Lesson Exercises

Complete the exercises from today’s two lessons:

1. Base R Exercises
2. Visualizing Tabular Data Exercises

Challenge Exercises

Work through as many of the following challenge exercises as you can in the remaining time. Take a few minutes to ponder each challenge on your own, but feel free to engage with fellow participants and instructors for guidance or ideas.

Challenge 1

Fix each of the following common data frame subsetting errors. The pums data frame was read in during the lesson on Visualizing Tabular Data.

• Fix pums[pums$AGEP = 21, ] so it returns all rows where AGEP equals 21.
• Fix pums[-1:4, ] so it returns all but the first four rows.
• Fix pums[pums$WAGP <= 20,000] so it returns all rows where WAGP is less than or equal to 20,000.
• Fix pums[pums$AGEP == 18 | 21, ] so it returns all rows where AGEP equals 18 or 21.

Challenge 2

Were you to ever interview for a “data scientist” postition, you may be asked to complete this common challenge (possibly using multiple approaches). Write a “for loop” in the R language that calculates the Fibonacci sequence up through it’s 12th entry. Start your script with the line fib <- c(1, 1), and recall that c combines vectors.

Challenge 3

Create a data frame from scratch that will have three columns and 40 rows as follows. In a column named “id”, put a repeating sequence from 1 to 10. For a column named “group”, create a factor from an alternating seqence of 10 as followed by 10 bs (hint: ?seq). In a colume named “var” put 40 random samples from a uniform distribution (hint: ?runif) in order from smallest to largest (hint: ?sort). Plot the data with a smooth line showing the relationship between the increasing id and var with different colors for each group.

Challenge 4

Try that Fibonacci challenge again, but use an approach called “recursion”. That is, write a function that calls itself (i.e. a recursion) to calculate any Fibonacci number. Just because this is an “advanced” approach doesn’t mean it’s a good one: don’t try it for any number over your age!

Solutions

Solution 1

• pums[pums$AGEP == 21, ]
• pums[-(1:4), ]
• pums[pums$WAGP <= 20000, ]
• pums[pums$AGEP == 18 | pums$AGEP == 21, ]

Solution 2

fib <- c(1, 1)
for (i in 3:12) {
  fib[i] <- fib[i - 1] + fib[i - 2]
}

Solution 3

df <- data.frame(
  id = 1:10,
  group = rep(c('a', 'b'), each = 10),
  prob = sort(runif(40)))
ggplot(df, aes(x = id, y = prob, color = group)) +
  geom_point() +
  geom_smooth(method = 'lm')

Solution 4

fibn <- function(n) {
 if (n <= 2) {
   fib <- 1
 } else if (n > 2) {
   fib <- fibn(n - 1) + fibn(n - 2)
 }
 return(fib)
}
fibn(12)

Note that this recursive solution is dangerously inefficient, but could be greatly improved with memoization.

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