1. Vaccination data analyses with R and R commander Lars T. Fadnes & Halvor Sommerfelt Centre for International Health University of Bergen

3. Aim for this session Introduce a brilliant tool Give examples on use of the tool Guide to further knowledge

4. What is R? R is a free software environment that includes a set of base packages for graphics, math, and statistics. You can make use of specialized packages contributed by R users or write your own new functions.

5. Why R? Very powerful Developing extremely quickly Working on different platforms (not only Microsoft Windows…) Free of all costs

6. Why don’t all use R?

7. What is R commander?

9. Why R commander? Powerful Free of all costs Working on different platforms (not only Microsoft Windows…) Easy to learn and to use…

10. How to install R? For Windows: http://cran.r-project.org/bin/windows/base/ –Easy If here at UiB, the information department will fix it for you if you just ask them to add it for you For Linux (Ubuntu etc): -Very Easy -Just search for R-base-core in Synaptic Package Manager and add it -http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/installation-notes.htmlhttp://socserv.mcmaster.ca/jfox/Misc/Rcmdr/installation-notes.html

11. How to install R commander? -install.packages("Rcmdr", dependencies=TRUE)

12. Some things to note first R is case-sensitive –help, Help, HELP and HELF are different… –Recommendation: Choose one style and stick to it Avoid using shortcut keys in R commander! If it’s something you don’t know? –There are lot’s of good information on the web Particularly for R

13. How to start? Open R –Load packages Rcmdr or write library(Rcmdr)

14. Menu File Menu: –items for loading and saving script files; –for saving output and the R workspace; –and for exiting Edit Menu: –items (Cut, Copy, Paste, etc.) for editing the contents of the script and output windows. –Right clicking in the script or output window also brings up an edit “context” menu. Data –Submenus containing menu items for reading and manipulating data. Statistics –Submenus containing menu items for a variety of basic statistical analyses.

15. Menu Graphs –Menu items for creating simple statistical graphs. Models –Menu items and submenus for obtaining numerical summaries, confidence intervals, hypothesis tests, diagnostics, and graphs for a statistical model, and for adding diagnostic quantities, such as residuals, to the data set. Distributions –Probabilities, quantiles, and graphs of standard statistical distributions (to be used, for example, as a substitute for statistical tables) and samples from these distributions. Tools –Menu items for loading R packages unrelated to the Rcmdr package (e.g., to access data saved in another package), and for setting some options. Help Menu –items to obtain information about the R Commander (including this manual). As well, each R Commander dialog box has a Help button (see below).

16. Script Window –R commands generated by the R Commander –You can also type R commands directly into the script window or the R Console –The main purpose of the R Commander, however, is to avoid having to type commands. Output Window –Printed output Messages Window –Displays error messages, warnings, and notes Graphics Device window –When you create graphs, these will appear in a separate window outside of the main R Commander window.

17. Easily available function:

19. Let’s get started… Change directory –Find the folder where you placed your data file Import data –Give it the name: Vaccine Save workspace as –Give a name to your file

20. Data Types Vectors –including continuous variables Factors –Nominal/ categorical Matrices, arrays and data frames Lists http://www.statmethods.net/input/datatypes.html

21. Define the datatypes Variables in dataset IDNO –ID number:categorical = factor Adjuvant –Categorical = factor: 0: Placebo1: Adjuvant Day0 –Continuous (vector) Day30 –Continuous (vector)

22. –Variables are now defined as vectors –Factors (categorical variables needs to be defined)

23. Now we’re ready to answer some scientific questions…

24. Vaccination response Effect of vaccination is often calculated by measuring antibodies before and after vaccination –Response = concentration after vaccination concentration before vaccination Compute new variable: response –concentration before vaccinationDay0 –concentration after vaccinationDay30

25. Does the new variable look reasonable? View data set Histogram

26. Summarize variable Calculate mean, median and standard deviation for –response for each group Numerical summaries

27. Placebo: adjuvant = no Adjuvant: adjuvant = yes Mean, standard deviation, the percentiles and number of cases

28. Doing calculations for subset group

29. Before making the ”Placebo” subset, remember to select main dataset (Vaccine) –You can now easily change between the datasets

30. Make a histogram of the response First for the adjuvant dataset Then for the adjuvant dataset And for the placebo dataset –The histograms will be printed in the R window (not inside R commander) –Right click on the graph and you can copy it as metafile to paste it into a document, print it or save it

31. Box plot Box plot for response –By AdjuvantCat –(for complete Vaccine dataset)

32. Does it look normally distributed?

33. We might need to do a logarithmic transformation of the variable –Compute new variable: logresponse Does it look more normally distributed (histogram)?

