# 评论以 # 开始

# R 语言原生不支持 多行注释
# 但是你可以像这样来多行注释

# 在窗口里按回车键可以执行一条命令


###################################################################
# 不用懂编程就可以开始动手了
###################################################################

data()	# 浏览内建的数据集
data(rivers)	# 北美主要河流的长度（数据集）
ls()	# 在工作空间中查看「河流」是否出现
head(rivers)	# 撇一眼数据集
# 735 320 325 392 524 450
length(rivers)	# 我们测量了多少条河流？
# 141
summary(rivers)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  135.0   310.0   425.0   591.2   680.0  3710.0 
stem(rivers)	# 茎叶图（一种类似于直方图的展现形式）
#
#  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))	# 查看数据集的方式既不是标准形式，也不是取log后的结果! 看起来，是钟形曲线形式的基本数据集

#  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


hist(rivers, col="#333333", border="white", breaks=25)	# 试试用这些参数画画 （译者注：给 river 做统计频数直方图，包含了这些参数：数据源，颜色，边框，空格）
hist(log(rivers), col="#333333", border="white", breaks=25)	#你还可以做更多式样的绘图

# 还有其他一些简单的数据集可以被用来加载。R 语言包括了大量这种 data()
data(discoveries)
plot(discoveries, col="#333333", lwd=3, xlab="Year", main="Number of important discoveries per year")
# 译者注：参数为（数据源，颜色，线条宽度，X 轴名称，标题）
plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year", main="Number of important discoveries per year")


# 除了按照默认的年份排序，我们还可以排序来发现特征
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) # 译者注：茎叶图（数据，放大系数）
#
#  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 




#基本的统计学操作也不需要任何编程知识

#随机生成数据
round(runif(7, min=.5, max=6.5))
# 译者注：runif 产生随机数，round 四舍五入
# 1 4 6 1 4 6 4

# 你输出的结果会和我们给出的不同，除非我们设置了相同的随机种子 random.seed(31337)


#从标准高斯函数中随机生成 9 次
rnorm(9)
# [1]  0.07528471  1.03499859  1.34809556 -0.82356087  0.61638975 -1.88757271
# [7] -0.59975593  0.57629164  1.08455362









#########################
# 基础编程
#########################

# 数值

#“数值”指的是双精度的浮点数
5	# 5
class(5)	# "numeric"
5e4	# 50000				# 用科学技术法方便的处理极大值、极小值或者可变的量级
6.02e23	# 阿伏伽德罗常数#
1.6e-35	# 布朗克长度

# 长整数并用 L 结尾
5L	# 5  
#输出5L
class(5L)	# "integer"

# 可以自己试一试？用 class() 函数获取更多信息
# 事实上，你可以找一些文件查阅 `xyz` 以及xyz的差别
# `xyz` 用来查看源码实现，?xyz 用来看帮助

# 算法
10 + 66	# 76
53.2 - 4	# 49.2
2 * 2.0	# 4
3L / 4	# 0.75
3 %% 2	# 1

# 特殊数值类型
class(NaN)	# "numeric"
class(Inf)	# "numeric"
class(-Inf)	# "numeric"		# 在以下场景中会用到 integrate( dnorm(x), 3, Inf ) -- 消除 Z 轴数据

# 但要注意，NaN 并不是唯一的特殊数值类型……
class(NA)	# 看上面
class(NULL)	# NULL


# 简单列表
c(6, 8, 7, 5, 3, 0, 9)	# 6 8 7 5 3 0 9
c('alef', 'bet', 'gimmel', 'dalet', 'he')	
c('Z', 'o', 'r', 'o') == "Zoro"	# FALSE FALSE FALSE FALSE

# 一些优雅的内置功能
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

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"


# Access the n'th element of a list with list.name[n] or sometimes list.name[[n]]
# 使用 list.name[n] 来访问第 n 个列表元素，有时候需要使用 list.name[[n]]
letters[18]	# "r"
LETTERS[13]	# "M"
month.name[9]	# "September"
c(6, 8, 7, 5, 3, 0, 9)[3]	# 7 



