Tidy data.frame with repeated column names
with tidyverse we can do :
library(tidyverse)
toy %>%
repair_names(sep="_") %>%
pivot_longer(-(1:3),names_to = c(".value","id"), names_sep="_") %>%
select(-id)
#> # A tibble: 15 x 7
#> file_path Condition Trial.Num A B C ID
#> <fct> <fct> <int> <int> <int> <int> <fct>
#> 1 root/some.extension Baseline 1 2 3 5 car
#> 2 root/some.extension Baseline 1 2 1 7 bike
#> 3 root/some.extension Baseline 1 4 9 0 plane
#> 4 root/thing.extension Baseline 2 3 6 45 car
#> 5 root/thing.extension Baseline 2 5 4 4 bike
#> 6 root/thing.extension Baseline 2 9 5 4 plane
#> 7 root/else.extension Baseline 3 4 4 6 car
#> 8 root/else.extension Baseline 3 7 5 4 bike
#> 9 root/else.extension Baseline 3 68 7 56 plane
#> 10 root/uniquely.extension Treatment 1 5 3 7 car
#> 11 root/uniquely.extension Treatment 1 1 7 37 bike
#> 12 root/uniquely.extension Treatment 1 9 8 7 plane
#> 13 root/defined.extension Treatment 2 6 7 3 car
#> 14 root/defined.extension Treatment 2 4 6 8 bike
#> 15 root/defined.extension Treatment 2 9 0 8 plane
#> Warning message:
#> Expected 2 pieces. Missing pieces filled with `NA` in 4 rows [1, 2, 3, 4].
You can use the make.unique-function to create unique column names. After that you can use melt from the data.table-package which is able to create multiple value-columns based on patterns in the columnnames:
# make the column names unique
names(toy) <- make.unique(names(toy))
# let the 'Condition' column start with a small letter 'c'
# so it won't be detected by the patterns argument from melt
names(toy)[2] <- tolower(names(toy)[2])
# load the 'data.table' package
library(data.table)
# tidy the data into long format
tidy_toy <- melt(setDT(toy),
measure.vars = patterns('^A','^B','^C','^ID'),
value.name = c('A','B','C','ID'))
which gives:
> tidy_toy
file_path condition Trial.Num variable A B C ID
1: root/some.extension Baseline 1 1 2 3 5 car
2: root/thing.extension Baseline 2 1 3 6 45 car
3: root/else.extension Baseline 3 1 4 4 6 car
4: root/uniquely.extension Treatment 1 1 5 3 7 car
5: root/defined.extension Treatment 2 1 6 7 3 car
6: root/some.extension Baseline 1 2 2 1 7 bike
7: root/thing.extension Baseline 2 2 5 4 4 bike
8: root/else.extension Baseline 3 2 7 5 4 bike
9: root/uniquely.extension Treatment 1 2 1 7 37 bike
10: root/defined.extension Treatment 2 2 4 6 8 bike
11: root/some.extension Baseline 1 3 4 9 0 plane
12: root/thing.extension Baseline 2 3 9 5 4 plane
13: root/else.extension Baseline 3 3 68 7 56 plane
14: root/uniquely.extension Treatment 1 3 9 8 7 plane
15: root/defined.extension Treatment 2 3 9 0 8 plane
Another option is to use a list of column-indexes for measure.vars:
tidy_toy <- melt(setDT(toy),
measure.vars = list(c(4,8,12), c(5,9,13), c(6,10,14), c(7,11,15)),
value.name = c('A','B','C','ID'))
Making the column-names unique isn't necessary then.
A more complicated method that creates names that are better distinguishable by the patterns argument:
# select the names that are not unique
tt <- table(names(toy))
idx <- which(names(toy) %in% names(tt)[tt > 1])
nms <- names(toy)[idx]
# make them unique
names(toy)[idx] <- paste(nms,
rep(seq(length(nms) / length(names(tt)[tt > 1])),
each = length(names(tt)[tt > 1])),
sep = '.')
# your columnnames are now unique:
> names(toy)
[1] "file_path" "Condition" "Trial.Num" "A.1" "B.1" "C.1" "ID.1" "A.2"
[9] "B.2" "C.2" "ID.2" "A.3" "B.3" "C.3" "ID.3"
# tidy the data into long format
tidy_toy <- melt(setDT(toy),
measure.vars = patterns('^A.\\d','^B.\\d','^C.\\d','^ID.\\d'),
value.name = c('A','B','C','ID'))
which will give the same end-result.
As mentioned in the comments, the janitor-package can be helpful for this problem as well. The clean_names() works similar as the make.unique function. See here for an explanation.