Data Visualization with ggplot2 - Exercise Solutions.R

#========================Data Visualization with ggplot2=======================
library("tidyverse")

#------------01 First Steps------------

# Exercice01--------------

# Run ggplot(data = mpg) what do you see?
ggplot(data = mpg)
# This code creates an empty plot. The ggplot() function creates the background
# of the plot, but since no layers were specified with geom function, nothing 
# is drawn.

# Exercice02--------------

# How many rows are in mpg? How many columns?
nrow(mpg)
#> [1] 234
ncol(mpg)
#> [1] 11
# There are 234 rows and 11 columns in the mpg data frame.

# The glimpse() function also displays the number of rows and columns in a data
# frame.
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", "…


# Exercice03--------------

# What does the drv variable describe? Read the help for ?mpg to find out.
ggplot2::mpg?
#The drv variable is a categorical variable which categorizes cars into
#front-wheels, rear-wheels, or four-wheel drive

# Exercice04--------------

# Make a scatter plot of hwy vs. cyl

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

# Exercice05--------------

# What happens if you make a scatter plot of class vs drv? Why is the plot 
# not useful?
ggplot(mpg, aes(x = class, y = drv)) +
  geom_point()
# The resulting scatterplot has only a few points.
#-----------------------------------------------------------------------------
# A scatter plot is not a useful display of these variables since both drv and 
# class are categorical variables. Since categorical variables typically take 
# a small number of values, there are a limited number of unique combinations 
# of (x, y) values that can be displayed. In this data, drv takes 3 values and 
# class takes 7 values, meaning that there are only 21 values that could be 
# plotted on a scatterplot of drv vs. class. In this data, there 12 values of 
# (drv, class) are observed.
#-----------------------------------------------------------------------------
count(mpg, drv, class)
#> # A tibble: 12 x 3
#>   drv   class          n
#>   <chr> <chr>      <int>
#> 1 4     compact       12
#> 2 4     midsize        3
#> 3 4     pickup        33
#> 4 4     subcompact     4
#> 5 4     suv           51
#> 6 f     compact       35
#> # … with 6 more rows

ggplot(mpg, aes(x = class, y = drv)) +
  geom_count()

# The second is geom_tile() which uses a color scale to show the number of 
# observations with each (x, y) value

mpg %>%
  count(class, drv) %>%
  ggplot(aes(x = class, y = drv)) +
  geom_tile(mapping = aes(fill = n))

# In the previous plot, there are many missing tiles. These missing tiles 
# represent unobserved combinations of class and drv values. These missing 
# values are not unknown, but represent values of (class, drv) where n = 0. 
# The complete() function in the tidyr package adds new rows to a data frame 
# for missing combinations of columns. The following code adds rows for missing 
# combinations of class and drv and uses the fill argument to set n = 0 for those 
# new rows.

mpg %>%
  count(class, drv) %>%
  complete(class, drv, fill = list(n = 0)) %>%
  ggplot(aes(x = class, y = drv)) +
  geom_tile(mapping = aes(fill = n))


#------------02 Aesthetic mappings------------

# Exercice01--------------

# What’s gone wrong with this code? Why are the points not blue?

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

# The argumentcolour = "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().

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

The following code does produces the expected result.

# Exercice02--------------

# 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?

# The following list contains the categorical variables in mpg:

      # manufacturer
      # model
      # trans
      # drv
      # fl
      # class

# The following list contains the continuous variables in mpg:

      # displ
      # year
      # cyl
      # cty
      # hwy
# In the printed data frame, angled brackets at the top of each column provide 
# type of each variable.

mpg

#> # A tibble: 234 x 11
#>   manufacturer model displ  year   cyl trans      drv     cty   hwy fl    class 
#>   <chr>        <chr> <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr> 
#> 1 audi         a4      1.8  1999     4 auto(l5)   f        18    29 p     compa…
#> 2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa…
#> 3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa…
#> 4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa…
#> 5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa…
#> 6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa…
#> # … with 228 more rows

# Those with <chr> above their columns are categorical, while those with <dbl> or
# <int> are continuous. The exact meaning of these types will be discussed in 
# “Chapter 15: Vectors”.

