1. Intro to R Stephanie Lee Dept of Sociology, CSSCR University of Washington September 2009

2. Class Outline I.What is R? II.The R Environment III.Reading in Data IV.Viewing and Manipulating Data V.Data Analysis

3. What is R? R is frequently thought of as another statistics package, like SPSS, Stata or SAS. While many people use R for statistical analysis, R is actually a full programming environment.

4. What is R? R is completely command-driven. There are very few menu items, so you must use the R language to do anything. Another important distinction between traditional stats packages and R is that R is object-oriented.

5. Why Use R? Free! Extremely flexible Many additional packages available Excellent graphics Disadvantages Steep learning curve Difficult data entry

6. Download R Download R: http://cran.r-project.org Available for Linux, MacOS, and Windows

7. The R Environment A traditional stats program like SPSS or Stata only contains one rectangular dataset at a time. All analysis is done on the current dataset. In contrast, the R environment is like a sandbox. It can contain a large number of different objects.

8. The R Environment R is also function-driven. The functions act on objects and return objects. Functions themselves are objects, too! function works its black-box magic! Input Arguments (Objects) Output (Objects)

9. Rectangular Dataset (Excel, SPSS, Stata, SAS) Variable 1Variable 2Variable 3 Case 1 Case 2 Case 3 Case 4 Case 5

10. R Environment (Object-Oriented) Function 1 Function 2 Results Vector 1 Vector 2 Matrix Data Frame String Numeric Value

11. Help Function help(function name) help.search(“search term”) Note: R is case-sensitive! Try: help(help), ls()

12. Help Function Sometimes one help file will contain information for several functions. Usage: Shows syntax for command and required arguments (input) and any default values for arguments. Value: the output object of the function

13. Setting Up Our Data > library(datasets) > mtcars > ?mtcars > write.csv(mtcars, “C:/temp/cars.csv”)

14. Creating Objects Assignment operator: = or <- Objects need to be assigned a name, otherwise they get dumped to main window, not saved to the environment. c() is a useful function for creating vectors

15. Reading in Data read.table(filename,...) > cars = read.csv(C:/temp/cars.csv) I prefer the CSV (comma-separated values) format. Almost every stats program will export to this format.

16. Viewing Data What does the dataset look like? > str(cars) > colnames(cars) > dim(cars) > nrow(cars) > ncol(cars) You can also assign row/col names with these functions.

17. Common Mode Types ModePossible Values Logical TRUE or FALSE or NA Integer Whole numbers Numeric Real numbers Character Single character or String (in double quotes)

18. Common Object Types ObjectModes More than one mode? vector Logical, Char, or Numeric No factor Logical, Char, or Numeric No matrix Logical, Char, or Numeric No data frame Logical, Char, and Numeric Yes

19. Creating Objects ObjectCreate Function vector c(), vector() factor factor() matrix matrix() data frame data.frame()

20. Viewing Data: Indexing datasetname[rownum, columnnum] > cars[1,4] displays value at row 1, column 4 > cars[2:5, 6] displays rows 2-5, column 6

21. Viewing Data: Indexing > cars[, 2] displays all rows, column 2 > cars[4,] displays row 4, all columns

22. Viewing Data You can also access columns (variables) using the ‘$’ symbol if the data frame has column names: > cars$mpg > cars$wt

23. Manipulating Data Now we can give that first column (variable) a better name than “X”. > colnames(cars) = c(“name”, colnames(cars)[2:ncol(cars)])

24. Manipulating Data > str(cars) R has the unfortunate habit of trying to turn vectors of character strings into factors (categorical data). > cars$name = as.character(cars$name)

25. Manipulating Data: Operators Arithmetic: + - * / ^ Comparison < less than > greater than <= less than or equal to >= greater than or equal to == is equal to != is not equal to Logical ! not & and | or xor() exclusive or

26. Manipulating Data Viewing subsets of data using column names and operators: > cars[cars$vs == 1,] > cars[cars$cyl >= 6,] > cars$name[cars$hp > 100] > cars$name[cars$wt > 3]

27. Analyzing Data What do the variables look like? > table(cars$gear) > hist(cars$qsec) > mean(cars$mpg) > sd(cars$mpg) > cor(cars$mpg, cars$wt) > mean(cars$mpg[cars$cyl == 4])

28. Manipulating Data Transforming variables: > wt.lb = cars$wt * 1000 This creates a new vector called wt.lb of length 32 (our number of cases).

29. Manipulating Data We can use wt.lb without “adding” it to our dataframe. But if you like the rectangular dataset concept, you can column bind it to the existing dataframe: > cars = cbind(cars, wt.lb)

30. Data Analysis Hypothesis Testing t.test, prop.test Regression lm(), glm()

31. Data Analysis: OLS Regression > regr = lm(cars$mpg ~ wt.lb + cars$hp + cars$cyl) The output of the regression is also an object. We’ve named it regr. > summary(regr)

32. Saving Data You can use write.csv() or write.table() to save your dataset. When you quit R, it will ask if you want to save the workspace. This includes all the objects you have created, but it does not include the code you’ve written. You can also use save.image() to save the workspace. You should always save your code in a *.r file.

33. Other Useful Functions > ifelse() > is.na() > match() > merge() > apply() > order() > sort()

34. Other Resources Main R website: http://www.r-project.orghttp://www.r-project.org UW CSSS Intro to R UW CSDE Intro to R UCLA Statistical Computing http://www.ats.ucsla.edu/stat

35. Advanced Topics More on factors Lists (data type) Loops String manipulation Writing your own functions Graphics