É¯èª a Continuous Variable Can Not Be Mapped to Shape

3.3.1 Solutions

The following questions stem from Section 3.3 of Chapter 3 (Data Visualization) in R for Data Science. The "solutions" offer some answers. Issues and pull requests are very welcome here.

Load packages

1. What's gone wrong with this code? Why are the points not blue?

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

The color aesthetic needs to be set outside of the aes function, as follows:

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

2. Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset.)

Categorical: manufacturer, model, year, trans, drv, fl, and class.

Continuous: displ, cyl, cty, and hwy.

How can you see this information when you run mpg?

## # A tibble: 234 x 11 ##    manufacturer model    displ  year   cyl trans   drv     cty   hwy fl    ##    <chr>        <chr>    <dbl> <int> <int> <chr>   <chr> <int> <int> <chr> ##  1 audi         a4        1.80  1999     4 auto(l… f        18    29 p     ##  2 audi         a4        1.80  1999     4 manual… f        21    29 p     ##  3 audi         a4        2.00  2008     4 manual… f        20    31 p     ##  4 audi         a4        2.00  2008     4 auto(a… f        21    30 p     ##  5 audi         a4        2.80  1999     6 auto(l… f        16    26 p     ##  6 audi         a4        2.80  1999     6 manual… f        18    26 p     ##  7 audi         a4        3.10  2008     6 auto(a… f        18    27 p     ##  8 audi         a4 quat…  1.80  1999     4 manual… 4        18    26 p     ##  9 audi         a4 quat…  1.80  1999     4 auto(l… 4        16    25 p     ## 10 audi         a4 quat…  2.00  2008     4 manual… 4        20    28 p     ## # ... with 224 more rows, and 1 more variable: class <chr>

Running mpg displays the mpg data as a tibble. Under the variables' names, you can see their corresponding variable types, which are enclosed within arrows (e.g., below manufacturer, you see <chr>). Characters (chr), factors (fctr), and logicals (lgl) are all categorical variables, whereas integers (int) and floating point numbers (dbl) are both continuous variables.

3. Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

For the color aesthetic, continuous variables get mapped along a color spectrum.

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

Encoding cyl, the number of cylinders in a vehicle, as a categorical variable "fixes" this issue, such that vehicles with different numbers of cylinders get assigned to different colors.

ggplot(data              =              mpg)              +              geom_point(mapping              =              aes(x              =              displ,              y              =              hwy,              color              =              as.factor(cyl)))

For continuous variables that are discrete and have a small number values, the next plot shows that the size aesthetic behaves the same with continuous variables as it does with categorical variables.

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

For the shape aesthetic, R returns an error message (i.e., "Error: A continuous variable cannot be mapped to shape").

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

4. What happens if you map the same variable to multiple aesthetics?

Mapping a variable to the x or y axis as well as to another aesthetic creates redundancy. For example, in the next plot, the y axis and the legend provide redundant information.

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

5. What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point.)

The stroke aesthetic modifies the width of a data point's borders. It only works for shapes 21-24.

ggplot(data              =              mpg)              +              geom_point(mapping              =              aes(x              =              displ,              y              =              hwy),              shape              =              22,              stroke              =              .8)

6. What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)?

ggplot uses logical arguments (like aes(colour = displ < 5)) to map values from data (like displ == 6) to levels of aesthetic properties (like "red"). In this case, the decision rule determines what color the data points get assigned.

ggplot(data              =              mpg)              +              geom_point(mapping              =              aes(x              =              displ,              y              =              hwy,              color              =              displ              <              5))

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Source: https://github.com/jeffboichuk/s2r4ds/blob/master/3.3.1.md

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