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# Comments start with hashtags.
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# 评论以 # 开始
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# You can't make a multi-line comment per se,
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# but you can stack multiple comments like so.
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# 你不能在每一个se下执行多个注释,
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# 但是你可以像这样把命注释内容堆叠起来.
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# in Windows, hit COMMAND-ENTER to execute a line
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# 在windows下,点击回车键来执行一条命令
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# R 语言原生不支持 多行注释
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# 但是你可以像这样来多行注释
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# 在窗口里按回车键可以执行一条命令
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###################################################################
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# Stuff you can do without understanding anything about programming
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# 素材可以使那些不懂编程的人同样得心用手
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# 不用懂编程就可以开始动手了
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###################################################################
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data() # Browse pre-loaded data sets
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data() # 浏览预加载的数据集
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data(rivers) # Lengths of Major North American Rivers
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data() # 浏览内建的数据集
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data(rivers) # 北美主要河流的长度（数据集）
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ls() # Notice that "rivers" appears in the workspace
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ls() # 在工作站中查看”河流“文件夹是否出现
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head(rivers) # peek at the dataset
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head(rivers) # 浏览数据集
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ls() # 在工作空间中查看「河流」是否出现
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head(rivers) # 撇一眼数据集
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# 735 320 325 392 524 450
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length(rivers) # how many rivers were measured?
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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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length(rivers) # 测量了多少条河流
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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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#查看”河流“数据集的特征
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# 最小值. 1st Qu. 中位数 平均值 最大值
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# 135.0 310.0 425.0 591.2 680.0 3710.0
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stem(rivers) #stem-and-leaf plot (like a histogram)
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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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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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# 小数点向|右边保留两位数字
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#
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# 0 | 4
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# 2 | 011223334555566667778888899900001111223333344455555666688888999
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@ -62,11 +44,10 @@ stem(rivers) #茎叶图（一种类似于直方图的展现形式）
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# 34 |
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# 36 | 1
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stem(log(rivers)) #Notice that the data are neither normal nor log-normal! Take that, Bell Curve fundamentalists.
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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
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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
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# The decimal point is 1 digit(s) to the left of the |
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# 小数点向|左边保留一位数字
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#
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# 48 | 1
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# 50 |
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@ -88,35 +69,26 @@ stem(log(rivers)) #查看数据集的方式既不是标准形式，也不是取l
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# 82 | 2
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hist(rivers, col="#333333", border="white", breaks=25) #play around with these parameters
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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）
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hist(log(rivers), col="#333333", border="white", breaks=25) #you'll do more plotting later
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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）
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hist(rivers, col="#333333", border="white", breaks=25) #play around with these parameters
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hist(rivers, col="#333333", border="white", breaks=25) #运行同济频数直方图的这些参数
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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）
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hist(log(rivers), col="#333333", border="white", breaks=25) #你还可以做更多式样的绘图
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#Here's another neat data set that comes pre-loaded. R has tons of these. data()
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#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
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# 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()
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data(discoveries)
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#数据集（发现）
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plot(discoveries, col="#333333", lwd=3, xlab="Year", main="Number of important discoveries per year")
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#绘图（发现，颜色负值，宽度负值，X轴名称，主题：Number of important discoveries per year）
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# 译者注：参数为（数据源，颜色，线条宽度，X 轴名称，标题）
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plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year", main="Number of important discoveries per year")
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#rather than leaving the default ordering (by year) we could also sort to see what's typical
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#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
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sort(discoveries) #给（发现）分类
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# 除了按照默认的年份排序，我们还可以排序来发现特征
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sort(discoveries)
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# [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
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# [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
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# [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
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# [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
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stem(discoveries, scale=2)
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#茎叶图（发现，在原来的基础上降尺度扩大两倍）
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#
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stem(discoveries, scale=2) # 译者注：茎叶图（数据，放大系数）
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#
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# The decimal point is at the |
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# 小数点在|
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#
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# 0 | 000000000
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# 1 | 000000000000
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@ -133,32 +105,26 @@ stem(discoveries, scale=2)
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# 12 | 0
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max(discoveries)
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#最大值(发现)
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# 12
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summary(discoveries)
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#数据集特征(发现)
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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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#Basic statistical operations don't require any programming knowledge either
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#基本的统计学操作也不需要任何编程知识
