# Chapter 12 Plotting

“Make it informative, then make it pretty”

There are two major sets of tools for creating plots in R:

Note that other plotting facilities do exist (notably `lattice`

), but base and `ggplot2`

are by far the most popular.

## 12.1 The Dataset

For the following examples, we will be using the `gapminder`

dataset we have used previously. Gapminder is a country-year dataset with information on life expectancy, among other things.

## 12.2 R Base Graphics

The **basic** call takes the following form:

We will also introduce a base R command to help us with creating these plots. To reference a specific column in a dataset, we use a `$`

with the following syntax: `dataset$column`

. See this in action below, where we plot `gdpPercap`

against `lifeExp`

:

### 12.2.1 Scatter and Line Plots

The `type`

argument accepts the following character indicators:

`"p"`

: Point/scatter plots (default plotting behavior).

`"l"`

: Line graphs.

```
# Note that "line" does not create a smooth line, just connected points
plot(x = gap$gdpPercap, y = gap$lifeExp, type="l")
```

`"b"`

: Both line and point plots.

### 12.2.2 Histograms and Density Plots

Histograms display the frequency of different values of a variable.

Histograms require a `breaks`

argument, which determines the number of bins in the plot. Let’s play around with different `breaks`

values:

Density plots are similar; they visualize the distribution of data over a continuous interval.

```
# Create a density object (NOTE: Be sure to remove missing values)
age.density <- density(x=gap$lifeExp, na.rm=T)
# Plot the density object
plot(x=age.density)
```

Density passes a `bw`

parameter, which determines the plot’s “bandwidth.”

### 12.2.3 Labels

Here is the basic call with popular labeling arguments:

From the previous example…

`plot(x = gap$gdpPercap, y = gap$lifeExp, type="p", xlab="GDP per cap", ylab="Life Expectancy", main="Life Expectancy ~ GDP") # Add labels for axes and overall plot`

### 12.2.4 Axis and Size Scaling

Currently it is hard to see the relationship between the points due to some strong outliers in GDP per capita. We can change the scale of units on the x-axis using scaling arguments.

Here is the basic call with popular scaling arguments:

From the previous example…

```
# Create a basic plot
plot(x = gap$gdpPercap, y = gap$lifeExp, type="p")
# Limit gdp (x-axis) to between 1,000 and 20,000
plot(x = gap$gdpPercap, y = gap$lifeExp, xlim = c(1000,20000))
# Limit gdp (x-axis) to between 1,000 and 20,000, increase point size to 2
plot(x = gap$gdpPercap, y = gap$lifeExp, xlim = c(1000,20000), cex=2)
# Limit gdp (x-axis) to between 1,000 and 20,000, decrease point size to 0.5
plot(x = gap$gdpPercap, y = gap$lifeExp, xlim = c(1000,20000), cex=0.5)
```

### 12.2.5 Graphical Parameters

We can change the points with a number of graphical options:

- Colors

```
library(dplyr)
colors() %>%
head(20)# View first 20 elements of the color vector
#> [1] "white" "aliceblue" "antiquewhite" "antiquewhite1"
#> [5] "antiquewhite2" "antiquewhite3" "antiquewhite4" "aquamarine"
#> [9] "aquamarine1" "aquamarine2" "aquamarine3" "aquamarine4"
#> [13] "azure" "azure1" "azure2" "azure3"
#> [17] "azure4" "beige" "bisque" "bisque1"
```

Another option: R Color Infographic

- Point Styles and Widths

```
# Change point style to crosses
plot(x = gap$gdpPercap, y = gap$lifeExp, type="p", pch=3)
# Change point style to filled squares
plot(x = gap$gdpPercap, y = gap$lifeExp, type="p", pch=15)
# Change point style to filled squares and increase point size to 3
plot(x = gap$gdpPercap, y = gap$lifeExp, type="p", pch=15, cex=3)
# Change point style to "w"
plot(x = gap$gdpPercap, y = gap$lifeExp, type="p", pch="w")
# Change point style to "$" and increase point size to 2
plot(x = gap$gdpPercap, y = gap$lifeExp, type="p", pch="$", cex=2)
```

- Line Styles and Widths

```
# Line plot with solid line
plot(x = gap$gdpPercap, y = gap$lifeExp, type="l", lty=1)
# Line plot with medium dashed line
plot(x = gap$gdpPercap, y = gap$lifeExp, type="l", lty=2)
# Line plot with short dashed line
plot(x = gap$gdpPercap, y = gap$lifeExp, type="l", lty=3)
# Change line width to 2
plot(x = gap$gdpPercap, y = gap$lifeExp, type="l", lty=3, lwd=2)
# Change line width to 5
plot(x = gap$gdpPercap, y = gap$lifeExp, type="l", lwd=5)
# Change line width to 10 and use dash-dot
plot(x = gap$gdpPercap, y = gap$lifeExp, type="l", lty=4, lwd=10)
```

### 12.2.6 Annotations, Reference Lines, and Legends

- Text

We can add text to an arbitrary point on the graph like this:

```
# Plot the line first
plot(x = gap$gdpPercap, y = gap$lifeExp, type="p")
# Now add the label
text(x=40000, y=50, labels="Evens Out", cex = .75)
```

We can also add labels for every point by passing in a vector of text:

```
# First randomly select rows for a smaller gapaset
small <- gap %>% sample_n(100)
# Plot the line first
plot(x = small$gdpPercap, y = small$lifeExp, type="p")
# Now add the label
text(x = small$gdpPercap, y = small$lifeExp, labels = small$country)
```

- Reference Lines

```
# Plot the line
plot(x = gap$gdpPercap, y = gap$lifeExp, type="p")
# Now the guides
abline(v=40000, h=75, lty=2)
```

## 12.3 ggplot2

#### Why `ggplot`

?

