Writing Functions! Part I!

Transcription

1 Writing Functions! Part I!

2 In your mat219_class project 1. Create a new R script or R notebook called wri7ng_func7ons 2. Include this code in your script or notebook: library(tidyverse) library(gapminder) gapminder <- gapminder::gapminder

3 checking validity of arguments

4 Test functions

5 max_minus_min <- func7on(x) { if(!is.numeric(x)){ stop( expected input is a numeric vector.\n, actual input was of class, class(x)[1]) } max(x) min(x) } If a func7on will get used again in different contexts, it is good to check the validity of the arguments Rule of Repair: When you must fail, fail noisily and as soon as possible

6 Your Turn 1 The max and min of a numeric vector are special cases of a quantile: min = 0 quantile Q1 = 0.25 quantile median = 0.5 quantile Q3 = 0.75 quantile max = 1 quantile Suppose we sometimes want max min, sometime Q3 Q1 (the IQR), and sometimes the difference between two other quantiles. Write a function that takes the difference of two specified quantiles for a numeric vector. Hint: consider using quantile().

7 quan7le(gapminder$lifeexp) boxplot(gapminder$lifeexp, plot = FALSE)$stats quan7le(gapminder$lifeexp, probs = 0.5) median(gapminder$lifeexp) quan7le(gapminder$lifeexp, probs = c(0.25, 0.75)) take some 7me to understand how quan7le() works

8 first step: get something to work the_probs <- c(0.25, 0.75) the_quan7les <- quan7le(gapminder$lifeexp, probs = the_probs) max(the_quan7les) - min(the_quan7les)

9 second step: turn the code into a func7on quan7le_diff <- func7on(x, probs) { the_quan7les <- quan7le(x = x, probs = probs) max(the_quan7les) - min(the_quan7les) } quan7le_diff(gapminder$lifeexp, probs = c(0.25, 0.75)) IQR(gapminder$lifeExp)

10 second step: turn the code into a func7on quan7le_diff <- func7on(x, probs) { the_quan7les <- quan7le(x = x, probs = probs) max(the_quan7les) - min(the_quan7les) } quan7le_diff(gapminder$lifeexp, probs = c(0, 1)) max_minus_min(gapminder$lifeexp)

11 default arguments

12 max_minus_min(gapminder$lifeexp) quan7le_diff(gapminder$lifeexp) it would be nice if our generaliza7on worked the same as our old func7on when we wanted it to

13 quan7le_diff <- func7on(x, probs = c(0, 1)) { the_quan7les <- quan7le(x = x, probs = probs) max(the_quan7les) - min(the_quan7les) } quan7le_diff(gapminder$lifeexp) max_minus_min(gapminder$lifeexp)

14 the argument

15 ?quan7le

16 quan7le_diff <- func7on(x, probs = c(0, 1), ) { the_quan7les <- quan7le(x = x, probs = probs, ) max(the_quan7les) - min(the_quan7les) } quan7le_diff(c(1, 2, NA), na.rm = TRUE)

17 iteration

18 Setup Run the following code to simulate exam scores for five students: set.seed(42) stu1 <- runif(10, 50, 100) stu2 <- runif(10, 50, 100) stu3 <- runif(10, 50, 100) stu4 <- runif(10, 50, 100) stu5 <- runif(10, 50, 100)

19 Task 2 For each student, compute the mean exam score after dropping the lowest score. Accomplish this task by writing a function called grade.

20 Goal: compute the mean exam score after dropping the lowest score. 1. Get something that works (sum(stu1) min(stu1)) / (length(stu1) 1)

21 Goal: compute the mean exam score after dropping the lowest score. 1. Get something that works 2. Turn that code into a func7on grade <- func7on(x) { (sum(x) min(x)) / (length(x) 1) } grade(stu1)

22 grade <- func7on(x) { (sum(x) min(x)) / (length(x) 1) } grade(stu1) grade(stu4) grade(stu2) grade(stu5) grade(stu3)

23 map()

24

25 exams <- list(stu1, stu2, stu3, stu4, stu5) map(exams, mean)

26

27

28 Task 3 Create a vector of type double that contains the grade (mean after removing the lowest score) for each student.

29 exams <- list(stu1, stu2, stu3, stu4, stu5) exams %>% map_dbl(grade)

30 Extra arguments exams <- list(stu1, stu2, stu3, stu4, stu5) exams %>% map_dbl(quan7le_diff)

31 Extra arguments exams <- list(stu1, stu2, stu3, stu4, stu5) exams %>% map_dbl(quan7le_diff, probs = c(0.25, 0.75))

32 operating on a selection of variables

33 Task 3 Write a function that computes the z-score for a numeric vector. Name the function zscore.

34 zscore <- func7on(x) { (x mean(x)) / sd(x) }

35 gapminder %>% mutate(gdppercap = zscore(gdppercap)) gapminder %>% mutate_at(vars(lifeexp:gdppercap), zscore) mutate_at() lets you select variables using the same syntax as the select() verb.

36 gapminder %>% mutate(gdppercap = zscore(gdppercap)) gapminder %>% mutate_if(is.numeric, zscore) mutate_if() lets you select variables based on a logical test (called the predicate).

37 The following data manipula7on verbs have *_at(), *_if(), and *_all() variants: mutate() transmute() summarise() filter() select() rename() arrange group_by()

38 dm <- 7bble(x = c(34, 145, 6544, 32, 129), y = c(345, 1452, 644, 312, 6129)) dm %>% mutate(z = sqrt(x))

39 dm <- 7bble(x = c(34, 145, 6544, 32, 129), y = c(345, 1452, 644, 312, 6129)) dm %>% mutate(z = reverse_int(x)) reverse_int <- func7on(x){ reverse <- 0 while (x!= 0){ rem <- x %% 10 reverse <- reverse * 10 + rem x <- x %/% 10 } reverse }

40 dm <- 7bble(x = c(34, 145, 6544, 32, 129), y = c(345, 1452, 644, 312, 6129)) dm %>% mutate(z = map_dbl(x, reverse_int)) reverse_int <- func7on(x){ reverse <- 0 while (x!= 0){ rem <- x %% 10 reverse <- reverse * 10 + rem x <- x %/% 10 } reverse }