👀 Views: 64 💬 Answers: 1 📅 Created: 2025-06-14

dplyr dataframe merge R

I'm collaborating on a project where I've searched everywhere and can't find a clear answer. I'm working with an scenario when trying to merge two data frames using the `dplyr` package and the `left_join` function. The question arises because one of the key columns is of type `character` in one data frame and `factor` in the other, leading to unexpected results. Here's a simplified example: ```r library(dplyr) df1 <- data.frame(id = c('1', '2', '3'), value = c(10, 20, 30)) df2 <- data.frame(id = factor(c('1', '2', '4')), description = c('A', 'B', 'C')) result <- left_join(df1, df2, by = 'id') print(result) ``` This should ideally give me a merged data frame showing the rows from `df1` along with the matching description from `df2`. However, the output seems incorrect: ``` id value description 1 1 10 A 2 2 20 B 3 3 30 <NA> ``` I expected to see a `NA` in the description for the id '3', but is there a way to ensure that the join is performed correctly when the data types are different? I've tried using `mutate` to convert the `factor` to `character` beforehand, like this: ```r df2 <- df2 %>% mutate(id = as.character(id)) result <- left_join(df1, df2, by = 'id') print(result) ``` This worked, but I'm wondering if there's a best practice or a more efficient way to handle such situations without needing to convert data types manually. Any advice or solutions would be greatly appreciated! For context: I'm using R on Debian. I'd love to hear your thoughts on this. I'm developing on Ubuntu 20.04 with R. How would you solve this?