What Is R Programming Language?

Total Page:16

File Type:pdf, Size:1020Kb

What Is R Programming Language? 2 / 25 Lecture 1: Introduction to R and RStudio 3 / 27 R Basics Comparison What is R with other technologies History RStudio PROGRAMMING Why Learn Applications Companies Features using R 4 / 27 What is R Programming Language? Provides Statistical and Graphical Techniques Provides Well Designed Quality Posts Open Source Programming Language WHAT IS ? R is Available Runs on Wide as a Free Software Variety of Platform 5 / 27 What is R Programming Language? R is an open-source programming language that facilitates statistical computing and graphical libraries. Being open-source, R enjoys community support of avid developers who work on releasing new packages, updating R and making it a steadfast programming package for Data Science. • With the help of R, one can perform various statistical operations. • You can obtain it for free from the website www.r-project.org. • It is driven by command lines. • Each command is executed when the user enters them into the prompt. 6 / 27 History of R It Was created by R is the top Google Ross Ihaka & search for advanced Robert Gentleman analytics software Its First version It is Used by more Was released than 2 million March 1995 people worldwide It was Named after It is the Most used The first letter of PROGRAMMING LANGUAGE Data Science the founders language after SQL 7 / 27 Evaluation of R Programming • R language was developed by Ross Ihaka and Robert Gentleman in 1990. • Ross Ihaka and Robert Gentleman both worked together in the statistics department at Auckland University, New Zealand. • R showed its first appearance in the year 1993. • R language is based upon the S language. • S language was developed mainly by John Chambers in 1970 at Bell laboratories. • R software is an open source software, and it is a GNU (General Public License) based project. • R language along with the software, was developed by the group of people known as ‘R Core Team’. 8 / 27 Why Learn R Programming Language Free and Gateway to Open-Source tool lucrative career Large community Has a robust of users visualization library Popular for WHY LEARN? Goes to language for academic research Stats. & Data Science Independent Used in almost platform every industry 9 / 27 Why Learn R Programming Language • With R, you can perform statistical analysis, data analysis as well as machine learning. We can create objects, functions and packages in it. R is platform-independent and can be used across multiple operating systems. R is free owing to its open-source GNU licensing and can be installed by anyone. • R consists of a robust and aesthetic collection of graphical libraries like ggplot2, plotly and many more. With these libraries, you can make visually appealing and elegant visualizations. • R is most widely used by various industries. Only the academic avenues in the past made use of R but industries are now using R as their primary tool for statistical modeling. The most profound industry that makes use of R is the Data Science industry and the several underlying industries that it comprises of industries like health, finance, banking, manufacturing and many more. • There are about 2 million job openings for R programmers worldwide. Companies hire R programmers for many roles like data analysts, business analysts, data visualization experts, and business intelligence experts. 10 / 27 Features of R Programming Open-Source Machine Learning Operations Complete Language Support Extensions Analytical Support Community Support FEATURES Quality Plotting & Graphing Simple and Easy to Understand Comparable and often superior in power Object-oriented and functional to commercial products in most senses programming structure 11 / 27 Features of R Programming • R is a comprehensive programming language that provides support for procedural programming involving functions as well as object-oriented programming with generic functions. • There are more than 10,000 packages in the repository of R programming. With these packages, one can make use of functions to facilitate easier programming. • Being an interpreter-based language, R produces a machine-independent code that is portable in nature. Furthermore, it facilitates easy debugging of errors in the code. • R facilitates complex operations with vectors, arrays, data frames as well as other data objects that have varying sizes. • R provides robust facilities for data handling and storage. • R can be easily integrated with many other technologies and frameworks like Hadoop and HDFS. It can also integrate with other programming languages like C, C++, Python, Java, FORTRAN, and JavaScript. 