Getting started

Download R

What is R? Open-source, free, stastical computing software.

Go to the R website, and download the appropriate software for your operating system.

Download RStudio

What is Rstudio? A nice GUI (graphical user interface) for writing code, analyzing data and building packages with R.

Go to the RStudio website. and download the appropriate software for your operating system.

Rstudio Layout

Rstudio has four main panels that allow you to organize your code and have a better user experience developing and using R code:

Source panel: where scripts are edited
Console panel: where code is executed and output is printed
Environment panel: where objects are stored
Files/Plots/Help: navigate files and folders, view plots, read and access help documentation for packages and functions

R as a calculator

Open a new .R script by clicking File $\rightarrow$ New File $\rightarrow$ R Script. We can execute code by highlighting it in a source file and clicking the run button.

We can also execute code by typing it directly into the console and pressing enter.

We can do basic mathematical computations using +,-, *, /, and ().

Note: pi is hard coded into R.

2+3

## [1] 5

6*7

## [1] 42

pi*1

## [1] 3.141593

6*3/2-3

## [1] 6

6*3/(2-3)

## [1] -18

1/0

## [1] Inf

Try some on your own!

What about more complicated functions like exsponents, square roots, trig functions and the natural logarithm?

3^2

## [1] 9

2^(3+2)

## [1] 32

sqrt(9)

## [1] 3

sin(2*pi)

## [1] -2.449294e-16

log(1000)

## [1] 6.907755

exp(6.907755)

## [1] 999.9997

Note: computes the natural logarithm. See to compute a logarithm with a different base.

Objects in R

Numbers, formulas, and other statistical information can be stored as objects in R.

Objects are given names and then are stored in the working environment. Once they are stored, the information remainds “hidden” until referenced again. If you are using RStudio the object names can be seen in the Environment module.

Notes:

Object names are case-sensitive
Object names should be meaningful and short (``best practice’’)
<- is most common, but = is acceptable

x <- 2 #Creates the object "x"
x    #Shows the contents of object "x"

## [1] 2

y = 3.5
y

## [1] 3.5

h <- "practice"  #You can also put strings in an object
h

## [1] "practice"

Objects can be numbers, strings, matrices, or even more complicated R objects.
Examples of R object types:

the computing basics: logical, integer, numeric, vector, matrix, list
data.frame, tibble
factor
lm object (linear model object)
formula
sf object (special feature, for spatial data)

There are handy functions for seeing whether or not an object is of a certain type. These functions are usually important for debugging and making sure you are inputing the proper object type into a function argument.

is.integer(y)

## [1] FALSE

is.numeric(y)

## [1] TRUE

is.character(h)

## [1] TRUE

is.matrix(x)

## [1] FALSE

The function will list all of the objects that have been created:

ls()

## [1] "h" "x" "y"

Here we see we have in our environment the objects we created . If we assign a new value to it will overwrite what is already stored in that object

x     #Original object that we created above.

## [1] 2

x <- 7  #New object with same name
x     #New object

## [1] 7

Data types

The most atomic object in R will exist having one of those data types, described below. An atomic object of the data type can have a value, NA which represents an observation with no data (e.g., a missing measurement), or NULL which isn’t really a value at all, but can still have the data type class.

You will encounter other data types, such as Date or POSIXct if you are working with dates or time stamps. These other data types are extensions of the fundamental data types.

To determine what data type an object is, use is(obj), str(obj), or class(obj).

is("a")

## [1] "character"           "vector"              "data.frameRowLabels"
## [4] "SuperClassMethod"

str(TRUE)

##  logi TRUE

class(123.45)

## [1] "numeric"

class(as.integer(1000))

## [1] "integer"

n <- as.numeric(999999999999999999999)

class(n)

## [1] "numeric"

R data structures

There are 5 basic data structures in R, as shown in the graphic:

vector (all elements must be of same class)
matrix (all elements must be of same class)
array (all elements must be of same class)
data frame (all elements within a column must be of same class, but columns can be of different classes)
list (each element of the list can be of a different class)

Documentation

One convenient feature in is the documenation for all functions. If you want to learn more about a function and its arguments you can simply type a question mark in front of the function name or use the function.

?log
help("log")

Question According to the above help files, what function (or functions) would you need to use for a base 10 logarithm?

There are a few other functions that can help you understand how to use functions or recall their arguments.

help.search("log")
args(log)

R Markdown

Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. RMarkdown is a nice program within RStudio that allows us to typset documents and incorporate code and output. This is helpful for writing labs for you all to follow along with, but also to create reproducible and replicable research. It allows you to avoid writing scripts to produce all your output and then copy and pasting that output into a word processing software which may induce unnecessary errors in your work.

Go to File $\rightarrow$ New File $\rightarrow$ R Markdown. Select Knit to HTML

It will open up a new template .Rmd file for you to begin editing that will look like this:

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

data(cars)
summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

A handy Rstudio/R Markdown cheatsheet can be found here https://rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf. It gives summaries of the most used Markdown syntax aspects (e.g. headings/subheadings, bullet lists, numbered lists), the types of files R Markdown can create and examples of how R and R Markdown interact.

This is an example of an an R Markdown file that will be used to create a .html file. This is the best option if you don’t have much math to write or do not need to share a .pdf.

Why use R Markdown?

Reproducibility: the results in the final come directly from the code include in the file
Replicability: a collaborator or another researcher or student can directly replicate your analysis using the code in the chunks and there is room for much more commentary than in a regular script file.
Debugging: every time you knit a .Rmd file, it executes the code from start to finish in a clean environment. it will not compile if there are errors in the

Chunks

While not nicely named, R code can be included in an R Markdown file in what we call a “chunk”. Properties of the chunk are adjusted within the curly braces. The cheatsheet under the heading “Embed code with knitr syntax”. The most common arguments I use are

echo: logical argument determining whether the code in the subsequent chunk will be printed in the document created
eval: logical argument determining whether or not the code in the subsequent chunk will be evaluated by `{R}$ or just printed in the document.
fig.cap: provides a caption for a figure
fig.align: aligns figure on “left”, “right” or “center”

For example, this figure was created with echo = FALSE and eval = TRUE.

Chunks allow you to break up your code into smaller pieces that a reader can understand. It’s great for writing reports/homeworks so that you can include code immediately followed by its output.

`purl()`

You can use the function purl() on a .Rmd document and it will create a new .R file with only the R code in the chunks. Copy and paste the following code line by line into your document to create a document called GettingStartedinR.R containing only the R code in this document.

install.packages('knitr', dependencies = T)
library(knitr)
purl('GettingStartedinR.Rmd')

Formatting

All of this is available on the cheat sheet, but here we will briefly cover basic text and document formatting.

Bold/Italics

Use asterisks (*) around a word or phrase to make it italicized.

Use double asterisks (**) around a word or phrase to make it bold.

In R Markdown, these formatting tools will create highlighted text.

Math mode

If you want to write equations you can use (\$) like in LaTeX.

Wrap an equation in a single \$ to write an equation in “math mode”: $y = x$.

Wrap an equation in double dollar signs (\$\$) to make an equation block in math mode: \[ y = x. \]

You can also include raw LaTeX code, and use and equation or align environment:

\begin{equation} y = x. \end{equation}

\[\begin{equation} y = x. \end{equation}\]

Getting Started with R & RStudio

Jessica Godwin