From 772370c5184822e61fa81da878d3853bb06a33a6 Mon Sep 17 00:00:00 2001 From: Akira Hirose Date: Wed, 16 Jul 2014 14:51:56 +0900 Subject: Japanese version. Just on a middle of way. --- ja-jp/r-jp.html.markdown | 782 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 782 insertions(+) create mode 100644 ja-jp/r-jp.html.markdown (limited to 'ja-jp') diff --git a/ja-jp/r-jp.html.markdown b/ja-jp/r-jp.html.markdown new file mode 100644 index 00000000..26d8403f --- /dev/null +++ b/ja-jp/r-jp.html.markdown @@ -0,0 +1,782 @@ +--- +language: R +contributors: + - ["e99n09", "http://github.com/e99n09"] + - ["isomorphismes", "http://twitter.com/isomorphisms"] +translators: + - ["akirahirose", "https://www.facebook.com/akira.hirose"] +filename: learnr-jp.r +lang: ja-jp +--- + + +R は統計計算用の言語です。 +データの取得やクリーニング、統計処理やグラフ作成をするために使える、たくさんのライブラリがあります。また、LaTeX文書からRコマンドを呼び出すこともできます。 + + +```python +# コメント行は、#で開始します + + +# コメントを複数の行に分けたい場合は、 +# このように、コメント行を複数連続させるとできます + + +# WindowsやMacでは、 COMMAND-ENTERで1行のコマンド実行ができます + + + + + + +############################################################################# +# プログラミングがわからなくとも使えるコマンド類 +############################################################################# + + +# この節では、プログラミングがわからなくとも使える便利なRコマンドを紹介します +# 全てを理解できなくとも、まずはやってみましょう！ + + +data() # 既にロードされているデータを閲覧します +data(rivers) # "北米にある大きな川の長さ"データを取得します +ls() # "rivers" がワークスペースに表示されました +head(rivers) # データの先頭部分です +# 735 320 325 392 524 450 + + +length(rivers) # 何本の川がデータにある? +# 141 +summary(rivers) # 統計的に要約するとどうなる? +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 135.0 310.0 425.0 591.2 680.0 3710.0 + + +# 茎葉図（ヒストグラムに似た図）を描く +stem(rivers) + + +# The decimal point is 2 digit(s) to the right of the | +# +# 0 | 4 +# 2 | 011223334555566667778888899900001111223333344455555666688888999 +# 4 | 111222333445566779001233344567 +# 6 | 000112233578012234468 +# 8 | 045790018 +# 10 | 04507 +# 12 | 1471 +# 14 | 56 +# 16 | 7 +# 18 | 9 +# 20 | +# 22 | 25 +# 24 | 3 +# 26 | +# 28 | +# 30 | +# 32 | +# 34 | +# 36 | 1 + + +stem(log(rivers)) # このデータは、正規分布でも対数正規分布でもないので注意！ +# 特に正規分布原理主義のみなさん + + +# The decimal point is 1 digit(s) to the left of the | +# +# 48 | 1 +# 50 | +# 52 | 15578 +# 54 | 44571222466689 +# 56 | 023334677000124455789 +# 58 | 00122366666999933445777 +# 60 | 122445567800133459 +# 62 | 112666799035 +# 64 | 00011334581257889 +# 66 | 003683579 +# 68 | 0019156 +# 70 | 079357 +# 72 | 89 +# 74 | 84 +# 76 | 56 +# 78 | 4 +# 80 | +# 82 | 2 + + +# ヒストグラム作成 +hist(rivers, col="#333333", border="white", breaks=25) # これらのパラメータをつかいます +hist(log(rivers), col="#333333", border="white", breaks=25) # いろいろな使い方ができます + + +# 別のロード済データでやってみましょう。Rには、いろいろなデータがロードされています。 +data(discoveries) +plot(discoveries, col="#333333", lwd=3, xlab="Year", + main="Number of important discoveries per year") +plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year", + main="Number of important discoveries per year") + + +# 年次のソートだけではなく、 +# 標準的な並べ替えもできます +sort(discoveries) +# [1] 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 +# [26] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 +# [51] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 +# [76] 4 4 4 4 5 5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 8 9 10 12 + + +stem(discoveries, scale=2) +# +# The decimal point is at the | +# +# 0 | 000000000 +# 1 | 000000000000 +# 2 | 00000000000000000000000000 +# 3 | 00000000000000000000 +# 4 | 000000000000 +# 5 | 0000000 +# 6 | 000000 +# 7 | 0000 +# 8 | 0 +# 9 | 0 +# 10 | 0 +# 11 | +# 12 | 0 + + +max(discoveries) +# 12 +summary(discoveries) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.0 2.0 3.0 3.1 4.0 12.0 + + +# サイコロを振ります +round(runif(7, min=.5, max=6.5)) +# 1 4 6 1 4 6 4 +# 私と同じrandom.seed(31337)を使わない限りは、別の値になります + + +# ガウス分布を9回生成します +rnorm(9) +# [1] 0.07528471 1.03499859 1.34809556 -0.82356087 