# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(nycflights13)

CH19 Functions

Introductions

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)
)
# 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$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))

df
## # A tibble: 10 × 4
##        a      b     c     d
##    <dbl>  <dbl> <dbl> <dbl>
##  1 0.332 0.216  0.782 1    
##  2 0.765 0      0.473 0.519
##  3 1     0.0919 0.498 0.448
##  4 0     0.440  0.943 0.511
##  5 0.809 0.810  0.373 0.168
##  6 0.831 0.368  0     0.308
##  7 0.516 0.202  1     0    
##  8 0.524 0.0361 0.210 0.256
##  9 0.519 0.0667 0.708 0.575
## 10 0.424 1.41   0.253 0.522
rescale <- function(x) {
  
  # body
  x <- (x - min(x, na.rm = TRUE)) /
(max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
  
  # return values
  return(x)
}
df$a <- rescale(df$a)
df$b <- rescale(df$a)
df$c <- rescale(df$a)
df$d <- rescale(df$a)

df
## # A tibble: 10 × 4
##        a     b     c     d
##    <dbl> <dbl> <dbl> <dbl>
##  1 0.332 0.332 0.332 0.332
##  2 0.765 0.765 0.765 0.765
##  3 1     1     1     1    
##  4 0     0     0     0    
##  5 0.809 0.809 0.809 0.809
##  6 0.831 0.831 0.831 0.831
##  7 0.516 0.516 0.516 0.516
##  8 0.524 0.524 0.524 0.524
##  9 0.519 0.519 0.519 0.519
## 10 0.424 0.424 0.424 0.424

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 negative, the function must stop")
  }
  
}
3 %>% detect_sign()
## [1] 3
0 %>% detect_sign()
## [1] 0
# -1 %>% detect_sign()

Function 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
  • another that takes a data frame as the input

Return Values