Simple function winsorizes data at the specified percentile.

## Usage

winsorizor(d, percentile, values, na.rm = TRUE)

## Arguments

d

A vector, matrix, data frame, or data table to be winsorized

percentile

The percentile bounded by [0, 1] to winsorize data at. If a data frame or matrix is provided for the data, this should have the same length as the number of columns, or it will be repeated for all.

values

If values are specified, use these instead of calculating by percentiles. Should be a data frame with columns named “low”, and “high”. If a data frame or matrix is provided for the data, there should be as many rows for values to winsorize at as there are columns in the data.

na.rm

A logical whether to remove NAs.

## Value

winsorized data. Attributes are included to list the exact values (for each variable, if a data frame or matrix) used to winsorize at the lower and upper ends.

## Examples

dev.new(width = 10, height = 5)
par(mfrow = c(1, 2))
hist(as.vector(eurodist), main = "Eurodist")
hist(winsorizor(as.vector(eurodist), .05), main = "Eurodist with lower and upper\n5% winsorized")

library(data.table)
dat <- data.table(x = 1:5)
dat[, y := scale(1:5)]
#>        x          y
#>    <int>      <num>
#> 1:     1 -1.2649111
#> 2:     2 -0.6324555
#> 3:     3  0.0000000
#> 4:     4  0.6324555
#> 5:     5  1.2649111
winsorizor(dat\$y, .01)
#> Warning: Atomic type with no dimensions, coercing to a numeric vector.
#> To remove this warning, try wrapping the data in as.numeric() or
#> otherwise coercing to a vector prior to passing to winsorizor().
#> [1] -1.2396128 -0.6324555  0.0000000  0.6324555  1.2396128
#> attr(,"winsorizedValues")
#>         low     high percentile
#> 1 -1.239613 1.239613       0.01

## make a copy of the data table
winsorizor(dat, .01)
#>        x          y
#>    <num>      <num>
#> 1:  1.04 -1.2396128
#> 2:  2.00 -0.6324555
#> 3:  3.00  0.0000000
#> 4:  4.00  0.6324555
#> 5:  4.96  1.2396128

winsorizor(mtcars, .01)
#>       mpg cyl    disp     hp  drat      wt    qsec vs am gear carb
#> 1  21.000   6 160.000 110.00 3.900 2.62000 16.4600  0  1    4 4.00
#> 2  21.000   6 160.000 110.00 3.900 2.87500 17.0200  0  1    4 4.00
#> 3  22.800   4 108.000  93.00 3.850 2.32000 18.6100  1  1    4 1.00
#> 4  21.400   6 258.000 110.00 3.080 3.21500 19.4400  1  0    3 1.00
#> 5  18.700   8 360.000 175.00 3.150 3.44000 17.0200  0  0    3 2.00
#> 6  18.100   6 225.000 105.00 2.760 3.46000 20.2200  1  0    3 1.00
#> 7  14.300   8 360.000 245.00 3.210 3.57000 15.8400  0  0    3 4.00
#> 8  24.400   4 146.700  62.00 3.690 3.19000 20.0000  1  0    4 2.00
#> 9  22.800   4 140.800  95.00 3.920 3.15000 22.0692  1  0    4 2.00
#> 10 19.200   6 167.600 123.00 3.920 3.44000 18.3000  1  0    4 4.00
#> 11 17.800   6 167.600 123.00 3.920 3.44000 18.9000  1  0    4 4.00
#> 12 16.400   8 275.800 180.00 3.070 4.07000 17.4000  0  0    3 3.00
#> 13 17.300   8 275.800 180.00 3.070 3.73000 17.6000  0  0    3 3.00
#> 14 15.200   8 275.800 180.00 3.070 3.78000 18.0000  0  0    3 3.00
#> 15 10.400   8 468.280 205.00 2.930 5.25000 17.9800  0  0    3 4.00
#> 16 10.400   8 460.000 215.00 3.000 5.39951 17.8200  0  0    3 4.00
#> 17 14.700   8 440.000 230.00 3.230 5.34500 17.4200  0  0    3 4.00
#> 18 32.400   4  78.700  66.00 4.080 2.20000 19.4700  1  1    4 1.00
#> 19 30.400   4  75.700  55.10 4.775 1.61500 18.5200  1  1    4 2.00
#> 20 33.435   4  72.526  65.00 4.220 1.83500 19.9000  1  1    4 1.00
#> 21 21.500   4 120.100  97.00 3.700 2.46500 20.0100  1  0    3 1.00
#> 22 15.500   8 318.000 150.00 2.760 3.52000 16.8700  0  0    3 2.00
#> 23 15.200   8 304.000 150.00 3.150 3.43500 17.3000  0  0    3 2.00
#> 24 13.300   8 350.000 245.00 3.730 3.84000 15.4100  0  0    3 4.00
#> 25 19.200   8 400.000 175.00 3.080 3.84500 17.0500  0  0    3 2.00
#> 26 27.300   4  79.000  66.00 4.080 1.93500 18.9000  1  1    4 1.00
#> 27 26.000   4 120.300  91.00 4.430 2.14000 16.7000  0  1    5 2.00
#> 28 30.400   4  95.100 113.00 3.770 1.54462 16.9000  1  1    5 2.00
#> 29 15.800   8 351.000 264.00 4.220 3.17000 14.5310  0  1    5 4.00
#> 30 19.700   6 145.000 175.00 3.620 2.77000 15.5000  0  1    5 6.00
#> 31 15.000   8 301.000 312.99 3.540 3.57000 14.6000  0  1    5 7.38
#> 32 21.400   4 121.000 109.00 4.110 2.78000 18.6000  1  1    4 2.00

winsorizor(matrix(1:9, 3), .01)
#>      [,1] [,2] [,3]
#> [1,] 1.02 4.02 7.02
#> [2,] 2.00 5.00 8.00
#> [3,] 2.98 5.98 8.98
#> attr(,"winsorizedValues")
#>    low high percentile
#> 1 1.02 2.98       0.01
#> 2 4.02 5.98       0.01
#> 3 7.02 8.98       0.01

rm(dat) # clean up