Iurie
Reputation: 2258
Concatenate data frames horizontally within a loop in R
I use the bellow function for comparisons between group levels with the pairwise.wilcox.test:
pairwise.wilcox <- function(data, x, factor) {
require(reshape2)
pw <- data.frame()
for(i in x){
pw <- pairwise.wilcox.test(data[ ,i], factor, p.adj = "bonf")
#pairwise.wilcox.test output is reformatted
pw <- melt(pw[[3]])
#the first two columns of the reformatted output are merged
pw$Var1 = paste(pw$Var2, pw$Var1, sep=" - ")
#the second column is dropped
drops <- c("Var2")
pw <- pw[,!(names(pw) %in% drops)]
colnames(pw) <- c("Compared groups", "p-value")
print(pw)
}
}
pairwise.wilcox(tabel2, c(2:4), tabel2$lot)
This is an excerpt of the output:
Compared groups p-value
1 1StL - 3StL 1
2 1StL - IP 1
3 1StL - ISR 1
4 1StL - ISU 1
5 1StL - StM 1
6 3StL - 3StL NA
7 3StL - IP 1
...
Compared groups p-value
1 1StL - 3StL 1,0000000
2 1StL - IP 0,1092484
3 1StL - ISR 1,0000000
4 1StL - ISU 1,0000000
5 1StL - StM 1,0000000
6 3StL - 3StL NA
7 3StL - IP 1,0000000
...
Compared groups p-value
1 1StL - 3StL 1
2 1StL - IP 1
3 1StL - ISR 1
4 1StL - ISU 1
5 1StL - StM 1
6 3StL - 3StL NA
7 3StL - IP 1
...
What I want is to concatenate horizontally these data frames by the common first column, so the resulted output to looks like this:
Compared groups p-value p-value p-value
1 1StL - 3StL 1 1,0000000 1
2 1StL - IP 1 0,1092484 1
3 1StL - ISR 1 1,0000000 1
4 1StL - ISU 1 1,0000000 1
5 1StL - StM 1 1,0000000 1
6 3StL - 3StL NA NA NA
7 3StL - IP 1 1,0000000 1
...
How to do this? Keep in mind that I have more variables.
My data set (not all data):
structure(list(lot = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("1StL",
"3StL", "IP", "ISR", "ISU", "StM"), class = "factor"), RS.5_1 = c(2,
3, 3, 2, 2, 2, 2, 3, 1, 1, 2, 3, 3, 2, 2, 1, 3, 2, 3, 2, 3, 1,
3, 1, 3, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 3, 2, 2, 2, 3, 3,
3, 3, 2, 3, 3, 3, 2, 2, 2, 2, 3, 2, 3, 3, 2, 2, 1, 3, 2, 2, 3,
2, 2, 3, 3, 3, 3, 3, 2, 2, 2, 2, 1, 3, 3, 3, 3, 3, 3, 1, 1, 3,
2, 1, 2, 3, 2, 2, 3, 3, 3, 2, 3, 1, 3, 2, 2, 1, 1, 3, 2, 1, 3,
1, 2, 2, 3, 2, 3, 2, 2, 2, 1, 3, 1, 2, 1, 1, 2, 2, 2, 3, 2, 2,
2, 1, 3, 3, 1, 1, 2, 2, 2, 3, 1, 2, 2, 2, 1, 2, 2, 1, 3, 3, 3,
2, 2, 2, 3, 2, 1, 2, 3, 3, 1, 1, 2, 2, 2, 3, 1, 1, 1, 2, 3, 2,
2, 3, 2, 2, 3, 3, 3, 1, 3, 3, 1, 2, 3, 3, 3, 1, 3, 3, 3, 3, 1,
3, 1, 3, 1, 2, 3, 3, 2, 3, 3, 1, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 2, 1, 3, 2, 3, 2, 2, 2, 1, 3,
