add xiaoqi's translation

zh-cn/r-cn.html.markdown

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+
+# 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
+
+