R for IR. Created by Narren Brown, Grinnell College, and Diane Saphire, Trinity University

Transcription

1 R for IR Created by Narren Brown, Grinnell College, and Diane Saphire, Trinity University For presentation at the June 2013 Meeting of the Higher Education Data Sharing Consortium Table of Contents I. Downloading and Installing R. 1 II. Downloading and Installing R Commander.. 7 III. Using R Commander a. The R Commander Windows b. Importing and Viewing Data i. Data sets provided in R. 12 ii. Importing one of your own data sets. 14 iii. Viewing your data. 16 iv. Tips if you are having problems importing Excel data 17 c. Summarizing Data i. Quick summary of active data set. 19 ii. Numerical summaries iii. Tables iv. Aggregating data.. 26 d. Creating New Variables.. 28 i. Recoding a variable ii. Computing a new variable e. Filtering Data. 33 f. Graphing.. 35 g. Inferential statistics.. 39 i. T-test. 39 ii. Least squares regression. 42 iii. Logistic regression IV. Moving Beyond R Commander to R V. Useful Websites... 50

2 I. DOWNLOADING AND INSTALLING R Directions for downloading R can be found at R is available for Windows, Mac OS X and Linux. As of June, 2013, the latest release is We will show screen shots for a Windows download. To Download R for Windows, click on the Download R for Windows link: 1

3 Next, click on install R for the first time : Click the Download R for Windows link: If asked if you want to run or save, click run. You may also be asked whether to allow the program to access your computer allow access. You will be asked to select a language. 2

4 Next you will see a Welcome Wizard. Click Next. And an information screen. After you have finished reading every word of the license, click Next. If you want to select a different folder from the default for installing the program you can do that next: 3

5 If you have a 64 bit machine, you can install a 64 bit version instead of a 32 bit version. Here I m installing the 32-bit version. Probably no need to use anything other than the default startup options, so click next: 4

6 If you want to select a different folder for the shortcuts, you can do that on this next screen. On the Select Additional Tasks window, you will likely want to just leave the automatically checked items as they are: 5

7 And, finally, the program will install. You will eventually see this window. Click Finish. You should now see the R icon on your desktop: 6

8 II. DOWNLOADING AND INSTALLING R COMMANDER R commander is a graphical user interface that will automatically generate for you some of the common R commands that you might use. Hard core users of R may want to type in their own command line code, but for those of you just starting up, it will likely be easier to let R Commander do this work for you. To get R Commander, double-click on the R desktop icon. This will open a window called the R Console. At the command line (which starts with a >) type the command as seen in the screen shot below. Hit the enter key. install.packages( Rcmdr ) If asked if you want to use a personal Library, say yes. Next, if you don t specify a personal library folder, it will use a default (for me, it was C:/Users/dsaphire/Documents/R/win-library/3.0). You will be asked to select a CRAN mirror. What the heck is that? These are locations that store updated versions of R from which users can download R and related plug ins, like R Commander. You want to pick one that is closest to you to minimize network traffic. I selected USA (TX 1) since I m in Texas. Here s what the bottom of the list looks like: 7

9 Now R Commander will download and your R Console will look like this: 8

10 To run R Commander, at the command line (the next >) type library(rcmdr) and hit the enter key. The first time you do this, you will be asked if you want to install some other packages used by R Commander. Answer Yes. Next you will be asked to specify the location from which you want to install the packages. Just take the default CRAN response. Click OK. A bunch of packages will be installed. And finally you will see the R Commander window! It looks like this: 9

11 In the future, when you want to use R Commander, you will just click on the R desktop icon and type at the first prompt (>). library(rcmdr) 10

12 The R Commander Windows III. USING R COMMANDER After opening the R program and typing library(rcmdr) at the command prompt, the window below will appear. The various components of the R Commander window: Drop Down Menus Script Window: R commands generated by GUI are displayed here. You can type commands directly here, select by highlighting and then send code by pressing the Submit button (lower right corner below window) Output Window DARK BLUE: output RED: command that generated output Message Window: RED: Error GREEN: Warnings BLUE: Other information 11

13 Importing and Viewing Data Data sets provided in R. R actually provides several datasets for analysis. If you click on the menu button Data Data in packages List data sets in packages, the menu shown below will appear. 12

14 Once this menu is open, scroll down and find an interesting data set. Double click on that data set and it will be loaded. Note: You need an internet connection to access these data sets. 13

