Goals of this course. Crash Course in R. Getting Started with R. What is R? What is R? Getting you setup to use R under Windows

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1 Oxford Spring School, April 2013 Effective Presentation ti Monday morning lecture: Crash Course in R Robert Andersen Department of Sociology University of Toronto And Dave Armstrong Department of Political Science University of Wisconsin, Milwaukee Goals of this course What is R? Getting you setup to use R under Windows RStudio, RCommander Resources Getting help, documents, books etc. Add-on packages Called libraries in R Working with data Manipulating data recode function in the car library Some statistical models An brief introduction to graphs 2 What is R? A free, open-source implementation of the S language for data analysis and graphics A complete programming language Available for various operating systems (including Linux, Mac and Windows). Supported by a comprehensive help system and a large international community of users. Increasingly used in advanced social-science research, as well as in many other disciplines. In constant flux. Not guaranteed by anyone to be fit for any purpose! 3 Getting Started with R The main source for R and everything connected with it is the Comprehensive R Archive Network (CRAN): Here you will find self-extracting ti and installing zip files for Windows, Linux and Mac operating systems You will also find documentation of various sorts: The official documentation (on CRAN, or installed as part of the R help system) includes: An Introduction to R R Data Import/Export R Installation and Administration, mainly for Unix users who want to have R set up in nonstandard ways. Not needed for basic Windows installation. Writing R Extensions, covers how to create your own packages, write R help files, etc. Also various unofficial user guides and course notes, of varying utility, at CRAN under contributed. 4

2 Some Books to Consider (not on CRAN) Fox, J and S. Weisberg (2010). An R Companion to Applied Regression. 2 nd Edition. Sage. See Krause, A and M. Olson (2002) The Basics of S-PLUS, Third Edition. Springer. Venables, W N and Ripley, B D (2002). Modern Applied Statistics with S. 4 th edition. Springer. See Venables, W N and Ripley, B D (2000). S Programming. Springer. Pinheiro, J C and Bates, D M (2000). Mixed-effects Models in S and S-Plus. Springer. Murrell, P. (2006). R Graphics. Chapman & Hall. 5 Using an Editor Notepad will do but there are much better alternatives R-WinEDT R plug-in for the shareware program WinEDT WinEDT is available for a samll charge at Once WinEDT is installed on your computer, simply install and load the RWinEdt package for R. (more about installing packages later) RCommander Also provides many a menu-driven front-end for R Great for teaching (very similar to SPSS) Simply install and load the Rcmdr package for R RStudio Perhaps now the most commonly used Basically acts as a new front-end for R After installing the most recent version of R, simply download the self-installer for RStudio and double click it 6 Add-on Packages (1) There are many exceptional add-on programs for R Some of interest to social scientists are: car: functions and data connected with John Fox s An R and S-Plus Companion to Applied Regression (Sage, 2002) effects: effect displays foreign: functions for exchanging data with other systems such as SPSS and Stata gam, mgcv and VGAM: generalized additive models gamm: generalized additive mixed models gmodels: various usual functions, including CrossTable lme4 and nlme: mixed models MASS: functions and data connected with Venables and Ripley, Modern Applied Statistics with S. Functions for robust regression, ordered regression etc. nnet: multinomial i l logit models 7 Add-on Packages (2) qvalc: quasi variances for dummy regressors relimp: Relative importance of sets of predictors roblm and robustbase: various methods for robust regression sandwich: robust standard errors survival: duration modeling (survival analysis) sem: structural equation models sm: various methods for smoothing data Zelig: brings together many packages in a way that more closely resembles Stata 8

3 Add-on Packages (3) Add on packages are easily installed from the menus within R Packages load package Or alternatively by typing directly into the console: >install.packages( package name") Once installed, the package must be loaded into the current interactive R session Many packages contain datasets. These must also be loaded and attached to be used The number symbol # below is used to insert comments R will not read anything after it (only works for a single line) 9 Getting Help in R A number of different types of help are available by clicking the help menu: Documentation on all installed packages is available in a web browser by clicking help html help The official manuals can be loaded in PDF format by clicking help manuals Help about individual functions and objects can also be obtained within R by typing help(data) or?data,, for help on something whose name is known help.search( ordinal ), to search all the installed help files for occurrence of a particular text string aprops( stem ), to look for stem in the names of objects available in the current R session If all else fails, use the R-help list: 10 Getting data in (1) Entering data directly The concatenate function, c, combines individual cases together into a vector The cbind (columns bind) and rbind (rows bind) functions combine vectors together into a matrix The data.frame function makes the matrix and a data frame object Getting data in (2) External datasets For rectangular data in a text file, use read.table(): Mydata<-read.table( dataname.txt, header=true) header=true signifies that the first contains variable names The foreign library imports data files from other formats: 11 use.value.labels=true labels converts SPSS value labels to categories. If you specify FALSE, all variables will be treated as quantitative For Stata use read.dtadta For Excel, use read.table, read.csv, read.delim 12

4 Re-specifying variables To make a numerically coded variable into an unordered factor (categorical variable): Recoding Variables using the recode function in the car package Recoding into a quantitative variable: Recoding into an unordered factor: To make a numerical variable into an ordered factor: Finally, it is important to note for recoding that the code for missing data in R is simply NA Some useful functions to get you started Data frames data.frame, rbind, cbind, edit, na.omit, subset, nrow, ncol, length, merge Manipulating variables ifelse, scale, log, ^, recode (car library), seq as.factor, as.ordered, relevel, levels, as.numeric Summarizing variables mean, standard deviation, fivenum, summary, table, CrossTable (gmodels library), by Saving Data Frames # as a rectangular text file write.table(new.data, file="c:/temp/dataname.txt") # Notice the 'forward' leaning slashes! # alternatively, as a CSV file: write.csv(new.data, file="c:/temp/dataname.csv", row.names=false) # 'row.names=false' removes numbered row # names 15 16

5 S Modeling Language The S modeling language has a similar notation for most types of models Model specification generally takes the following form: Response ~ Independent Variables Where the tilde sign (~) is interpreted as regressed on For the general linear model, terms represent additive components as in the regression equation itself Some examples of formulas are: 17 Some Commonly used Models lm: : linear regression by least squares rlm: robust linear models (package MASS) roblm: robust linear models (package roblm) glm: generalized linear models (logit, probit, Poisson, Gamma etc.) glmrob: robust generalized linear models (package robustbase) gam: generalized additive models using the mgcv package or gam package multinom: multinomial logit models (package nnet) polr: ordered logit models (package MASS) lme: mixed models (package nlme) lmer: mixed models (package lme4) survreg and coxph: survival models or event history analysis (package survival) 18 Useful functions for model objects 19 20

6 Graphs in R All graphs are drawn on a chosen device either until a new device is started, or the device is closed dev.off() Some commonly used graphics devices are postscript( mygraphs.ps ) Necessary for LaTeX pdf( mygraph.pdf ) Necessary for PDF LaTeX windows() The default graphics device Especially useful when saving figures in RStudio As you will see in this course, graphs in R are very flexible. A small graph example (1) A small graph example (2) Florida votes by county DADE Graphs in R This afternoon Dave will give a more detailed lecture on making graphs, both generally and in R BUSH PALM.BEACH BUCHANAN 23 24