Week 12: Code Along 11

R for Data Science: Chapter 19

# Load package
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Introduction

When should you write a function?

# For reproducible data
set.seed(1234)

# Create a data frame
df <- tibble::tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)

# Rescale each column
df$a <- (df$a - min(df$a, na.rm = TRUE)) / 
  (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))
df$b <- (df$b - min(df$b, na.rm = TRUE)) / 
  (max(df$b, na.rm = TRUE) - min(df$b, na.rm = TRUE))
df$c <- (df$c - min(df$c, na.rm = TRUE)) / 
  (max(df$c, na.rm = TRUE) - min(df$c, na.rm = TRUE))
df$d <- (df$d - min(df$d, na.rm = TRUE)) / 
  (max(df$d, na.rm = TRUE) - min(df$d, na.rm = TRUE))

rescale <- function(x) {
    
    # body
    x <- (x - min(df$a, na.rm = TRUE)) / 
  (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
    
    # return values
    return(x)
    
}

df$a <- rescale(df$a)
df$b <- rescale(df$b)
df$c <- rescale(df$c)
df$d <- rescale(df$d)

Functions are for humans and computers

Good

input_select() input_checkbox() input_text()

Not so good

select_input() checkbox_input() text_input()

Conditional execution

detect_sign <- function(x) {
    if(x > 0){
        message("Value is positive")
        print(x)
    } else if(x == 0) {
        warning("Value is not positive, but it can be accepted")
        print(x)
    } else {
        stop("Value is negative, the function must stop")
        print(x)
    }
}

3 %>% detect_sign()

## Value is positive

## [1] 3

0 %>% detect_sign()

## Warning in detect_sign(.): Value is not positive, but it can be accepted

## [1] 0

# -1 %>% detect_sign()

Functional arguments

?mean

x <- c(1:10, 100, NA)
x

##  [1]   1   2   3   4   5   6   7   8   9  10 100  NA

x %>% mean()

## [1] NA

x %>% mean(na.rm = TRUE)

## [1] 14.09091

x %>% mean(na.rm = TRUE, trim = 0.1)

## [1] 6

mean_remove_na <- function(x, na.rm = TRUE, ...) {
    
    avg <- mean(x, na.rm = na.rm, ...)
    
    return(avg)
}

x %>% mean_remove_na()

## [1] 14.09091

x %>% mean_remove_na(na.rm = FALSE)

## [1] NA

x %>% mean_remove_na(trim = 0.1)

## [1] 6

two types of functions - one that takes a vector as the input - one that takes a data frame as input

Return values