Data Visualization in R with ggplot2: A Beginner Tutorial


A well-known common is assumed to have stated, “A great sketch is best than an extended speech.” That recommendation could have come from the battlefield, but it surely’s relevant in a number of different areas — together with knowledge science. “Sketching” out our knowledge by visualizing it utilizing ggplot2 in R is extra impactful than merely describing the tendencies we discover.


Sketching out the design for a home communicates far more clearly than making an attempt to explain it with phrases. The identical factor is usually true for knowledge — and that is the place knowledge visualization with ggplot2 is available in!

This is the reason we visualize knowledge. We visualize knowledge as a result of it’s simpler to be taught from one thing that we are able to see slightly than learn. And fortunately for knowledge analysts and knowledge scientists who use R, there is a tidyverse package referred to as ggplot2 that makes knowledge visualization a snap!

On this weblog publish, we’ll discover ways to take some knowledge and produce a visualization utilizing R. To work by it, it is best if you have already got an understanding of R programming syntax, however you do not have to be an knowledgeable or have any prior expertise working with ggplot2.

Introducing the Information

The National Center for Health Statistics has been monitoring United States mortality tendencies since 1900. They’ve compiled data on life expectancy and dying charge of United States residents.

We want to know the way life expectancy has been altering by time. With advances in medication and expertise, we might count on that life expectancy could be rising, however we gained’t know for certain till we take a look!

If you happen to’d like to breed the graphs we’ll create on this weblog publish, obtain the info set here and observe alongside! 

(Undecided how one can work with R in your private laptop? Take a look at how to get started with RStudio!)

What’s in a Graph?

Earlier than we dive into the publish, some context is required. There are a lot of kinds of visualizations on the market, however most of them will boil right down to the next:

line chart

We will break down this plot into its elementary constructing blocks:

  1. The information used to create the plot:

line chart

  1. The axes of the plot:

line chart

  1. The geometric shapes used to visualise the info. On this case, a line:

line chart

  1. The labels or annotations that can assist a reader perceive the plot:

line chart

Breaking down a plot into layers is vital as a result of it’s how the ggplot2 package understands and builds a plot. The ggplot2 bundle is likely one of the packages within the tidyverse, and it’s chargeable for visualization. As you proceed studying by the publish, hold these layers in thoughts.

Importing the Information

To be able to begin on the visualization, we have to get the info into our workspace. We’ll carry within the tidyverse packages and use the read_csv() perform to import the info. Now we have our knowledge named as life_expec.csv, so that you’ll have to rename it in accordance with the way you identify the file.

life_expec <- read_csv("life_expec.csv")

Let’s see what knowledge we’re working with:

[1] "Yr"    "Race"        “Intercourse"         "Avg_Life_Expec"    "Age_Adj_Death_Rate"

We will see that point is encoded by way of years through the Yr column. There are two columns that enable us to differentiate between completely different race and intercourse classes. Lastly, the final two columns correspond to life expectancy and dying charge.

Let’s have a fast take a look at the info to see the way it appears to be like like for one explicit 12 months:

life_expec %>%
  filter(Yr == 2000)

For the 12 months 2000, there are 9 knowledge factors:

## # A tibble: 9 x 5
##    Yr Race      Intercourse        Avg_Life_Expec Age_Adj_Death_Rate
## 1  2000 All Races Each Sexes           76.8               869
## 2  2000 All Races Feminine               79.7               731.
## 3  2000 All Races Male                 74.3              1054.
## 4  2000 Black     Each Sexes           71.8              1121.
## 5  2000 Black     Feminine               75.1               928.
## 6  2000 Black     Male                 68.2              1404.
## 7  2000 White     Each Sexes           77.3               850.
## 8  2000 White     Feminine               79.9               715.
## 9  2000 White     Male                 74.7              1029.

One 12 months has 9 completely different rows, each equivalent to a unique demographic division. For this visualization, we’ll give attention to america total, so we’ll have to filter the info down accordingly:

life_expec <- life_expec %>%
  filter(Race == "All Races", Intercourse == "Each Sexes")

The information is in place, so we are able to pipe it right into a ggplot() perform to start making a graph. We use the ggplot() perform to point that we need to create a plot.