13 / 27 Advantages and Disadvantages of R Open Source Weak Origin Data Wrangling Data Handling Array of Packages Basic Security Quality Plotting and Graphing Complicated Language Platform Independent Slower Speed Machine Learning Operations Consuming All Available Memory Continuously Growing 14 / 27 How R is better than other Technologies 15 / 27 IDEs for R If you use R under Windows or Mac OS X, then a graphical user interface (GUI) is available to you. This consists of a command-line interpreter, facilities for displaying plots and help pages, and a basic text editor. There are several editors and IDEs that facilitate GUI features for executing R scripts. Some of the useful editors that support the R programming language are: • Emacs + ESS • Eclipse/Architect • RStudio • Revolution-R • Live-R 16 / 27 Other IDEs and Editors There are many more editors that you can use to write R code. Here’s a quick roundup of a few more possibilities: • JGR [pronounced “Jaguar”] is a Java-based GUI for R, essentially a souped-up version of the stock R GUI. • Tinn-R is a fork of the editor TINN that has extensions specifically to help you write R code. • SciViews-K, from the same team that makes Tinn-R, is an extension for the KomodoIDE to work with R. • Vim-R is a plug-in for Vim that provides R integration. • NppToR plugs into Notepad++ to give R integration 17 / 27 RStudio Features of RStudio: • RStudio provides various tools and features that allow you to boost your code productivity. • It can also be accessed over the web and is cross-platform in nature. • It facilitates automatic checking of updates so that you don’t have to check for them manually. • It provides support for recovery in case of file loss. • With RStudio, you can manage the data more efficiently. 19 / 27 Components of RStudio • Source – In the top left corner of the screen is the text editor that allows you to work within source scripting. You can enter multiple lines in this source. Furthermore, users can save the R scripts to files that are stored in local memory. • Console – This is present on the bottom left corner of the main window of R Studio. It facilitates interactive scripting in R. • Workspace and History – In the top right corner, you will find the R workspace and the history window. This will give you the list of all the variables that were created in the environment session. Furthermore, you can also view the list of past commands that were executed by R. 20 / 27 Application of R Programming 21 / 27 Application of R Programming • R is used in finance and banking sectors for detecting fraud, reducing customer churn rate and for making future decisions. • R is also used by bioinformatics to analyze strands of genetic sequences, for performing drug discovery and also in computational neuroscience. • R is used in social media analysis to discover potential customers in online advertising. Companies also use social media information to analyze customer sentiments for making their products better. • E-Commerce companies make use of R to analyze the purchases made by the customers as well as their feedbacks. • Manufacturing companies use R to analyze customer feedback. They also use it to predict future demand to adjust their manufacturing speeds and maximize profits. 22 / 27 Companies Using R 23 / 27 How to Install R and RStudio We have to follow three basic steps in the same order to run R and RStudio on your system. • Install R • Then, install RStudio 24 / 27 1. Install R • https://www.r-project.org/ • https://cran.r-project.org/mirrors.html • https://cran.um.ac.ir/ Download R for Windows: https://cran.um.ac.ir/bin/windows/ Download R for (Mac) OS X: https://cran.um.ac.ir/bin/macosx/ Download R for Linux: https://cran.um.ac.ir/bin/linux/ 25 / 27 2. Install RStudio • https://rstudio.com/ • https://rstudio.com/products/rstudio/#rstudio-desktop • https://rstudio.com/products/rstudio/download/ 26 / 27 Suggestion of Books to Read 2012 2013 2015 2018 2015 2019 2017 2017 2019 2019 Thank You and Good Luck Any Questions?.
Recommended publications
  • The R and Omegahat Projects in Statistical Computing
    The R and Omegahat Projects in Statistical Computing Brian D. Ripley [email protected] http://www.stats.ox.ac.uk/∼ripley Outline • Statistical Computing – History – S – R • Application and Comparisons – Web servers – Embedding — Medieval Chant – R vs S-PLUS • Omegahat and Component Systems – The Omegahat project – Components — GGobi – The future? Statistical Computing and S Scene-setting: Statistical Computing 1980 Mainly Fortran programming, or PL/I (SAS). Batch computing (SAS, BMDP, SPSS, Genstat) with restricted range of platforms. Some small interactive systems (GLIM 3.77, Minitab). Very poor interactive graphics (2400 baud to a Tektronix storage tube if you were lucky). Flatbed and drum plotters, microfilm for publication-quality output off-line. Mainly home-brew solutions in research. (GLIM macros?) 1990 PCs become widespread, but FPUs still uncommon. Sun etc workstations available for researchers, and for teaching in a few places. Graphics could be pretty good (postscript printers, ca 1000 × 1000 pixel screens), but often was not, and mono text terminals were still widespread. C was beginning to be used, as more portable than Fortran. (Few PC Fortran compilers then and now.) Still SAS, SPSS etc as batch programs. S beginning to be make an impact on research and teaching. 2001 Little spread in machine speed (min 500MHz, max 1.5GHz), fast FPUs are universal. Colour everywhere, usually 24-bit colour. The video-games generation is now at university. Few people would dream of writing a complete program for a research idea: prototype and distribute in a higher-level language such as S or Matlab or Gauss or Ox or ...