# CHARACTERS
#特性
# There's no difference between strings and characters in R
# 字符串和字符在R语言中没有区别
"Horatio"	# "Horatio"
#字符输出"Horatio"
class("Horatio") # "character"
#字符串输出("Horatio") # "character"
substr("Fortuna multis dat nimis, nulli satis.", 9, 15)	# "multis "
#提取字符串("Fortuna multis dat nimis, nulli satis.", 第9个到15个之前并输出)	
gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.")	# "Fortøna møltis dat nimis, nølli satis."
#替换字符春，用ø替换u



# LOGICALS
#逻辑值

# booleans
#布尔运算
class(TRUE)	# "logical"
#定义为真，逻辑型
class(FALSE)	# "logical"
#定义为假，逻辑型
# Behavior is normal
#表现的标准形式
TRUE == TRUE	# TRUE
TRUE == FALSE	# FALSE
FALSE != FALSE	# FALSE
FALSE != TRUE	# TRUE
# Missing data (NA) is logical, too
#缺失数据也是逻辑型的
class(NA)	# "logical"
#定义NA为逻辑型



# FACTORS
#因子
# The factor class is for categorical data
#因子是分类数据的定义函数
# which can be ordered (like childrens' grade levels)
#可以使有序的（就像儿童的等级水平）
# or unordered (like gender)
#也可以是无序的(就像性别）
levels(factor(c("female", "male", "male", "female", "NA", "female")))	# "female" "male"   "NA" 
#c("female", "male", "male", "female", "NA", "female")向量，变量是字符型，levels factor（）因子的等级水平

factor(c("female", "female", "male", "NA", "female"))    
#  female female male   NA     female
# Levels: female male NA

data(infert)	#Infertility after Spontaneous and Induced Abortion
#数据集（感染） 自然以及引产导致的不育症
levels(infert$education)	# "0-5yrs"  "6-11yrs" "12+ yrs"
#等级（感染与教育程度） 输出



# VARIABLES
#变量

# Lots of way to assign stuff
#许多种方式用来分配素材
x = 5 # this is possible
#x = 5可能的
y <- "1" # this is preferred
#y <- "1" 优先级的
TRUE -> z # this works but is weird
#输出真实的，存在一个超自然数满足条件

# We can use coerce variables to different classes
#我们还可以使用枪支变量去进行不同的定义
as.numeric(y)	# 1
#定义数值型
as.character(x)	# "5"
#字符型


# LOOPS
#循环

# We've got for loops
#循环语句
for (i in 1:4) {
  print(i)
}
#定义一个i，从1-4输出

# We've got while loops
#我们可以获取循环结构
a <- 10
while (a > 4) {
	cat(a, "...", sep = "")
	a <- a - 1
}
#把10负值为a，a＜4，输出文件（a,"...",sep="" ),跳出继续下一个循环取a=a-1,如此循环，直到a=10终止
# Keep in mind that for and while loops run slowly in R
#在R语言中牢记 for和它的循环结构
# Operations on entire vectors (i.e. a whole row, a whole column)
#牢记矢量中附带的操作（例如，整行和整列）
# or apply()-type functions (we'll discuss later) are preferred
#或者优先使用()-函数,稍后会进行讨论

# IF/ELSE
#判断分支

# Again, pretty standard
#再一次，看这些优雅的标准
if (4 > 3) {
	print("Huzzah! It worked!")
} else {
	print("Noooo! This is blatantly illogical!")
}

# =>
# [1] "Huzzah! It worked!"

# FUNCTIONS
#功能函数

# Defined like so:
#定义如下
jiggle <- function(x) {
	x+ rnorm(x, sd=.1)	#add in a bit of (controlled) noise
	return(x)
}
#把功能函数x负值给jiggle，

# Called like any other R function:
jiggle(5)	# 5±ε. After set.seed(2716057), jiggle(5)==5.005043

#########################
# Fun with data: vectors, matrices, data frames, and arrays
# 数据参数：向量，矩阵，数据框，数组，
#########################

