Datamining r 1st

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Datamining r 1st

  1. 1. Rsesejun@is.ocha.ac.jp 2010/10/14
  2. 2. R• http://r-project.org/ DL • Mac, Win, Linux• S-Plus•• Interactive shell•• :)
  3. 3. • Applications R • Version 2.6 ( ) • R project DL• 1+1[RET]> 1+1 > 8/3[1] 2 [1] 2.666667> 3*6 > as.integer(8/3)[1] 18 [1] 2> 3^3 > 8%%3[1] 27 [1] 2
  4. 4. & > c(1,2,3) [1] 1 2 3> x <- 2 > c(1,2,3) + c(4,5,6)> y <- 3 [1] 5 7 9> x*y > c(1,2,3) * c(4,5,6)[1] 6 [1] 4 10 18> x^y[1] 8 > c(1,2,3) * 2 [1] 2 4 6 > c(1,2,3) / 2 [1] 0.5 1.0 1.5 > v <- c(1,2,3) > w <- v + 3 > w [1] 4 5 6 > v*w [1] 4 10 18
  5. 5. > v <- c(3,2,5,7,2,4,3,1,4)> length(v)[1] 9> max(v)[1] 7> min(v)[1] 1> mean(v)[1] 3.444444> median(v)[1] 3> unique(v)[1] 3 2 5 7 4 1> sort(v)[1] 1 2 2 3 3 4 4 5 7> order(v)[1] 8 2 5 1 7 6 9 3 4> hist(v)> help(max)
  6. 6. > v <- c(3,2,5,7,2,4,3,1,4)> hist(v, main="My First Histgram", col="gray")> hist(v, col="gray", main="My First Histgram")> w <- sort(v)> plot(v,w)> plot(w,v)
  7. 7. > seq(1,4)[1] 1 2 3 4> 1:4[1] 1 2 3 4> seq(1,5,by=2)[1] 1 3 5> rep(1,4)[1] 1 1 1 1> rep(1:3,2)[1] 1 2 3 1 2 3> v <- c(3,2,5,7,2,4,3,1,4)> v[1][1] 3> v[c(1,3,5)][1] 3 5 2> v[c(5,3,1)][1] 2 5 3> v[c(F,F,T,T,F,F,T,T,F)][1] 5 7 3 1
  8. 8. > x <- 3> x[1] 3> x == 3[1] TRUE> x == 5[1] FALSE> x < 5[1] TRUE> v <- c(3,2,5,7,2,4,3,1,4)> v == c(3,3,3,3,3,3,3,3,3)[1] TRUE FALSE FALSE FALSEFALSE FALSE TRUE FALSE FALSE> v == 3[1] TRUE FALSE FALSE FALSEFALSE FALSE TRUE FALSE FALSE> v < 3[1] FALSE TRUE FALSE FALSETRUE FALSE FALSE TRUE FALSE
  9. 9. > v <- c(3,2,5,7,2,4,3,1,4)> v < 3[1] FALSE TRUE FALSE FALSETRUE FALSE FALSE TRUE FALSE> v[v<3][1] 2 2 1> v[v>3][1] 5 7 4 4> v[v>3 & v<7][1] 5 4 4> (1:length(v))[v<3][1] 2 5 8> sum(v>3)[1] 4> v %in% c(2,3,4)[1] TRUE TRUE FALSE FALSETRUE TRUE TRUE FALSE TRUE> v[v %in% c(2,3,4)][1] 3 2 2 4 3 4
  10. 10. > runif(10,min=0,max=1) [1] 0.45189074 0.15543373 0.04654874 0.56946222 0.06086409 [6] 0.64340708 0.91820279 0.28365751 0.91056890 0.61600679> n <- 10> hist(runif(n,min=0,max=1), main=paste("n=",n,sep=""))> n <- 10000> hist(runif(n,min=0,max=1), main=paste("n=",n,sep=""))
  11. 11. .> n <- 10> x <- runif(n,min=0,max=1)> x [1] 0.9308879 0.6457174 0.7480667 0.9277555 0.2432229 0.7852049 [7] 0.9005295 0.3948717 0.3442392 0.7808671> x < 0.3 [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE> sum(x < 0.3)[1] 1> sum(x < 0.3)/n[1] 0.1> n <- 10000> x <- runif(n,min=0,max=1)> sum(x < 0.3)/n[1] 0.3013> n <- 10000> x <- rnorm(n,mean=0,sd=1)> sum(x < 0.3)/n[1] 0.6125> sum(x > 1.0)/n[1] 0.1591
  12. 12. > m <- matrix((1:9)**2,nrow=3)> m [,1] [,2] [,3][1,] 1 16 49[2,] 4 25 64[3,] 9 36 81> m[c(2,3),c(2,3)] [,1] [,2][1,] 25 64[2,] 36 81> m[2,][1] 4 25 64> m[c(1,2),] [,1] [,2] [,3][1,] 1 16 49[2,] 4 25 64> m[,2][1] 16 25 36> m<50 [,1] [,2] [,3][1,] TRUE TRUE TRUE[2,] TRUE TRUE FALSE[3,] TRUE TRUE FALSE
  13. 13. > m <- matrix((1:9)**2,nrow=3)> solve(m) [,1] [,2] [,3][1,] 1.291667 -2.166667 0.9305556[2,] -1.166667 1.666667 -0.6111111[3,] 0.375000 -0.500000 0.1805556> eigen(m)$values[1] 112.9839325 -6.2879696 0.3040371$vectors [,1] [,2] [,3][1,] -0.3993327 -0.8494260 0.7612507[2,] -0.5511074 -0.4511993 -0.6195403[3,] -0.7326760 0.2736690 0.1914866> v <- c(3,2,5,7,2,4,3,1,4)> t(v) %*% v [,1][1,] 133
  14. 14. R• R ≠• • if for• R • • apply family ( R apply, sapply, lapply ) ••
  15. 15. • R WEB• R-Tips: • http://cse.naro.affrc.go.jp/takezawa/r-tips/r.html• RjpWiki • http://www.okada.jp.org/RWiki/• R

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