Box Package

Reusable and Modular R Code with the Box Package

Introduction to the box package

box is one of my new favorite packages in the R ecosystem. Developed by Konrad Rudolph, its main purpose is to allow us to organize the code in a much more modular way, mainly through two mechanisms, as indicated in its page of pkgdown:

• Allows you to write modular code by treating R code files and folders as independent (potentially nested) modules, without requiring the user to package the code in a package (wow!)

• It provides a new syntax for importing reusable code (both packages and modules) that is more powerful and less error prone than classic library or require by limiting the number of names that are available.

Import Reusable Code

First of all, we are going to explain the example that we can find in the box web page itself (remember not to do library (box), as you will get an error):

 1 2 3 4 5 6  box::use( purrr, # 1 tbl = tibble, # 2 dplyr = dplyr[filter, select], # 3 stats[st_filter = filter, ...] # 4 ) 

What are we indicating with this box :: use statement?

1. First, the purrr package is imported and its functions are made accessible through the$operator:  1  purrr$reduce(c(1:10), sum) 
## [1] 55

1. Second, the tibble package with the alias tbl is imported, so we access its functions as follows:
  1 2 3 4 5 6 7 8 9 10  library(dplyr) set.seed(123) df <- tbl$tibble(date = seq.Date(from = as.Date('2020-12-31'), length.out = 8, by = 'quarter'), value = rnorm(8)) %>% tbl$add_column(ID = 'base', .before = 'date') df 
## # A tibble: 8 x 3
##   ID    date         value
##   <chr> <date>       <dbl>
## 1 base  2020-12-31 -0.560
## 2 base  2021-03-31 -0.230
## 3 base  2021-07-01  1.56
## 4 base  2021-10-01  0.0705
## 5 base  2021-12-31  0.129
## 6 base  2022-03-31  1.72
## 7 base  2022-07-01  0.461
## 8 base  2022-10-01 -1.27

1. Third, the dplyr library is imported and also attach is used on the names dplyr::filter and dplyr::select.
 1  select(df, value) 
## # A tibble: 8 x 1
##     value
##     <dbl>
## 1 -0.560
## 2 -0.230
## 3  1.56
## 4  0.0705
## 5  0.129
## 6  1.72
## 7  0.461
## 8 -1.27

 1  filter(df, value > 0.5) 
## # A tibble: 2 x 3
##   ID    date       value
##   <chr> <date>     <dbl>
## 1 base  2021-07-01  1.56
## 2 base  2022-03-31  1.72

 1  dplyr$first(df$date) 
## [1] "2020-12-31"

1. Atachs all the functions of the stats package (this is what the three ellipsis represents) and also uses the alias st_filter for the filter function. In this way we can have the filter functions of dplyr and stats coexisting at the same time and without risk of errors.

Reusable Modules

Perhaps one of the biggest advantages of box is the use of reusable modules without the need to create a package for it. Now that we have seen a short introduction on how to work with box to handle packages, let’s see how we can use this package to load a module. The first thing we must do is create a script with the functions that we want to import, in our example case it will be these two functions:

  1 2 3 4 5 6 7 8 9 10 11  #' @export subscribe = function () { message('You should subscribe to my NewsletteR!') } #' @export bye = function () { message('I hope you liked this post :) I hope to see you soon here again!') } 

First, we must modify the options(‘box.path’) to the path where we have our script so that we are able to import it correctly.

 1 2 3  options(box.path = getwd()) box::use(./box_post) 

Once imported, we can access the functions found in the script through the $ operator:  1  box_post$subscribe() 
## You should subscribe to my NewsletteR!


I hope you liked this post, see you in the next one!

 1  box_post\$bye() 
## I hope you liked this post :) I hope to see you soon here again!