# Load packages

# Core
library(tidyverse)
library(tidyquant)

CH19 Functions

Introduction

When should you write a function?

# For reproducible work
set.seed(1234)
# Create a data frame
df <- tibble::tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)
# Re-scale 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$b, na.rm = TRUE) - min(df$a, 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(x, na.rm = TRUE)) / 
  (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
    
    # return value
    return(x)
    
}
df$a <- rescale(df$a)
df$a <- rescale(df$b)
df$a <- rescale(df$c)
df$a <- rescale(df$d)

df
## # A tibble: 10 × 4
##        a      b     c     d
##    <dbl>  <dbl> <dbl> <dbl>
##  1 1     0.216  0.782 1    
##  2 0.519 0      0.473 0.519
##  3 0.448 0.0919 0.498 0.448
##  4 0.511 0.440  0.943 0.511
##  5 0.168 0.810  0.373 0.168
##  6 0.308 0.368  0     0.308
##  7 0     0.202  1     0    
##  8 0.256 0.0361 0.210 0.256
##  9 0.575 0.0667 0.708 0.575
## 10 0.522 1.41   0.253 0.522

Functions are for humans and computers

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 negetive, the function must stop")
        print(x)
        }
}

3 %>% detect_sign()
## [1] 3
0 %>% detect_sign()
## [1] 0
1 -1 %>% detect_sign()
## [1] 1
## [1] 0

Function Arguements

?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

Return values