Brief introduction to R Lecturenotes1_R .ppt

2 Outline  Why R, and R Paradigm  References, Tutorials and links  R Overview  R Interface  R Workspace  Help  R Packages  Input/Output  Reusing Results

3 Why R? It's free! It runs on a variety of platforms including Windows, Unix and MacOS. It provides an unparalleled platform for programming new statistical methods in an easy and straightforward manner. It contains advanced statistical routines not yet available in other packages. It has state-of-the-art graphics capabilities.

4 R has a Steep Learning Curve First, while there are many introductory tutorials (covering data types, basic commands, the interface), none alone are comprehensive. In part, this is because much of the advanced functionality of R comes from hundreds of user contributed packages. Hunting for what you want can be time consuming, and it can be hard to get a clear overview of what procedures are available.

5 R has a Learning Curve The second reason is more transient. As users of statistical packages, we tend to run one controlled procedure for each type of analysis. Think of PROC GLM in SAS. We can carefully set up the run with all the parameters and options that we need. When we run the procedure, the resulting output may be a hundred pages long. We then sift through this output pulling out what we need and discarding the rest.

6 R paradigm is different Rather than setting up a complete analysis at once, the process is highly interactive. You run a command (say fit a model), take the results and process it through another command (say a set of diagnostic plots), take those results and process it through another command (say cross-validation), etc. The cycle may include transforming the data, and looping back through the whole process again. You stop when you feel that you have fully analyzed the data.

7 How to download?  Google it using R or CRAN (Comprehensive R Archive Network)  http://www.r-project.org

8 Tutorials Each of the following tutorials are in PDF format.  P. Kuhnert & B. Venables, An Introduction to R: Software for Statistical Modeling & Compu ting  J.H. Maindonald, Using R for Data Analysis and Graphics  B. Muenchen, R for SAS and SPSS Users  W.J. Owen, The R Guide  D. Rossiter, Introduction to the R Project for Statistical Computing for Use at the ITC  W.N. Venebles & D. M. Smith, An Introduction to R

9 Web links  Paul Geissler's excellent R tutorial  Dave Robert's Excellent Labs on Ecological Analysis  Excellent Tutorials by David Rossitier  Excellent tutorial an nearly every aspect of RMOST of these notes follow this web page format  Introduction to R by Vincent Zoonekynd  R Cookbook  Data Manipulation Reference

10 Web links  R time series tutorial  R Concepts and Data Types  Interpreting Output From lm()  The R Wiki  An Introduction to R  Import / Export Manual  R Reference Cards

11 Web links  KickStart  Hints on plotting data in R  Regression and ANOVA  Appendices to Fox Book on Regression  JGR a Java-based GUI for R [Mac|Windows|Linux]  A Handbook of Statistical Analyses Using R(Brian S. Everitt and Torsten Hothorn)

12 R Overview R is a comprehensive statistical and graphical programming language and is a dialect of the S language: 1988 - S2: RA Becker, JM Chambers, A Wilks 1992 - S3: JM Chambers, TJ Hastie 1998 - S4: JM Chambers R: initially written by Ross Ihaka and Robert Gentleman at Dep. of Statistics of U of Auckland, New Zealand during 1990s. Since 1997: international “R-core” team of 15 people with access to common CVS archive.

13 R Overview You can enter commands one at a time at the command prompt (>) or run a set of commands from a source file. There is a wide variety of data types, including vectors (numerical, character, logical), matrices, data frames, and lists. To quit R, use >q()

14 R Overview Most functionality is provided through built-in and user-created functions and all data objects are kept in memory during an interactive session. Basic functions are available by default. Other functions are contained in packages that can be attached to a current session as needed

15 R Overview A key skill to using R effectively is learning how to use the built-in help system. Other sections describe the working environment, inputting programs and outputting results, installing new functionality through packages and etc. A fundamental design feature of R is that the output from most functions can be used as input to other functions. This is described in reusing results.

16 R Interface Start the R system, the main window (RGui) with a sub window (R Console) will appear In the `Console' window the cursor is waiting for you to type in some R commands.

18 R Introduction  Results of calculations can be stored in objects using the assignment operators:  An arrow (<-) formed by a smaller than character and a hyphen without a space!  The equal character (=).

