CODEWITHSUNDEEP
rsampleresampling
User981636
User981636

Reputation: 3621

Oversampling with sample function

I would like to create a mtcars dataset where all cylinders have 100 observations. For that, I would sample with replacement the existing observations.

I have tried the following code that, for some reason, does not produce 300 observations. 

library(data.table)
mtcars <- data.table(mtcars)
resampling <- list()
set.seed(3)

cyl <- sort(unique(as.character(mtcars$cyl)))
for (i in 1:length(cyl)){

  min_obs_cyl <- 100
  dat_cyl <- mtcars[cyl == as.numeric(cyl[i]) ]
  resampling[[  cyl[i]  ]] <- dat_cyl[sample(1:nrow(dat_cyl),
                                             size = (min_obs_cyl - nrow(mtcars[cyl == cyl[i] ])),
                                                 replace = T),]
}

resampling_df <- do.call("rbind", resampling)
mtcars_oversample <- rbind(mtcars, resampling_df)

I get 307 observations. Anyone knows what I am doing wrong?

Upvotes: 1

Views: 520

Answers (3)

thelatemail
thelatemail

Reputation: 93813

I think in this case, you can do the the sampling within groups using data.table's by= functionality. sample from the .I row counter within each cyl group, and then use this row identifier to sub-select the rows from the original set:

mtcars[mtcars[, sample(.I, 100, replace=TRUE), by=cyl]$V1,]
#      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#  1: 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#  2: 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#  3: 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#  4: 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#  5: 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
# ---                                                    
#296: 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#297: 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#298: 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#299: 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#300: 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3

If you need to specify different counts for each group, here's one option. The special .BY object stores the value of the by= argument as a list.

grpcnt <- setNames(c(50,100,70), unique(mtcars$cyl))
#  6   4   8 
# 50 100  70 
mtcars[mtcars[, sample(.I, grpcnt[as.character(.BY[[1]])], replace=TRUE), by=cyl]$V1]

Upvotes: 2

Shree
Shree

Reputation: 11140

Here's another way using dplyr::slice

mtcars %>%
  group_by(cyl) %>%
  slice(sample(n(), 100, replace = T)) %>%
  ungroup()

Upvotes: 3

Marius
Marius

Reputation: 60070

For an alternative solution, you can use dplyr and do:

library(dplyr)

mtcars %>%
    group_by(cyl) %>%
    do(sampled = sample_n(., size = 100, replace = TRUE)) %>%
    select(-cyl) %>%
    unnest()

Upvotes: 4

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