1. Introduction to Exploratory Descriptive Data Analysis in S-Plus Jagdish S. Gangolly State University of New York at Albany

2. Simple Structures I: Arithmetic Operators Arithmetic Operators – *, /, +, and -. –Avoid amguity by using parantheses, eg., (7+2)*3, since 7+2*3=13 and not 27. –Multiplication and division are evaluated before addition & subtraction. Raising to a power (^ or **) takes precedence over everything else.

3. Simple Structures I: Assignments Assignments: X x or x_3 or x=3 Not a good idea to use underscore for assignment or the equals sign. To see the value of a variable x : X or print(x) To remove a variable x : Rm(x)

4. Simple Structures II: Concatenation Concatenation: –Used to create vectors of any length > X <- c(1.5, 2, 2.5) > X 1.5 2.0 2.5 > X^2 2.25 4.00 6.25.c can be used with any type of data

5. Simple Structures III: Sequence Sequence command –Seq(lower, upper, increment) Some examples: seq(1,35,5): 1 6 11 16 21 26 31 seq(5,15,1.5): 5 6.5 8.0 9.5 11 12.5 14.0 seq(50,25,-5): 50 45 40 35 30 25

6. Simple Structures IV: Replicate Replicate command: to generate data that follow a regular pattern: Some examples: rep(8,5): 8 8 8 8 8 rep(“8”, 5): “8” “8” “8” “8” “8” rep(c(0,”ab”),2):“0” “ab” “0” “ab” rep(1:4, 1:4): 1 2 2 3 3 3 4 4 4 4 Rep(1:3, rep(2,3)): 1 1 2 2 3 3 Rep(c(1,8,7),length=5)):1 8 7 1 8

7. Simple Structures V: Expressions > X <- seq(2,10,2) > Y <- 1:5 > Z <- ((3*x^2+2*y)/((x+y)*(x-y)))^(0.5) > X 2 4 6 8 10 > Y 1 2 3 4 5 >Z 2.160247 2.081666 2.054805 2.041241 2.033060

8. Simple Structures VI: Logical Operators < Less Than > Greater than <= Less than or equal to >= Greater than or equal to == Equal to != Not equal to

9. Simple Structures VII Index Brackets: Square brackets are used to index vectors and matrices. > x <- seq(0,20,10) > x[2] 10 > x[5] NA > x[c(1,3)] 0 20 > x[-1] 10 20

10. Data Manipulation I: Frames & matrices I Matrices: two-dimensional vectors (have row and column indices Arrays: General data structure in S-Plus –Zero-dimensional: scalar –One-dimensional: vector –Two-dimensional: matrix –Three to eight-dimensional: arrays The data in a matrix must all be of the same datatype (usually numeric datatypes)

11. Data Manipulation I: Frames & matrices II The columns in dataframes can be of different datatypes Lists: The most general datatype in S-Plus

12. Data Manipulation I: Matrices I Reading data –S-Plus is very finicky about format of input data –To read a table: Read.table(“filename”) The first column must be rownames The first row must be column names The top left cell must be empty Space/tab the default column delimiters See the example “fasb103.txt” in the directory /db4/teach/acc522/ on the machine cayley.ba.albany.edu and play around with it.

13. Data Manipulation I: matrices II Read.table and as.matrix(): x <- Read.table(“filename”) as.matrix(x) Enter data directly: Matrix(data, nrow, ncol, byrow=F) Example: x <- Matrix(1:6, nrow=2, byrow=T) dim(x): (2 X 3) Dimnames(x): (NULL)

14. Data Manipulation I: matrices III Elements of matrices are accessed by specifying the row and column indices. Example: data <- c(227,8,1.3,1534,58,1.2,2365,82,1.8) dountries <- c(“austria”, “france”, “germany”) variables <- c(“gdp”, “pop”, “inflation”) country.data <- matrix(data,nrow=3,byrow=T) dimnames(country.data)<- list(countries,variables) Country.data[1:2,2:3] : pop and inflation of austria & france

15. S-Plus Graphics I To open a graphics window: motif() You can adjust the color scheme and print options through the drop-down menu on the motif window. To plot two variables x and y, plot(x,y) Example: (sine curve) plot(1:100, sin(1:100/10))