exercise.md

library(lobstr)

Q-1:

1:5^2 1:5*2

Q0: Fix each of the following common data frame subsetting errors:

mtcars[mtcars$cyl = 4, ]

mtcars[-1:4, ]

mtcars[mtcars$cyl <= 5]

mtcars[mtcars$cyl == 4 | 6, ]

Q1: Why is 1 == "1" true? Why is -1 < FALSE true? Why is "one" < 2 false?

Q1.5: What does the following code return? Why? Describe how each of the three c’s is interpreted.

c <- 10
c(c = c)

Q2: What does dim() return when applied to a 1-dimensional vector? When might you use NROW() or NCOL()?

Q3: Can you have a data frame with zero rows? What about zero columns?

Q4: Explain the relationship between a, b, c, and d in the following code:

a <- 1:10
b <- a
c <- b
d <- 1:10

Q5: Brainstorm as many ways as possible to extract the third value from the cyl variable in the mtcars dataset.

Q6: Why does the following code work?

x <- 1:10
if (length(x)) "not empty" else "empty"
#> [1] "not empty"

x <- numeric()
if (length(x)) "not empty" else "empty"
#> [1] "empty"

Q7: When the following code is evaluated, what can you say about the vector being iterated?

xs <- c(1, 2, 3)
for (x in xs) {
  xs <- c(xs, x * 2)
}
xs
#> [1] 1 2 3 2 4 6
#> 

Q8: What does the following code tell you about when the index is updated?

for (i in 1:3) {
  i <- i * 2
  print(i) 
}
#> [1] 2
#> [1] 4
#> [1] 6
#> 

Q9: The following code accesses the mean function in multiple ways. Do they all point to the same underlying function object? Verify this with lobstr::obj_addr().

mean
base::mean
get("mean")
evalq(mean)
match.fun("mean")

Q10: Test your knowledge of vector coercion rules by predicting the output of the following uses of c():

c(1, FALSE)      
c("a", 1)        
c(TRUE, 1L)      

Q11: What sort of object does table() return? What is its type? What attributes does it have? How does the dimensionality change as you tabulate more variables?

Q12: What does df[is.na(df)] <- 0 do? How does it work?

Q13: What does the following function return? Make a prediction before running the code yourself.

f <- function(x) {
  f <- function(x) {
    f <- function() {
      x ^ 2
    }
    f() + 1
  }
  f(x) * 2
}
f(10)

Q14: Write your own version of + that pastes its inputs together if they are character vectors but behaves as usual otherwise. In other words, make this code work:

1 + 2
#> [1] 3

"a" + "b"
#> [1] "ab"
#> 

Q15: Create infix versions of the set functions intersect(), union(), and setdiff(). You might call them %n%, %u%, and %/% to match conventions from mathematics.