My data is:
df <- data.frame(a = 1:2,
x = 1:2,
b = 1:2,
y = 3:4,
x_2 = 1:2,
y_2 = 3:4,
c = 1:2,
x_3 = 5:6,
y_3 = 1:2)
I now want to put together the x vars, and the y vars so that the order of columns would be:
a, x, x_2, x_3, b, y, y_2, y_3, c
I thought, I could use tidyverse's relocate function in combination with lapply or map or reduce (?), but it doesn't work out.
E.g. if I do:
move_names <- c("x", "y")
library(tidyverse)
moved_data <- lapply(as.list(move_names), function(x)
{
df <- df |>
relocate(!!!syms(paste0(x, "_", 2:3)),
.after = all_of(x))
}
)
It does the moving for x and y separately, but it creates separate list, but I want to have just my original df with relocated columns.
Update:
I should have been clear that my real data frame has ~500 columns where the to-be-moved columns are all over the place. So providing the full vector of desired column name order won't be feasible.
What I instead have: I have the names of my original columns, i.e. x and y, and I have the names of the to-be-moved columns, i.e. x_2, x_3, y_2, y_3.
Ok, this is probably the worst workaround ever and I don't really understand what exactly I'm doing (especially with the <<-), but it is does the trick.
My general idea after realizing the problem a bit more with the help of you guys here was to "loop" through both of my x and y names, remove these new _2 and _3 columns from the vector of column names and re-append them after their "base" x and y columns.
search_names <- c("x", "y")
df_names <- names(df)
new_names <- lapply(search_names, function(x)
{
start <- which(df_names == x)
without_new_names <- setdiff(df_names, paste0(x, "_", 2:3))
df_names <<- append(without_new_names, values = paste0(x, "_", 2:3), after = start)
})[[length(search_names)]]
df |>
relocate(any_of(new_names))
a x x_2 x_3 b y y_2 y_3 c
1 1 1 1 5 1 3 3 1 1
2 2 2 2 6 2 4 4 2 2