Set the vector v1 equal to the following: 11, 13, 15,
17, 19, …, 99, 101.
v1 <- seq(11, 101, by = 2)
v1## [1] 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
## [20] 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85
## [39] 87 89 91 93 95 97 99 101Set the vector v2 equal to the following: “A” “A” “B”
“B” “C” “C” “D” “D” “E” “E” (note the letters are all uppercase).
v2 <- c("A","A","B","B","C","C","D","D","E","E")
v2## [1] "A" "A" "B" "B" "C" "C" "D" "D" "E" "E"Set the vector v3 equal to the words “dog” 10 times,
“cat” 9 times, “fish” 6 times, and “ferret” 1 time. (Here, you will have
to check documentation for the rep() function, and/or the
textbook.)
v3 <- c(rep("dog", 10),
rep("cat", 9),
rep("fish", 6),
rep("ferret", 1))
v3## [1] "dog" "dog" "dog" "dog" "dog" "dog" "dog" "dog"
## [9] "dog" "dog" "cat" "cat" "cat" "cat" "cat" "cat"
## [17] "cat" "cat" "cat" "fish" "fish" "fish" "fish" "fish"
## [25] "fish" "ferret"Use map() functions to create a list of 10 vectors of
100 numbers sampled from 10 random normal distributions with means of 1
to 10 (in steps of 1) and SDs of 5. Assign this list to the object
samples.
NOTE: In map(), you can define arguments of the function
after providing the name of the function. The vector/list provided will
iterate across the first argument that is NOT provided.
set.seed(321) # ensures exact reproducibility
samples <- purrr::map(1:10, rnorm, n = 100, sd = 5)
glimpse(samples)## List of 10
## $ : num [1:100] 9.525 -2.56 -0.39 0.402 0.38 ...
## $ : num [1:100] -4.52 -2.59 7.67 6.09 -3.87 ...
## $ : num [1:100] -3.499 13.599 5.727 3.777 -0.428 ...
## $ : num [1:100] -0.74 -6.08 -4.68 14.72 1.62 ...
## $ : num [1:100] 4.024 7.473 6.216 -1.377 0.559 ...
## $ : num [1:100] 1.73 4.685 -0.392 12.4 13.367 ...
## $ : num [1:100] 3.038 5.423 17.136 12.216 0.745 ...
## $ : num [1:100] 9.04 6.25 15.95 12.5 6.71 ...
## $ : num [1:100] 4.966 8.36 8.174 12.382 -0.157 ...
## $ : num [1:100] 8.574 3.255 0.734 21.196 9.338 ...Use map() functions to create a vector of the sample
means from the list samples in the previous question. Then,
specifically do the same thing but create a vector of the sample means
in which the means are character strings.
sample_means <- purrr::map_dbl(samples, mean)
sample_means_char <- purrr::map_chr(samples, ~as.character(mean(.x)))
glimpse(sample_means)## num [1:10] 1.05 1.99 3.82 4.42 5.52 ...glimpse(sample_means_char)## chr [1:10] "1.04533834298671" "1.99224355267195" "3.81900699890789" ...Write a for loop that produces the exact same output as
the output in 4a. Use glimpse to look at both lists at
once, to compare them.
NOTE: Look carefully at the for loop in the slides.
set.seed(321)
samples_for <- list()
for(i in 1:10){
samples_for[[i]] <- rnorm(n = 100, mean = i, sd = 5)
}
glimpse(samples)## List of 10
## $ : num [1:100] 9.525 -2.56 -0.39 0.402 0.38 ...
## $ : num [1:100] -4.52 -2.59 7.67 6.09 -3.87 ...
## $ : num [1:100] -3.499 13.599 5.727 3.777 -0.428 ...
## $ : num [1:100] -0.74 -6.08 -4.68 14.72 1.62 ...
## $ : num [1:100] 4.024 7.473 6.216 -1.377 0.559 ...
## $ : num [1:100] 1.73 4.685 -0.392 12.4 13.367 ...
## $ : num [1:100] 3.038 5.423 17.136 12.216 0.745 ...
## $ : num [1:100] 9.04 6.25 15.95 12.5 6.71 ...
## $ : num [1:100] 4.966 8.36 8.174 12.382 -0.157 ...
## $ : num [1:100] 8.574 3.255 0.734 21.196 9.338 ...glimpse(samples_for)## List of 10
## $ : num [1:100] 9.525 -2.56 -0.39 0.402 0.38 ...
## $ : num [1:100] -4.52 -2.59 7.67 6.09 -3.87 ...
## $ : num [1:100] -3.499 13.599 5.727 3.777 -0.428 ...
## $ : num [1:100] -0.74 -6.08 -4.68 14.72 1.62 ...
## $ : num [1:100] 4.024 7.473 6.216 -1.377 0.559 ...
## $ : num [1:100] 1.73 4.685 -0.392 12.4 13.367 ...
## $ : num [1:100] 3.038 5.423 17.136 12.216 0.745 ...
## $ : num [1:100] 9.04 6.25 15.95 12.5 6.71 ...
## $ : num [1:100] 4.966 8.36 8.174 12.382 -0.157 ...
## $ : num [1:100] 8.574 3.255 0.734 21.196 9.338 ...