This code produces a clean graph (as we see under). However it now “is aware of” to make use of the life_expec knowledge, though we do not see it charted but.


Constructing the Axes

Now that we’ve ready the info, we are able to begin constructing our visualization. The following layer that we have to set up are the axes. We’re thinking about how life expectancy adjustments with time, so this means what our two axes are: Yr and Avg_Life_Expec.

To be able to specify the axes, we have to use the aes() perform. aes is brief for “aesthetic”, and it’s the place we inform ggplot what columns we need to use for various elements of the plot. We are attempting to have a look at life expectancy by time, so which means Yr will go to the x-axis and Avg_Life_Expec will go to the y-axis.

life_expec %>%
  ggplot(aes(x = Yr, y = Avg_Life_Expec))

With the addition of the aes() perform, the graph now is aware of what columns to attribute to the axes:


However discover that there’s nonetheless nothing on the plot! We nonetheless want to inform ggplot() what sort of shapes to make use of to visualise the relationships between Yr and Avg_Life_Expec.

Specifying Geoms

Usually once we consider visualizations, we usually take into consideration the kind of graph because it’s actually the shapes that we see that inform us a lot of the data. Whereas the ggplot2 bundle offers us a number of flexibility by way of selecting a form to attract the info, it’s price taking a while to contemplate which one is finest for our query.

We are attempting to visualise how life expectancy has modified by time. Because of this there must be a approach for us to match the previous immediately with the long run. In different phrases, we wish a form that helps present a relationship between two consecutive years. For this, a line graph is nice.

To create a line graph with ggplot(), we use the geom_line() perform. A geom is the identify for the precise form that we need to use to visualise the info. All the capabilities which might be used to attract these shapes have geom in entrance of them. geom_line() creates a line graph, geom_point() creates a scatter plot, and so forth.

life_expec %>%
  ggplot(aes(x = Yr, y = Avg_Life_Expec)) +

Discover how after the usage of the ggplot() perform, we begin to add extra layers to it utilizing a + signal. That is vital to notice as a result of we use %>% to inform ggplot() what knowledge to perform. After utilizing ggplot(), we use + so as to add extra layers to the plot.


This graph is precisely what we have been searching for! Taking a look on the common pattern, life expectancy has grown over time.

We might cease the plot right here if we have been simply wanting on the knowledge shortly, however that is hardly ever the case. Extra widespread is that you simply’ll be making a visualization for a report or for others in your staff. On this case, the plot just isn’t full: if we have been to present it to a teammate with no context, they wouldn’t perceive the plot. Ideally, your entire plots ought to be capable to clarify themselves by the annotations and titles.

Including a Title and Axis Labels

At present the graph retains the column names because the labels for each of the axes. That is adequate for Yr, however we’ll need to change up the y-axis. To be able to change the axis labels for a plot, we are able to use the labs() perform and add it as a layer onto the plot. labs() can change each the axis labels in addition to the title, so we’ll incorporate that right here.

life_expec %>% 
  ggplot(aes(x = Yr, y = Avg_Life_Expec)) + 
  geom_line() + 
    title = "United States Life Expectancy: 100 Years of Change",
    y = "Common Life Expectancy (Years)"

Our remaining polished graph is:


Conclusion: ggplot2 is Highly effective!

In just a few strains of code, we produced an important visualization that tells us every little thing we have to find out about life expectancy for the overall inhabitants in america. Visualization is a vital talent for all knowledge analysts, and R makes it straightforward to select up.

Take a look at our Data Analyst in R path for those who’re thinking about studying extra! The Information Analyst in R path features a course on data visualization in R utilizing ggplot2, the place you’ll discover ways to:

  • Visualize adjustments over time utilizing line graphs.
  • Use histograms to grasp knowledge distributions.
  • Evaluate graphs utilizing bar charts and field plots.
  •  Perceive relationships between variables utilizing scatter plots.


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