学ぶ Boxplots for Distribution Analysis

Data Visualization in R with ggplot2

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Boxplots are a powerful tool for visualizing the distribution of numerical data, especially when you want to compare distributions across categories. Each boxplot summarizes a dataset using five key statistics: the minimum value, the first quartile (Q1), the median, the third quartile (Q3), and the maximum value. The box itself represents the interquartile range (IQR), which contains the middle 50% of the data. The line inside the box marks the median. Whiskers extend from the box to the smallest and largest values within 1.5 times the IQR from the quartiles, while points outside this range are plotted individually as outliers. This makes boxplots excellent for spotting differences in spread, central tendency, and the presence of outliers across groups.


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library(ggplot2)

# Create a data frame with test scores for three classes
scores <- data.frame(
  class = rep(c("A", "B", "C"), each = 20),
  score = c(
    rnorm(20, mean = 75, sd = 10),
    rnorm(20, mean = 80, sd = 12),
    rnorm(20, mean = 70, sd = 8)
  )
)

# Create a boxplot comparing scores across classes
ggplot(scores, aes(x = class, y = score)) +
  geom_boxplot(fill = "skyblue", color = "darkblue") +
  labs(
    title = "Test Score Distribution by Class",
    x = "Class",
    y = "Test Score"
  )

In this boxplot code, you use ggplot() to initialize the plot with the scores data frame. The aesthetics mapping assigns the categorical variable class to the x-axis and the numerical variable score to the y-axis. The geom_boxplot() layer draws a boxplot for each class, using color and fill to improve clarity. The resulting plot allows you to quickly compare the median, spread, and potential outliers for test scores across classes A, B, and C. When reading the plot, look for differences in the heights of the boxes and positions of the medians to assess which class has higher or lower scores, as well as the consistency of scores within each group.