15 Importing one of your own data sets. Here we show how to import one of your SPSS data sets. You can also import from text and Excel files in a similar fashion. If you experience problems importing Excel data, see our Tips if you are having problems importing Excel data below. We start as we did before. Click on menu button Data Import data from SPSS data set. Once the steps above have been completed, this pop up will appear. R will ask you to name the data set. We have named the data set for this presentation HEDS. 14

16 Once you have named your data set, R will open this window. From here, navigate to your desired data set and double click. The R Commander window will now look like: R Commander automatically generates the R syntax needed to import the data. From this window we can highlight/edit commands and submit them. We can also copy and paste them into R s syntax window to save and re-run in the future. The command syntax that was used to get the data set. Unrecognized record type 7 will sometimes occur when reading in data sets created in newer versions of SPSS. It does not affect the data. Notice the NOTE! It tells us about the data set we just loaded. 15

17 Viewing your data. Now that your data is loaded there are two ways to view the data set in memory. One way is to click on Edit data set and the other is to click on View data set. Below is the result of clicking on Edit data set. As the name implies by accessing this function you can edit the data directly. If you were to click on View data set the data would have appeared in grayscale and editing would not be allowed. 16

18 Tips if you are having problems importing Excel data. Theoretically, you can import your data from Excel into R using R Commander similarly to the way in which we demonstrated importing it from SPSS above: I have sometimes experienced problems with this, although they appear to have been resolved in the most recent release. In case you have problems, here are two tips: (1) Sometimes R has trouble handling spaces. If you are having trouble importing an Excel file, try getting rid of spaces. Remove all spaces in the path to the file and in the file name. If you are still having problems, you can try removing spaces from the variable names, and in the values themselves. Values like Trinity University or Grinnell College would become TrinityUniversity and GrinnellCollege. In older versions you would sometimes end up with the pieces on either side of the space in different columns in your R data set. You can remove spaces easily in Excel by finding and replacing all spaces with nothing. In the Find and Replace window I have entered a space in the Find what box and left the Replace with box blank: 17

19 Here is the result after clicking Replace All: (2) Older versions of R and/or R Commander have sometimes crashed when I have tried to import data from Excel. You can try to skin this cat by first saving the data from Excel in comma delimited format (CSV) and then importing into R/R Commander. To do this, select CSV (comma delimited) from the Save as type drop down when saving your Excel file: Then import into R/R Commander as a text file: Similarly, you can try saving the Excel as Formatted Text (space delimited) and importing. Either way, be sure to view your data and check it carefully once it is imported to be sure it imported correctly. Again, these issues appear to be resolved in the most current versions. 18

20 Summarizing Data Quick Summary of Active Data Set. A feature I like to use is Statistics Summaries Active data set. This feature will provide you with descriptive statistics for all variables. Numerical Summaries. R makes getting descriptive statistics fairly simple. Clicking on Statistics Summaries Numerical summaries will produce the pop-up menu below. 19

21 Select the variables (hold down the CTRL key to get more than one) and the summary statistics that you want. Click OK. 20

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23 You may want separate sets of summary statistics for the different values of a categorical variable. For instance, we might want to see numerical summaries of SAT scores separately for students who were retained to year two and students who were not. To do this in R Commander, first select SAT as the variable, select the summary statistics you want, then click on Summarize by groups and select RetainedToYr2. Here s what the output looks like: 22

24 Tables. You can create tables of counts or percents and conduct common tests such as a Chi-square test on the tabulated data. To create a two-way table using R and R Commander, select Statistics-> Contingency tables -> Two-way table. Here I am creating a table of the dichotomous variable indicating whether the student was retained to year two or not versus the recoded variable we created above indicating whether the student s SAT score was low, medium, or high. I have chosen to view the output in terms of row percentages. (To get counts, select the No percentages option.) I have also opted to perform a Chi-square test of independence. 23

25 Results appear in the output window. Similarly, you can create multi-way tables. Here is an example of adding a gender dimension to the table we created above. In this example I have chosen to display counts instead of percentages. 24

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27 Aggregating Data. In the descriptive statistics section above we computed summary SAT statistics for those students who were retained to year two and those who were not. R can easily compute aggregate statistics (such as mean, median, sum, standard deviation, min, max, etc.) for any numerical variable by the values of a categorical data and store those aggregated values in a new dataset. In this example, I have computed the mean SAT and mean financial need for students who were retained and for those who were not. NOTE: If we select the Other (specify) option, then we can specify our own function. Commonly used built in functions in R are median, sd, min, or max. There is not a count function if you want counts, see the section in this document on Tables. 26

