Title: Creating Beautiful ggplot Scatter Plots with geom_point in R: A Comprehensive Guide – R Lesson 11

Introduction

Welcome to R Lesson 11, where we explore the power of ggplot2 and the geom_point function to create stunning scatter plots in R. Scatter plots are a vital tool for data visualization, allowing you to identify patterns, trends, and relationships between variables in your data. In this comprehensive guide, we will walk you through creating a ggplot scatter plot using geom_point, providing extra tips and insights to help you become a data visualization pro. We recommend a few books to help you further develop your R programming and data visualization skills. This post is designed for easy integration into a WordPress blog.

Video

Recommended Books

To further enhance your understanding of R programming and data manipulation, we recommend the following books (as an Amazon Associate, I may earn a small commission from these links):

1. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
2. Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
3. The Kaggle Book: Data analysis and machine learning for competitive data science
4. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Creating a Scatter Plot with ggplot2 and geom_point

The ggplot2 package is a powerful and flexible data visualization tool in R, based on the Grammar of Graphics principles. It allows you to create complex and customizable plots using a simple and intuitive syntax. The geom_point function is used to create scatter plots in ggplot2.

Here is a step-by-step guide on how to create a scatter plot using ggplot2 and geom_point:

1. Install and load the ggplot2 package:

install.packages("ggplot2")
library(ggplot2)

2. Prepare your data: Ensure your data is stored in a data frame format, with columns representing the variables you want to plot.

Example:

data <- data.frame(x = c(1, 2, 3, 4, 5),
                   y = c(2, 4, 1, 6, 3))

3. Create the scatter plot using ggplot() and geom_point():

scatter_plot <- ggplot(data, aes(x = x, y = y)) +
                geom_point()

print(scatter_plot)

Customizing Your Scatter Plot

With ggplot2, you can easily customize your scatter plot to improve its appearance and convey more information.

1. Change the point color, shape, and size:

scatter_plot <- ggplot(data, aes(x = x, y = y)) +
                geom_point(color = "blue", shape = 19, size = 3)

print(scatter_plot)

2. Add a title, and customize axis labels and themes:

library(ggthemes)

scatter_plot <- ggplot(data, aes(x = x, y = y)) +
                geom_point(color = "blue", shape = 19, size = 3) +
                ggtitle("Scatter Plot of X and Y") +
                xlab("X Axis Label") +
                ylab("Y Axis Label") +
                theme_minimal()

print(scatter_plot)

ggplot Line by Line

This R code creates a scatter plot using the ggplot2 package. Let’s break down each part of the code to explain the different parameters to a beginner programmer.

1. scatter_plot <-: This line assigns the result of the following ggplot function to a variable called scatter_plot.
2. ggplot(data, aes(x = x, y = y)): The base ggplot function initializes a new ggplot object.
   • data: This is the input data frame containing the data to be plotted.
   • aes(x = x, y = y): This is the aesthetic mapping function that defines how variables in the data are mapped to the visual properties of the plot. In this case, x and y are mapped to their respective axes.
3. geom_point(color = “blue”, shape = 19, size = 3): This is the function that adds points to the scatter plot.
   • color = “blue”: This sets the color of the points to blue.
   • shape = 19: This sets the shape of the points to a filled circle (shape number 19).
   • size = 3: This sets the size of the points to 3.
4. ggtitle(“Scatter Plot of X and Y”): This function adds a title to the plot with the text “Scatter Plot of X and Y”.
5. xlab(“X Axis Label”): This function adds a label to the x-axis with the text “X Axis Label”.
6. ylab(“Y Axis Label”): This function adds a label to the y-axis with the text “Y Axis Label”.
7. theme_minimal(): This function applies the minimal theme to the plot, which is a clean and simple theme with minimal styling.

The ‘+’ symbol between each function call adds layers to the ggplot object. When you run this code, it will create a scatter plot with the specified properties and store it in the scatter_plot variable. You can then display the plot by simply calling scatter_plot in the R console.

Recommended Books

To further enhance your understanding of R programming and data manipulation, we recommend the following books (as an Amazon Associate, I may earn a small commission from these links):

1. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
2. Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
3. The Kaggle Book: Data analysis and machine learning for competitive data science
4. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python