Description
Let’s work on the mpg dataset in the package ggplot2. You can use the following code
to load the data. Use necessary code to read the description of the dataset, which contains
234 samples and 11 variables.
# Install the package if you never did
install . packages (” ggplot 2″)
# Load the pacakge
library ( ggplot 2)
# Load the mpg dataset
data (“mpg”)
Problem 1 (1 × 6 = 6 points)
Let’s first clean the data:
(a) Turn the variable cyl to an ordered factor variable with levels “4”, “5”, “6”, and “8”
(b) Turn the variable trans to a factor variable, of which unique values are “auto” and
“manu” (Hint: use the function substr() to extract substrings in a character vector
before converting to a factor vector)
(c) Turn the variable drv to an ordered factor variable with levels “f”, “r”, and “4”,
(d) Turn the variable fl to a factor variable, of which unique values are “gasoline”,
“diesel”, and “other” (Hint, “other” should include “e” and “c” in the original variable: “e” for E85, which is an ethanol fuel blend of 85% ethanol fuel and 15% gasoline
and “c” for compressed natural gas)
(e) Turn the variable class to an ordered factor variable with levels “2seater”, “subcompact”, “compact”, “midsize”, “suv”, “minivan”, and “pickup”
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Country Manufacturer
United States Chevrolet, Dodge, Ford, Jeep, Lincoln, Mercury, Pontiac
Japan Honda, Nissan, Subaru, Toyota
Germany Audi, Volkswagen
South Korea Hyundai
Great Britain Land Rover
(f) Create a new variable of country to indicate the manufacturer base location (Hint:
You can refer to the above table)
Hint: You should get the following response after applying the function str() on the
cleaned dataset
$ manufacturer : chr ” audi ” ” audi ” ” audi ” ” audi ” …
$ model : chr “a4” “a4” “a4” “a4” …
$ displ : num 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 …
$ year : int 1999 1999 2008 2008 1999 1999 2008 199
9 1999 2008 …
$ cyl : Factor w / 4 levels “4”,”5″,”6″,”8″: 1 1 1 1
3 3 3 1 1 1 …
$ trans : Factor w / 2 levels ” auto “,” manu “: 1 2 2 1 1
2 1 2 1 2 …
$ drv : Factor w / 3 levels “f”,”r”,”4″: 1 1 1 1 1 1
1 3 3 3 …
$ cty : int 18 21 20 21 16 18 18 18 16 20 …
$ hwy : int 29 29 31 30 26 26 27 26 25 28 …
$ fl : Factor w / 3 levels ” diesel “,” gasoline ” ,..:
2 2 2 2 2 2 2 2 2 2 …
$ class : Factor w / 7 levels “2 seater “,” subcompact ”
,..: 3 3 3 3 3 3 3 3 3 3 …
$ country : chr ” germany ” ” germany ” ” germany ” ” germany
” …
Problem 2 (1 × 4 = 4 points)
Let’s analyze the data. For each figure, please make it complete/readable, in other words,
it should include all the label information, title, and legend if necessary.
(a) Draw a bar plot of the variable country and arrange the country in decreasing order in
terms of the number of samples. Which country has the most samples in this dataset?
Which has the least?
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(b) Summarize what a typical U.S. car looks like, in terms of engine displacement (i.e.
displ), number of cylinders (i.e. cyl), type of transmission (i.e. trans), drive type
(i.e. drv), fuel type (i.e. fl), and type of car (i.e. class)? (Hint: Use the function
table() to find the mode for each of the above discrete univariate data)
(c) Make a boxplot of the combined miles per gallon (i.e. (cty + hwy)/2) of U.S. cars
and Japan cars, respectively, and report their means, medians, standard deviations,
and IQRs.
(d) Make a histogram of the engine displacement (i.e. displ) of U.S. cars and Japan cars,
respectively, and describe their shapes.
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