Add helper function to add random missingness

[njtierney]

rather than needing to do something like:

x <- 1:10
x
#>  [1]  1  2  3  4  5  6  7  8  9 10
x[sample(x = length(x), size = 5)] <- NA
x
#>  [1] NA NA  3 NA NA  6  7  8  9 NA

add_n_na <- function(x, n_na){
  x[sample(x = vctrs::vec_size(x), size = n_na)] <- NA
  x
}

x <- 1:10
x
#>  [1]  1  2  3  4  5  6  7  8  9 10
add_n_na(x, 3)
#>  [1]  1  2  3  4 NA  6  7 NA NA 10

^{Created on 2022-04-05 by the reprex package (v2.0.1)}

Session info

sessioninfo::session_info()
#> ─ Session info  🇻🇺  ⏺️  🕰️   ───────────────────────────────────────────────────
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#> ──────────────────────────────────────────────────────────────────────────────

[njtierney]

set_prop_miss <- function(x, prop = 0.1) {
  x[sample(seq_along(x) <= prop * length(x))] <- NA
  x
}

set_n_miss <- function(x, n = 1) {
  x[sample(seq_along(x) <= n)] <- NA
  x
}

library(tidyverse)
library(naniar)

df <- tibble(
  x = rnorm(100),
  y = rpois(100, lambda = 5)
)

df
#> # A tibble: 100 × 2
#>         x     y
#>     <dbl> <int>
#>  1  1.53      4
#>  2  0.281     7
#>  3  0.609    10
#>  4  2.18      7
#>  5 -0.256     9
#>  6 -0.565     5
#>  7  0.827     4
#>  8 -0.878     4
#>  9 -1.14      8
#> 10  1.17      8
#> # … with 90 more rows

set_prop_miss(df$x, 0.1) %>% prop_miss()
#> [1] 0.1
set_prop_miss(df$x, 0.5) %>% prop_miss()
#> [1] 0.5
set_prop_miss(df$x, 0.75) %>% prop_miss()
#> [1] 0.75

set_n_miss(df$x, 2) %>% n_miss()
#> [1] 2
set_n_miss(df$x, 10) %>% n_miss()
#> [1] 10
set_n_miss(df$x, 50) %>% n_miss()
#> [1] 50

^{Created on 2023-01-31 with reprex v2.0.2}

Session info

sessioninfo::session_info()
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#> ──────────────────────────────────────────────────────────────────────────────

Note that add_n_miss and add_prop_miss are already functions for adding helper columns on the proportion and number of missing values to a dataset