Creating Graphs with ggplot2

1. Introduction

This tutorial will focus on creating graphs to effectively communicate key patterns within a dataset. While many software packages allow the user to make basic plots, it can be challenging to create plots that are customized to address a specific idea. While there are numerous ways to create graphs, this tutorial will focus on the R package ggplot2, created by Hadley Wickham.

The key function used in ggplot2 is ggplot()the grammar of graphics plot. This function is different from other graphics functions because it uses a particular grammar inspired by Leland Wilkinson’s landmark book, The Grammar of Graphics, that focused on thinking about, reasoning with and communicating with graphics. It enables layering of independent components to create custom graphics.

Data: In this tutorial, we will use the AmesHousing data, which provides information on the sales of individual residential properties in Ames, Iowa from 2006 to 2010. The data set contains 2930 observations, and a large number of explanatory variables involved in assessing home values. A full description of this dataset can be found here.

To start, we will focus on just a few variables:

• SalePrice: The sale price of the home
• GrLivArea: The above ground living area in the home (in square feet)
• Fireplaces: The number of fireplaces in the home
• KitchenQuality: The quality rating of the kitchen (Excellent, Good, Average, Fair, or Poor)

Select the Run Code button on the right to run the code in the section below.

# The code below uses the head() function to view the first four lines of the AmesHousing data.

2. The structure of the ggplot function

All ggplot functions must have at least three components:

• data frame: In this activity we will be using the AmesHousing data.
• geom: this determines the type of geometric shape used to display the data, such as line, bar, point, or area.
• aes: this determines how variables in the data are mapped to visual properties (aesthetics) of geoms. This can include x position, y position, color, shape, fill, and size.

Thus the structure of a graphic made with ggplot() could have the following form:

ggplot(data, aes(x, y)) + geom_line()

In the following histogram we use SalePrice as our x variable.
# Create a histogram of housing prices
ggplot(data=AmesHousing, mapping = aes(SalePrice)) + geom_histogram()

In the above code, the terms data= and mapping= are not required, but are used for clarification. For example, the following code will produce identical results:
ggplot(AmesHousing, aes(SalePrice)) + geom_histogram().

In the following scatterplot we use GrLivArea as our x variable and SalePrice as or y variable.
# Create a scatterplot of above ground living area by sales price
ggplot(data=AmesHousing, mapping= aes(x=GrLivArea, y=SalePrice)) + geom_point()

Questions:

1. Create a scatterplot using ggplot with Fireplaces as the x-axis and SalePrice as the y-axis.
2. Two forms for ggplot functions: The following two lines of code above would provide identical results.
• ggplot(data, aes(x, y)) + geom_line()
• ggplot(data) + geom_line(aes(x, y))

In the first case, the aes is set as the default for all geoms. In essense, the same x and y variables are used throughout the entire graphic. However, as graphics get more complex, it is often best to creating local aes mappings for each geom as shown in the second line of code.

Modify the code for histogram above so that the aes is listed within the geom. However the resulting graph should look identical to the one above.

3. Making modifications to graphics using ggplot2

It is easy to make modifications to the color, shape and transparency of the points in a scatterplot.

ggplot(data=AmesHousing) +
geom_point(mapping = aes(x=log(GrLivArea), y=log(SalePrice), color=KitchenQuality), alpha = .5, shape = 1)

• In the code above, adjust alpha to any values between 0 and 1. How does changing alpha modify the points on a graph?
• Adjust the shape values. For example, what symbol is used when shape = 1?
• Run the above code using geom_point(aes(x=log(GrLivArea), y=log(SalePrice)), color = "violet", alpha = .5,shape = 10). What color corresponds to Kitchen Quality = Good?

Notice that fixed color names are given in quotes, color = "navy". However, if we select colors based upon a variable from our data frame, we treat it as an explanatory variable, and place it within the aes part of our code.

The scatterplot above suffers from overplotting, that is, many values are being plotted on top of each other many times. We can use the alpha argument to adjust the transparency of points so that higher density regions are darker. By default, this value is set to 1 (non-transparent), but it can be changed to any number between 0 and 1, where smaller values correspond to more transparency. Another useful technique is to use the facet option to render scatterplots for each level of an additional categorical variable, such as kitchen quality. In ggplot2, this is easily done using the facet_grid() layer.

# Create distinct scatterplots for each type of kitchen quality
ggplot(data=AmesHousing) +
geom_point(aes(x=log(GrLivArea), y=log(SalePrice)), alpha = .5,shape = 10) +
facet_grid(. ~ KitchenQuality)

4. Adding layers to graphics using the ggplot function

In the following code, we layer additional components onto the two graphs shown in the previous section.

ggplot(data=AmesHousing) +
geom_histogram(mapping = aes(SalePrice/100000),
breaks=seq(0, 7, by = 1), col="red", fill="lightblue") +
geom_density(mapping = aes(x=SalePrice/100000, y = (..count..)))  +
labs(title="Figure 1: Housing Prices in Ames, Iowa (in \$100,000)",
x="Sale Price of Individual Homes")

Remarks:

• The histogram geom transforms the SalePrice, modifies the bin size and changes the color.
• geom_density overlays a density curve on top of the histogram.
• Typically density curves and histrograms have very different scales, here we use y = (..count..) to modify the density. Alternatively, we could specify aes(x = SalePrice/100000, y = (..density..)) in the histogram geom.
• The labs() command adds a title and an x-axis label. A y-axis label can also be added by using y = " “.

