Introduction to R Programming

Exercise 1

Open RStudio and explore the programme. Make sure you can identify the console, the script editor, the environment window, the packages window and the help window!

In the script window, write code that creates a vector composed of 5 single-digit integers. To concatinate a series of values, use the c() function, with each argument separated by a comma. So, a character vector of length three could be c("a", "b", "c"). Assign your integer vector using the assignment operator <- to an object with a name of your choosing.

my_vec <- c(1,2,3,4,5)

Multiply your vector by 3, and assign the output to a new object. Print the values of your new object.

my_new_vec <- my_vec * 3
print(my_new_vec)

## [1]  3  6  9 12 15

Add together the two objects that you have created to far, printing the result. Note that R operates on vectors element-wise.

print(my_new_vec + my_vec)

## [1]  4  8 12 16 20

Create a logical vector of length five, again using the c() function. Make sure that you have a mix of TRUE and FALSE values in the vector. Use the logical vector to subset the numeric vector that you created in question 2 and print the result.

my_logical_vec <- c(T, T, T, F, F)
print(my_new_vec[my_logical_vec])

## [1] 3 6 9

Subset to just the first two elements of the numeric vector that you created in question 2 and assign the result to have the name my_short_vector.

my_short_vector <- my_new_vec[c(1,2)]

Exercise 2

This exercise relates to the College data set, which comes from An Introduction to Statistical Learning by James et al 2013.. It contains a number of variables for 777 different universities and colleges in the US.

The variables are

Private : Public/private indicator
Apps : Number of applications received
Accept : Number of applicants accepted
Enroll : Number of new students enrolled
Top10perc : New students from top 10% of high school class
Top25perc : New students from top 25% of high school class
F.Undergrad : Number of full-time undergraduates
P.Undergrad : Number of part-time undergraduates
Outstate : Out-of-state tuition
Room.Board : Room and board costs
Books : Estimated book costs
Personal : Estimated personal spending
PhD : Percent of faculty with Ph.D.’s
Terminal : Percent of faculty with terminal degree
S.F.Ratio : Student/faculty ratio
perc.alumni : Percent of alumni who donate
Expend : Instructional expenditure per student
Grad.Rate : Graduation rate

You can either download the .csv file containing the data from the MY591 moodle page, or read the data in directly from the website.

Use the read.csv() function to read the data into R. Call the loaded data college. Make sure that you have the directory set to the correct location for the data. You can load this in R directly from the website, using:

college <- read.csv("http://www-bcf.usc.edu/~gareth/ISL/College.csv")

Or you can load it from a saved file, using:

college <- read.csv("path_to_my_file/College.csv")

Look at the data using the View() function. You should notice that the first column is just the name of each university. We don’t really want R to treat this as data. However, it may be handy to have these names for later. Try the following commands:

rownames(college) <- college[, 1] 
View(college)

You should see that there is now a row.names column with the name of each university recorded. This means that R has given each row a name corresponding to the appropriate university. R will not try to perform calculations on the row names. However, we still need to eliminate the first column in the data where the names are stored. Try

college <- college[, -1] 
View(college)

Now you should see that the first data column is Private. Note that another column labeled row.names now appears before the Private column. However, this is not a data column but rather the name that R is giving to each row.

Use the str() function to look at the structure of the data. Which of the variables are numeric? Which are integer? Which are factors?

str(college)

## 'data.frame':    777 obs. of  18 variables:
##  $ Private    : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Apps       : int  1660 2186 1428 417 193 587 353 1899 1038 582 ...
##  $ Accept     : int  1232 1924 1097 349 146 479 340 1720 839 498 ...
##  $ Enroll     : int  721 512 336 137 55 158 103 489 227 172 ...
##  $ Top10perc  : int  23 16 22 60 16 38 17 37 30 21 ...
##  $ Top25perc  : int  52 29 50 89 44 62 45 68 63 44 ...
##  $ F.Undergrad: int  2885 2683 1036 510 249 678 416 1594 973 799 ...
##  $ P.Undergrad: int  537 1227 99 63 869 41 230 32 306 78 ...
##  $ Outstate   : int  7440 12280 11250 12960 7560 13500 13290 13868 15595 10468 ...
##  $ Room.Board : int  3300 6450 3750 5450 4120 3335 5720 4826 4400 3380 ...
##  $ Books      : int  450 750 400 450 800 500 500 450 300 660 ...
##  $ Personal   : int  2200 1500 1165 875 1500 675 1500 850 500 1800 ...
##  $ PhD        : int  70 29 53 92 76 67 90 89 79 40 ...
##  $ Terminal   : int  78 30 66 97 72 73 93 100 84 41 ...
##  $ S.F.Ratio  : num  18.1 12.2 12.9 7.7 11.9 9.4 11.5 13.7 11.3 11.5 ...
##  $ perc.alumni: int  12 16 30 37 2 11 26 37 23 15 ...
##  $ Expend     : int  7041 10527 8735 19016 10922 9727 8861 11487 11644 8991 ...
##  $ Grad.Rate  : int  60 56 54 59 15 55 63 73 80 52 ...

