Combine and aggregate multiple data.frames - r

Question

I have a collection of .csv files each consisting of the same number of rows and columns. Each file contains observations (column 'value') of some test subjects characterised by A, B, C and takes the form similar to the following:
A B C value
1 1 1 0.5
1 1 2 0.6
1 2 1 0.1
1 2 2 0.2
. . . .
Suppose each file is read into a separate data frame. What would be the most efficient way to combine these data frames into a single data frame in which 'value' column contains means, or generally speaking, results of some function call over all 'value' rows for a given test subject. Columns A, B and C are constant across all files and can be viewed as keys for these observations.
Thank you for your help.

Answer 1

This should be pretty easy, assuming that the files are all ordered in the same way:
dflist <- lapply(dir(pattern='csv'), read.csv)
# row means:
rowMeans(do.call('cbind', lapply(dflist, `[`, 'value')))
# other function `myfun` applied to each row:
apply(do.call('cbind', lapply(dflist, `[`, 'value')), 1, myfun)

Answer 2

Here is another solution in the case where the keys might be in any order, or maybe missing:
n <- 10 # of csv files to create
obs <- 10 # of observations
# create test files
for (i in 1:n){
df <- data.frame(A = sample(1:3, obs, TRUE)
, B = sample(1:3, obs, TRUE)
, C = sample(1:3, obs, TRUE)
, value = runif(obs)
)
write.csv(df, file = tempfile(fileext = '.csv'), row.names = FALSE)
}
# read in the data
input <- lapply(list.files(tempdir(), "*.csv", full.names = TRUE)
, function(file) read.csv(file)
)
# put dataframe together and the compute the mean for each unique combination
# of A, B & C assuming that they could be in any order.
input <- do.call(rbind, input)
result <- lapply(split(input, list(input$A, input$B, input$C), drop = TRUE)
, function(sect){
sect$value[1L] <- mean(sect$value)
sect[1L, ]
}
)
# create output DF
result <- do.call(rbind, result)
result