# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(ggrepel)

Functions

When should you write a function

# For reproducible work
set.seed(1234)

# Create 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))
square <- function(var) {
    
    # body
    squared_value <- var * var
    
    # return value
    return(squared_value)

    
}
rescale <- function(x) {
    
    x <- (x - min(x, na.rm = TRUE)) / 
  (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
    
    # Return value
    return(x)
    
}

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

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 cector as the input
  • one that takes a data frame as the input

Return values