How to join (merge) data frames (inner, outer, left, right) in R?
Joining (or merging) data frames in R is a crucial task for data wrangling and analysis. Whether you need an inner, outer, left, or right join, R provides multiple methods to unify data from different sources, enabling you to get the most out of your datasets.
2. Base R merge() Function
2.1 Inner Join
- Usage:
merge(df1, df2, by = "common_column") - Result: Returns only matching rows from both data frames, discarding unmatched rows.
2.2 Outer Join (Full)
- Usage:
merge(df1, df2, by = "common_column", all = TRUE) - Result: Returns all rows from both data frames, matching data where possible. Unmatched rows get
NA.
2.3 Left Join
- Usage:
merge(df1, df2, by = "common_column", all.x = TRUE) - Result: Returns all rows from
df1and only matching rows fromdf2. Unmatched rows indf2appear asNA.
2.4 Right Join
- Usage:
merge(df1, df2, by = "common_column", all.y = TRUE) - Result: Returns all rows from
df2and only matching rows fromdf1. Unmatched rows indf1appear asNA.
3. Using dplyr Joins
After loading dplyr (library(dplyr)), you can do:
- Inner Join:
df1 %>% inner_join(df2, by = "common_column") - Outer (Full) Join:
df1 %>% full_join(df2, by = "common_column") - Left Join:
df1 %>% left_join(df2, by = "common_column") - Right Join:
df1 %>% right_join(df2, by = "common_column")
This syntax often feels more readable, especially when chaining multiple data transformations.
4. Next Steps and Helpful Resources
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