之前一阵子，在EDX上学习了R语言的一门基础课程，这里做个总结。这门课程主要侧重于R的数据结构的介绍，当然也介绍了它的基本的绘图手段。

工作空间相关

ls()

## character(0)

rm(a)

## Warning in rm(a): 找不到对象'a'

ls()

## character(0)

基本数据类型

  1. logical

    • TRUE/FALSE/NA/T/F(推荐使用完整形式)/某些时候的0与非0
  2. numeric
    • integer is numeric
    • numeric not always integer
  3. character

Other atomic types:

  • double: higher precision
  • complex: complex numbers
  • raw: store raw bytes

is.*()返回括号内内容是否是*对应类型。

# logical

TRUE

## [1] TRUE

class(TRUE)

## [1] "logical"

FALSE

## [1] FALSE

class(NA)

## [1] "logical"

T

## [1] TRUE

F

## [1] FALSE

# numeric

2

## [1] 2

class(2)

## [1] "numeric"

2.5

## [1] 2.5

class(2.5)

## [1] "numeric"

2L

## [1] 2

class(2L) 

## [1] "integer"

is.numeric(2)

## [1] TRUE

is.numeric(2L)

## [1] TRUE

#integer is numeric

#numeric not always integer

is.integer(2)

## [1] FALSE

is.integer(2L)

## [1] TRUE

# character

 "I love data science!"

## [1] "I love data science!"

 class("I love data science!")

## [1] "character"

强制转换

as.*()返回括号内内容转化为*对应类型后的结果，有些情况无法转换。

as.numeric(TRUE)

## [1] 1

as.numeric(FALSE)

## [1] 0

as.character(4)

## [1] "4"

as.numeric("4.5")

## [1] 4.5

as.integer("4.5")

## [1] 4

as.numeric("Hello")

## Warning: 强制改变过程中产生了NA

## [1] NA

向量 Vector

  • Sequence of data elements
  • Same basic type
    • Automatic coercion if necessary
  • character, numeric, logical
  • Single value = Vector

创建 c()或者利用:

# c()

drawn_suits <- c("hearts", "spades", "diamonds",

                   "diamonds", "spades")

drawn_suits

## [1] "hearts"   "spades"   "diamonds" "diamonds" "spades"

is.vector(drawn_suits)

## [1] TRUE

# :

1:5

## [1] 1 2 3 4 5

is.vector(1:5)

## [1] TRUE

命名 names()

remain <- c(11, 12, 11, 13)

suits <- c("spades", "hearts", "diamonds", "clubs")

names(remain) <- suits

remain 

##   spades   hearts diamonds    clubs

##       11       12       11       13

#or

remain <- c(spades = 11, hearts = 12,

              diamonds = 11, clubs = 13)

remain

##   spades   hearts diamonds    clubs

##       11       12       11       13

#or

remain <- c("spades" = 11, "hearts" = 12,

              "diamonds" = 11, "clubs" = 13)

remain

##   spades   hearts diamonds    clubs

##       11       12       11       13

单值仍为向量

my_apples <- 5

my_oranges <- "six"

is.vector(my_apples)

## [1] TRUE

is.vector(my_oranges)

## [1] TRUE

length(my_apples)

## [1] 1

length(my_oranges)

## [1] 1

强制变换

drawn_ranks <- c(7, 4, "A", 10, "K", 3, 2, "Q")

drawn_ranks

## [1] "7"  "4"  "A"  "10" "K"  "3"  "2"  "Q"

class(drawn_ranks)

## [1] "character"

基本运算

很自然的可以由单数的运算推广出来。

# with number: +-*/

earnings <- c(50, 100, 30)

earnings * 3

## [1] 150 300  90

earnings^2

## [1]  2500 10000   900

# with vector: +-*/

earnings <- c(50, 100, 30)

expenses <- c(30, 40, 80)

bank <- earnings - expenses

## sum() >

sum(bank)

## [1] 30

earnings > expenses

## [1]  TRUE  TRUE FALSE

## multiplication and division are done element-wise!

earnings * c(1, 2, 3)

## [1]  50 200  90

子集

三种索引方式

  • 序号（R从1开始）
  • 名字 —— names()的利用
  • 逻辑值

remain <- c(spades = 11, hearts = 12,

              diamonds = 11, clubs = 13)

remain[1]