28 A new data set called AggregatedData is created and looks like this: Again, this button will allow you to toggle among the data sets that R is now holding in memory (we are currently at 3) NOTE: The note tells us that the new data set has 2 rows, and 3 columns as you can see in the previous screen shot. 27

29 Creating New Variables Recoding a variable. As an example of how to recode a variable, we will create a factor variable (R speak for string or categorical) that segments students based on SAT into low, medium, and high categories. Click on Data Manage variables in active data set Recode variables this will lead to a pop up box titled Recode Variables appearing. 28

30 When the pop up box to the left ( ) appears, the next few steps are simple. In some ways R thinks much like SPSS. Here I recode the SAT variable into an ordinal variable with three values the bottom quartile, the middle 50 th, and the top quartile. Here I state value ranges with a : and else. NOTE: R is very sensitive to spaces don t use them! Computing a new variable. To create a new numerical variable that is, for instance, a weighted combination of GPA and SAT we click on Data Manage variables in active data set Compute New Variable this will lead to a pop up box titled Compute New Variable appearing. 29

31 The syntax used for the recode and computed variable are here 30

32 Using the View data set button, we can see that our data set now looks like this: 31

33 R comes equipped with features that will automatically recode variables. Data Manage variables in active data set Standardize variables will produce standardized variables. Once the pop-up below appears select the variables that you would like to standardize (convert to z- scores). Hold down the CTRL key to select multiple variables. Click OK. Once the transformations are complete, the note window will update you as to the number of columns now in the data set. I added standardized versions of AcadIndex, FinNeed, GPA, and SAT, so there are now 14 columns. 32

34 Filtering Data In R, the closest thing to the Select Cases feature in SPSS is Subset. There are two ways to Subset the data in R. One way is to select columns and make a new data set of them. The second way is to select rows, or to filter the data based on some predetermined criteria. This process will make a new dataset. Suppose we only wanted to look at students who had SAT scores in the lowest quartile. We would click on Data Active data set Subset active data set. Once this pop-up appears we have to write the expression that will allow us to subset the data. R uses the following logical operators: Logical Symbol Name/Use == Equality/Indicates equal to!= Inequality/Indicates not equal to & And Or is.na(varname) Include missing!is.na(varname) Exclude missing > Greater than < Less than >= Greater than or equal to <= Less than or equal to 33

35 The syntax SAT<=1220 told R to create a new data set where only the students with an SAT score less than or equal to the 25 th percentile are included. I named the new data set LowSAT. (Note that alternatively we could have used the recoded variable, SATlmh, that we created above and the subset expression SATlmh== low.) R both produces this new data set and loads it. It also will tell you how many observations met your criteria in the NOTE section. NOTE: My intention was to keep all the variables in the data set so I left the box Include all variables checked. And my new data set includes 185 students and 14 variables. 34

36 To toggle between data sets click here and a pop-up will appear allowing you to flip back and forth. There is a lot of information in the NOTEs! 35

37 Graphing. Graphing is fairly straightforward in R Commander. To create a histogram click Graphs Histogram You have choices when asking for a histogram. You can plot either the Frequency counts, Percentages or the Densities. (In a density histogram, the vertical axis is labeled so that the total area in the histogram s bars is 1.) Your histogram will appear as a window in the console screen in its own pop-up box to the left of where you first typed in library(rcmdr). The graphing features of R are top rate, although only a limited number of them are available through R Commander. Once you get comfortable with R Commander, you may want to explore altering the syntax it generates to modify your graphs. (More on this later in the Moving Beyond R Commander to R section.) 36

38 Next we create a scatterplot. By default, the options that are checked as options in the scatterplot GUI are Marginal boxplots, Leastsquares line, Smooth line (a locally weighted polynomial regression known as LOESS), and Show spread (confidence interval). I have entered x-axis and y-axis labels. 37

39 To plot multiple scatterplots of various pairs of your variables at the same time you can use the scatterplot matrix option. Here we have plotted every combination of GPA, SAT, and FinNeed (financial need). Since the diagonal of the scatterplot matrix would not be useful (e.g., SAT versus SAT would be a straight line), you can select to use the space on the diagonal for a variety of different plots of each single variable. Here we have chosen to create density plots, which are an excellent way of determining the distribution of your data. 38

40 Inferential Statistics T-test. The T-test is probably the most widely used hypothesis test in statistics. Here we test to see if the mean SAT score of students retained to year two is significantly different from that of those not retained. To conduct a T-test is fairly straightforward. Click on Statistics Means Independent samples t-test 39

