README

Automatically map complex operations safely or quietly, and quickly see the captured side effects.

The collateral package extends the power of purrr::safely() and purrr::quietly(), providing variants of map()—map_safely() and map_quietly()—that automatically apply the appropriate function wrapper and print the resulting output nicely.

This is especially useful for tidy workflows: you can nest() a data frame, apply a complex operation (such as building a regression model or building and saving a plot) to each group and quickly see which groups need to be inspected in more detail:

Installation

We’ll submit to CRAN soon, but for now, use devtools to install collateral:

devtools::install_github('rensa/collateral')

Example

library(tidyverse)
library(collateral)

test =
  # tidy up and trim down for the example
  mtcars %>%
  rownames_to_column(var = "car") %>%
  as_data_frame() %>%
  select(car, cyl, disp, wt) %>%
  # spike some rows in cyl == 4 to make them fail
  mutate(wt = dplyr::case_when(
    wt < 2 ~ -wt,
    TRUE ~ wt)) %>%
  # nest and do some operations quietly()
  nest(-cyl) %>%
  mutate(qlog = map_quietly(data, ~ log(.$wt)))

test
#> # A tibble: 3 x 4
#>     cyl data              qlog
#>   <dbl> <list>            <collat>
#> 1     6 <tibble [7 x 3]>  R O _ _
#> 2     4 <tibble [11 x 3]> R O _ W
#> 3     8 <tibble [14 x 3]> R O _ _

collateral uses pillar to style output, so supported terminals will also color the output! Properly styling knitted output is still on the to-do list, though.

If you’re a fan of iterating over two or more inputs in lockstep, then map2() and pmap() variants are also provided:

collateral also provides tally_*() and has_() functions to help you filter or summarise your side effects, as well as a summary() mehod. If you haven’t used a list-column workflow before, take a look at the vignette to see how it’s done!

collateral

Installation

Example

Support