R: Transforming (n*P) * N data frame into n * (N*P) - r

Question

I'm using R and I have a data frame called df which has (n*P) rows and N columns.
C1 C2 ... CN-1 CN
1-1 100 36 ... 136 76
1-2 120 -33 ... 87 42
1-3 150 14 ... 164 24
:
1-n 20 36 ... 136 76
2-1 109 26 ... 166 87
2-2 -33 87 ... 42 24
2-3 100 36 ... 136 76
:
2-n 100 36 ... 136 76
:
P-1 150 14 ... 164 24
P-2 100 36 ... 765 76
P-3 150 14 ... 164 94
:
P-n 10 26 ... 106 76
And I want to transform this data frame into a data frame with n rows and (N*P) columns. The new data frame, df.new, should look like
C1-1 C2-1 ... CN-1-1 CN-1 C1-2 C2-2 ... CN-1-2 CN-2 ... C1-P C2-P ... CN-1-P CN-P
R1 100 36 ... 136 76 20 36 ... 136 76 ... 150 14 ... 164 24
R2 120 -33 ... 87 42 109 26 ... 166 87 ... 100 36 ... 765 76
:
:
Rn 20 36 ... 136 76 100 36 ... 136 76 ... 10 26 ... 106 76
That is to say, the first N columns of df.new are rbind of rows 1-1, 2-1, 3-1, ... , P-1 of df. The next N columns of df.new are rbind of rows 1-2, 2-2, 3-2, ... , P-2 of df. It follows till the last N columns of df.new which will be composed of rows rows 1-n, 2-n, 3-n, ... , P-n of df. (R1 of df.new is cbind of rows 1-1, 1-2,...,1-n. R2 of df.new is cbind of rows 2-1, 2-2,...,2-n. Rn of df.new is cbind of rows P-1, P-2,...,P-n.)
n, P and N are variables so the value of them depend on the case. I tried to create df.new using for loops but doesn't work well.
Here is my try which I kind of gave up.
for (j in 1:n) {
df.new <- data.frame(matrix(vector(), 1, dim(df)[2],
dimnames = list(c(), colnames(df))),
stringsAsFactors=F)
for (i in 1:nrow(df)) {
if (i %% n == 0) {
df.new <- rbind(df.new, df[i,])
} else if (i %% n == j) {
df.new <- rbind(df.new, df[i,])
}
}
assign(paste0("df.new", j), df.new)
}

Answer 1

library(dplyr)
library(tidyr)
library(tibble)
df %>%
rownames_to_column("rowname") %>%
separate(rowname, c("rowname_prefix", "rowname_suffix"), "-") %>%
gather(col_name, value, -rowname_prefix, -rowname_suffix) %>%
mutate(col_name = paste(col_name, rowname_prefix, sep="-")) %>%
select(-rowname_prefix) %>%
spread(col_name, value) %>%
mutate(rowname_suffix = paste0("R", rowname_suffix)) %>%
column_to_rownames("rowname_suffix")
Output is:
C1-1 C1-2 C1-3 C2-1 C2-2 C2-3 C3-1 C3-2 C3-3 C4-1 C4-2 C4-3
R1 100 109 150 36 26 14 136 166 164 76 87 24
R2 120 -33 100 -33 87 36 87 42 765 42 24 76
R3 150 100 150 14 36 14 164 136 164 24 76 94
R4 20 100 10 36 36 26 136 136 106 76 76 76
Sample data:
df <- structure(list(C1 = c(100L, 120L, 150L, 20L, 109L, -33L, 100L,
100L, 150L, 100L, 150L, 10L), C2 = c(36L, -33L, 14L, 36L, 26L,
87L, 36L, 36L, 14L, 36L, 14L, 26L), C3 = c(136L, 87L, 164L, 136L,
166L, 42L, 136L, 136L, 164L, 765L, 164L, 106L), C4 = c(76L, 42L,
24L, 76L, 87L, 24L, 76L, 76L, 24L, 76L, 94L, 76L)), .Names = c("C1",
"C2", "C3", "C4"), class = "data.frame", row.names = c("1-1",
"1-2", "1-3", "1-4", "2-1", "2-2", "2-3", "2-4", "3-1", "3-2",
"3-3", "3-4"))
# C1 C2 C3 C4
#1-1 100 36 136 76
#1-2 120 -33 87 42
#1-3 150 14 164 24
#1-4 20 36 136 76
#2-1 109 26 166 87
#2-2 -33 87 42 24
#2-3 100 36 136 76
#2-4 100 36 136 76
#3-1 150 14 164 24
#3-2 100 36 765 76
#3-3 150 14 164 94
#3-4 10 26 106 76