R - ggplot2

Goals
  • Learn how to make your data frame for ggplot2
  • Learn to make a x versus y plot
  • Learn how to add elements to your plot
  • Learn to make a boxplot
  • Learn how to ask ChatGPT for ggplot2

This tutorial will give an introduction to plotting with the ggplot2 package, which is the modern way to make general plots with R. I will talk tomorrow about plots of spatial data.

This is oriented to those with little or no experience using ggplot2 or those who have tried it and gotten frustrated. The goal is to get you started. Once you have the basics, there are a many many ggplot tutorials online – though personally the basics are all I need.

The goal is to give you enough of an understanding of ggplot2 so that you can create prompts for ChatGPT. You need to know the basics of ggplot2 to understand ChatGPT’s code and know what sort of things to ask ChatGPT. But if you are lost, you can always tell it what you want and ask it to ask you questions.

Load the library

library(ggplot2)

Basic x versus y plot

Workflow

Let’s say you wanted to make a simple x versus y plot. Here’s your ggplot() workflow.

  • Decide on x.
  • Decide on y.
  • Make a data frame with x and y.
  • Create the plot object with a call to ggplot() to tell it the data frame and then what the x and y to use: aes(x=..., y=...).
  • Add points or lines to the plot with geom_line() or geom_point().

Create data frame

val <- mtcars$mpg
x <- mtcars$hp
df <- data.frame(x=x, val=val)

ggplot call

p1 <- ggplot(df, aes(x=x, y=val)) + 
        geom_point() # plot a line
p1 # plot it

p1 is a ggplot object.

class(p1)
[1] "gg"     "ggplot"

We can add elements to a ggplot object

p1 + geom_line()

An x y plot 
ggplot(df, aes(x=x, y=val)) +
 geom_point() +
 geom_line()

Changing the whole look

ggplot uses themes to set the look of your plot and you can change the whole look by setting a different theme. You can also just tweak one element of the plot’s existing theme. Note because we fixed the line and point colors, we override some theme elements (eg, line colors). See ?theme_bw to see all the themes. See ?theme to learn how to change one element of your plot design.

p1 + theme_classic()

Using ChatGPT

Boxplots

Now that you see the basic structure of a simple x versus y plot, let’s use ChatGPT to help us make plots.

Prompt

I have a data frame with species, gender and bill length. I want to make a boxplot by species with ggplot2 where the genders are separate and side by side.

This what ChatGPT gave me. Try using this prompt and adding more info until you get a plot you like.

# Load the ggplot2 library
library(ggplot2)

# Sample data frame (replace with your actual data)
data <- data.frame(
  species = c("Species A", "Species A", "Species B", "Species B", "Species C", "Species C"),
  gender = c("Male", "Female", "Male", "Female", "Male", "Female"),
  bill_length = c(10.2, 9.8, 11.5, 10.7, 9.3, 9.9)
)

# Create the side-by-side boxplot
ggplot(data, aes(x = species, y = bill_length, fill = gender)) +
  geom_boxplot(position = position_dodge(width = 0.8)) +
  labs(title = "Boxplot of Bill Length by Species and Gender",
       x = "Species",
       y = "Bill Length") +
  scale_fill_manual(values = c("Male" = "blue", "Female" = "red"))

Giving your data to ChatGPT

You can paste in the top of your data (if it is csv) and ChatGPT can figure out the structure. Then you can say what you want to do with this data.

