README.md

multimeter

Inspect data pipelines (especially magrittr pipelines) for before-after changes during tricky operations.

Installation

You can install the latest development version from GitHub with:

# install.packages("remotes")
remotes::install_github("bvancil/multimeter")

Example

The basic Multimeter class powers the mm_get_*_meter() functions, which are easier to use.

library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.0.5
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(multimeter)

Monitoring missingness during operations

# Function to intentionally introduce some NAs
maybe_na <- function(x, prob = 0.2) {
  n <- base::length(x)
  missing_of_type_x <- vctrs::vec_cast(NA, to = vctrs::vec_ptype(x))
  dplyr::if_else(stats::runif(n) < prob, missing_of_type_x, x)
}

missing_meter <- multimeter::mm_get_missing_meter()
set.seed(2021L + 06L + 28L)
starwars2 <- dplyr::starwars %>%
  missing_meter$probe() %>% 
  dplyr::mutate(homeworld = maybe_na(homeworld)) %>% 
  missing_meter$probe()
#> # A tibble: 1 x 3
#>   column    frac_missing_before frac_missing_after
#>   <chr>                   <dbl>              <dbl>
#> 1 homeworld               0.115              0.241

You can access this comparison through

missing_meter$comparison
#> # A tibble: 1 x 3
#>   column    frac_missing_before frac_missing_after
#>   <chr>                   <dbl>              <dbl>
#> 1 homeworld               0.115              0.241

Summarizing changes in specific columns

value_meter <- multimeter::mm_get_value_meter()
mtcars2 <- mtcars %>%
  value_meter$probe(cyl) %>%
  dplyr::mutate(square_error_from_five = (cyl - 5)^2) %>%
  value_meter$probe(square_error_from_five)
#>   cyl square_error_from_five  n
#> 1   4                      1 11
#> 2   6                      1  7
#> 3   8                      9 14

You can access a summary of changed column values and frequency counts through

value_meter$comparison
#>   cyl square_error_from_five  n
#> 1   4                      1 11
#> 2   6                      1  7
#> 3   8                      9 14

It would be nice to do this automatically, perhaps by decorating dplyr::mutate.

Contributing

Run to install development packages:

renv::restore()

Run before committing:

source("R/dev/before_commit.R")

Code of Conduct

Please note that the multimeter project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.