Japanese version. Just on a middle of way.

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+---
+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
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