0.61638975 -1.88757271 +# [7] -0.59975593 0.57629164 1.08455362 + + + + + + +################################################## +# データ型と基本計算 +################################################## + + +# ここからは、プログラミングをつかうチュートリアルです +# この節ではRで重要なデータ型の、整数型、数字型、文字型、論理型と因子型をつかいます +# 他にもいろいろありますが、まずは最小限必要な、これらから始めましょう + + +# 整数型 +# 整数型の長さは、Lで指定します +5L # 5 +class(5L) # "integer" +# (?class を実行すると、class()関数についてさらなる情報が得られます) +# Rでは、この5Lのような単一の値は、長さ1のベクトルとして扱われます +length(5L) # 1 +# 整数型のベクトルはこのようにつくります +c(4L, 5L, 8L, 3L) # 4 5 8 3 +length(c(4L, 5L, 8L, 3L)) # 4 +class(c(4L, 5L, 8L, 3L)) # "integer" + + +# 数字型 +# 倍精度浮動小数点数です +5 # 5 +class(5) # "numeric" +# くどいですが、すべてはベクトルです +# 1つ以上の要素がある数字のベクトルも、作ることができます +c(3,3,3,2,2,1) # 3 3 3 2 2 1 +# 指数表記もできます +5e4 # 50000 +6.02e23 # アボガドロ数 +1.6e-35 # プランク長 +# 無限大、無限小もつかえます +class(Inf) # "numeric" +class(-Inf) # "numeric" +# 例のように、"Inf"を使ってください。integrate( dnorm(x), 3, Inf); +# Z-スコア表が必要なくなります + + +# 基本的な計算 +# 数を計算できます +# 整数と整数以外の数字を両方使った計算をすると、結果は整数以外の数字になります +10L + 66L # 76 # 整数足す整数は整数 +53.2 - 4 # 49.2 # 整数引く数字は数字 +2.0 * 2L # 4 # 数字かける整数は数字 +3L / 4 # 0.75 # 整数割る数字は数字 +3 %% 2 # 1 # 二つの数字を割った余りは数字 +# 不正な計算は "not-a-number"になる +0 / 0 # NaN +class(NaN) # "numeric" +# You can do arithmetic on two vectors with length greater than 1, +# so long as the larger vector's length is an integer multiple of the smaller +c(1,2,3) + c(1,2,3) # 2 4 6 + + +# CHARACTERS +# There's no difference between strings and characters in R +"Horatio" # "Horatio" +class("Horatio") # "character" +class('H') # "character" +# Those were both character vectors of length 1 +# Here is a longer one: +c('alef', 'bet', 'gimmel', 'dalet', 'he') +# => +# "alef" "bet" "gimmel" "dalet" "he" +length(c("Call","me","Ishmael")) # 3 +# You can do regex operations on character vectors: +substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis " +gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis." +# R has several built-in character vectors: +letters +# => +# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" +# [20] "t" "u" "v" "w" "x" "y" "z" +month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec" + + +# LOGICALS +# In R, a "logical" is a boolean +class(TRUE) # "logical" +class(FALSE) # "logical" +# Their behavior is normal +TRUE == TRUE # TRUE +TRUE == FALSE # FALSE +FALSE != FALSE # FALSE +FALSE != TRUE # TRUE +# Missing data (NA) is logical, too +class(NA) # "logical" +# Here we get a logical vector with many elements: +c('Z', 'o', 'r', 'r', 'o') == "Zorro" # FALSE FALSE FALSE FALSE FALSE +c('Z', 'o', 'r', 'r', 'o') == "Z" # TRUE FALSE FALSE FALSE FALSE + + +# FACTORS +# The factor class is for categorical data +# Factors can be ordered (like childrens' grade levels) or unordered (like gender) +factor(c("female", "female", "male", "NA", "female")) +# female female male NA female +# Levels: female male NA +# The "levels" are the values the categorical data can take +levels(factor(c("male", "male", "female", "NA", "female"))) # "female" "male" "NA" +# If a factor vector has length 1, its levels will have length 1, too +length(factor("male")) # 1 +length(levels(factor("male"))) # 1 +# Factors are commonly seen in data frames, a data structure we will cover later +data(infert) # "Infertility after Spontaneous and Induced Abortion" +levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs" + + +# NULL +# "NULL" is a weird one; use it to "blank out" a vector +class(NULL) # NULL +parakeet +# => +# [1] "beak" "feathers" "wings" "eyes" +parakeet <- NULL +parakeet +# => +# NULL + + +# TYPE COERCION +# Type-coercion is when you force a value to take on a different type +as.character(c(6, 8)) # "6" "8" +as.logical(c(1,0,1,1)) # TRUE FALSE TRUE TRUE +# If you put elements of different types into a vector, weird coercions happen: +c(TRUE, 4) # 1 4 +c("dog", TRUE, 