Here are some random R tips and tricks that might be useful to you, or more likely, useful to me when I forget how I did the thing and need to remind myself.
1. Error safe mapping
Have you ever run into the problem where you map over a dataset or a
vector of values but sometimes encounter an error? This can be extremely
annoying because even if the error is rare it interrupts the entire
process. This could be an external API call that fails randomly or some
other error prone function applied to a list. As it turns out it isn’t
hard to make map_dfr() just keep going if it returns an error. How? By
using purrr::possibly()!
error_prone_function <- function(x) {
if(runif(1) < 0.5) stop("Random error encountered!")
x
}
# Stopping on any error
diamonds |> group_by(cut) |> group_split() |> map_dfr(~error_prone_function(.x))
## Error in `map()`:
## ℹ In index: 4.
## Caused by error in `error_prone_function()`:
## ! Random error encountered!
# Returning empty tibble for a group when encountering an error
diamonds |> group_by(cut) |> group_split() |> map_dfr(~possibly(error_prone_function)(.x))
## # A tibble: 23,161 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 2 0.86 Fair E SI2 55.1 69 2757 6.45 6.33 3.52
## 3 0.96 Fair F SI2 66.3 62 2759 6.27 5.95 4.07
## 4 0.7 Fair F VS2 64.5 57 2762 5.57 5.53 3.58
## 5 0.7 Fair F VS2 65.3 55 2762 5.63 5.58 3.66
## 6 0.91 Fair H SI2 64.4 57 2763 6.11 6.09 3.93
## 7 0.91 Fair H SI2 65.7 60 2763 6.03 5.99 3.95
## 8 0.98 Fair H SI2 67.9 60 2777 6.05 5.97 4.08
## 9 0.84 Fair G SI1 55.1 67 2782 6.39 6.2 3.47
## 10 1.01 Fair E I1 64.5 58 2788 6.29 6.21 4.03
## # ℹ 23,151 more rows
This isn’t something we always want to do. In the example above it will silently drop any group that throws an error. That can be dangerous. A more advanced solution would still return the group name but no data.