Chapter 3

3.2.4 Exercises:

library(ggplot2)

df <- mpg

1. Run ggplot(data = mpg). What do you see?

empty plot

ggplot(mpg)

2. How many rows are in mpg? How many columns?

234 row and 11 column

df

3.3.1 Exercises:

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

it works, but it gives us a bad visualization ,Because it is redundant information, in most cases avoid mapping a single variable to multiple aesthetics.

ggplot(df)+
  geom_point(mapping = aes(x = hwy,y = displ, color = hwy, size = hwy))

*6. what happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)? Note, you’ll also need to specify x and y.**

ggplot() function is behave as if a temporary variable was added to the data with values equal to the result of the expression But In case year>200 the result is a logical variable

ggplot(mpg, aes(x = displ, y = hwy, colour = year>2000)) +
  geom_point()

3.5.1 Exercises:

3. What plots does the following code make? What does . do?

will facet by values of drv on the y-axis.

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

will facet cyl based on displ and hwy

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

3.6.1 Exercises:

3.Will these two graphs look different? Why/why not?

no, they will give us the same result

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

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

##3. What does show.legend = FALSE do? What happens if you remove it? It’s will result in the plot having a legend displaying the mapping between colors and drv #Why do you think I used it earlier in the chapter? Because In that example, the legend isn’t necessary since looking up the values associated with each color isn’t necessary to make that point.

ggplot(data = mpg) +
  geom_smooth(
    mapping = aes(x = displ, y = hwy, colour = drv),
    show.legend = FALSE
  )

**3.7.1 Exercises:

5.In our proportion bar chart, we need to set group = 1. Why? In other words what is the problem with these two graphs?

the problem with these two plots is that all bars have the same height, group = 1 will make the plot after calculating proportion with respect to the whole data set.

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, y = after_stat(prop)))

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = color, y = after_stat(prop)))

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))