Convert data frame from wide to long with 2 variables - r

Question

I have the following wide data frame (mydf.wide):
DAY JAN F1 FEB F2 MAR F3 APR F4 MAY F5 JUN F6 JUL F7 AUG F8 SEP F9 OCT F10 NOV F11 DEC F12
1 169 0 296 0 1095 0 599 0 1361 0 1746 0 2411 0 2516 0 1614 0 908 0 488 0 209 0
2 193 0 554 0 1085 0 1820 0 1723 0 2787 0 2548 0 1402 0 1633 0 897 0 411 0 250 0
3 246 0 533 0 1111 0 1817 0 2238 0 2747 0 1575 0 1912 0 705 0 813 0 156 0 164 0
4 222 0 547 0 1125 0 1789 0 2181 0 2309 0 1569 0 1798 0 1463 0 878 0 241 0 230 0
I want to produce the following "semi-long":
DAY variable_month value_month value_F
1 JAN 169 0
I tried:
library(reshape2)
mydf.long <- melt(mydf.wide, id.vars=c("YEAR","DAY"), measure.vars=c("JAN","FEB","MAR","APR","MAY","JUN","JUL","AUG","SEP","OCT","NOV","DEC"))
but this skip the F variable and I don't know how to deal with two variables...

Answer 1

This is one of those cases where reshape(...) in base R is a better option.
months <- c(2,4,6,8,10,12,14,16,18,20,22,24) # column numbers of months
F <- c(3,5,7,9,11,13,15,17,19,21,23,25) # column numbers of Fn
mydf.long <- reshape(mydf.wide,idvar=1,
times=colnames(mydf.wide)[months],
varying=list(months,F),
v.names=c("value_month","value_F"),
direction="long")
colnames(mydf.long)[2] <- "variable_month"
head(mydf.long)
# DAY variable_month value_month value_F
# 1.JAN 1 JAN 169 0
# 2.JAN 2 JAN 193 0
# 3.JAN 3 JAN 246 0
# 4.JAN 4 JAN 222 0
# 1.FEB 1 FEB 296 0
# 2.FEB 2 FEB 554 0
You can also do this with 2 calls to melt(...)
library(reshape2)
months <- c(2,4,6,8,10,12,14,16,18,20,22,24) # column numbers of months
F <- c(3,5,7,9,11,13,15,17,19,21,23,25) # column numbers of Fn
z.1 <- melt(mydf.wide,id=1,measure=months,
variable.name="variable_month",value.name="value_month")
z.2 <- melt(mydf.wide,id=1,measure=F,value.name="value_F")
mydf.long <- cbind(z.1,value_F=z.2$value_F)
head(mydf.long)
# DAY variable_month value_month z.2$value_F
# 1 1 JAN 169 0
# 2 2 JAN 193 0
# 3 3 JAN 246 0
# 4 4 JAN 222 0
# 5 1 FEB 296 0
# 6 2 FEB 554 0

Answer 2

melt() and dcast() are available from the reshape2 and data.table packages. The recent versions of data.table allow to melt multiple columns simultaneously. The patterns() parameter can be used to specify the two sets of columns by regular expressions:
library(data.table) # CRAN version 1.10.4 used
regex_month <- toupper(paste(month.abb, collapse = "|"))
mydf.long <- melt(setDT(mydf.wide), measure.vars = patterns(regex_month, "F\\d"),
value.name = c("MONTH", "F"))
# rename factor levels
mydf.long[, variable := forcats::lvls_revalue(variable, toupper(month.abb))][]
DAY variable MONTH F
1: 1 JAN 169 0
2: 2 JAN 193 0
3: 3 JAN 246 0
4: 4 JAN 222 0
5: 1 FEB 296 0
...
44: 4 NOV 241 0
45: 1 DEC 209 0
46: 2 DEC 250 0
47: 3 DEC 164 0
48: 4 DEC 230 0
DAY variable MONTH F
Note that "F\\d" is used as regular expression in patterns(). A simple "F" would have catched FEB as well as F1, F2, etc. producing unexpected results.
Also note that mydf.wide needs to be coerced to a data.table object. Otherwise, reshape2::melt() will be dispatched on a data.frame object which doesn't recognize patterns().
Data
library(data.table)
mydf.wide <- fread(
"DAY JAN F1 FEB F2 MAR F3 APR F4 MAY F5 JUN F6 JUL F7 AUG F8 SEP F9 OCT F10 NOV F11 DEC F12
1 169 0 296 0 1095 0 599 0 1361 0 1746 0 2411 0 2516 0 1614 0 908 0 488 0 209 0
2 193 0 554 0 1085 0 1820 0 1723 0 2787 0 2548 0 1402 0 1633 0 897 0 411 0 250 0
3 246 0 533 0 1111 0 1817 0 2238 0 2747 0 1575 0 1912 0 705 0 813 0 156 0 164 0
4 222 0 547 0 1125 0 1789 0 2181 0 2309 0 1569 0 1798 0 1463 0 878 0 241 0 230 0",
data.table = FALSE)