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@@ -18,8 +18,8 @@ head(rivers) # 撇一眼数据集
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length(rivers) # 我们测量了多少条河流？
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# 141
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summary(rivers)
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# Min. 1st Qu. Median Mean 3rd Qu. Max.
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# 135.0 310.0 425.0 591.2 680.0 3710.0
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# Min. 1st Qu. Median Mean 3rd Qu. Max.
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# 135.0 310.0 425.0 591.2 680.0 3710.0
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stem(rivers) # 茎叶图（一种类似于直方图的展现形式）
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#
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# The decimal point is 2 digit(s) to the right of the |
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@@ -34,14 +34,14 @@ stem(rivers) # 茎叶图（一种类似于直方图的展现形式）
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# 14 | 56
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# 16 | 7
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# 18 | 9
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# 20 |
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# 20 |
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# 22 | 25
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# 24 | 3
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# 26 |
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# 28 |
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# 30 |
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# 32 |
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# 34 |
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# 26 |
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# 28 |
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# 30 |
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# 32 |
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# 34 |
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# 36 | 1
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@@ -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
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# The decimal point is 1 digit(s) to the left of the |
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#
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# 48 | 1
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# 50 |
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# 50 |
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# 52 | 15578
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# 54 | 44571222466689
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# 56 | 023334677000124455789
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@@ -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
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# 74 | 84
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# 76 | 56
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# 78 | 4
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# 80 |
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# 80 |
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# 82 | 2
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@@ -101,15 +101,15 @@ stem(discoveries, scale=2) # 译者注：茎叶图（数据，放大系数）
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# 8 | 0
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# 9 | 0
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# 10 | 0
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# 11 |
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# 11 |
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# 12 | 0
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max(discoveries)
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# 12
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summary(discoveries)
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# Min. 1st Qu. Median Mean 3rd Qu. Max.
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# 0.0 2.0 3.0 3.1 4.0 12.0
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# Min. 1st Qu. Median Mean 3rd Qu. Max.
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# 0.0 2.0 3.0 3.1 4.0 12.0
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@@ -151,7 +151,7 @@ class(5) # "numeric"
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1.6e-35 # 布朗克长度
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# 长整数并用 L 结尾
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5L # 5
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5L # 5
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#输出5L
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class(5L) # "integer"
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@@ -178,7 +178,7 @@ class(NULL) # NULL
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# 简单列表
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c(6, 8, 7, 5, 3, 0, 9) # 6 8 7 5 3 0 9
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c('alef', 'bet', 'gimmel', 'dalet', 'he')
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c('alef', 'bet', 'gimmel', 'dalet', 'he')
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c('Z', 'o', 'r', 'o') == "Zoro" # FALSE FALSE FALSE FALSE
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# 一些优雅的内置功能
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@@ -200,119 +200,80 @@ month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "D
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letters[18] # "r"
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LETTERS[13] # "M"
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month.name[9] # "September"
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c(6, 8, 7, 5, 3, 0, 9)[3] # 7
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c(6, 8, 7, 5, 3, 0, 9)[3] # 7
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# CHARACTERS
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#特性
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# There's no difference between strings and characters in R
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# 字符串和字符在R语言中没有区别
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# 字符串
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# 字符串和字符在 R 语言中没有区别
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"Horatio" # "Horatio"
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#字符输出"Horatio"
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class("Horatio") # "character"
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#字符串输出("Horatio") # "character"
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substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis "
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#提取字符串("Fortuna multis dat nimis, nulli satis.", 第9个到15个之前并输出)
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gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis."
