Piping %>% in R has been around since the debut of library(magrittr) in 2014 and has been adopted by some of the most popular packages on CRAN including library(tidyverse). library(purrr) is a relatively new package, released in 2016, that uses simple syntax for adding powerful functional programming tools to R. This quick data(mtcars) run through aims to show the potential of pairing these two packages.

Magrittr

library(magrittr)

Stefan Milton Bache

• Faster coding
• Eliminate nested functions

Six Rules of the pipe:

# 1. By default the left-hand side (LHS) will be piped in as the first argument of the function appearing on the right-hand side (RHS).
LHS %>% some_fxn(...) = some_fxn(LHS, ...)

# 2. %>% may be used in a nested fashion, e.g. it may appear in expressions within arguments.
summarise(mtcars %>% filter(cyl == 4), avg_mpg = mean(mpg))

# 3. When the LHS is needed at a position other than the first, one can use the dot,'.', as placeholder.
some_chr_vector %>% gsub("find", "replace", .)

# 4. The dot in a formula is not confused with a placeholder.
list_of_dataframes %>% map(~ lm(response ~ ., data = .))

# 5. Whenever only one argument is needed, the LHS, then one can omit the empty parentheses.
some_object %>% class

# 6. A pipeline with a dot (.) as LHS will create a unary function.
mean_rm <- . %>% mean(na.rm = T)

The reciprocal pipe %<>%

Hands down my favorite tool in R. This special pipe is shorthand for storing the result from the RHS evalution by overwriting the original LHS value. This is very useful with certain functions and can save a bunch of typing and intermediate variables in common data-munging.

some_chr_vector %<>% gsub("find", "replace", .) # now its saved
some_chr_vector %<>% factor(levels = c("a","b")) # ready for ggplot()

Hadley doesn’t like these %<>%, due to principles of explicit definition, so thats why they don’t load with tidyverse. I think they are beyond swell and hope you do too.

Trend setting

To play nicely with magrittr, functions should be defined with the data argument first, not like lm(formula, data, ...) or qplot(x, y = NULL, ..., data). This allows minimal typing and maximum foucs on the series of actions. New tidyverse packages are designed with this in mind and purrr is no different.

Purrr

library(purrr)

• Simplify annonymous functions
• Bring some “traditonal” functional programming to R
• Use variants to control return value

Sugar + function(){..} = ~

Since annonymous functions are everywhere in R, lapply(..., function(x){ anything custom } ), purrr’s map(.x, .f, ...) is directly aimed at sweetening their syntax. Syntactic sugar makes for code that is easier to understand and easier to type. The core function map() is the tidy-alernative for apply() and is the major workhorse function of the package, let's look at its syntax features…

# no need for () if required args == 1
mtcars %>% split(.$cyl) %>% map(class) # if args > 1 use ~ and magrittr's . syntax mtcars %>% split(.$cyl) %>%
map(~ lm(mpg ~ gear, data = .)) # same rules for flexible positioning

map_df() and friends

Maybe the most useful map variant is map_df, which wraps the common syntax do.call(rbind, your_list) after the original mapping. Instead of using map(your_list, ...) %>% do.call(rbind, .), use map_df(your_list, ...). This is very useful for reading in raw data, dir(raw_folder) %>% map_df(read.csv). And map_df takes the argument .id = ... which allows for the storage of list element names in a new var. If the argument .id = is omitted then the name information is omitted from the resulting tibble.

Other helper functions like map_chr, map_lgl and map_dbl are similar to vapply and are very useful for controlling output types.

walk() is the imaginary friend

If you ever just want to call a function for it’s side effect(s), like when printing plots, walk is a nice option. walk will silently evaluate and functions just like map would, but without any console output and it returns the list (or vector) that was passed in unchanged. This is really useful for outputing anything, like writing a list of tibbles to RDS or capturing a list of plots in your prefered graphics device.

But why?

Pipes are syntactic sugar, they make code chunks easier to digest. This important not only for you, but also anyone else that comes behind and uses your code. Using pipelines shows the steps required to contrust variables sequentially and seeing it in series makes debugging easier. Pipes also help reduce the number of unimportant intermediate variables that are generated keeping your environmnet free from clutter so you can focus on the important pieces.

Data cleaning

Let look at the first 6 rows of mtcars sorted by mpg:

# prep data
data(mtcars)
mtcars %<>% rownames_to_column() # bc this should be the default
mtcars$cyl %<>% as.factor # useful later on for group_by() and split() # equivilant code head(arrange(mtcars, mpg)) mtcars %>% arrange(mpg) %>% head While both of the lines above will return identical results, the pipeline version offers several syntactic advantages over the traditional version. • Alleviates the need for nested functions. • Pipelines make following logic easier. • Complex procedures are presented in steps. Gets better with more steps As the number of steps increases and the manipulation task become more complex, pipelines become even better. For example let’s group mtcars by number of cyl and then pull out the top 4 cars for mpg from each cyl group, then take the average of each numeric column. # this ? summarise_if(top_n(group_by(mtcars, cyl), 4, mpg),is.numeric, mean) # or this ? mtcars %>% group_by(cyl) %>% top_n(4, mpg) %>% # doing arrange %>% head from above summarise_if(is.numeric, mean) Again both code chunks are execution identical, but we can really start to see the benefits how pipes help break code down into single digestible operations. Instead of looking at a single confusing line of nested function calls, we have a line by line break down of each step in sequential order. Modeling Now lets run through the ?map example and see these two packages playing nicely. The linear model of the relationship between disp and mpg for each cyl level. Thanks to ggplot2::stat_summary(), maybe the best plotting function ever. ggplot(mtcars, aes(disp, mpg, color = cyl)) + geom_point(size = 4) + stat_smooth(method = "lm", se = F) Suppose we want to make the same linear models ourselves. Perhaps for hypothesis testing or coefficient extraction. mtcars %>% split(.$cyl) %>%
map(~ lm(mpg ~ disp, data = .)) -> lm_fits # not my fav but useful

Now we have a nice little list, which is the best, because we could continue on applying functions with map( fun(x) ). This template is nice for confidence intervals, predicting or just about anything you do in R.

Easy extraction

Grabbing the data from any R object can be a pain, but library(broom) added tidy() which has methods for doing just that to most classes in R!

We want to extract the coefs from each model and re-plot the lms with geom_abline()

lm_fits %<>%
map_df(broom::tidy, .id = "cyl") %>% # love this map_df(tidy) combo!!!
select(cyl, term, estimate) %>%
geom_abline(data = lm_fits, aes(slope = disp, intercept = (Intercept), color = cyl))
I hope you can begin to see the value of using pipes They can help keep your code environment clean and clutter free. Pipelines can also help when they left “open”, with the results returning, because the objects are easy to investigate, which makes the next step easy to see. Bug squashing is easy with pipelines, adding a View on the end of the line or highlighting and running before the %>%, lets you check into any code at any line.