Basic Plotting in R

What You’ll Learn 📚

• Core concepts of plotting in R
• Key terminology
• Simple to complex plotting examples
• Common questions and troubleshooting

Introduction to Plotting in R

Plotting is a powerful way to visualize data, making it easier to understand and communicate insights. R, a popular language for statistical computing, offers a variety of tools for creating stunning plots. Let’s start with some core concepts.

Core Concepts

• Data Visualization: The graphical representation of information and data.
• Plot: A graphical display of data points in a coordinate system.
• Axis: The reference lines on a plot, typically horizontal (x-axis) and vertical (y-axis).
• ggplot2: A widely-used R package for creating complex plots from data in a data frame.

Key Terminology

Here’s a quick glossary to get you started:

• Data Frame: A table or 2D array-like structure in R, where each column contains values of one variable and each row contains one set of values from each column.
• Layer: In ggplot2, plots are built in layers, allowing you to add different elements like points, lines, and text.
• Geom: Short for geometric object, it’s the visual representation of data in a plot (e.g., points, lines).

Getting Started with Plotting

Setup Instructions

Before we begin, make sure you have R and RStudio installed. You’ll also need to install the ggplot2 package if you haven’t already. Open RStudio and run the following command:

install.packages('ggplot2')

Simple Example: Scatter Plot

# Load the ggplot2 package
library(ggplot2)

# Create a simple data frame
data <- data.frame(x = c(1, 2, 3, 4, 5), y = c(3, 7, 8, 5, 9))

# Create a scatter plot
ggplot(data, aes(x = x, y = y)) +
  geom_point()

Progressively Complex Examples

Example 1: Line Plot

# Create a line plot
ggplot(data, aes(x = x, y = y)) +
  geom_line()

Example 2: Bar Plot

# Create a bar plot
ggplot(data, aes(x = factor(x), y = y)) +
  geom_bar(stat = 'identity')

Example 3: Histogram

# Create a histogram
# Using a different data set for demonstration
hist_data <- data.frame(values = rnorm(1000))
ggplot(hist_data, aes(x = values)) +
  geom_histogram(binwidth = 0.5)

Common Questions and Troubleshooting

1. Why isn't my plot displaying?

   Ensure you've run all necessary code, including loading libraries and creating data frames. Check for typos in your code.

2. What does 'aes' mean?

   aes stands for aesthetic mappings, defining how data is mapped to visual properties.

3. How do I change the color of my plot?

   Add color = 'blue' inside aes() or directly in geom_point() for a specific color.

4. Can I save my plot?

   Yes! Use ggsave('plot.png') after your plot code to save it as an image.

5. Why do I get a 'could not find function' error?

   This usually means a package isn't loaded. Make sure to use library() to load necessary packages.

Troubleshooting Common Issues

Ensure your data is correctly formatted and all necessary libraries are loaded before plotting.

Remember, practice makes perfect! Try creating different types of plots with your own data to solidify your understanding. 🎨

Practice Exercises

• Create a scatter plot using a new data set with at least 10 points.
• Experiment with different plot types (e.g., boxplot, density plot) using ggplot2.
• Try customizing your plots by changing colors, adding titles, and modifying axis labels.

For more information, check out the ggplot2 documentation.