# Chapter 5 Introduction to Data Graphics

Data graphics provide one of the most accessible, compelling, and expressive modes to investigate and depict patterns in data. This chapter presents examples of standard kinds of data graphics: what they are used for and how to read them. To start, you’ll make simple examples of graphics using an interactive tool. Later, in Chapter 6, you’ll see a unifying framework — a grammar — for describing and specifying graphics, so that you can create custom graphics types that support displaying data in a purposeful way.

There are many different genres of data graphics, and many different variations on each genre. Here are some commonly encountered kinds.

• Scatterplots showing relationships between two or more variables.
• Displays of distribution, such as histograms.
• Bar charts, comparing values of a single variable across groups.
• Maps, showing how a variable relates to geography.
• Network diagrams, showing how entities are connected to one another.

## 5.1 Scatter plots

The main purpose of a scatter plot is to show the relationship between two variables across several or many cases. Most often, there is a Cartesian coordinate system in which the x-axis represents one variable and the y-axis the value of a second variable.

## Example: Growing up

The NCHS data frame gives medical and morphometric measurements of individual people. The scatter plot in Figure shows the relationship between two of the variables, height and age. Each dot is one case. The position of that dot signifies the value of the two variables for that case. Figure 5.1: A scatter plot.

Scatterplots are useful for visualizing a simple relationship between two variables. For instance, you can see in Figure 5.1 the familiar pattern of growth in height from birth to the late teens.

## 5.2 Displays of Distribution

A histogram shows how many cases fall into given ranges of the variable. For instance, Figure 5.2 is a histogram of heights from NCHS. The most common height is about 1.65 m — that’s the location of the tallest bar. Only a handful are taller than 2.0 m. Figure 5.2: A histogram.

A simple alternative to a histogram is a frequency polygon. Frequency polygons let you break things up by other variables. Figure 5.3 shows the distribution of height for each sex, separately. Figure 5.3: A frequency polygon.

## 5.3 Bar Charts

The familiar bar chart is effective when the objective is to compare a few different quantities.

## 5.4 Example: Smoking and death

Based on the NCHS data, how likely is a person to have died during the follow-up period, based on their age and whether they smoke? It’s easy to compare bars to their neighbors. From Figure 5.4, for instance, you can see that at each age, non-smokers were more likely to survive. Figure 5.4: A bar chart

## 5.5 Maps

Using a map to display data geographically helps both to identify particular cases and to show spatial patterns and discrepancy. The map in Figure 5.5 shows oil production in each country. That is, the shading of each country represents the variable oilProd from DataComputing::CountryData. This sort of map, where the fill color of each region reflects the value of a variable, is sometimes called a choropleth map. Figure 5.5: A choropleth map.

## 5.6 Networks

A network is a set of connections, called edges, between elements, called vertices. A vertex corresponds to a case. The network describes which vertices are connected to other vertices.

The DataComputing::NCI60 data set is about the genetics of cancer. The data set looks at more than 40,000 probes for the expression of genes, in each of 60 cancers. In the network here, a vertex is a given cell line. Every vertex is depicted as a dot. The dot’s color and label gives the type of cancer involved. These are Ovarian, Colon, Central Nervous System, Melanoma, Renal, Breast, and Lung cancers. The edges between vertices show pairs of cell lines that had a strong correlation in gene expression. (#fig:Fig-6, cancer-network)A network diagram

The network shows that the melanoma cell lines (ME) are closely related to each other and not so much to other cell lines. The same is true for colon cancer cell lines (CO) and for central nervous system (CN) cell lines.

## 5.7 Constructing Graphics Interactively

There is a simple pattern to creating a data graphic:

1. Choose or create the glyph-ready data frame that will be graphed.
2. Select the kind of graphic: scatterplot, bar chart, map, etc.
3. Decide which variables from the data frame will be assigned to which roles in the graphic: $$x$$- and $$y$$-coordinates, bar lengths, colors, sizes, etc. This is called mapping a variable to a graphical attribute.

Chapter 8 introduces R commands such as ggplot() for drawing graphics. In this chapter, you will use interactive programs to generate the graphics and the corresponding R commands. The resulting R commands can be pasted into an R chunk in an .Rmd file.

Never put the interactive commands into an .Rmd file because there is no one to interact with the session created when compiling. Instead, use the interactive commands in the console to generate graphics commands to paste into an R chunk.

The DataComputing package provides several interactive graphing functions.Instructions for installing DataComputing and other packages are posted at http://Data-Computing.org/ under “Software and Data.” They are interactive in allowing you to use the mouse to specify which variables map to which graphical attributes. The functions are:

• scatterGraphHelper()
• barGraphHelper()
• distributionGraphHelper()
• WorldMap()
• USMap()

The first argument to each of these functions is a data frame whose cases you want to display graphically. The map-making functions also require two more arguments: key= specifies the variable in the data frame that identifies the country or state for each case. fill= specifies the variable to be used for shading each country. Drawing networks will be introduced later.

## 5.8 Scatter Plots

Figure 5.6: The interactive plot tab created using scatterGraphHelper(CountryData). By default, the first two quantitative variables in a data frame are mapped to the $$x$$- and $$y$$-axes. Change this using the drop-down menu to make the scatter plot of interest to you. Consider the relationship between birth rate and death rate among the countries in CountryData. The variables birth and death that give these rates (in births per 1000 people per year).

