scenarios when merging multiple data frames on multiple keys using dplyr - unexpected results
I keep running into I'm having trouble merging multiple data frames using `dplyr::left_join()` in R, and I need to seem to get the expected results. I have three data frames: `df1`, `df2`, and `df3`. I want to merge them by two keys, `id` and `date`, but I'm getting unexpected duplicates in the final result. Here's the setup: ```R library(dplyr) # Sample data frames df1 <- data.frame(id = c(1, 1, 2), date = as.Date(c('2023-01-01', '2023-01-01', '2023-01-02')), value1 = c(10, 20, 30)) df2 <- data.frame(id = c(1, 2), date = as.Date(c('2023-01-01', '2023-01-02')), value2 = c(100, 200)) df3 <- data.frame(id = c(1, 1, 2), date = as.Date(c('2023-01-01', '2023-01-01', '2023-01-02')), value3 = c(1000, 2000, 3000)) ``` I first merged `df1` and `df2` like this: ```R merged1 <- left_join(df1, df2, by = c('id', 'date')) ``` Then, I attempted to merge `merged1` with `df3`: ```R final_result <- left_join(merged1, df3, by = c('id', 'date')) ``` However, when I look at `final_result`, I see multiple rows for the id `1` on the date `2023-01-01`, which results in a Cartesian product effect. The output is: ```R id date value1 value2 value3 1 1 2023-01-01 10 100 1000 2 1 2023-01-01 10 100 2000 3 1 2023-01-01 20 100 1000 4 1 2023-01-01 20 100 2000 5 2 2023-01-02 30 200 3000 ``` I was expecting to have a single entry for each combination, but instead, I have multiple rows for the combinations of `df1` and `df3`. I tried using `distinct()` after merging, but it doesn't seem to solve the underlying scenario. Can anyone guide to understand why this is happening and how I can fix it? I'm using `dplyr` version 1.0.10. I'm on CentOS using the latest version of R. I'm open to any suggestions. I'm using R LTS in this project.