19 R Introduction  These objects can then be used in other calculations. To print the object just enter the name of the object. There are some restrictions when giving an object a name:  Object names cannot contain `strange' symbols like !, +, -, #.  A dot (.) and an underscore ( ) are allowed, also a name starting with a dot.  Object names can contain a number but cannot start with a number.  R is case sensitive, X and x are two different objects, as well as temp and temP.

20 An example > # An example > x <- c(1:10) > x[(x>8) | (x<5)] > # yields 1 2 3 4 9 10 > # How it works > x <- c(1:10) > x >1 2 3 4 5 6 7 8 9 10 > x > 8 > F F F F F F F F T T > x < 5 > T T T T F F F F F F > x > 8 | x < 5 > T T T T F F F F T T > x[c(T,T,T,T,F,F,F,F,T,T)] > 1 2 3 4 9 10

21 R Introduction  To list the objects that you have in your current R session use the function ls or the function objects. > ls() [1] "x" "y"  So to run the function ls we need to enter the name followed by an opening ( and a closing ). Entering only ls will just print the object, you will see the underlying R code of the the function ls. Most functions in R accept certain arguments. For example, one of the arguments of the function ls is pattern. To list all objects starting with the letter x: > x2 = 9 > y2 = 10 > ls(pattern="x") [1] "x" "x2"

22 R Introduction  If you assign a value to an object that already exists then the contents of the object will be overwritten with the new value (without a warning!). Use the function rm to remove one or more objects from your session. > rm(x, x2)  Lets create two small vectors with data and a scatterplot. z2 <- c(1,2,3,4,5,6) z3 <- c(6,8,3,5,7,1) plot(z2,z3) title("My first scatterplot")

23 R Warning ! R is a case sensitive language. FOO, Foo, and foo are three different objects

24 R Introduction > x = sin(9)/75 > y = log(x) + x^2 > x [1] 0.005494913 > y [1] -5.203902 > m <- matrix(c(1,2,4,1), ncol=2) > m > [,1] [,2] [1,] 1 4 [2,] 2 1 > solve(m) [,1] [,2] [1,] -0.1428571 0.5714286 [2,] 0.2857143 -0.1428571

25 R Workspace Objects that you create during an R session are hold in memory, the collection of objects that you currently have is called the workspace. This workspace is not saved on disk unless you tell R to do so. This means that your objects are lost when you close R and not save the objects, or worse when R or your system crashes on you during a session.

26 R Workspace When you close the RGui or the R console window, the system will ask if you want to save the workspace image. If you select to save the workspace image then all the objects in your current R session are saved in a file .RData. This is a binary file located in the working directory of R, which is by default the installation directory of R.

27 R Workspace  During your R session you can also explicitly save the workspace image. Go to the `File‘ menu and then select `Save Workspace...', or use the save.image function. ## save to the current working directory save.image() ## just checking what the current working directory is getwd() ## save to a specific file and location save.image("C:Program FilesRR-2.5.0 bin.RData")

28 R Workspace If you have saved a workspace image and you start R the next time, it will restore the workspace. So all your previously saved objects are available again. You can also explicitly load a saved workspace, that could be the workspace image of someone else. Go the `File' menu and select `Load workspace...'.

29 R Workspace Commands are entered interactively at the R user prompt. Up and down arrow keys scroll through your command history. You will probably want to keep different projects in different physical directories.

30 R Workspace R gets confused if you use a path in your code like c:mydocumentsmyfile.txt This is because R sees "" as an escape character. Instead, use c:my documentsmyfile.txt or c:/mydocuments/myfile.txt

31 R Workspace getwd() # print the current working directory ls() # list the objects in the current workspace setwd(mydirectory) # change to mydirectory setwd("c:/docs/mydir")

32 R Workspace #view and set options for the session help(options) # learn about available options options() # view current option settings options(digits=3) # number of digits to print on output # work with your previous commands history() # display last 25 commands history(max.show=Inf) # display all previous commands

33 R Workspace # save your command history savehistory(file="myfile") # default is ".Rhistory" # recall your command history loadhistory(file="myfile") # default is ".Rhistory“

34 R Help Once R is installed, there is a comprehensive built-in help system. At the program's command prompt you can use any of the following: help.start() # general help help(foo) # help about function foo ?foo # same thing apropos("foo") # list all function containing string foo example(foo) # show an example of function foo

35 R Help # search for foo in help manuals and archived mailing lists RSiteSearch("foo") # get vignettes on using installed packages vignette() # show available vingettes vignette("foo") # show specific vignette

36 R Datasets R comes with a number of sample datasets that you can experiment with. Type > data( ) to see the available datasets. The results will depend on which packages you have loaded. Type help(datasetname) for details on a sample dataset.

