Intro to dplyr

Lesson 6 of 14 in Coddy's Data Manipulation in R course.

The dplyr package is a powerful tool for data manipulation in R. It provides a set of functions that make it easy to transform and analyze data frames efficiently. In this lesson, we'll focus on three key functions: select(), filter(), and mutate().

Loading dplyr

Here is an example of how to load the package:

# Load dplyr
library(dplyr)

`select()` Function

The select() function allows you to choose specific columns from a data frame:

# Create a sample data frame
df <- data.frame(
  name = c("Alice", "Bob", "Charlie"),
  age = c(25, 30, 35),
  city = c("New York", "London", "Paris")
)

# Select specific columns
result <- select(df, name, age)
print(result)

Output:

  name    age
1 Alice   25
2 Bob     30
3 Charlie 35

`filter()` Function

The filter() function helps you subset rows based on specific conditions:

# Filter rows where age is greater than 28
result <- filter(df, age > 28)
print(result)

Output:

  name     age city
1 Bob      30  London
2 Charlie  35  Paris

`mutate()` Function

The mutate() function allows you to create new columns or modify existing ones:

# Add a new column 'age_group'
result <- mutate(df, age_group = ifelse(age < 30, "Young", "Adult"))
print(result)

Output:

    name age    city age_group
1  Alice  25 New York     Young
2    Bob  30   London     Adult
3 Charlie  35    Paris     Adult

Pipe Operator `%>%`

dplyr introduces the pipe operator %>%, which allows you to chain multiple operations:

result <- df %>%
  filter(age > 28) %>%
  select(name, city) %>%
  mutate(location = paste(name, "lives in", city))

Test yourself

This lesson includes a short quiz. Start the lesson to answer it and track your progress.

Test yourself

This lesson includes a short quiz. Start the lesson to answer it and track your progress.

Test yourself

This lesson includes a short quiz. Start the lesson to answer it and track your progress.

Challenge

Easy

Process a data frame of employee information using dplyr functions. Perform the following operations:

Select only the columns "name", "age", and "salary"
Filter the data to include only employees who are 30 years or older
Add a new column called "bonus" that is 10% of the salary for employees aged 40 or above, and 5% for others
Arrange the data frame by salary in descending order
Print the resulting data frame.

Try it yourself

# Load required library
suppressPackageStartupMessages(library(dplyr))

# Read input
con <- file("stdin", "r")
input_data <- suppressWarnings(readLines(con))

# Create data frame from input
df <- read.csv(text = input_data, stringsAsFactors = FALSE)

# TODO: Write your code below to process the data frame
process_employee_data <- function(df) {
  # Your code here
  # Use dplyr functions to:
  # 1. Select columns
  # 2. Filter data
  # 3. Add bonus column
  # 4. Arrange by salary
  
  return(df)  # Replace this with your processed data frame
}

# Process the data frame
result <- process_employee_data(df)

# Print the result
print(result)