An Introduction to the R Commander

Transcription

1 An Introduction to the R Commander BIO/MAT 460, Spring 2011 Christopher J. Mecklin Department of Mathematics & Statistics Biomathematics Research Group Murray State University Murray, KY christopher.mecklin@murraystate.edu March, 2011

2 Outline 1. What is R and the R Commander? 2. Downloading and Installing the R Commander 3. Data Handling 4. Descriptive Statistics 5. Basic Graphs 6. Hypothesis Testing (t-tests and χ 2 tests) 7. Linear Models and Beyond

3 What is R? R is an open-source statistical programming environment that is available for free. R is similar to S, a statistical programming language developed at Bell Labs. The language is object-oriented and its syntax will remind the user of C++. In addition to the core, many many users have added onto R by submitting packages to perform many statistical tasks (over 2700 packages the last time I checked!).

4 What is R Commander? Apparently not everyone is fond of writing computer code. Some people would perform a graphical user interface for statistical analysis. The Rcmdr package, written by John Fox, provides such a GUI for R. This package utilizes many other R packages and can perform most standard statistical analyses, although more specialized tasks (and some basic ones as well ) cannot be performed with R Commander.

5 The Comprehensive R Archive Network CRAN (the comprehensive R Archive Network) is hosted on servers all around the world and is the home of all things R. Google CRAN or go to the URL

6 The Rcmdr package Once you have downloaded and started R (the desktop icon is a big blue R, go to Packages, then Set Cran mirror... Select a mirror site close to you.

7 The Rcmdr package Go to Packages, then Install Package(s)... Choose Rcmdr. Once that is done, type in library(rcmdr). If you get a prompt to download required packages, say Yes. Be patient, this might take several minutes. Don t try this with dial-up or DSL!

8 Getting Started Once you type in library(rcmdr), the following window should appear:

9 Now What? You need to get your data into R Commander before you can do any analyses. There are several options for data import, including: 1. Directly typing in data into the built-in data editor 2. Using a dataset built in to a R package 3. Importing data from a text file 4. Importing data from an EXCEL spreadsheet

10 Data Editor Go to Data, then New data set... to pull up the built-in data editor. I do not use it very often, as it is a bit difficult to use and I find it easier to enter all but the smallest data sets in a spreadsheet or a plain text file.

11 Built-in Datasets There are a number of built-in datasets in R that are handy for self-learning or teaching purposes. Go to Data, then Data in packages..., then Read data set from an attached package.

12 Importing from a text file Go to Data, then Import data, and then from text file, clipboard or URL... Name the data set you are creating, and choose the Field Separator for your delimiter (space, tab, comma, etc.) Use the first row for variable names.

13 View the data set I ALWAYS check to make sure the data has been properly imported. Click on the View Data Set button.

14 Importing from EXCEL You have have the need to import an EXCEL Spreadsheet into the R Commander. Choose Data, then Import data, and then from Excel, Access or dbase data set... Use first row for variable names.

15 Summary Statistics Descriptive statistics such as means, standard deviations, medians, and quartiles can be easily found by Statistics, then Summaries, and then either Active data set or Numerical Summaries...

16 Correlations Choose Statistics, then Summaries and Correlation matrix... Hold down the CTRL key while left-clicking your mouse to select which variables you want to use. > cor(uscereal[,c("calories","carbo","fat","protein")], use="complete.obs") calories carbo fat protein calories carbo fat protein

17 Graphs menu Many basic statistical graphs, including but not limited to: 1. Histograms 2. Scatterplots 3. Boxplots 4. Plot of means with error bars can be created under the Graphs menu. The graph will appear in the R Graphics window and can be saved in several formats by clicking on File and Save as. LaTeX friendly formats such as Postscript and PDF and Microsoft Office (blech!) friendly formats such as bmp, png, and jpeg are available.

18 Compare this elegant R scatterplot... Longevity (years) Gestation period (days)

19 With the ugly EXCEL version! 45 Longevity Longevity 20 Linear (Longevity)

20 Hypothesis Tests In BIO/MAT 460, we have studied the following hypothesis tests: 1. Independent samples t-test, assuming equal variances 2. χ 2 Test of Independence 3. χ 2 Goodness-of-Fit Test Unfortunately, the χ 2 Goodness-of-Fit test is not built into the R Commander, so you ll have to use the formulas. Sorry!

21 t-test The text file ExamScores has final exam scores from two groups; students that took the final on Monday (class== M ) and students that took it on Friday (class== F ). I will read in the text file and then will go to Statistics, Means, Independent samples t-test...

22 t-test output > t.test(exam~class, alternative= two.sided, conf.level=.95, var.equal=true, + data=examscores) Two Sample t-test data: exam by class t = , df = 33, p-value = alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: sample estimates: mean in group F mean in group M

23 χ 2 test of independence We are interested in testing if there is a significant relationship between result of a test of HPV (human papillomavirus) and age category. The following 2 6 contingency table contains data from the U.S. NHANES survey. Age Group HPT Test Positive Negative

24 Contingency Table Go to Statistics, then Contingency Tables, and Enter and analyze two-way tables... Enter the data.

25 χ 2 test output > rownames(.table) <- c( 1, 2 ) > colnames(.table) <- c( 1, 2, 3, 4, 5, 6 ) >.Table Counts >.Test <- chisq.test(.table, correct=false) >.Test Pearson s Chi-squared test data:.table X-squared = , df = 5, p-value = 1.155e-07 >.Test expected Expected Counts

26 What else can R Commander do? While that s probably enough for today, the R Commander can also do the following: 1. Simple and multiple linear regression 2. ANOVA 3. Generalized linear models (including logistic & Poisson regression) 4. Many nonparametric and multivariate methods

27 What can t R Commander do? There are many statistical procedures that are not built in to the R Commander. If you have the need for them, options include: 1. Writing R scripts (i.e. programs) and using the R console, with either the base packages or a package you downloaded. vegan is an example of a package not part of the base R distribution that I use frequently, mainly for computing biodiversity statistics. 2. People are starting to create Plug-Ins to add more features to the GUI. An example is RcmdrPlugin.survival, which adds standard survival analysis methods like logrank tests and Cox regression. 3. You are always able to write your own programs in case no packages or plug-ins exist or you just prefer to! 4. Thank you!