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#替换字符春，用ø替换u
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# LOGICALS
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#逻辑值
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# 逻辑值
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# booleans
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#布尔运算
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# 布尔值
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class(TRUE) # "logical"
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#定义为真，逻辑型
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class(FALSE) # "logical"
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#定义为假，逻辑型
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# Behavior is normal
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#表现的标准形式
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# 和我们预想的一样
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TRUE == TRUE # TRUE
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TRUE == FALSE # FALSE
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FALSE != FALSE # FALSE
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FALSE != TRUE # TRUE
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# Missing data (NA) is logical, too
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#缺失数据也是逻辑型的
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# 缺失数据（NA）也是逻辑值
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class(NA) # "logical"
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#定义NA为逻辑型
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# FACTORS
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#因子
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# The factor class is for categorical data
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#因子是分类数据的定义函数
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# which can be ordered (like childrens' grade levels)
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#可以使有序的（就像儿童的等级水平）
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# or unordered (like gender)
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#也可以是无序的(就像性别）
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levels(factor(c("female", "male", "male", "female", "NA", "female"))) # "female" "male" "NA"
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#c("female", "male", "male", "female", "NA", "female")向量，变量是字符型，levels factor（）因子的等级水平
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# 因子
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# 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）
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levels(factor(c("female", "male", "male", "female", "NA", "female"))) # "female" "male" "NA"
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factor(c("female", "female", "male", "NA", "female"))
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factor(c("female", "female", "male", "NA", "female"))
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# female female male NA female
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# Levels: female male NA
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data(infert) #Infertility after Spontaneous and Induced Abortion
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#数据集（感染） 自然以及引产导致的不育症
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data(infert) # 自然以及引产导致的不育症
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levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs"
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#等级（感染与教育程度） 输出
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# VARIABLES
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#变量
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# 变量
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# Lots of way to assign stuff
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#许多种方式用来分配素材
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x = 5 # this is possible
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#x = 5可能的
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y <- "1" # this is preferred
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#y <- "1" 优先级的
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TRUE -> z # this works but is weird
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#输出真实的，存在一个超自然数满足条件
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# 有许多种方式用来赋值
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x = 5 # 这样可以
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y <- "1" # 更推荐这样
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TRUE -> z # 这样可行，但是很怪
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# We can use coerce variables to different classes
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#我们还可以使用枪支变量去进行不同的定义
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#我们还可以使用强制转型
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as.numeric(y) # 1
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#定义数值型
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as.character(x) # "5"
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#字符型
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# 循环
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# LOOPS
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#循环
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# We've got for loops
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#循环语句
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# for 循环语句
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for (i in 1:4) {
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print(i)
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}
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#定义一个i，从1-4输出
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# We've got while loops
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#我们可以获取循环结构
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# while 循环
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a <- 10
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while (a > 4) {
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cat(a, "...", sep = "")
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a <- a - 1
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}
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#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
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# Keep in mind that for and while loops run slowly in R
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#在R语言中牢记 for和它的循环结构
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# Operations on entire vectors (i.e. a whole row, a whole column)
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#牢记矢量中附带的操作（例如，整行和整列）
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# or apply()-type functions (we'll discuss later) are preferred
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#或者优先使用()-函数,稍后会进行讨论
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# 记住，在 R 语言中 for / while 循环都很慢
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# 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）
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# IF/ELSE
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#判断分支
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# Again, pretty standard
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#再一次，看这些优雅的标准
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# 再来看这些优雅的标准
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if (4 > 3) {
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print("Huzzah! It worked!")
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} else {
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@@ -322,30 +283,25 @@ if (4 > 3) {
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# =>
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# [1] "Huzzah! It worked!"
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# FUNCTIONS
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#功能函数
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# 函数
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# Defined like so:
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#定义如下
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# 定义如下
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jiggle <- function(x) {
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x+ rnorm(x, sd=.1) #add in a bit of (controlled) noise
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x + rnorm(x, sd=.1) #add in a bit of (controlled) noise
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return(x)
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}
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#把功能函数x负值给jiggle，
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# Called like any other R function:
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jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043
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# 和其他 R 语言函数一样调用
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jiggle(5) # 5±ε. 使用 set.seed(2716057) 后， jiggle(5)==5.005043
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#########################
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# Fun with data: vectors, matrices, data frames, and arrays
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# 数据参数：向量，矩阵，数据框，数组，
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# 数据容器：vectors, matrices, data frames, and arrays
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#########################
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# ONE-DIMENSIONAL
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#单维度
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# 单维度
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# You can vectorize anything, so long as all components have the same type
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#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
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vec <- c(8, 9, 10, 11)
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vec <- c(8, 9, 10, 11)
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vec # 8 9 10 11
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# The class of a vector is the class of its components
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#矢量class表示这一组分的类型
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@@ -423,10 +379,10 @@ t(mat)
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mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog"))
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mat2
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# =>
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# [,1] [,2]
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# [1,] "1" "dog"
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# [2,] "2" "cat"
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# [3,] "3" "bird"
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# [,1] [,2]
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# [1,] "1" "dog"
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# [2,] "2" "cat"
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# [3,] "3" "bird"
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# [4,] "4" "dog"
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class(mat2) # matrix
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#定义mat2矩阵
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@@ -451,7 +407,7 @@ mat3
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# TWO-DIMENSIONAL (DIFFERENT CLASSES)
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##二维函数(不同的变量类型)
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# For columns of different classes, use the data frame
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# For columns of different classes, use the data frame
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利用数组可以将不同类型放在一起
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dat <- data.frame(c(5,2,1,4), c("dog", "cat", "bird", "dog"))
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#dat<-数据集(c(5,2,1,4), c("dog", "cat", "bird", "dog"))
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@@ -484,7 +440,7 @@ dat[,"number"] # 5 2 1 4
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# You can make a two-dimensional table (sort of like a matrix)
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#你可以建立一个2维表格（类型和矩阵相似）
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array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
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#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
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#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
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# =>
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# [,1] [,2] [,3] [,4]
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# [1,] 1 4 8 3
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@@ -540,7 +496,7 @@ mat
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#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
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# over rows (MAR = 1) or columns (MAR = 2)
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#规定行列，其边界分别为1,2
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# That is, R does FUN to each row (or column) of X, much faster than a
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# That is, R does FUN to each row (or column) of X, much faster than a
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#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
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# for or while loop would do
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apply(mat, MAR = 2, myFunc)
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