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still on a middle way
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Akira Hirose
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@ -221,26 +221,25 @@ class(-Inf) # "numeric"
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# 不正な計算は "not-a-number"になる
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0 / 0 # NaN
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class(NaN) # "numeric"
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# You can do arithmetic on two vectors with length greater than 1,
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# so long as the larger vector's length is an integer multiple of the smaller
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# 長さが1より大きなベクター同士で計算ができます
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# どちらかが長い場合、短い方は何度も繰り返して使われます
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c(1,2,3) + c(1,2,3) # 2 4 6
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# CHARACTERS
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# There's no difference between strings and characters in R
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# 文字
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# Rでは、文字列と文字に区別がありません
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"Horatio" # "Horatio"
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class("Horatio") # "character"
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class('H') # "character"
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# Those were both character vectors of length 1
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# Here is a longer one:
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# 上記は両方とも、長さ1のベクターです
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# 以下は、より長いものです
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c('alef', 'bet', 'gimmel', 'dalet', 'he')
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# =>
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# "alef" "bet" "gimmel" "dalet" "he"
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length(c("Call","me","Ishmael")) # 3
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# You can do regex operations on character vectors:
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# 正規表現処理を文字ベクターに使えます
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substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis "
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gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis."
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# R has several built-in character vectors:
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# Rはいくつかの文字ベクターを組み込みで持っています
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letters
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# =>
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# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
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@ -248,40 +247,40 @@ letters
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month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
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# LOGICALS
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# In R, a "logical" is a boolean
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# 論理
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# Rでは、Booleanは論理（logical）型です
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class(TRUE) # "logical"
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class(FALSE) # "logical"
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# Their behavior is normal
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# 以下は正しい動きです
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TRUE == TRUE # TRUE
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TRUE == FALSE # FALSE
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FALSE != FALSE # FALSE
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FALSE != TRUE # TRUE
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# Missing data (NA) is logical, too
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# 無いデータ (NA) も論理型です
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class(NA) # "logical"
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# Here we get a logical vector with many elements:
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# 以下のようにすると、複数の要素を持つ、論理型ベクターが返ります
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c('Z', 'o', 'r', 'r', 'o') == "Zorro" # FALSE FALSE FALSE FALSE FALSE
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c('Z', 'o', 'r', 'r', 'o') == "Z" # TRUE FALSE FALSE FALSE FALSE
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# FACTORS
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# The factor class is for categorical data
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# Factors can be ordered (like childrens' grade levels) or unordered (like gender)
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# ファクター
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# ファクタークラスは、カテゴリカルデータようのクラスです
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# ファクターは、子供の学年のように順序がつけられるものか、性別のように順序がないものがあります
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factor(c("female", "female", "male", "NA", "female"))
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# female female male NA female
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# Levels: female male NA
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# The "levels" are the values the categorical data can take
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# "levels" は、カテゴリカルデータがとりうる値を返します
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levels(factor(c("male", "male", "female", "NA", "female"))) # "female" "male" "NA"
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# If a factor vector has length 1, its levels will have length 1, too
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# ファクターベクターの長さが1ならば、そのlevelも1です
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length(factor("male")) # 1
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length(levels(factor("male"))) # 1
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# Factors are commonly seen in data frames, a data structure we will cover later
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# ファクターは、この後で紹介するデータフレーム（というデータ型）内で、よくみられます
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data(infert) # "Infertility after Spontaneous and Induced Abortion"
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levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs"
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# NULL
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# "NULL" is a weird one; use it to "blank out" a vector
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# "NULL" は変わった型です。ベクターを空にするときに使います
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class(NULL) # NULL
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parakeet
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# =>
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@ -292,11 +291,11 @@ parakeet
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# NULL
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# TYPE COERCION
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# Type-coercion is when you force a value to take on a different type
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# 型の強制
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# 型の強制は、ある値を、強制的にある型として利用する事です
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as.character(c(6, 8)) # "6" "8"
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as.logical(c(1,0,1,1)) # TRUE FALSE TRUE TRUE
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# If you put elements of different types into a vector, weird coercions happen:
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# さまざまな要素が入っているベクターに対して型の強制を行うと、おかしなことになります
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c(TRUE, 4) # 1 4
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c("dog", TRUE, 4) # "dog" "TRUE" "4"
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as.numeric("Bilbo")
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@ -306,8 +305,8 @@ as.numeric("Bilbo")
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# NAs introduced by coercion
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# Also note: those were just the basic data types
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# There are many more data types, such as for dates, time series, etc.