3, 3, 1, 2, 3, 1, 1, 3, 3, 3, 2, 2, 1, 1, 2, 3, 1, 1, 2, 1, 1,
2, 3, 3, 3, 1, 2, 3, 2, 2, 2, 2, 3, 3, 1, 1, 3, 3, 2, 2, 2, 1,
3, 2, 2, 2, 2, 1, 3, 2, 2, 3, 3, 3, 1, 3, 2, 3, 2, 2, 2, 3, 2,
3, 3, 2, 2, 2, 1, 2, 2, 2, 2, 2, 3, 2, 1, 2, 2, 1, 3, 2, 2, 2,
3, 3, 2, 1, 2, 1, 2, 3, 3, 2, 3, 3, 2, 1, 3, 1, 2, 2, 2, 3, 1,
2, 3, 3, 3, 3, 2, 2, 2, 2, 3, 3, 2, 1, 2, 2, 3, 1, 3, 1, 3, 3,
3, 3, 3, 3, 2, 3, 3, 3, 2, 1, 1, 1, 3, 1, 1, 1, 3, 2, 2, 2, 3,
3, 1, 3, 1, 2, 1, 1, 3, 3, 3, 2, 1, 1, 1, 2, 3, 1, 3, 3, 3, 2,
3, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 3, 3, 2, 2, 2, 1, 1, 1,
3, 3, 3, 3, 3, 2, 3, 3, 1, 1, 3, 2, 2, 2, 3, 1, 3, 2, 3, 3, 3,
2, 2, 3, 1, 2, 3, 3, 2, 3, 2, 3, 3, 3, 2, 2, 1, 3, 2, 3, 3, 2,
3, 3, 3, 2, 1, 3, 3, 1, 1, 3, 1, 3, 3, 3, 3, 1, 2, 3, 2, 3, 2,
3, 1, 3, 2, 2, 2, 2, 2, 1, 2, 1, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2,
1, 2, 3, 3, 3, 3, 2, 2, 3, 1, 2, 3, 2, 1, 3, 2, 3, 3, 3, 3, 3,
3, 3, 3, 3, 1, 3, 1, 3, 3, 3, 3, 3, 2, 2, 1, 3, 3, 3, 3, 2, 3,
3, 3, 2, 2, 3, 2, 1, 3, 1, 1, 2, 2, 3, 3, 3, 3, 2, 2, 3, 2, 2,
2, 2, 2, 1, 3, 1, 2, 3, 1, 3, 3, 2, 2, 3), RS.5_2 = c(2, 2, 3,
3, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 3, 2, 1, 1, 2, 2, 2, 1,
2, 1, 2, 2, 1, 1, 3, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1,
1, 3, 3, 1, 1, 2, 2, 1, 1, 3, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2,
2, 1, 3, 3, 2, 1, 1, 1, 2, 2, 2, 2, 3, 2, 3, 1, 1, 1, 1, 2, 1,
2, 2, 2, 2, 1, 1, 1, 1, 3, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1,
1, 3, 2, 1, 1, 1, 1, 1, 2, 1, 3, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2,
3, 1, 2, 2, 1, 1, 2, 3, 1, 2, 2, 2, 1, 2, 1, 1, 2, 3, 1, 1, 1,
1, 3, 2, 2, 3, 3, 3, 2, 1, 1, 1, 1, 2, 2, 2, 2, 3, 2, 2, 2, 1,
1, 1, 3, 1, 2, 2, 1, 3, 1, 2, 1, 2, 3, 1, 1, 3, 1, 2, 3, 1, 2,
3, 1, 3, 3, 2, 2, 1, 3, 1, 1, 1, 2, 2, 3, 1, 1, 2, 1, 1, 1, 1,
1, 1, 2, 2, 2, 2, 1, 1, 1, 3, 1, 1, 2, 3, 1, 3, 1, 1, 1, 3, 3,
1, 1, 1, 1, 1, 2, 2, 3, 2, 1, 1, 3, 2, 3, 1, 2, 2, 2, 2, 2, 3,
1, 1, 2, 3, 3, 2, 1, 1, 1, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2,
2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1,
1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 3, 2, 1, 1, 1, 2,
2, 2, 1, 2, 1, 1, 1, 2, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 1, 2, 1,
1, 3, 1, 2, 1, 2, 3, 1, 1, 1, 2, 2, 3, 3, 1, 3, 1, 2, 3, 2, 3,
2, 1, 1, 1, 3, 1, 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1,
2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 3, 1, 3, 3, 3, 1, 3, 2,