37 R Packages  One of the strengths of R is that the system can easily be extended. The system allows you to write new functions and package those functions in a so called `R package' (or `R library'). The R package may also contain other R objects, for example data sets or documentation. There is a lively R user community and many R packages have been written and made available on CRAN for other users. Just a few examples, there are packages for portfolio optimization, drawing maps, exporting objects to html, time series analysis, spatial statistics and the list goes on and on.

38 R Packages  When you download R, already a number (around 30) of packages are downloaded as well. To use a function in an R package, that package has to be attached to the system. When you start R not all of the downloaded packages are attached, only seven packages are attached to the system by default. You can use the function search to see a list of packages that are currently attached to the system, this list is also called the search path. > search() [1] ".GlobalEnv" "package:stats" "package:graphics" [4] "package:grDevices" "package:datasets" "package:utils" [7] "package:methods" "Autoloads" "package:base"

39 R Packages To attach another package to the system you can use the menu or the library function. Via the menu: Select the `Packages' menu and select `Load package...', a list of available packages on your system will be displayed. Select one and click `OK', the package is now attached to your current R session. Via the library function: > library(MASS) > shoes $A [1] 13.2 8.2 10.9 14.3 10.7 6.6 9.5 10.8 8.8 13.3 $B [1] 14.0 8.8 11.2 14.2 11.8 6.4 9.8 11.3 9.3 13.6

40 R Packages  The function library can also be used to list all the available libraries on your system with a short description. Run the function without any arguments > library() Packages in library 'C:/PROGRA~1/R/R-25~1.0/library': base The R Base Package Boot Bootstrap R (S-Plus) Functions (Canty) class Functions for Classification cluster Cluster Analysis Extended Rousseeuw et al. codetools Code Analysis Tools for R datasets The R Datasets Package DBI R Database Interface foreign Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, dBase, ... graphics The R Graphics Package

41 R Packages install = function() { install.packages(c("moments","graphics","Rcmdr","hexbin"), repos="http://lib.stat.cmu.edu/R/CRAN") } install()

42 R Conflicting objects  It is not recommended to do, but R allows the user to give an object a name that already exists. If you are not sure if a name already exists, just enter the name in the R console and see if R can find it. R will look for the object in all the libraries (packages) that are currently attached to the R system. R will not warn you when you use an existing name. > mean = 10 > mean [1] 10  The object mean already exists in the base package, but is now masked by your object mean. To get a list of all masked objects use the function conflicts. > [1] "body<-" "mean"

43 R Conflicting objects The object mean already exists in the base package, but is now masked by your object mean. To get a list of all masked objects use the function conflicts. > conflicts() [1] "body<-" "mean“ You can safely remove the object mean with the function rm() without risking deletion of the mean function. Calling rm() removes only objects in your working environment by default.

44 Source Codes you can have input come from a script file (a file containing R commands) and direct output to a variety of destinations. Input The source( ) function runs a script in the current session. If the filename does not include a path, the file is taken from the current working directory. # input a script source("myfile")

45 Output Output The sink( ) function defines the direction of the output. # direct output to a file sink("myfile", append=FALSE, split=FALSE) # return output to the terminal sink()

46 Output The append option controls whether output overwrites or adds to a file. The split option determines if output is also sent to the screen as well as the output file. Here are some examples of the sink() function. # output directed to output.txt in c:projects directory. # output overwrites existing file. no output to terminal. sink("myfile.txt", append=TRUE, split=TRUE)

47 Graphs To redirect graphic output use one of the following functions. Use dev.off( ) to return output to the terminal. Function Output to pdf("mygraph.pdf") pdf file win.metafile("mygraph.wmf") windows metafile png("mygraph.png") png file jpeg("mygraph.jpg") jpeg file bmp("mygraph.bmp") bmp file postscript("mygraph.ps") postscript file

48 Redirecting Graphs # example - output graph to jpeg file jpeg("c:/mygraphs/myplot.jpg") plot(x) dev.off()

49 Reusing Results One of the most useful design features of R is that the output of analyses can easily be saved and used as input to additional analyses. # Example 1 lm(mpg~wt, data=mtcars) This will run a simple linear regression of miles per gallon on car weight using the dataframe mtcars. Results are sent to the screen. Nothing is saved.

