learnxinyminutes-docs/zh-cn/r-cn.html.markdown

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.namen

使用 list.name[n] 来访问第 n 个列表元素，有时候需要使用 list.namen

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