Introduction to Combining and Reshaping Data

Data often comes from multiple sources and needs to be merged or reshaped for analysis. In this lesson, you will learn how to combine and restructure data in R.

Combining Data Frames

You can combine data frames using functions like rbind() (row-wise) and cbind() (column-wise).

df1 <- data.frame(ID = c(1, 2), Score = c(90, 85))
df2 <- data.frame(ID = c(3, 4), Score = c(78, 92))
df_combined <- rbind(df1, df2)

Merging Data Frames

To merge two data frames by a common column, use merge().

df1 <- data.frame(ID = c(1, 2, 3), Name = c('Alice', 'Bob', 'Charlie'))
df2 <- data.frame(ID = c(1, 2, 3), Score = c(90, 85, 88))
merged_df <- merge(df1, df2, by = 'ID')

Using dplyr for Joins

The dplyr package provides functions like left_join() and inner_join() for merging data.

library(dplyr)
joined_df <- left_join(df1, df2, by = 'ID')

Pivoting Data with tidyr

Reshaping data is useful for summarizing and analyzing different formats.

Widening Data (pivot_wider)

Use pivot_wider() to convert long format data to wide format.

library(tidyr)
data_wide <- pivot_wider(df, names_from = Category, values_from = Value)

Longer Data (pivot_longer)

Use pivot_longer() to convert wide format data to long format.

data_long <- pivot_longer(df, cols = c('Jan', 'Feb'), names_to = 'Month', values_to = 'Sales')

Sorting Data

You can sort data frames using the arrange() function from dplyr.

df_sorted <- arrange(df, desc(Score))

Summary

In this lesson, you learned:

• How to combine and merge data frames.
• How to reshape data using pivoting functions.
• How to sort and structure data efficiently.