sourçoïse : Source and Cache

CRAN status Lifecycle: stable R-CMD-check metacran downloads CRAN_latest_release_date Codecov test coverage

{sourcoise} (pronouced [suʁsɔiz] as sourcçoïse) is a package that provides tools for running an R script and caching the results (saving them to disk). The aim is to be able to execute (quickly) code that accesses files or an API and which, in the absence of updates, always produces the same result. When the API is likely to block (or if you don’t have an internet connection), this avoids failure of the rendering of a qmd document or a quarto website.

The script code that retrieves the data from a file or an API has to be isolated, and that is improving reproducibility. sourcoise() can be called in a sourcoise() which allows modularity. The cache is persistent (on disk, possibly synched with github) across sessions of the same project. The package provides tools, for checking the cache, sourcoise_status(), and refreshing it on demand, sourcoise_refresh(), or at a given frequency through a parameter of sourcoise().

Installation

{sourcoise} can be installed from CRAN:

install.packages("sourcoise")

The development version can be installed from github:

devtools::install_gitub("xtimbeau/sourcoise")

# or (if pak is installed)
pak::pak("xtimbeau/sourcoise")

Usage

To populate a graph or table with data, put the code in a script r ("mon_script.r"), ending the script with a return(data_pour_le_graphique). In the .qmd or .rmd (or also an R script) we have the instructions for the graph in a r code chunk:

```r
library(tidyverse)
library(sourcoise)

mes_datas <- sourcoise("mon_script.r")
ggplot(mes_datas) + <<graph code>>

```

The first time the script is run completely, subsequent calls will use the cache on disk, persistent across session of the same project (R project, ), unless the cache is invalidated.

To check the status of cache, just call sourcoise_status(). It will scan your project and collect info about your cached data. To refresh everything, call sourcoise_refresh(), it will execute all scripts and refresh data in the cache. Options are available to filter what really need to be refreshed.

Many benefits

  1. time saving when code execution takes a long time (accessing an API, downloading large amounts of data, major processing). Reading an excel file can also take a long time. The time taken to access cached data depends on its size, but even for large data (and there’s no reason why it should be that large), the order of magnitude is a few milliseconds (4 to 10ms) for execution when data is cached, thanks to optimization and {memoise}.

  2. the cache is transferable via github. It’s in a (hidden) folder, but saved in the project folder and committed and pushed on github. The cache produced on a workstation can therefore be accessed via pull on other workstations, without the need to re-execute the code (and thanks to a smart naming scheme, without too much conflicts).

  3. if the source code triggers an error, you can override it: In the case of a package that is not installed, missing data (for example, an absolute path in the code), or an API that blocks (such as the OECD API, particularly unreliable), then sourcoise() tries to take the result of the last successful execution (if cached). Although this can be problematic, i.e. an unreported error and use of an ivalid cache, it has the enormous advantage of not blocking the process and allows to handle the error in parallel.

  4. sourcoise() cleverly searches for the source file in the project and executes the code in a local environment, changing the working directory to the one where the source code is located. This makes it possible to call the source code (the script r mon_script.r passed as a parameter to sourcoise("mon_script.r") scripts r data files .csv or .xlsx which are saved in the same directory as the mon_script.r. You can therefore reuse the code without having to worry about modifying the paths, which are relative to the folder where mon_script.r is. Then, the code is resusable anywhere in the project or elsewhere. Note that it is done with simple copy of source scripts and does not compare to a common code called in many situation, which involves another approach (a package and functions for instance).

  5. This provides an embryo of reproducibility by designating the script that produces the data and thus allowing to complement the code chunk with a reference to reproduce it. It is possible to propose the code to be downloaded in the qmd.

Coming soon