Welcome back! In the previous lesson, we learned how to gather and spread data using the tidyr package in R. These techniques helped reshape our data to meet various analytical needs. Now, we're going to dive into another essential aspect of data wrangling: splitting and combining columns. Get ready to learn and practice these powerful data manipulation techniques using separate and unite functions.

In this lesson, you will learn how to:

1. Separate: Split a single column into multiple columns based on a delimiter. This is especially useful when you have concatenated data that needs to be split into distinct pieces.
2. Unite: Combine multiple columns into a single column by using a specified delimiter. This can be useful for creating a summary or identifier from multiple data fields.

Let's look at an example to illustrate these functions:

R
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1# Suppress package startup messages for a cleaner output
2suppressPackageStartupMessages(library(tidyr))
3suppressPackageStartupMessages(library(dplyr))
4
5# Create a tibble with concatenated values
6concat_df <- tibble(
7  FullName_Age = c("Alice_30", "Bob_25", "Charlie_35")
8)
9
10# Separate the concatenated columns
11separated_df <- concat_df %>%
12  separate(FullName_Age, into = c("FullName", "Age"), sep = "_")
13
14# Unite columns back into one
15united_df <- separated_df %>%
16  unite(FullName_Age, FullName, Age, sep = "_")

We'll get to see exactly how both functions work during the upcoming practice session!

Understanding how to split and combine columns is crucial for data cleansing and preparation. Often, you'll encounter data that needs to be disaggregated for detailed analysis or reunited for simplification. For instance, combining first and last names or splitting a full address into street, city, and postal code are common tasks. Mastering these techniques will save you a significant amount of time and add flexibility to your data manipulation skills.

Excited to enhance your data wrangling skills? Let's jump into the practice section and apply these powerful techniques to make your data cleaner and more analyzable.