From 0fc4b4d5d53a677cfc8c9889af328427c2fa6e24 Mon Sep 17 00:00:00 2001 From: alswl Date: Sat, 24 Aug 2013 23:27:50 +0800 Subject: add xiaoqi's translation --- zh-cn/r-cn.html.markdown | 691 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 691 insertions(+) create mode 100644 zh-cn/r-cn.html.markdown (limited to 'zh-cn') diff --git a/zh-cn/r-cn.html.markdown b/zh-cn/r-cn.html.markdown new file mode 100644 index 00000000..1bd83c60 --- /dev/null +++ b/zh-cn/r-cn.html.markdown @@ -0,0 +1,691 @@ + +# Comments start with hashtags. +# 评论以 # 开始 + +# You can't make a multi-line comment per se, +# but you can stack multiple comments like so. +# 你不能在每一个se下执行多个注释, +# 但是你可以像这样把命注释内容堆叠起来. +# in Windows, hit COMMAND-ENTER to execute a line +# 在windows下,点击回车键来执行一条命令 + + +################################################################### +# Stuff you can do without understanding anything about programming +# 素材可以使那些不懂编程的人同样得心用手 +################################################################### + +data() # Browse pre-loaded data sets +data() # 浏览预加载的数据集 +data(rivers) # Lengths of Major North American Rivers +data(rivers) # 北美主要河流的长度（数据集） +ls() # Notice that "rivers" appears in the workspace +ls() # 在工作站中查看”河流“文件夹是否出现 +head(rivers) # peek at the dataset +head(rivers) # 浏览数据集 +# 735 320 325 392 524 450 +length(rivers) # how many rivers were measured? +# 141 +length(rivers) # 测量了多少条河流 +summary(rivers) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 135.0 310.0 425.0 591.2 680.0 3710.0 +#查看”河流“数据集的特征 +# 最小值. 1st Qu. 中位数 平均值 最大值 +# 135.0 310.0 425.0 591.2 680.0 3710.0 +stem(rivers) #stem-and-leaf plot (like a histogram) +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 | +# 小数点向|右边保留两位数字 +# +# 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)) #Notice that the data are neither normal nor log-normal! Take that, Bell Curve fundamentalists. +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 | +# 小数点向|左边保留一位数字 +# +# 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) #play around with these parameters +hist(rivers, col="#333333", border="white", breaks=25) #给river做统计频数直方图，包含了这些参数（名称，颜色，边界，空白） +hist(log(rivers), col="#333333", border="white", breaks=25) #you'll do more plotting later +hist(log(rivers), col="#333333", border="white", breaks=25) #稍后你还可以做更多的绘图，统计频数直方图，包含了这些参数（river数据集的log值，颜色，边界，空白） +hist(rivers, col="#333333", border="white", breaks=25) #play around with these parameters +hist(rivers, col="#333333", border="white", breaks=25) #运行同济频数直方图的这些参数 + +#Here's another neat data set that comes pre-loaded. R has tons of these. data() +#这里还有其他一些简洁的数据集可以被提前加载。