This week in R (2025-03-14)

This week in R (2025-03-14)

Happy Friday everyone! Here's some news from the world of R this week:

  • Pick your poison: Albert Rapp has written a blog post comparing common data manipulation tasks in both {dplyr} and {data.table}. They are the two most popular frameworks for working with tabular data and both have different strengths and weaknesses. The article is well produced and makes cool use of tabs to enable comparing the two package's syntax side-by-side!
  • Spruce up pkgdown: Murielle Delmotte has produced an article diving into the various options you can set for a {pkgdown} documentation site. The post looks at how you can change the structure, theme and styling of your site with a few well-placed options in your pkgdown.yml file. If you're looking to make your documentation site stand out and make an impact for your users, check out Murielle's post.
  • Double language docs: Isabella Velasquez has written up an article based on a discussion at a recent coffee and chat with Hadley Wickham and Wes Mckinney, where a question was asked around documentation for both R and Python users. The article (and video of the discussion) talks about a couple of options for having tabs which can be globally switched - allowing users to select whether they want to see R or Python code and having that reflected in all code chunks - neat!
  • More time to shine: Jumping Rivers have extended the deadline for submissions to talk at Shiny in Production 2025. You now have until the 3rd of April to submit a talk. The article here gives some ideas of the topics they are particularly interested in seeing submissions about, and as someone who has spoken at Shiny in Production before I highly recommend you consider submitting a proposal!

  • Did you know:?you can use the base R function Vectorize() to vectorize your functions?

my_fun <- function(x) {
  if (x == 1) return("A")
  if ((x %% 2) == 0) return("B")
  return("C")
}

my_fun(1:5)
#> Error in if (x == 1) return("A"): the condition has length > 1

my_v_fun <- Vectorize(my_fun)

my_v_fun(1:5)
#> [1] "A" "B" "C" "B" "C"        

New versions:

  • {httr2} v1.1.1 - new streaming and verbosity features plus bug fixes.

I post updates like this every week so if you're interested feel free to follow. Comment below if there's something interesting you found out this week too!

Darakhshan Nehal

Educator |Biochemistry |AMU

1 周

You can include this to the list : Johnson & Johnson's Open Source Journey in R https://m.youtube.com/watch?v=_bqeYh2kNgY

Isabella V.

Sr. Product Marketing Manager at Posit PBC

1 周

Thank you, thank you! Loving the other highlights, too!

Allan Engelhardt

Value and growth in the data economy | Management Decision Analytics ? Innovation ? Growth ? Data Science

1 周

`base::Vectorize()`, which is wrapper around `mapply`, is slower than a `for` loop: https://thatdatatho.com/vectorization-r-purrr/

Claire Turner-Tait

Senior Product Manager - Verifications at Experian

1 周

Where is VictoRRRRR RRRRRay ?

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