maskr

maskr introduces the masked class that suppresses or “masks” printing of certain elements of an atomic vector while keeping the underlying data available for computation. Masked vectors can include numeric, logical character or factor data.

Installation

maskr can be installed from CRAN using:

install.packages('maskr')

You can also install the development version of maskr from GitHub:

devtools::install_github('inpowell/maskr')

Example

library(maskr)

Masked vectors can be built from atomic vectors using masked():

x <- 0:8
masked(x, 0 < x & x < 5)
#> <integer+masked[9]>
#>    0 n.p. n.p. n.p. n.p.    5    6    7    8

Here, the masked cells are indicated by “n.p.” (not published) by default. We can change how they are presented using the maskr.replacement option:

options(maskr.replacement = '*')
masked(x, 0 < x & x < 5)
#> <integer+masked[9]>
#> 0 * * * * 5 6 7 8
options(maskr.replacement = NULL)

Other types of atomic vectors can be masked as well:

masked(letters, letters %in% c('a', 'e', 'i', 'o', 'u'))
#> <character+masked[26]>
#> n.p. b    c    d    n.p. f    g    h    n.p. j    k    l    m    n    n.p. p    
#> q    r    s    t    n.p. v    w    x    y    z

We can also use this to control which data gets displayed in data frames and cross-tables.

tabular <- tibble::tibble(
  Activity = gl(4, 4, 16, labels = c("I", "II", "III", "Total")),
  Region = gl(4, 1, 16, labels = c("A", "B", "C", "Total")),
  Count = as.integer(c(
    10,  25,   5,  40,
    16,  13,  11,  40,
    17,  20,  24,  61,
    43,  58,  40, 141
  ))
)
suppress <- rep(FALSE, 16L)
suppress[c(5, 8, 9, 12)] <- TRUE
tabular$Count <- masked(tabular$Count, suppress)
tabular
#> # A tibble: 16 × 3
#>    Activity Region     Count
#>    <fct>    <fct>  <int+msk>
#>  1 I        A             10
#>  2 I        B             25
#>  3 I        C              5
#>  4 I        Total         40
#>  5 II       A           n.p.
#>  6 II       B             13
#>  7 II       C             11
#>  8 II       Total       n.p.
#>  9 III      A           n.p.
#> 10 III      B             20
#> 11 III      C             24
#> 12 III      Total       n.p.
#> 13 Total    A             43
#> 14 Total    B             58
#> 15 Total    C             40
#> 16 Total    Total        141

This works with tidyverse reshaping functions like pivot_wider():

tidyr::pivot_wider(tabular, names_from = 'Region', values_from = 'Count')
#> # A tibble: 4 × 5
#>   Activity         A         B         C     Total
#>   <fct>    <int+msk> <int+msk> <int+msk> <int+msk>
#> 1 I               10        25         5        40
#> 2 II            n.p.        13        11      n.p.
#> 3 III           n.p.        20        24      n.p.
#> 4 Total           43        58        40       141

Masked vectors support basic arithmetic, so for example we can find percentages while maintaining the correct masking pattern.

tabular |>
  dplyr::group_by(Activity) |>
  dplyr::mutate(Percent = 100 * Count / Count[Region == 'Total'])
#> # A tibble: 16 × 4
#> # Groups:   Activity [4]
#>    Activity Region     Count   Percent
#>    <fct>    <fct>  <int+msk> <dbl+msk>
#>  1 I        A             10      25  
#>  2 I        B             25      62.5
#>  3 I        C              5      12.5
#>  4 I        Total         40     100  
#>  5 II       A           n.p.      n.p.
#>  6 II       B             13      n.p.
#>  7 II       C             11      n.p.
#>  8 II       Total       n.p.      n.p.
#>  9 III      A           n.p.      n.p.
#> 10 III      B             20      n.p.
#> 11 III      C             24      n.p.
#> 12 III      Total       n.p.      n.p.
#> 13 Total    A             43      30.5
#> 14 Total    B             58      41.1
#> 15 Total    C             40      28.4
#> 16 Total    Total        141     100

Notice that where we have divided by a masked cell, the percentage is also masked.

Using masked vectors, as opposed to just replacing values we want to suppress with missing values, means we can always recover our data before we publish it:

tabular$Count <- unmask(tabular$Count)
tabular
#> # A tibble: 16 × 3
#>    Activity Region Count
#>    <fct>    <fct>  <int>
#>  1 I        A         10
#>  2 I        B         25
#>  3 I        C          5
#>  4 I        Total     40
#>  5 II       A         16
#>  6 II       B         13
#>  7 II       C         11
#>  8 II       Total     40
#>  9 III      A         17
#> 10 III      B         20
#> 11 III      C         24
#> 12 III      Total     61
#> 13 Total    A         43
#> 14 Total    B         58
#> 15 Total    C         40
#> 16 Total    Total    141