1. An introduction to R: get familiar with R Guangxu Liu Bio7932

2. 1. Download and installation of R 2. Get familiar with R 3. Input data and read data from file 4. Commonly used R code 5. Something need to be aware 6. Reference for R Step by step proc. Outline

3. 1.Downloading R official website: www.R-project.org www.R-project.org Where to download R.

4. After we choose the correct server and computer system, we will get something like below. What we want is here!

5. Choose again?!! Here it is!

6. Installation: Proc: Simply use the download installation file. Simply use the download installation file. Available options: Choose install direction Choose install direction language (English, simple Chinese only) ? language (English, simple Chinese only) ? Set up the methods for viewing help information(html, txt, chm) Set up the methods for viewing help information(html, txt, chm) Select components, customize …. Select components, customize ….

7. 2. Get familiar with R (Sorry about using chinese version) File; Edit; Others; Package; Help function Basic introduction of R, such as Version… Working place, where we write code, see the verbal or numerial results

8. 3. Input data and read data from file Basic proc: Basic proc: x<-function() x<-function() x=data name; <- assignment; function()=the methods for reading data x=data name; <- assignment; function()=the methods for reading data Function(): Function(): c() for example, x<-c(1,2,3,4,5) c() for example, x<-c(1,2,3,4,5) read.table() read.table() scan() scan() Read data using Rcmdr package Read data using Rcmdr package

9. Examples: Read data with Rcmdr package Load package from package option

10. Rcmdr

11. Rcmdr windows (right windows below)

12. Rcmdr-Data-Inport data (Thanks god, Rcmdr is in English!) File can be read by Rcmdr includes txt,spss,mintab, stata,excel….

13. For example: open the excel file contains the data, select the data you want to input and copy it by right click and copy or ctl+c. Then switch back to Rcmdr windows and choose data input from txt & clipboard

14. Data name 1.Variable name in first line or not 2. NA How the data are separated (Tabs here for excel)

15. All setting looks fine now Read the data

16. The R code for what we did Output window? Do not show graphic results General information of the data

17. An example show how t-test results displayed in output windows T test R code Results form the t-test

18. Lot ’ s functions here, such as make graph, load statistics package

19. R code for box plot If we make graph (box plot for example)

20. Graph result shows in R graphics window (right windows)

21. View data and edit your data View data window shows the data View data and edit data after data input

22. Edit data in the data edit windows

23. 4. Commonly used R code ls() show all the available data (objects) ls() show all the available data (objects) in the work space or memory in the work space or memory rm() delete data, remove objects rm() delete data, remove objects ? get online help about *** ? get online help about *** lm() fit a simple regression and look at it lm() fit a simple regression and look at it resid() residuals from one model resid() residuals from one model fitted() fits from one model fitted() fits from one model plot() scatter plot plot() scatter plot 5. Something needs to be kept in mind Capital letter matters: v≠V Data (Objects) are vector or matrix Some examples

24. VCL and vcl are different data objects Commends: don’t use capital letter at all is a good idea Use ls() show the data in workspace

25. Use “?ls” to learn more about ls() fuction Online help (don’t need internet connection in fact)

26. Use ?lm learn more about lm() There are lot’s components in lm() Details about all those components

27. A simple example (without data input part) Step 1: Read and built a new data with name f Use f to view the data f in workspace Data from fertilization data of Liu (2006), and we know there should have a simple linear relationship between ln and density. Density=log(sperm/egg density) ln=log(fertilized egg/unfertilized egg). So the model will be ln=a+b*density+error

28. Use fm<-lm(ln~density,data=f) to build up the linear model fm<-lm(ln~density,data=f) Assign fm as the linear model Model is based on ln and density, use ln as dependent and density as variable There ln and density come from

29. Use fm to view fm Shows what’s fm

30. Use summary(fm) to see more details Details of fm, including df, SE, t value and P

31. Use plot(fitted(fm),resid(fm),xlab="fitted",ylab="residual",main="residual vs fits") check residual vs fits. Graph shown on the right side window Use plot(fitted(fm),resid(fm),xlab="fitted",ylab="residual",main="residual vs fits") check residual vs fits. Graph shown on the right side window Here xlab(),ylab(),main() are R codes to give x,y and the whole graph labels.

32. 6.Reference for R (http://www.r-project.org) This makes me change my presentation title to “An introduction to R: get familiar with R” 1.Free reference on R official website 2. Practical Regression and Anova using R, Julian J. Faraway, 2002

33. Thanks Picture above from www.R-project.org