Visualizing Data

Why visualize data?

Data visualization is a graphical approach for quickly communicating key insights and sharing information. The ggplot2 package in the tidyverse offers a way to create beautiful and customizable charts. Creating charts with code might initially seem slow and challenging. However, learning to create with code will simplify and automate the data analysis process, ultimately saving time and effort.

What we’ll learn in this section

By the end of this section, we’ll be able to create the following two charts:

What is ggplot2?

ggplot2 is a data visualization package that is part of the tidyverse . It is based on a concept called the grammar of graphics, which is a way of thinking about creating graphics in a structured and consistent way.

If you don’t have tidyverse or ggplot2 installed, you can install using:

 install.packages("tidyverse")
 
# or 
 
install.packages("ggplot2") 

And then, you can load the library using:

 library(tidyverse)
 
# or
 
library(ggplot2) 

Making a Bar Chart

To create a ggplot, we pass the name of the dataset and then the x and y axis columns inside the aes() function.

 ggplot(dataset, aes(x = x_variable, y = y_variable))  

We can add a new layer using + and specify the type of chart we want to create. In this case, we are creating a bar chart using the geom_bar function.

 ggplot(data, aes(x = x_variable, y = y_variable)) +
  geom_bar() 

Inside the geom_bar() function we specify the type of statistic we want to use.

In this case, we’ll use stat = "identity" which uses the values we have in a column.

 ggplot(data, aes(x = x_variable, y = y_variable)) +
  geom_bar(stat = "identity") 

 ggplot(flowers, aes(x = name, y = sunlight)) + 
  geom_bar(stat = "identity") 

Adding Text

A chart is more informative when we add text to it. We can add text to the chart using the labs() function.

 labs(alt = "Alternative text for the chart",
     title = "Title of the chart",
     subtitle = "Subtitle of the chart",
     x = "Label for the x-axis",
     y = "Label for the y-axis",
     caption = "Caption for the chart") 

The alt argument is used to add alternative text to the chart. This is essential for screen readers to read aloud the text for those who may experience vision challenges. The title and subtitle give context to the chart. The x and y arguments are used to label the x and y axes. The caption argument is a good place to add the source of the data.

Formatting Theme Elements

We can change the appearance of the chart by adding themes. We will use the inbuilt theme_minimal() function to remove the grid lines and add a white background.

And we’ll add modifications to the theme by removing the x-axis grid lines and moving the title to the top of the plot area.

 theme_minimal() +
theme(panel.grid.major.x = element_blank(),
      panel.grid.minor.x = element_blank(),
      plot.title.position = "plot") 

Making a Scatter Plot

A scatter plot shows the relationship between two numerical variables.

In this section, we’ll make this plot:

We can create a scatter plot using the geom_point() function.

The geom_point() function takes in two arguments: x and y. We pass an aesthetic or aes() inside mapping. And then inside aes() we add our x and y variables.

 ggplot(data, aes(x = x_variable, y = y_variable)) +
  geom_point() 

Adding Titles and Labels

Review

Nice work! In this section, we learned how to create simple charts using ggplot2. We learned how to create a bar chart and a scatter plot. We also learned how to customize the appearance of the charts, add titles and labels, and change the color of the bars and points.