R: Reshaping data as multiple columns into rows - r

Question

I have a df which includes multiple columns, which you could find my templete below. I would like to reshape as columns into rows in R. I am sure it is possible with tidyr::gather() function but I can not manage it.
If someone could help me I would be glad!
Best wishes
# Df I have
A1 A2 A3 A4 B1 B2 B3 B4 C1 C2 C3 C4 D1 D2 D3 D4
X1 X2 X3 X4 a b c d e f g h i j k l
Y1 Y2 Y3 Y4 m n o p
Z1 Z2 Z3 Z4 r s t u w v y z
# Df I would like to reshape
Col1 Col2 Col3 Col4
X1 X2 X3 X4 a b c d
X1 X2 X3 X4 e f g h
X1 X2 X3 X4 i j k l
Y1 Y2 Y3 Y4 m n o p
Z1 Z2 Z3 Z4 r s t u
Z1 Z2 Z3 Z4 w v y z

Answer 1

We could also do this with a single pivot_longer
library(dplyr)
library(tidyr)
library(stringr)
df %>%
pivot_longer(cols = -id, names_to = c("grp", ".value"),
names_sep="(?<=[A-Z])(?=[0-9])", values_drop_na = TRUE) %>%
select(-grp) %>%
rename_at(-1, ~ str_c('Col', .))
# A tibble: 7 x 5
# id Col1 Col2 Col3 Col4
# <int> <chr> <chr> <chr> <chr>
#1 1 a b c d
#2 1 e f g h
#3 1 i j k l
#4 2 m n o p
#5 2 q <NA> <NA> <NA>
#6 3 r s t u
#7 3 w v y z
data
df <- structure(list(id = 1:3, A1 = c("a", "m", "r"), A2 = c("b", "n",
"s"), A3 = c("c", "o", "t"), A4 = c("d", "p", "u"), B1 = c("e",
"q", "w"), B2 = c("f", NA, "v"), B3 = c("g", NA, "y"), B4 = c("h",
NA, "z"), C1 = c("i", NA, NA), C2 = c("j", NA, NA), C3 = c("k",
NA, NA), C4 = c("l", NA, NA), D1 = c(NA, NA, NA), D2 = c(NA,
NA, NA), D3 = c(NA, NA, NA), D4 = c(NA, NA, NA)), class = "data.frame",
row.names = c("1",
"2", "3"))

Answer 2

I bet there are more elegant solutions, but this one uses tidyr and dplyr:
Suppose your data looks like
> df
# A tibble: 3 x 17
id A1 A2 A3 A4 B1 B2 B3 B4 C1 C2 C3 C4 D1 D2 D3 D4
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 1 a b c d e f g h i j k l NA NA NA NA
2 2 m n o p q NA NA NA NA NA NA NA NA NA NA NA
3 3 r s t u w v y z NA NA NA NA NA NA NA NA
I replaced your X1 X2 X3 X4, ... by an indexing column and I added on q in column B1.
Using
df %>%
pivot_longer(cols=matches("\\d$"),
names_to = c("set"),
names_pattern = ".(.)") %>%
pivot_wider(names_from="set",
names_prefix="Col",
values_fn = list) %>%
unnest(matches("\\d$")) %>%
rowwise() %>%
filter(sum(is.na(c_across(matches("\\d$")))) != ncol(.) - 1) # -1 because of the indexing column
returns
# A tibble: 7 x 5
# Rowwise:
id Col1 Col2 Col3 Col4
<dbl> <chr> <chr> <chr> <chr>
1 1 a b c d
2 1 e f g h
3 1 i j k l
4 2 m n o p
5 2 q NA NA NA
6 3 r s t u
7 3 w v y z