Use the summary() function to produce a numerical summary of the variables in the data set.

summary(college)

##  Private        Apps           Accept          Enroll       Top10perc    
##  No :212   Min.   :   81   Min.   :   72   Min.   :  35   Min.   : 1.00  
##  Yes:565   1st Qu.:  776   1st Qu.:  604   1st Qu.: 242   1st Qu.:15.00  
##            Median : 1558   Median : 1110   Median : 434   Median :23.00  
##            Mean   : 3002   Mean   : 2019   Mean   : 780   Mean   :27.56  
##            3rd Qu.: 3624   3rd Qu.: 2424   3rd Qu.: 902   3rd Qu.:35.00  
##            Max.   :48094   Max.   :26330   Max.   :6392   Max.   :96.00  
##    Top25perc      F.Undergrad     P.Undergrad         Outstate    
##  Min.   :  9.0   Min.   :  139   Min.   :    1.0   Min.   : 2340  
##  1st Qu.: 41.0   1st Qu.:  992   1st Qu.:   95.0   1st Qu.: 7320  
##  Median : 54.0   Median : 1707   Median :  353.0   Median : 9990  
##  Mean   : 55.8   Mean   : 3700   Mean   :  855.3   Mean   :10441  
##  3rd Qu.: 69.0   3rd Qu.: 4005   3rd Qu.:  967.0   3rd Qu.:12925  
##  Max.   :100.0   Max.   :31643   Max.   :21836.0   Max.   :21700  
##    Room.Board       Books           Personal         PhD        
##  Min.   :1780   Min.   :  96.0   Min.   : 250   Min.   :  8.00  
##  1st Qu.:3597   1st Qu.: 470.0   1st Qu.: 850   1st Qu.: 62.00  
##  Median :4200   Median : 500.0   Median :1200   Median : 75.00  
##  Mean   :4358   Mean   : 549.4   Mean   :1341   Mean   : 72.66  
##  3rd Qu.:5050   3rd Qu.: 600.0   3rd Qu.:1700   3rd Qu.: 85.00  
##  Max.   :8124   Max.   :2340.0   Max.   :6800   Max.   :103.00  
##     Terminal       S.F.Ratio      perc.alumni        Expend     
##  Min.   : 24.0   Min.   : 2.50   Min.   : 0.00   Min.   : 3186  
##  1st Qu.: 71.0   1st Qu.:11.50   1st Qu.:13.00   1st Qu.: 6751  
##  Median : 82.0   Median :13.60   Median :21.00   Median : 8377  
##  Mean   : 79.7   Mean   :14.09   Mean   :22.74   Mean   : 9660  
##  3rd Qu.: 92.0   3rd Qu.:16.50   3rd Qu.:31.00   3rd Qu.:10830  
##  Max.   :100.0   Max.   :39.80   Max.   :64.00   Max.   :56233  
##    Grad.Rate     
##  Min.   : 10.00  
##  1st Qu.: 53.00  
##  Median : 65.00  
##  Mean   : 65.46  
##  3rd Qu.: 78.00  
##  Max.   :118.00

What is the mean and standard deviation of the Enroll and Top10Perc variables?

mean(college$Enroll)

## [1] 779.973

mean(college$Top10perc)

## [1] 27.55856

sd(college$Enroll)

## [1] 929.1762

sd(college$Top10perc)

## [1] 17.64036

Now remove the 10th through 85th observations. What is the mean and standard deviation of the Enroll and Top10Perc variables in the subset of the data that remains?

college <- college[-c(15:85),]
mean(college$Enroll)

## [1] 784.8867

mean(college$Top10perc)

## [1] 27.51841

sd(college$Enroll)

## [1] 928.6599

sd(college$Top10perc)

## [1] 17.61432

What is the range of the Books variable?

range(college$Books)

## [1]  110 2340

Use the pairs() function to produce a scatterplot matrix of the first five columns or variables of the data. Recall that you can reference the first five columns of a matrix A using A[,1:5].

pairs(college[,1:5])

Use the plot() function to produce a scatter plot of S.F.Ratio versus Grad.Rate. Give the axes informative labels.

plot(college$S.F.Ratio, college$Grad.Rate, xlab = "Student/Faculty Ratio", ylab = "Graduation Rate")

Compete with your neighbour to make the prettiest plot. You might want to look at ?plot and ?par for some ideas. If you are feeling very keen, try using ggplot but you will need to load the ggplot library first: library(ggplot2).

plot(college$S.F.Ratio, college$Grad.Rate, 
     xlab = "Student/Faculty Ratio", ylab = "Graduation Rate",
     main = "A really nice plot",
     pch = 19, col = "gray", bty = "n")

library(ggplot2)
ggplot(data = college, aes(x = S.F.Ratio, y = Grad.Rate, col = Private)) + 
  geom_point()+
  xlab("Student/Faculty Ratio")+
  ylab("Graduation Rate")+
  ggtitle("A really nice plot")+
  facet_grid(~Private)

Introduction to R Programming

Exercises Solutions

Tom Paskhalis

Exercise 1

Exercise 2