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# 追記: ここで紹介したのは、基本的な型だけです
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# 実際には、日付（dates）や時系列（time series）など、いろいろな型があります
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@ -315,40 +314,40 @@ as.numeric("Bilbo")
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##################################################
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# Variables, loops, if/else
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# 変数、ループ、もし/ほかに（if/else）
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##################################################
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# A variable is like a box you store a value in for later use.
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# We call this "assigning" the value to the variable.
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# Having variables lets us write loops, functions, and if/else statements
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# 変数は、ある値を後で使うために入れておく、箱のようなものです
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# 箱に入れることを、変数に値を代入する、といいます
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# 変数を使うと、ループや関数、if/else 分岐を利用できます
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# VARIABLES
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# Lots of way to assign stuff:
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x = 5 # this is possible
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y <- "1" # this is preferred
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TRUE -> z # this works but is weird
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# 変数
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# 代入する方法はいろいろあります
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x = 5 # これはできます
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y <- "1" # これがおすすめです
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TRUE -> z # これも使えますが、変です
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# LOOPS
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# We've got for loops
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# ループ
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# forでループできます
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for (i in 1:4) {
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print(i)
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}
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# We've got while loops
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# whileでループできます
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a <- 10
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while (a > 4) {
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cat(a, "...", sep = "")
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a <- a - 1
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}
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# Keep in mind that for and while loops run slowly in R
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# Operations on entire vectors (i.e. a whole row, a whole column)
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# or apply()-type functions (we'll discuss later) are preferred
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# Rでは、forやwhileは遅いことを覚えておいてください
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# 処理を行う場合は、ベクター丸ごと処理する（つまり、行全体や、列全体）を指定して行うか、
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# 後述する、apply()系の関数を使うのがお勧めです
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# IF/ELSE
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# Again, pretty standard
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# ごく普通のif文です
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if (4 > 3) {
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print("4 is greater than 3")
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} else {
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@ -358,14 +357,14 @@ if (4 > 3) {
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# [1] "4 is greater than 3"
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# FUNCTIONS
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# Defined like so:
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# 関数
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# 以下のように定義します
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jiggle <- function(x) {
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x = x + rnorm(1, sd=.1) #add in a bit of (controlled) noise
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x = x + rnorm(1, sd=.1) #すこしだけ（制御された）ノイズを入れます
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return(x)
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}
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# Called like any other R function:
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jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043
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# 他のR関数と同じように呼びます
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jiggle(5) # 5±ε. set.seed(2716057)をすると、jiggle(5)==5.005043
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@ -373,26 +372,26 @@ jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043
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###########################################################################
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# Data structures: Vectors, matrices, data frames, and arrays
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# データ構造: ベクター、行列、データフレーム、配列
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###########################################################################
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# ONE-DIMENSIONAL
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# 1次元
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# Let's start from the very beginning, and with something you already know: vectors.
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# まずは基本からです。すでにご存じのベクターからです
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vec <- c(8, 9, 10, 11)
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vec # 8 9 10 11
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# We ask for specific elements by subsetting with square brackets
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# (Note that R starts counting from 1)
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# 特定の要素を、[角括弧]による指定で取り出せます
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# (Rでは、最初の要素は1番目と数えます)
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vec[1] # 8
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letters[18] # "r"
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LETTERS[13] # "M"
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month.name[9] # "September"
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c(6, 8, 7, 5, 3, 0, 9)[3] # 7
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# We can also search for the indices of specific components,
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# 特定のルールに当てはまる要素を見つけることもできます
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which(vec %% 2 == 0) # 1 3
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# grab just the first or last few entries in the vector,
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# 最初か最後の数個を取り出すこともできます
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head(vec, 1) # 8
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tail(vec, 2) # 10 11
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# or figure out if a certain value is in the vector
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