# Load packages

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

Functions

When Should you write a function

# for reproducible work
set.seed(1234)
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))
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
rescale <- function(x) {
    
    # Body
    x <- (x - min(x, na.rm = TRUE)) / 
    (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
    
    #
    return(x)
}
# Rescale
# Rescale each Column
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
square <- function(var) {
    
squared_value <- var * var

#Return Value
return(squared_value)}

Functions are for humans and computers

labels should be clear

Functions with similar use should start with the same term

Conditional execution

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

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

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, trim = .1)
## [1] 6
mean_remove_na <- function(x, na.rm = TRUE, ...)
{
    
    ave <- mean(x, na.rm =na.rm, ...)
    return(ave)
}
x %>% mean_remove_na()
## [1] 14.09091
x %>% mean_remove_na(na.rm = FALSE)
## [1] NA
x %>% mean_remove_na(trim = .1)
## [1] 6

Two types of functions

#one that takes a vector as an input # one that takes a data frame as the input