50 Reusing Results # Example 2 fit <- lm(mpg~wt, data=mtcars) This time, the same regression is performed but the results are saved under the name fit. No output is sent to the screen. However, you now can manipulate the results. str(fit) # view the contents/structure of "fit“ The assignment has actually created a list called "fit" that contains a wide range of information (including the predicted values, residuals, coefficients, and more.

51 Reusing Results # plot residuals by fitted values plot(fit$residuals, fit$fitted.values) To see what a function returns, look at the value section of the online help for that function. Here we would look at help(lm). The results can also be used by a wide range of other functions. # produce diagnostic plots plot(fit)

53 Outline  Data Types  Importing Data  Keyboard Input  Database Input  Exporting Data  Viewing Data  Variable Labels  Value Labels  Missing Data  Date Values

54 Data Types R has a wide variety of data types including scalars, vectors (numerical, character, logical), matrices, dataframes, and lists.

55 Vectors a <- c(1,2,5.3,6,-2,4) # numeric vector b <- c("one","two","three") # character vector c <- c(TRUE,TRUE,TRUE,FALSE,TRUE,FALSE) #logical vector Refer to elements of a vector using subscripts. a[c(2,4)] # 2nd and 4th elements of vector

56 Matrices All columns in a matrix must have the same mode(numeric, character, etc.) and the same length. The general format is mymatrix <- matrix(vector, nrow=r, ncol=c, byrow=FALSE,dimnames=list(char_vector_rownames, char_vector_colnames)) byrow=TRUE indicates that the matrix should be filled by rows. byrow=FALSE indicates that the matrix should be filled by columns (the default). dimnames provides optional labels for the columns and rows.

57 Matrices # generates 5 x 4 numeric matrix y<-matrix(1:20, nrow=5,ncol=4) # another example cells <- c(1,26,24,68) rnames <- c("R1", "R2") cnames <- c("C1", "C2") mymatrix <- matrix(cells, nrow=2, ncol=2, byrow=TRUE, dimnames=list(rnames, cnames)) #Identify rows, columns or elements using subscripts. x[,4] # 4th column of matrix x[3,] # 3rd row of matrix x[2:4,1:3] # rows 2,3,4 of columns 1,2,3

58 Arrays Arrays are similar to matrices but can have more than two dimensions. See help(array) for details.

59 Data frames A data frame is more general than a matrix, in that different columns can have different modes (numeric, character, factor, etc.). 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") #variable names

60 Data frames There are a variety of ways to identify the elements of a dataframe . myframe[3:5] # columns 3,4,5 of dataframe myframe[c("ID","Age")] # columns ID and Age from dataframe myframe$X1 # variable x1 in the dataframe

61 Lists An ordered collection of objects (components). A list allows you to gather a variety of (possibly unrelated) objects under one name. # example of a list with 4 components - # a string, a numeric vector, a matrix, and a scaler w <- list(name="Fred", mynumbers=a, mymatrix=y, age=5.3) # example of a list containing two lists v <- c(list1,list2)

62 Lists Identify elements of a list using the [[]] convention. mylist[[2]] # 2nd component of the list

63 Factors Tell R that a variable is nominal by making it a factor. The factor stores the nominal values as a vector of integers in the range [ 1... k ] (where k is the number of unique values in the nominal variable), and an internal vector of character strings (the original values) mapped to these integers. # variable gender with 20 "male" entries and # 30 "female" entries gender <- c(rep("male",20), rep("female", 30)) gender <- factor(gender) # stores gender as 20 1s and 30 2s and associates # 1=female, 2=male internally (alphabetically) # R now treats gender as a nominal variable summary(gender)

64 Useful Functions length(object) # number of elements or components str(object) # structure of an object class(object) # class or type of an object names(object) # names c(object,object,...) # combine objects into a vector cbind(object, object, ...) # combine objects as columns rbind(object, object, ...) # combine objects as rows ls() # list current objects rm(object) # delete an object newobject <- edit(object) # edit copy and save a newobject fix(object) # edit in place

65 Importing Data Importing data into R is fairly simple. For Stata and Systat, use the foreign package. For SPSS and SAS I would recommend the Hmisc package for ease and functionality. See the Quick-R section on packages, for information on obtaining and installing the these packages. Example of importing data are provided below.

