## Exercise 2 : Basic R operations

1. Open up your RStudio Project from Exercise 1 and either create a new R script or continue with your previous R script. Make sure you include any metadata you feel is appropriate (title, description of task, date of creation etc). Don’t forget to comment out your metadata with a # at the beginning of the line.

1. Let’s use R as a fancy calculator. Find the natural log, log to the base 10, log to the base 2, square root and the natural antilog of 12.43. See Section 2.1 of the Introduction to R book for more information on mathematical functions in R. Don’t forget to write your code in RStudio’s script editor and source the code into the console.

log(12.43)              # natural log
log10(12.43)            # log to base 10
log2(12.43)             # log to base 2
log(12.43, base = 2)    # alternative log to base 2
sqrt(12.43)             # square root
exp(12.43)              # exponent

1. Next, use R to determine the area of a circle with a diameter of 20 cm and assign the result to a variable called area_circle. Google is your friend if you can’t remember the formula! Also, remember that R already knows about pi.

area_circle <- pi * (20/2)^2

1. Now for something a little more tricky. Calculate the cube root of 14 x 0.51. You might need to think creatively for a solution (hint: think exponents), and remember that R follows the usual order of mathematical operators so you might need to use brackets in your code (see this page if you’ve never heard of this).

(14 * 0.51)^(1/3)

1. Ok, you’re now ready to explore one of R’s basic (but very useful) data structures - vectors. A vector is a sequence of elements (or components) that are all of the same data type (see Section 2.4 and Section 3.2.1 for an introduction to vectors). Although technically not correct it might be useful to think of a vector as something like a single column in a spreadsheet. There are a multitude of ways to create vectors in R but you will use the concatenate function c() to create a vector called weight containing the weight (in kg) of 10 children: 69, 62, 57, 59, 59, 64, 56, 66, 67, 66 (Section 2.3 shows you how to do this).

weight <- c(69, 62, 57, 59, 59, 64, 56, 66, 67, 66)

1. You can now do stuff to your weight vector. Get R to calculate the mean, variance, standard deviation, range of weights and the number of children of your weights vector (Section 2.3). Next, extract the weights for the first five children and store these weights in a new variable called first_five. Remember, you will need to use the square brackets [ ] to extract (aka indexing, subsetting) elements from a variable. Section 2.4.1 introduces using the [] notation.

mean(weight)                                # calculate mean
var(weight)                                 # calculate variance
sd(weight)                                  # calculate standard deviation
range(weight)                               # range of weight values
length(weight)                              # number of observations

first_five <- weights[1:5]                  # extract first 5 weight values
first_five <- weights[c(1, 2, 3, 4, 5)]     # alternative method

1. We’re now going to use the the c() function again to create a vector called height containing the height (in cm) of the same 10 children: 112, 102, 83, 84, 99, 90, 77, 112, 133, 112. Use the summary() function to summarise these data. Extract the height of the 2nd, 3rd, 9th and 10th child and assign these heights to a variable called some_child. Also extract all the heights of children less than or equal to 99 cm and assign to a variable called shorter_child.

height <- c(112, 102, 83, 84, 99, 90, 77, 112, 133, 112)

summary(height)   # summary statistics of height variable

some_child <- height[c(2, 3, 9, 10)]      # extract the 2nd, 3rd, 9th, 10th height

shorter_child <- height[height <= 99]     # extract all heights less than or equal to 99

1. Now you can use the information in your weight and height variables to calculate the body mass index (BMI) for each child. The BMI is calculated as weight (in kg) divided by the square of the height (in meters). Store the results of this calculation in a variable called bmi. Note: you don’t need to do this calculation for each child individually, you can use both vectors in the BMI equation – this is called vectorisation (see Section 2.4.4 of the Introduction to R book).