    [Show full text]
  • Hadley Wickham, the Man Who Revolutionized R Hadley Wickham, the Man Who Revolutionized R · 51,321 Views · More Stats
    12/15/2017 Hadley Wickham, the Man Who Revolutionized R Hadley Wickham, the Man Who Revolutionized R · 51,321 views · More stats Share https://priceonomics.com/hadley-wickham-the-man-who-revolutionized-r/ 1/10 12/15/2017 Hadley Wickham, the Man Who Revolutionized R “Fundamentally learning about the world through data is really, really cool.” ~ Hadley Wickham, prolific R developer *** If you don’t spend much of your time coding in the open-source statistical programming language R, his name is likely not familiar to you -- but the statistician Hadley Wickham is, in his own words, “nerd famous.” The kind of famous where people at statistics conferences line up for selfies, ask him for autographs, and are generally in awe of him. “It’s utterly utterly bizarre,” he admits. “To be famous for writing R programs? It’s just crazy.” Wickham earned his renown as the preeminent developer of packages for R, a programming language developed for data analysis. Packages are programming tools that simplify the code necessary to complete common tasks such as aggregating and plotting data. He has helped millions of people become more efficient at their jobs -- something for which they are often grateful, and sometimes rapturous. The packages he has developed are used by tech behemoths like Google, Facebook and Twitter, journalism heavyweights like the New York Times and FiveThirtyEight, and government agencies like the Food and Drug Administration (FDA) and Drug Enforcement Administration (DEA). Truly, he is a giant among data nerds. *** Born in Hamilton, New Zealand, statistics is the Wickham family business: His father, Brian Wickham, did his PhD in the statistics heavy discipline of Animal Breeding at Cornell University and his sister has a PhD in Statistics from UC Berkeley.
    [Show full text]
  • R Generation [1] 25
    IN DETAIL > y <- 25 > y R generation [1] 25 14 SIGNIFICANCE August 2018 The story of a statistical programming they shared an interest in what Ihaka calls “playing academic fun language that became a subcultural and games” with statistical computing languages. phenomenon. By Nick Thieme Each had questions about programming languages they wanted to answer. In particular, both Ihaka and Gentleman shared a common knowledge of the language called eyond the age of 5, very few people would profess “Scheme”, and both found the language useful in a variety to have a favourite letter. But if you have ever been of ways. Scheme, however, was unwieldy to type and lacked to a statistics or data science conference, you may desired functionality. Again, convenience brought good have seen more than a few grown adults wearing fortune. Each was familiar with another language, called “S”, Bbadges or stickers with the phrase “I love R!”. and S provided the kind of syntax they wanted. With no blend To these proud badge-wearers, R is much more than the of the two languages commercially available, Gentleman eighteenth letter of the modern English alphabet. The R suggested building something themselves. they love is a programming language that provides a robust Around that time, the University of Auckland needed environment for tabulating, analysing and visualising data, one a programming language to use in its undergraduate statistics powered by a community of millions of users collaborating courses as the school’s current tool had reached the end of its in ways large and small to make statistical computing more useful life.