## spades

##     11

remain["spades"]

## spades

##     11

remain[c(4, 1)] # 此法可以用来交换或者抽取特定位置的元素

##  clubs spades

##     13     11

remain[c("clubs", "spades")]

##  clubs spades

##     13     11

# 逻辑值索引，短的会被自动循环使用

remain[c(TRUE, FALSE)]

##   spades diamonds

##       11       11

remain[c(TRUE, FALSE, TRUE, FALSE)]

##   spades diamonds

##       11       11

# 负索引，“all but it”，返回除此之外的元素

remain[-1]

##   hearts diamonds    clubs

##       12       11       13

remain[-c(1, 2)]

## diamonds    clubs

##       11       13

#remain[-"spades"] #can't work

矩阵 Matrix

  • Vector: 1D array of data elements
  • Matrix: 2D array of data elements
  • Rows and columns
  • One atomic vector type

创建 matrix()

默认按列填充

# 直接创建

matrix(1:6, nrow = 2)

##      [,1] [,2] [,3]

## [1,]    1    3    5

## [2,]    2    4    6

matrix(1:6, ncol = 3)

##      [,1] [,2] [,3]

## [1,]    1    3    5

## [2,]    2    4    6

matrix(1:6, nrow = 2, byrow = TRUE)

##      [,1] [,2] [,3]

## [1,]    1    2    3

## [2,]    4    5    6

# 循环创建

matrix(1:3, nrow = 2, ncol = 3)

##      [,1] [,2] [,3]

## [1,]    1    3    2

## [2,]    2    1    3

matrix(1:4, nrow = 2, ncol = 3)

## Warning in matrix(1:4, nrow = 2, ncol = 3): 数据长度[4]不是矩阵列数[3]的整

## 倍数

##      [,1] [,2] [,3]

## [1,]    1    3    1

## [2,]    2    4    2

# 组合创建

cbind(1:3, 1:3)

##      [,1] [,2]

## [1,]    1    1

## [2,]    2    2

## [3,]    3    3

rbind(1:3, 1:3)

##      [,1] [,2] [,3]

## [1,]    1    2    3

## [2,]    1    2    3

m <- matrix(1:6, byrow = TRUE, nrow = 2)

rbind(m, 7:9)

##      [,1] [,2] [,3]

## [1,]    1    2    3

## [2,]    4    5    6

## [3,]    7    8    9

cbind(m, c(10, 11))

##      [,1] [,2] [,3] [,4]

## [1,]    1    2    3   10

## [2,]    4    5    6   11

命名

rownames(), colnames()

m <- matrix(1:6, byrow = TRUE, nrow = 2)

rownames(m) <- c("row1", "row2")

m

##      [,1] [,2] [,3]

## row1    1    2    3

## row2    4    5    6

colnames(m) <- c("col1", "col2", "col3")

m

##      col1 col2 col3

## row1    1    2    3

## row2    4    5    6

# 直接命名

m <- matrix(1:6, byrow = TRUE, nrow = 2,

              dimnames = list(c("row1", "row2"),

                              c("col1", "col2", "col3")))

m

##      col1 col2 col3

## row1    1    2    3

## row2    4    5    6

强制转换

num <- matrix(1:8, ncol = 2)

num

##      [,1] [,2]

## [1,]    1    5

## [2,]    2    6

## [3,]    3    7

## [4,]    4    8

char <- matrix(LETTERS[1:6], nrow = 4, ncol = 3)

char

##      [,1] [,2] [,3]

## [1,] "A"  "E"  "C"

## [2,] "B"  "F"  "D"

## [3,] "C"  "A"  "E"

## [4,] "D"  "B"  "F"

num <- matrix(1:8, ncol = 2)

char <- matrix(LETTERS[1:6], nrow = 4, ncol = 3)

cbind(num, char)

##      [,1] [,2] [,3] [,4] [,5]

## [1,] "1"  "5"  "A"  "E"  "C"

## [2,] "2"  "6"  "B"  "F"  "D"

## [3,] "3"  "7"  "C"  "A"  "E"

## [4,] "4"  "8"  "D"  "B"  "F"

子集运算

m <- matrix(sample(1:15, 12), nrow = 3)

rownames(m) <- c("r1", "r2", "r3")

colnames(m) <- c("a", "b", "c", "d")

m

##     a  b  c  d

## r1  7  5  6 10

## r2  3  9 12  8

## r3 15 13  2  4

m[1,3]

