Welcome! In today's lesson, we are diving into styling plots. Styling is crucial to make plots visually attractive and insightful. We will delve into various aspects of plot style with R's ggplot2, enhancing the aesthetics of our plots as we progress. Let's get started!

In ggplot2, each plot can be styled differently. Here's an example with a basic line plot:

library(ggplot2)
x = c(1, 2, 3, 4, 5)
y = c(1, 4, 9, 16, 25)
df = data.frame(x, y)
plot <- ggplot(df, aes(x = x, y = y)) + geom_line()

Have you ever wanted to change these defaults? Fortunately, ggplot2 allows you to do just that with the color and linetype parameters:

plot <- ggplot(df, aes(x = x, y = y)) + 
geom_line(color = "red", linetype = "dashed")

Voila! Our line is now red and dashed!

The fun part is that ggplot2 offers many color options (like 'green', 'blue', 'cyan', etc.) and line types (like 'solid', 'dotted', 'dashed', etc.). This flexibility allows you to create personalized and differentiated line plots.

Markers can significantly enhance your plot's aesthetics and readability by highlighting the data points. With ggplot2, we can add markers using the geom_point function and the shape parameter:

plot <- ggplot(df, aes(x = x, y = y)) + 
geom_line(color = "red", linetype = "dashed") + 
geom_point(shape = 1)

Commonly used markers include 1 (circle), 4 (cross), 8 (star), 15 (filled square), 18 (plus), and more.

Good labels make plots easy to understand. Let's add a title, x-label, y-label, and a legend to our plot to make it more self-explanatory:

plot <- ggplot(df, aes(x = x, y = y)) + 
geom_line(color = "red", linetype = "dashed") +
labs(title = "Square Numbers", x = "Numbers", y = "Squares")

Our plot now carries much more information!