Getting Prepped

library(tidyverse)

Part 1: More ggplotting

Scenario: Do cars with big engines use more fuel than cars with small engines?

OK so you probably know the answer but let’s make the justification as precise as possible.

Let’s use the mpg data frame found in ggplot2. The data frame contains observations collected by the US Environment Protection Agency on 38 car models. To see all of the unique values, we can run

unique(mpg$manufacturer)

##  [1] "audi"       "chevrolet"  "dodge"      "ford"       "honda"     
##  [6] "hyundai"    "jeep"       "land rover" "lincoln"    "mercury"   
## [11] "nissan"     "pontiac"    "subaru"     "toyota"     "volkswagen"

which is equivalent to the following as a tibble

mpg %>%
  select(manufacturer) %>%
  distinct()

## # A tibble: 15 x 1
##    manufacturer
##    <chr>       
##  1 audi        
##  2 chevrolet   
##  3 dodge       
##  4 ford        
##  5 honda       
##  6 hyundai     
##  7 jeep        
##  8 land rover  
##  9 lincoln     
## 10 mercury     
## 11 nissan      
## 12 pontiac     
## 13 subaru      
## 14 toyota      
## 15 volkswagen

glimpse(mpg)

## Rows: 234
## Columns: 11
## $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", …
## $ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", …
## $ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2…
## $ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 20…
## $ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8,…
## $ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "aut…
## $ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "…
## $ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, …
## $ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, …
## $ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "…
## $ class        <chr> "compact", "compact", "compact", "compact", "compact", "…

ggplot(mpg, aes(x = manufacturer, fill = manufacturer)) +
  geom_bar() +
  geom_text(stat = 'count', 
             aes(label = after_stat(count)), 
             vjust = -0.5) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, 
                                   vjust = 1.3,
                                   hjust = 1)) 

mpg %>%
  select(manufacturer) %>%
  group_by(manufacturer) %>%
  tally() %>%
  ungroup()

## # A tibble: 15 x 2
##    manufacturer     n
##  * <chr>        <int>
##  1 audi            18
##  2 chevrolet       19
##  3 dodge           37
##  4 ford            25
##  5 honda            9
##  6 hyundai         14
##  7 jeep             8
##  8 land rover       4
##  9 lincoln          3
## 10 mercury          4
## 11 nissan          13
## 12 pontiac          5
## 13 subaru          14
## 14 toyota          34
## 15 volkswagen      27

Get the `mpg` tibble %>%
  Only show me the column `manufacturer` %>%
   Perform any wrangling by car manufacturer %>%
   Count by car manufacturer %>%
   Dont do any more wrangling by car manufacturer

mpg %>%
  select(manufacturer) %>%
  tally() 

## # A tibble: 1 x 1
##       n
##   <int>
## 1   234

names(mpg)

##  [1] "manufacturer" "model"        "displ"        "year"         "cyl"         
##  [6] "trans"        "drv"          "cty"          "hwy"          "fl"          
## [11] "class"

ggplot(data = mpg) + 
  geom_point(aes(x = displ, y = hwy))

Part 2: Aesthetics

Aesthetics are more than just coloring a plot, you can define directions, shapes, sizes, types, and provide a useful way to bring in a third variable into a static two dimensional image.

So how is this done? Well in a nutshell you can add a third variable to a two dimensional scatterplot by mapping it to an aesthetic. Recall that an aesthetic is a visual property of the objects in your plot.

You can convey information about your data by mapping the aesthetics in your plot to the variables in your dataset. For example, you can map the colors of your points to the class variable to reveal the class of each car.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class)) 

or if you like British English, colour can be used interchangeably

As a reminder, to map an aesthetic to a variable, associate the name of the aesthetic to the name of the variable inside aes(). ggplot2 will automatically assign a unique level of the aesthetic - here a unique color - to each unique value of the variable, a process known as scaling.

ggplot2 will also add a legend that explains which levels correspond to which values but you can turn that off as well.

With that said, we mapped class to the color aesthetic, and though pretty² that may not be the best method since the graphic is a bit hard to decipher immediately. Instead, we could have mapped class to the size aesthetic in the same way.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class)) 

## Warning: Using size for a discrete variable is not advised.

So what’s up with the warning? In this case, the exact size of each point would reveal its class affiliation. So the warning is telling us that mapping an unordered variable (class) to an ordered aesthetic (size) is not a good idea.

