Condensing a data frame in R to one row

I have a list of data frames, and the data frames in the list look something like this where the columns x_, y_, and t_ all have the same values in each role and the only thing differnt are the var1, var2, and var3 values:

Here is the code for an example of the data frame above:

df <- data.frame(x_ = c(1,1,1),
                 y_ = c(1,1,1),
                 t_ = c(1,1,1),
                 var1 = c(5, NA, NA),
                 var2 = c(NA, 9, NA),
                 var3 = c(NA, NA, 20))

I would like to get the data frames to look something like this, where I can condense the data into a single row:

Is there a good way to do this?

>Solution :

One potential option is to fill the NAs then remove duplicate lines, e.g.

library(tidyverse)
library(vctrs)

df <- data.frame(x_ = c(1,1,1),
                 y_ = c(1,1,1),
                 t_ = c(1,1,1),
                 var1 = c(5, NA, NA),
                 var2 = c(NA, 9, NA),
                 var3 = c(NA, NA, 20))

df2 <- df %>%
  mutate(across(everything(),
                ~vec_fill_missing(.x, direction = "downup")))
df2
#>   x_ y_ t_ var1 var2 var3
#> 1  1  1  1    5    9   20
#> 2  1  1  1    5    9   20
#> 3  1  1  1    5    9   20

df2 %>%
  distinct()
#>   x_ y_ t_ var1 var2 var3
#> 1  1  1  1    5    9   20

If you have NAs for every line, this will have NA in the final distinct row:

df3 <- data.frame(x_ = c(1,1,1),
                 y_ = c(1,1,1),
                 t_ = c(1,1,1),
                 var1 = c(5, NA, NA),
                 var2 = c(NA, 9, NA),
                 var3 = c(NA, NA, NA))

df4 <- df3 %>%
  mutate(across(everything(),
                ~vec_fill_missing(.x, direction = "downup")))
df4
#>   x_ y_ t_ var1 var2 var3
#> 1  1  1  1    5    9   NA
#> 2  1  1  1    5    9   NA
#> 3  1  1  1    5    9   NA

df4 %>%
  distinct()
#>   x_ y_ t_ var1 var2 var3
#> 1  1  1  1    5    9   NA

^{Created on 2023-03-17 with reprex v2.0.2}