R commander an introduction

Transcription

1 R commander an introduction free, user-friendly, and powerful software Ho Kim SCHOOL OF PUBLIC HEALTH, SNU

2 Useful sites R is a free software with powerful tools The Comprehensive R Archives Network -> Windows -> base -> Download R for Windows Textbook : Simple R by John Verzani

3 Features of R R is free. R is open-source and runs on UNIX, Windows and Macintosh. R has an excellent built-in help system. R has excellent graphing capabilities. Students can easily migrate to the commercially supported S-Plus program if commercial software is desired. R's language has a powerful, easy to learn syntax with many built-in statistical functions. The language is easy to extend with user-written functions. R is a computer programming language. For programmers it will feel more familiar than others and for new computer users, the next leap to programming will not be so large.

4 Starting the R

5 Data manipulation Data input Data types Importing data Exporting data Viewing data Value labels Missing data Data management Variables Operators Sorting data Merging data Subsetting data Source: (Quick r)

6 [Data Input] Data types 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 a[c(2,4)] #2nd and 4th elements of vector Matrices # generates 5 x 4 numeric matrix y<-matrix(1:20, nrow=5,ncol=4) y[,4] # 4th column of matrix y[3,] # 3rd row of matrix y[2:4,1:3] # rows 2,3,4 of columns 1,2,3

7 [Data Input] Data types Dataframes 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 Lists # 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) Factors gender <- c(rep("male",20), rep("female", 30)) gender <- factor(gender) # R now treats gender as a nominal variable summary(gender)

8 [Data Input] Importing data From CSV file malaria <-read.table("c:\\r_data\\malaria.csv", header=true, sep=",") From Excel library(rodbc) channel <- odbcconnectexcel("c:\\r_data\\malaria.xls") malaria <- sqlfetch(channel, "mal") *odbcconnectexcel is only usable with 32-bit Windows From txt file malaria <- read.table("c:\\ R_data\\malaria.txt", header=true, sep="\t")

9 [Data Input] Exporting data To an CSV file write.table(malaria, "C:\\ R_data\\mal01.csv", row.names=f) To a tab delimited text file write.table(malaria, "C:\\ R_data\\mal02.txt", sep="\t", row.names=f)

10 Viewing data ls() # list objects in the working environment names(malaria) # list the variables in malaria str(malaria) # list the structure of malaria levels(malaria $v1) # list levels of factor v1 in malaria malaria$v1<-factor(malaria$mal) dim(malaria) # dimensions of an malaria class(malaria) # class of an malaria (numeric, matrix, dataframe, etc) malaria # print malaria head(malaria, n=10) # print first 10 rows of malaria tail(malaria, n=5) # print last 5 rows of malaria summary(malaria)

11 Value labels # variable v1 is coded 1, 2 or 3 # we want to attach value labels 1=red, 2=blue, 3=green v1<-c(1,1,1,2,2,3) v2 <- factor(v1, levels = c(1,2,3), labels = c("red", "blue", "green"))

12 Missing data Testing for missing values y <- c(1,2,3,na) is.na(y) # returns a vector (F F F T) Recoding values to missing malaria[malaria$age==99, age"] <- NA Excluding missing values from analyses x <- c(1,2,na,3) mean(x) # returns NA mean(x, na.rm=true) # returns 2

13 Help > help(mean) >?mean

14 Data manipulation Data input Data types Importing data Exporting data Viewing data Value labels Missing data Data management Variables Operators Sorting data Merging data Subsetting data

15 [Data management] Variables Recoding variables # create 2 age categories malaria$agecat <- ifelse(malaria$age >7, c( student"), c( baby")) attach(malaria) malaria$agecat2[age > 7] <- "student" malaria$agecat2[age <= 7] <- "baby" detach(malaria)

16 [Data management] Operators Comparison operators == equals!= not equals <= less than or equals >= greater than or equals = assignment (same as <- ) Logical operators & and or! not

17 [Data management] Sorting Data # sort by mal newdata <- malaria[order(malaria$mal),] # sort by mal and age newdata2 <- malaria[order(malaria$mal, malaria$age),] #sort by mal (ascending) and age (descending) newdata3 <- malaria[order(malaria$mal, -malaria$age),] Avoid Attach command when sorting the data

18 [Data management] Merging Data Raw dataset malaria2<-read.table("c:\\r_data\\malaria.csv", header=true, sep=",") Adding rows extra<-read.table ("C:\\R_data\\extra15.csv",header=T, sep=",") malaria3<-rbind(malaria2,extra) Adding columns region<-read.table ("C:\\R_data\\region.csv", header=t, sep=",") malaria4<-merge(malaria3, region, by="subject")

19 [Data management] Subsetting Data mal.1 <- subset(malaria,mal==1) summary(mal.1) mal.baby <- subset(malaria, mal == 1 & age < 8)

20 Installing R commander You need to first install R and then R commander.

21 Starting the R commander > library(rcmdr)

22 R commander windows

23 Importing datasets

24 Select the data set by clicking on this box

25 Checking continuous variables Statistics->Means options Single-sample t-test Independent samples t-test Paired t-test One-way ANOVA Multi-way ANOVA

26 Q Read Pepers.xls. Test whether the mean of angle is zero or not. Write down the null and alternative hypotheses. 26

27 single-sample t-test (Pepers.xls) Statistics > Means > Single-sample t-test (Enter the proposed mean (Null hypothesis: mu=))

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29 1.2 Suppose that the mean of angle is already known to be 2. And you wan to claim that it is not true based on your data. Write down your hypotheses for this claim. * Perform statistical test with R commander. What do you conclude? 29

30 single-sample t-test (Pepers.xls) Statistics > Summaries > Shapiro-Wilk test of normality This is a hypothesis tests with the null hypothesis that the data comes from a normal distribution.