66 From A Comma Delimited Text File # first row contains variable names, comma is separator # assign the variable id to row names # note the / instead of on mswindows systems mydata <- read.table("c:/mydata.csv", header=TRUE, sep=",", row.names="id")

67 From Excel The best way to read an Excel file is to export it to a comma delimited file and import it using the method above. On windows systems you can use the RODBC package to access Excel files. The first row should contain variable/column names. # first row contains variable names # we will read in workSheet mysheet library(RODBC) channel <- odbcConnectExcel("c:/myexel.xls") mydata <- sqlFetch(channel, "mysheet") odbcClose(channel)

68 From SAS  # save SAS dataset in trasport format libname out xport 'c:/mydata.xpt'; data out.mydata; set sasuser.mydata; run;  library(foreign) #bsl=read.xport(“mydata.xpt")

69 Keyboard Input Usually you will obtain a dataframe by importing it from SAS, SPSS, Excel, Stata, a database, or an ASCII file. To create it interactively, you can do something like the following. # create a dataframe from scratch age <- c(25, 30, 56) gender <- c("male", "female", "male") weight <- c(160, 110, 220) mydata <- data.frame(age,gender,weight)

70 Keyboard Input You can also use R's built in spreadsheet to enter the data interactively, as in the following example. # enter data using editor mydata <- data.frame(age=numeric(0), gender=character(0), weight=numeric(0)) mydata <- edit(mydata) # note that without the assignment in the line above, # the edits are not saved!

71 Exporting Data There are numerous methods for exporting R objects into other formats . For SPSS, SAS and Stata. you will need to load the foreign packages. For Excel, you will need the xlsReadWrite package.

72 Exporting Data To A Tab Delimited Text File write.table(mydata, "c:/mydata.txt", sep="t") To an Excel Spreadsheet library(xlsReadWrite) write.xls(mydata, "c:/mydata.xls") To SAS library(foreign) write.foreign(mydata, "c:/mydata.txt", "c:/mydata.sas", package="SAS")

73 Viewing Data There are a number of functions for listing the contents of an object or dataset. # list objects in the working environment ls() # list the variables in mydata names(mydata) # list the structure of mydata str(mydata) # list levels of factor v1 in mydata levels(mydata$v1) # dimensions of an object dim(object)

74 Viewing Data There are a number of functions for listing the contents of an object or dataset. # class of an object (numeric, matrix, dataframe, etc) class(object) # print mydata mydata # print first 10 rows of mydata head(mydata, n=10) # print last 5 rows of mydata tail(mydata, n=5)

75 Variable Labels R's ability to handle variable labels is somewhat unsatisfying. If you use the Hmisc package, you can take advantage of some labeling features. library(Hmisc) label(mydata$myvar) <- "Variable label for variable myvar" describe(mydata)

76 Variable Labels Unfortunately the label is only in effect for functions provided by the Hmisc package, such as describe(). Your other option is to use the variable label as the variable name and then refer to the variable by position index. names(mydata)[3] <- "This is the label for variable 3" mydata[3] # list the variable

77 Value Labels To understand value labels in R, you need to understand the data structure factor. You can use the factor function to create your own value lables. # variable v1 is coded 1, 2 or 3 # we want to attach value labels 1=red, 2=blue,3=green mydata$v1 <- factor(mydata$v1, levels = c(1,2,3), labels = c("red", "blue", "green")) # variable y is coded 1, 3 or 5 # we want to attach value labels 1=Low, 3=Medium, 5=High

78 Value Labels mydata$v1 <- ordered(mydata$y, levels = c(1,3, 5), labels = c("Low", "Medium", "High")) Use the factor() function for nominal data and the ordered() function for ordinal data. R statistical and graphic functions will then treat the data appropriately. Note: factor and ordered are used the same way, with the same arguments. The former creates factors and the later creates ordered factors.

