1. Introduction

It is often necessary to create 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.

There are two key functions that are used in ggplot2:

library(ggplot2)
library(dplyr)

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.

# The csv file should be imported into rstudio:
AmesHousing <- read.csv("https://raw.githubusercontent.com/ds4stats/r-tutorials/master/data-viz/data/AmesHousing.csv")
# str(AmesHousing)

2. The basic structure of the ggplot function

All ggplot functions must have at least three components:

Thus the simplest code for a graphic made with ggplot() would have one of the the following forms:

These two lines of code provide identical results. In the first case, the aes is set for all geoms, meaning the same x and y variables are mapped to any geoms that are added. Because more complex graphics can include multiple geoms, it can be advantageous to locally define the aes for each geom as shown in the second line of code.

# Create a histogram of housing prices
ggplot(data=AmesHousing) + geom_histogram(mapping = aes(x = SalePrice))

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

# Create a scatterplot of above ground living area by sales price
ggplot(data=AmesHousing) + geom_point(mapping= aes(x=GrLivArea, y=SalePrice))

Question #1 Open your R Script “Lab2_Name1_Name2” containing answers to the previous lab’s questions and add a comment beneath your earlier answers indicating all subsequent work pertains to the ggplot lab.

Beneath this comment, write code that does the following:

  1. Creates the same histogram as the example above, but modifies the code so that the aes is not within the geom.
  2. Creates a scatterplot using ggplot with Fireplaces as the x-axis and SalePrice as the y-axis.

3. Customizing graphics using the ggplot function

In this section, we layer additional components onto the two graphs shown above.

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 9: Housing Prices in Ames, Iowa (in $100,000)", 
          x="Sale Price of Individual Homes")

Remarks:

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 10: Housing Prices in Ames, Iowa")

ggplot(data=AmesHousing) + 
  geom_point(aes(x=log(GrLivArea), y=log(SalePrice), color=KitchenQual),shape=2, size=2) + 
  geom_smooth(aes(x=log(GrLivArea), y=log(SalePrice), color=KitchenQual), 
          method=loess, size=1) +                        
  labs(title="Figure 11: Housing Prices in Ames, Iowa") 

ggplot(data=AmesHousing) +
  geom_point(mapping = aes(x=log(GrLivArea), y=log(SalePrice), color=KitchenQual)) +
  geom_smooth(mapping = aes(x=log(GrLivArea), y=log(SalePrice), color=KitchenQual), 
      method=lm, se=FALSE, fullrange=TRUE) +                             
  facet_grid( ~ Fireplaces) +                      
  labs(title="Figure 12: Housing Prices in Ames, Iowa")

Remarks:

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.

Question #2: Add a comment indicating you’re answering question 2, beneath the comment write code that does the following:

  1. Creates a scatterplot using YearBuilt as the explanatory variable and SalePrice as the response variable. Include a regression line, a title, and labels for the x and y axes.
  2. Modifies the scatterplot in Part A by replacing the regression line with a loess smoother, and coloring the points by the overall condition of the home, OverallCond.

4. 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, which is a variable that contains a set of numeric codes that map to 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 = PavedDrive)) +
  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:

Question #3: Add a comment indicating you’re answering question 3, beneath the comment answer the following:

  1. In the density plot above, explain what the color, fill, and alpha commands are used for. Hint: try running the code with and without these commands or use the Data Visualization Cheat Sheet.
  2. In the boxplot, what is done by the code coord_flip()?
  3. Create a new boxplot, similar to the one above, but use theme_bw() instead of the given theme command. Explain how this changes the graph.

5. Practice

Question #4: Add a comment to your lab write-up indicating you’re working on question 4. This question requires you to use the dplyr package to manipulate the dataset before making any graphics.

  1. Restrict the AmesHousing data to only sales under normal conditions. In other words, SaleCondition == "Norm"
  2. Create a new variable called TotalSqFt = GRLivArea + TotalBsmtSF and remove any homes with more than 3000 total square feet.
  3. With this modified dataset, create a graphic involving no more than three explanatory variables that best illustrates how to predict home’s sale price. For example, Figure 12 uses a linear model of kitchen quality, above ground square footage, and number of fireplaces to predict sale price.

Additional resources