Row-wise expansion of data.frame [duplicate] - r

Question

This question already has answers here:
De-aggregate / reverse-summarise / expand a dataset in R [duplicate]
(4 answers)
Closed 6 years ago.
I basically want do the opposite of ddply(df, columns.to.preserve, numcolwise(FUNCTION).
Suppose I have
d <- data.frame(
count=c(2,1,3),
summed.value=c(50,20,30),
averaged.value=c(35,80,20)
)
count summed.value averaged.value
1 2 50 35
2 1 20 80
3 3 30 20
I want to do a row expansion of this data.frame based on the count column while specifying what kind of operation I want to apply to the other columns.
Here is the kind of result I'm looking for:
> d2
count summed.value averaged.value
1 1 25 35
2 1 25 35
3 1 20 80
4 1 10 20
5 1 10 20
6 1 10 20
Any there built in functions within dplyr or other packages that does this kind of operation?
Edit: This is different from the De-aggregate / reverse-summarise / expand a dataset in R question because I want to go further and actually apply different functions to columns within the table I wish to expand. There are also more useful and answers on this post.

Answer 1

Use dplyr and tidyr, you can do a rowwise transformation for the summed.value which produces a list for each cell and then unnest the column should give you what you need:
library(dplyr); library(tidyr)
d %>% rowwise() %>% summarise(summed.value = list(rep(summed.value/count, count)),
averaged.value = averaged.value, count = 1) %>% unnest()
# Source: local data frame [6 x 3]
# averaged.value count summed.value
# <dbl> <dbl> <dbl>
# 1 35 1 25
# 2 35 1 25
# 3 80 1 20
# 4 20 1 10
# 5 20 1 10
# 6 20 1 10
Another way is to use data.table, where you can specify the row number as group variable, and the data table will automatically expand it:
library(data.table)
setDT(d)
d[, .(summed.value = rep(summed.value/count, count), averaged.value, count = 1), .(1:nrow(d))]
[, nrow := NULL][]
# summed.value averaged.value count
#1: 25 35 1
#2: 25 35 1
#3: 20 80 1
#4: 10 20 1
#5: 10 20 1
#6: 10 20 1

Answer 2

There is a function untable in package reshape for getting the inverse of a table. Then divide the variables that need dividing by count via mutate_at (or mutate_each). mutate_at was introduced in dplyr_0.5.0.
First the untable:
library(reshape)
untable(d, num = d$count)
count summed.value averaged.value
1 2 50 35
1.1 2 50 35
2 1 20 80
3 3 30 20
3.1 3 30 20
3.2 3 30 20
Then the mutate_at for dividing summed.value and count by count:
library(dplyr)
untable(d, num = d$count) %>%
mutate_at(vars(summed.value, count), funs(./count))
count summed.value averaged.value
1 1 25 35
2 1 25 35
3 1 20 80
4 1 10 20
5 1 10 20
6 1 10 20

Answer 3

Here's a both simple and fully vecotrized base R approach
transform(d[rep(1:nrow(d), d$count), ],
count = 1,
summed.value = summed.value/count)
# count summed.value averaged.value
# 1 1 25 35
# 1.1 1 25 35
# 2 1 20 80
# 3 1 10 20
# 3.1 1 10 20
# 3.2 1 10 20
Or similarly, using data.table
library(data.table)
res <- setDT(d)[rep(1:.N, count)][, `:=`(count = 1, summed.value = summed.value / count)]
res
# count summed.value averaged.value
# 1: 1 25 35
# 2: 1 25 35
# 3: 1 20 80
# 4: 1 10 20
# 5: 1 10 20
# 6: 1 10 20

Answer 4

A base R solution: It tries to replicate each row by the value of the count column and then divide count and summed.value columns by count.
mytext <- 'count,summed.value,averaged.value
2,50,35
1,20,80
3,30,20'
mydf <- read.table(text=mytext,header=T,sep = ",")
mydf <- do.call(rbind,apply(mydf, 1, function(x) {
tempdf <- t(replicate(x[1],x,simplify = T))
tempdf[,1] <- tempdf[,1]/x[1]
tempdf[,2] <- tempdf[,2]/x[1]
return(data.frame(tempdf))
}))
count summed.value averaged.value
1 25 35
1 25 35
1 20 80
1 10 20
1 10 20
1 10 20