I have two dataframes, one of which contains a subset of IDs and columns of the other (but has different values).
ds1 <- data.frame(id = c(1:4),
d1 = "A",
d2 = "B",
d3 = "C")
ds2 <- data.frame(id = c(1,2),
d1 = "W",
d2 = "X")
I am hoping to use dplyr on d1 to find the shared columns, and replace their values with those found in d2, matching on ID. I can mutate them one at a time like this:
ds1 %>%
mutate(d1 = ifelse(id %in% ds2$id, ds2$d1[ds2$id==id],d1),
d2 = ifelse(id %in% ds2$id, ds2$d2[ds2$id==id],d2))
In my real situation, I am needing to do this 47 times, however. With the robustness of across()
, I feel there is a better way. I am open to non-dplyr solutions as well.
using rows_update
library(tidyverse)
ds1 <- data.frame(id = c(1:4),
d1 = "A",
d2 = "B",
d3 = "C")
ds2 <- data.frame(id = c(1,2),
d1 = "W",
d2 = "X")
rows_update(x = ds1, y = ds2, by = "id")
#> id d1 d2 d3
#> 1 1 W X C
#> 2 2 W X C
#> 3 3 A B C
#> 4 4 A B C
Created on 2021-05-11 by the reprex package (v2.0.0)