- More elegant and compact code than R base graphics.
- More aesthetically pleasing defaults than
`lattice`

. - Very powerful for exploratory data analysis.
- Follows a grammar, just like any language.
- It defines basic components that make up a sentence. In this case, the grammar defines components in a plot.
*G*rammar of*g*raphics originally coined by Lee Wilkinson.

### 12.3.1 Grammar

The general call for `ggplot2`

looks like this:

The *grammar* involves some basic components:

**Data**: A dataframe.**Aes**thetics: How your data are represented visually, i.e., its “mapping.” Which variables are shown on the x- and y-axes, as well as color, size, shape, etc.**Geom**etry: The geometric objects in a plot – points, lines, polygons, etc.

The key to understanding `ggplot2`

is thinking about a figure in layers, just like you might do in an image editing program like Photoshop, Illustrator, or Inkscape.

Let’s look at an example:

So the first thing we do is call the `ggplot`

function. This function lets R know that we are creating a new plot, and any of the arguments we give the `ggplot`

function are the global options for the plot: They apply to all layers on the plot.

We have passed in two arguments to `ggplot`

. First, we told `ggplot`

what ** data** we wanted to show on our figure – in this example, the

`gapminder`

data we read in earlier.For the second argument, we passed in the ** aes** function, which tells

`ggplot`

how variables in the data map to aesthetic properties of the figure – in this case, the x and y locations. Here we told `ggplot`

we wanted to plot the `lifeExp`

column of the `gapminder`

dataframe on the x-axis, and the `gdpPercap`

column on the y-axis.Notice that we did not need to explicitly pass `aes`

these columns (e.g., `x = gapminder$lifeExp`

). This is because `ggplot`

is smart enough to know to look in the data for that column!

By itself, the call to `ggplot`

is not enough to draw a figure:

We need to tell `ggplot`

how we want to visually represent the data, which we do by adding a new ** geom** layer. In our example, we used

`geom_point`

, which tells `ggplot`

we want to visually represent the relationship between x and y as a scatterplot of points:```
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp)) +
geom_point()
# Same as
# my_plot <- ggplot(data = gap, aes(x = gdpPercap, y = lifeExp))
# my_plot + geom_point()
```

#### Challenge 1.

Modify the example so that the figure visualizes how life expectancy has changed over time.

Hint: The `gapminder`

dataset has a column called `year`

, which should appear on the x-axis.

### 12.3.2 Anatomy of `aes`

In the previous examples and challenge, we have used the `aes`

function to tell the scatterplot `geom`

about the **x** and **y** locations of each point. Another aesthetic property we can modify is the point **color**.

Normally, specifying options like `color="red"`

or `size=10`

for a given layer results in its contents being red and quite large. Inside the `aes()`

function, however, these arguments are given entire variables whose values will then be displayed using different realizations of that aesthetic.

**Color** is not the only aesthetic argument we can set to display variation in the data. We can also vary by shape, size, etc.

### 12.3.3 Layers

In the previous challenge, you plotted `lifExp`

over time. Using a scatterplot probably is not the best for visualizing change over time. Instead, let’s tell `ggplot`

to visualize the data as a line plot:

Instead of adding a `geom_point`

layer, we have added a `geom_line`

layer. We have also added the `**by**`

aesthetic, which tells `ggplot`

to draw a line for each country.

But what if we want to visualize both lines and points on the plot? We can simply add another layer to the plot:

```
ggplot(data = gap, aes(x=year, y=lifeExp, by=country, color=continent)) +
geom_line() +
geom_point()
```

It is important to note that each layer is drawn on top of the previous layer. In this example, the points have been drawn on top of the lines. Here is a demonstration:

```
ggplot(data = gap, aes(x=year, y=lifeExp, by=country)) +
geom_line(aes(color=continent)) +
geom_point()
```

In this example, the aesthetic mapping of **color** has been moved from the global plot options in `ggplot`

to the `geom_line`

layer, so it no longer applies to the points. Now we can clearly see that the points are drawn on top of the lines.

#### Challenge 2.

Switch the order of the point and line layers from the previous example. What happened?

### 12.3.4 Labels

Labels are considered to be their own layers in `ggplot`

.

```
# Add x- and y-axis labels
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp, color=continent)) +
geom_point() +
xlab("GDP per capita") +
ylab("Life Expectancy") +
ggtitle("My fancy graph")
```

So are scales:

### 12.3.5 Transformations and Stats

`ggplot`

also makes it easy to overlay statistical models over the data. To demonstrate, we will go back to an earlier example:

We can change the scale of units on the x-axis using the `scale`

functions, which control the mapping between the data values and visual values of an aesthetic.