4) # "dog" "TRUE" "4" +as.numeric("Bilbo") +# => +# [1] NA +# Warning message: +# NAs introduced by coercion + + +# Also note: those were just the basic data types +# There are many more data types, such as for dates, time series, etc. + + + + + + +################################################## +# Variables, loops, if/else +################################################## + + +# A variable is like a box you store a value in for later use. +# We call this "assigning" the value to the variable. +# Having variables lets us write loops, functions, and if/else statements + + +# VARIABLES +# Lots of way to assign stuff: +x = 5 # this is possible +y <- "1" # this is preferred +TRUE -> z # this works but is weird + + +# LOOPS +# We've got for loops +for (i in 1:4) { + print(i) +} +# We've got while loops +a <- 10 +while (a > 4) { + cat(a, "...", sep = "") + a <- a - 1 +} +# Keep in mind that for and while loops run slowly in R +# Operations on entire vectors (i.e. a whole row, a whole column) +# or apply()-type functions (we'll discuss later) are preferred + + +# IF/ELSE +# Again, pretty standard +if (4 > 3) { + print("4 is greater than 3") +} else { + print("4 is not greater than 3") +} +# => +# [1] "4 is greater than 3" + + +# FUNCTIONS +# Defined like so: +jiggle <- function(x) { + x = x + rnorm(1, sd=.1) #add in a bit of (controlled) noise + return(x) +} +# Called like any other R function: +jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043 + + + + + + +########################################################################### +# Data structures: Vectors, matrices, data frames, and arrays +########################################################################### + + +# ONE-DIMENSIONAL + + +# Let's start from the very beginning, and with something you already know: vectors. +vec <- c(8, 9, 10, 11) +vec # 8 9 10 11 +# We ask for specific elements by subsetting with square brackets +# (Note that R starts counting from 1) +vec[1] # 8 +letters[18] # "r" +LETTERS[13] # "M" +month.name[9] # "September" +c(6, 8, 7, 5, 3, 0, 9)[3] # 7 +# We can also search for the indices of specific components, +which(vec %% 2 == 0) # 1 3 +# grab just the first or last few entries in the vector, +head(vec, 1) # 8 +tail(vec, 2) # 10 11 +# or figure out if a certain value is in the vector +any(vec == 10) # TRUE +# If an index "goes over" you'll get NA: +vec[6] # NA +# You can find the length of your vector with length() +length(vec) # 4 +# You can perform operations on entire vectors or subsets of vectors +vec * 4 # 16 20 24 28 +vec[2:3] * 5 # 25 30 +any(vec[2:3] == 8) # FALSE +# and R has many built-in functions to summarize vectors +mean(vec) # 9.5 +var(vec) # 1.666667 +sd(vec) # 1.290994 +max(vec) # 11 +min(vec) # 8 +sum(vec) # 38 +# Some more nice built-ins: +5:15 # 5 6 7 8 9 10 11 12 13 14 15 +seq(from=0, to=31337, by=1337) +# => +# [1] 0 1337 2674 4011 5348 6685 8022 9359 10696 12033 13370 14707 +# [13] 16044 17381 18718 20055 21392 22729 24066 25403 26740 28077 29414 30751 + + +# TWO-DIMENSIONAL (ALL ONE CLASS) + + +# You can make a matrix out of entries all of the same type like so: +mat <- matrix(nrow = 3, ncol = 2, c(1,2,3,4,5,6)) +mat +# => +# [,1] [,2] +# [1,] 1 4 +# [2,] 2 5 +# [3,] 3 6 +# Unlike a vector, the class of a matrix is "matrix", no matter what's in it +class(mat) # => "matrix" +# Ask for the first row +mat[1,] # 1 4 +# Perform operation on the first column +3 * mat[,1] # 3 6 9 +# Ask for a specific cell +mat[3,2] # 6 + + +# Transpose the whole matrix +t(mat) +# => +# [,1] [,2] [,3] +# [1,] 1 2 3 +# [2,] 4 5 6 + + +# Matrix multiplication +mat %*% t(mat) +# => +# [,1] [,2] [,3] +# [1,] 17 22 27 +# [2,] 22 29 36 +# [3,] 27 36 45 + + +# cbind() sticks vectors together column-wise to make a matrix +mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog")) +mat2 +# => +# [,1] [,2] +# [1,] "1" "dog" +# [2,] "2" "cat" +# [3,] "3" "bird" +# [4,] "4" "dog" +class(mat2) # matrix +# Again, note what happened! +# Because matrices must contain entries all of the same class, +# everything got converted to the character class +c(class(mat2[,1]), class(mat2[,2])) + + +# rbind() sticks vectors together row-wise to make a matrix +mat3 <- rbind(c(1,2,4,5), c(6,7,0,4)) +mat3 +# => +# [,1] [,2] [,3] [,4] +# [1,] 1 2 4 5 +# [2,] 6 7 0 4 +# Ah, everything of the same class. No coercions. Much better. + + +# TWO-DIMENSIONAL (DIFFERENT CLASSES) + + +# For columns of different types, use a data frame +# This data structure is so useful for statistical programming, +# a version of it was added to Python in the package "pandas". + + +students <- data.frame(c("Cedric","Fred","George","Cho","Draco","Ginny"), + c(3,2,2,1,0,-1), + c("H", "G", "G", "R", "S", "G")) +names(students) <- c("name", "year", "house") # name the columns +class(students) # "data.frame" +students +# => +# name year house +# 1 Cedric 3 H +# 2 Fred 2 G +# 3 George 2 G +# 4 Cho 1 R +# 5 Draco 0 S +# 6 Ginny -1 G +class(students$year) # "numeric" +class(students[,3]) # "factor" +# find the dimensions +nrow(students) # 6 +ncol(students) # 3 +dim(students) # 6 3 +# The data.frame() function converts character vectors to factor vectors +# by default; turn this off by setting stringsAsFactors = FALSE when +# you create the data.frame +?data.frame + + +# There are many twisty ways to subset data frames, all subtly unalike +students$year # 3 2 2 1 0 -1 +students[,2] # 3 2 2 1 0 -1 +students[,"year"] # 3 2 2 1 0 -1 + + +# An augmented version of the data.frame structure is the data.table +# If you're working with huge or panel data, or need to merge a few data +# sets, data.table can be a good choice. Here's a whirlwind tour: +install.packages("data.table") # download the package from CRAN +require(data.table) # load it +students <- as.data.table(students) +students # note the slightly different print-out +# => +# name year house +# 1: Cedric 3 H +# 2: Fred 2 G +# 3: George 2 G +# 4: Cho 1 R +# 5: Draco 0 S +# 6: Ginny -1 G +students[name=="Ginny"] # get rows with name == "Ginny" +# => +# name year house +# 1: Ginny -1 G +students[year==2] # get rows with year == 2 +# => +# name year house +# 1: Fred 2 G +# 2: George 2 G +# data.table makes merging two data sets easy +# let's make another data.table to merge with students +founders <- data.table(house=c("G","H","R","S"), + founder=c("Godric","Helga","Rowena","Salazar")) +founders +# => +# house founder +# 1: G Godric +# 2: H Helga +# 3: R Rowena +# 4: S Salazar +setkey(students, house) +setkey(founders, house) +students <- founders[students] # merge the two data sets by matching "house" +setnames(students, c("house","houseFounderName","studentName","year")) +students[,order(c("name","year","house","houseFounderName")), with=F] +# => +# studentName year house houseFounderName +# 1: Fred 2 G Godric +# 2: George 2 G Godric +# 3: Ginny -1 G Godric +# 4: Cedric 3 H Helga +# 5: Cho 1 R Rowena +# 6: Draco 0 S Salazar + + +# data.table makes summary tables easy +students[,sum(year),by=house] +# => +# house V1 +# 1: G 3 +# 2: H 3 +# 3: R 1 +# 4: S 0 + + +# To drop a column from a data.frame or data.table, +# assign it the NULL value +students$houseFounderName <- NULL +students +# => +# studentName year house +# 1: Fred 2 G +# 2: George 2 G +# 3: Ginny -1 G +# 4: Cedric 3 H +# 5: Cho 1 R +# 6: Draco 0 S + + +# Drop a row by subsetting +# Using data.table: +students[studentName != "Draco"] +# => +# house studentName year +# 1: G Fred 2 +# 2: G George 2 +# 3: G Ginny -1 +# 4: H Cedric 3 +# 5: R Cho 1 +# Using data.frame: +students <- as.data.frame(students) +students[students$house != "G",] +# => +# house houseFounderName studentName year +# 4 H Helga Cedric 