Lesson 2

Welcome back! In our previous lesson, we learned how to select and filter data using the `dplyr`

package in R. Now that you have the foundational skills to manage and clean your data, it's time to move on to summarizing and grouping. This lesson will enhance your ability to derive meaningful insights from your datasets by grouping and summarizing information efficiently.

In this lesson, you'll discover how to:

**Group data**by one or more variables using the`group_by`

function.**Summarize data**to calculate aggregate statistics like the mean, median, or sum using the`summarize`

function.

We'll continue using simple data frame examples to illustrate these concepts. Here’s a step-by-step guide:

First, let's create a simple data frame that we'll use for grouping and summarizing:

R`1# Example data frame 2data <- data.frame( 3 Group = c("A", "A", "B", "B"), 4 Score = c(85, 95, 78, 92) 5) 6 7# Print the example data frame 8print(data) 9 10# Output: 11# Group Score 12# 1 A 85 13# 2 A 95 14# 3 B 78 15# 4 B 92`

In this data frame, we have two columns: `Group`

and `Score`

. The `Group`

column specifies the group to which each observation belongs, while the `Score`

column provides the scores.

Now, let's use the `group_by`

function to group our data by the `Group`

column:

R`1# Example data frame 2data <- data.frame( 3 Group = c("A", "A", "B", "B"), 4 Score = c(85, 95, 78, 92) 5) 6# Group data by Group 7grouped_data <- data %>% 8 group_by(Group) 9 10# Printed the grouped data 11print(grouped_data) 12 13# Output of grouped_data: 14# A tibble: 4 × 2 15# Groups: Group [2] 16# Group Score 17# <chr> <dbl> 18# 1 A 85 19# 2 A 95 20# 3 B 78 21# 4 B 92`

The `%>%`

symbol is the pipe operator, which allows you to chain multiple functions together seamlessly. The `group_by`

function is used to specify the column(s) to group by. In this case, we group by the `Group`

column.

Next, let's summarize the data to calculate the mean score for each group using the `summarize`

function:

R`1# Example data frame 2data <- data.frame( 3 Group = c("A", "A", "B", "B"), 4 Score = c(85, 95, 78, 92) 5) 6# Group data by Group 7grouped_data <- data %>% 8 group_by(Group) 9 10# Summarize the data by mean score 11summary <- grouped_data %>% 12 summarize(mean_score = mean(Score)) 13 14# Print the summarized data 15print(summary) 16 17# Output of summary: 18# A tibble: 2 × 2 19# Group mean_score 20# <chr> <dbl> 21# 1 A 90 22# 2 B 85`

The `summarize`

function allows us to calculate aggregate statistics. Here, we calculate the mean score for each group and store it in a new column named `mean_score`

.

Summarizing and grouping data allows you to understand patterns and trends that may not be evident by looking at raw data. Whether you're comparing test scores across different classes or sales across different regions, the ability to group and summarize data helps you make informed decisions based on aggregated information. Mastering these techniques will make your data analysis more robust and insightful.

Ready to dig into summarizing and grouping data? Let's dive into the practice section and explore these powerful tools together.