How to move data from one column to another in R - r

Question

I am trying to move data from one column to another, due to the underlying forms being filled out incorrectly.
In the form it asks for information on a household and asks for their age(AGE) and gender(SEX) for each member, allowing up to 5 people per household. However some users have filled in information for person 1,3 and 4, but not filled in any info for person 2 because they filled out person 2 incorrectly, crossed out the details and have filled person 2 details into the person 3 boxes etc.
The data looks like this (ref 1 and 5 are correct in this data, all others are incorrect)
df <- data.frame(
ref = c(1, 2, 3, 4, 5, 6),
AGE1 = c(45, 36, 26, 47, 24, NA),
AGE2 = c(NA, 24, NA, 13, 57, 28),
AGE3 = c(NA, NA, 35, NA, NA, 26),
AGE4 = c(NA, NA, 15, 11, NA, NA),
AGE5 = c(NA, 15, NA, NA, NA, NA),
SEX1 = c("M", "F", "M", "M", "M", NA),
SEX2 = c(NA, "M", NA, "F", "F", "F"),
SEX3 = c(NA, NA, "M", NA, NA, "M"),
SEX4 = c(NA, NA, "F", "F", NA, NA),
SEX5 = c(NA, "F", NA, NA, NA, NA)
)
This is what the table looks like currently
(I have replaced NA with - to make reading easier)
ref
AGE1
AGE2
AGE3
AGE4
AGE5
SEX1
SEX2
SEX3
SEX4
SEX5
1
45
-
-
-
-
M
-
-
-
-
2
36
24
-
-
15
F
M
-
-
F
3
26
-
35
15
-
M
-
M
F
-
4
47
13
-
11
-
M
F
-
F
-
5
24
57
-
-
-
M
F
-
-
-
6
-
28
26
-
-
-
F
M
-
-
but i would like it to look like this
ref
AGE1
AGE2
AGE3
AGE4
AGE5
SEX1
SEX2
SEX3
SEX4
SEX5
1
45
-
-
-
-
M
-
-
-
-
2
36
24
15
-
-
F
M
F
-
-
3
26
35
15
-
-
M
M
F
-
-
4
47
13
11
-
-
M
F
F
-
-
5
24
57
-
-
-
M
F
-
-
-
6
28
26
-
-
-
F
M
-
-
-
Is there a way of correcting this using dplyr? If not, is there another way in R of correcting the data

Answer 1

Here is a way using dplyr and tidyr. The approach involves pivoting the data to longer format, sorting the NA values to the end, renumbering the column names, and the pivoting to wide form again.
library(dplyr)
library(tidyr)
df <- data.frame(ref, AGE1, AGE2, AGE3, AGE4, AGE5,
SEX1, SEX2, SEX3, SEX4, SEX5)
df %>%
mutate(across(starts_with("AGE"), as.character)) %>%
pivot_longer(2:11) %>%
separate(name, into = c("cat", "num"), 3) %>%
arrange(is.na(value)) %>%
group_by(ref, cat) %>%
mutate(num = seq_along(value)) %>%
ungroup() %>%
arrange(cat) %>%
unite(name, cat, num, sep = "") %>%
pivot_wider(id_cols = ref) %>%
mutate(across(starts_with("AGE"), as.numeric))
# A tibble: 6 x 11
ref AGE1 AGE2 AGE3 AGE4 AGE5 SEX1 SEX2 SEX3 SEX4 SEX5
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 1 45 NA NA NA NA M NA NA NA NA
2 2 36 24 15 NA NA F M F NA NA
3 3 26 35 15 NA NA M M F NA NA
4 4 47 13 11 NA NA M F F NA NA
5 5 24 57 NA NA NA M F NA NA NA
6 6 28 26 NA NA NA F M NA NA NA