Demo

Arranging plots into grids

Dynamically

ggplot() will dynamically break your data into plots for you using facet_wrap().

df1 <- data.frame(t=1:10, val=10:1, name="df1")
df2 <- data.frame(t=1:20, val=1:20+100, name="df2")
df <- rbind(df1, df2)
p1 <- ggplot(df, aes(x=t, y=val, col=name)) + 
  geom_line() +
  facet_wrap(~name)
p1

We can “free” the scales.

p1 + facet_wrap(~name, scales="free")

Manually

We can arrange plots into a grid manually with gridExtra::grid.arrange() (from the gridExtra package).

library(gridExtra)
df <- data.frame(t = 1:10, val = rnorm(10))
p1 <- ggplot(df, aes(x=t, y=val)) + geom_line() + geom_point()
p2 <- ggplot(df, aes(x=t, y=val+100)) + geom_line()
p3 <- ggplot(df, aes(x=t, y=100*val)) + geom_point()
df2 <- data.frame(t = 1:4, val = 1:4, se=.1*(1:4))
p4 <- ggplot(df2, aes(x=t, y=val)) + geom_col()
gridExtra::grid.arrange(p1, p2, p3, p4)

Instead of gridExtra and grid.arrange(), check out the patchwork package. It does similar jobs as grid.arrange() but is better.

Let’s do two plots in one column but add new info to plot 1.

gridExtra::grid.arrange(p1+geom_smooth(method="lm"), 
                        p4+geom_errorbar(aes(ymin=val-se, ymax=val+se), width=0.3), ncol=1)
`geom_smooth()` using formula = 'y ~ x'

Getting your data in the right format

You need your data in long format.

Let’s say you had temperature data for 3 rivers. It would be common to have your data like this. This is wide format.

Date, River1, River2, River3
1, 45, 35, 20
2, 25, 30, 21

You need to get this in long format like so.

Date, River, Temperature
1, River1, 45
1, River2, 35
1. River3, 20
2, River1, 25
2, River2, 30
2, River3, 21

You do this with code not manually! This is hard to remember how to do, so just ask ChatGPT. Pass the data into ChatGPT and ask it how to put it in long format.

Prompt

how can I read a text string of my data in

# Your data as a text string
data_string <- "Date, River1, River2, River3
1, 45, 35, 20
2, 25, 30, 21"

# Use read.table() to read the data from the text string
df <- read.table(text = data_string, header = TRUE, sep = ",")

# Print the resulting data frame
print(df)
  Date River1 River2 River3
1    1     45     35     20
2    2     25     30     21
Prompt

How do I convert that to long format

library(tidyr)
df_long <- gather(df, key = "River", value = "Value", -Date)
df_long
  Date  River Value
1    1 River1    45
2    2 River1    25
3    1 River2    35
4    2 River2    30
5    1 River3    20
6    2 River3    21

Creating plot templates

If you are creating plots with the same features over and over, you can hold the features in a list and add that on to your plot.

Example where I want all my plots to have red points and a regression line:

p3 <- geom_point(col="red")
p2 <- geom_smooth(method="lm")
p4 <- xlab("MPG")
p5 <- ylab("HP")
# pt is my template
pt <- list(p2, p3, p4, p5)

p1 <- ggplot(mtcars, aes(x=mpg, y=hp)) + pt
p2 <- ggplot(mpg, aes(x=hwy,y=cyl)) + pt
grid.arrange(p1, p2)

Summary

ggplot is very powerful and will make your plotting workflow more efficient and much much faster. It takes a little while to get the hang of, but you do not need to be a ggplot wizard. Just the basics here will take you a long way. ChatGPT knows ggplot2 and will help you.

A good set of basic ggplot commands when you are starting:

  • ggplot
  • geom_line()
  • geom_point()
  • geom_col()
  • ggtitle(), xlab(), ylab(), xlim(), ylim()
  • Themes. Use ?theme_bw to see them.
  • gridExtra::grid.arrange(..., nrow, ncol)
  • facet_wrap()
  • Changing the color, line, and points manually is a bit painful, but often unavoidable. Get to know the scale_xyz_manual() functions when you need to do that. ?scale_color_manual to find them all.

If you want to see some examples of what ggplot2 can do, look at some galleries. Knowing what ggplot2 can do will give you ideas for what you can ask ChatGPT to create for you.

There are also many tutorials on ggplot2