Difficulty merging data frames with different row orders in R using dplyr
I'm stuck on something that should probably be simple. I've hit a wall trying to I'm relatively new to this, so bear with me. I'm relatively new to this, so bear with me... I'm having trouble merging two data frames in R using the `dplyr` package. I have two data frames, `df1` and `df2`, which both contain a column called `id`. The scenario arises because the rows in `df2` are not ordered the same way as in `df1`. Here’s a simplified version of what I’m working with: ```r library(dplyr) df1 <- data.frame(id = c(1, 2, 3, 4), value1 = c('A', 'B', 'C', 'D')) df2 <- data.frame(id = c(4, 3, 2, 1), value2 = c('W', 'X', 'Y', 'Z')) ``` I want to perform a left join on these two data frames based on the `id` column. The expected output should be: ``` id | value1 | value2 1 | A | NA 2 | B | Y 3 | C | X 4 | D | W ``` However, when I run the following code: ```r df_merged <- left_join(df1, df2, by = "id") ``` I get the output as: ``` id | value1 | value2 1 | A | NA 2 | B | NA 3 | C | NA 4 | D | NA ``` It seems like the join isn’t working as expected. I’ve checked for typos in the column names and confirmed that the `id` values exist in both data frames, but I still get an unexpected result. Is there something I'm missing about how `left_join` works with unordered data frames? I also tried using `arrange()` to sort both data frames before merging, but it didn’t help resolve the scenario. Any insights would be greatly appreciated! This is part of a larger service I'm building. What am I doing wrong? This is part of a larger CLI tool I'm building. For context: I'm using R on Debian. Has anyone dealt with something similar? Could this be a known issue?