Answer 2

Here's a way using dplyr and tidyr library.
library(dplyr)
library(tidyr)
df %>%
pivot_longer(cols = -ref,
names_to = c('.value', 'num'),
names_pattern = '([A-Z]+)(\\d+)') %>%
arrange(ref, AGE, SEX) %>%
group_by(ref) %>%
mutate(num = row_number()) %>%
ungroup %>%
pivot_wider(names_from = num, values_from = c(AGE, SEX))
# ref AGE_1 AGE_2 AGE_3 AGE_4 AGE_5 SEX_1 SEX_2 SEX_3 SEX_4 SEX_5
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
#1 1 45 NA NA NA NA M NA NA NA NA
#2 2 15 24 36 NA NA F M F NA NA
#3 3 15 26 35 NA NA F M M NA NA
#4 4 11 13 47 NA NA F F M NA NA
#5 5 24 57 NA NA NA M F NA NA NA
#6 6 26 28 NA NA NA M F NA NA NA

Answer 3

Try the base code below
u1 <- reshape(
setNames(df, sub("(\\d)", ".\\1", names(df))),
direction = "long",
idvar = "ref",
varying = -1
)
u2 <- reshape(
transform(
u1[with(u1, order(is.na(AGE), is.na(SEX))), ],
time = ave(time, ref, FUN = seq_along)
),
direction = "wide",
idvar = "ref"
)
out <- u2[match(names(df),sub("\\.","",names(u2)))]
and you will get
> out
ref AGE.1 AGE.2 AGE.3 AGE.4 AGE.5 SEX.1 SEX.2 SEX.3 SEX.4 SEX.5
1.1 1 45 NA NA NA NA M <NA> <NA> <NA> <NA>
2.1 2 36 24 15 NA NA F M F <NA> <NA>
3.1 3 26 35 15 NA NA M M F <NA> <NA>
4.1 4 47 13 11 NA NA M F F <NA> <NA>
5.1 5 24 57 NA NA NA M F <NA> <NA> <NA>
6.2 6 28 26 NA NA NA F M <NA> <NA> <NA>
data
df <- data.frame(
ref = c(1, 2, 3, 4, 5, 6),
AGE1 = c(45, 36, 26, 47, 24, NA),
AGE2 = c(NA, 24, NA, 13, 57, 28),
AGE3 = c(NA, NA, 35, NA, NA, 26),
AGE4 = c(NA, NA, 15, 11, NA, NA),
AGE5 = c(NA, 15, NA, NA, NA, NA),
SEX1 = c("M", "F", "M", "M", "M", NA),
SEX2 = c(NA, "M", NA, "F", "F", "F"),
SEX3 = c(NA, NA, "M", NA, NA, "M"),
SEX4 = c(NA, NA, "F", "F", NA, NA),
SEX5 = c(NA, "F", NA, NA, NA, NA)
)

Answer 4

Here is a solution using package dedupewider:
library(dedupewider)
df <- data.frame(
ref = c(1, 2, 3, 4, 5, 6),
AGE1 = c(45, 36, 26, 47, 24, NA),
AGE2 = c(NA, 24, NA, 13, 57, 28),
AGE3 = c(NA, NA, 35, NA, NA, 26),
AGE4 = c(NA, NA, 15, 11, NA, NA),
AGE5 = c(NA, 15, NA, NA, NA, NA),
SEX1 = c("M", "F", "M", "M", "M", NA),
SEX2 = c(NA, "M", NA, "F", "F", "F"),
SEX3 = c(NA, NA, "M", NA, NA, "M"),
SEX4 = c(NA, NA, "F", "F", NA, NA),
SEX5 = c(NA, "F", NA, NA, NA, NA)
)
age_moved <- na_move(df, cols = names(df)[grepl("^AGE\\d$", names(df))]) # 'right' direction is by default
sex_moved <- na_move(age_moved, cols = names(df)[grepl("^SEX\\d$", names(df))])
sex_moved
#> ref AGE1 AGE2 AGE3 AGE4 AGE5 SEX1 SEX2 SEX3 SEX4 SEX5
#> 1 1 45 NA NA NA NA M <NA> <NA> NA NA
#> 2 2 36 24 15 NA NA F M F NA NA
#> 3 3 26 35 15 NA NA M M F NA NA
#> 4 4 47 13 11 NA NA M F F NA NA
#> 5 5 24 57 NA NA NA M F <NA> NA NA
#> 6 6 28 26 NA NA NA F M <NA> NA NA