79 Missing Data In R, missing values are represented by the symbol NA (not available) . Impossible values (e.g., dividing by zero) are represented by the symbol NaN (not a number). Unlike SAS, R uses the same symbol for character and numeric data. Testing for Missing Values is.na(x) # returns TRUE of x is missing y <- c(1,2,3,NA) is.na(y) # returns a vector (F F F T)

80 Missing Data Recoding Values to Missing # recode 99 to missing for variable v1 # select rows where v1 is 99 and recode column v1 mydata[mydata$v1==99,"v1"] <- NA Excluding Missing Values from Analyses Arithmetic functions on missing values yield missing values. x <- c(1,2,NA,3) mean(x) # returns NA mean(x, na.rm=TRUE) # returns 2

81 Missing Data The function complete.cases() returns a logical vector indicating which cases are complete. # list rows of data that have missing values mydata[!complete.cases(mydata),] The function na.omit() returns the object with listwise deletion of missing values. # create new dataset without missing data newdata <- na.omit(mydata)

82 Missing Data Advanced Handling of Missing Data Most modeling functions in R offer options for dealing with missing values. You can go beyond pairwise of listwise deletion of missing values through methods such as multiple imputation. Good implementations that can be accessed through R include Amelia II, Mice, and mitools.

83 Date Values Dates are represented as the number of days since 1970- 01-01, with negative values for earlier dates. # use as.Date( ) to convert strings to dates mydates <- as.Date(c("2007-06-22", "2004-02-13")) # number of days between 6/22/07 and 2/13/04 days <- mydates[1] - mydates[2] Sys.Date( ) returns today's date. Date() returns the current date and time.

84 Date Values The following symbols can be used with the format( ) function to print dates. Symbol Meaning Example %d day as a number (0-31) 01-31 %a %A abbreviated weekday unabbreviated weekday Mon Monday %m month (00-12) 00-12 %b %B abbreviated month unabbreviated month Jan January %y %Y 2-digit year 4-digit year 07 2007

85 Date Values # print today's date today <- Sys.Date() format(today, format="%B %d %Y") "June 20 2007"

87 Outline  Creating New Variable  Operators  Built-in functions  Control Structures  User Defined Functions  Sorting Data  Merging Data  Aggregating Data  Reshaping Data  Sub-setting Data  Data Type Conversions

88 Introduction Once you have access to your data, you will want to massage it into useful form. This includes creating new variables (including recoding and renaming existing variables), sorting and merging datasets, aggregating data, reshaping data, and subsetting datasets (including selecting observations that meet criteria, randomly sampling observation, and dropping or keeping variables).

89 Introduction Each of these activities usually involve the use of R's built-in operators (arithmetic and logical) and functions (numeric, character, and statistical). Additionally, you may need to use control structures (if-then, for, while, switch) in your programs and/or create your own functions. Finally you may need to convert variables or datasets from one type to another (e.g. numeric to character or matrix to dataframe).

90 Creating new variables  Use the assignment operator <- to create new variables. A wide array of operators and functions are available here.  # Three examples for doing the same computations mydata$sum <- mydata$x1 + mydata$x2 mydata$mean <- (mydata$x1 + mydata$x2)/2 attach(mydata) mydata$sum <- x1 + x2 mydata$mean <- (x1 + x2)/2 detach(mydata)  mydata <- transform( mydata, sum = x1 + x2, mean = (x1 + x2)/2 )

91 Creating new variables Recoding variables  In order to recode data, you will probably use one or more of R's control structures.  # create 2 age categories mydata$agecat <- ifelse(mydata$age > 70, c("older"), c("younger")) # another example: create 3 age categories attach(mydata) mydata$agecat[age > 75] <- "Elder" mydata$agecat[age > 45 & age <= 75] <- "Middle Aged" mydata$agecat[age <= 45] <- "Young" detach(mydata)

92 Creating new variables Recoding variables  In order to recode data, you will probably use one or more of R's control structures.  # create 2 age categories mydata$agecat <- ifelse(mydata$age > 70, c("older"), c("younger")) # another example: create 3 age categories attach(mydata) mydata$agecat[age > 75] <- "Elder" mydata$agecat[age > 45 & age <= 75] <- "Middle Aged" mydata$agecat[age <= 45] <- "Young" detach(mydata)

93 Creating new variables Renaming variables  You can rename variables programmatically or interactively.  # rename interactively fix(mydata) # results are saved on close # rename programmatically library(reshape) mydata <- rename(mydata, c(oldname="newname")) # you can re-enter all the variable names in order # changing the ones you need to change. The limitation # is that you need to enter all of them! names(mydata) <- c("x1","age","y", "ses")

