I am having some trouble with joining data frames with dplyr, where I would like to ignore the NAs.
The data that I have is quite big, but a simplified version looks like:
id <- c("id1", "id2", "id3", "id4")
A <- c("E", "F", "G", NA)
B <- c("T", NA, "N", "T")
C <- c(NA, "T", "U", NA)
df <- data.frame(A, B, C)
id A B C
1 id1 E T NA
2 id2 F NA T
3 id3 G N U
4 id4 NA T NA
I have an entry that I would like to match with df, which is e.g.:
df2 <- data.frame(A = "E", B = "T", C = "M")
A B C
1 E T M
As a result I would like to obtain all rows from df that match with df2, but the NAs should be ignored. So the result should look like this:
id A B C
1 id1 E T NA
2 id4 NA T NA
I was trying to do this with semi_join, but it did not work so far:
result <- df %>%
group_by(n = seq(n())) %>%
do(modify_if(., is.na, ~NULL) %>%
semi_join(df2, by = c("A", "B", "C"))) %>%
ungroup %>%
select(-n)
Which results in:
Error: `by` can't contain join column `C` which is missing from LHS
Call `rlang::last_error()` to see a backtrace
Who knows the answer?
Here's a solution with a mix of tidyverse and base R. I think this is pretty clear, but I'd be interested in a pure tidyverse implementation that isn't completely contrived.
The idea is to first expand all entries in df and df2 and then filter through all the columns using a loop.
The data:
id <- c("id1", "id2", "id3", "id4")
A <- c("E", "F", "G", NA)
B <- c("T", NA, "N", "T")
C <- c(NA, "T", "U", NA)
df <- data.frame(id, A, B, C, stringsAsFactors = F) # Make sure to use strings not factors
df2 <- data.frame(A = "E", B = "T", C = "M", stringsAsFactors = F)
Code:
library(tidyr)
results <- crossing(df, df2)
select_columns <- c("A", "B", "C")
for(col in select_columns) {
keep <- is.na(results[[col]]) | results[[col]] == results[[paste0(col, 1)]]
results <- results[keep,, drop=F]
}
results <- results %>% dplyr::select(id, A:C) %>% distinct
results
id A B C
1 id1 E T <NA>
2 id4 <NA> T <NA>