Welcome back! You've already built a strong foundation with dplyr by learning how to select, rename, filter, slice, mutate, and relocate data. Now, let’s take those skills a step further. In this lesson, we will learn about chaining multiple dplyr functions for powerful and efficient data manipulation. We will also introduce the arrange function to help you sort your data for better insights.
In this lesson, you will learn how to chain multiple dplyr functions together to perform complex data manipulations in a concise and readable manner. Here is a glimpse of what you will achieve:
R1suppressPackageStartupMessages(library(dplyr)) 2 3# Sample dataframe 4sample_df <- tibble::tibble( 5 ID = 1:5, 6 Name = c("John", "Jane", "Alex", "Emily", "David"), 7 Age = c(28, 22, 35, 29, 40), 8 Salary = c(50000, 60000, 70000, 80000, 90000) 9) 10 11# Chaining multiple dplyr functions 12final_df <- sample_df %>% 13 mutate(Bonus = Salary * 0.1) %>% 14 filter(Age > 25) %>% 15 select(Name, Age, Salary, Bonus) %>% 16 arrange(desc(Age)) 17 18print(final_df)
For example, in the code above, you can see how we:
Bonus that calculates 10% of the Salary.Age is greater than 25.Name, Age, Salary, and Bonus columns.Age in descending order using arrange() (the dplyr equivalent of the order function we used in the previous course)We'll be using the %>% operator to chain these functions together, making the code more readable and efficient!
Chaining functions streamlines your data manipulation tasks, making your code more efficient and easier to read. By mastering these techniques, you can build robust data pipelines that are essential for data analysis and reporting. This skill is particularly useful when dealing with large datasets, enabling you to perform complex operations with simple, readable code.
Ready to unlock the full potential of dplyr? Let’s start the practice section and harness the power of chaining functions together!