reshape wide to long with character suffixes instead of numeric suffixes
r, reshape
Solution
This works (to specify to varying what columns go with who):
reshape(dadmom, direction="long", varying=list(c(2, 4), c(3, 5)),
sep="", v.names=c("name", "inc"), timevar="dadmom",
times=c("d", "m"))
So you actually have nested repeated measures here; both name and inc for mom and dad. Because you have more than one series of repeated measures you have to supply a `list` to varying that tells `reshape` which group gets stacked on the other group.
So the two approaches to this problem are to provide a list as I did or to rename the columns the way the R beast likes them as you did.
See my recent blogs on base `reshape` for more on this (particularly the second link deals with this):
reshape (part I)
reshape (part II)
Problem
Inspired by a comment from @gsk3 on a question about reshaping data, I started doing a little bit of experimentation with reshaping data where the variable names have character suffixes instead of numeric suffixes. As an example, I'll load the `dadmomw` dataset from one of the UCLA ATS Stata learning webpages (see "Example 4" on the webpage). Here's what the dataset looks like: ``` library(foreign) dadmom <- read.dta("https://stats.idre.ucla.edu/stat/stata/modules/dadmomw.dat") dadmom # famid named incd namem incm # 1 1 Bill 30000 Bess 15000 # 2 2 Art 22000 Amy 18000 # 3 3 Paul 25000 Pat 50000 ``` When trying to reshape from this wide format to long, I run into a problem. Here's what I do to reshape the data. ``` reshape(dadmom, direction="long", idvar=1, varying=2:5, sep="", v.names=c("name", "inc"), timevar="dadmom", times=c("d", "m")) # famid dadmom name inc # 1.d 1 d 30000 Bill # 2.d 2 d 22000 Art # 3.d 3 d 25000 Paul # 1.m 1 m 15000 Bess # 2.m 2 m 18000 Amy # 3.m 3 m 50000 Pat ``` Note the swapped column names for "name" and "inc"; changing `v.names` to `c("inc", "name")` doesn't solve the problem. `reshape` seems very picky about wanting the columns to be named in a fairly standard way. For example, I can reshape the data correctly (and easily) if I first rename the columns: ``` dadmom2 <- dadmom # Just so we can continue experimenting with the original data # Change the names of the last four variables to include a "." names(dadmom2)[2:5] <- gsub("(d$|m$)", "\\.\\1", names(dadmom2)[2:5]) reshape(dadmom2, direction="long", idvar=1, varying=2:5, timevar="dadmom") # famid dadmom name inc # 1.d 1 d Bill 30000 # 2.d 2 d Art 22000 # 3.d 3 d Paul 25000 # 1.m 1 m Bess 15000 # 2.m 2 m Amy 18000 # 3.m 3 m Pat 50000 ``` My questions are: - Why is R swapping the columns in the example I've provided? - Can I get to this result with base R `reshape` without changing the variable names before reshaping? - Are there other approaches that could be considered instead of `reshape`?