Moving data from right to left column in a tibble - r

Question

I have a tibble with information about diagnoses:
data <- tibble(
id = c(1:10),
diagnosis_1 = c("F32", "F431", "R58", "S32", "F11", NA, NA, "Y67", "F32", "Z032"),
diagnosis_2 = c(NA, NA, NA, NA, NA, NA, "G35", NA, NA, NA),
diagnosis_3 = c("F40", NA, "R67", "F431", NA, "F60", "S58", "R68", "F11", NA),
diagnosis_4 = c(NA, NA, "F65", NA, "F19", NA, NA, "F32", NA, NA)
)
As a part of the cleaning process, I have removed all diagnoses not fulfilling certain criteria (i.e. not starting with the letter F, G, or Z). With the following code:
data$diagnosis_1[str_sub(data$diagnosis_1, 1,1) %in% c("R", "S", "Y")] <- NA
data$diagnosis_2[str_sub(data$diagnosis_2, 1,1) %in% c("R", "S", "Y")] <- NA
data$diagnosis_3[str_sub(data$diagnosis_3, 1,1) %in% c("R", "S", "Y")] <- NA
data$diagnosis_4[str_sub(data$diagnosis_4, 1,1) %in% c("R", "S", "Y")] <- NA
Ending up with this tibble:
I now need to move the data to the left to fill the columns from left to right (i.e diagnosis_1 not being empty if diagnosis_2, diagnosis_3 or diagnosis_4 has data). I have tried using ifelse() as it is vectorized but I can`t seem to get it to work with several nested ifelse().
ifelse(is.na(data$diagnosis_1), data$diagnosis_2, data$diagnosis_1))
All suggestions are much appreciated.
Edit: adding expected output:

Answer 1

Using dplyr and tidyr. Reshape from wide to long, exclude "^RSY" and NA diagnosis, reshape long to wide.
library(dplyr)
library(tidyr)
gather(data, key = "k", value = "v", -id) %>%
filter(!(grepl("^[R|S|Y]", v) | is.na(v))) %>%
group_by(id) %>%
mutate(diagN = paste0("diagnosis_", row_number())) %>%
select(-k) %>%
spread(key = "diagN", value = "v") %>%
ungroup()
# # A tibble: 10 x 3
# id diagnosis_1 diagnosis_2
# <int> <chr> <chr>
# 1 1 F32 F40
# 2 2 F431 NA
# 3 3 F65 NA
# 4 4 F431 NA
# 5 5 F11 F19
# 6 6 F60 NA
# 7 7 G35 NA
# 8 8 F32 NA
# 9 9 F32 F11
# 10 10 Z032 NA

Answer 2

We first replace values which start with either "R", "S" or "Y" to NA and then left shift the non-NA values.
data[-1] <- lapply(data[-1], function(x) replace(x, grepl("^[R|S|Y]", x), NA))
data[] <- t(apply(data, 1, function(x) `length<-`(na.omit(x), length(x))))
data
# A tibble: 10 x 5
# id diagnosis_1 diagnosis_2 diagnosis_3 diagnosis_4
# <chr> <chr> <chr> <chr> <chr>
# 1 " 1" F32 F40 NA NA
# 2 " 2" F431 NA NA NA
# 3 " 3" F65 NA NA NA
# 4 " 4" F431 NA NA NA
# 5 " 5" F11 F19 NA NA
# 6 " 6" F60 NA NA NA
# 7 " 7" G35 NA NA NA
# 8 " 8" F32 NA NA NA
# 9 " 9" F32 F11 NA NA
#10 10 Z032 NA NA NA
Shifting the non-NA value to left has been taken from David's answer from here. You can try any other approach to shift values from the same question as well.

