Why dplyr simplifies data wrangling in R Programming - See It in Action

Why dplyr simplifies data wrangling

📖 Scenario: You have a small dataset of sales records for a store. You want to find out which products sold more than 50 units and see their total sales. Doing this manually is slow and confusing. Using dplyr in R makes this easy and clear.

🎯 Goal: Build a simple R script that uses dplyr to filter products with sales over 50 units and then select their names and total sales.

📋 What You'll Learn

Create a data frame called sales_data with product names and units sold

Create a threshold variable called min_units set to 50

Use dplyr functions filter() and select() to get products with units sold greater than min_units and only show product names and units sold

Print the resulting filtered data frame

💡 Why This Matters

🌍 Real World

Data analysts often need to quickly filter and select important parts of large datasets. dplyr makes this easy and readable.

💼 Career

Knowing dplyr is essential for data science and analytics jobs that use R, as it speeds up data cleaning and preparation.

1

Create the sales data frame

Create a data frame called sales_data with these exact entries: product column with values 'Apples', 'Bananas', 'Cherries', 'Dates', and 'Elderberries', and units_sold column with values 30, 60, 45, 80, and 20 respectively.

R Programming

# Create the sales_data data frame with product and units_sold columns
# Your code here

Need a hint?

Use data.frame() with two columns named product and units_sold.

2

Set the minimum units threshold

Create a variable called min_units and set it to 50.

R Programming

sales_data <- data.frame(
  product = c('Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'),
  units_sold = c(30, 60, 45, 80, 20)
)
# Create min_units variable and set it to 50
# Your code here

Need a hint?

Just assign 50 to a variable named min_units.

3

Filter and select with dplyr

Load the dplyr library. Then create a new data frame called filtered_sales by filtering sales_data to keep only rows where units_sold is greater than min_units. Use select() to keep only the product and units_sold columns.

R Programming

sales_data <- data.frame(
  product = c('Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'),
  units_sold = c(30, 60, 45, 80, 20)
)
min_units <- 50

# Load dplyr and create filtered_sales with filter() and select()
# Your code here

Need a hint?

Use library(dplyr) to load the package. Then use the pipe %>% to chain filter() and select().

4

Print the filtered sales data

Print the filtered_sales data frame to show the products with units sold greater than min_units.

R Programming

library(dplyr)
sales_data %
  filter(units_sold > min_units) %>%
  select(product, units_sold)

# Print filtered_sales
# Your code here

Need a hint?

Use print(filtered_sales) to show the filtered data.