31 Q Read pulse.xls. What kinds of test can be used to see the difference between pre and post variables. * Write down null and alternative hypothesis. 2.2 Perform parametric and non-parametric tests for the above hypothesis using R commander. What do you conclude? 31

32 paired t-test (Pepers.xls) Statistics > Means > Paired t-test

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34 paired t-test (Pepers.xls) Statistics > nonparametric tests > Pairedsamples Wilcoxon test

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36 Q What would be the purpose of analyzing insul.xls. 3.2 Perform explanatory analysis (Statistics>Summaries) using R commander. Interpret the results. 3.3 What are the hypotheses to compare the glucose levels of 5 groups? 3.4 Perform ANOVA using R commander and interpret the results. 3.5 Perform post-hoc analyses to explain the differences between groups. 3.6 How would you compare group A (conc=1,2) and group B (conc=4,5)? Do this using R commander. 36

37 insul.xls Effect of glucose concentration on Insulin Measured the amount of insulin secretion after administration of five different concentrations of glucose into pancreatic tissue (animal experiments) Characteristics for each group Statistics > Summaries (according to the study objective) Graphs (according to the study objective) variable conc must be declared as a factor variable! 37

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40 Conc 1,2 < 3 < conc 4,5 Graphs->Boxplot

41 insul.xls One-Way ANOVA Statistics > Means > One-way ANOVA Pairwise comparisons of means Tukey post-hoc comparison procedure (default)

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43 t-test for (1,2) vs (4,5) comparison Re-define variables Data > Manage variable in active data set > Recode variables > select conc variable New variable name or prefix for multiple recodes : new Enter recode directives 1:2=1; 3=NA; 4:5=2 conc=3 as a missing Equality of variance test should be carried out before the t-test Statistics > Variances > Two variances F-test the variances are equal Statistics > Means > Independent samples t-test Mean concentration difference between two new groups (variances are assumed to be equal) Significant Insul.xls 43

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45 Variance ratio test of the two groups: Statistics > Variances > Two variances F-test

46 Independent samples t-test (equal variances)

47 Insul.xls Nonparametric way of comparing (1,2) vs (4,5) Statistics > Nonparametric tests > Two sample Wilcoxon test 47

48 taillite2.sav data vehtype='vehicle Type group='group - Light On=1 Light Off=2 position='light Position speedzn='speed Zone resptime='response Time follotme='following Time in Vedio Frames folltmec='following Time in Categories ; resptme(continounous) difference by Vehtype(dichotomous) variable=> Analysis of variance? Looking at only Group=1 48

49 Q Read taillite2.sav. What is the aim of analyzing this data? 4.2 Apply ANOVA to see the differences of resptime by the level of vehtype. 4.3 Test the normality assumption. 4.4 Perform a non-parametric test to check the differences of resptime by the level of vehtype. 4.5 Do log-transformation and normality check. 4.6 Do ANOVA with log transformed variable. 4.7 Perform a non-parametric test for the log transformed variable. 4.8 Compare the results of (4.2 and 4.6), (4.4 and 4.7) and explain. 49

50 taillite2.sav data Trying ANOVA Statistics > Means > One-way ANOVA Response variable : resptime, Groups : vehtype Grouping variables should be converted as factor variables (Data > Manage variable in active data set > Convert numeric variables to factors) A significant difference between Vehtypes on resptime? 50

51 taillite2.sav data Normality test Statistics > Summaries > Shapiro-Wilk test of normality For normality test for Vehtype, by(taillite2$resptime, taillite2$vehtype, shapiro.test) Reject the null!! ANOVA can not be conducted. 51

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53 taillite2.sav data Trying nonparametric way (Kruskal-Wallis test) Statistics > Nonparametric tests > Kruskal-Wallis test p=0.259 No difference between groups! 53

54 taillite2.sav data Data > Manage variable in active data set > Compute new variable New variable name : lresp Expression to compute : log(resptime) Normality test for lresp Edit command line as by(taillite2$lresp, taillite2$vehtype, shapiro.test) 54

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56 taillite2.sav data Trying ANOVA with lresp p=0.063 What do you conclude? 56

57 electric.xls housize = 'House Size' income = 'Family Income aircapac = 'Air Conditioning Capacity applindx = 'Appliance Index family = 'Number of Family Members peak = 'Peak Hour Electric Load' ; Aim: Selecting variables that affect the variable peak (Maximum amount of electricity) and finding the regression equation Statistics > Fit models > Linear regression Create command line first if you want to use the stepwise method for model selection (use step(model) function) 57

58 Q Read eletric.xls and explain the purpose of the analysis. 5.2 Perform step-wise regression using peak as a dependent variable. Interpret the results. (Exclude variable family) Statistics -> Fit models -> Linear Regression 58

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61 3D graphics

62 Rcmdr R commander was developed as an easy to use graphical user interface (GUI) for R Rcmdr is not perfect yet, but has been updated Expecting menu screen in Korean and Korean fonts variability