Reshaping untidy and unbalanced dataset from wide to long [duplicate] - r

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This question already has an answer here:
How do I convert a wide dataframe to a long dataframe for a multilevel structure with 'quadruple nesting'?
(1 answer)
Closed 6 years ago.
I have a dataset (data) that looks like this:
ID,ABC.BC,ABC.PL,DEF.BC,DEF.M,GHI.PL
SB0005,C01,D20,C01a,C01b,D20
BC0013,C05,D5,C05a,NA,D5
I want to reshape it from wide-to-long format to get something like this:
ID,FC,Type,Var
SB0005,ABC,BC,C01
SB0005,ABC,PL,D20
SB0005,DEF,BC,C01a
SB0005,DEF,M,C01b
SB0005,GHI,PL,D20
BC0013,ABC,BC,C05
BC0013,ABC,PL,D5
BC0013,DEF,BC,C05a
# BC0013,DEF,M,NA (This row need not be in the dataset as I will remove it later)
BC0013,GHI,PL,D5
The usual reshape package does not work as the dataset is unbalanced. I also tried Reshape from splitstackshape but it does not give me what I want.
library(splitstackshape)
vary <- grep("\\.BC$|\\.PL$|\\.M$", names(data))
stubs <- unique(sub("\\..*$", "", names(data[vary])))
Reshape(data, id.vars=c("ID"), var.stubs=stubs, sep=".")
ID,time,ABC,DEF,GHI
SB0005,1,C01,C01a,D20
BC0013,1,C05,C05a,D5
SB0005,2,D20,C01b,NA
BC0013,2,D5,NA,NA
SB0005,3,NA,NA,NA
BC0013,3,NA,NA,NA
Appreciate any suggestions, thanks!
Providing the output of dput(data) as requested
structure(list(ID = structure(c(2L, 1L), .Label = c("BC0013",
"SB0005"), class = "factor"), ABC.BC = structure(1:2, .Label = c("C01",
"C05"), class = "factor"), ABC.PL = structure(1:2, .Label = c("D20",
"D5"), class = "factor"), DEF.BC = structure(1:2, .Label = c("C01a",
"C05a"), class = "factor"), DEF.M = structure(1:2, .Label = c("C01b",
"NA"), class = "factor"), GHI.PL = structure(1:2, .Label = c("D20",
"D5"), class = "factor")), .Names = c("ID", "ABC.BC", "ABC.PL",
"DEF.BC", "DEF.M", "GHI.PL"), row.names = c(NA, -2L), class = "data.frame")

Answer 1

You need to reshape your data into long format first and then you can spit the variable column into to columns. With splitstackshape you could do:
library(splitstackshape) # this will also load 'data.table' from which the 'melt' function is used
cSplit(melt(mydf, id.vars = 'ID'),
'variable',
sep = '.',
direction = 'wide')[!is.na(value)]
which results in:
ID value variable_1 variable_2
1: SB0005 C01 ABC BC
2: BC0013 C05 ABC BC
3: SB0005 D20 ABC PL
4: BC0013 D5 ABC PL
5: SB0005 C01a DEF BC
6: BC0013 C05a DEF BC
7: SB0005 C01b DEF M
8: SB0005 D20 GHI PL
9: BC0013 D5 GHI PL
An alternative with tidyr:
library(tidyr)
mydf %>%
gather(var, val, -ID) %>%
separate(var, c('FC','Type')) %>%
filter(!is.na(val))