expr <- rlang::expr(c(...))The {tidyselect} package powers column selection for {dplyr}, {tidyr} and {recipes} functions but it’s also quite straight-forward to include its functonality in other user-built functions. Here’s an example which replaces a percentage of a data frame by NA. It works by using the tidyselect::eval_select function to select columns and then replaces a portion of the data with NA values.
Column selections are passed to the function through dot-dot-dot, allowing an arbitrary number of columns to be selected. The first line of the function:
returns a defused expression, and the second:
pos <- tidyselect::eval_select(expr, data = data)resumes execution, returning a vector of positions that match the selection. More details on how to implement tidyselect interfaces is available at the tidyselect pkgdown page.
#' populate data frame with missing data
#'
#' Replace a proportion of data in a data frame with missing values
#'
#' @param data A data frame
#' @param p proporion (between 0 and 100) of data in column to be flagged as missing
#' @param ... `<tidy-select>` One or more unquoted expressions separated by commas
#'
#' @return data frame
#'
#' @importFrom rlang expr
#' @importFrom tidyselect vars_select
#' @export
add_missing_df <- function(data, ..., p = 10) {
expr <- rlang::expr(c(...))
pos <- tidyselect::eval_select(expr, data = data)
if (length(pos) > 0) {
for (posn in pos) {
missing_rows <- sample(nrow(data), size = as.integer(nrow(data) * p / 100))
if (length(missing_rows) > 0) {
data[missing_rows, posn] <- NA
}
}
}
return(data)
}Examples
All of the {tidyselect} selectors may be used, along with the {magrittr} pipe if loaded. Some examples of use are:
# remove 40% of data in columns mpg to hp
add_missing_df(mtcars, p=40, mpg:hp) mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 NA 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 NA NA 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 NA 108.0 NA 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive NA 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 NA 8 360.0 NA 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 NA 146.7 NA 3.69 3.190 20.00 1 0 4 2
Merc 230 NA 4 140.8 NA 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 NA NA 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 NA 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE NA 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 NA 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC NA 8 275.8 NA 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 NA 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial NA 8 NA 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 NA 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 NA 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 NA NA 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 NA 120.1 NA 3.70 2.465 20.01 1 0 3 1
Dodge Challenger NA NA NA NA 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 NA 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 NA NA 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 NA 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 NA NA 79.0 NA 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 NA NA 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 NA 3.77 1.513 16.90 1 1 5 2
Ford Pantera L NA 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 NA 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 NA NA 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 NA NA 109 4.11 2.780 18.60 1 1 4 2# remove 10% of data in columns that start with "d" (dist and drat)
mtcars %>% add_missing_df(tidyselect::starts_with("d")) mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 NA 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 NA 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 NA 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 NA 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 NA 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 NA 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2