Selecting rows maintaining distribution percentage? - r

Question

I have an existing data frame with a variable "grade" indicating the type of row/observation. My goal is to select from another dataframe more of these types of rows while not exceeding a maximum percentage for each grade type in my existing data frame. I have defined a named vector with the grade allocations:
gradeAllocation <- c("A" = 0, "B" = 0, "C" = .25, "D" = .40, "E" = .20, "F" = .10, "G" = .05)
This represents the maximum percent of each type of grade in my data frame. Now, lets say I want to select from another data frame a mixture of grades but I dont want to select too many where after the selection would give me more than the max percentage per grade type. I would be basically doing this process in a loop for each new data set that becomes available but want to keep the max distribution given by the gradeAllocation vector.
Is there a package/function that can help here? Any thoughts for custom code?
Thanks, John

Answer 1

So as #Mr.Flick points out, there is no guarantee that this will be possible. In your gradeAllocation the sampling distribution sums to 1. If your test dataset has no "D", for example, it will not be possible to create a sample with at most 25% C, 15% E, 10% F, 5% G, and no A or B.
Also, because the sampling distribution sums to 1, if the sample size you want is N, then the number of samples of each grade must be given by N * gradeAllocation. Here is a method that takes advantage of that fact, starting with a dataset that has 700 samples and is uniformly distributed (same number in each grade), and we extract a random sample of 100 with the distribution given by gradeAllocation.
# sample dataset: 700 observations, grade distribution is uniform
set.seed(1) # for reproducible example
data <- data.frame(grade=rep(LETTERS[1:7],each=100),x=rnorm(700))
# desired distribution in the sample
gradeAllocation <- c(A=0, B=0, C=.25, D=.40, E=.20, F=.10, G=.05)
# you start here...
N <- 100 # sample size
get.sample<- function(g) data[sample(which(data$grade==g),N*gradeAllocation[g]),]
result <- do.call(rbind,lapply(LETTERS[1:7],get.sample))
# confirm distribution of grades in the sample
table(result$grade)
# A B C D E F G
# 0 0 25 40 20 10 5

Answer 2

Here's one approach
Generate some data
nOriginal <- 1000
df1 <- data.frame(grade=sample(c('A','B','C','D','E','F','G'),1000,replace=TRUE),
indx=seq(1:nOriginal))
Get the rows that correspond to each grade
idx_a=which(df1$grade=='A')
idx_b=which(df1$grade=='B')
idx_c=which(df1$grade=='C')
idx_d=which(df1$grade=='D')
idx_e=which(df1$grade=='E')
idx_f=which(df1$grade=='F')
idx_g=which(df1$grade=='G')
Sample the rows based on the prescribed distribution which should sum to one.
location <- c("A" = 0, "B" = 0, "C" = .25, "D" = .40, "E" = .20, "F" = .10, "G" = .05)
nSamples = 200
samp_idx_a <- sample(idx_a,nSamples*location["A"])
samp_idx_b <- sample(idx_b,nSamples*location["B"])
samp_idx_c <- sample(idx_c,nSamples*location["C"])
samp_idx_d <- sample(idx_d,nSamples*location["D"])
samp_idx_e <- sample(idx_e,nSamples*location["E"])
samp_idx_f <- sample(idx_f,nSamples*location["F"])
samp_idx_g <- sample(idx_g,nSamples*location["G"])
df_2 <- df1[c(samp_idx_a, samp_idx_b, samp_idx_c, samp_idx_d,
samp_idx_e, samp_idx_f, samp_idx_g),]
Check the results
(percent_A = sum(df_2$grade=="A")/nrow(df_2)*100)
(percent_B = sum(df_2$grade=="B")/nrow(df_2)*100)
(percent_C = sum(df_2$grade=="C")/nrow(df_2)*100)
(percent_D = sum(df_2$grade=="D")/nrow(df_2)*100)
(percent_E = sum(df_2$grade=="E")/nrow(df_2)*100)
(percent_F = sum(df_2$grade=="F")/nrow(df_2)*100)
(percent_G = sum(df_2$grade=="G")/nrow(df_2)*100)