An appropriate graphic modality is a scatter plot: birth rate against death rate. To make the graph, use the software appropriate for this modality, namely scatterGraphHelper(). As an argument, give the data frame from which the variables are taken: CountryData.

scatterGraphHelper(CountryData)

That one simple command gives two essential details: what data frame to use and what modality of graph to make. After giving that command, something like Figure 5.6 should appear in your “Plots” tab.

Notice three components of the plots tab:

1. A coordinate grid with dots.
2. A menu for mapping variables to attributes.
3. A small gear icon: If you don’t see the menu on your system, click on the gear icon. If the menu runs off the bottom of the screen, make the “Plots” tab taller.

By default, the first two quantitative variables, area and pop, are being used to define the frame. This is not usually what you want.

You can use the scatterGraphHelper() menu (Figure 5.7) set the frame to be death versus birth. The overall pattern is U-shaped; both low and high birth rates are associated with high death rates, while birth rates in the middle tend to have lower death rates.

Figure 5.7: Using the scatterGraphHelper() menu to set the variables displayed on the $$x$$- and $$y$$-axes to be birth and rates. The glyphs in scatterplots — dots here — can have graphical attributes besides from their position in the frame. Standard ones include fill color, shape, size, transparency, and border color. In Figure 5.8, life expectancy is mapped to size. You can see that, for countries with high life expectancy, the death rate is high when the birth rate is low.That’s because when life expectancy is high and birth rate is low, the population tends to be older. Older populations have higher death rates. For countries with low life expectancies, the death rate is high when the birth rate is low.

Figure 5.8: Graphical attributes such as size, shape, and color can be used to represent additional variables. Here, dot size reflects a country’s life expectancy. ## 5.9 Distributions

Frequency polygons or histograms are appropriate for showing how the different values are distributed.

distributionGraphHelper(NCHS, format = "frequency polygon") Figure 5.9: The distribution of body mass index shown with a frequency polygon separately for each sex.

Consider, for instance, how body mass index varies across the subjects in the NCHS data. For a distribution, the measured quantity is mapped to the $$x$$-axis. The $$y$$-values are set by the number of cases at the corresponding $$x$$-value. In the distributionGraphHelper() menu shown in Figure 5.10, sex has been mapped to line type, producing the chart in Figure 5.9.

Figure 5.10: Setting the variable mappings for the frequency polygon plot in Figure 5.9 using the distributionGraphHelper() menu. ## 5.10 Bar Plots

Bar charts use a glyph whose length reflects the value to be presented. Depending on how variables are mapped to graphical attributes, the plots can tell different aspects of the story.

For instance, the individual ballot choices in the 2013 mayoral election in Minneapolis look like this:

Precinct First Second Third Ward
P-01 CAM WINTON DON SAMUELS MARK ANDREW W-12
P-06 BETSY HODGES undervote undervote W-10
P-01 CAM WINTON CHRISTOPHER ROBIN ZIMMERMAN undervote W-12
P-07 BETSY HODGES MARK ANDREW undervote W-7
P-05 DAN COHEN DON SAMUELS MARK ANDREW W-4
P-07 CAM WINTON OLE SAVIOR undervote W-1

You might be interested to make a bar chart of the number of first-choice votes that each candidate received. In this case, as is typical, a bit of data wrangling is called for to create glyph-ready data. For now, don’t worry about the following commands, which will be introduced later.

FirstPlaceTally <-
Minneapolis2013 %>%
rename(candidate=First) %>%
group_by(candidate) %>%
summarise(total = n())

Table 5.1: First place vote tallies in the Minneapolis2013 data

candidate total
ALICIA K. BENNETT 351
BETSY HODGES 28935
BILL KAHN 97
BOB FINE 2094
CAM WINTON 7511
CAPTAIN JACK SPARROW 264
… and so on for 38 rows altogether.

There were 38 candidates in the election (!), but most got only a small number of votes. The results can be displayed effectively with a bar chart.

barGraphHelper(FirstPlaceTally)

Figure 5.11: A bar chart showing the number of first-place votes given to each candidate.  Figure 5.12: The mappings for the vote-tally bar chart in Figure 5.11

The chart shows at a glance that there are just a handful of major candidates. For this plot, the total variable was mapped to the y-axis, the candidate was mapped to the x-axis, and the candidates were ordered from lowest to highest total of votes. Look closely and you’ll see that “undervote” (meaning no candidate was chosen) beat 29 of the candidates.

## 5.11 Making Maps

Showing a variable in a geographical map requires two data frames:

1. A shape file giving latitude and longitude of points on the boundaries.
2. The data frame for the variable that is to be plotted.

There are shape files for all sorts of geographic entities: countries, states, counties, precincts, and so on. To simplify things, the DataComputing package provides two functions: WorldMap() with country boundaries and USMap() with state boundaries in the US. The shape file is pre-set for these functions; you need only provide a data frame with the name of countries (or states) and the variable to be plotted. Figure 5.13 shows how fertility varies from country to country. Figure 5.13: A choropleth map of fertility (children born per woman)

CountryData %>%
WorldMap(key = "country", fill = "fert")                                                                                               + theme(legend.position = "top")