# R tips ## packages ```R # data processing install.packages("tidyverse") install.packages("data.table") # date install.packages("lubridate") # visualization install.packages("psych") install.packages("scales") install.packages("GGally") # http://www.ggplot2-exts.org/gallery/ # dev install.packages("devtools") install.packages("testthat") install.packages("roxygen2") # machine learning install.packages("caret") #install.packages("ranger") install.packages("randomForest") install.packages("xgboost") # recommendation install.packages("arules") install.packages("arulesViz") # stats modeling install.packages("glmnet") # time series install.packages("xts") install.packages("forecast") install.packages("zoo") # web app install.packages("shiny") # reporting install.packages("rmarkdown") install.packages("formatR") # R notebook # bayse install.packages("rstan") #install.packages("dlm") install.packages("") ``` ## Paste from Clip Board ```R # win/Linux? data <- read.table("clipboard") # mac data <- read.table(pipe("pbpaste")) ``` ## basic functions ```R # rangeではない(python脳, range -> output min/max seq(1, 9) # 1 to 9 seq(1, 9, by = 0.1) # step seq(1, 9, length.out = 5) # n = 5 # log log(2, base = exp(1)) # shuffle/sampling sample(1:5) sample(1:5, 3) sample(1:5, 10, replace=TRUE) # zero matrix matrix(0, nrow=2, ncol=2) # identity matrix diag(nrow=2) ``` ## repeat / replicate ```R rep(1:3, 2) [1] 1 2 3 1 2 3 # byrowにしたいときはt() で転置 replicate(2, 1:3) [,1] [,2] [1,] 1 1 [2,] 2 2 [3,] 3 3 ``` ## sub(tract) vector from matrix ```R (mat <- matrix(1:6, ncol=2)) [,1] [,2] [1,] 1 4 [2,] 2 5 [3,] 3 6 # MARGIN: 1 -> row, 2 -> col sweep(mat, 2, mat[1, ]) [,1] [,2] [1,] 0 0 [2,] 1 1 [3,] 2 2 # matrixではなくdataframeの場合、data.matrixで変換してから対応する # matrixにしてもカラムは消えない ``` ## apply系 ```R # MARGIN = c(1,2) のとき成分ごとに適用 # sumはnumpyと同じで1つのスカラーに集約してしまうので、applyが必要 apply(df, MARGIN, sum) ``` ## dot/outer product ```R 1:3 %*% 1:3 [,1] [1,] 14 1:3 %o% 1:3 [,1] [,2] [,3] [1,] 1 2 3 [2,] 2 4 6 [3,] 3 6 9 ``` ## list ```R # append l <- list() l <- c(l, list(1)) l <- c(l, list(2), list(3)) ``` ## dummy ```R install.packages("dummies") library(dummies) a <- 1:5 df <- data.frame(a=a) dummies::dummy(a) dummies::dummy("a", df) ``` ## duplicated / drop duplicated ```R df <- data.frame(d1=c(1,1,2,3,3,4,5,6,6), d2=c(1,1,2,3,3,4,5,5,6)) df$df_duplicated <- duplicated(df[, c("d1", "d2")]) df$d1_duplicated <- duplicated(df$d1) df$d1_duplicated_fromlast <- duplicated(df$d1, fromLast = T) df$d1_duplicated_all <- duplicated(df$d1) | duplicated(df$d1, fromLast = T) # => https://stackoverflow.com/questions/7854433/finding-all-duplicate-rows-including-elements-with-smaller-subscripts # drop duplicated unique(df$d1) df %>% select(d1, d2) %>% distinct() # drop duplicated/base d1 and drop d2 # drop duplicated/base d1 and keep d2 df %>% select(d1, d2) %>% distinct(d1) df %>% select(d1, d2) %>% distinct(d1, .keep_all = T) ``` ## 標準化 ```R scale(iris[, -5]) == scale(iris[, -5], center = T, scale = T) # center, scaleは標準化でなくてもよい # 上記2つに数値を渡すこともできるので、min-max scaleもできる ``` ## PCA ```R # デフォルトの変換対象は分散共分散行列(center = T, scale = F) iris_pca <- prcomp(iris[, -5]) # 固有分解 iris_cov_eigen <- eigen(cov(iris[, -5])) ## 固有ベクトル iris_pca$rotation == iris_cov_eigen$vectors ## 固有値 iris_pca$sdev ^ 2 == iris_cov_eigen$values # 変換後データ と 元の行列 iris_pca$x scale(iris[, -5], scale = F) == (iris_pca$x %*% t(iris_pca$rotation)) # 寄与率 iris_pca_summary <- summary(iris_pca) iris_pca_summary ``` ## docallによるlist展開 ```R # cbindにベクトルを渡す do.call(cbind, list(1:2, 3:4, 5:6)) [,1] [,2] [,3] [1,] 1 3 5 [2,] 2 4 6 ## 引数が足りない場合 myfunc <- function(a, b, c, bias1, bias2 = 2) { return(sum(a, b, c) + bias1 + bias2) } do.call(myfunc, list(1:2, 3:4, bias1 = 1)) (function (a, b, c, bias1, bias2 = 2) でエラー: 引数 "c" がありませんし、省略時既定値もありません ## デフォルト引数 do.call(myfunc, list(1:2, 3:4, 5:6, bias1 = 1)) [1] 24 ## 可変長引数関数 variadic_func <- function(..., bias1, bias2 = 2) { return(sum(...) + bias1 + bias2) } do.call(variadic_func, list(1:2, 3:4, bias1 = 1)) [1] 13 do.call(variadic_func, list(1, 2, 3, 4, 5, bias1 = 1)) [1] 18 ``` ## ggplot ```R library(ggplot2) library(gridExtra) # 複数グラフ plots <- list() plots[[1]] <- ggplot2::ggplot() + geom_point(aes(x, y1), data = df) plots[[2]] <- ggplot2::ggplot() + geom_point(aes(x, y2), data = df) gridExtra::marrangeGrob(plots, nrow=1, ncol=2) # or do.call(gridExtra::grid.arrange, c(plots, nrow=1, ncol=2)) # violin geom_violin # for pca install.packages("ggbiplot") library(ggbiplot) ``` ## time series ```R data("AirPassengers") class(AirPassengers) # => [1] "ts" tsp(AirPassengers) # => start end frequency # plot plot(AirPassengers) decomposed <-decompose(AirPassengers) plot(decomposed) # observed, trend, seasonal, random ## 変化率 log近似とdefault plot(diff(log(AirPassengers))) plot(diff(AirPassengers) / AirPassengers) ## (偏)自己相関 acf(AirPassengers) pacf(AirPassengers) # TODO ``` ## Rで共分散と相関係数を求める(自作と {stats}) ```R ## 数式をプログラムに落とし込む練習 # Σとか行列・ベクトルを見ても怖がらないように # 落ち着けば問題なさそう # 作っているうちにイメージしやすくなった # forがダサく見える… # もっといい方法があったら知りたい v1 <- 1:9 v2 <- sample(v1) m <- matrix(sample(v2), 3) # cor (function(m){ len <- dim(m)[1] mat.cor <- diag(rep(1, len)) for (k in 1:len) { for (j in 1:len) { if (k == j) next if (k > j) { cor.kj <- mat.cor[j, k] } else { d1 <- m[, k] - mean(m[, k]) d2 <- m[, j] - mean(m[, j]) cor.kj <- sum(d1 * d2) / (sqrt(sum(d1 ** 2)) * sqrt(sum(d2 ** 2))) } mat.cor[k, j] <- cor.kj } } return(mat.cor) })(m) cor(m) # cov (function(m){ len <- dim(m)[1] mat <- matrix(0, len, len) for (k in 1:len) { for (j in 1:len) { d1 <- m[, k] - mean(m[, k]) d2 <- m[, j] - mean(m[, j]) mat[k, j] <- sum(d1 * d2) } } return(mat / (len - 1)) })(m) cov(m) ``` ## Rで変数削除 ```R library(dplyr) library(caret) df.filtered <- df %>% select(-caret::nearZeroVar(df)) %>% select(-caret::findCorrelation(cor(.), cutoff = 任意の閾値)) # おまけ # tidyr　はたいでぃーあーる ``` ## 他記事のリンク #### [Rで簡単に前処理＋クロス集計＋α](http://qiita.com/ksomemo/items/e5df50251402369b978d) #### [Rでの前処理として覚えたこと](http://qiita.com/ksomemo/items/c80802a16e75dd38ea8a) #### [Rのseqを使って先月を求めるときの注意点](http://qiita.com/ksomemo/items/2c399a35b8edf087237c) #### [not installed package names](http://qiita.com/ksomemo/items/b6ed564ff247261bd31f) - もう一回インストールしたら嵌りそうなもの