    [Show full text]
  • Statistics with Free and Open-Source Software
    Free and Open-Source Software • the four essential freedoms according to the FSF: • to run the program as you wish, for any purpose • to study how the program works, and change it so it does Statistics with Free and your computing as you wish Open-Source Software • to redistribute copies so you can help your neighbor • to distribute copies of your modified versions to others • access to the source code is a precondition for this Wolfgang Viechtbauer • think of ‘free’ as in ‘free speech’, not as in ‘free beer’ Maastricht University http://www.wvbauer.com • maybe the better term is: ‘libre’ 1 2 General Purpose Statistical Software Popularity of Statistical Software • proprietary (the big ones): SPSS, SAS/JMP, • difficult to define/measure (job ads, articles, Stata, Statistica, Minitab, MATLAB, Excel, … books, blogs/posts, surveys, forum activity, …) • FOSS (a selection): R, Python (NumPy/SciPy, • maybe the most comprehensive comparison: statsmodels, pandas, …), PSPP, SOFA, Octave, http://r4stats.com/articles/popularity/ LibreOffice Calc, Julia, … • for programming languages in general: TIOBE Index, PYPL, GitHut, Language Popularity Index, RedMonk Rankings, IEEE Spectrum, … • note that users of certain software may be are heavily biased in their opinion 3 4 5 6 1 7 8 What is R? History of S and R • R is a system for data manipulation, statistical • … it began May 5, 1976 at: and numerical analysis, and graphical display • simply put: a statistical programming language • freely available under the GNU General Public License (GPL) → open-source
    [Show full text]
  • A History of R (In 15 Minutes… and Mostly in Pictures)
    A history of R (in 15 minutes… and mostly in pictures) JULY 23, 2020 Andrew Zief!ler Lunch & Learn Department of Educational Psychology RMCC University of Minnesota LATIS Who am I and Some Caveats Andy Zie!ler • I teach statistics courses in the Department of Educational Psychology • I have been using R since 2005, when I couldn’t put Me (on the EPSY faculty board) SAS on my computer (it didn’t run natively on a Me Mac), and even if I could have gotten it to run, I (everywhere else) couldn’t afford it. Some caveats • Although I was alive during much of the era I will be talking about, I was not working as a statistician at that time (not even as an elementary student for some of it). • My knowledge is second-hand, from other people and sources. Statistical Computing in the 1970s Bell Labs In 1976, scientists from the Statistics Research Group were actively discussing how to design a language for statistical computing that allowed interactive access to routines in their FORTRAN library. John Chambers John Tukey Home to Statistics Research Group Rick Becker Jean Mc Rae Judy Schilling Doug Dunn Introducing…`S` An Interactive Language for Data Analysis and Graphics Chambers sketch of the interface made on May 5, 1976. The GE-635, a 36-bit system that ran at a 0.5MIPS, starting at $2M in 1964 dollars or leasing at $45K/month. ’S’ was introduced to Bell Labs in November, but at the time it did not actually have a name. The Impact of UNIX on ’S' Tom London Ken Thompson and Dennis Ritchie, creators of John Reiser the UNIX operating system at a PDP-11.
    [Show full text]
  • The Grammar of Graphics: an Introduction to Ggplot2
    The Grammar of Graphics: An Introduction to ggplot2 Jefferson Davis Research Analytics Historical background • In 1970s John Chambers, Rick Becker, and Allan Wilks develop S and S+ at Bell Labs • Bell System monopoly was broken up in 1982 • Late 80s some attempt to commercialize S/S+ but already too many non-commercial implementations • Ross Ihaka and Robert Gentleman produce R in early 1990s • Main developement now by Comprehensive R Archive Network (CRAN) R: some useful tidbits • Use left arrow for variable assignment x <- 5 • Can concatenate with c() c(4,5) [1] 4 5 c("baby","elephant”) [1] "baby" "elephant" • Download packages with the install() command install.packages("ggplot2") install.packages("ggthemes") • Make packages available with library() library(ggplot2) library(ggthemes) • Get help with ? ? ggplot Statistical Graphics x y=x2 0.0 0.00 0.1 0.01 0.2 0.04 0.3 0.09 0.4 0.16 0.5 0.25 0.6 0.36 0.7 0.49 0.8 0.64 0.9 0.81 1.0 1.00 1.1 1.21 1.2 1.44 1.3 1.69 Statistical Graphics Graphics can convey meaning without displaying any particular quantatative data. Statistical Graphics Statistical Graphics The dataset The diamond dataset carat cut color clarity depth table price x y z 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 Very 0.24 Good J VVS2 62.8 57 336 3.94 3.96 2.48 The dataset The dataset library(ggplot2) head(diamonds)[,1:4] # A tibble: 6 × 4 carat cut
    [Show full text]
  • Data Analysis in R
    Data analysis in R IML Machine Learning Workshop, CERN Andrew John Lowe Wigner Research Centre for Physics, Hungarian Academy of Sciences Downloading and installing R You can follow this tutorial while I talk 1. Download R for Linux, Mac or Windows from the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/ I Latest release version 3.3.3 (2017-03-06, “Another Canoe”) RStudio is a free and open-source integrated development environment (IDE) for R 2. Download RStudio from https://www.rstudio.com/ I Make sure you have installed R first! I Get RStudio Desktop (free) I Latest release version 1.0.136 Rstudio RStudio looks something like this. RStudio RStudio with source editor, console, environment and plot pane. What is R? I An open source programming language for statistical computing I R is a dialect of the S language I S is a language that was developed by John Chambers and others at Bell Labs I S was initiated in 1976 as an internal statistical analysis environment — originally implemented as FORTRAN libraries I Rewritten in C in 1988 I S continues to this day as part of the GNU free software project I R created by Ross Ihaka and Robert Gentleman in 1991 I First announcement of R to the public in 1993 I GNU General Public License makes R free software in 1995 I Version 1.0.0 release in 2000 Why use R? I Free I Runs on almost any standard computing platform/OS I Frequent releases; active development I Very active and vibrant user community I Estimated ~2 million users worldwide I R-help and R-devel mailing lists, Stack Overflow I Frequent conferences; useR!, EARL, etc.