# glimpse() is another function that concisely displays the type of each column 
# in the data frame:
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", "…

# Exercice03--------------

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

# The variable cty, city highway miles per gallon, is a continuous variable.
ggplot(mpg, aes(x = displ, y = hwy, colour = cty)) +
  geom_point()

# Instead of using discrete colors, the continuous variable uses a scale that 
# varies from a light to dark blue color.

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

# When mapped to size, the sizes of the points vary continuously as a function 
# of their size.

ggplot(mpg, aes(x = displ, y = hwy, shape = cty)) +
  geom_point()
#> Error: A continuous variable can not be mapped to shape

# Exercice04--------------

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

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

# In the above plot, hwy is mapped to both location on the y-axis and color, 
# and displ is mapped to both location on the x-axis and size. The code works 
# and produces a plot, even if it is a bad one. Mapping a single variable to 
# multiple aesthetics is redundant. Because it is redundant information, in 
# most cases avoid mapping a single variable to multiple aesthetics.


# Exercice05--------------

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

# 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.

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


# Exercice06--------------

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

ggplot(mpg, aes(x = displ, y = hwy, colour = displ < 5)) +
  geom_point()

# 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 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.

# This also explains why, in Exercise 3.3.1, the expression colour = "blue" 
# created a categorical variable with only one category: “blue”.

#------------03 Common problems------------

# 	No exercises


#------------04  Facets------------

# Exercice01--------------

# What happens if you facet on a continuous variable?
# Let’s see.

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

# The continuous variable is converted to a categorical variable, and the plot 
# contains a facet for each distinct value.

# Exercice02--------------

# What do the empty cells in plot with facet_grid(drv ~ cyl) mean? How do they 
# relate to this plot?

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

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

# The empty cells (facets) in this plot are combinations of drv and cyl that have 
# no observations. These are the same locations in the scatter plot of drv and cyl
# that have no points.

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


# Exercice03--------------

# What plots does the following code make? What does . do?
  
# The symbol . ignores that dimension when faceting. For example, drv ~ . facet
# by values of drv on the y-axis.

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

# While, . ~ cyl will facet by values of cyl on the x-axis.

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

# Exercice04--------------

# Take the first faceted plot in this section:

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

# What are the advantages to using faceting instead of the colour aesthetic? 
# What are the disadvantages? How might the balance change if you had a larger 
# dataset?
  
# In the following plot the class variable is mapped to color.

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


# Exercice05--------------

# Read ?facet_wrap. What does nrow do? What does ncol do? What other options 
# control the layout of the individual panels? Why doesn’t facet_grid() have nrow 
# and ncol variables?

  # The arguments nrow (ncol) determines the number of rows (columns) to use when 
# aying out the facets. It is necessary since facet_wrap() only facets on one 
# variable.

# The nrow and ncol arguments are unnecessary for facet_grid() since the number 
# of unique values of the variables specified in the function determines the 
# number of rows and columns.


# Exercice06--------------

# When using facet_grid() you should usually put the variable with more unique 
# levels in the columns. Why?
  
# There will be more space for columns if the plot is laid out horizontally 
# (landscape).

#------------05 Geometric objects-------------

# Exercice01--------------

# What geom would you use to draw a line chart? A boxplot? A histogram? 
# An area chart?
  
      # line chart: geom_line()
      # boxplot: geom_boxplot()
      # histogram: geom_histogram()
      # area chart: geom_area()


# Exercice02--------------

# Run this code in your head and predict what the output will look like. Then, run
# the code in R and check your predictions.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth(se = FALSE)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

# This code produces a scatter plot with displ on the x-axis, hwy on the y-axis,
# and the points colored by drv. There will be a smooth line, without standard 
# errors, fit through each drv group.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth(se = FALSE)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'


# Exercice03--------------

# What does show.legend = FALSE do? What happens if you remove it? Why do you 
# think I used it earlier in the chapter?
  
# The theme option show.legend = FALSE hides the legend box.

# Consider this example earlier in the chapter.

ggplot(data = mpg) +
  geom_smooth(
    mapping = aes(x = displ, y = hwy, colour = drv),
    show.legend = FALSE
  )
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

# In that plot, there is no legend. Removing the show.legend argument or setting
# show.legend = TRUE will result in the plot having a legend displaying the
# mapping between colors and drv.

ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy, colour = drv))
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

# Exercice04--------------

# What does the se argument to geom_smooth() do?
  
# It adds standard error bands to the lines.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth(se = TRUE)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

# By default se = TRUE:

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth()
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'


# Exercice05--------------

# Will these two graphs look different? Why/why not?

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

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

# No. Because both geom_point() and geom_smooth() will use the same data and 
# mappings. They will inherit those options from the ggplot() object, so the 
# mappings don’t need to specified again.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth()
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'


ggplot() +
  geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'


# Exercice06--------------

# Recreate the R code necessary to generate the following graphs.

# The following code will generate those plots.

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(se = FALSE)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(group = drv), se = FALSE) +
  geom_point()

ggplot(mpg, aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth(se = FALSE)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(colour = drv)) +
  geom_smooth(se = FALSE)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(colour = drv)) +
  geom_smooth(aes(linetype = drv), se = FALSE)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'


ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(size = 4, color = "white") +
  geom_point(aes(colour = drv))



#------------06 Statistical transformations------------


# Exercice01--------------

# What is the default geom associated with stat_summary()? How could you rewrite
# the previous plot to use that geom function instead of the stat function?


