Argthtjtr

I have a dataframe with multiple columns containing, inter alia, words and their position in sentences. For some positions, there's more rows than for other positions. Here's a mock example:

df <- data.frame(

  word = sample(LETTERS, 100, replace = T),

  position = sample(1:5, 100, replace = T)

)

head(df)

  word position

1    K        1

2    R        5

3    J        2

4    Y        5

5    Z        5

6    U        4

Obviously, the tranches of 'position' are differently sized:

table(df$position)

 1  2  3  4  5 

15 15 17 28 25

To make the different tranches more easily comparable I'd like to draw equally sized samples on the variable 'position' within one dataframe. This can theoretically be done in steps, such as these:

df_pos1 <- df[df$position==1,]

df_pos1_sample <- df_pos1[sample(1:nrow(df_pos1), 3),]



df_pos2 <- df[df$position==2,]

df_pos2_sample <- df_pos2[sample(1:nrow(df_pos2), 3),]



df_pos3 <- df[df$position==3,]

df_pos3_sample <- df_pos3[sample(1:nrow(df_pos3), 3),]



df_pos4 <- df[df$position==4,]

df_pos4_sample <- df_pos4[sample(1:nrow(df_pos4), 3),]



df_pos5 <- df[df$position==5,]

df_pos5_sample <- df_pos5[sample(1:nrow(df_pos5), 3),]

and so on, to finally combine the individual samples in a single dataframe:

df_samples <- rbind(df_pos1_sample, df_pos2_sample, df_pos3_sample, df_pos4_sample, df_pos5_sample)

but this procedure is cumbersome and error-prone. A more economical solution might be a for loop. I've tried this code so far, which, however, returns, not a combination of the individual samples for each position value but a single sample drawn from all values for 'position':

df_samples <-c()

for(i in unique(df$position)){

   df_samples <- rbind(df[sample(1:nrow(df[df$position==i,]), 3),])

}

df_samples

   word position

13    D        2

2     R        5

12    G        3

4     Y        5

16    Z        3

11    S        3

6     U        4

14    J        3

9     O        5

1     K        1

What's wrong with this code and how can it be improved?

Consider by to split data frame by position with needed sampling. Then rbind all dfs together outside the loop with do.call().

df_list <- by(df, df$position, function(sub) sub[sample(1:nrow(sub), 3),])



final_df <- do.call(rbind, df_list)

Currently you index the entire (not subsetted) data frame in each iteration. Also, you are using rbind inside a for loop which is memory-intensive and not advised.

Specifically,

• 
  by is the object-oriented wrapper to tapply and essentially splits a data frame into subsets by factor(s) and passes each subset into a defined function. Here sub is just the name of subsetted variable (can be named anything). The result here is a list of data frames.


• 
  do.call essentially runs a compact version of an expanded call across multiple elements where rbind(df1, df2, df3) is equivalent to do.call(rbind, list(df1, df2, df3)). The key here to note is rbind is not called inside a loop (avoiding the danger of growing complex objects like a data frame inside an iteration) but once outside the loop.

Each time you run the loop you are overwriting the last entry. Try:

df_samples <- data.frame()

df_samples <- rbind(df_samples, df[sample(1:nrow(df[df$position==i,]), 3),])

We can use data.table with a group by sample of the row index .I and use that to subset the dataset. This would be very efficient

i1 <- setDT(df)[, sample(.I, 3), position]$V1

df[i1]

Or use sample_n from tidyverse

library(tidyverse)

df %>% 

   group_by(position) %>% 

   sample_n(3)

Or as a function

f1 <- function(data) {

     data as.data.table(data)

     i1 <- data[, sample(.I, 3), by = position]$V1

     data[i1]

    }