34. Check the logresponse Are the means and medians similar for both groups (placebo and adjuvant)? –Numerical summary (as earlier, but with logrespons) Check with an independent sample t-test –Adjuvant vs logresponse –Is the response different among those getting adjuvant?

35. Will this be confirmed with a robust test not assuming normal distribution? Check with a –non-paramethric »two-sample wilcoxon test (log rank test) »Use the ’Exact’ test –Will the test be different when using response or logresponse »Why or why not?

36. Recoding and making new variables Different types of examples on how to recode in the box: –Data  manage variables value = ”factor” –Factor can be either number or word value, value, value = ”factor” –Listed with comma value:value = ”factor” –From lowest to highest values elseall other values NAmissing

38. How was the baseline immunological response (before vaccination)? –Variable: Day0 Numerical summary Let us take a look into the upper quartile specifically –Make subsets: ”UpperQuartile” ”OtherQuartiles”

39. Is baseline response associated with response after intervention? We can first repeat the t-tests for the subsets ”UpperQuartile” ”OtherQuartiles” –Independent sample t-test »logresponse »AdjuvantCat

40. What is the results? –What are the differences between the means What does it mean?

41. Is intervention associated with response? Linear regression analysis with the full dataset –Statistics  Fit model  Linear model Dependant variable (left box): –logresponse Independent variable: –AdjuvantCat

42. Is baseline response associated with response after intervention? Linear regression analysis with the full dataset –Statistics  Fit model  Linear model Dependant variable (left box): –logresponse Independent variables (right box with ’+’ between each): –AdjuvantCat –UpperQuartileDay0

43. Is baseline response associated with response after intervention? Linear regression analysis with the full dataset –Statistics  Fit model  Linear model Dependant variable (left box): –logresponse Independent variables (right box with ’+’ between each): –AdjuvantCat –Day0 (continuous)

44. What will you conclude? Is adjuvant/placebo associated with response? Is initial immune response associated with response after vaccination?

46. How to save? Save R workspace as… –This will save your data (in the R format) Save output as… –This will save your output –Another strategy is to cut and paste what you want to save Always save the commands –essential if you want to re-run the analyses later –WordPad is a better option than Word etc (does not autocorrect - change to upper case etc)

47. How to write and a command? Simply write the command in the script window, mark it and click ’Submit’ or press Ctrl+R

48. Nice to know: When writing comments in the syntax, start with the following sign# If you are uncertain about a function, use google or help(name-of-function)

49. Are there differenses in syntax between R and R commander? Some few: –Commands that extend over more than one line should have the second and subsequent lines indented by one or more spaces or tabs; all lines of a multiline command must be submitted simultaneously for execution. –Commands that include an assignment arrow (<-) will not generate printed output, even if such output would normally appear had the command been entered in the R Console [the command print(x <- 10), for example]. On the other hand, assignments made with the equals sign (=) produce printed output even when they normally would not (e.g., x = 10). –Commands that produce normally invisible output will occasionally cause output to be printed in the output window. This behaviour can be modified by editing the entries of the log-exceptions.txt file in the R Commander’s etc directory. –Blocks of commands enclosed by braces, i.e., {}, are not handled properly unless each command is terminated with a semicolon (;). This is poor R style, and implies that the script window is of limited use as a programming editor. For serious R programming, it would be preferable to use the script editor provided by the Windows version of R itself, or – even better – a programming editor.

50. True or false quiz R was the program that gave their shareholders most profit last year? R commander only works for Ms Windows? R has even more functions than R commander? R is built by a few people with a secret source-code? There are a lot of enthusiastic people working with R providing help to their peers in the R forum?

51. Further reading: The R Commander A Basic-Statistics Graphical User Interface to R - John Fox 2005.pdf –http://www.jstatsoft.org/v14/i09/paperhttp://www.jstatsoft.org/v14/i09/paper Getting started with the R Commander: a basic-statistics graphical user interface to R –http://socserv.mcmaster.ca/jfox/Getting-Started-with-the-Rcmdr.pdfhttp://socserv.mcmaster.ca/jfox/Getting-Started-with-the-Rcmdr.pdf Quick-R: magnificent guide –http://www.statmethods.net/http://www.statmethods.net/ http://cran.r-project.org/manuals.html

52. R help forum: http://r.789695.n4.nabble.com/

54. You have learnt some basic skills and can now experiment with the program yourself

55. Questions and comments