From 6b9ac70e5efc9163fd2919b35559e9ea9d7d1703 Mon Sep 17 00:00:00 2001 From: alswl Date: Wed, 18 Sep 2013 21:26:38 +0800 Subject: transalted 50% --- zh-cn/r-cn.html.markdown | 156 +++++++++++++++++------------------------------ 1 file changed, 56 insertions(+), 100 deletions(-) diff --git a/zh-cn/r-cn.html.markdown b/zh-cn/r-cn.html.markdown index d377703e..68867d92 100644 --- a/zh-cn/r-cn.html.markdown +++ b/zh-cn/r-cn.html.markdown @@ -18,8 +18,8 @@ head(rivers) # 撇一眼数据集 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 +# 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 | @@ -34,14 +34,14 @@ stem(rivers) # 茎叶图（一种类似于直方图的展现形式） # 14 | 56 # 16 | 7 # 18 | 9 -# 20 | +# 20 | # 22 | 25 # 24 | 3 -# 26 | -# 28 | -# 30 | -# 32 | -# 34 | +# 26 | +# 28 | +# 30 | +# 32 | +# 34 | # 36 | 1 @@ -50,7 +50,7 @@ stem(log(rivers)) # 查看数据集的方式既不是标准形式，也不是取 # The decimal point is 1 digit(s) to the left of the | # # 48 | 1 -# 50 | +# 50 | # 52 | 15578 # 54 | 44571222466689 # 56 | 023334677000124455789 @@ -65,7 +65,7 @@ stem(log(rivers)) # 查看数据集的方式既不是标准形式，也不是取 # 74 | 84 # 76 | 56 # 78 | 4 -# 80 | +# 80 | # 82 | 2 @@ -101,15 +101,15 @@ stem(discoveries, scale=2) # 译者注：茎叶图（数据，放大系数） # 8 | 0 # 9 | 0 # 10 | 0 -# 11 | +# 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 +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.0 2.0 3.0 3.1 4.0 12.0 @@ -151,7 +151,7 @@ class(5) # "numeric" 1.6e-35 # 布朗克长度 # 长整数并用 L 结尾 -5L # 5 +5L # 5 #输出5L class(5L) # "integer" @@ -178,7 +178,7 @@ 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('alef', 'bet', 'gimmel', 'dalet', 'he') c('Z', 'o', 'r', 'o') == "Zoro" # FALSE FALSE FALSE FALSE # 一些优雅的内置功能 @@ -200,119 +200,80 @@ month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "D letters[18] # "r" LETTERS[13] # "M" month.name[9] # "September" -c(6, 8, 7, 5, 3, 0, 9)[3] # 7 +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 +# 字符串 + +# 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 +# 逻辑值 -# booleans -#布尔运算 +# 布尔值 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 +# 和我们预想的一样 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 +# 缺失数据（NA）也是逻辑值 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 +# 因子 +# 因子是为数据分类排序设计的（像是排序小朋友们的年级或性别） +levels(factor(c("female", "male", "male", "female", "NA", "female"))) # "female" "male" "NA" -factor(c("female", "female", "male", "NA", "female")) +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 +data(infert) # 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 +# 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 +# 有许多种方式用来赋值 +x = 5 # 这样可以 +y <- "1" # 更推荐这样 +TRUE -> z # 这样可行，但是很怪 -# 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 +#我们还可以使用强制转型 as.numeric(y) # 1 -#定义数值型 as.character(x) # "5" -#字符型 - -# LOOPS -#循环 +# 循环 -# We've got for loops -#循环语句 +# for 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 +# while 循环 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 + +# 记住，在 R 语言中 for / while 循环都很慢 +# 建议使用 apply()（我们一会介绍）来错做一串数据（比如一列或者一行数据） # IF/ELSE -#判断分支 -# Again, pretty standard -#再一次，看这些优雅的标准 +# 再来看这些优雅的标准 if (4 > 3) { print("Huzzah! It worked!") } else { @@ -322,30 +283,25 @@ if (4 > 3) { # => # [1] "Huzzah! It worked!" -# FUNCTIONS -#功能函数 +# 函数 -# Defined like so: -#定义如下 +# 定义如下 jiggle <- function(x) { - x+ rnorm(x, sd=.1) #add in a bit of (controlled) noise + 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 +# 和其他 R 语言函数一样调用 +jiggle(5) # 5±ε. 使用 set.seed(2716057) 后， 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， +# 数据容器：vectors, matrices, data frames, and arrays ######################### -# ONE-DIMENSIONAL -#单维度 +# 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 <- 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 @@ -423,10 +379,10 @@ t(mat) mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog")) mat2 # => -# [,1] [,2] -# [1,] "1" "dog" -# [2,] "2" "cat" -# [3,] "3" "bird" +# [,1] [,2] +# [1,] "1" "dog" +# [2,] "2" "cat" +# [3,] "3" "bird" # [4,] "4" "dog" class(mat2) # matrix #定义mat2矩阵 @@ -451,7 +407,7 @@ mat3 # TWO-DIMENSIONAL (DIFFERENT CLASSES) ##二维函数(不同的变量类型) -# For columns of different classes, use the data frame +# 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")) @@ -484,7 +440,7 @@ dat[,"number"] # 5 2 1 4 # 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 +#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 @@ -540,7 +496,7 @@ mat #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 +# 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) -- cgit v1.2.3