Stat 480: Graphics with ggplot2

Questions for the fbi data

Plan for answers

• Explore how one (or more) variables are distributed: barchart or histogram
• Explore how two variables are related: scatterplot, boxplot, tile plot
• Explore how two variables are related, conditioned on other variables: facetting, color & other aesthetics

Different version of the data

For the exploration, we will use a different shape of the fbi data - a wide form:

library(classdata)
str(fbiwide)

## 'data.frame':    2749 obs. of  14 variables:
##  $ State              : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ Abb                : chr  "AL" "AL" "AL" "AL" ...
##  $ Year               : int  1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 ...
##  $ Population         : int  3302000 3358000 3347000 3407000 3462000 3517000 3540000 3566000 3531000 3444165 ...
##  $ Violent.crime      : int  5564 5283 6115 7260 6916 8098 8448 8288 8842 10185 ...
##  $ Murder             : int  427 316 340 316 395 384 415 421 485 404 ...
##  $ Legacy.rape        : int  252 218 192 397 367 341 371 396 494 637 ...
##  $ Rape               : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ Robbery            : int  630 754 828 992 992 1124 1167 1462 1448 1731 ...
##  $ Aggravated.assault : int  4255 3995 4755 5555 5162 6249 6495 6009 6415 7413 ...
##  $ Property.crime     : int  32541 35829 38521 46290 48215 53740 57079 62997 66248 75214 ...
##  $ Burglary           : int  11205 11722 12614 15898 16398 18551 20227 22403 23559 26739 ...
##  $ Larceny.theft      : int  18801 21306 22874 26713 28115 30583 31682 34508 36644 40779 ...
##  $ Motor.vehicle.theft: int  2535 2801 3033 3679 3702 4606 5170 6086 6045 7696 ...

Scatterplots

• need two continuous variables
• we’ll be using the ggplot2 framework for plotting

Why ggplot2

• Wildly popular package for statistical graphics: over 2.5 million downloads from CRAN in 2017 (several thousand times per day)
• Developed by Hadley Wickham (An ISU Alumni)
• Designed to adhere to good graphical practices
• Constructs plots using the concept of layers
• Supports a wide variety plot types and extensions
• http://ggplot2.org/book/ or Hadley’s book ggplot2: Elegant Graphics for Data Analysis for reference

Grammar of Graphics

A graphical representation (plot) consists of:

1. mappings (aes): data variables are mapped to graphical elements
2. layers: geometric elements (geoms, such as points, lines, rectangles, text, …) and statistical transformations (stats, are identity, counts, bins, …)
3. scales: map values in the data space to values in an aesthetic space (e.g. color, size, shape, but also position)
4. coordinate system (coord): normally Cartesian, but pie charts use e.g. polar coordinates
5. facetting: for small multiples (subsets) and their arrangement
6. theme: fine-tune display items, such as font and its size, color of background, margins, …

Scatterplots in ggplot2

aes allows us to specify mappings; scatterplots need a mapping for x and a mapping for y:

ggplot(data = fbiwide, aes(x = Burglary, y = Murder)) +
  geom_point()

ggplot(data = fbiwide, aes(x = log(Burglary), y = log(Murder))) +
  geom_point()

ggplot(data = fbiwide, aes(x = log(Burglary), 
                           y = log(Motor.vehicle.theft))) +
  geom_point()

Revision - Interpreting Scatterplots

• Big patterns
  • Form and direction
  • Strength
• Small patterns
• Deviations from the pattern
  • Outliers

Form

Is the plot linear? Is the plot curved? Is there a distinct pattern in the plot? Are there multiple groups?

Strength

Does the plot follow the form very closely? Or is there a lot of variation?

Direction

Is the pattern increasing? Is the plot decreasing?

Positively: Above (below) average in one variable tends to be associated with above (below) average in another variable.

Negatively: Opposite pattern.

1. Form: Linear
2. Strength: Strong, very close to a straight line.
3. Direction: Two variables are positively associated.
4. No outliers.

1. Form: Roughly linear, two distinct groups (more than 40% and less than 40%.)
2. Strength: not strong. Data points are scattered.
3. Direction: Negatively Associated.
4. Outliers: None.

Aesthetics

Can map other variables to size or colour

ggplot(aes(x = log(Burglary), y = log(Motor.vehicle.theft),
           colour=State), data=fbiwide) + geom_point()

ggplot(aes(x = log(Burglary), y = log(Motor.vehicle.theft),
           colour=Year), data=fbiwide) + geom_point()

ggplot(aes(x = log(Burglary), y = log(Motor.vehicle.theft),
           size=Population), data=fbiwide) + geom_point()

other aesthetics: shape