BMIdata.txt DT DT <- read.table("bmidata.txt") DT head(dt) names(dt) str(dt)

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みりあさわなか
5 years ago
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1 ?read.table read.table(file, header = FALSE, sep = "", quote = "\" ", dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"), row.names, col.names, as.is =!stringsasfactors, na.strings = "NA", colclasses = NA, nrows = -1, skip = 0, check.names = TRUE, fill =!blank.lines.skip, strip.white = FALSE, blank.lines.skip = TRUE, comment.char = "#", allowescapes = FALSE, flush = FALSE, stringsasfactors = default.stringsasfactors(), fileencoding = "", encoding = "unknown", text, skipnul = FALSE header: FALSE TRUE ( ) sep: ""( ) "," (CSV) fileencoding: Shift-JIS UTF-8 ( )

2 BMIdata.txt DT DT <- read.table("bmidata.txt") DT head(dt) names(dt) str(dt)

3 > DT <- read.table("bmidata.txt") > DT # Jirou M Tei M Yumi F Miki F Sacho F Taichi M Ichiro M Nobuo M

4 head > head(dt) V1 V2 V3 V4 1 Name Sex Height Weight 2 Yuri F Miwa F Saki F Taiki M Tarou M (Name, Sex,...) V1 V4 header = TRUE

5 names str > names(dt) [1] "V1" "V2" "V3" "V4" > str(dt) data.frame : 28 obs. of 4 variables: $ V1: Factor w/ 19 levels "Aki","Daiki",..: $ V2: Factor w/ 3 levels "F","M","Sex": $ V3: Factor w/ 7 levels "155.6","158.3",..: $ V4: Factor w/ 7 levels "52.3","54.3",..: > names str < Factor Height, Weight

6 > DT <- read.table("bmidata.txt",header=true) > names(dt) [1] "Name" "Sex" "Height" "Weight" > str(dt) data.frame : 27 obs. of 4 variables: $ Name : Factor w/ 18 levels "Aki","Daiki",..: $ Sex : Factor w/ 2 levels "F","M": $ Height: num $ Weight: num str Factor num

7 CSV MS-Office Excel Shift-JIS LibreOffice Calc UTF-8 Windows Shift-JIS Mac/Linux UTF-8 # UTF-8 Shift-JIS : Score.csv incomplete final line found by readtableheader on Score.csv # Shift-JIS UTF-8 <f1> : : Score2.csv incomplete final line found by readtableheader on Score2.csv

8 1 () t- ( 10 )

9 DataA.csv > DA <- read.table("dataa.csv",sep=",",header=t) > head(da) X32.25 # > DA <- read.table("dataa.csv",sep=",",header=f) # > head(da) V1 # DA V1

10 Shapiro-Wilk DT$V1 > summary(da$v1) # Min. 1st Qu. Median Mean 3rd Qu. Max > shapiro.test(da$v1) Shapiro-Wilk normality test data: DA$V1 W = , p-value = W: p: p W p W p

11 lattice R lattice > install.packages("lattice") Installing package into... # # install.packages() library() library(lattice)

12 (1) > summary(da$v1) # Min. 1st Qu. Median Mean 3rd Qu. Max > library(lattice) # lattice > densityplot(da$v1) #

13 (2) Q-Q qqnorm lattice > qqnorm(da$v1) # Q-Q > qqline(da$v1) # Q-Q

14 > histogram(da$v1) # > bwplot(da$v1) # Box-Whisker plot, Box Plot

15 DataAB.txt A, B lattice X Type A A A B B > DT <- read.table("dataab.txt",header=t) > str(dt) data.frame : 400 obs. of 2 variables: $ X : num $ Type: Factor w/ 2 levels "A","B":

16 2 densityplot lattice () Type A, B 2 ~ X X > library(lattice) > densityplot(~ X Type, data=dt) # > densityplot(~ DT$X DT$Type) #

17 Q-Q (lattice ) Q-Q qqnorm 2 par() 2 qqnorm() qqnorm() > par(mfrow=c(1,2)) > XA <- DT$X[DT$Type=="A"] # Type A > XB <- DT$X[DT$Type=="B"] # Type A > qqnorm(xa,main="a") # "A" > qqline(xa) > qqnorm(xb,main="b") # "B" > qqline(xb) lattice qqmath() lattice qqmath() densityplot()