3 +# 5 R Rowena Cho 1 +# 6 S Salazar Draco 0 + + +# MULTI-DIMENSIONAL (ALL ELEMENTS OF ONE TYPE) + + +# Arrays creates n-dimensional tables +# All elements must be of the same type +# You can make a two-dimensional table (sort of like a matrix) +array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4)) +# => +# [,1] [,2] [,3] [,4] +# [1,] 1 4 8 3 +# [2,] 2 5 9 6 +# You can use array to make three-dimensional matrices too +array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2)) +# => +# , , 1 +# +# [,1] [,2] +# [1,] 2 8 +# [2,] 300 9 +# [3,] 4 0 +# +# , , 2 +# +# [,1] [,2] +# [1,] 5 66 +# [2,] 60 7 +# [3,] 0 847 + + +# LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES) + + +# Finally, R has lists (of vectors) +list1 <- list(time = 1:40) +list1$price = c(rnorm(40,.5*list1$time,4)) # random +list1 +# You can get items in the list like so +list1$time # one way +list1[["time"]] # another way +list1[[1]] # yet another way +# => +# [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 +# [34] 34 35 36 37 38 39 40 +# You can subset list items like any other vector +list1$price[4] + + +# Lists are not the most efficient data structure to work with in R; +# unless you have a very good reason, you should stick to data.frames +# Lists are often returned by functions that perform linear regressions + + +################################################## +# The apply() family of functions +################################################## + + +# Remember mat? +mat +# => +# [,1] [,2] +# [1,] 1 4 +# [2,] 2 5 +# [3,] 3 6 +# Use apply(X, MARGIN, FUN) to apply function FUN to a matrix X +# over rows (MAR = 1) or columns (MAR = 2) +# That is, R does FUN to each row (or column) of X, much faster than a +# for or while loop would do +apply(mat, MAR = 2, jiggle) +# => +# [,1] [,2] +# [1,] 3 15 +# [2,] 7 19 +# [3,] 11 23 +# Other functions: ?lapply, ?sapply + + +# Don't feel too intimidated; everyone agrees they are rather confusing + + +# The plyr package aims to replace (and improve upon!) the *apply() family. +install.packages("plyr") +require(plyr) +?plyr + + + + + + +######################### +# Loading data +######################### + + +# "pets.csv" is a file on the internet +# (but it could just as easily be be a file on your own computer) +pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv") +pets +head(pets, 2) # first two rows +tail(pets, 1) # last row + + +# To save a data frame or matrix as a .csv file +write.csv(pets, "pets2.csv") # to make a new .csv file +# set working directory with setwd(), look it up with getwd() + + +# Try ?read.csv and ?write.csv for more information + + + + + + +######################### +# Plots +######################### + + +# BUILT-IN PLOTTING FUNCTIONS +# Scatterplots! +plot(list1$time, list1$price, main = "fake data") +# Regressions! +linearModel <- lm(price ~ time, data = list1) +linearModel # outputs result of regression +# Plot regression line on existing plot +abline(linearModel, col = "red") +# Get a variety of nice diagnostics +plot(linearModel) +# Histograms! +hist(rpois(n = 10000, lambda = 5), col = "thistle") +# Barplots! +barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow")) + + +# GGPLOT2 +# But these are not even the prettiest of R's plots +# Try the ggplot2 package for more and better graphics +install.packages("ggplot2") +require(ggplot2) +?ggplot2 +pp <- ggplot(students, aes(x=house)) +pp + geom_histogram() +ll <- as.data.table(list1) +pp <- ggplot(ll, aes(x=time,price)) +pp + geom_point() +# ggplot2 has excellent documentation (available http://docs.ggplot2.org/current/) + + + + + + +``` + + +## How do I get R? + + +* Get R and the R GUI from [http://www.r-project.org/](http://www.r-project.org/) +* [RStudio](http://www.rstudio.com/ide/) is another GUI \ No newline at end of file -- cgit v1.2.3