useful functions

My sticky note filled up quickly after I only added setNames() on it, with related functions for dealing with object names, in base R and beyond! (Un)Setting object names: stats::setNames(), unname() and rlang::set_names() I noticed a function ending with something like this: blop <- function() { # code creating the df data.frame # ... names(df) <- c("col1", "col2") df } It struck me as simplifiable by:

Time for me to throw away my sticky note after sharing what I wrote on it! grep(...) not which(grepl(...)) Recently I caught myself using which(grepl(...)), animals <- c("cat", "bird", "dog", "fish") which(grepl("i", animals)) #> [1] 2 4 when the simpler alternative is animals <- c("cat", "bird", "dog", "fish") grep("i", animals) #> [1] 2 4 And should I need the values instead of the indices, I know I shouldn’t write

Straight from my sticky note, three functions that I like a lot, despite their not being new at all… But maybe new to some of you? sprintf(), the dependency-free but less neat “glue” Imagine I want to tell you who I am. I could write name <- whoami::fullname() github_username <- whoami::gh_username() glue::glue("My name is {name} and you'll find me on GitHub as {github_username}!") #> My name is Maëlle Salmon and you'll find me on GitHub as maelle!

My sticky note is full! And luckily all functions on it can be squeezed into a similar topic: making things smaller! Make lists smaller with purrr::compact(), purrr::keep(), purrr::discard() Once upon a time there was a list (isn’t this the beginning of all R scripts?!) my_list <- list( name = "Maëlle", city = "Nancy", r_problems_encountered = Inf, python_skills = NULL ) Imagine you want to get rid of NULL elements.

There are already three functions of note on a piece of paper on my desk, so it’s time to blog about them! This post was featured on the R Weekly podcast by Eric Nantz and Mike Thomas. How does this package depend on this other package? pak::pkg_deps_explain() The pak package by Gábor Csárdi makes installing packages easier. If I need to start working on a package, I clone it, then run pak::pak() to install and update its dependencies.

I (only! but luckily!) recently got introduced to the magic of purrr::reduce(). Thank you, Tobias! I was told about it right as I was unhappily using many for loops in a package1, for lack of a better idea. In this post I’ll explain how purrr::reduce() helped me reduce my for loop usage. I also hope that if I’m doing something wrong, someone will come forward and tell me! This post was featured on the R Weekly podcast by Eric Nantz and Mike Thomas.

Following my recent post on three useful (to me) R patterns, I’ve written down three other things on a tiny sticky note. This post will allow me to throw away this beaten down sticky note, and maybe to show you one element you didn’t know? nzchar(): “a fast way to find out if elements of a character vector are non-empty strings” One of my favorite testing technique is the escape hatch strategy, about which I wrote a post on the R-hub blog: you make part of your code responsive to an environment variable, and you locally set that environment variable in your tests.

This post was featured on the R Weekly highlights podcast hosted by Eric Nantz and Mike Thomas. I’m happy to report that I thought “oh but I know a better way to write that code!” a few times lately when reading old scripts of mine, or scripts by others. It’s a good feeling because it shows progress! I’ve tooted about all three things I’ll present in this post: After reading Julia Evans’ post about blogging, I decided to train the blogging muscle a bit using these low-hanging fruits/toots1.