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tidyr >

If you are learning R, one of the most useful tools you can learn is how to handle and manipulate data. The dplyr package makes this process easy and enjoyable. This write-up will guide you through the basics of data manipulation using dplyr, step by step.

What is dplyr?

dplyr is an R package designed for working with data. It helps you select, filter, arrange, and summarize your data in a simple way. Think of it as a toolbox for cleaning and organizing data to make it ready for analysis.

Why Use dplyr?

  1. Simple and Clear: The functions in dplyr are easy to understand.
  2. Fast: It works quickly even with large datasets.
  3. Chaining Commands: You can perform multiple steps in one line of code using the pipe operator (%>%).

Installing and Loading dplyr

Before you can use dplyr, you need to install it. Open R and type:

install.packages("dplyr")

Once installed, load the package by typing:

library(dplyr)

Basic Functions in dplyr

Here are some key functions in dplyr and what they do:

1. select()

Use this function to pick specific columns from your data.

Example:

data <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30), Score = c(90, 85))
select(data, Name, Score)

This will return only the “Name” and “Score” columns.

2. filter()

Use this to select rows based on conditions.

Example:

filter(data, Age > 25)

This will return rows where Age is greater than 25.

3. mutate()

This adds new columns or changes existing ones.

Example:

mutate(data, Score_Doubled = Score * 2)

This will add a new column called “Score_Doubled”.

4. arrange()

Use this to sort rows in ascending or descending order.

Example:

arrange(data, Age)

This will sort the data by Age in ascending order.

5. summarize()

This creates a summary of your data, such as the average, maximum, or minimum of a column.

Example:

summarize(data, Average_Score = mean(Score))

This will return the average of the “Score” column.

6. group_by()

This groups your data by a specific column, often used with summarize().

Example:

data <- data.frame(Name = c("Alice", "Bob", "Alice"), Score = c(90, 85, 95))
grouped_data <- group_by(data, Name)
summarize(grouped_data, Average_Score = mean(Score))

This will calculate the average score for each name.

The Pipe Operator %>%

The %>% operator allows you to chain commands together. Instead of writing many separate lines of code, you can combine them into one.

Example:

result <- data %>%
  filter(Age > 25) %>%
  mutate(Score_Doubled = Score * 2) %>%
  arrange(Score_Doubled)
print(result)

Here, the data is filtered, a new column is added, and the rows are sorted—all in one step.

Example Workflow

Let’s work through a complete example. Imagine we have this dataset:

students <- data.frame(
  Name = c("Alice", "Bob", "Charlie", "Diana"),
  Age = c(25, 22, 23, 24),
  Score = c(90, 85, 88, 92)
)

Now, let’s:

  1. Select only the “Name” and “Score” columns.
  2. Filter rows where “Score” is greater than 85.
  3. Arrange the rows by “Score” in descending order.

Code:

result <- students %>%
  select(Name, Score) %>%
  filter(Score > 85) %>%
  arrange(desc(Score))
print(result)
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