::map with an anonymous function
    result <- purrr::map(vec, ~ .x * 2)
    
    # Print the result
    print(result)
    [[1]]
    [1] 2
    
    [[2]]
    [1] 4
    
    [[3]]
    [1] 6
    
  • dplyr piping data - difference between `.` and `.x`
  • Function argument naming conventions (`.x` vs `x`). Se purrr::map

negate()

How to select non-numeric columns using dplyr::select_if

library(tidyverse)
iris %>% select_if(negate(is.numeric))

pmap()

?pmap - Map over multiple input simultaneously (in "parallel")

# Create two lists with multiple elements
list1 <- list(1, 2, 3)
list2 <- list(10, 20, 30)

# Define a function to add the elements of each list
my_func <- function(x, y) {
  x + y
}

# Use pmap to apply the function to each element of the lists in parallel
result <- pmap(list(list1, list2), my_func); result
[[1]]
[1] 11

[[2]]
[1] 22

[[3]]
[1] 33

A more practical example when we want to run analysis or visualization on each element of some group/class variable. nest() + pmap().

# Create a data frame
df <- mpg %>% 
  filter(manufacturer %in% c("audi", "volkswagen")) %>% 
  select(manufacturer, year, cty)

# Nest the data by manufacturer
df_nested <- df %>% 
  nest(data = -manufacturer)

# Create a function that takes a data frame and creates a ggplot object
my_plot_func <- function(data, manuf) {
     ggplot(data, aes(x = year, y = cty)) +
         geom_point() +
         ggtitle(manuf)
 }

# Use pmap to apply the function to each element of the list-column in df_nested
df_nested_plot <- df_nested %>% 
     mutate(plot = pmap(list(data, manufacturer), my_plot_func))

df_nested_plot[[1]]

Another example: fitting regressions for data in each group

library(tidyverse)

# create example data
data <- tibble(
  x = rnorm(100),
  y = rnorm(100),
  group = sample(c("A", "B", "C"), 100, replace = TRUE)
)

# create a nested dataframe
nested_data <- data %>% 
  nest(data = -group)

# define a function that runs linear regression on each dataset
lm_func <- function(data) {
  lm(y ~ x, data = data)
}

# apply lm_func() to each row of the nested dataframe
results <- nested_data %>% 
  mutate(model = pmap(list(data), lm_func))

reduce

Reducing my for loop usage with purrr::reduce()

filter, subset data

Four Filters for Functional (Programming) Friends

purrr vs base R

https://purrr.tidyverse.org/dev/articles/base.html

forcats

https://forcats.tidyverse.org/

JAMA retraction after miscoding – new Finalfit function to check recoding

outer()

Genomic sequence

  • chartr
> yourSeq <- "AAAACCCGGGTTTNNN"
> chartr("ACGT", "TGCA", yourSeq)
[1] "TTTTGGGCCCAAANNN"

broom

  • broom: Convert Statistical Analysis Objects into Tidy Tibbles
  • Especially the tidy() function.
    R> str(survfit(Surv(time, status) ~ x, data = aml))
    List of 17
     $ n        : int [1:2] 11 12
     $ time     : num [1:20] 9 13 18 23 28 31 34 45 48 161 ...
     $ n.risk   : num [1:20] 11 10 8 7 6 5 4 3 2 1 ...
     $ n.event  : num [1:20] 1 1 1 1 0 1 1 0 1 0 ...
    ...
    
    R> tidy(survfit(Surv(time, status) ~ x, data = aml))
    # A tibble: 20 x 9
        time n.risk n.event n.censor estimate std.error conf.high conf.low strata         
       <dbl>  <dbl>   <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl> <chr>          
     1     9     11       1        0   0.909     0.0953     1       0.754  x=Maintained   
     2    13     10       1        1   0.818     0.142      1       0.619  x=Maintained   
    ...
    18    33      3       1        0   0.194     0.627      0.664   0.0569 x=Nonmaintained
    19    43      2       1        0   0.0972    0.945      0.620   0.0153 x=Nonmaintained
    20    45      1       1        0   0       Inf         NA      NA      x=Nonmaintained
    
  • Tables from journal papers
  • Multiple univariate models
    library(tidyverse)
    library(broom)
    
    mtcars %>%
      select(-mpg) %>%
      names() %>%
      map_dfr(~ tidy(lm(as.formula(paste("mpg ~", .x)), data = mtcars)))
    # A tibble: 20 × 5
    #   term        estimate std.error statistic  p.value
    #   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
    # 1 (Intercept)  37.9      2.07       18.3   8.37e-18
    # 2 cyl          -2.88     0.322      -8.92  6.11e-10
    # 3 (Intercept)  29.6      1.23       24.1   3.58e-21
    # 4 disp         -0.0412   0.00471    -8.75  9.38e-10
    
  • Multivariate model
    lm(mpg ~ ., data = mtcars) |> tidy()
    # A tibble: 11 × 5
    #   term        estimate std.error statistic p.value
    #   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
    # 1 (Intercept)  12.3      18.7        0.657  0.518 
    # 2 cyl          -0.111     1.05      -0.107  0.916 
    # 3 disp          0.0133    0.0179     0.747  0.463
    

lobstr package - dig into the internal representation and structure of R objects

lobstr 1.0.0

Other packages

Great R packages for data import, wrangling, and visualization

Great R packages for data import, wrangling, and visualization

cli package

tidytext

https://juliasilge.shinyapps.io/learntidytext/

tidytuesdayR

install.packages("tidytuesdayR")
library("tidytuesdayR")
tt_datasets(2020)  # get the exact day of the data we want to load
coffee_ratings <- tt_load("2020-07-07")
print(coffee_ratings)  #  readme(coffee_ratings)

janitor

funneljoin