In the code below we create three scatterplots of the log of the above ground living area by the log of sales price

ggplot(data=AmesHousing, aes(x=log(GrLivArea), y=log(SalePrice)) ) +
geom_point(shape = 3, color = "darkgreen") +
geom_smooth(method=lm,  color="green") +
labs(title="Figure 2: Housing Prices in Ames, Iowa")
ggplot(data=AmesHousing) +
geom_point(aes(x=log(GrLivArea), y=log(SalePrice), color=KitchenQuality),shape=2, size=2) +
geom_smooth(aes(x=log(GrLivArea), y=log(SalePrice), color=KitchenQuality),
method=loess, size=1) +
labs(title="Figure 3: Housing Prices in Ames, Iowa")
ggplot(data=AmesHousing) +
geom_point(mapping = aes(x=log(GrLivArea), y=log(SalePrice), color=KitchenQuality)) +
geom_smooth(mapping = aes(x=log(GrLivArea), y=log(SalePrice), color=KitchenQuality),
method=lm, se=FALSE, fullrange=TRUE) +
facet_grid(. ~ Fireplaces) +
labs(title="Figure 4: Housing Prices in Ames, Iowa")

Remarks:

• geom_point is used to create a scatterplot. As shown in Figure 2, multiple shapes can be used as points. The Data Visualization Cheat Sheet lists several shape options
• geom_smooth adds a fitted line through the data.
• method=lm specifies a linear regression line. method=loess creates a smooth fit curve.
• se=FALSE removes the shaded confidence regions around each line.
• fullrange=TRUE extends all regression lines to the same length
• facet_grid and facet_wrap commands are used to create multiple plots. In Figure 4, we have created separate scatterplots based upon the number of fireplaces.
• When assigning fixed characteristics, (such as color, shape or size), the commands occur outside the aes, as in Figure 2, color="green". When characteristics are dependent on the data, the command should occur within the aes, such as in Figure 3, color=KitchenQuality.

In the above examples, only a few geoms are listed. The ggplot2 website lists each geom and gives detailed examples of how they are used.

Questions:

1. Create a histogram of the above ground living area, GrLivArea. You may get a warning related to bin size. Try specifying the breaks for the histogram, breaks=seq(0, 6000, by = 1000) or breaks=seq(0, 6000, by = 100).
# A histogram of above ground living area
ggplot(data=AmesHousing) +
geom_histogram(mapping = aes(GrLivArea))
1. Create a scatterplot using Fireplaces as the explanatory variable and SalePrice as the response variable. Include a regression line, a title, and labels for the x and y axes.
# Create a scatterplot of above ground living area by sales price
ggplot(data=AmesHousing, aes(x=Fireplaces, y=SalePrice)) +
geom_point() +
geom_smooth(method=lm) +
labs(title="Housing Prices in Ames, Iowa", x="Fireplaces", y = "Sale Price")

5. Additional Considerations with R graphics

Influence of data types on graphics: If you use the str command after reading data into R, you will notice that each variable is assigned one of the following types: Character, Numeric (real numbers), Integer, Complex, or Logical (TRUE/FALSE). In particular, the variable Fireplaces in considered an integer. In the code below we try to color and fill a density graph by an integer value. Notice that the color and fill commands appear to be ignored in the graph.
# str(AmesHousing)
ggplot(data=AmesHousing) +
geom_density(aes(SalePrice, color = Fireplaces,  fill = Fireplaces))

In the following code, we use the dplyr package to modify the AmesHousing data; we first restrict the dataset to only houses with less than three fireplaces and then create a new variable, called Fireplace2. The as.factor command creates a factor, wich is a variable that contains a set of numeric codes with character-valued levels. Notice that the color and fill command now work properly.

# Create a new data frame with only houses with less than 3 fireplaces
AmesHousing2 <- filter(AmesHousing, Fireplaces < 3)
# Create a new variable called Fireplace2
AmesHousing2 <-mutate(AmesHousing2,Fireplace2=as.factor(Fireplaces))
#str(AmesHousing2)

ggplot(data=AmesHousing2) +
geom_density(aes(SalePrice, color = Fireplace2,  fill = Fireplace2), alpha = 0.2)

Customizing graphs: In addition to using a data frame, geoms, and aes, several additional components can be added to customize each graph, such as: stats, scales, themes, positions, coordinate systems, labels, and legends. We will not discuss all of these components here, but the materials in the references section provide detailed explanations. In the code below we provide a few examples on how to customize graphs.

ggplot(AmesHousing2, aes(x = Fireplace2, y = SalePrice, color = KitchenQuality)) +
geom_boxplot(position = position_dodge(width = 1)) +
coord_flip()+
labs(title="Housing Prices in Ames, Iowa") +
theme(plot.title = element_text(family = "Trebuchet MS", color = "blue", face="bold", size=12, hjust=0))

Remarks:

• position is used to address geoms that would take the same space on a graph. In the above boxplot, position_dodge(width = 1) adds a space between each box. For scatterplots, position = position_jitter() puts spaces between overlapping points.
• theme is used to change the style of a graph, but does not change the data or geoms. The above code is used to modify only the title in a boxplot. A better approach for beginners is to choose among themes that were created to customize the overall graph. Common examples are theme_bw(), theme_classic(), theme_grey(), and theme_minimal(). You can also install the ggthemes package for many more options.

Questions:

1. In the boxplot, what is done by the code coord_flip()?
2. Create a new boxplot, similar to the one above, but use theme_bw() instead of the given theme command. Explain how the graph changes.
3. Use the tab completion feature in RStudio (type theme and hit the Tab key to see various options) to determine what theme is the default for most graphs in ggplot.