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Shift missing values to facilitate missing data exploration/visualisation

Source: R/shadow-shifters.R
shadow_shift.Rd

shadow_shift transforms missing values to facilitate visualisation, and has different behaviour for different types of variables. For numeric variables, the values are shifted to 10% below the minimum value for a given variable plus some jittered noise, to separate repeated values, so that missing values can be visualised along with the rest of the data.

Usage

shadow_shift(...)

Arguments

...

arguments to impute_below().

Details

[Deprecated]

See also

add_shadow_shift() cast_shadow_shift() cast_shadow_shift_label()

Examples

airquality$Ozone
#>   [1]  41  36  12  18  NA  28  23  19   8  NA   7  16  11  14  18  14  34   6
#>  [19]  30  11   1  11   4  32  NA  NA  NA  23  45 115  37  NA  NA  NA  NA  NA
#>  [37]  NA  29  NA  71  39  NA  NA  23  NA  NA  21  37  20  12  13  NA  NA  NA
#>  [55]  NA  NA  NA  NA  NA  NA  NA 135  49  32  NA  64  40  77  97  97  85  NA
#>  [73]  10  27  NA   7  48  35  61  79  63  16  NA  NA  80 108  20  52  82  50
#>  [91]  64  59  39   9  16  78  35  66 122  89 110  NA  NA  44  28  65  NA  22
#> [109]  59  23  31  44  21   9  NA  45 168  73  NA  76 118  84  85  96  78  73
#> [127]  91  47  32  20  23  21  24  44  21  28   9  13  46  18  13  24  16  13
#> [145]  23  36   7  14  30  NA  14  18  20
shadow_shift(airquality$Ozone)
#> Warning: `shadow_shift()` was deprecated in naniar 1.1.0.
#> ℹ Please use `impute_below()` instead.
#>   [1]  41.00000  36.00000  12.00000  18.00000 -19.72321  28.00000  23.00000
#>   [8]  19.00000   8.00000 -18.51277   7.00000  16.00000  11.00000  14.00000
#>  [15]  18.00000  14.00000  34.00000   6.00000  30.00000  11.00000   1.00000
#>  [22]  11.00000   4.00000  32.00000 -17.81863 -19.43853 -15.14310  23.00000
#>  [29]  45.00000 115.00000  37.00000 -16.17315 -14.65883 -17.85609 -13.29299
#>  [36] -16.16323 -19.60935  29.00000 -19.65780  71.00000  39.00000 -13.40961
#>  [43] -13.53728  23.00000 -19.65993 -16.48342  21.00000  37.00000  20.00000
#>  [50]  12.00000  13.00000 -17.17718 -16.74073 -13.65786 -16.78786 -12.30098
#>  [57] -13.33171 -16.77414 -17.08225 -15.98818 -19.17558 135.00000  49.00000
#>  [64]  32.00000 -14.27138  64.00000  40.00000  77.00000  97.00000  97.00000
#>  [71]  85.00000 -13.51764  10.00000  27.00000 -13.48998   7.00000  48.00000
#>  [78]  35.00000  61.00000  79.00000  63.00000  16.00000 -16.92150 -16.60335
#>  [85]  80.00000 108.00000  20.00000  52.00000  82.00000  50.00000  64.00000
#>  [92]  59.00000  39.00000   9.00000  16.00000  78.00000  35.00000  66.00000
#>  [99] 122.00000  89.00000 110.00000 -14.78907 -16.19151  44.00000  28.00000
#> [106]  65.00000 -19.73591  22.00000  59.00000  23.00000  31.00000  44.00000
#> [113]  21.00000   9.00000 -18.92235  45.00000 168.00000  73.00000 -14.86296
#> [120]  76.00000 118.00000  84.00000  85.00000  96.00000  78.00000  73.00000
#> [127]  91.00000  47.00000  32.00000  20.00000  23.00000  21.00000  24.00000
#> [134]  44.00000  21.00000  28.00000   9.00000  13.00000  46.00000  18.00000
#> [141]  13.00000  24.00000  16.00000  13.00000  23.00000  36.00000   7.00000
#> [148]  14.00000  30.00000 -14.83089  14.00000  18.00000  20.00000
if (FALSE) {
library(dplyr)
airquality %>%
    mutate(Ozone_shift = shadow_shift(Ozone))
}