# Load packages

# Core
library(tidyverse)
library(lubridate)
library(nycflights13)

Ch19 Functions

Introduction

When should you write a function?

# For reproductible work
set.seed(1234)

# Create a data frame
df <- tibble::tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)
#rnorm: random value
# Rescale each column

df$a <- (df$a - min(df$a, na.rm = TRUE)) / 
  (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))
#$ extract column a
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$a, na.rm = TRUE)) / 
  (max(df$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 0.153  0.782  0.336 
##  2 0.765 0      0.473 -0.145 
##  3 1     0.0651 0.498 -0.216 
##  4 0     0.311  0.943 -0.153 
##  5 0.809 0.573  0.373 -0.496 
##  6 0.831 0.260  0     -0.356 
##  7 0.516 0.143  1     -0.664 
##  8 0.524 0.0255 0.210 -0.409 
##  9 0.519 0.0472 0.708 -0.0897
## 10 0.424 1      0.253 -0.142
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$b <- rescale(df$b)
df$c <- rescale(df$c)
df$d <- rescale(df$d)

df
## # A tibble: 10 × 4
##        a      b     c     d
##    <dbl>  <dbl> <dbl> <dbl>
##  1 0.332 0.153  0.782 1    
##  2 0.765 0      0.473 0.519
##  3 1     0.0651 0.498 0.448
##  4 0     0.311  0.943 0.511
##  5 0.809 0.573  0.373 0.168
##  6 0.831 0.260  0     0.308
##  7 0.516 0.143  1     0    
##  8 0.524 0.0255 0.210 0.256
##  9 0.519 0.0472 0.708 0.575
## 10 0.424 1      0.253 0.522

Functions are for humans and computers

  • Function’s name as short as possible, and very clear (what it does)

  • Consistency!!

  • If family: start with the same word (ex.: input_select, input_text, etc.)

  • Label your code with # before section, make it easier to read

Conditional execution

detect_sign <- function(x) {
    
    if(x > 0) {
        message("Value is positive")
        print(x)
    } else if(x==0) {
        warning("Value is not positive, by it can be accepted")
        print(x)
    } else {
        stop("Value is negative, the function must stop")
        print(x)
        # will not print the function because of the 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
# ...
# provide additional arguments to other functions we are creating
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