# ONE-DIMENSIONAL
#单维度
# You can vectorize anything, so long as all components have the same type
#你可以将任何东西矢量化，因此所有的组分都有相同的类型
vec <- c(8, 9, 10, 11) 
vec	#  8  9 10 11
# The class of a vector is the class of its components
#矢量class表示这一组分的类型
class(vec)	# "numeric"
# If you vectorize items of different classes, weird coercions happen
#如果你强制的将不同类型的classes矢量化，会发生超自然形式的函数，例如都转变成数值型、字符型
c(TRUE, 4)	# 1 4
c("dog", TRUE, 4)	# "dog"  "TRUE" "4"

# We ask for specific components like so (R starts counting from 1)
#我们可以找寻特定的组分，例如这个例子（R从1算起）
vec[1]	# 8
# We can also search for the indices of specific components,
#我们也可以从这些特定组分中找寻这些指标
which(vec %% 2 == 0)	# 1 3
# or grab just the first or last entry in the vector
#抓取矢量中第1个和最后一个字符
head(vec, 1)	# 8
tail(vec, 1)	# 11
#如果指数结束或不存在即"goes over" 可以获得NA
# If an index "goes over" you'll get NA:
vec[6]	# NA
# You can find the length of your vector with length()
#你也可以找到矢量的长度
length(vec)	# 4

# You can perform operations on entire vectors or subsets of vectors
#你可以将整个矢量或者子矢量集进行展示
vec * 4	# 16 20 24 28
#
vec[2:3] * 5	# 25 30
# and there are many built-in functions to summarize vectors
#这里有许多内置的功能函数，并且可对矢量特征进行总结
mean(vec)	# 9.5
var(vec)	# 1.666667
sd(vec)	# 1.290994
max(vec)	# 11
min(vec)	# 8
sum(vec)	# 38

# TWO-DIMENSIONAL (ALL ONE CLASS)
#二维函数

# You can make a matrix out of entries all of the same type like so:
#你可以建立矩阵，保证所有的变量形式相同
mat <- matrix(nrow = 3, ncol = 2, c(1,2,3,4,5,6))
#建立mat矩阵，3行2列，从1到6排列，默认按列排布
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
#访问第一行的字符
mat[1,]	# 1 4
# Perform operation on the first column
#优先输入第一列，分别×3输出
3 * mat[,1]	# 3 6 9
# Ask for a specific cell
#访问特殊的单元，第3行第二列
mat[3,2]	# 6
# Transpose the whole matrix
#转置整个矩阵，变成2行3列
t(mat)
# =>
#      [,1] [,2] [,3]
# [1,]    1    2    3
# [2,]    4    5    6

# cbind() sticks vectors together column-wise to make a matrix
把两个矩阵按列合并，形成新的矩阵
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
#定义mat2矩阵
# Again, note what happened!
#同样的注释
# Because matrices must contain entries all of the same class,
#矩阵必须包含同样的形式
# everything got converted to the character class
#每一个变量都可以转化成字符串形式
c(class(mat2[,1]), class(mat2[,2]))

# rbind() sticks vectors together row-wise to make a matrix
#按行合并两个向量，建立新的矩阵
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
# Aah, everything of the same class. No coercions. Much better.

# TWO-DIMENSIONAL (DIFFERENT CLASSES)
##二维函数(不同的变量类型)

# For columns of different classes, use the data frame 
利用数组可以将不同类型放在一起
dat <- data.frame(c(5,2,1,4), c("dog", "cat", "bird", "dog"))
#dat<-数据集(c(5,2,1,4), c("dog", "cat", "bird", "dog"))
names(dat) <- c("number", "species") # name the columns
#给每一个向量命名
class(dat)	# "data.frame"
#建立数据集dat
dat
# =>
#   number species
# 1      5     dog
# 2      2     cat
# 3      1    bird
# 4      4     dog
class(dat$number)	# "numeric"
class(dat[,2])	# "factor"
# The data.frame() function converts character vectors to factor vectors
#数据集，将字符特征转化为因子矢量

