Creating a Faceted Histogram in R with ggplot2
Scenario: You have a dataset of customer feedback for an e-commerce platform. You want to visualize the distribution of customer satisfaction scores across different product categories.
R
# Create a sample customer_data datasetcustomer_data <- data.frame( Satisfaction_Score = c(4, 5, 3, 2, 5, 4, 4, 3, 5, 2, 1, 5), Product_Category = c("Electronics", "Clothing", "Electronics", "Clothing", "Home Decor", "Electronics", "Clothing", "Home Decor", "Clothing", "Electronics", "Home Decor", "Clothing"))# Load the necessary librarieslibrary(ggplot2)# Create a ggplot objectplot <- ggplot(data = customer_data, aes(x = Satisfaction_Score)) + geom_histogram(binwidth = 1, fill = "blue") + labs(title = "Distribution of Customer Satisfaction Scores")# Add facets based on product categoriesfaceted_plot <- plot + facet_wrap(~Product_Category, scales = "free")# Display the faceted plotprint(faceted_plot) |
Output
The code generates a faceted histogram that visualizes the distribution of customer satisfaction scores. The plot is divided into three facets, each corresponding to a different product category: “Electronics,” “Clothing,” and “Home Decor.” Within each facet, the histogram displays the distribution of satisfaction scores for the respective product category. This visualization allows for a quick comparison of satisfaction score distributions across different product categories.
Plotting multiple groups with facets in ggplot2
Data visualization is an essential aspect of data analysis and interpretation. We can more easily examine and comprehend data thanks to it. You may make many kinds of graphs in R, a popular computer language for data research, to show your data. For a thorough understanding while working with complicated datasets or several variables, it becomes essential to display multiple graphs concurrently. Faceting, commonly referred to as tiny multiples or trellis plots, is useful in this situation.
A data visualization approach called faceting includes making a grid of smaller plots, each of which shows a portion of the data. A categorical variable or group of categorical variables determines these subsets. Faceting is a potent tool in your data analysis toolbox since it helps you visualize links and trends within various subsets of your data.