tidyverse function to `mutate_sample`? - r

Question

I'm looking to mutate a column for a random sample, e.g., mutate_sample. Does anyone know whether there is a dplyr/other tidyverse verb for this? Below is a reprex for the behavior I am looking for and an attempt to functionalize (which isn't running because I'm struggling with quasiquotation in if_else).
library(dplyr)
library(tibble)
library(rlang)
# Setup -------------------------------------------------------------------
group_size <- 10
group_n <- 1
my_cars <-
mtcars %>%
rownames_to_column(var = "model") %>%
mutate(group = NA_real_, .after = model)
# Code to create mutated sample -------------------------------------------
group_sample <-
my_cars %>%
filter(is.na(group)) %>%
slice_sample(n = group_size) %>%
pull(model)
my_cars %>%
mutate(group = if_else(model %in% group_sample, group_n, group)) %>%
head()
#> model group mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 Mazda RX4 NA 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> 2 Mazda RX4 Wag 1 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> 3 Datsun 710 1 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> 4 Hornet 4 Drive NA 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> 5 Hornet Sportabout NA 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> 6 Valiant NA 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# Function to create mutated sample ---------------------------------------
#
# Note: doesn't run because of var in if_else
# mutate_sample <- function(data, var, id, n, value) {
# # browser()
# sample <-
# data %>%
# filter(is.na({{var}})) %>%
# slice_sample(n = n) %>%
# pull({{id}})
#
# data %>%
# mutate(var = if_else({{id}} %in% sample, value, {{var}}))
# }
#
# mutate_sample(my_cars, group, model, group_size, group_n)
Created on 2020-10-21 by the reprex package (v0.3.0)
Looking through SO, I found this related post:
Mutate column as input to sample

Answer 1

I think you could achieve your goal with this two options.
With dplyr:
mtcars %>% mutate(group = sample(`length<-`(rep(group_n, group_size), n())))
or with base R:
mtcars[sample(nrow(mtcars), group_size), "group"] <- group_n
If you need an external function to handle it, you could go with:
mutate_sample <- function(.data, .var, .size, .value) {
mutate(.data, {{.var}} := sample(`length<-`(rep(.value, .size), n())))
}
mtcars %>% mutate_sample(group, group_size, group_n)
or
mutate_sample_rbase <- function(.data, .var, .size, .value) {
.data[sample(nrow(.data), size = min(.size, nrow(.data))),
deparse(substitute(.var))] <- .value
.data
}
mtcars %>% mutate_sample(group, group_size, group_n)
Note that if .size is bigger than the number of rows of .data, .var will be a constant equal to .value.
EDIT
If you're interested in keeping the old group, I suggest you another way to handle the problem:
library(dplyr)
# to understand this check out ?sample
resample <- function(x, ...){
x[sample.int(length(x), ...)]
}
# this is to avoid any error in case you choose a size bigger than the available rows to select in one group
resample_max <- function (x, size) {
resample(x, size = min(size, length(x)))
}
mutate_sample <- function(.data, .var, .size, .value) {
# creare column if it doesnt exist
if(! deparse(substitute(.var)) %in% names(.data)) .data <- mutate(.data, {{.var}} := NA)
# replace missing values randomly keeping existing non-missing values
mutate(.data, {{.var}} := replace({{.var}}, resample_max(which(is.na({{.var}})), .size), .value))
}
group_size <- 10
mtcars %>%
mutate_sample(group, group_size, 1) %>%
mutate_sample(group, group_size, 2)
#> mpg cyl disp hp drat wt qsec vs am gear carb group
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 NA
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 NA
#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 2
#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 NA
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 NA
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 2
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 NA
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 NA
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 2
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 1
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 NA
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 2
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 1
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 1
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 2
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 NA
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 NA
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 1
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 1
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 2
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 1
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 2
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 2
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 NA
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 2
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 1
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 NA
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 2
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 NA
Notice that this solution works even with grouped_df class (what you get after a dplyr::group_by): from each group [made by dplyr::group_by] a sample of .size units will be selected.
mtcars %>%
group_by(am) %>%
mutate_sample(group, 10, 1) %>%
ungroup() %>%
count(group)
#> # A tibble: 2 x 2
#> group n
#> <dbl> <int>
#> 1 1 20 # two groups, each with 10!
#> 2 NA 12