R Basics

Functions

R Packages

library(tidyverse)

Indexing

  • Indexing

  • if/else Statements

  • Built-in Functions

  • User-built functions

Indexing

Within an R object, you can access an element by indexing it.

Indexing tells R which values to output.

Vectors

A vector can be indexed by adding [] after the object’s name and specifying the number of each element.

letters
letters[13]

Matrices

A matrix can be indexed by adding [] after the object’s name and specifying the number of each element. Separate the values by commas for specific indexes.

x <- matrix(1:40, nrow = 4)

Data Frames

Data frames can be indexed using the $ operator and [].

mtcars[,"mpg"]

Lists

Lists can be indexed using the [[]] for a specific element of a list.

toy_list <- list(x = letters,
                 y = mtcars,
                 z = list(x = diag(rep(1, 5),
                          y = matrix(1:40, nrow = 5),
                          z = band_members)))

if/else Statements

  • Indexing

  • if/else Statements

  • Built-in Functions

  • User-built functions

if/else Statements

if/else statements are used to conduct specific tasks depending on the conditions

if Statement

An if statement is used to if you want R to perform a specific function if a certain condition is met. An if statement will only run a task if a logical is returned. You will need type if, followed by the condition (as a logical) in parentheses, then the task.

Example

x <- sample(-10:10,1)
if (x > 0){
  print("Positive")
}
print(x)

else statement

An else statement will conduct a different task if the if statement does not conduct the tasks.

Example

x <- sample(-10:10,1)
if (x > 0 ){
  print("Positive")
} else {
  print("Non-positive")
} 
print(x)

Chain if/else statement

If you have more than two options, you can chain if/else statements by adding an if statement immediately after the word else.

Example

x <- sample(-10:10,1)
if (x > 0 ){
  print("Positive")
} else if (x == 0) {
  print("Zero")
} else {
  print("Negative")
}
print(x)

Built-in Functions

  • Indexing

  • if/else Statements

  • Built-in Functions

  • User-built functions

Built-in Functions

There are several available functions in R to conduct specific statistical methods or tasks

Help Documentation

Section Description
Description Provides a brief introduction of the function
Usage Provides potential usage of the function
Arguments Arguments that the function can take
Details An in depth description of the function
Value Provides information of the output produced by the function
Notes Any need to know information about the function
Authors Developers of the function
References References to the model and function
See Also Provide information of supporting functions
Examples Examples of the function

Generic Functions

Several R objects have a known class attached to it. A specialized object designed to be read by generic functions, such as summary() and plot().

For example, the summary() is a generic for several types of functions: summary.aov(), summary.lm(), summary.glm(), and many more.

Commonly-used Function

Functions Description
aov() Fits an ANOVA Model
lm() Fits a linear model
glm() Fits a general linear model
t.test() Conducts a t-test

User-built functions

  • Indexing

  • if/else Statements

  • Built-in Functions

  • User-built functions

User-built functions

  • Functions created by the user for analysis

  • Needs to be ran once to the R environment

  • Will be lost when R session is closed

Anatomy

name_of_function <- function(data_1, data_2 = NULL, 
                             argument_1, argument_2 = TRUE, argument_3 = NULL,
                             ...){
  # Conduct Task
  # Conduct Task
  output_object <- Tasks
  return(output_object)
}

  • function: used to construct the function

  • data1: first data argument that needs to supplied

  • data2: second data argument that does not need to be supplied

  • argument1: first argument must be supplied to alter function

  • argument2: second argument to alter function, set to TRUE

  • argument3: third argument that does not need to be supplied

  • …: additional arguments supplied to other functions

Example

Create a function for

\[ y = \ln(x^2) \]

Example

Create a function for

\[ f(x) = \left\{\begin{array}{cc} x^3 & x<0\\ x^2 + 5 & \mathrm{otherwise} \end{array} \right. \]

Example

Create a function for

\[ f(x,y) = \left\{\begin{array}{cc} x^3 e^y & x<0\ \\ x^2 + 5 + \ln(y) & \mathrm{otherwise} \end{array} \right. \]

Example

Create the function that allows your to compute the z-score of a specific value x using the sampling distribution from a set of data (y vector):

\[ z = \frac{x-\bar y}{\sqrt{s^2_{y}/n_y}} \]