Issue with combining multiple data frames using dplyr's bind_rows resulting in unexpected column types in R 4.3
I'm sure I'm missing something obvious here, but After trying multiple solutions online, I still can't figure this out. I've been trying to combine several data frames with different structures using `dplyr::bind_rows`, but I'm facing some unexpected behavior. Specifically, I'm seeing that columns that I expect to be numeric are being coerced to character types in the resulting data frame, which is causing issues down the line when I try to perform calculations. Here’s a simplified version of what I’m doing: ```r library(dplyr) # Data frames with different structures df1 <- data.frame(id = 1:3, value = c(10, 20, 30)) df2 <- data.frame(id = 4:5, value = c("40", "50")) df3 <- data.frame(id = 6, value = c(60)) # Attempting to combine them combined_df <- bind_rows(df1, df2, df3) ``` However, when I check the structure of `combined_df` using `str(combined_df)`, I see that the `value` column is of type `character` instead of `numeric`: ``` 'data.frame': 5 obs. of 2 variables: $ id : int 1 2 3 4 5 6 $ value : chr "10" "20" "30" "40" "50" "60" ``` I've tried coercing the `value` column back to numeric after combining, but I keep running into warnings about NAs being introduced: ```r combined_df$value <- as.numeric(combined_df$value) # Warning message: # NAs introduced by coercion ``` I suspect this issue stems from `df2` where the `value` column is stored as character. Is there a best practice or a way to ensure that the columns maintain their original types when using `bind_rows`? Also, would using `mutate` before combining be a recommended approach in this case? Any insights would be greatly appreciated! This is part of a larger web app I'm building. Is there a better approach?