Customizing Colors and Aesthetics
In this, we’ll continue to use the same dataset but focus on customizing colors and aesthetics of the faceted plots.
R
# Load the necessary librarieslibrary(ggplot2)# Create a sample customer_data dataset with satisfaction scores and spendingcustomer_data <- data.frame( Satisfaction_Score = c(4, 5, 3, 2, 5, 4, 4, 3, 5, 2, 1, 5), Spending_Amount = c(100, 150, 80, 60, 200, 120, 130, 90, 180, 70, 50, 210), Product_Category = c("Electronics", "Clothing", "Electronics", "Clothing", "Home Decor", "Electronics", "Clothing", "Home Decor", "Clothing", "Electronics", "Home Decor", "Clothing"))# Create a ggplot object for scatterplot with custom colorsscatterplot <- ggplot(data = customer_data, aes( x = Satisfaction_Score, y = Spending_Amount)) + geom_point(aes(color = Product_Category), size = 3) + labs(title = "Scatterplot of Satisfaction vs. Spending")# Create a ggplot object for histogram with custom colorshistogram <- ggplot(data = customer_data, aes(x = Satisfaction_Score)) + geom_histogram(binwidth = 1, fill = "lightblue") + labs(title = "Distribution of Satisfaction Scores")# Create faceted plots with custom colorsfaceted_plots <- scatterplot + facet_wrap(~Product_Category, scales = "free") + theme_minimal() + scale_color_manual(values = c("Electronics" = "red", "Clothing" = "blue", "Home Decor" = "green"))histogram_facet <- histogram + facet_wrap(~Product_Category, scales = "free") + theme_minimal() + scale_fill_manual(values = c("Electronics" = "red", "Clothing" = "blue", "Home Decor" = "green"))# Display the faceted scatterplotprint(faceted_plots)# Display the faceted histogramprint(histogram_facet) |
Output
- We use the scale_color_manual and scale_fill_manual functions to alter the colors of the scatterplot points and histogram fills based on product categories.
- The faceted plots’ custom colors—”red” for electronics, “blue” for clothing, and “green” for home décor—make it simpler to tell apart the various categories.
The data points in the faceted scatterplot plot now have configurable colors. The caption at the top-right shows the color-coding for each category, and each facet relates to a certain product category.
Additionally, the histogram faceted plot uses unique colors for various product categories. When comparing the distribution of satisfaction levels, this makes it easier to visually distinguish the groups.
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.