    [Show full text]
  • Ross Ihaka and Robert Gentleman Year
    MULTI-FILES LOADER file_01.csv GGPLOT2 data(aes) + geom + labels R (Statistical computing) files <- list.files(pattern = “file_.*csv”) %>% ggplot(data = df, aes(x_col, y_col)) + geom() CHEET SHEET BY @CHARLSTOWN lapply(files, read_csv) %>% canvas + layer +...+ labs > general structure CREATOR: ROSS IHAKA AND ROBERT GENTLEMAN bind_rows() YEAR: 1995 ggplot(data = df) > canvas level STEABLE RELEASE (2020): 3.6.2 DATA FRAMES ACTIONS dplyr & tidyr aes(x_col, y_col) > aesthetics TOP FROM R: DPLYR, GGPLOT2, ESQUISSE, BIO DPLYR FUNCTIONS df %>% function geom_point() > geometry level CONDUCTOR, SHINY, LUBRIDATE, KNITR, TIDYR. rename(c_new = c1) > rename columns AESTHETICS aes(*) select(col) > select column ELEMENTAL LIBRARIES library( * ) select(-col) > drop column x_col, y_col > sets x,y elements library(dplyr) > Easy commands filter(c == “value”) > select rows label=labels > set ticks labels library(ggplot2) > Generate plots mutate(col = (c1+2)) > column mutations color > sets color values library(tidyverse) > Tidy data transmute(c1=c, c2=c/2) > drop & built cols alpha > sets the opacity library(kableExtra) > Table visualization arrange(des(c1)) > order by column fill = ‘color’ > sets the filled color library(knitr) > Regular exprs. join(df2) > join 2 data frames shape = class > set groups by class library(readr) > Files Reader group_by(col) > silent grooping group = class > sets plot by class library(stringr) > Strings treatment summarise(c* = max(c)) > after grooping shape > geometry shape size = 4 > sets the size TIDYR FUNCTIONS df %>% function
    [Show full text]
  • Chapter 1 Introduction to R
    Chapter 1 Introduction to R Math 3210 Dr. Zeng Outline I Introduction to R language I Introduction to RStudio I Introduction to R markdown Introduction to the R language The Package I R is a statistical computer program made available through the Internet under the General Public License (GPL). I R provides an enviroment in which you can perform statistical analysis and produce graphics. The R History I R is based on the computer language S, developed by John Chambers and others at Bell Laboratories in 1976. I In 1993 Robert Gentleman and Ross Ihaka at University of Auckland wanted to experiment with the langauge, so they developed an implementation, and named it R. I They made it open source in 1995. Its users are free to see how it is written and thousands of people around the world have contributed to its development. Design of the R System I The primary R system is available from the Comprehensive R Archive Network (CRAN) which hosts many add-on packages that can be used to extend the functionality of R. The R system is divided into 2 conceptual parts: I The ‘base’ R system that you download from CRAN: It contains the base package which is required to run R and contains the most fundamental functions. Other basic packages such as as stats, graphics, grid, cluster, et al. are included. To see which packages are loaded, run I Everything else: There are over 4000 add-on packages on CRAN that have been developed by users around the world and avaliable for download freely.
