functionswithr.Rpres

functions with R
========================================================
author: Hezi Buba & Irene Steves
date: 18/12/2018
autosize: true

Presentation materials: https://github.com/ecodatasci-tlv/functions 

Why write functions?
========================================================
Sometimes, we tend to repeat ourselves when coding: Repeating similar analyses, getting data ready for plots, etc.
Functions have multiple advantages over copy and pasting chuncks of code:
- Naming a function improve readability of code ("write codes for humans")
- Changing paramters and conditions in one place.
- Eliminate chance of mistakes due to copy-pasting.

When to write functions?
=======================================================
A good rule of thumb is to not copy and paste code more than twice.

```{r eval=F}
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))
```


Writing functions
========================================================

![](spotify-howtobuildmvp.gif)

>"It is faster to make a four-inch mirror then a six-inch mirror than to make a six-inch mirror." 

****

1. Start with a limited but working chuck of code.
2. Rewrite it as a function. (psst.. FUN snippet..)
```{r eval =F}
name <- function(variables) {
  
}
```

3. Test it. OPTIONAL: conditional stopping of functions.
4. Name it. 
5. Use it in your code or within a more complicated function. 

How much code do we want to encapsule in a funcion?
========================================================
Suprisingly, not so much. Focus on your function doing just one thing.

Most of R's functions are less than 12 lines long!


Naming functions
========================================================
Function names should be kept short yet informative. Remember: functions are meant to help codes be reusable and readable.

What are good names for these two functions?

```{r eval = F}
f1 <- function(string, prefix) {
  substr(string, 1, nchar(prefix)) == prefix
}
f2 <- function(x) {
  if (length(x) <= 1) return(NULL)
  x[-length(x)]
}
```


Anonymous functions
========================================================
Sometimes, you won't save the function like seen below, but rather - use it directly in a code:

```{r}
matrix_of_numbers <- matrix(1:100,10,10)
apply(matrix_of_numbers,2,function(x) x^2)
```


Iterations
========================================================
To further streamline your code, use iterations to repeat chuncks of code.
Most basic are `for` and `while` loops. However, there are more ways to iterate code.

Iterations have three main components: an output, a sequence to iterate over, and the body of code.

Vector allocation will save you a lot of time!
========================================================

```{r eval=F}
library(tictoc)
n_times <- 50000
tic()
a <- NULL
for(i in seq_len(n_times)){
    a <- c(a, i^2)
    a
}
toc()
tic()
a <- vector("double", n_times)
for(i in seq_len(n_times)){
    a[i] <- i^2
}
toc()
```

Sequence
========================================================
`for (i in (1:10))` is a sequence. So is `while TRUE`. 
`for (i in seq_along(vector))` is a better way of sequencing if you might get a vector of length 0 like so:

```{r}
y <- vector("double", 0)
seq_along(y)
#> integer(0)
1:length(y)
#> [1] 1 0
```

for i in unique(df$column)
========================================================
That's a common phrase when working with data.  So common that there is a tidyverse package that does it for you.

![](purrr.png) We will discuss it shortly...

R is a functional language 
========================================================
That means you can wrap loops within a function and just call that function when neccessary.
Remember - limit your copy and paste as much as possible!

```{r}
library(tidyverse)
data <- tibble(a=rnorm(10),
               b=rnorm(10))
col_means <- function(dataframe){
  output <- vector("double",ncol(dataframe))
  for (i in seq_along(dataframe)){
    output[[i]] <- mean(dataframe[[i]])
  }
  return(output)
}
col_means(data)
```


99 bottles of beer on the wall
========================================================

Create a function that returns the full song for any number of any vessel (bottles,cans, even boxes... ) of any drink (But no Jägermeister please):

>99 bottles of beer on the wall, 99 bottles of beer. Take one down, pass it around - 98 bottles of beer on the wall