I have a data.frame that looks like this.
df <-
tibble::tribble(
~name, ~age, ~birthday, ~address, ~favorite_fruit,
'josh', '26', '1998-04-14', '1 main st', 'banana',
'jason', '2000-09-01', '2 front st', 'apple', NA,
'nate', '1992-dec-25', '3 oak st', 'blueberry', NA
)
df
#> # A tibble: 3 x 5
#> name age birthday address favorite_fruit
#> <chr> <chr> <chr> <chr> <chr>
#> 1 josh 26 1998-04-14 1 main st banana
#> 2 jason 2000-09-01 2 front st apple <NA>
#> 3 nate 1992-dec-25 3 oak st blueberry <NA>
As you might notice, age data was not collected for all people (ignoring that I could calculate it from birthday). Therefore, all values in some rows after the name column are incorrectly shifted to the left by one column. My ideal output would be NAs in the age column and the correct values in all other columns. How can I achieve this? If possible, a tidyverse solution is preferred.
You could use shift_row_values() from the hacksaw package:
library(hacksaw)
library(dplyr)
bind_cols(select(df, name),
select(df, -name) |>
shift_row_values(.dir = "right", at = is.na(df$favorite_fruit)))
# name age birthday address favorite_fruit
# <chr> <chr> <chr> <chr> <chr>
# 1 josh 26 1998-04-14 1 main st banana
# 2 jason NA 2000-09-01 2 front st apple
# 3 nate NA 1992-dec-25 3 oak st blueberry