18 2 Y (xi,a+bxi) (x2,y2) h2 (xi,yi) hi y = a + b x h1 (x1,y1) X h i a; b (ax + b ) sx 2 x 1 ; x 2 ; : : : ; x n s xy x, y b = s xy s 2 x ; a = ȳ ` b x

19 y = a + b 1 x 1 + b 2 x b p x p b 1 ; b 2 ; : : : b 1 b 2. b p = s x1 x 1 s x1 x 2 s x1 x p s x2 x 1 s x2 x 2 s x2 x p s xp x 1 s xp x 2 s xp x p `1 = s x1 y s x2 y. s xp y

20 R X Y DT <- read.table("xy10.dat",header=true) result = lm(y ~ X, data = DT) # summary(result) # plot(y ~ X, data = DT) # abline(result) #

21 result = lm(y ~ X, data = DT) (linear model) result lm Y ~ X, data = DT DT X Y summary(result) result plot(y ~ X, data = DT) DT X Y abline(result)

22 Call: lm(formula = Y ~ X, data = DT) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) X *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: 3.54 on 8 degrees of freedom Multiple R-squared: ,Adjusted R-squared: F-statistic: on 1 and 8 DF, p-value:

23 Residuals: Coefficients: Intercept, X Estimate Std. Error t value t Pr(> t ), p ( p ) Residual standard error: Multiple R-squared: ( ) 2 Adjusted R-squared: ( ) 2 ( ) F-Statistic: F p

24 TestScore.txt Eng Math Sci Art Eng, Math, Sci Art

25 Result <- read.table("textscore.txt",header=t) cor(result) ## Eng Math Sci Art Eng Math Sci Art

26 cor(result) pairs(result)

27 Result.fit <- lm(art ~ Eng + Math + Sci, data = Result) # + summary(result.fit) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) * Eng Math * Sci * --- Signif. codes: 1 Residual standard error: on 26 degrees of freedom 0 *** ** 0.01 * Multiple R-squared: 0.554,Adjusted R-squared: F-statistic: on 3 and 26 DF, p-value: 8.859e-05 Math, Sci Eng

29 m0 m25 m50 m75 w0 w25 w50 w75 Algeria Cameroon Madagascar Mauritius Reunion Seychelles South Africa(B) South Africa(W) Tunisia Canada Costa Rica Dominican Rep Ecuador mxx, wxx xx ( )

30 R ## life <- read.table("lifeexp.txt",header=t) ## country country <- row.names(life) ## dist <- dist(life) postscript("lifeexp.ps",horizontal=f, width=7, height=7,onefile=true) ## plot(hclust(dist, method = "complete"), labels = country, # xlab = " ", ylab = " ", main = " ")

32 TibetScull.txt Type Length Breadth Height Fheight Fbreadth Type "1" "1" "2" "1" "3" "1" "4" "1" "5" "1" "19" "2" "20" "2" "21" "2" "31" "2" "32" "2"

33 Length Breadth Height Fheight Fbreadth A B library(mass) # MASS DT <- read.table("tibetscull.txt",header=t) dis <- lda(type ~ Length + Breadth + Height + Fheight + Fbreadth, data = DT, prior = c(0.5,0.5)) # ##. Type newscull <- read.table("newscull.txt",header=t) predict(dis, newdata = newscull) #

34 A, B , $class [1] 1 2 Levels: 1 2 $posterior 1 2 A B

35 ( ) Musicchoice.txt ffl 2 MData <- read.table("musicchoice.txt",header=t) ## MData ## chisq.test(mdata)

36 Pearson s Chi-squared test data: MData X-squared = , df = 6, p-value = p = ffl (df) = 6 0.5%

37 The R Tips 2 R, 2009 ( ) R, 2009 ( ) A. R ABC,2012 B. R S-PLUS,2012 R 2010 RjpWiki R Wiki The R Project for Statistical Computing, R *

R R-console R R Rscript R-console GUI 1

November 2015 R R-console R R Rscript R-console GUI 1 2 X Y 1 11.04 21.03 2 15.76 24.75 3 17.72 31.28 4 9.15 11.16 5 10.10 18.89 6 12.33 24.25 7 4.20 10.57 8 17.04 33.99 9 10.50 21.01 10 8.36 9.68 x =