Counting Arguments in the Tidyverse
Before we start anything, I’d like to mention that most of the hard work came from nsaunders and his great blog post Idle thoughts lead to R internals: how to count function arguments.
Let’s get started. The aim of this blog is to capture the number of arguments present in each function with packages of the {tidyverse}. First we need to load the necessary packages
library("tidyverse")
library("tidytext")
Now we need to grab the relevant {tidyverse} packages
tpkg = tidyverse_packages()
tpkg[17] = "readxl"
head(tpkg)
## [1] "broom" "cli" "crayon" "dplyr" "dbplyr" "forcats"
We’ve had to reset the 17th element to {readxl} as it gets loaded as readxl\n(>=, which breaks the next block of code. Now we also need to load in the tidyverse packages. Doing this one by one would be a pain, so I’ve used map()
map(tpkg, library, character.only = TRUE)
Now for the actual analysis I’m just going to whack the full code in now, then go through it line by line.
pkg = tpkg %>%
as_tibble() %>%
rename(package = value) %>%
rowwise() %>%
mutate(funcs = paste0(ls(paste0("package:", package)), collapse = ",")) %>%
unnest_tokens(func, funcs, token = stringr::str_split,
pattern = ",", to_lower = FALSE) %>%
filter(is.function(get(func, pos = paste0("package:", package)))) %>%
mutate(num_args = length(formalArgs(args(get(func, pos = paste0("package:", package)))))) %>%
ungroup()
This is what the head of pkg looks like
head(pkg)
## # A tibble: 6 x 3
## package func num_args
##
## 1 broom augment 2
## 2 broom augment_columns 8
## 3 broom bootstrap 3
## 4 broom confint_tidy 4
## 5 broom finish_glance 2
## 6 broom fix_data_frame 3
Lines 1-4
Lines 1-4 look like this
tpkg %>%
as_tibble() %>%
rename(package = value) %>%
rowwise() %>%
Here we are grabbing, the tidyverse packages character vector, converting it to a tibble and renaming the column. We then use rowwise() so that we can work in a row-wise fashion.
Line 5
mutate(funcs = paste0(ls(paste0("package:", package)), collapse = ",")) %>%
To get a character vector back of the objects within a package, we do ls("package:package_name"). However, we want to store this as a single string, so we need to use our old friend paste0() to do so. We then use mutate to attach this to the data frame. Our data from now looks like this
## Source: local data frame [6 x 2]
## Groups:
##
## # A tibble: 6 x 2
## package funcs
##
## 1 broom argument_glossary,augment,augment_columns,bootstrap,column_gloss…
## 2 cli ansi_hide_cursor,ansi_show_cursor,ansi_with_hidden_cursor,bg_bla…
## 3 crayon %+%,bgBlack,bgBlue,bgCyan,bgGreen,bgMagenta,bgRed,bgWhite,bgYell…
## 4 dplyr %>%,add_count,add_count_,add_row,add_rownames,add_tally,add_tall…
## 5 dbplyr add_op_single,as.sql,base_agg,base_no_win,base_odbc_agg,base_odb…
## 6 forcats %>%,as_factor,fct_anon,fct_c,fct_collapse,fct_count,fct_cross,fc…
Lines 6 - 7
unnest_tokens(func, funcs, token = stringr::str_split,
pattern = ",", to_lower = FALSE) %>%
As we’ve stored the function names as a single string, we can now apply some {tidytext} to turn our data into long data! We do this using the unnest_tokens() function. Here we are taking the funcs variable, turning it into func by splitting it up using str_split() from {stringr}. The data now looks like this
## Source: local data frame [6 x 2]
## Groups:
##
## # A tibble: 6 x 2
## package func
##
## 1 broom argument_glossary
## 2 broom augment
## 3 broom augment_columns
## 4 broom bootstrap
## 5 broom column_glossary
## 6 broom confint_tidy
Line 8
filter(is.function(get(func, pos = paste0("package:", package)))) %>%
Now, not every object inside a package is a function. We can use is.function() to test this. However, as our function names are stored as strings, we must wrap them in the get() function. For instance,
is.function("augment")
## [1] FALSE
is.function(get("augment"))
## [1] TRUE
What if we have conflicts in function names? We can also specify the package our function is from, using the argument pos
is.function(get("augment", pos = "package:broom"))
## [1] TRUE
We can then use this condition within a filter command to remove any objects that aren’t functions
Lines 9 - end
mutate(num_args = length(formalArgs(args(get(func, pos = paste0("package:", package)))))) %>%
ungroup()
It is possible to withdraw the arguments of a function using the formalArgs() function. However, this does not work on primitive functions
formalArgs(get("add", pos = "package:magrittr"))
## NULL
formalArgs(get("augment", pos = "package:broom"))
