WELCOME!

This Presentation Covers...

• How to install the R toolchain for data science

  • The R project for statistical computing

  • The RStudio integrated development environment (IDE)

  • The Rtools suite of compilers (optional)

    • Required for building R packages containing compiled code

    • Required for installing R packages from GitHub containing compiled code

  • Git/GitHub (optional)

    • Easily collaborate on Rprojects with other programmers

    • Seamlessly integrates with RStudio

• The basics of R

  • What R is and how R differs from many other languages

  • Advantages and disadvantages of using R

  • Creating and manipulating R objects

  • Accessing help resources

• Literate programming with Rmarkdown

  • Render code, text, images, and \(\mathscr{equations}\) simultaneously in documents & presentations

WHAT IS R?

In a Nutshell, R is...

• An open-source, interpreted computer language and suite of statistical operators for calculations on vectors and matrices

• An integrated collection of tools and graphical facilities for data analysis and reproducible research

• A well-developed programming language which includes conditionals, loops, user-defined recursive functions, and input-output facilities

• One of the fastest growing technical programming languages in the world

IEEE Ranking Of Top Programming Languages

Where Did R Come From?

• The S programming language

  • Computing language originally developed at Bell Labs (1976) for data analysis

  • Licensed by AT&T/Lucent to Insightful Corp. under the product name: S-Plus.

• The R programming language

  • Written by two statisticians Ross Ihaka and Robert Gentleman at the University of Auckland

  • Released as an open source implementation of the S language (R plays on name "S")

  • Since 1997: international R-core team (~15 people) and thousands of code writers and statisticians share their work via R packages

  • R packages work like apps for smartphones - making it more useful

  • Currently, 9683 packages are available on the Comprehensive R Archive Network (CRAN)

HOW DOES R COMPARE TO OTHER LANGUAGES

R vs. Proprietary Source Languages

ADVANTAGES AND DISADVANATAGES OF USING R

Advantages

• Only open-source language with a standardized set of development tools to extend the environment

• Gives full access to the programming environment, easily see what functions are doing, fix bugs, and extend software

• Promotes reproducible research by providing open and accessible tools for data analysis and reporting

• Provides a forum allowing researchers to explore and expand the methods used to analyze data

• Scientists around the world are the co-owners to the software tools needed to carry out research

• The product of thousands of experts in many fields - R is CUTTING EDGE

Disadvantages

• Many percieve the R learning curve to be steep, minimal GUI
  Addressed by RStudio

  • In reality, R's learning curve is no steeper than that of any other language

  • R has a flexible syntax - that's not always consistent

• Little commercial support; new users complain of hostility on R help sites

• Figuring out correct methods to use a function on your own can be frustrating

• Working with large datasets is limited by RAM
  Addressed by data.table package

• Data prep can be messier and more mistake prone in R vs. SPSS or SAS
  Addressed by dplyr and tidyr pacakges

IS R FAST? (YES AND NO)

Human efficiency vs. Machine efficiency

• R is a fast language that has been purposefully implemented in such a way as to make it run slower

• Recall, R was written by statisticians - for statisticians

• Many languages are designed to produce numerical results as fast as possible Machine efficiency

  • Compiled languages like C, C++, and FORTRAN can produce numerical results extremely fast (about 30-60x faster than R)

  • However, compiled code can be difficult to for statisticians and non-developers work with

• R was built to help non-developers produce results and high quality graphics fast human efficiency

UNDERSTANDING THE R ENVIRONMENT

Basic Structure of R

• R was initially created as a functional programming language but has since taken on attributes of an object-oriented programming

• To understand computations in R, remember these five things

  • 1. Everything that exists is an object...almost
  • 2. Everything that happens is a function call
  • 3. Function call means that a function-class object "calls" a particular method
  • 4. Every object and function call has an associated environment
  • 5. Lexical scoping rules define how objects and functions can interact

Everything in R is an OBJECT...Almost

• Functions, vectors, datasets, character strings... are stored as objects

• Graphics are written out and are NOT stored as objects

• Objects are classified by two criteria:

  • MODE: how objects are stored in R - character, numeric, logical, factor, list, & function

  • CLASS: how objects are treated by functions - vector,list, matrix, array, data frame & hundreds of other classes created by specific functions

• Scripting is the process of matching objects and function calls such that the desired output objects (e.g., statistical results) and graphics are returned.

Assigning names to objects - R is CasE sENsItiVE!!

• R has three methods to assign names to objects

  • Left assignment <-

  • Left deep assignment <<-

  • Right assigment ->

  • Right deep assignment ->>

  • Equals sign =

• In most cases R treats each of these assignment methods the same, however the R style guide provide a best practice to ensure consistency

  • Use <- and -> to define objects (i.e. x <- 4)

  • Use = to assign values to function arguments (i.e. sqrt(x = 4))

  • The use of <<- and ->> should be minimized as it can have unintended side effects (if you don't know what is does don't use it)

• Object naming rules

  • Variable names can only contain letters and numbers separated by "." or "_"

  • Variable Names CANNOT begin with a number

var <- 5  ### Left assignment

200 -> Var ### Right assignment

meaning.of_life = 42 ### Or equal sign

var; Var; meaning.of_life

[1] 5

[1] 200

[1] 42

HELPFUL FUNCTIONS

Functions to access R object properties

• There are several functions to view the properties of an object

mode(object.name)  ### Returns an object's mode 

class(object.name) ### Returns an object's class

attributes(object.name) ### Returns any attributes associated with an object

str(object.name) ### Returns a complete list of properties assigned to an object

Functions to manage active objects

• Errors can result if you accidentally overwrite an object in the current environment

• These functions are helpful for managing objects defined in the current working environment

ls( ) ### Returns a list of active objects in the current working environment

rm( ) ### Removes an object from the current working environment

rm(list = ls()) ### Removes all objects from the current working environment (use carefully)

Functions to manage your local file structure

• Errors can result if a script, saved in one file location, calls a file in another location

• These functions are helpful for managing your local file structure

• Additionally, creating projects in RStudio can help avoid these errors

getwd( ) ### Returns the location of the current working directory

setwd("C:/Users/Desktop") ### Reassigns the location of the working directory

file.choose( ) ### Opens a new file explorer window to choose a file

Help Resources Within R

?cos ### Searches the local package library for "cos"

??xyz ### Searches the full R documentation for "xyz" - also help(xyz)

vignette() ### Lists "how-to" demos available for each package in the library

help.search("t.test") ### Provides a categorized search of R documentation for "t.test"

Online Resources for Help

• Quick R http://www.statmethods.net/

  • Expands upon book: R in Action 2nd ed., Robert I. Kabacoff August, 2011, Mannings Publication Company, ISBN 9781935182399

• R-bloggers http://www.r-bloggers.com/

  • An aggregation of content collected from bloggers who write about R to help R users connect and follow the "R blogosphere"

• StackOverflow http://stackoverflow.com/questions/tagged/r/

  • A question and answer site for professional and enthusiast programmers