    [Show full text]
  • Introduction to R: Part V Annex
    Introduction to R: Part V Annex Alexandre Perera i Lluna1;2 1Centre de Recerca en Enginyeria Biomèdica (CREB) Departament d’Enginyeria de Sistemes, Automàtica i Informàtica Industrial (ESAII) Universitat Politècnica de Catalunya mailto:[email protected] 2Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) Jan 2011 / Introduction to R Universitat Rovira i Virgili Integration to other Languages Writing parallel code in R Bibliography ContentsI 1 Integration to other Languages R4Calc, OpenOffice LATEX R integration into Latex, Sweave 2 Writing parallel code in R Parallel code: when, how and why? R, Rmpi, Snow 3 Bibliography Alexandre Perera i Lluna; Introduction to R: Part V Integration to other Languages R4Calc, OpenOffice Writing parallel code in R LATEX Bibliography R integration into Latex, Sweave R and Calc R and Calc is an extension to Calc application, part of the OO.o suite (OpenOffice.org): Allows to see R objects from the OO.o menu R should be started with Rserve running: library(Rserve) Rserve() in OO.o, Use the menu bar R Add-on > Rdump() It will open a dialog, input rnorm(10) cor.test(c(1,2,3,4), c(1,2,2,3)) A new sheet will open with the result of the link to R http: //wiki.services.openoffice.org/wiki/R_and_Calc Alexandre Perera i Lluna; Introduction to R: Part V Integration to other Languages R4Calc, OpenOffice Writing parallel code in R LATEX Bibliography R integration into Latex, Sweave What is latex? LATEX (written as LaTeX in plain text, pronounced as /l\'atej/ ) is: Is a document markup language Is document preparation system for the TEX typesetting program Originally written in 1984 by Leslie Lamport at SRI International Only to the language in which documents are written, not to the text editor itself.
    [Show full text]
  • Big Data Analytics Using R
    International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 07 | July-2016 www.irjet.net p-ISSN: 2395-0072 Big Data Analytics Using R Sanchita Patil MCA Department, Vivekanand Education Society's Institute of Technology, Chembur, Mumbai – 400074. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - R is an open-source data analysis environment into picture when the CPU time for the calculation takes and programming language .The process of converting data longer than the process of designing a model. Data sets that into knowledge, insight and understanding is Data analysis, contain up to millions of records can easily be processed which is a critical part of statistics. For the effective processing with standard R. Data sets with almost one million to one and analysis of big data, it allows users to conduct a number of billion records can also be processed in R, but requires some tasks that are essential. R consists of numerous ready-to-use additional effort. Worldwide, millions of statisticians as well statistical modelling algorithms and machine learning which as data scientists use R in order to solve their most allow users to create reproducible research and develop data challenging problems in the field, right from quantitative products. Although big data processing may be accomplished marketing to computational biology. R have become the with other tools as well, it is when one steps on to the data most popular language for data science and an most analysis that R really stands unique, owing to the huge amount essential tool for analytics-driven companies such as Google, of built-in statistical formulae and third-party algorithms Facebook, LinkedIn and Finance .
    [Show full text]
  • Intro to R Materials Research Laboratory (MRL) Center for Scientific Computing (CSC) [email protected] MRL 2066B
    Fuzzy Rogers Research Computing Administrator Intro to R Materials Research Laboratory (MRL) Center for Scientific Computing (CSC) [email protected] MRL 2066B Sharon Solis Paul Weakliem Research Computing Consultant CNSI Research Computing Support Enterprise Technology Services (ETS) California NanoSystems Institute (CNSI) Center for Scientific Computing (CSC) Center for Scientific Computing (CSC) [email protected] [email protected] Elings Hall 3229 Elings Hall 3231 CSC Workshops, Fall 2018 Other Research IT Jim Woods Marine Science Institute [email protected] Letters & Science IT [email protected] (805) 893-4357 Ted Cabeen Michael Colee Steve Miley Glenn Schiferl Life Sciences Earth Research Institute Bren School of Environmental Physics Chemistry (ERI) Science & Management [email protected] [email protected] [email protected] [email protected] 2 Pre-class Instructions: This lesson assumes you have the R software and the development environment RStudio installed on your computer. R can be downloaded here. RStudio can be downloaded here. You will need the Desktop version for your computer. You also need to download some files to follow this lesson: 1. Make a new folder in your Desktop called r-novice-inflammation. 2. Download r-novice-inflammation-data.zip and move the file to this folder. 3. If it’s not unzipped yet, double-click on it to unzip it. You should end up with a new folder called data. 3 What is R? - R is a programming language and free software environment for statistical computing and graphics - R was initially written by Robert Gentleman and Ross Ihaka—also known as “R & R” of the Statistics Department of the University of Auckland in 1992.
    [Show full text]