学ぶ Summarizing and Grouping Data

セクション 1. 章 4

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When working with data, you often need to calculate summary statistics - like averages, counts, or totals - for different groups within your dataset. The group_by and summarize functions in the Tidyverse are essential tools for these tasks. The group_by function allows you to specify one or more columns to define groups in your data. Once your data is grouped, you can use summarize to perform calculations within each group, such as finding the mean, sum, or count. This approach helps you gain insights into patterns or differences across categories, such as comparing average sales by region or counting the number of entries per department.


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library(dplyr)
options(crayon.enabled = FALSE)

# Example data frame
data <- data.frame(
  department = c("HR", "Finance", "HR", "IT", "Finance", "IT", "HR"),
  salary = c(50000, 60000, 52000, 70000, 61000, 72000, 51000)
)

# Calculating mean salary and count of employees by department
summary_stats <- data %>%
  group_by(department) %>%
  summarize(
    mean_salary = mean(salary),
    employee_count = n()
  )

print(summary_stats)

After performing grouped operations, your data retains its grouping structure. This can lead to unexpected results if you continue manipulating the data without first removing the grouping. The ungroup function is important because it clears groupings, returning your data to a regular, ungrouped state. This ensures that subsequent operations are performed on the entire dataset rather than within the previously defined groups. Always consider whether you need to use ungroup after summarizing, especially before further transformations or analyses.


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library(dplyr)
options(crayon.enabled = FALSE)

# Example data frame
data <- data.frame(
  department = c("HR", "Finance", "HR", "IT", "Finance", "IT", "HR"),
  salary = c(50000, 60000, 52000, 70000, 61000, 72000, 51000)
)

# Calculating mean salary by department (grouped)
summary_stats <- data %>%
  group_by(department) %>%
  summarize(mean_salary = mean(salary))

# Removing grouping with ungroup
summary_stats_ungrouped <- summary_stats %>%
  ungroup()

# Adding a new column showing overall mean salary (not by department)
overall_mean <- mean(data$salary)
summary_stats_ungrouped$overall_mean <- overall_mean

print(summary_stats_ungrouped)

タスク

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Use the provided students data frame to calculate summary statistics by class and add an overall minimum test score column. Follow these steps:

Group the students data frame by the class column;
Use the summarize function to calculate the average test score mean and the maximum max test score for each class;
Remove the grouping using the ungroup function;
Add a new column to your summary showing the overall minimum min test score across all classes;
Assign your final result to a variable named class_summary.

This task helps you practice grouping, summarizing, ungrouping, and adding summary columns in the context of student test scores.

解答

実践的な練習のためにデスクトップに切り替える下記のオプションのいずれかを利用して、現在の場所から続行する

すべて明確でしたか？

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セクション 1. 章 4

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