Easily re-using self-written functions: the power of gist + code snippet duo
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Quite often data processing or analysis needs bring us to write own functions. Sometimes these self-defined functions are only meaningful and useful within a certain workflow or even a certain script. But other self-written functions may be more generic and reusable in other circumstances. For example, one may want to have a version of ggsave() that always enforces bg = 'snow', or a theme_own() function with pre-saved preferences. Self-written functions live in {.GlobalEnv} and have to be re-defined in every new R session. Copying the same lines of code across projects can be boring. How to “bookmark” the useful little own functions and reuse them easier in other projects? This post offers an elegant solution.
One obvious way to store self-written functions would be to write an own package and have a easy access to these function via library() calls. While this may be quite comfortable for own coding purposes, the objective downside of this approach is replicability of the code – once the code leaves your specific machine, one would have to install your package in order to run the code. This seems an overkill to store a couple of occasional arbitrarily useful functions.
I suggest a more convenient approach: store the functions as GitHub gists and call them using the handy devtools::source_gist(). This allows to load self-written functions from standalone R scripts. And to avoid copying manually the lines of code that source a certain gist we may use code snippets. Let me give you an example.
I want to re-use a ggplot2 theme with certain preferred parameters. Here are the lines of code that define my theme_ik() function.
theme_ik <- function(
base_size = 12,
base_family = "sans",
labs_color = "#074949",
axis_color = "#002F2F",
bg_color = "#eeffff",
grid_color = "#ccffff"
){
theme_minimal(base_size = base_size, base_family = base_family)+
theme(
plot.title = element_text(size = base_size*2, face = 2, color = labs_color),
plot.subtitle = element_text(color = labs_color),
plot.caption = element_text(color = labs_color),
axis.title = element_text(color = axis_color),
axis.text = element_text(color = axis_color),
plot.background = element_rect(color = NA, fill = bg_color),
legend.position = "bottom",
panel.spacing = unit(1, "lines"),
panel.grid.major = element_line(color = grid_color),
panel.grid.minor = element_blank(),
line = element_line(lineend = "round")
)
}
I store these lines of R code as a gist here. Next, I only need to supply the ID part of the gist URL (https://gist.github.com/ikashnitsky/653e1040a07364ae82b1bb312501a184) to the devtools::source_gist() function and it will execute the script stored in the gist, which will result in function theme_ik() appearing in my {.GlobalEnv}. With the second line of code I set the default ggplot2 theme to my self-written one.
devtools::source_gist("653e1040a07364ae82b1bb312501a184")
theme_set(theme_ik())
Now, the final element of this recipe is to save the two lines above as a code snippet. I’m using RStudio, but code snippets are available in any decent IDE. To add a custom snippet we need to navigate to Tools --> Edit Code Snippets.... In the new window just add a custom snippet making sure to respect the indentation. 1
1 Note that in the left tabset one can choose what kind of snippets to add. In R scripts snippets are called with TAB, in rmarkdown documents the hot-key for snippets is SHIFT+TAB.
That’s it. Save the modified snippets, the new one is ready to be used. Now, when I type thm and then press TAB, thm transforms into the two lines of code that source the specific gist and set the custom theme to theme_ik(). Any ggplot that I will produce next in this R session will have my preferred theme defaults.
swiss %>%
ggplot(aes(x = Agriculture, y = Fertility))+
geom_point()+
labs(
title = "Fertility and rurality in Swiss cantones, 1888"
)
My preferred ggplot2 theme here is optimized to produce plots that look nicely in my blog. 2 Happy coding with snippets and easily re-usable custom functions!
2 In case you read this anywhere else, this blog post is available at https://ikashnitsky.github.io/2023/gist-snippet
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