94 Arithmetic Operators Operator Description + addition - subtraction * multiplication / division ^ or ** exponentiation x %% y modulus (x mod y) 5%%2 is 1 x %/% y integer division 5%/%2 is 2

95 Logical Operators Operator Description < less than <= less than or equal to > greater than >= greater than or equal to == exactly equal to != not equal to !x Not x x | y x OR y x & y x AND y isTRUE(x) test if x is TRUE

96 Control Structures  R has the standard control structures you would expect. expr can be multiple (compound) statements by enclosing them in braces { }. It is more efficient to use built- in functions rather than control structures whenever possible.

97 Control Structures  if-else  if (cond) expr if (cond) expr1 else expr2  for  for (var in seq) expr  while  while (cond) expr  switch  switch(expr, ...)  ifelse  ifelse(test,yes,no)

98 Control Structures  # transpose of a matrix # a poor alternative to built-in t() function mytrans <- function(x) { if (!is.matrix(x)) { warning("argument is not a matrix: returning NA") return(NA_real_) } y <- matrix(1, nrow=ncol(x), ncol=nrow(x)) for (i in 1:nrow(x)) { for (j in 1:ncol(x)) { y[j,i] <- x[i,j] } } return(y) }

99 Control Structures  # try it z <- matrix(1:10, nrow=5, ncol=2) tz <- mytrans(z)

100 R built-in functions Almost everything in R is done through functions. Here I'm only referring to numeric and character functions that are commonly used in creating or recoding variables. Note that while the examples on this page apply functions to individual variables, many can be applied to vectors and matrices as well.

101 Numeric Functions Function Description abs(x) absolute value sqrt(x) square root ceiling(x) ceiling(3.475) is 4 floor(x) floor(3.475) is 3 trunc(x) trunc(5.99) is 5 round(x, digits=n) round(3.475, digits=2) is 3.48 signif(x, digits=n) signif(3.475, digits=2) is 3.5 cos(x), sin(x), tan(x) also acos(x), cosh(x), acosh(x), etc. log(x) natural logarithm log10(x) common logarithm exp(x) e^x

Applied Statistical Computing and Graphics 102 Character Functions Function Description substr(x, start=n1, stop=n2) Extract or replace substrings in a character vector. x <- "abcdef" substr(x, 2, 4) is "bcd" substr(x, 2, 4) <- "22222" is "a222ef" grep(pattern, x , ignore.case=FALSE, fixed=FALSE) Search for pattern in x. If fixed =FALSE then pattern is a regular expression. If fixed=TRUE then pattern is a text string. Returns matching indices. grep("A", c("b","A","c"), fixed=TRUE) returns 2 sub(pattern, replacement, x, ignore.case =FALSE, fixed=FALSE) Find pattern in x and replace with replacement text. If fixed=FALSE then pattern is a regular expression. If fixed = T then pattern is a text string. sub("s",".","Hello There") returns "Hello.There" strsplit(x, split) Split the elements of character vector x at split. strsplit("abc", "") returns 3 element vector "a","b","c" paste(..., sep="") Concatenate strings after using sep string to seperate them. paste("x",1:3,sep="") returns c("x1","x2" "x3") paste("x",1:3,sep="M") returns c("xM1","xM2" "xM3") paste("Today is", date()) toupper(x) Uppercase tolower(x) Lowercase

103 Stat/Prob Functions  The following table describes functions related to probaility distributions. For random number generators below, you can use set.seed(1234) or some other integer to create reproducible pseudo-random numbers.