Basically you are trying to plot a categorical variable - the manufacturers of cars - against a continuous variable - a vehicle’s fuel efficiency on the highway - and its telling you that you probably shouldn’t do that³.

Ok well let’s try something else to distinguish the types of vehicles.

What about the alpha level better known as transparency, or how much of the color can you see through?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))

## Warning: Using alpha for a discrete variable is not advised.

No? Maybe the shape then

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.

## Warning: Removed 62 rows containing missing values (geom_point).

Uh oh. Something is missing from the plot! So what happened to the SUVs? ggplot2 will only use six shapes at a time. By default, additional groups will go unplotted when you use the shape aesthetic so remember this and you’ll save yourself from throwing a computer.

If you are a controlling person, you can also set the aesthetic properties of your geom manually. For example, we can make all of the points in our plot blue

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

or even WVU Blue

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "#002855")

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, 
                           color = "blue"))

Part 3: Grouping

Maybe you’d like to use multiple colors. Let try something fun based off of this palette

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), 
             color = c("#34738f", "#122f3d", "#be3e2b", "#ed8a45", "#f6de6c"))

Uh oh. That didn’t work. Why not?

Well to ggplot there are 234 different data points and it doesn’t know how you want that color palette distributed. By the power of mathematics, 5 < 234 so you get an error. To fix it, you either assign 1 color to every data point or 234 different colors. Neither of these sound great. So what can we do?

Well we can group by category. That may help. If you run

mpg$class

##   [1] "compact"    "compact"    "compact"    "compact"    "compact"   
##   [6] "compact"    "compact"    "compact"    "compact"    "compact"   
##  [11] "compact"    "compact"    "compact"    "compact"    "compact"   
##  [16] "midsize"    "midsize"    "midsize"    "suv"        "suv"       
##  [21] "suv"        "suv"        "suv"        "2seater"    "2seater"   
##  [26] "2seater"    "2seater"    "2seater"    "suv"        "suv"       
##  [31] "suv"        "suv"        "midsize"    "midsize"    "midsize"   
##  [36] "midsize"    "midsize"    "minivan"    "minivan"    "minivan"   
##  [41] "minivan"    "minivan"    "minivan"    "minivan"    "minivan"   
##  [46] "minivan"    "minivan"    "minivan"    "pickup"     "pickup"    
##  [51] "pickup"     "pickup"     "pickup"     "pickup"     "pickup"    
##  [56] "pickup"     "pickup"     "suv"        "suv"        "suv"       
##  [61] "suv"        "suv"        "suv"        "suv"        "pickup"    
##  [66] "pickup"     "pickup"     "pickup"     "pickup"     "pickup"    
##  [71] "pickup"     "pickup"     "pickup"     "pickup"     "suv"       
##  [76] "suv"        "suv"        "suv"        "suv"        "suv"       
##  [81] "suv"        "suv"        "suv"        "pickup"     "pickup"    
##  [86] "pickup"     "pickup"     "pickup"     "pickup"     "pickup"    
##  [91] "subcompact" "subcompact" "subcompact" "subcompact" "subcompact"
##  [96] "subcompact" "subcompact" "subcompact" "subcompact" "subcompact"
## [101] "subcompact" "subcompact" "subcompact" "subcompact" "subcompact"
## [106] "subcompact" "subcompact" "subcompact" "midsize"    "midsize"   
## [111] "midsize"    "midsize"    "midsize"    "midsize"    "midsize"   
## [116] "subcompact" "subcompact" "subcompact" "subcompact" "subcompact"
## [121] "subcompact" "subcompact" "suv"        "suv"        "suv"       
## [126] "suv"        "suv"        "suv"        "suv"        "suv"       
## [131] "suv"        "suv"        "suv"        "suv"        "suv"       
## [136] "suv"        "suv"        "suv"        "suv"        "suv"       
## [141] "suv"        "compact"    "compact"    "midsize"    "midsize"   
## [146] "midsize"    "midsize"    "midsize"    "midsize"    "midsize"   
## [151] "suv"        "suv"        "suv"        "suv"        "midsize"   
## [156] "midsize"    "midsize"    "midsize"    "midsize"    "suv"       
## [161] "suv"        "suv"        "suv"        "suv"        "suv"       
## [166] "subcompact" "subcompact" "subcompact" "subcompact" "compact"   
## [171] "compact"    "compact"    "compact"    "suv"        "suv"       
## [176] "suv"        "suv"        "suv"        "suv"        "midsize"   
## [181] "midsize"    "midsize"    "midsize"    "midsize"    "midsize"   
## [186] "midsize"    "compact"    "compact"    "compact"    "compact"   
## [191] "compact"    "compact"    "compact"    "compact"    "compact"   
## [196] "compact"    "compact"    "compact"    "suv"        "suv"       
## [201] "pickup"     "pickup"     "pickup"     "pickup"     "pickup"    
## [206] "pickup"     "pickup"     "compact"    "compact"    "compact"   
## [211] "compact"    "compact"    "compact"    "compact"    "compact"   
## [216] "compact"    "compact"    "compact"    "compact"    "compact"   
## [221] "compact"    "subcompact" "subcompact" "subcompact" "subcompact"
## [226] "subcompact" "subcompact" "midsize"    "midsize"    "midsize"   
## [231] "midsize"    "midsize"    "midsize"    "midsize"

