I have the following dataset, of which a subset is:
structure(list(Sex = c("Male", "Male", "Female", "Male", "Male", "Male"
), AgeGroup = structure(c(2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Young",
"Old"), class = "factor"), FPT_Values_LEFT = c(0.615962446,
NA, 0.466983199, 0.607728601, 0.534221292, 0.577650726), FPT_Values_RIGHT = c(0.534368277,
NA, 0.456424206, 0.589604795, 0.564736903, 0.484459132)), row.names = c(NA,
6L), class = "data.frame")
So:
Sex AgeGroup FPT_Values_LEFT FPT_Values_RIGHT
1 Male Old 0.6159624 0.5343683
2 Male Old NA NA
3 Female Old 0.4669832 0.4564242
4 Male Old 0.6077286 0.5896048
5 Male Old 0.5342213 0.5647369
6 Male Old 0.5776507 0.4844591
I would like to run an ANOVA of AgeGroup*SIDE*value but need to get the dataset to include a side column that corresponds to either FPT_Values_LEFT or FPT_Value_RIGHT.
I have used varitions of melt but its not accurately lining up by side with the values.
DF$SIDE <- c("Left", "Right")
reshape::melt(id="SIDE")
or
reshape::melt(id=c("SIDE", "AgeGroup"))
reshape::melt(id=c("SIDE", "AgeGroup"), measure.vars = c("FPT_Value_LEFT", "FPT_Value_RIGHT"))
I know I can transpose and append the data to move the right values under left, and copy the sex and age columns to match, then add a side column. I am not sure if there is a more efficient method though.
Expected output:
Sex AgeGroup FPT_Values SIDE
1 Male Old 0.6159624 Left
2 Male Old NA Left
3 Female Old 0.4669832 Left
4 Male Old 0.6077286 Left
5 Male Old 0.5342213 Left
6 Male Old 0.5776507 Left
1 Male Old 0.5343683 Right
2 Male Old NA Right
3 Female Old 0.4564242 Right
4 Male Old 0.5896048 Right
5 Male Old 0.5647369 Right
6 Male Old 0.4844591 Right
Using data.table you can do:
> library(data.table)
> melt(as.data.table(DF), id = c("Sex", "AgeGroup"))[, variable := sub("FPT_Values_", "", variable)][]
Sex AgeGroup variable value
1: Male Old LEFT 0.6159624
2: Male Old LEFT NA
3: Female Old LEFT 0.4669832
4: Male Old LEFT 0.6077286
5: Male Old LEFT 0.5342213
6: Male Old LEFT 0.5776507
7: Male Old RIGHT 0.5343683
8: Male Old RIGHT NA
9: Female Old RIGHT 0.4564242
10: Male Old RIGHT 0.5896048
11: Male Old RIGHT 0.5647369
12: Male Old RIGHT 0.4844591
If you want to continue using melt from "reshape2" rather than using data.table, you can achieve the same by doing:
transform(reshape2::melt(DF, c("Sex", "AgeGroup")), variable = sub("FPT_Values_", "", variable))
However, I would recommend updating to data.table or converting your code to using tidyr.