R- Introduction
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The document provides an introduction to the R programming language. It discusses that R is an open-source programming language for statistical analysis and graphics. It can run on Windows, Unix and MacOS. The document then covers downloading and installing R and R Studio, the R workspace, basics of R syntax like naming conventions and assignments, working with data in R including importing, exporting and creating calculated fields, using R packages and functions, and resources for R help and tutorials.
R- Introduction
- 1. Introduction to R Venkat Reddy
- 2. • • • • • • • • What is R R basics Working with R R packages R Functions Importing & Exporting data Creating calculated fields R Help & Tutorials Data Analysis Course Venkat Reddy Contents 2
- 3. • Programming “environment” • Runs on a variety of platforms including Windows, Unix and MacOS. • Provides an unparalleled platform for programming new statistical methods in an easy and straightforward manner. • Object-oriented • Open source • Excellent graphics capabilities • Supported by a large user network Data Analysis Course Venkat Reddy R 3
- 4. Downloading Data Analysis Course Venkat Reddy • Google it using R or CRAN (Comprehensive R Archive Network) • http://www.r-project.org 4
- 5. Data Analysis Course Venkat Reddy R 5
- 6. Data Analysis Course Venkat Reddy R Studio 6
- 7. • • • • • 2+2 log(10) help(log) summary(airquality) demo(graphics) # pretty pictures... Data Analysis Course Venkat Reddy R-Demo 7
- 8. R-Basics :Naming convention • must start with a letter (A-Z or a-z) • can contain letters, digits (0-9), and/or periods “.” • mydata different from MyData Data Analysis Course Venkat Reddy • R is a case sensitive language. 8
- 9. R-Basics :Assignment • “<-” used to indicate assignment • Assignment to an object is denoted by ”<-” or ”->” or ”=”. • If you see a notation ”= =”, you’ll looking at a comparison operator. – Many other notations can be found from the documentation for the Base package or R. Data Analysis Course Venkat Reddy • x<-c(1,2,3,4,5,6,7) • x<-c(1:7) • x<-1:4 9
- 10. • during an R session, all objects are stored in a temporary, working memory • Commands are entered interactively at the R user prompt. Up and down arrow keys scroll through your command history. • list objects ls() • remove objects rm() • data() Data Analysis Course Venkat Reddy Workspace 10
- 11. • • • • • • • • • • x <- round(rnorm(10,mean=20,sd=5)) # simulate data x mean(x) m <- mean(x) m x- m # notice recycling (x - m)^2 sum((x - m)^2) data() UKgas Data Analysis Course Venkat Reddy Demo: Working with R 11
- 12. • R consists of a core and packages. Packages contain functions that are not available in the core. • Collections of R functions, data, and compiled code • Well-defined format that ensures easy installation, a basic standard of documentation, and enhances portability and reliability • When you download R, already a number (around 30) of packages are downloaded as well. • You can use the function search to see a list of packages that are currently attached to the system, this list is also called the search path. • search() Data Analysis Course Venkat Reddy R Packages 12
- 13. • Select the `Packages' menu and select `Install package...', a list of available packages on your system will be displayed. • Select one and click `OK', the package is now attached to your current R session. Via the library function • The library can also be used to list all the available libraries on your system with a short description. Run the function without any arguments Data Analysis Course Venkat Reddy R packages 13
- 14. Demo: R packages Data Analysis Course Venkat Reddy • Install cluster package • Install plyr package (for string operations) 14
- 15. Importing Data • Reading CSV file • X <-read.csv(“file.csv") • reads in data from an external file • d <- read.table("myfile", header=TRUE) • R has ODBC for connecting to other programs • R gets confused if you use a path in your code like c:mydocumentsmyfile.txt • This is because R sees "" as an escape character. Instead, use c:my documentsmyfile.txt or c:/mydocuments/myfile.txt Data Analysis Course Venkat Reddy • read.table() 15
- 16. Demo: Importing data • petrol<-read.csv("C:UsersVENKATGoogle DriveTrainingRDataPetrol_Consuption.csv") • sales_data<-read.table("C:UsersVENKATGoogle DriveTrainingRDatasales.txt") • sales_data<-read.table("C:UsersVENKATGoogle DriveTrainingRDatasales.txt",header=TRUE) Data Analysis Course Venkat Reddy • Reading CSV file 16
