Histograms and Scatterplots
We’ll utilize a dataset in this code that comprises customer satisfaction ratings and the related dollar amounts of purchases. To see the distribution of satisfaction ratings and the connection between contentment and expenditure, we’ll make 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 scatterplotscatterplot <- ggplot(data = customer_data, aes(x = Satisfaction_Score, y = Spending_Amount)) + geom_point(color = "green", size = 3) + labs(title = "Scatterplot of Satisfaction vs. Spending")# Create a ggplot object for histogramhistogram <- ggplot(data = customer_data, aes(x = Satisfaction_Score)) + geom_histogram(binwidth = 1, fill = "blue") + labs(title = "Distribution of Satisfaction Scores")# Create faceted plotsfaceted_plots <- scatterplot + facet_wrap(~Product_Category, scales = "free") + geom_smooth(method = "lm", color = "red") # Add regression lines# Customize the histogram facethistogram_facet <- histogram + facet_wrap(~Product_Category, scales = "free") + theme_minimal() # Apply a minimal theme# Display the faceted scatterplotprint(faceted_plots)# Display the faceted histogramprint(histogram_facet) |
Output
geom_smooth()` using formula = ‘y ~ x’
- Two ggplot objects are made: one for a histogram of satisfaction ratings and another for a scatterplot of satisfaction vs spending.
- We provide options for customization, such as altering the scatterplot’s point color and size and giving the histogram a minimalistic look.
- Both scatterplots and histograms are made with faceted plots that are organized by product categories.
- Regression lines are added to the scatterplot’s faceted plot to show how expenditure and satisfaction are related.
The code generates two faceted plots:
- The scatterplot visualizes the relationship between satisfaction scores and spending amounts, with separate facets for different product categories. Regression lines indicate the trend within each category.
- The histogram facets display the distribution of satisfaction scores across product categories, each with its own histogram. The minimal theme is applied to this facet.
These plots provide a comprehensive view of customer satisfaction, spending patterns, and their distributions across product categories, making it easier to analyze and draw insights from the data.
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.