问题
I have a dataframe that has recurring columns (the interval is 5).
my dataframe at the moment
So this is how it looks: I have 5 type of columns and they repeat time over time. The recurring columns have a suffix in their name, this can be removed/renamed as well, so that they would all match.
What I would like to do is to transpose these recurring columns to rows, so that I would have only 5 columns in the end (Dates, PX_LAST, PX_HIGH, PX_VOLUME, Name). Then I would be able to group the dataframe by Dates, Name etc and do many other things.
I tried some manipulations with pipe operator %>%, but it didn't really work at the moment. Since I don't have any ideas left, I thought, that maybe you could help me out.
Thanks in advance!
回答1:
One option would be to split
the data into a list
of data.frame based on the column names and then rbind
them together
nm1 <- sub("\\.\\d+", "", names(dft))
i1 <- ave(seq_along(dft), nm1, FUN = seq_along)
out <- do.call(rbind, lapply(split.default(dft, i1),
function(x) setNames(x, sub("\\.\\d+", "", names(x)))))
row.names(out) <- NULL
out
# Date Age
#1 1 21
#2 2 15
#3 1 32
#4 2 12
Or another option is to loop through the unique
names, subset the data, unlist
, and convert to data.frame
un1 <- unique(nm1)
setNames(data.frame(lapply(un1,
function(x) unlist(dft[grep(x, names(dft))]))), un1)
data
dft <- data.frame("Date" = 1:2, "Age" = c(21,15), "Date" = 1:2, "Age" = c(32,12))
来源:https://stackoverflow.com/questions/55759687/how-to-reshape-dataframe-and-transpose-recurring-columns-to-dataframe-rows