DAA01

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6 dat<-data.frame( condition score=c(78,70,66,76,78,76,88, 76, 76,72,60,72,70,72,84,70), cond=c(rep('low',8), rep('high',8))) score high low summary(aov(score ~ cond, data = dat)) Df Sum Sq Mean Sq F value Pr(>F) cond Residuals

8 dat.lm <- lm(ani~otouto, data=dat) abline(dat.lm, col = 'red',lwd = 2.5)

9 Polynomial regression plot(grade~study,data=dat,pch=20,xlab = "hours studied",xlim=c(3,27), ylim =c(0,110),cex=2) x = seq(0,30,0.1) y = *x *x^2 lines(x,y,col='red',lwd=3)

10 ( ) = exp ( b 0 + b 1 shoesize) 1+ exp( b 0 + b 1 shoesize) P M Coefficients: gender Estimate Std. Error z value Pr(> z ) (Intercept) e-06 *** shoesize e-06 *** P(M 23)=1/(1+exp(-1*( *23)))= P(M 25)=1/(1+exp(-1*( *25)))= P(M 27)=1/(1+exp(-1*( *27)))= age

11 Ability! ",$ %&''(%) =! ",$ %&''(%) = + +,-./ ,-./ Item Characteristic Curves Probability V1 V2 V3 V4 V5 V6 V7 V8 V9 V

12 > dat.pca$loadings Loadings: Comp.1 Comp.2 Comp.3 writing thesis interview Comp writing f hesis c erview b a h g i e d Comp.1

13 dat<-read.csv(" header=true, row.names=1) > dat admirable useful good big strong fast noisy young honest firm busy monk banker comic_artist designer nursery_teach professor med_doctor police_officer journalist sailor athlete author actor flight_attn

14 dat.cluster=hclust(dist(dat),method="average ) plot(dat.cluster,cex=1.5) Cluster Dendrogram Height banker monk professor author flight_attn actor comic_artist designer sailor athlete nursery_teach med_doctor police_officer journalist dist(dat) hclust (*, "average")

15 Height banker dat.pca=princomp(dat) biplot(dat.pca) monk professor author flight_attn actor Cluster Dendrogram comic_artist designer dist(dat) hclust (*, "average") sailor athlete nursery_teach med_doctor police_officer journalist Comp young comic_artist designer actor flight_attn busy nursery_teach good noisy honest admirable sailor big useful journalist athlete fast strong police_officer banker med_doctor author firm professor monk Comp.1

17 > # [1] 6 > # [1] 0 > 1*2*3 # [1] 6 > 10/2 # [1] 5

18 > 2^4 # [1] 16 > 10%/%3 # [1] 3 > 10%%3 # [1] 1

19 > 1==1 #equal [1] TRUE > 1==2 [1] FALSE > 1!=2 #not equal [1] TRUE > 1<1.1 [1] TRUE

20 > 1 == c(1,2) [1] TRUE FALSE > all(1 == c(1,2)) [1] FALSE > any(1 == c(1,2)) [1] TRUE

21 > a=1:10 [1] > which(a<5) [1] > b=10:1 [1] > which(b<5) [1]

22 c(var1,var2,...,varn) > x<-c(1,2,3,4) > x [1] > x2<-c(x,5,6,7,8) > x2 [1]

23 c(var1,var2,...,varn) > y=c('a0','a1','b0','b1') > y [1] "a0" "a1" "b0" "b1 > z=c(x,y) > z [1] "1" "2" "3" "4" "a0" "a1" "b0" "b1"

24 seq(start, end, increment/decrement) > x<-seq(1,4,1) [1] > x<-seq(0,40,10) [1] > x<-seq(10,2,-2) [1] > x<-1:4 # [1] > x<-10:1 # - [1]

25 rep(x, times) > x<-rep(1,4) (1) [1] > x<-rep(c(1,7,87),3) (2) [1,7,87] [1] > x<-rep(1:4,3) (3) 1:4 seq [1] > x<-sort(rep(1:4,3)) (4) (3) [1]

27 > x<-matrix(1:8, nrow=2) [,1] [,2] [,3] [,4] [1,] [2,] > x<-matrix(1:8, nrow=2,byrow=t) [,1] [,2] [,3] [,4] [1,] [2,]

28 data01<-data.frame(score = c(2,4,3,4), dose = c(rep(10,2),rep(100,2)), condition = rep(c('exp','control'),2)) > data01 score dose condition exp control exp control

29 dat01<-read.csv(" header=t) > dat01 x y z

30 dat02<-read.csv(" header=t, row.name=1) > dat02 x y z katsuo wakame tarachan

31 > dat03<-read.table(" header=t, row.name=4) > dat03 x y z sazae masuo tarachan

32 matrix M[ ] > dat03 x y z sazae masuo tarachan > dat03[1,1] #1 1 [1] 11

33 n M[n, ] m M[, m] > dat03 x y z sazae masuo tarachan > dat03[2,] # x y z masuo > dat03[,1] #1 [1]

34 M$varName > dat03 x y z sazae masuo tarachan > dat03$x [1] > dat03$y [1] > dat03$z [1]

35 > dat03 x y z sazae masuo tarachan > colnames(dat03)<-c("var1","var2","var3") > dat03 var1 var2 var3 sazae masuo tarachan

36 Dat03 score dat03 var1 var2 var3 Name Conditionn, var1, var2, var3 > dat04 score name condition 1 11 sazae var1 > dat masuo var2 var1 var2 var tarachan var3 sazae sazae var1 masuo masuo var tarachan var3 tarachan sazae var masuo var tarachan var3

37 score var1 var2 var3 Name Conditionn, var1, var2, var3 > dat04<-data.frame(score=c(dat03$var1,dat03$var2,dat03$var3), name=rep(rownames(dat03),3), condition = rep(c("var1","var2","var3"),3))

38 dat<-read.csv(" > head(dat) shoesize header=t); h gender M M M M F F > head(dat) shoesize height (meter) gender M M M M F F

39 mean(dat$shoesize[dat$gender == "M"]) [1] # mean(dat$shoesize[dat$gender == "F"]) [1] # mean(dat$shoesize[dat$h > 180]) [1] 27.5 # 180cm

DAA02

DAA02 c(var1,var2,...,varn) > x x [1] 1 2 3 4 > x2 x2 [1] 1 2 3 4 5 6 7 8 c(var1,var2,...,varn) > y=c('a0','a1','b0','b1') > y [1] "a0" "a1" "b0" "b1 > z=c(x,y) > z [1] "1" "2"