# The “previous plot” referred to in the question is the following.

ggplot(data = diamonds) +
  stat_summary(
    mapping = aes(x = cut, y = depth),
    fun.min = min,
    fun.max = max,
    fun = median
  )
# The arguments fun.ymin, fun.ymax, and fun.y have been deprecated and replaced 
# with fun.min, fun.max, and fun in ggplot2 v 3.3.0.

# The default geom for stat_summary() is geom_pointrange(). The default stat for
# geom_pointrange() is identity() but we can add the argument stat = "summary" to
# use stat_summary() instead of stat_identity()

ggplot(data = diamonds) +
  geom_pointrange(
    mapping = aes(x = cut, y = depth),
    stat = "summary"
  )
#> No summary function supplied, defaulting to `mean_se()`

# The resulting message says that stat_summary() uses the mean and sd to 
# calculate the middle point and endpoints of the line. However, in the original 
# plot the min and max values were used for the endpoints. To recreate the 
# original plot we need to specify values for fun.min, fun.max, and fun.

ggplot(data = diamonds) +
  geom_pointrange(
    mapping = aes(x = cut, y = depth),
    stat = "summary",
    fun.min = min,
    fun.max = max,
    fun = median
  )


# Exercice02--------------

# What does geom_col() do? How is it different to geom_bar()?

# The geom_col() function has different default stat than geom_bar(). 
# The default stat of geom_col() is stat_identity(), which leaves the data as is. 
# The geom_col() function expects that the data contains x values and y values 
# which represent the bar height.

# The default stat of geom_bar() is stat_count(). The geom_bar() function only 
# expects an x variable. The stat, stat_count(), preprocesses input data by 
# counting the number of observations for each value of x. The y aesthetic uses 
# the values of these counts.


# Exercice03--------------

# Most geoms and stats come in pairs that are almost always used in concert. 
# Read through the documentation and make a list of all the pairs. What do they 
# have in common?


# Exercice04--------------

# What variables does stat_smooth() compute? What parameters control its 
# behavior?

# Exercice05--------------

# If group = 1 is not included, then all the bars in the plot will have the 
# same height, a height of 1. The function geom_bar() assumes that the groups 
# are equal to the x values since the stat computes the counts within the group.

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

# The problem with these two plots is that the proportions are calculated within
# the groups.

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

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

# The following code will produce the intended stacked bar charts for the case 
# with no fill aesthetic.

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

# With the fill aesthetic, the heights of the bars need to be normalized.

ggplot(data = diamonds) + 
  geom_bar(aes(x = cut, y = ..count.. / sum(..count..), fill = color))



#------------07 Position adjustments------------


# Exercice01--------------

# What is the problem with this plot? How could you improve it?

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

# There is overplotting because there are multiple observations for each 
# combination of cty and hwy values.

# I would improve the plot by using a jitter position adjustment to decrease 
# overplotting.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point(position = "jitter")

# The relationship between cty and hwy is clear even without jittering the 
# points but jittering shows the locations where there are more observations.


# Exercice02--------------

# What parameters to geom_jitter() control the amount of jittering?

# From the geom_jitter() documentation, there are two arguments to jitter:

     # width controls the amount of horizontal displacement, and
     # height controls the amount of vertical displacement.

# The defaults values of width and height will introduce noise in both 
# directions. Here is what the plot looks like with the default values of height 
# and width.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point(position = position_jitter())

# However, we can change these parameters. Here are few a examples to understand
# how these parameters affect the amount of jittering. Whenwidth = 0 there is no 
# horizontal jitter.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(width = 0)

# When width = 20, there is too much horizontal jitter.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(width = 20)

# When height = 0, there is no vertical jitter.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(height = 0)

# When height = 15, there is too much vertical jitter.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(height = 15)

# When width = 0 and height = 0, there is neither horizontal or vertical jitter,
# and the plot produced is identical to the one produced with geom_point().