bmi <- weight/(height/100)^2    # don't forget to convert height to meters

1. Now let’s practice a very useful skill - creating sequences (honestly it is…). First use the seq() function to create a sequence of numbers ranging from 0 to 1 in steps of 0.1 (this is also a vector by the way) and assign this sequence to a variable called seq1. If you’re unsure how to do this then see Section 2.3 of the book for more information.

seq1 <- seq(from = 0, to = 1, by = 0.1)

1. Next, create a sequence from 10 to 1 in steps of 0.5 and assign to a variable called seq2 (Hint: you may find it easier to include the rev() function in your code).

seq2 <- rev(seq(from = 1, to = 10, by = 0.5))

1. Let’s go sequence crazy! Generate the following sequences. You will need to experiment with the arguments to the rep() function to generate these sequences (see Section 2.3 for some clues):

• 1 2 3 1 2 3 1 2 3
• “a” “a” “a” “c” “c” “c” “e” “e” “e” “g” “g” “g”
• “a” “c” “e” “g” “a” “c” “e” “g” “a” “c” “e” “g”
• 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3
• 1 1 1 1 1 2 2 2 2 3 3 3 4 4 5
• 4 sevens, 3 twos, 1 eight and 5 ones
rep(1:3, times = 3)
rep(c("a", "c", "e", "g"), each = 3)
rep(c("a", "c", "e", "g"), times = 3)
rep(1:3, each = 3, times = 2)
rep(1:5, times = 5:1)
rep(c(7, 2, 8, 1), times = c(4, 3, 1, 5))

1. Ok, back to the variable height you created in Q7. Sort the values of height into ascending order (shortest to tallest) and assign the sorted vector to a new variable called height_sorted. See Section 2.4.3 for an introduction to sorting and ordering vectors. Now sort all heights into descending order and assign the new vector a name of your choice.

height_sorted <- sort(height)

height_rev <- rev(sort(height))

1. Let’s give the children some names. Create a new vector called child_names with the following names of the 10 children: "Alfred", "Barbara", "James", "Jane", "John", "Judy", "Louise", "Mary", "Ronald", "William".

child_names <- c("Alfred", "Barbara", "James", "Jane", "John", "Judy", "Louise", "Mary", "Ronald", "William")

1. A really useful (and common) task is to sort the values of one variable by the order of another variable. To do this you will need to use the order() function in combination with the square bracket notation [ ] (see Section 2.4.3 of the book for more details). Create a new variable called names_sort to store the names of the children sorted by child height (from shortest to tallest). Who is the shortest? who is the tallest child? If you’re not sure how to do this, please ask one of the instructors.

height_ord <- order(height)   # get the indexes of the heights, smallest to tallest
names_sort <- child.names[height_ord]     # Louise is shortest, Ronald is tallest

1. Now order the names of the children by descending values of weight and assign the result to a variable called weight_rev. Who is the heaviest? Who is the lightest?

weight_ord <- rev(order(weight))
weight_rev <- child_names[weight_ord]     # Alfred is heaviest, Louise is lightest

1. Almost there! In R, missing values are usually represented with an NA. Missing data can be tricky to deal with in R (and in statistics more generally) and cause some surprising behaviour when using some functions (see Section 2.4.5 of the Introduction to R book). To explore this a little further let’s create a vector called mydata with the values 2, 4, 1, 6, 8, 5, NA, 4, 7. Notice the value of the 7th element of mydata is missing and represented with an NA. Now use the mean() function to calculate the mean of the values in mydata. What does R return? Confused? Next, take a look at the help page for the function mean(). Can you figure out how to alter your use of the mean() function to calculate the mean without this missing value?

mydata <- c(2, 4, 1, 6, 8, 5, NA, 4, 7)
mean(mydata)    # returns NA!

mean(mydata, na.rm = TRUE)    # returns 4.625

1. Finally, list all variables in your workspace that you have created in this exercise. Remove the variable seq1 from the workspace using the rm() function.

ls()          # list all variables in workspace
rm(seq1)      # remove variable seq1 from the workspace
ls()          # check seq1 has been removed

End of Exercise 2