# There are many twisty ways to subset data frames, all subtly unalike
#这里有许多种生成数据集的方法，所有的都很巧妙但又不相似
dat$number	# 5 2 1 4
dat[,1]	# 5 2 1 4
dat[,"number"]	# 5 2 1 4

# MULTI-DIMENSIONAL (ALL OF ONE CLASS)
#多维函数
# Arrays creates n-dimensional tables
#利用数组创造一个n维的表格
# You can make a two-dimensional table (sort of like a matrix)
#你可以建立一个2维表格（类型和矩阵相似）
array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
#数组(c(c(1,2,4,5),c(8,9,3,6)),有前两个向量组成，2行4列 
# =>
#      [,1] [,2] [,3] [,4]
# [1,]    1    4    8    3
# [2,]    2    5    9    6
# You can use array to make three-dimensional matrices too
#你也可以利用数组建立一个三维的矩阵
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)
#列表（多维的，不同类型的）

# Finally, R has lists (of vectors)
#R语言有列表的形式
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
#你可以获得像上面列表的形式
list1$time
# You can subset list items like vectors
#你也可以获取他们的子集，一种类似于矢量的形式
list1$price[4]

#########################
# The apply() family of functions
#apply()函数家族的应用
#########################

# Remember mat?
#输出mat矩阵
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
#使用(X, MARGIN, FUN)将一个function功能函数根据其特征应用到矩阵x中
# over rows (MAR = 1) or columns (MAR = 2)
#规定行列，其边界分别为1,2
# That is, R does FUN to each row (or column) of X, much faster than a 
#即就是，R定义一个function使每一行/列的x快于一个for或者while循环
# for or while loop would do
apply(mat, MAR = 2, myFunc)
# =>
#      [,1] [,2]
# [1,]    3   15
# [2,]    7   19
# [3,]   11   23
# Other functions: ?lapply, ?sapply
其他的功能函数，

# Don't feel too intimidated; everyone agrees they are rather confusing
#不要被这些吓到，许多人在此都会容易混淆
# The plyr package aims to replace (and improve upon!) the *apply() family.
#plyr程序包的作用是用来改进family函数家族

install.packages("plyr")
require(plyr)
?plyr

#########################
# Loading data
#########################

# "pets.csv" is a file on the internet
#"pets.csv" 是网上的一个文本
pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv")
#首先读取这个文本
pets
head(pets, 2) # first two rows
#显示前两行
tail(pets, 1) # last row
#显示最后一行

# To save a data frame or matrix as a .csv file
#以.csv格式来保存数据集或者矩阵
write.csv(pets, "pets2.csv") # to make a new .csv file
#输出新的文本pets2.csv
# set working directory with setwd(), look it up with getwd()
#改变工作路径setwd()，查找工作路径getwd()

# Try ?read.csv and ?write.csv for more information
#试着做一做以上学到的，或者运行更多的信息

#########################
# Plots
#画图
#########################

# Scatterplots!
#散点图
plot(list1$time, list1$price, main = "fake data")
#作图，横轴list1$time，纵轴list1$price，主题fake data
# Regressions!
#退回
linearModel <- lm(price  ~ time, data = list1)
# 线性模型，数据集为list1，以价格对时间做相关分析模型
linearModel # outputs result of regression
#输出拟合结果，并退出
# Plot regression line on existing plot
#将拟合结果展示在图上，颜色设为红色
abline(linearModel, col = "red")
# Get a variety of nice diagnostics
#也可以获取各种各样漂亮的分析图
plot(linearModel)

# Histograms!
#直方图
hist(rpois(n = 10000, lambda = 5), col = "thistle")
#统计频数直方图（）

# Barplots!
#柱状图
barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow"))
#作图，柱的高度负值c(1,4,5,1,2），各个柱子的名称"red","blue","purple","green","yellow"

# Try the ggplot2 package for more and better graphics
#可以尝试着使用ggplot2程序包来美化图片
install.packages("ggplot2")
require(ggplot2)
?ggplot2