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter(height = 0, width = 0)

# The default values of height and width in geom_jitter() are non-zero, 
# so unless both height and width are explicitly set set 0, there will be some 
# jitter.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter()


# Exercice03--------------

# Compare and contrast geom_jitter() with geom_count().

# The geom geom_jitter() adds random variation to the locations points of 
# the graph. In other words, it “jitters” the locations of points slightly. 
# This method reduces overplotting since two points with the same location are 
# unlikely to have the same random variation.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter()

# However, the reduction in overlapping comes at the cost of slightly changing 
# the x and y values of the points.

#The geom geom_count() sizes the points relative to the number of observations. 
#Combinations of (x, y) values with more observations will be larger than those 
# with fewer observations.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_count()

ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) +
  geom_jitter()

ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) +
  geom_count()

# Combining geom_count() with jitter, which is specified with the position 
# argument to geom_count() rather than its own geom, helps overplotting a little.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) +
  geom_count(position = "jitter")


# Exercice04--------------

# What’s the default position adjustment for geom_boxplot()? Create 
a visualization of the mpg dataset that demonstrates it.

The default position for geom_boxplot() is "dodge2", which is a shortcut 
# for position_dodge2. This position adjustment does not change the vertical 
# position of a geom but moves the geom horizontally to avoid overlapping other 
# geoms. See the documentation for position_dodge2() for additional discussion 
# on how it works.

# When we add colour = class to the box plot, the different levels of the drv 
# variable are placed side by side, i.e., dodged.

ggplot(data = mpg, aes(x = drv, y = hwy, colour = class)) +
  geom_boxplot()

# If position_identity() is used the boxplots overlap

ggplot(data = mpg, aes(x = drv, y = hwy, colour = class)) +
  geom_boxplot(position = "identity")




#------------08 Coordinate systems------------


# Exercice01--------------

# Turn a stacked bar chart into a pie chart using coord_polar().

# A pie chart is a stacked bar chart with the addition of polar coordinates. 
# Take this stacked bar chart with a single category.

ggplot(mpg, aes(x = factor(1), fill = drv)) +
  geom_bar()

# Now add coord_polar(theta="y") to create pie chart.

ggplot(mpg, aes(x = factor(1), fill = drv)) +
  geom_bar(width = 1) +
  coord_polar(theta = "y")

# The argument theta = "y" maps y to the angle of each section. If coord_polar()
# is specified without theta = "y", then the resulting plot is called a 
# bulls-eye chart.

ggplot(mpg, aes(x = factor(1), fill = drv)) +
  geom_bar(width = 1) +
  coord_polar()


# Exercice02--------------

# What does labs() do? Read the documentation.

# The labs function adds axis titles, plot titles, and a caption to the plot.

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot() +
  coord_flip() +
  labs(y = "Highway MPG",
       x = "Class",
       title = "Highway MPG by car class",
       subtitle = "1999-2008",
       caption = "Source: http://fueleconomy.gov")

# The arguments to labs() are optional, so you can add as many or as few 
# of these as are needed.

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot() +
  coord_flip() +
  labs(y = "Highway MPG",
       x = "Year",
       title = "Highway MPG by car class")

# The labs() function is not the only function that adds titles to plots. 
# The xlab(), ylab(), and x- and y-scale functions can add axis titles. 
# The ggtitle() function adds plot titles.

# Exercice03--------------

# What’s the difference between coord_quickmap() and coord_map()?

# The coord_map() function uses map projections to project the three-dimensional
# Earth onto a two-dimensional plane. By default, coord_map() uses the Mercator 
# projection. This projection is applied to all the geoms in the plot. 
# The coord_quickmap() function uses an approximate but faster map projection. 
# This approximation ignores the curvature of Earth and adjusts the map for 
# the latitude/longitude ratio. The coord_quickmap() project is faster than 
# coord_map() both because the projection is computationally easier, 
# and unlike coord_map(), the coordinates of the individual geoms do not need 
# to be transformed.

# See the coord_map() documentation for more information on these functions 
# and some examples.

# Exercice04--------------

# What does the plot below tell you about the relationship between city 
# and highway mpg? Why is coord_fixed() important? What does geom_abline() do?

# The function coord_fixed() ensures that the line produced by geom_abline() is 
# at a 45-degree angle. A 45-degree line makes it easy to compare the highway 
# and city mileage to the case in which city and highway MPG were equal.

p <- ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() +
  geom_abline()
p + coord_fixed()

# If we didn’t include coord_fixed(), then the line would no longer have an 
# angle of 45 degrees

p


#------------09 The layered grammar of graphics------------

# No Exercices