Edit the code chunks below and knit the document. You can pipe your objects to glimpse() or print() to display them.
Set the vector v1 equal to the following: 11, 13, 15, 17, 19, …, 99, 101 (use a function; don’t just type all the numbers).
v1 <- seq(11, 101, by = 2) %>% print()## [1] 11 13 15 17 19 21 23 25 27 29 31 33 35 37
## [15] 39 41 43 45 47 49 51 53 55 57 59 61 63 65
## [29] 67 69 71 73 75 77 79 81 83 85 87 89 91 93
## [43] 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 <- rep(LETTERS[1:5], each = 2) %>% print()## [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.
pets <- c("dog", "cat", "fish", "ferret")
pet_n <- c(10, 9, 6, 1)
v3 <- rep(pets, times = pet_n) %>% print()## [1] "dog" "dog" "dog" "dog" "dog" "dog"
## [7] "dog" "dog" "dog" "dog" "cat" "cat"
## [13] "cat" "cat" "cat" "cat" "cat" "cat"
## [19] "cat" "fish" "fish" "fish" "fish" "fish"
## [25] "fish" "ferret"Use apply() or map() functions to create a list of 11 vectors of 100 numbers sampled from 11 random normal distributions with means of 0 to 1.0 (in steps of 0.1) and SDs of 1. Assign this list to the object samples. Set the seed to 321 before you generate the random numbers to ensure reproducibility.
set.seed(321)
mu <- seq(0, 1, 0.1)
samples <- map(mu, rnorm, n = 100)
# alternatively
samples <- lapply(mu, rnorm, n = 100)Use apply() or map() functions to create a vector of the sample means from the list samples in the previous question.
sample_means <- map_dbl(samples, mean)
## alternatively
sample_means <- sapply(samples, mean) %>% print()## [1] 0.08304484 0.21226580 0.16840128 0.12930317 0.53216771
## [6] 0.40842952 0.72589166 0.63446436 0.81040369 1.02317536
## [11] 0.91126680Write a function called my_add that adds two numbers (x and y) together and returns the results.
my_add <- function(x, y) {
x+y
}Create a vector testing your function my_add. Every item in the vector should evaluate to TRUE if your function is working correctly.
my_add_test <- c(
my_add(1, 2) == 3,
my_add(10, 20) == 30,
my_add(-1, -3) == -4,
my_add(0.2, 0.334) == 0.534
) %>% print()## [1] TRUE TRUE TRUE TRUECopy the function my_add above and add an error message that returns “x and y must be numbers” if x or y are not both numbers.
my_add <- function(x, y) {
if (!is.numeric(x) | !is.numeric(y)) stop("x and y must be numbers")
x+y
}Create a tibble called dat that contains 20 rows and three columns: id (integers 101 through 120), pre and post (both 20-item vectors of random numbers from a normal distribution with mean = 0 and sd = 1). Set seed to 90210 to ensure reproducible values.
set.seed(90210)
dat <- tibble(
id = 101:120,
pre = rnorm(20),
post = rnorm(20)
) %>% print()## # A tibble: 20 x 3
## id pre post
## <int> <dbl> <dbl>
## 1 101 -0.444 0.664
## 2 102 -0.515 -1.78
## 3 103 1.18 -1.21
## 4 104 1.57 -1.40
## 5 105 0.0934 1.83
## 6 106 0.165 -1.43
## 7 107 -0.339 -2.17
## 8 108 -0.780 -0.789
## 9 109 0.386 0.172
## 10 110 -1.83 -1.24
## 11 111 -0.544 0.319
## 12 112 -0.138 -1.63
## 13 113 0.296 0.395
## 14 114 -0.434 -1.85
## 15 115 -0.502 -2.53
## 16 116 0.113 0.279
## 17 117 -0.781 0.824
## 18 118 0.916 -0.622
## 19 119 0.179 0.928
## 20 120 0.0141 0.266Run a two-tailed, paired-samples t-test comparing pre and post. (check the help for t.test)
t <- t.test(dat$post, dat$pre, paired = TRUE) %>% print()##
## Paired t-test
##
## data: dat$post and dat$pre
## t = -1.5392, df = 19, p-value = 0.1402
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.1299798 0.1722842
## sample estimates:
## mean of the differences
## -0.4788478Use broom::tidy to save the results of the t-test in question 8 in a table called stats.
stats <- t.test(dat$post, dat$pre, paired = TRUE) %>%
broom::tidy() %>%
print()## # A tibble: 1 x 8
## estimate statistic p.value parameter conf.low conf.high
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -0.479 -1.54 0.140 19 -1.13 0.172
## # … with 2 more variables: method <chr>, alternative <chr>Create a function called report_t that takes a data table as an argument and returns the result of a two-tailed, paired-samples t-test between the columns pre and post in the following format:
“The mean increase from pre-test to post-test was #.###: t(#) = #.###, p = 0.###, 95% CI = [#.###, #.###].”
Hint: look at the function paste0() (simpler) or sprintf() (complicated but more powerful).
NB: Make sure all numbers are reported to three decimal places (except degrees of freedom).
report_t <- function(data) {
stats <- t.test(data$post, data$pre, paired = TRUE) %>%
broom::tidy()
diff <- pull(stats, estimate) %>% round(3)
t <- pull(stats, statistic) %>% round(3)
p <- pull(stats, p.value) %>% round(3)
df <- pull(stats, parameter)
ci1 <- pull(stats, conf.low) %>% round(3)
ci2 <- pull(stats, conf.high) %>% round(3)
paste0("The mean increase from pre-test to post-test was ", diff,
": t(", df, ") = ", t,
", p = ", p,
", 95% CI = [", ci1, ", ", ci2, "].")
}# sprintf() is a complicated function, but can be easier to use in long text strings with a lot of things to replace
report_t <- function(data) {
stats <- t.test(data$post, data$pre, paired = TRUE) %>%
broom::tidy()
sprintf("The mean increase from pre-test to post-test was %.3f: t(%.0f) = %.3f, p = %.3f, 95%% CI = [%.3f, %.3f].",
pull(stats, estimate),
pull(stats, parameter),
pull(stats, statistic),
pull(stats, p.value),
pull(stats, conf.low),
pull(stats, conf.high)
)
}Use inline R to include the results of report_t() on the dat data table in a paragraph below.
The mean increase from pre-test to post-test was -0.479: t(19) = -1.539, p = 0.140, 95% CI = [-1.130, 0.172].