👀 Views: 48 💬 Answers: 1 📅 Created: 2025-06-27

r dplyr data-manipulation R

I'm not sure how to approach I've looked through the documentation and I'm still confused about I'm experiencing issues when trying to merge two data frames using `dplyr::full_join()` in R, where the columns I want to join on have different data types. The data frames `df1` and `df2` are structured as follows: ```r # Example data frame 1 df1 <- data.frame(id = c(1, 2, 3), name = c('Alice', 'Bob', 'Charlie'), stringsAsFactors = FALSE) # Example data frame 2 df2 <- data.frame(id = c('1', '2', '4'), score = c(85, 90, 75), stringsAsFactors = FALSE) ``` I'm attempting to join these two data frames on the `id` column, but since `df1$id` is numeric and `df2$id` is character, I'm getting unexpected results. The code I used is: ```r library(dplyr) result <- full_join(df1, df2, by = 'id') ``` When I run this, the output does not include the expected rows from both data frames, and I see NA values where I expect matches. I also tried converting the `id` in `df1` to character using `as.character()` before the join: ```r df1$id <- as.character(df1$id) result <- full_join(df1, df2, by = 'id') ``` However, this still results in some NA values in the `score` column of the resulting data frame. The output I receive is: ```r # A tibble: 4 × 3 id name score <chr> <chr> <dbl> 1 1 Alice NA 2 2 Bob 90 3 3 Charlie NA 4 4 NA 75 ``` It seems like there's still a mismatch even after the conversion. Is there a best practice to ensure that I can merge these data frames correctly when the join columns are of different types? Any help would be greatly appreciated! This is part of a larger REST API I'm building. Any suggestions would be helpful. This is part of a larger REST API I'm building. What's the correct way to implement this?