Change multiple values in a dataframe based on two other values - r

Question

If anyone mind lending some knowledge... What I am trying to do is make a new dataframe based on the below data frame values.
id value
ant 10
cat 4
cat 6
dog 5
dog 3
dog 2
fly 9
What I want to do next is, in sequential order I want to make a dataframe that looks like the following.
Every time we see a new id, we create a column. The max value is 10 so there should be 10 rows.
Our first word is ant and so therefore for every row of ant, I would like a 0.
Our next column is cat. We have 2 values and what I would like to do is for the first value we see, the first 4 rows must be 0 which is followed by 6 rows of 1.
Same logic for dog, with first five rows as 0 and next three rows as 1 and last 2 as 0.
Fly has only 9 rows of 0 and the last row should contain NA.
It should look like this
ant cat dog fly
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 1 0 0
0 1 1 0
0 1 1 0
0 1 1 0
0 1 0 0
0 1 0 NA
I know how to do this the long way by
newdf <- data.frame(matrix(2, ncol = length(unique(df[,"id"])) , nrow = 10))
newdf$X1[1:10] <- 0
newdf$X2[1:4] <- 0
newdf$X2[5:10] <- 1
...
However, is there any way to do this more efficiently? Note that my actual data will have roughly 50 rows so that's why I am looking for a more efficient way to complete this!

Answer 1

Here's a tidyverse answer -
library(dplyr)
library(tidyr)
df %>%
group_by(id) %>%
mutate(val = rep(c(0, 1), length.out = n())) %>%
uncount(value) %>%
mutate(row = row_number()) %>%
complete(row = 1:10) %>%
pivot_wider(names_from = id, values_from = val) %>%
select(-row)
# ant cat dog fly
# <dbl> <dbl> <dbl> <dbl>
# 1 0 0 0 0
# 2 0 0 0 0
# 3 0 0 0 0
# 4 0 0 0 0
# 5 0 1 0 0
# 6 0 1 1 0
# 7 0 1 1 0
# 8 0 1 1 0
# 9 0 1 0 0
#10 0 1 0 NA
For each id we assign an alternate 0, 1 value and use uncount to repeat the rows based on the count. Get the data in wide format so that we have a separate column for each id.
data
df <- structure(list(id = c("ant", "cat", "cat", "dog", "dog", "dog",
"fly"), value = c(10, 4, 6, 5, 3, 2, 9)), row.names = c(NA, -7L
), class = "data.frame")

Answer 2

You can try the following base R code
maxlen <- with(df, max(tapply(value, id, sum)))
list2DF(
lapply(
with(df, split(value, id)),
function(x) {
`length<-`(
rep(rep(c(0, 1), length.out = length(x)), x),
maxlen
)
}
)
)
which gives
ant cat dog fly
1 0 0 0 0
2 0 0 0 0
3 0 0 0 0
4 0 0 0 0
5 0 1 0 0
6 0 1 1 0
7 0 1 1 0
8 0 1 1 0
9 0 1 0 0
10 0 1 0 NA