R语言包括大量这种类型的数据集 +data(discoveries) +#数据集（发现） +plot(discoveries, col="#333333", lwd=3, xlab="Year", main="Number of important discoveries per year") +#绘图（发现，颜色负值，宽度负值，X轴名称，主题：Number of important discoveries per year） + + + +#rather than leaving the default ordering (by year) we could also sort to see what's typical +#宁可舍弃也不执行排序（按照年份完成）我们可以分类来查看这是那些类型 +sort(discoveries) #给（发现）分类 +# [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) +#茎叶图（发现，在原来的基础上降尺度扩大两倍） +# +# The decimal point is at the | +# 小数点在| +# +# 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) +#最大值(发现) +# 12 + +summary(discoveries) +#数据集特征(发现) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.0 2.0 3.0 3.1 4.0 12.0 + + + + +#Basic statistical operations don't require any programming knowledge either +#基本的统计学操作也不需要任何编程知识 + +#roll a die a few times +#随机输出数据 +round(runif(7, min=.5, max=6.5)) +#round(产生均匀分布的随机数，进行四舍五入(7个, 最小值为0.5, max=6.5)) +# 1 4 6 1 4 6 4 + +#your numbers will differ from mine unless we set the same random.seed(31337) +#你输出的结果将会与我们给出的不同，除非我们设置了同样的随机种子 random.seed(31337) + + +#draw from a standard Gaussian 9 times +#从标准高斯函数中随机的提取9次结果 +rnorm(9) +# [1] 0.07528471 1.03499859 1.34809556 -0.82356087 0.61638975 -1.88757271 +# [7] -0.59975593 0.57629164 1.08455362 + + + + + + + + + +######################### +# Basic programming stuff +# 基本的编程素材 +######################### + +# NUMBERS + +# "numeric" means double-precision floating-point numbers +#“数值”指的是双精度的浮点数 +5 # 5 +class(5) # "numeric" +#定义(5)为数值型变量 # "numeric" +5e4 # 50000 #handy when dealing with large,small,or variable orders of magnitude +#5×104次方 可以手写输入改变数量级的大小将变量扩大 +6.02e23 # Avogadro's number +#阿伏伽德罗常数 +1.6e-35 # Planck length +#布朗克长度 + +# long-storage integers are written with L +#长存储整数并用L书写 +5L # 5 +#输出5L +class(5L) # "integer" +#（5L）的类型， 整数型 + +# Try ?class for more information on the class() function +#可以自己试一试？用class()功能函数定义更多的信息 +# In fact, you can look up the documentation on `xyz` with ?xyz +#事实上，你可以找一些文件查阅“xyz”以及xyz的差别 +# or see the source for `xyz` by evaluating xyz +#或者通过评估xyz来查看“xyz”的来源 + +# Arithmetic +#算法 +10 + 66 # 76 +53.2 - 4 # 49.2 +2 * 2.0 # 4 +3L / 4 # 0.75 +3 %% 2 # 1 + +# Weird number types +#超自然数的类型 +class(NaN) # "numeric" +class(Inf) # "numeric" +class(-Inf) # "numeric" #used in for example integrate( dnorm(x), 3, Inf ) -- which obviates Z-score tables +#定义以上括号内的数均为数值型变量，利用实例中的整数（正态分布函数（X）,3,Inf ）消除Z轴列表 + +# but beware, NaN isn't the only weird type... +# 但要注意，NaN并不是仅有的超自然类型。。。 +class(NA) # see below +#定义（NA）下面的部分会理解 +class(NULL) # NULL +#定义（NULL）无效的 + + +# SIMPLE LISTS +#简单的数据集 +c(6, 8, 7, 5, 3, 0, 9) # 6 8 7 5 3 0 9 +#输出数值型向量（6 8 7 5 3 0 9） +c('alef', 'bet', 'gimmel', 'dalet', 'he') +#输出字符型变量# "alef" "bet" "gimmel" "dalet" "he" +c('Z', 'o', 'r', 'o') == "Zoro" # FALSE FALSE FALSE FALSE +#输出逻辑型变量FALSE FALSE FALSE FALSE + +#some more nice built-ins +#一些优雅的内置功能 +5:15 # 5 6 7 8 9 10 11 12 13 14 15 +#从5-15输出，以进度为1递增 + +seq(from=0, to=31337, by=1337) +#输出序列（从0到31337，以1337递增） +# [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 +#字符型变量，26个 +# [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" +#表示月份的变量 + + +# Access the n'th element of a list with list.name[n] or sometimes list.name[[n]] +#访问数据集名字为[n]的第n个元素 + +letters[18] # "r" +#访问其中的第18个变量 +LETTERS[13] # "M" +#用大写访问其中的第13个变量 +month.name[9] # "September" +#访问名字文件中第9个变量 +c(6, 8, 7, 5, 3, 0, 9)[3] # 7 +#访问向量中的第三个变量 + + + +# 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 + + -- cgit v1.2.3