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Histograms and Boxplots
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Histograms and boxplots are commonly used to understand the distribution of numerical data. Both plots help in identifying patterns, spread, and unusual values within a dataset. The ggplot2 package in R provides simple functions to create these plots.
A histogram is used to show the distribution of a single numerical variable. It divides the data into intervals called bins and displays the number of observations in each bin. This helps in understanding the shape of the data, such as whether it is symmetrical, skewed, or contains multiple peaks.
library(ggplot2)
ggplot(data = mtcars, aes(x = mpg)) +
geom_histogram()In this example, the histogram shows the distribution of miles per gallon values from the mtcars dataset.
A boxplot is used to display the spread and central tendency of data. It shows the median, quartiles, and potential outliers in the dataset. Boxplots are especially useful when comparing distributions across categories.
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg)) +
geom_boxplot()In this example, the boxplot compares the distribution of miles per gallon for different cylinder categories.
The table below summarizes the key differences between histograms and boxplots.
| Feature | Histogram | Boxplot |
|---|---|---|
| Purpose | Shows the distribution of a numerical variable | Shows spread, median, quartiles, and outliers |
| Data Type | Single numerical variable | Numerical variable, often grouped by categories |
| Main Use | Understanding shape and frequency | Comparing distributions and detecting outliers |
| Visual Elements | Bars representing frequency | Box, whiskers, and median line |
Histograms and boxplots are essential tools for exploratory data analysis. They help analysts understand the structure of the data before performing more advanced statistical analysis or modeling.
