Week 2 – Introduction to R.

Why use R?

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. – http://cran.csiro.au/doc/manuals/r-release/R-intro.html

This is a valid question considering that most languages/frameworks, including CUDA have statistical analysis libraries built in. Hopefully running through some introductory exercises will reveal the benefits.

Associated GUI’s and extensions:

• Weka – Specific for machine learning algorithms
• R Commander – Data analysis GUI

Install on Ubuntu 12.04:

1. sudo echo "deb http://cran.csiro.au/bin/linux/ubuntu precise/" >> /etc/apt/sources.list
   sudo apt-get update
   sudo apt-get install r-base

   Then to enter the R command line interface, $ R

   For starters, will run through an intro from UCLA: http://www.ats.ucla.edu/stat/r/seminars/intro.htm

   Within the R command line interface if a package is to be used it must first be installed:

   install.packages()

   • foreign – package to read data files from other stats packages
   • xlsx – package (requires Java to be installed, same architecture as your R version, also the rJava package and xlsxjars package)
   • reshape2 – package to easily melt data to long form
   • ggplot2 – package for elegant data visualization using the Grammar of Graphics
   • GGally – package for scatter plot matrices
   • vcd – package for visualizing and analyzing categorical data

   install.packages("xlsx")
   install.packages("reshape2")
   install.packages("ggplot2")
   install.packages("GGally")
   install.packages("vcd")

   Pre-requisites:

   sudo apt-get install openjdk-7-*
   sudo ln -s /usr/lib/jvm/java-7-openjdk-amd64/bin/java /etc/alternatives/java
   sudo R CMD javareconf

   Preparing session:

   After installing R and the packages needed for a task if these packages are needed in the current session they must be included:

   require(foreign)
   require(xlsx)

   After attaching all of the required packages to the current session, confirmation can be completed via:

   sessionInfo()

   R code can be entered into the command line directly or saved to a script which can be run inside a session using the ‘source’ function.

   Help can be attained using ? preceding a function name.

   Entering Data:

   R is most compatible with datasets stored as text files, ie: csv.

   Base R contains functions read.table and read.csv see the help files on these functions for many options.

   # comma separated values
   dat.csv <- read.csv("http://www.ats.ucla.edu/stat/data/hsb2.csv")
   # tab separated values
   dat.tab <- read.table("http://www.ats.ucla.edu/stat/data/hsb2.txt", header=TRUE, sep = "\t")
   

   Datasets from other statistical analysis software can be imported using the foreign package:

   require(foreign)
   # SPSS files
   dat.spss <- read.spss("http://www.ats.ucla.edu/stat/data/hsb2.sav", to.data.frame=TRUE)
   # Stata files
   dat.dta <- read.dta("http://www.ats.ucla.edu/stat/data/hsb2.dta")

   If converting excel spreadsheets to CSV is too much of a hassle the xlxs package we imported will do the job:

   # these two steps only needed to read excel files from the internet
   f <- tempfile("hsb2", fileext=".xls")
   download.file("http://www.ats.ucla.edu/stat/data/hsb2.xls", f, mode="wb")
   dat.xls <- read.xlsx(f, sheetIndex=1)

   Viewing Data:

   # first few rows
   head(dat.csv)
   # last few rows
   tail(dat.csv)
   # variable names
   colnames(dat.csv)
   # pop-up view of entire data set (uncomment to run)
   View(dat.csv)
   

   Datasets that have been read in are stored as data frames which have a matrix structure. The most common method of indexing is object[row,column] but many others are available.

   # single cell value
   dat.csv[2, 3]
   # omitting row value implies all rows; here all rows in column 3
   dat.csv[, 3]
   # omitting column values implies all columns; here all columns in row 2
   dat.csv[2, ]
   # can also use ranges - rows 2 and 3, columns 2 and 3
   dat.csv[2:3, 2:3]

   Variables can also be accessed via their names:

   # get first 10 rows of variable female using two methods
   dat.csv[1:10, "female"]
   dat.csv$female[1:10]

   The c function is used to combine values of common type together to form a vector:

   # get column 1 for rows 1, 3 and 5
   dat.csv[c(1, 3, 5), 1]
   ## [1]  70  86 172
   # get row 1 values for variables female, prog and socst
   dat.csv[1, c("female", "prog", "socst")]
   ##   female prog socst
   ## 1      0    1    57

   Creating colnames:

   colnames(dat.csv) <- c("ID", "Sex", "Ethnicity", "SES", "SchoolType", "Program", 
       "Reading", "Writing", "Math", "Science", "SocialStudies")
   
   # to change one variable name, just use indexing
   colnames(dat.csv)[1] <- "ID2"

   Saving data:

   #write.csv(dat.csv, file = "path/to/save/filename.csv")
   #write.table(dat.csv, file = "path/to/save/filename.txt", sep = "\t", na=".")
   #write.dta(dat.csv, file = "path/to/save/filename.dta")
   #write.xlsx(dat.csv, file = "path/to/save/filename.xlsx", sheetName="hsb2")
   # save to binary R format (can save multiple datasets and R objects)
   #save(dat.csv, dat.dta, dat.spss, dat.txt, file = "path/to/save/filename.RData")
   #change workspace directory
   setwd("/home/a/Desktop/R/testspace1")