FAIR-ifying R Software: Basic Operation

Key Points

Introduction to RStudio	Using RStudio can make programming in R much more productive.
Preparations	Objects are created on demand whenever a value is assigned to them. `apply`ing the mean calculation across rows or columns has shortcuts, but other statistics may need `?apply` or `?purrr`. Use `plot` to create simple visualizations.
Creating Functions	Define a function using `name <- function(...arguments...) {...body...}`. Specify default values for arguments when defining a function using `name = value` in the argument list. Call a function using `name(arg1 = value, ...)`. R looks for variables in the current stack frame before looking for them at the top level. Make code more readable by passing arguments preferably by name. Arguments can be passed by matching based on name, by position, or by omitting them (in which case the default value is used). Use `?name` or `??name` to find the help page of a function. Write formal roxygen2 comments for your functions and generate help pages from those.
Making Packages In R	A package is the basic unit of reusability in R. Every package must have a `DESCRIPTION` file and an `R` directory containing code. We have to fill these manually. A FAIR `DESCRIPTION` means adding rich metadata, best in a machine-readable format. A `NAMESPACE` file is needed as well, and a `man` directory containing documentation, but both can be autogenerated. RStudio provides a package skeleton, shortcuts like `Build and install`, and a `Git` pane.
Unit-Testing And Test-driven Development	Changing code is not always necessary, but often useful. Tests provide a safety net for changing code. Practice more TDD at exercism.io
Tidying & Packaging Datasets	Spreadsheets incentivise the wide data format, which may spread variables across columns. If variables in different dataset are comparable methodologically, their variable/column names should be spelled exactly alike. (Raw) Data can be packaged by itself (and/or alongside related cleaning and analysis code).

Basic Operation

# this is a comment in R
Use x <- 3 to assign a value, 3, to a variable, x
R counts from 1, unlike many other programming languages (e.g., Python)
length(thing) returns the number of elements contained in the variable collection
c(value1, value2, value3) creates a vector
container[i] selects the i’th element from the variable container

List objects in current environment ls()

Remove objects in current environment rm(x)

Remove all objects from current environment rm(list = ls())

Control Flow

Create a conditional using if, else if, and else

if(x > 0){
	print("value is positive")
} else if (x < 0){
	print("value is negative")
} else{
	print("value is neither positive nor negative")
}

create a for loop to process elements in a collection one at a time

for (i in 1:5) {
	print(i)
}

This will print:

Use == to test for equality
- 3 == 3, will return TRUE,
- 'apple' == 'orange' will return FALSE
X & Y is TRUE is both X and Y are true
X | Y is TRUE if either X or Y, or both are true

Functions

Defining a function:

is_positive <- function(integer_value){
	if(integer_value > 0){
	   TRUE
	}
	else{
	   FALSE
	{
}

In R, the last executed line of a function is automatically returned

Specifying a default value for a function argument

increment_me <- function(value_to_increment, value_to_increment_by = 1){
	value_to_increment + value_to_increment_by
}

increment_me(4), will return 5

increment_me(4, 6), will return 10

Call a function by using function_name(function_arguments)
apply family of functions: apply(), sapply(), lapply(), and mapply()

apply(dat, MARGIN = 2, mean) will return the average (mean) of each column in dat

Packages

Install package by using install.packages("package-name")
Update packages by using update.packages("package-name")
Load packages by using library("package-name")

Glossary

argument: A value given to a function or program when it runs. The term is often used interchangeably (and inconsistently) with parameter.
call stack: A data structure inside a running program that keeps track of active function calls. Each call’s variables are stored in a stack frame; a new stack frame is put on top of the stack for each call, and discarded when the call is finished.
comma-separated values (CSV): A common textual representation for tables in which the values in each row are separated by commas.
comment: A remark in a program that is intended to help human readers understand what is going on, but is ignored by the computer. Comments in Python, R, and the Unix shell start with a # character and run to the end of the line; comments in SQL start with --, and other languages have other conventions.
conditional statement: A statement in a program that might or might not be executed depending on whether a test is true or false.
dimensions (of an array): An array’s extent, represented as a vector. For example, an array with 5 rows and 3 columns has dimensions (5,3).
documentation: Human-language text written to explain what software does, how it works, or how to use it.
encapsulation: The practice of hiding something’s implementation details so that the rest of a program can worry about what it does rather than how it does it.
for loop: A loop that is executed once for each value in some kind of set, list, or range. See also: while loop.
function body: The statements that are executed inside a function.
function call: A use of a function in another piece of software.
function composition: The immediate application of one function to the result of another, such as f(g(x)).
index: A subscript that specifies the location of a single value in a collection, such as a single pixel in an image.
loop variable: The variable that keeps track of the progress of the loop.
notional machine: An abstraction of a computer used to think about what it can and will do.
parameter: A variable named in the function’s declaration that is used to hold a value passed into the call. The term is often used interchangeably (and inconsistently) with argument.
pipe: A connection from the output of one program to the input of another. When two or more programs are connected in this way, they are called a “pipeline”.
return statement: A statement that causes a function to stop executing and return a value to its caller immediately.
silent failure: Failing without producing any warning messages. Silent failures are hard to detect and debug.
slice: A regular subsequence of a larger sequence, such as the first five elements or every second element.
stack frame: A data structure that provides storage for a function’s local variables. Each time a function is called, a new stack frame is created and put on the top of the call stack. When the function returns, the stack frame is discarded.
standard input (stdin): A process’s default input stream. In interactive command-line applications, it is typically connected to the keyboard; in a pipe, it receives data from the standard output of the preceding process.
standard output (stdout): A process’s default output stream. In interactive command-line applications, data sent to standard output is displayed on the screen; in a pipe, it is passed to the standard input of the next process.
string: Short for “character string”, a sequence of zero or more characters.
while loop: A loop that keeps executing as long as some condition is true. See also: for loop.