Using ggplot2 is a lot like baking. One batter, many delicacy:

The base batter/dough of a few main ingredient can be made into different desert through different technique of baking. You cook the batter in a pan you get pancake, in an oven you get a cake, and deep fried give you corn dog!

The structure of how ggplot2 work is basically the same:

The main ‘batter’ function where you will feed your data is “ggplot()”. You will able to specify what data frame you are using to build the graph, which vector in your data frame is your x and y axis, and even which color to distinguish your categorical variable. The cooking process is done with the variety of “geom_” function. Using geom_bar() on your ggplot() will yield a bar graph, geom_point() will yield a scatter plot, and geom_boxplot() will make a box plot! Within each geom_ function, there are more parameters that will allow you to customize further. Pretty cool and simple eh?

Further customization can be added on with even more function! Simply do + function() and it will add on to the existing graph!

data(iris) #Load in the built in iris data set
head(iris) #create a data frame object using iris

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

This is your iris data frame! Notice how it is very similar to an excel sheet. Each column are vectors. Each vector name are present at the very top i.e: Sepal.Length, Sepal.Width Petal.Length, Petal.Width, Species. Each rows show the element of each vector at a numbered position, ie: Row 6 contain 5.4, 3.9, 1.7, 0.4, setosa for each respective aforementioned vector. For now, only worry about how column vector create the basic structure of your data frame since the vectors is what you will use to create graphs.

Basic Boxplot

library(ggplot2) # load in the ggplot2 library to be able to access all the ggplot2 function, use install.packages(ggplot2) if not installed. Then load the library
ggplot(iris, aes(x = Species,y = Sepal.Width, fill = Species)) + # Your batter
  geom_boxplot() # Your cooking method

See how the original ggplot is “cooked” with geom_boxplot() in the code? This is the essence of creating graph in ggplot and R in general! We will dive deeper into the code later but for now, let add frosting and other goods to our cake!

By adding frosting to a cake, you make it appropriate for a birthday, wedding, etc. Editting your graph is needed to make it presentable for publication.

library(viridisLite) #load in colorblind friendly color
ggplot(iris, aes(x = Species,y = Sepal.Width, fill = Species)) + # Your batter
  geom_boxplot() + # Your cooking method
  xlab("Species of Iris") + # X-axis title
  ylab("Sepal Width") + # Y-axis title
  scale_x_discrete(labels = c('Setosa', 'Versicolor', 'Virginica')) + # change x axis element label
  scale_fill_manual(values = viridis(3)) + #Make the color colorblind friendly!
  theme_bw() + #change the general theme look of the graph! 
  theme(legend.position="none") + #remove legend
  geom_jitter() #add jitter

Look how beautiful this graph is compared to the original base graph! It is clear, precise, and colorblind friendly! More modification of the base ggplot graph can be achieve by adding on more and more function with “+”. This is the equivalent to frosting and decorating your cake! Now that we understand the basic structures, let make the boxplot step by step! Proceed to the Box plot tab at the top.