scenarios in using `dplyr::mutate` to create new columns based on conditions involving multiple data frames
I need some guidance on I recently switched to I'm migrating some code and I'm trying to configure I'm stuck on something that should probably be simple..... I'm having trouble creating new columns in a data frame using conditions that involve values from another data frame. I want to add a column in `df1` that checks if a value in `df1$col1` exists in `df2$colA` and assigns a corresponding value from `df2$colB`. I'm using R 4.3.1 and the `dplyr` package. Here's the code I've tried: ```r library(dplyr) # Sample data frames df1 <- data.frame(col1 = c('A', 'B', 'C', 'D')) df2 <- data.frame(colA = c('B', 'C', 'E'), colB = c(1, 2, 3)) # Attempting to create a new column based on conditions result <- df1 %>% mutate(new_col = if_else(col1 %in% df2$colA, df2$colB[match(col1, df2$colA)], NA_integer_)) ``` However, I'm working with the behavior: `behavior in if_else(.data$col1 %in% df2$colA, ...) : argument "true" is missing, with no default.` I understand that this behavior may be happening because the lengths of the vectors in the `if_else` function are mismatched, but I'm not sure how to fix it. I tried using `left_join` instead: ```r result <- df1 %>% left_join(df2, by = c("col1" = "colA")) %>% mutate(new_col = coalesce(colB, NA_integer_)) ``` This does create the new column, but it doesn't handle cases where `df1$col1` has values not present in `df2$colA` correctly, as I still get NA in those cases. Can someone guide to understand how to achieve this correctly? Thanks in advance! Any examples would be super helpful. I'm on Linux using the latest version of R. Am I approaching this the right way? Has anyone dealt with something similar? For context: I'm using R on Debian.