Lære Merging and Joining Data Frames | Core R Data Structures for EDA

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Definition

Merging and joining refer to combining two or more data frames based on one or more common columns, known as keys. Merging is the general process of bringing datasets together, while joining describes specific methods for aligning rows according to key values. These operations enable you to create richer, more informative tables by integrating related information from separate sources, which is essential for comprehensive exploratory data analysis.

When you work with real-world data, information is often spread across multiple tables. To analyze such data effectively, you need to bring these tables together in a meaningful way. Joins are specialized forms of merging that connect rows from two data frames based on shared key columns. The most common types of joins are inner join, left join, and right join.

Inner join: Includes only the rows with matching key values in both data frames; unmatched rows are excluded;
Left join: Includes all rows from the left (first) data frame and adds matching rows from the right (second) data frame; if there is no match, missing values are filled in for the right-side columns;
Right join: Includes all rows from the right data frame and adds matching rows from the left data frame; if there is no match, missing values are filled in for the left-side columns.

Each type of join serves a different purpose. Use an inner join when you want only the records that exist in both tables. Choose a left join if you want to keep all data from your primary table and supplement it with information from another. Use a right join when your focus is on retaining all records from the secondary table, regardless of whether they match in the primary table.


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# Create two sample data frames
df1 <- data.frame(
  id = c(1, 2, 3, 4),
  name = c("Alice", "Bob", "Carol", "David")
)

df2 <- data.frame(
  id = c(2, 3, 4, 5),
  score = c(85, 90, 95, 80)
)

# Inner join: only ids present in both df1 and df2
inner_merged <- merge(df1, df2, by = "id", all = FALSE)
print("Inner join result:")
print(inner_merged)

# Left join: all rows from df1, matching rows from df2
left_merged <- merge(df1, df2, by = "id", all.x = TRUE)
print("Left join result:")
print(left_merged)

# Right join: all rows from df2, matching rows from df1
right_merged <- merge(df1, df2, by = "id", all.y = TRUE)
print("Right join result:")
print(right_merged)

When joining data frames, you may encounter duplicate keys—situations where the key column contains repeated values in one or both tables. In such cases, the join operation produces all possible combinations of matching rows, which can significantly increase the number of rows in the result. Additionally, missing values can appear in the output if a row in one data frame does not have a corresponding match in the other. Handling these issues often requires careful inspection of your keys, cleaning up duplicates, or deciding how to treat missing data after the join. Being aware of these challenges helps you maintain data integrity and ensures your merged data frames are suitable for analysis.

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