Lernen Combining and Joining Data | Data Cleaning and Wrangling Essentials

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Definition

Data joining and combining are essential operations in data wrangling. Joining refers to merging datasets based on shared key columns, such as combining customer information with transaction records using a customer ID. Combining often means appending datasets, like stacking survey results from different months or merging additional columns from a related source. You need these techniques when you have information split across multiple tables or files and want to analyze a complete dataset.

When working with multiple datasets, the dplyr package provides powerful join functions to merge data frames by key columns. The main join functions include:

inner_join(): returns only rows with matching keys in both datasets;
left_join(): returns all rows from the left dataset and matches from the right;
right_join(): returns all rows from the right dataset and matches from the left;
full_join(): returns all rows from both datasets, matching where possible.

Each function helps you control how much data is retained depending on your analysis needs.


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# Simulate two data frames
library(dplyr)

students <- data.frame(
  id = c(1, 2, 3, 4),
  name = c("Alice", "Bob", "Carol", "David")
)

scores <- data.frame(
  id = c(2, 3, 4, 5),
  score = c(88, 92, 95, 79)
)

# Perform an inner join to keep only matching ids
result <- inner_join(students, scores, by = "id")
print(result)

Understanding the differences between join types is crucial for accurate data merging. Use inner_join() when you want only records that exist in both datasets. Choose left_join() if you want all records from your main dataset, even if there are no matches in the other. right_join() is similar, but keeps all records from the right-hand dataset. full_join() is the most inclusive, keeping all rows from both datasets and filling in missing values with NA where there is no match. The choice depends on whether you want to preserve all data from one source, only overlapping records, or every possible record.


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# Simulate two data frames to demonstrate binding
df1 <- data.frame(
  id = 1:3,
  value = c("A", "B", "C")
)

df2 <- data.frame(
  id = 4:5,
  value = c("D", "E")
)

# Bind rows: stack data frames vertically
combined_rows <- bind_rows(df1, df2)
print(combined_rows)

# Bind columns: combine side by side
df3 <- data.frame(
  score = c(90, 85, 88, 92, 80)
)
combined_cols <- bind_cols(combined_rows, df3)
print(combined_cols)

You will often join or combine data when merging survey results collected at different times, appending new records to an existing database, or when you want to enrich a dataset with additional variables from another source. These operations ensure your data is complete and ready for analysis, making them fundamental skills for any data wrangler.

1. What is the difference between inner_join() and left_join()?

2. When would you use `bind_rows()` instead of a join?

3. Why is it important to check key columns before joining datasets?

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Abschnitt 1. Kapitel 11

Combining and Joining Data

1. What is the difference between inner_join() and left_join()?

2. When would you use bind_rows() instead of a join?

3. Why is it important to check key columns before joining datasets?

2. When would you use `bind_rows()` instead of a join?