Question

1. On R:

1. Run ggplot(data = mpg). What do you see?
2. How many rows are in mpg? How many columns?
3. What does the drv variable describe? Read the help for ?mpg to find out.
4. Make a scatterplot of hwy vs cyl.
5. What happens if you make a scatterplot of class vs drv? Why is the plot not useful?
6. What’s gone wrong with this code? Why are the points not blue?
7. ggplot(data = mpg) +
8. geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
1. Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?
10. Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?
11. What happens if you map the same variable to multiple aesthetics?
12. What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point)
13. What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)?

Answer 1

A-  ggplot(data = mpg) function creates the background of the plot, but no layer is specified with geom function, nothing is drawn.

B- To get the dimensions of a data matrix, we can simply use the function ‘dim()’. The rows and columns in mpg is [1] 234 11 .

C- The drv variable is a categorical variable which is used to categorize cars into front wheels , rear wheels or four wheel drive.

            f = front-wheel drive, r = rear wheel drive, 4 = four wheel drive

D - ggplot(mpg, aes(x = hwy, y = cyl)) + geom_point() this function creates the scatterplot of hwy and cyl

E- when we make a scatterplot of class vs drv the resulting scatterplot will have only few points.

Since div and class variables are categorical variable and categorical variables typically take a small number of values so there are a limited number of unique combinations of (x, y) values that can be displayed. Therefore scatter plot is not used to display these variables.

F- The argument colour = "blue" is included within the mapping argument, and as such, it is treated as an aesthetic, which is a mapping between a variable and a value. In the expression, colour = "blue", "blue" is interpreted as a categorical variable which only takes a single value "blue". If this is confusing, consider how colour = 1:234 and colour = 1 are interpreted by aes().

G- categorical variables in mpg

• model
• trans
• drv
• fl
• class

continuous variables in mpg

• displ
• year
• cyl
• cty
• hwy

glimpse() function displays the type of each column.

J- When a continuous value is mapped to shape, it gives an error. Though we could split a continuous variable into discrete categories and use a shape aesthetic, this would conceptually not make sense. A numeric variable has an order, but shapes do not. It is clear that smaller points correspond to smaller values, or once the color scale is given, which colors correspond to larger or smaller values. But it is not clear whether a square is greater or less than a circle.

K- Mapping a same variable to multiple aesthetics is redundant. Because it is redundant information, in most cases avoid mapping a single variable to multiple aesthetics.

L- Stroke changes the size of the border for shapes (21-25). These are filled shapes in which the color and size of the border can differ from that of the filled interior of the shape. For example,

ggplot(mtcars, aes(wt, mpg)) +
  geom_point(shape = 21, colour = "black", fill = "white", size = 5, stroke = 5)

M- Aesthetics can also be mapped to expressions like displ < 5. The ggplot() function behaves as if a temporary variable was added to the data with with values equal to the result of the expression. In this case, the result of displ < 5 is a logical variable which takes values of TRUE or FALSE.