1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 3, 3, 1, 1, 2, 1, 1, 2, 3, 2,
3, 3, 3, 2, 2, 1, 1, 2, 1, 3, 2, 3, 3, 2, 3, 1, 3, 3, 3, 2, 2,
3, 1, 2, 3, 3, 2, 3, 1, 3, 3, 3, 1, 2, 1, 3, 2, 3, 3, 2, 3, 1,
1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 3, 1, 2, 1, 3, 3, 2, 2, 1, 2, 1,
3, 3, 2, 2, 3, 2, 2, 2, 1, 3, 3, 3, 1, 1, 1, 1, 2, 1, 3, 1, 2,
3, 2, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 3, 2, 2, 3, 3, 3, 1, 1,
3, 3, 1, 3, 1, 2, 3, 2, 1, 1, 1, 1, 2, 3, 3, 3, 2, 1, 1, 2, 1,
2, 1, 1, 3, 1, 2, 2, 2, 1, 1, 3, 1, 3, 2, 1, 1, 1, 1, 1, 1, 3,
1, 2, 1, 1, 1, 3, 1, 1, 1, 1, 2, 1), RS.5_3 = c(1, 3, 3, 3, 1,
1, 3, 2, 3, 2, 1, 3, 1, 3, 3, 3, 3, 1, 1, 1, 2, 1, 2, 1, 3, 1,
2, 1, 3, 2, 1, 1, 3, 1, 2, 2, 1, 2, 3, 3, 1, 1, 2, 3, 3, 2, 3,
3, 1, 2, 1, 2, 3, 1, 3, 1, 2, 3, 3, 2, 1, 3, 1, 2, 1, 2, 3, 2,
2, 3, 2, 1, 1, 1, 3, 2, 1, 3, 3, 1, 3, 1, 2, 2, 3, 3, 2, 2, 3,
2, 2, 2, 2, 1, 2, 3, 3, 2, 2, 1, 2, 3, 1, 1, 3, 1, 1, 1, 1, 3,
2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 1, 1, 2, 1, 1, 2, 2, 2, 3, 1, 2,
2, 2, 1, 3, 1, 2, 1, 2, 2, 1, 3, 3, 2, 3, 3, 3, 1, 1, 3, 3, 1,
1, 2, 2, 3, 1, 1, 2, 1, 2, 3, 2, 2, 2, 2, 3, 2, 1, 2, 2, 2, 2,
3, 1, 3, 3, 3, 3, 2, 1, 1, 2, 3, 1, 3, 3, 3, 2, 2, 2, 2, 3, 2,
3, 1, 3, 2, 2, 3, 1, 2, 1, 1, 3, 1, 3, 1, 2, 2, 2, 1, 1, 1, 1,
3, 3, 1, 1, 3, 2, 3, 2, 1, 3, 3, 3, 2, 1, 3, 3, 3, 3, 3, 3, 1,
1, 1, 1, 3, 3, 3, 2, 2, 1, 3, 3, 3, 3, 2, 3, 3, 2, 3, 2, 2, 1,
3, 3, 3, 3, 1, 1, 1, 3, 3, 1, 2, 1, 1, 3, 1, 3, 2, 1, 3, 2, 2,
3, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 3, 2, 2, 2, 1, 1, 1, 3, 1, 3,
1, 2, 2, 1, 3, 2, 1, 2, 1, 1, 2, 3, 3, 3, 2, 1, 2, 1, 3, 2, 3,
1, 3, 3, 1, 3, 1, 1, 3, 3, 2, 2, 2, 1, 2, 1, 3, 2, 2, 1, 1, 3,
2, 1, 1, 2, 3, 2, 2, 3, 1, 2, 3, 3, 1, 3, 2, 2, 1, 3, 3, 3, 3,
1, 2, 2, 2, 3, 1, 3, 2, 3, 2, 2, 3, 1, 3, 2, 2, 3, 3, 1, 1, 1,
1, 2, 1, 2, 1, 3, 1, 2, 1, 1, 1, 3, 2, 3, 3, 3, 1, 3, 3, 1, 1,
1, 1, 2, 2, 1, 1, 1, 1, 1, 3, 3, 1, 1, 2, 1, 1, 3, 3, 3, 3, 3,
3, 2, 3, 1, 2, 2, 1, 3, 1, 3, 2, 2, 3, 1, 3, 3, 3, 2, 1, 3, 1,
3, 3, 3, 2, 2, 1, 3, 3, 3, 2, 2, 1, 3, 3, 3, 3, 1, 3, 1, 1, 3,
1, 1, 1, 2, 1, 3, 2, 2, 3, 1, 1, 2, 3, 3, 3, 1, 2, 3, 1, 3, 2,
3, 3, 3, 2, 3, 3, 2, 3, 2, 3, 2, 3, 1, 2, 3, 3, 3, 1, 2, 3, 2,
1, 3, 2, 1, 1, 3, 1, 1, 1, 1, 3, 3, 1, 3, 3, 3, 2, 3, 1, 3, 3,
3, 1, 3, 2, 3, 2, 1, 1, 2, 1, 3, 3, 3, 3, 3, 3, 3, 1, 2, 3, 1,
1, 3, 1, 1, 3, 3, 1, 1, 1, 1, 3, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2,
