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-rw-r--r--zh-cn/r-cn.html.markdown156
1 files 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)