```
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp, color=continent)) +
geom_point() +
scale_x_log10()
```

The `log10`

function applied a transformation to the values of the `gdpPercap`

column before rendering them on the plot, so that each multiple of 10 now only corresponds to an increase in 1 on the transformed scale, e.g., a GDP per capita of 1,000 is now 3 on the y-axis, a value of 10,000 corresponds to 4 on the x-axis, and so on. This makes it easier to visualize the spread of data on the x-axis.

We can fit a simple relationship to the data by adding another layer, `geom_smooth`

:

```
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp, color=continent)) +
geom_point() +
scale_x_log10() +
geom_smooth(method="lm")
#> `geom_smooth()` using formula 'y ~ x'
```

Note that we have 5 lines, one for each region, because of the ** color** option in the global

`aes`

function. But if we move it, we get different results:```
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp)) +
geom_point(aes(color=continent)) +
scale_x_log10() +
geom_smooth(method="lm")
#> `geom_smooth()` using formula 'y ~ x'
```

So, there are two ways an aesthetic can be specified. Here, we set the ** color** aesthetic by passing it as an argument to

`geom_point`

. Previously in the lesson, we used the `aes`

function to define a *mapping*between data variables and their visual representation.

We can make the line thicker by setting the ** size** aesthetic in the

`geom_smooth`

layer:```
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp)) +
geom_point(aes(color=continent)) +
scale_x_log10() +
geom_smooth(method="lm", size = 1.5)
#> `geom_smooth()` using formula 'y ~ x'
```

#### Challenge 3.

Modify the color and size of the points on the point layer in the previous example so that they are fixed (i.e., not reflective of continent).

Hint: Do not use the `aes`

function.

### 12.3.6 Facets

Earlier, we visualized the change in life expectancy over time across all countries in one plot. Alternatively, we can split this out over multiple panels by adding a layer of **facet** panels:

### 12.3.7 Legend and Scale Manipulations

When creating plots with `ggplot`

, you will notice that legends are sometimes automatically produced. Additionally, you will often need to transform the axis scales, similar to the modifcations we made with the base plots.

We can easily set axis limits with `xlim`

and `ylim`

layers, or with the `limits`

argument withing `scale_x_log10`

:

```
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp)) +
geom_point() +
scale_x_log10(limits = c(100, 100000)) +
ylim(0, 100)
#> Warning: Removed 3 rows containing missing values (geom_point).
```

There are many other axis features we can change. For example, we can change the angle of an axis text with `theme`

or reverse the direction of an axis with `scale_x_reverse`

or `scale_y_reverse`

. Stack Overflow is a great resource for a variety of axis transformations.

```
library(scales)
#>
#> Attaching package: 'scales'
#> The following object is masked from 'package:purrr':
#>
#> discard
#> The following object is masked from 'package:readr':
#>
#> col_factor
ggplot(data = gap, aes(x = gdpPercap, y = lifeExp)) +
geom_point() +
theme(axis.text.x = element_text(angle=45)) +
scale_x_reverse()
```

Legend manipulations can be a little trickier. Let’s consider a plot where we group our observations by continent:

```
legend_plot <- ggplot(data = gap, aes(x = log(gdpPercap), y = lifeExp, color=continent)) +
geom_point() +
scale_x_log10()
legend_plot
```

First, let’s change the legend position:

We can also remove the title of the legend for self-explanatory groupings:

We can reverse the order of the groups with the `guides`

layer. Here, we specify that we want to reverse `color`

, because that was the original aesthetic that we specified to create the groupings and the legend:

Finally, we can also change the legend title by modifying the label for `color`

:

And we can change the legend labels and colors. Again, we are using `scale_color_manual`

because we originally specified the groups with the `**color**`

aesthetic:

### 12.3.8 Putting Everything Together

Here are some other common `geom`

layers:

**Bar Plots**

```
# Count of lifeExp bins
ggplot(data = gap, aes(x = lifeExp)) +
geom_bar(stat="bin")
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# With color representing regions
ggplot(data = gap, aes(x = lifeExp, fill = continent)) +
geom_bar(stat="bin")
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```

**Box Plots**

This is just a taste of what you can do with `ggplot2`

.

RStudio provides a really useful cheat sheet of the different layers available, and more extensive documentation is available on the `ggplot2`

website.

Finally, if you have no idea how to change something, a quick Google search will usually send you to a relevant question and answer on Stack Overflow with reusable code to modify!

#### Challenge 4.

Create a density plot of GDP per capita, filled by continent.

Advanced:

- Transform the x-axis to better visualize the data spread.
- Add a facet layer to panel the density plots by year.

## 12.4 Saving Plots

There are two basic image types:

**Raster/Bitmap**(.png, .jpeg)

Every pixel of a plot contains its own separate coding; not so great if you want to resize the image.

**Vector**(.pdf, .ps)

Every element of a plot is encoded with a function that gives its coding conditional on several factors; this is great for resizing.

**Exporting with ggplot**