1, 1, 3, 1, 1, 3, 3, 2, 1, 1)), .Names = c("lot", "RS.5_1", "RS.5_2",
"RS.5_3"), row.names = c(NA, -582L), class = "data.frame")
Upvotes: 0
Views: 352
Answers (2)
jlhoward
Reputation: 59425
Not sure how much this adds, but you asked how to implement this in a function:
pw.wcx <- function(data,x,factor){
result <- Reduce(function(x,y)merge(x,y,by="Var1"),
lapply(data[x],function(d){
pw <- pairwise.wilcox.test(d,factor,p.adj="bonf")[[3]]
transform(melt(pw),Var1=paste(Var2,Var1,sep="-"))[-2]}),
accumulate=FALSE)
colnames(result) <- c("Compared Groups",names(data[x]))
result
}
result <- pw.wcx(tabel2,2:4,tabel2$lot)
head(result)
# Compared Groups RS.5_1 RS.5_2 RS.5_3
# 1 1StL-3StL 1 1.0000000 1
# 2 1StL-IP 1 0.1092484 1
# 3 1StL-ISR 1 1.0000000 1
# 4 1StL-ISU 1 1.0000000 1
# 5 1StL-StM 1 1.0000000 1
# 6 3StL-3StL NA NA NA
So this generates a list of data frames using:
lapply(data[x],function(d){
pw <- pairwise.wilcox.test(d,factor,p.adj="bonf")[[3]]
transform(melt(pw),Var1=paste(Var2,Var1,sep="-"))[-2]})
then combines them using Reduce(...) as in the other answer.
EDIT: Response to OP's comments.
pw.wcx <- function(data,x,factor){
result <- Reduce(function(x,y)merge(x,y,by="Var1"),
lapply(x,function(i){
pw <- pairwise.wilcox.test(data[,i],factor,p.adj="bonf")[[3]]
transform(melt(pw,value.name=names(data)[i]),Var1=paste(Var2,Var1,sep="-"))[-2]}),
accumulate=FALSE)
colnames(result)[1] <- "Compared Groups"
result
}
This version will avoid the warnings, but I do not recommend it. This basically uses lapply(...) as a glorified for-loop, which does not take advantage of R's very efficient list processing (e.g., the data frame is subsetted once per iteration using data[,i] instead of just once at the beginning, using data[x].
Upvotes: 2
JasonAizkalns
Reputation: 20483
Your structure statement is not working for me...I think this is the essence of your question?
df1 <- data.frame(group=c('A', 'B', 'C'), result1=c(1, 2, 3))
df2 <- data.frame(group=c('A', 'B', 'C'), result2=c(4, 5, 6))
df3 <- data.frame(group=c('A', 'B', 'C'), result3=c(7, 8, 9))
result.temp <- merge(df1, df2)
result <- merge(result.temp, df3)
result
# group result1 result2 result3
# 1 A 1 4 7
# 2 B 2 5 8
# 3 C 3 6 9
Otherwise, if they are always the same length, why not just cbind?
Per @Roland's advice in the comment:
df.list <- list(df1, df2, df3)
Reduce(function(x, y) merge(x, y, all=T, by=c("group")), df.list, accumulate=F)
Upvotes: 1
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