Applied Statistical Computing and Graphics 104 Function Description dnorm(x) normal density function (by default m=0 sd=1) # plot standard normal curve x <- pretty(c(-3,3), 30) y <- dnorm(x) plot(x, y, type='l', xlab="Normal Deviate", ylab="Density", yaxs="i") pnorm(q) cumulative normal probability for q (area under the normal curve to the right of q) pnorm(1.96) is 0.975 qnorm(p) normal quantile. value at the p percentile of normal distribution qnorm(.9) is 1.28 # 90th percentile rnorm(n, m=0,sd=1) n random normal deviates with mean m and standard deviation sd. #50 random normal variates with mean=50, sd=10 x <- rnorm(50, m=50, sd=10) dbinom(x, size, prob) pbinom(q, size, prob) qbinom(p, size, prob) rbinom(n, size, prob) binomial distribution where size is the sample size and prob is the probability of a heads (pi) # prob of 0 to 5 heads of fair coin out of 10 flips dbinom(0:5, 10, .5) # prob of 5 or less heads of fair coin out of 10 flips pbinom(5, 10, .5) dpois(x, lamda) ppois(q, lamda) qpois(p, lamda) rpois(n, lamda) poisson distribution with m=std=lamda #probability of 0,1, or 2 events with lamda=4 dpois(0:2, 4) # probability of at least 3 events with lamda=4 1- ppois(2,4) dunif(x, min=0, max=1) punif(q, min=0, max=1) qunif(p, min=0, max=1) runif(n, min=0, max=1) uniform distribution, follows the same pattern as the normal distribution above. #10 uniform random variates x <- runif(10)

105 Function Description mean(x, trim=0, na.rm=FALSE) mean of object x # trimmed mean, removing any missing values and # 5 percent of highest and lowest scores mx <- mean(x,trim=.05,na.rm=TRUE) sd(x) standard deviation of object(x). also look at var(x) for variance and mad(x) for median absolute deviation. median(x) median quantile(x, probs) quantiles where x is the numeric vector whose quantiles are desired and probs is a numeric vector with probabilities in [0,1]. # 30th and 84th percentiles of x y <- quantile(x, c(.3,.84)) range(x) range sum(x) sum diff(x, lag=1) lagged differences, with lag indicating which lag to use min(x) minimum max(x) maximum scale(x, center=TRUE, scale=TRUE) column center or standardize a matrix.

106 Other Useful Functions Function Description seq(from , to, by) generate a sequence indices <- seq(1,10,2) #indices is c(1, 3, 5, 7, 9) rep(x, ntimes) repeat x n times y <- rep(1:3, 2) # y is c(1, 2, 3, 1, 2, 3) cut(x, n) divide continuous variable in factor with n levels y <- cut(x, 5)

107 Sorting  To sort a dataframe in R, use the order( ) function. By default, sorting is ASCENDING. Prepend the sorting variable by a minus sign to indicate DESCENDING order. Here are some examples.  # sorting examples using the mtcars dataset data(mtcars) # sort by mpg newdata = mtcars[order(mtcars$mpg),] # sort by mpg and cyl newdata <- mtcars[order(mtcars$mpg, mtcars$cyl),] #sort by mpg (ascending) and cyl (descending) newdata <- mtcars[order(mtcars$mpg, -mtcars$cyl),]

108 Merging To merge two dataframes (datasets) horizontally, use the merge function. In most cases, you join two dataframes by one or more common key variables (i.e., an inner join). # merge two dataframes by ID total <- merge(dataframeA,dataframeB,by="ID") # merge two dataframes by ID and Country total <- merge(dataframeA,dataframeB,by=c("ID","Country"))

109 Merging ADDING ROWS To join two dataframes (datasets) vertically, use the rbind function. The two dataframes must have the same variables, but they do not have to be in the same order. total <- rbind(dataframeA, dataframeB) If dataframeA has variables that dataframeB does not, then either: Delete the extra variables in dataframeA or Create the additional variables in dataframeB and set them to NA (missing) before joining them with rbind.

110 Aggregating  It is relatively easy to collapse data in R using one or more BY variables and a defined function.  # aggregate dataframe mtcars by cyl and vs, returning means # for numeric variables attach(mtcars) aggdata <-aggregate(mtcars, by=list(cyl), FUN=mean, na.rm=TRUE) print(aggdata)  OR use apply

111 Aggregating  When using the aggregate() function, the by variables must be in a list (even if there is only one). The function can be built-in or user provided.  See also:  summarize() in the Hmisc package  summaryBy() in the doBy package

112 Data Type Conversion  Type conversions in R work as you would expect. For example, adding a character string to a numeric vector converts all the elements in the vector to character.  Use is.foo to test for data type foo. Returns TRUE or FALSE Use as.foo to explicitly convert it.  is.numeric(), is.character(), is.vector(), is.matrix(), is.data.frame() as.numeric(), as.character(), as.vector(), as.matrix(), as.data.frame)

Brief introduction to R Lecturenotes1_R .ppt

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