Answer 3

You can try a tidyverse
library(tidyverse)
data %>%
mutate_at(vars(starts_with("diagnosis")), funs(ifelse(str_sub(., 1, 1) %in% c("R", "S", "Y"), NA, .))) %>%
gather(k,v, -id) %>%
group_by(id) %>%
arrange(id) %>%
mutate(v=ifelse(k == "diagnosis_1", v[!is.na(v)][1], v)) %>%
spread(k, v)
# A tibble: 10 x 5
# Groups: id [10]
id diagnosis_1 diagnosis_2 diagnosis_3 diagnosis_4
<int> <chr> <chr> <chr> <chr>
1 1 F32 NA F40 NA
2 2 F431 NA NA NA
3 3 F65 NA NA F65
4 4 F431 NA F431 NA
5 5 F11 NA NA F19
6 6 F60 NA F60 NA
7 7 G35 G35 NA NA
8 8 F32 NA NA F32
9 9 F32 NA F11 NA
10 10 Z032 NA NA NA
As its unclear what OP wants (see discussion below) you can also try
data %>%
mutate_at(vars(starts_with("diagnosis")), funs(ifelse(str_sub(., 1, 1) %in% c("R", "S", "Y"), NA, .))) %>%
gather(k,v, -id) %>%
group_by(id) %>%
arrange(id) %>%
mutate(v=c(v[!is.na(v)], rep(NA, length(v) - length(v[!is.na(v)])))) %>%
spread(k, v)
# A tibble: 10 x 5
# Groups: id [10]
id diagnosis_1 diagnosis_2 diagnosis_3 diagnosis_4
<int> <chr> <chr> <chr> <chr>
1 1 F32 F40 NA NA
2 2 F431 NA NA NA
3 3 F65 NA NA NA
4 4 F431 NA NA NA
5 5 F11 F19 NA NA
6 6 F60 NA NA NA
7 7 G35 NA NA NA
8 8 F32 NA NA NA
9 9 F32 F11 NA NA
10 10 Z032 NA NA NA

Answer 4

You can use Reduce along with coalesce from dplyr, i.e.
df$diagnosis_1 <- Reduce(dplyr::coalesce, df[-1])
#id diagnosis_1 diagnosis_2 diagnosis_3 diagnosis_4
# <int> <chr> <chr> <chr> <chr>
# 1 1 F32 <NA> F40 <NA>
# 2 2 F431 <NA> <NA> <NA>
# 3 3 F65 <NA> <NA> F65
# 4 4 F431 <NA> F431 <NA>
# 5 5 F11 <NA> <NA> F19
# 6 6 F60 <NA> F60 <NA>
# 7 7 G35 G35 <NA> <NA>
# 8 8 F32 <NA> <NA> F32
# 9 9 F32 <NA> F11 <NA>
#10 10 Z032 <NA> <NA> <NA>

Answer 5

Below solution using function na_move from package dedupewider.
library(dedupewider)
na_move(data) # 'right' direction is by default
#> # A tibble: 10 x 5
#> id diagnosis_1 diagnosis_2 diagnosis_3 diagnosis_4
#> * <chr> <chr> <chr> <lgl> <lgl>
#> 1 1 F32 F40 NA NA
#> 2 2 F431 <NA> NA NA
#> 3 3 F65 <NA> NA NA
#> 4 4 F431 <NA> NA NA
#> 5 5 F11 F19 NA NA
#> 6 6 F60 <NA> NA NA
#> 7 7 G35 <NA> NA NA
#> 8 8 F32 <NA> NA NA
#> 9 9 F32 F11 NA NA
#> 10 10 Z032 <NA> NA NA

Answer 6

A tidyr update, using pivot_longer and unnest_wider.
dplyr 1.0.10 CRAN release: 2022-09-01
tidyr 1.2.1 CRAN release: 2022-09-08
Step 1: clean up data
library(dplyr)
library(tidyr)
data <- data %>%
mutate(across(starts_with("diag"), ~
replace(.x, grepl(paste0("^", c("R", "S", "Y"), collapse="|"), .x), NA)))
Step 2: left-compact data
data %>%
pivot_longer(starts_with("diag")) %>%
group_by(id) %>%
mutate(value = value[order(is.na(value))]) %>%
summarize(col = list(value)) %>%
unnest_wider(col, names_sep="_") %>%
setNames(colnames({{data}}))
# A tibble: 10 × 5
id diagnosis_1 diagnosis_2 diagnosis_3 diagnosis_4
<int> <chr> <chr> <chr> <chr>
1 1 F32 F40 NA NA
2 2 F431 NA NA NA
3 3 F65 NA NA NA
4 4 F431 NA NA NA
5 5 F11 F19 NA NA
6 6 F60 NA NA NA
7 7 G35 NA NA NA
8 8 F32 NA NA NA
9 9 F32 F11 NA NA
10 10 Z032 NA NA NA
data
data <- structure(list(id = 1:10, diagnosis_1 = c("F32", "F431", "R58",
"S32", "F11", NA, NA, "Y67", "F32", "Z032"), diagnosis_2 = c(NA,
NA, NA, NA, NA, NA, "G35", NA, NA, NA), diagnosis_3 = c("F40",
NA, "R67", "F431", NA, "F60", "S58", "R68", "F11", NA), diagnosis_4 = c(NA,
NA, "F65", NA, "F19", NA, NA, "F32", NA, NA)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -10L))