Reshaping count-summarised data into long form in R [duplicate] - r

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Repeat each row of data.frame the number of times specified in a column
(10 answers)
Closed 4 years ago.
Embarrassingly basic question, but if you don't know.. I need to reshape a data.frame of count summarised data into what it would've looked like before being summarised. This is essentially the reverse of {plyr} count() e.g.
> (d = data.frame(value=c(1,1,1,2,3,3), cat=c('A','A','A','A','B','B')))
value cat
1 1 A
2 1 A
3 1 A
4 2 A
5 3 B
6 3 B
> (summry = plyr::count(d))
value cat freq
1 1 A 3
2 2 A 1
3 3 B 2
If you start with summry what is the quickest way back to d? Unless I'm mistaken (very possible), {Reshape2} doesn't do this..

Answer 1

Just use rep:
summry[rep(rownames(summry), summry$freq), c("value", "cat")]
# value cat
# 1 1 A
# 1.1 1 A
# 1.2 1 A
# 2 2 A
# 3 3 B
# 3.1 3 B
A variation of this approach can be found in expandRows from my "SOfun" package. If you had that loaded, you would be able to simply do:
expandRows(summry, "freq")

Answer 2

There is a good table to dataframe function on the R cookbook website that you can modify slightly. The only modifications were changing 'Freq' -> 'freq' (to be consistent with plyr::count) and making sure the rownames were reset as increasing integers.
expand.dft <- function(x, na.strings = "NA", as.is = FALSE, dec = ".") {
# Take each row in the source data frame table and replicate it
# using the Freq value
DF <- sapply(1:nrow(x),
function(i) x[rep(i, each = x$freq[i]), ],
simplify = FALSE)
# Take the above list and rbind it to create a single DF
# Also subset the result to eliminate the Freq column
DF <- subset(do.call("rbind", DF), select = -freq)
# Now apply type.convert to the character coerced factor columns
# to facilitate data type selection for each column
for (i in 1:ncol(DF)) {
DF[[i]] <- type.convert(as.character(DF[[i]]),
na.strings = na.strings,
as.is = as.is, dec = dec)
}
row.names(DF) <- seq(nrow(DF))
DF
}
expand.dft(summry)
value cat
1 1 A
2 1 A
3 1 A
4 2 A
5 3 B
6 3 B