Introduction to to R Emily Kalah Gade University of Washington Credit to Kristin Siebel for development of much of this PowerPoint

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

What is R?  Full programming environment  Language: entirely command-driven  Object-oriented

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

Download R + Packages Download R: Available for Linux, MacOS, and Windows Packages Collection of functions for specific tasks (1000s of them) Come with reference manual and vignettes /sample code Search for packages relevant to your area of interest: Google scholar for papers introducing new packages R-bloggers

Hints to Remember R is case-sensitive: X is not the same as x Assignment operator: = or <- Objects need to be assigned a name, otherwise they get dumped to main window, not saved to the environment. Use a text editor, not MS Word! Using a basic Textpad, or even R’s built-in editor keeps extraneous symbols out of your code, and quotation marks non-directional

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.

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

R Environment (Object-Oriented): Objects have both Type and Mode Function 1 Function 2 Results Vector 1 Vector 2 Matrix Data Frame String Numeric Value

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)

Help Function help(function name) help.search(“search term”) Try: help(lm), ?lm, and help.search(“linear regression”) 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.

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

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)

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

Reading in Data read.table(filename,...) > sts = read.csv(“C:/temp/statex77.csv”) Use CSV (comma-separated values) format. Almost every stats program will export to this format.

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

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

Viewing Data: Indexing > sts[,2] displays all rows, column 2 > sts[4,] displays row 4, all columns > head(sts) shows the first 10 rows of the data frame

Viewing Data You can also access columns (variables) using the ‘$’ symbol if the data frame has column names: > sts$X[30:35]

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

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

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

Viewing Data: Using Operators Viewing subsets of data using column names and operators: > sts[sts$state == “Washington”,] > sts[sts$Illiteracy >= 1.0,] > sts$state[sts$Area > ] > sts$state[sts$Life.Exp > 70]

Analyzing Data What do the variables look like? > table(sts$Illiteracy) > hist(sts$Area) > mean(sts$Life.Exp) > sd(sts$Life.Exp) > cor(sts$Illiteracy, sts$HS.Grad) > mean(sts$Income[sts$Illiteracy >= 1.0])

Manipulating Data Transforming variables: > Pop.Density = sts$Population/sts$Area This creates a new vector called Pop.Density of length 50 (our number of cases).

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

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

Data Analysis: OLS Regression > m1 = lm(Income ~ Illiteracy + log(Pop.Density) + HS.Grad + Murder, data=sts) The output of the regression is also an object. We’ve named it m1. > summary(m1)

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.

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

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