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Introduction
Today I am going to make a short post on the R package {box} which was showcased to me quite nicely by Michael Miles. It was informative and I was able to immediately see the usefulness of the {box} library.
So what is ‘box’? Well here is the description straight from their site:
‘box’ allows organising R code in a more modular way, via two mechanisms:
- It enables writing modular code by treating files and folders of R code as independent (potentially nested) modules, without requiring the user to wrap reusable code into packages.
- It provides a new syntax to import reusable code (both from packages and from modules) which is more powerful and less error-prone than library or require, by limiting the number of names that are made available.
So let’s see how it all works.
< section id="function" class="level1">Function
The main portion of the script looks like this:
# Main script
# Script setup --------------------------------------
# Load box modules
box::use(. / box / global_options / global_options)
box::use(. / box / io / imports)
box::use(. / box / io / exports)
box::use(. / box / mod / mod)
# Load global options
global_options$set_global_options()
# Main script ---------------------------------------
# Load data, process it, and export results
all_data <- getOption('data_dir') |>
# Load all data
imports$load_all() |>
# Modify dataset
mod$modify_data() |>
# Export data
exports$export_data()
So what does this do? Well it is grabbing data from a predefined location, modifying it and then re-exporting it. Now let’s look at all the code that is behind it, which allows us to do these things and then you will see the power of using box
< section id="example" class="level1">Example
Let’s take a look at the global options settings.
# Set global options
#' @export
set_global_options <- function() {
options(
look_ups = 'look-ups/',
data_dir = 'data/input/'
)
}
Ok 6 lines, boxed down to one.
Now the import function.
# Function for importing data
#' @export
load_all <- function(file_path) {
box::use(purrr)
box::use(vroom)
file_path |>
# Get all csv files from folder
list.files(full.names = TRUE) |>
# Set list names
purrr$set_names(\(file) basename(file)) |>
# Load all csvs into list
purrr$map(\(file) vroom$vroom(file))
}
Now the modify_data function.
# Function for modifying data
#' @export
modify_data <- function(df_list) {
box::use(dplyr)
box::use(purrr)
map_fun <- function(df) {
df |>
dplyr$select(name:mass) |>
dplyr$mutate(lol = height * mass) |>
dplyr$filter(lol > 1500)
}
# Apply mapping function to list
purrr$map(df_list, map_fun)
}
Ok again, a big savings here, instead of the above we simply call mod$modify_data() which makes things clearner and also modular in that we can go to a very specific spot in our proejct to fix an error or add/subtract functionality.
Lastly the export.
# Function for exporting data
#' @export
export_data <- function(df_list) {
box::use(vroom)
box::use(purrr)
# Export data
purrr$map2(.x = df_list,
.y = names(df_list),
~vroom$vroom_write(x = .x,
file = paste0('data/output/',
.y),
delim = ','))
}
Voila! I think to even a fresh user, the power of boxing your functions is fairly apparent and to the advanced user, eyes are most likely glowing!
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