- 17. Exporting data • To A Tab Delimited Text File • write.table(mydata, "c:/mydata.txt", sep="t") • library(xlsReadWrite) • write.xls(mydata, "c:/mydata.xls") • To SAS • library(foreign) • write.foreign(mydata, "c:/mydata.txt", "c:/mydata.sas", package="SAS") Data Analysis Course Venkat Reddy • To an Excel Spreadsheet 17
- 18. Demo: Exporting data Data Analysis Course Venkat Reddy • write.table(sales_data, "C:UsersVENKATGoogle DriveTrainingRDatasales_export.txt", sep="t") • write.table(sales_data, "C:UsersVENKATGoogle DriveTrainingRDatasales_export.csv", sep=",") 18
- 19. R- Functions Numeric Functions Description abs(x) absolute value sqrt(x) square root ceiling(x) ceiling(3.475) is 4 floor(x) floor(3.475) is 3 trunc(x) trunc(5.99) is 5 round(x, digits=n) round(3.475, digits=2) is 3.48 signif(x, digits=n) signif(3.475, digits=2) is 3.5 cos(x), sin(x), tan(x) also acos(x), cosh(x), acosh(x), etc. log(x) natural logarithm log10(x) common logarithm exp(x) e^x Data Analysis Course Venkat Reddy Function 19
- 20. • • • • y<-abs(-20) x<-Sum(y+5) Z<-Log(x) round(x,1) Data Analysis Course Venkat Reddy Demo: Numeric Functions 20
- 21. Character Functions Description substr(x, start=n1, stop=n2) Extract or replace substrings in a character vector. x <- "abcdef" substr(x, 2, 4) is "bcd" substr(x, 2, 4) <- "22222" is "a222ef" grep(pattern, x , ignore.case=FALSE, fixed=FALSE) Search for pattern in x. If fixed =FALSE then pattern is a regular expression. If fixed=TRUE then pattern is a text string. Returns matching indices. grep("A", c("b","A","c"), fixed=TRUE) returns 2 sub(pattern, replacement, x, ignore.case =FALSE, fixed=FALSE) Find pattern in x and replace with replacement text. If fixed=FALSE then pattern is a regular expression. If fixed = T then pattern is a text string. sub("s",".","Hello There") returns "Hello.There" strsplit(x, split) Split the elements of character vector x at split. strsplit("abc", "") returns 3 element vector "a","b","c" paste(..., sep="") Concatenate strings after using sep string to seperate them. paste("x",1:3,sep="") returns c("x1","x2" "x3") paste("x",1:3,sep="M") returns c("xM1","xM2" "xM3") paste("Today is", date()) toupper(x) Uppercase Data Analysis Course Venkat Reddy Function 21
- 22. Demo :Character Functions Data Analysis Course Venkat Reddy • cust_id<-"Cust1233416" • id<-substr(cust_id, 5,10) • Up=toupper(cust_id) 22
- 23. Calculated Fields in R • • • • • • mydata$sum <- mydata$x1 + mydata$x2 mydata$mean <- (mydata$x1 + mydata$x2)/2 attach(mydata) mydata$sum <- x1 + x2 mydata$mean <- (x1 + x2)/2 detach(mydata) Data Analysis Course Venkat Reddy • Use the assignment operator <- to create new variables. A wide array of operators and functions are available here. 23
- 24. • • • • • • • • sales_data$reduce<-(sales_data$Sales)*0.2 View(sales_data) sales_data$new_sales<-sales_data$Sales- sales_data$reduce attach(petrol) ratio=Income/consum_mill_gallons View(petrol) petrol$ratio=Income/Consum_mill_gallons View(petrol) Data Analysis Course Venkat Reddy Demo Calculated Fields in R 24
- 25. • If you encounter a new command during the exercises, and you’d like to know what it does, please consult the documentation. All R commands are listed nowhere, and the only way to get to know new commands is to read the documentation files, so we’d like you to practise this youself. • Tutorials Each of the following tutorials are in PDF format. • P. Kuhnert & B. Venables, An Introduction to R: Software for Statistical Modeling & Computing • J.H. Maindonald, Using R for Data Analysis and Graphics • B. Muenchen, R for SAS and SPSS Users • W.J. Owen, The R Guide • D. Rossiter, Introduction to the R Project for Statistical Computing for Use at the ITC • W.N. Venebles & D. M. Smith, An Introduction to R Data Analysis Course Venkat Reddy R-Help 25
- 26. R-Tutorials Paul Geissler's excellent R tutorial Dave Robert's Excellent Labs on Ecological Analysis Excellent Tutorials by David Rossitier Excellent tutorial an nearly every aspect of R (c/o Rob Kabacoff) MOST of these notes follow this web page format • Introduction to R by Vincent Zoonekynd • R Cookbook • Data Manipulation Reference Data Analysis Course Venkat Reddy • • • • 26
- 27. Data Analysis Course Venkat Reddy Thank you 27