41 Once this pop up appears select the variable that you want to test. I wanted to know if the average SAT differed by whether or not the student was retained to year 2. 40

42 All of your pertinent information will appear in the output window. 41

43 Least squares regression. To conduct an ordinary least squares regression, click on Statistics Fit Models Linear Regression In R, the response variable is the dependent or Y variable. I asked R to estimate a regression model with GPA as the dependent variable using financial need and SAT as independent variables. All your important information appears in the output window. R even stars your variables based on the level of significance. Much like data sets, R will store multiple regression models in memory! All of your model statistics can be found here! 42

44 Logistic Regression. To conduct a logistic regression analysis in R using R Commander, select Fit models -> Generalized linear model. (A logistic regression model is a subclass of a broad class of models known as Generalized Linear Models.) You can select a name for your model I chose Retention. Then you will need to select the variables to use in your model. I first double-clicked on the variable RetainedToYr2 [factor] since that is my dependent or Y variable in this logistic regression. I then double clicked on SAT, GPA, and Gender to create the model statement you see below. In the Family box, select binomial. (For a logistic regression, the dependent variable is a yes-no or 0-1 type variable.) In the link function section, double click on logit. Then click OK. For a logistic regression, select the binomial family and the logit link function. 43

45 Most of the output for this example is shown below. (You need to scroll down the Output Window to see it all.) 44

46 IV. MOVING BEYOND R COMMANDER TO R R provides an extensive array of statistical analyses and graphics, only a subset of which can be utilized through the point-and-click GUI interface of R Commander. In this section, we show how you can start to tap into the larger capabilities of R that are beyond those included in R Commander. Example: Create a snazzy graph! R has extraordinary graphing capabilities, only a small number of which are made available through the R Commander interface. We created a fairly plain looking histogram of financial need above. Let s see how we could make that look more professional. Here s the code produced by R Commander when we created the histogram: Notice that the syntax R Commander generated calls the Hist function, specifies the FinNeed variable in the HEDS data set (HEDS$FinNeed), specifies frequency as the scale, specifies Sturges as the breaks (i.e. the method used to determine the number and sizes of the bins in the histogram), and specifies darkgray as the color (col). First, let s tweak this code to get a different color than darkgray. To do this, I need to know what color names are available. A Google search of R colors produces numerous links to sites and documents listing colors available in R. Seven pages of color swatches and their R names can be found at I would like my histogram to be blueviolet instead of darkgray. To do this, I modify the syntax in the Script Window, changing darkgray to blueviolet : 45

47 I then select the modified Hist statement in the Script Window and click on the Submit button. 46

48 In the R Console window (where you originally typed the command library(rcmdr)) you will now see your new blueviolet histogram: Alternatively, you could have typed (or copied and pasted) the entire Hist statement directly into the R Console, and hit enter, to get your histogram without ever using R Commander: 47

49 We have just made a histogram using a value for the color (col) argument that was not available through R Commander. Similarly, we can incorporate arguments into our hist statement that are not available through R Commander. To get the details of the Hist function, we can type help(hist) into the R Commander Script Window, select it, and then hit the Submit button: This will automatically take you to the website with the information about the histogram function, Here I find that the hist function has arguments main, xlab, and ylab for giving titles to the main histogram and the x and y axes. Below I have modified my hist function to use these arguments. I have done my work in the R Console, but you could have done it in the R Commander Script Window: 48

50 Here is my new histogram: The hist command for this histogram has gotten long it s hard to read in the screen shot above. If you try to start a new line by hitting enter, what you actually do is submit the command! To be able to split long syntax onto multiple lines, you need to enclose it in curly brackets {}: 49

51 R automatically created the + at the beginning of the second line of my long command. V. USEFUL WEBSITES The search function in your favorite browser can be very useful in finding materials for using R and R Commander. Many users have posted web sites, blog entries, documents of various types to help others use R and R Commander. Here are a few sites we suggest: R Commander: Fox, John (the author of R Commander), Getting Started With the R Commander. Started with the Rcmdr.pdf Karp, Natasha A., R Commander an Introduction. project.org/doc/contrib/karp Rcommander intro.pdf. Scott, Theresa A., An Introduction to the R Commander : A Basic Statistics Graphical User Interface (GUI) for R. R Commander Tutorial (no author listed) is available at commander tutorial1.pdf R: A manual on R for Beginners by Emmanuel Paradis is available at rdebuts_en.pdf. The manual helps explain how R works. If you are interested in making the move from R Commander to R, this would be a good place to begin. The Introducing R website by German Rodriguez of Princeton University, may be helpful to you. 50