There are certain situations where we want to transform right-skewed data before analysing it. Taking the log of right-skewed data often helps to make it more normally distributed.
Here are histograms of the MPG.highway
and MPG.city
variables.
qplot(MPG.city, data = Cars93, bins = 10)
qplot(MPG.highway, data = Cars93, bins = 10)
(a) Do the city and highway gas-mileage figures appear to have right-skewed distributions?
Your answer:
(b) Use the mutate()
and log()
functions to create a new data frame called Cars93.log
that has MPG.highway
and MPG.city
replaced with log(MPG.highway)
and log(MPG.city)
, respectively.
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(c) Run the histogram plotting commands again, this time using your new Cars93.log
dataset instead of Cars93
.
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(d) Do the distributions appear less skewed than before?
Your answer here:
(a) Use the table()
function to tabulate the data by DriveTrain and Origin.
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(b) Repeat part (a), this time using the count()
function.
(c) Does it looks like foreign car manufacturers had different Drivetrain preferences compared to US manufacturers?
Your answer:
(a) Write a function called isPassingGrade
whose input x
is a number, and which returns FALSE
if x
is lower than 50 and TRUE
otherwise.
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(b) Write a function called sendMessage
whose input x
is a number, and which prints Congratulations
if isPassingGrade(x)
is TRUE
and prints Oh no!
if isPassingGrade(x)
is FALSE
.
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(c) Write a function called gradeSummary
whose input x
is a number. Your function will return a list with two elements, named letter.grade
and passed
. The letter grade will be "A"
if x
is at least 90
. The letter grade will be "B"
if x
is between 80
and 90
. The letter grade will be "F"
if x
is lower than "80"
. If the student’s letter grade is an A or B, passed
should be TRUE; passed
should be FALSE otherwise.
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To check if your function works, try the following cases:
x = 91
should return
## $letter.grade
## [1] "A"
##
## $passed
## [1] TRUE
x = 62
should return
## $letter.grade
## [1] "F"
##
## $passed
## [1] FALSE