1. R-Studio and Revolution Analytics have built additional functionality on top of base R.

2. Revolution Analytics has moved onto the radar screen for predictive analytics http://www.forrester.com/pimages/rws/reprints/document/85601/oid/1-KWYFVB

3. Enter Commands View Results Write Code/ Program -Input Data -Analyze -Graphics Datasets, etc.

5. Character Vector: b <- c("one","two","three") numeric vector character vector Numeric Vector: a <- c(1,2,5.3,6,-2,4) Matrix: y<-matrix(1:20, nrow=5,ncol=4) Dataframe: d <- c(1,2,3,4) e <- c("red", "white", "red", NA) f <- c(TRUE,TRUE,TRUE,FALSE) mydata <- data.frame(d,e,f) names(mydata) <- c("ID","Color","Passed") List: w <- list(name="Fred", age=5.3) Data Structures Framework Source: Hadley Wickham

6. Actor Heights 1)Create Vectors of Actor Names, Heights, Date of Birth, Gender 2) Combine the 4 Vectors into a DataFrame

7. Numeric: e.g. heights String: e.g. names Dates: “12-03-2013 Factor: e.g. gender Boolean: TRUE, FALSE Variable Types

8. We use the c() function and list all values in quotations so that R knows that it is string data. ?c  Combine Values into a Vector or List Creating a Character / String Vector

9. Create a variable (aka object) called ActorNames: ActorNames <- c(“John", “Meryl”, “Jennifer", “Andre") Creating a Character / String Vector

10. Class, Length, Index class(ActorNames) length(ActorNames) ActorNames[2]

11. Create a variable called ActorHeights (inches): ActorHeights <- c(77, 66, 70, 90) Creating a Numeric Vector / Variable

12. Use the as.Date() function: ActorDoB <-as.Date(c("1930-10-27", "1949-06-22", "1990-08-15", "1946-05-19“ )) Each date has been entered as a text string (in quotations) in the appropriate format (yyyy-mm-dd). By enclosing these data in the as.Date() function, these strings are converted to date objects. Creating a Date Variable

13. Use the factor() function: ActorGender <- c(“male", “female", “female", “male“ ) class(ActorGender) ActorGender <- factor(ActorGender) Creating a Categorical / Factor Variable

14. Actor.DF <- data.frame(Name=ActorNames, Height=ActorHeights, BirthDate = ActorDob, Gender=ActorGender) Vectors and DataFrames dim(Actor.DF)

15. 1234 Actor.DF[4,3] # row 1, column 3 Actor.DF[1,3] # row 4, column 3 Actor.DF[1,] # row 1 Actor.DF[2:3,] # rows 2,3, all columns # column 2 Actor.DF[,2] Accessing Rows and Columns

16. > getwd() [1] "C:/Users/johnp_000/Documents" > setwd() getwd() setwd()

17. write.table(Actors.DF, “ActorData.txt", sep="\t", row.names = TRUE) write.csv(Actors.DF, “ActorData.csv") Write / Create a File

19. Add New Variable: Height -> Feet, Inches Actor.DF$Feet <- floor(Actor.DF$Height/12) Actor.DF$Inches <- Actor.DF$Height - (Actor.DF$Feet *12)

20. Sort Actor.DF[with(Actor.DF, order(-Height)), ]

21. Logical Operators / Filter Actor.DF$Height > 68 Actor.DF$Gender == "female" ?'[' Actor.DF[Actor.DF$Gender == "female",] http://www.statmethods.net/management/operators.html