that doesn’t help you much in figuring out the types of vehicles we have (yes we did find it before by plotting but assume we hadn’t). Unless you like to clean this type of mess, we simply want to find the unique values and moreover, for coloring purposes, how many we have.

Again to find a list of unique values, we can run

unique(mpg$class)

## [1] "compact"    "midsize"    "suv"        "2seater"    "minivan"   
## [6] "pickup"     "subcompact"

Well that’s better but I’m lazy and don’t want to count so let’s find the number of unique values

length(unique(mpg$class))

## [1] 7

Oh good! There are 7 so that implies when we plot by class (type of vehicle), we need to have seven colors.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

which we did earlier. But to fill it with what we want, we need to override the default colors

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, 
                           color = class)) +
  scale_color_manual(values = c("#490A3D", "#BD1550", "#E97F02", "#F8CA00", "#8A9B0F", "#95C7D8", "#FFBFF5"))

Note that since the class variable (type of vehicle) is discrete, we can use scale_color_manual(). Other discrete scales in ggplot can be found here

Continuous variables use other scales and you can fins more about that and other scales can be found here

Now those dots are pretty small. Let’s make them bigger.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class), size = 2) +
  scale_color_manual(values = c("#490A3D", "#BD1550", "#E97F02", "#F8CA00", "#8A9B0F", "#95C7D8", "#FFBFF5"))

That does look better but why is the size now within the geom_point aesthetic? If you don’t know, that’s ok! We’ll get to that later but for now, think about it.

ggplot(data = mpg) 
+ geom_point(mapping = aes(x = displ, y = hwy))

Part 4: Facets

Another way to add additional variables is with aesthetics. Another way, particularly useful for categorical variables, is to split your plot into facets, subplots that each display one subset of the data set.

To facet your plot by a single variable, you can use facet_wrap() or facet_grid(). Let’s fow now just concentrate on wrapping the data set.

The first argument of facet_wrap() should be a formula, which you create with ~ followed by a variable name - here “formula” is the name of a data structure in R, not a synonym for an “equation”.

The variable that you pass to facet_wrap() should be discrete so it can essentially be split apart.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class), size = 2) +
  scale_color_manual(values = c("#490A3D", "#BD1550", "#E97F02", "#F8CA00", "#8A9B0F", "#95C7D8", "#FFBFF5")) +
 facet_wrap(~ class, nrow = 2)

To facet your plot on the combination of two variables, use facet_grid() to your plot The first argument of this is also a formula. This time the formula should contain two variable names separated by a ~.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class), size = 2) +
  scale_color_manual(values = c("#490A3D", "#BD1550", "#E97F02", "#F8CA00", "#8A9B0F", "#95C7D8", "#FFBFF5")) +
  facet_grid(drv ~ cyl)

If you prefer to not facet particular rows or columns, you can just use a . instead of a variable name to imply the entire data set

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class), size = 2) +
  scale_color_manual(values = c("#490A3D", "#BD1550", "#E97F02", "#F8CA00", "#8A9B0F", "#95C7D8", "#FFBFF5")) +
  facet_grid(. ~ cyl)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = drv, y = cyl))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(drv ~ .)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(. ~ cyl)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)