A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.
We can break down this plot into its fundamental building blocks:
- The data used to create the plot:
- The axes of the plot:
- The geometric shapes used to visualize the data. In this case, a line:
- The labels or annotations that will help a reader understand the plot:
Breaking down a plot into layers is important because it is how the ggplot2
package understands and builds a plot. The ggplot2
package is one of the packages in the tidyverse
, and it is responsible for visualization. As you continue reading through the post, keep these layers in mind.
Importing the Data
In order to start on the visualization, we need to get the data into our workspace. We’ll bring in the tidyverse
packages and use the read_csv()
function to import the data. We have our data named as life_expec.csv
, so you’ll need to rename it according to how you name the file.
library(tidyverse)
life_expec <- read_csv("life_expec.csv")
Let’s see what data we’re working with:
colnames(life_expec)
[1] "Year" "Race" “Sex" "Avg_Life_Expec" "Age_Adj_Death_Rate"
We can see that time is encoded in terms of years via the Year
column. There are two columns that allow us to distinguish between different race and sex categories. Finally, the last two columns correspond to life expectancy and death rate.
Let’s have a quick look at the data to see how it looks like for one particular year:
life_expec %>%
filter(Year == 2000)
For the year 2000, there are nine data points:
## # A tibble: 9 x 5
## Year Race Sex Avg_Life_Expec Age_Adj_Death_Rate
##
## 1 2000 All Races Both Sexes 76.8 869
## 2 2000 All Races Female 79.7 731.
## 3 2000 All Races Male 74.3 1054.
## 4 2000 Black Both Sexes 71.8 1121.
## 5 2000 Black Female 75.1 928.
## 6 2000 Black Male 68.2 1404.
## 7 2000 White Both Sexes 77.3 850.
## 8 2000 White Female 79.9 715.
## 9 2000 White Male 74.7 1029.
One year has nine different rows, each one corresponding to a different demographic division. For this visualization, we’ll focus on the United States overall, so we’ll need to filter the data down accordingly:
life_expec <- life_expec %>%
filter(Race == "All Races", Sex == "Both Sexes")
The data is in a good place, so we can pipe it into a ggplot()
function to begin creating a graph. We use the ggplot()
function to indicate that we want to create a plot.
life_expec %>%
ggplot()
This code produces a blank graph (as we see below). But it now “knows” to use the life_expec
data, even though we don’t see it charted yet.
Building the Axes
Now that we’ve prepared the data, we can start building our visualization. The next layer that we need to establish are the axes. We are interested in looking at how life expectancy changes with time, so this indicates what our two axes are: Year
and Avg_Life_Expec
.
In order to specify the axes, we need to use the aes()
function. aes
is short for “aesthetic”, and it is where we tell ggplot
what columns we want to use for different parts of the plot. We are trying to look at life expectancy through time, so this means that Year
will go to the x-axis
and Avg_Life_Expec
will go to the y-axis.
life_expec %>%
ggplot(aes(x = Year, y = Avg_Life_Expec))
With the addition of the aes()
function, the graph now knows what columns to attribute to the axes:
But notice that there’s still nothing on the plot! We still need to tell ggplot()
what kind of shapes to use to visualize the relationships between Year
and Avg_Life_Expec
.
Specifying Geoms
Typically when we think of visualizations, we normally think about the type of graph since it’s really the shapes that we see that tell us most of the information. While the ggplot2
package gives us a lot of flexibility in terms of choosing a shape to draw the data, it’s worth taking some time to consider which one is best for our question.
We are trying to visualize how life expectancy has changed through time. This means that there should be a way for us to compare the past directly with the future. In other words, we want a shape that helps show a relationship between two consecutive years. For this, a line graph is great.
To create a line graph with ggplot()
, we use the geom_line()
function. A geom
is the name for the specific shape that we want to use to visualize the data. All of the functions that are used to draw these shapes have geom
in front of them. geom_line()
creates a line graph, geom_point()
creates a scatter plot, and so on.
life_expec %>%
ggplot(aes(x = Year, y = Avg_Life_Expec)) +
geom_line()
Notice how after the use of the ggplot()
function, we start to add more layers to it using a +
sign. This is important to note because we use %>%
to tell ggplot()
what data to function. After using ggplot()
, we use +
to add more layers to the plot.
This graph is exactly what we were looking for! Having a look at the general trend, life expectancy has grown over time.
We could stop the plot here if we were just looking at the data quickly, but this is rarely the case. More common is that you’ll be creating a visualization for a report or for others on your team. In this case, the plot is not complete: if we were to give it to a teammate with no context, they wouldn’t understand the plot. Ideally, all of your plots should be able to explain themselves through the annotations and titles.
Adding a Title and Axis Labels
Currently the graph keeps the column names as the labels for both of the axes. This is sufficient for Year
, but we’ll want to change up the y-axis. In order to change the axis labels for a plot, we can use the labs()
function and add it as a layer onto the plot. labs()
can change both the axis labels as well as the title, so we’ll incorporate that here.
life_expec %>%
ggplot(aes(x = Year, y = Avg_Life_Expec)) +
geom_line() +
labs(
title = "United States Life Expectancy: 100 Years of Change",
y = "Average Life Expectancy (Years)"
)
Our final polished graph is:
Conclusion: ggplot2 is Powerful!
In only a few lines of code, we produced a great visualization that tells us everything we need to know about life expectancy for the general population in the United States. Visualization is an essential skill for all data analysts, and R makes it easy to pick up.
Check out our Data Analyst in R path if you’re interested in learning more! The Data Analyst in R path includes a course on data visualization in R using ggplot2
, where you’ll learn how to:
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