## [1] "x" "..."
We can counter act this by wrapping the function in args() first. This method now works for both primitives and non-primitives
formalArgs(args(get("add", pos = "package:magrittr")))
## [1] "e1" "e2"
formalArgs(args(get("augment", pos = "package:broom")))
## [1] "x" "..."
To work out the number of these argument we simply wrap this expression in length().
The big reveal
pkg %>%
arrange(desc(num_args))
## # A tibble: 2,292 x 3
## package func num_args
##
## 1 ggplot2 theme 93
## 2 ggplot2 guide_colorbar 28
## 3 ggplot2 guide_colourbar 28
## 4 ggplot2 guide_legend 21
## 5 rstudioapi launcherSubmitJob 21
## 6 ggplot2 geom_dotplot 19
## 7 ggplot2 geom_boxplot 18
## 8 readr read_delim_chunked 18
## 9 readr read_delim 17
## 10 readr spec_delim 17
## # … with 2,282 more rows
So it turns out that theme() from {ggplot2} is king of the arguments, by a mile! The largest per package looks like this
We’re not done there! The 9 packages with the largest sum of arguments are
largest = pkg %>%
group_by(package) %>%
count() %>%
arrange(desc(n)) %>%
head(9) %>%
pull(package)
largest
## [1] "rlang" "ggplot2" "dplyr" "purrr" "lubridate"
## [6] "dbplyr" "readr" "rstudioapi" "httr"
We can plot a histogram, for each package, of the no. of arguments in each function like so..
pkg %>%
filter(package %in% largest) %>%
ggplot(aes(x = num_args)) +
geom_histogram(binwidth = 1, fill = "steelblue", col = "black") +
facet_wrap(~package) +
xlim(c(0, 25)) +
theme_minimal()
We can go a step further and retrieve the argument names as well. To do this we use the same technique as before with the functions
pkg %>%
rowwise() %>%
mutate(args = paste0(formalArgs(args(get(func, pos = paste0("package:", package)))),
collapse = ",")) %>%
unnest_tokens(arg, args, token = stringr::str_split,
pattern = ",", to_lower = FALSE) %>%
ungroup() %>%
count(arg) %>%
arrange(desc(n))
## # A tibble: 1,029 x 2
## arg n
##
## 1 ... 785
## 2 x 698
## 3 data 169
## 4 .x 120
## 5 "" 102
## 6 n 91
## 7 .f 90
## 8 position 90
## 9 mapping 79
## 10 na.rm 79
## # … with 1,019 more rows
The most commonly used arguments in the tidyverse are ... and x by some distance.
pkg %>%
rowwise() %>%
mutate(args = paste0(formalArgs(args(get(func, pos = paste0("package:", package)))),
collapse = ",")) %>%
unnest_tokens(arg, args, token = stringr::str_split,
pattern = ",", to_lower = FALSE) %>%
ungroup() %>%
group_by(package) %>%
count(arg) %>%
arrange(package, desc(n)) %>%
slice(2) %>%
arrange(desc(n))
## # A tibble: 26 x 3
## # Groups: package [26]
## package arg n
##
## 1 ggplot2 data 103
## 2 purrr .x 91
## 3 dplyr x 83
## 4 rlang ... 64
## 5 readr locale 44
## 6 lubridate ... 35
## 7 stringr pattern 23
## 8 dbplyr x 21
## 9 httr ... 21
## 10 tidyr ... 18
## # … with 16 more rows
So you can see that data is the most common argument within {ggplot2}, .x is the most common argument within {purrr} and so on…
That’s it for this blog post. Hope you’ve enjoyed!
