R による統計解析入門

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1 R May 31, 2016

2 R R R R Studio GUI R Console R Studio PDF URL

3 R R Console Windows, Mac GUI Unix R Studio GUI

4 R version ( ) -- "Wooden Christmas-Tree" Copyright (C) 2015 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R license() licence() R contributors() R R citation() demo() help() help.start() HTML q() R >

5 demo() Demos in package base : error.catching is.things recursion scoping More examples on catching and handling errors Explore some properties of R objects and is.foo() functions. Not for newbies! Using recursion for adaptive integration An illustration of lexical scoping. Demos in package grdevices : colors hclcolors A show of R s predefined colors() Exploration of hcl() space (stdin): q

6 base error.catching, is.things, recursion, scoping grdevices colors, hclcolors graphics Hershey, Japanese, graphics, image, persp, plotmath stats glm.vr, lm.glm, nlm, smooth colors, graphics, persp > demo(recursion)

7 R # > a <- 50 # = > b <- 30 # = > c <- a + b < c # [1] 80 # abc, name, x, y sum1( ) mean_total( ) Sum( ), Member.female( ) if else repeat while function for in next break TRUE FALSE NULL NA Inf NaN

8 data, sum, summary, mean, median, max, mean, sqrt, sin, cos Data, DATA, DT Data.sum, DATA.mean D 1 C, F, I, T > sum function (..., na.rm = FALSE).Primitive("sum") > Sum : Sum sum Sum

9 c > X <- c(10.2,20.5,-2.9,13.8,9.1,23.1) > X [1] > X[4] [1] 13.8 # 1 > X[2:4] [1] > X[-3] [1] # 3 > order(x) [1] # > sort(x) [1] sum, mean, median, min, max, length X

10 > var(x) # [1] > sd(x) # [1] > quantile(x) # 0% 25% 50% 75% 100% > quantile(x,0.25) # 25% > quantile(x,0.75) # 75% > summary(x) # Min. 1st Qu. Median Mean 3rd Qu. Max

11 , ff 2 = 1 n ` 1 nx (x i ` ) 2 ; ff = 1 q ff 2 n x 1 ; x 2 ; ; x n n ff 2 = 1 n nx (x i ` ) 2 ; ff = 1 q ff 2 25% 75% 5

12 > X <- c(10.2,20.5,-2.9,13.8,9.1,23.1) > Y <- c(6.8,10.1,5.4,9.1,9.1,1.1) > Z <- c(1.32,3.24,0.98,0.55,1.67,2.3) cbind (column) (bind) > Mat <- cbind(x,y,z) > Mat X Y Z [1,] [2,] [3,] [4,] [5,] [6,]

13 rbind (row) < rbind(x,y,z) [,1] [,2] [,3] [,4] [,5] [,6] X Y Z cbind > dim(mat) [1] 6 3 > dim(rbind(x,y,z)) [1] 3 6 > dim(mat)[1] # dim() [1] 6 > dim(mat)[2] [1] 3

14 > Mat[2,3] Z 3.24 > Mat[2,] X Y Z > Mat[,3] [1] > Mat[,] X Y Z [1,] [2,] [3,] [4,] [5,] [6,] Mat[,], Mat[2,], Mat[,3]

15 NA (not available) > V <- c(10,20,30,na,50,60) > sum(v) [1] NA > mean(v) [1] NA > var(v) [1] NA > sum(v,na.rm=true) [1] 170 > mean(v,na.rm=true) [1] 34 > var(v,na.rm=true) [1] 430 sum, mean, var NA na.rm = TRUE na.rm = T

16 list list > N <- c("a","b","c","d") > List <- list(mat=mat,val=v,name=n) > List $Mat X Y Z [1,] [2,] [3,] [4,] [5,] [6,] $val [1] NA $name [1] "a" "b" "c" "d"

17 $ > names(list) # List [1] "Mat" "val" "name" > List$Mat X Y Z [1,] [2,] [3,] [4,] [5,] [6,] > List$val [1] NA > List$name [1] "a" "b" "c" "d" R

18 (CSV ) Excel.xls,.xlsx Excel CSV xls, xlsx

19 Windows Windows R (C:, D: ) n R / Unix Windows CP932 (Shit-JIS) Mac, Linux UTF-8 Windows Rscript Windows Windows R C:\R C:\home\R Path (3.3.0 ) C:\Program Files\R\R-3.3.0\bin

20 BMI.txt Name Sex Height Weight Yuri F Miwa F Saki F Taiki M Tarou M Kei M > setwd("c:/r/mydata") # > BMI <- read.table("bmidata.txt",header=true) # > class(bmi) # BMI [1] "data.frame" > names(bmi) # [1] "Name" "Sex" "Height" "Weight"

21 > BMI # Name Sex Height Weight 1 Yuri F Miwa F Saki F Taiki M Tarou M Kei M > str(bmi) # BMI data.frame : 6 obs. of 4 variables: $ Name : Factor w/ 6 levels "Kei","Miwa","Saki",..: $ Sex : Factor w/ 2 levels "F","M": $ Height: num $ Weight: num > summary(bmi$height) # Height Min. 1st Qu. Median Mean 3rd Qu. Max > summary(bmi$sex) # F M 3 3

22 > BMI_F <- BMI[BMI$Sex=="F",] # > BMI_M <- BMI[BMI$Sex=="M",] # > max(bmi_f$height) # [1] > mean_h <- mean(bmi$height) # > Higher <- BMI[BMI$Height>mean_H,] # > Higher Name Sex Height Weight 4 Taiki M Tarou M Kei M

23 CSV Score.csv NA URL ,,,,,,,F,69,80,58,,F,92,60,78,,M,59,79,63,,M,NA,NA,89

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

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