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options(scipen=10000)
library(palmerpenguins)
library(emo)
library(magrittr)
library(flair)

## Pipes %>%

The ease of reading and writing code in R is a thing of beauty, and is made so due to the pipe! 💗 ❇️ 🆒.

Little bunny Foo Foo
Went hopping through the forest
Scooping up the field mice
And bopping them on the head

### Intermediate Steps

foo_foo <-  little_bunny()
foo_foo1 <- hop(foo_foo, through = forest)
foo_foo2 <- scoop(foo_foo1, up = field_mice)
foo_foo3 <- bop(foo_foo2, on = head)
• Main downside is that we need to name each intermediate step and with long data wrangling pipelines this can get tedious.
• The thought is that these extra copies also take up space, but they don’t. R cleverly handles that for us behind the scenes.
diamonds <- ggplot2::diamonds
diamonds2 <- diamonds %>%
dplyr::mutate(price_per_carat = price / carat)

pryr::object_size(diamonds)  # gives memory occupied by all its args
3.46 MB
pryr::object_size(diamonds2)
3.89 MB
pryr::object_size(diamonds, diamonds2) # collective size of both
3.89 MB

diamonds and diamonds2 have 10 columns in common. These are shared by both objects.

If we modify any columns then the number of columns in common reduces. This is what happens below, and hence the shared size increases.

diamonds\$carat[1] <- NA
pryr::object_size(diamonds)  # gives memory occupied by all its args
3.46 MB
pryr::object_size(diamonds2)
3.89 MB
pryr::object_size(diamonds, diamonds2) # collective size of both
4.32 MB

### Overwrite Original

foo_foo <-  little_bunny()
foo_foo <- hop(foo_foo, through = forest)
foo_foo <- scoop(foo_foo, up = field_mice)
foo_foo <- bop(foo_foo, on = head)
• Debugging is hard. Need to re-run pipeline to figure out where the error lies.
• The common variable being overwritten hides whats happening at each step. E.g. Foo foo is a bunny who is hopping through the forest, and on her way she scoops up some field mice. A person reading your code will get confused as to what happened with Foo foo if they read long pipelines, or worse will think Foo foo can bop something on its head sometimes, can hop through a forest sometimes etc. I.e. it’s hard to keep track of what Foo foo is doing.

### Function Composition

bop(
scoop(
hop(
foo_foo,
through = forest
),
up = field_mice
),
)
• This way gets me the most, I struggle to keep track of what is happening when reading from the inside out, and I waste loads of paper drawing up pipelines like this to figure out what’s the end goal of the pipeline (sadly most other languages don’t have the %>%)!

### The pipe

foo_foo <- little_bunny()
foo_foo %>%
hop(through = forest) %>%
scoop(up = field_mice) %>%
bop(on = head)

The authors remark that this is their favourite form, because it focusses on verbs, not nouns, and I am totally with them.

Foo foo hops through the forest, then scoops up field mice, then bops ’em on the head.

Behind the scenes magrittr creates a function with these steps and saves each in an intermediate object for us.

my_pipe <- function(.) {
. <- hop(., through = forest)
. <- scoop(., up = field_mice)
}
my_pipe(foo_foo)

The pipe does not work well for all functions though. Ones that use the current environment, functions using lazy evaluation like tryCatch().

### Other useful pipe operators

Call a function for its side effects.

rnorm(100) %>%
matrix(ncol = 2) %>%
plot() %>%
str()

 NULL

Our str() did not produce anything! 😭

Enter the tee operator given by (%T>%). Things like print(), plot(), View() etc. do not return anything and when you use them in a pipeline it pipes nothing into the next step in the pipeline. A pipeline expects the result of the previous step to “replace” the first argument in the subsequent step though so this breaks the pipeline.

The %T>% does it’s job and sends the result of the previous pipe to the one after it’s side job function.

rnorm(100) %>%
matrix(ncol = 2) %T>% # side job is to please plot
plot() %>%
str()

 num [1:50, 1:2] 1.443 1.012 0.606 0.761 -0.849 ...

The above takes matrix(rnorm(100, ncol = 2) and pipes it into str().

sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_South Africa.1252  LC_CTYPE=English_South Africa.1252
[3] LC_MONETARY=English_South Africa.1252 LC_NUMERIC=C
[5] LC_TIME=English_South Africa.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] flair_0.0.2          magrittr_1.5         emo_0.0.0.9000
[4] palmerpenguins_0.1.0 workflowr_1.6.2

loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6     pryr_0.1.4       pillar_1.4.6     compiler_3.6.3
[5] later_1.0.0      git2r_0.26.1     tools_3.6.3      digest_0.6.25
[9] lubridate_1.7.8  evaluate_0.14    lifecycle_0.2.0  tibble_3.0.3
[13] gtable_0.3.0     pkgconfig_2.0.3  rlang_0.4.7      yaml_2.2.1
[17] xfun_0.13        dplyr_1.0.0      stringr_1.4.0    knitr_1.28
[21] vctrs_0.3.2      generics_0.0.2   fs_1.4.1         tidyselect_1.1.0
[25] rprojroot_1.3-2  grid_3.6.3       glue_1.4.1       R6_2.4.1
[29] rmarkdown_2.4    purrr_0.3.4      ggplot2_3.3.0    whisker_0.4
[33] codetools_0.2-16 backports_1.1.6  scales_1.1.0     promises_1.1.0
[37] htmltools_0.5.0  ellipsis_0.3.1   assertthat_0.2.1 colorspace_1.4-1
[41] httpuv_1.5.2     stringi_1.4.6    munsell_0.5.0    crayon_1.3.4