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#roll a die a few times
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#随机输出数据
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#随机生成数据
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round(runif(7, min=.5, max=6.5))
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#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))
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# 译者注：runif 产生随机数，round 四舍五入
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# 1 4 6 1 4 6 4
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#your numbers will differ from mine unless we set the same random.seed(31337)
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#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)
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# 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)
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#draw from a standard Gaussian 9 times
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#从标准高斯函数中随机的提取9次结果
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#从标准高斯函数中随机生成 9 次
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rnorm(9)
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# [1] 0.07528471 1.03499859 1.34809556 -0.82356087 0.61638975 -1.88757271
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# [7] -0.59975593 0.57629164 1.08455362
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@ -172,100 +138,69 @@ rnorm(9)
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#########################
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# Basic programming stuff
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# 基本的编程素材
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# 基础编程
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#########################
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# NUMBERS
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# 数值
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# "numeric" means double-precision floating-point numbers
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#“数值”指的是双精度的浮点数
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5 # 5
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class(5) # "numeric"
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#定义(5)为数值型变量 # "numeric"
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5e4 # 50000 #handy when dealing with large,small,or variable orders of magnitude
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#5×104次方 可以手写输入改变数量级的大小将变量扩大
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6.02e23 # Avogadro's number
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#阿伏伽德罗常数
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1.6e-35 # Planck length
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#布朗克长度
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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
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6.02e23 # 阿伏伽德罗常数#
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1.6e-35 # 布朗克长度
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# long-storage integers are written with L
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#长存储整数并用L书写
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# 长整数并用 L 结尾
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5L # 5
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#输出5L
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class(5L) # "integer"
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#（5L）的类型， 整数型
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# Try ?class for more information on the class() function
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#可以自己试一试？用class()功能函数定义更多的信息
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# In fact, you can look up the documentation on `xyz` with ?xyz
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#事实上，你可以找一些文件查阅“xyz”以及xyz的差别
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# or see the source for `xyz` by evaluating xyz
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#或者通过评估xyz来查看“xyz”的来源
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# 可以自己试一试？用 class() 函数获取更多信息
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# 事实上，你可以找一些文件查阅 `xyz` 以及xyz的差别
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# `xyz` 用来查看源码实现，?xyz 用来看帮助
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# Arithmetic
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#算法
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# 算法
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10 + 66 # 76
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53.2 - 4 # 49.2
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2 * 2.0 # 4
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3L / 4 # 0.75
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3 %% 2 # 1
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# Weird number types
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#超自然数的类型
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# 特殊数值类型
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class(NaN) # "numeric"
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class(Inf) # "numeric"
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class(-Inf) # "numeric" #used in for example integrate( dnorm(x), 3, Inf ) -- which obviates Z-score tables
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#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
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class(-Inf) # "numeric" # 在以下场景中会用到 integrate( dnorm(x), 3, Inf ) -- 消除 Z 轴数据
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# but beware, NaN isn't the only weird type...
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# 但要注意，NaN并不是仅有的超自然类型。。。
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class(NA) # see below
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#定义（NA）下面的部分会理解
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# 但要注意，NaN 并不是唯一的特殊数值类型……
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class(NA) # 看上面
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class(NULL) # NULL
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#定义（NULL）无效的
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# SIMPLE LISTS
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#简单的数据集
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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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#输出数值型向量（6 8 7 5 3 0 9）
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c('alef', 'bet', 'gimmel', 'dalet', 'he')
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#输出字符型变量# "alef" "bet" "gimmel" "dalet" "he"
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c('Z', 'o', 'r', 'o') == "Zoro" # FALSE FALSE FALSE FALSE
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#输出逻辑型变量FALSE FALSE FALSE FALSE
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#some more nice built-ins
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#一些优雅的内置功能
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# 一些优雅的内置功能
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5:15 # 5 6 7 8 9 10 11 12 13 14 15
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#从5-15输出，以进度为1递增
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seq(from=0, to=31337, by=1337)
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#输出序列（从0到31337，以1337递增）
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# [1] 0 1337 2674 4011 5348 6685 8022 9359 10696 12033 13370 14707
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# [13] 16044 17381 18718 20055 21392 22729 24066 25403 26740 28077 29414 30751
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letters
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#字符型变量，26个
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# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
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# [20] "t" "u" "v" "w" "x" "y" "z"
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month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
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#表示月份的变量
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# Access the n'th element of a list with list.name[n] or sometimes list.name[[n]]
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#访问数据集名字为[n]的第n个元素
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# 使用 list.name[n] 来访问第 n 个列表元素，有时候需要使用 list.name[[n]]
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letters[18] # "r"
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#访问其中的第18个变量
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LETTERS[13] # "M"
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#用大写访问其中的第13个变量
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month.name[9] # "September"
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#访问名字文件中第9个变量
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c(6, 8, 7, 5, 3, 0, 9)[3] # 7
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#访问向量中的第三个变量
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