## [1] 6

m[3,] 

##  a  b  c  d

## 15 13  2  4

m[,3]

## r1 r2 r3

##  6 12  2

m[4] # 默认按列计数 

## [1] 5

m[2, c(2, 3)]

##  b  c

##  9 12

m[c(1, 2), c(2, 3)]

##    b  c

## r1 5  6

## r2 9 12

m[c(1, 3), c(1, 3, 4)]

##     a c  d

## r1  7 6 10

## r3 15 2  4

m["r2","c"]

## [1] 12

m[2,"c"]

## [1] 12

m[3, c("c", "d")]

## c d

## 2 4

m[c(FALSE, FALSE, TRUE),

    c(FALSE, TRUE, FALSE, TRUE)] 

##  b  d

## 13  4

m[c(FALSE, FALSE, TRUE),

    c(FALSE, TRUE)]

##  b  d

## 13  4

矩阵运算

  • colSums(), rowSums()
  • Standard arithmetic possible
  • Element-wise computation

the_fellowship <- c(316, 556)

two_towers <- c(343, 584)

return_king <- c(378, 742)

lotr_matrix <- rbind(the_fellowship, two_towers, return_king)

colnames(lotr_matrix) <- c("US", "non-US")

rownames(lotr_matrix) <- c("Fellowship", "Two Towers",

                             "Return King")

lotr_matrix

##              US non-US

## Fellowship  316    556

## Two Towers  343    584

## Return King 378    742

# 与数字 +-*/

lotr_matrix / 1.12 

##                   US   non-US

## Fellowship  282.1429 496.4286

## Two Towers  306.2500 521.4286

## Return King 337.5000 662.5000

lotr_matrix - 50

##              US non-US

## Fellowship  266    506

## Two Towers  293    534

## Return King 328    692

# 与矩阵 +-*/ (这里不是线性代数中的矩阵计算)

theater_cut <- matrix(c(50, 80, 100), nrow = 3, ncol = 2)

theater_cut

##      [,1] [,2]

## [1,]   50   50

## [2,]   80   80

## [3,]  100  100

lotr_matrix - theater_cut

##              US non-US

## Fellowship  266    506

## Two Towers  263    504

## Return King 278    642

# 与向量

lotr_matrix - c(50, 80, 100) #按列循环计算

##              US non-US

## Fellowship  266    506

## Two Towers  263    504

## Return King 278    642

因子 Factors

  • Factors for categorical variables
  • Limited number of different values
  • Belong to category

创建因子 factor()

blood <- c("B", "AB", "O", "A", "O", "O", "A", "B")

blood

## [1] "B"  "AB" "O"  "A"  "O"  "O"  "A"  "B"

blood_factor <- factor(blood) # 默认等级按照字母顺序定

blood_factor

## [1] B  AB O  A  O  O  A  B

## Levels: A AB B O

str(blood_factor)

##  Factor w/ 4 levels "A","AB","B","O": 3 2 4 1 4 4 1 3

# 自定义level

blood_factor2 <- factor(blood,

                          levels = c("O", "A", "B", "AB"))

blood_factor2

## [1] B  AB O  A  O  O  A  B

## Levels: O A B AB

str(blood_factor2)

##  Factor w/ 4 levels "O","A","B","AB": 3 4 1 2 1 1 2 3

Rename factor levels

blood <- c("B", "AB", "O", "A", "O", "O", "A", "B")

#1.1

blood_factor <- factor(blood)

levels(blood_factor) <- c("BT_A", "BT_AB", "BT_B", "BT_O")

#1.2

blood <- c("B", "AB", "O", "A", "O", "O", "A", "B")

blood_factor <- factor(blood)

factor(blood,

         levels = c("O", "A", "B", "AB"),

         labels = c("BT_O", "BT_A", "BT_B", "BT_AB"))

## [1] BT_B  BT_AB BT_O  BT_A  BT_O  BT_O  BT_A  BT_B

## Levels: BT_O BT_A BT_B BT_AB

#2

factor(blood, labels = c("BT_A", "BT_AB", "BT_B", "BT_O"))

## [1] BT_B  BT_AB BT_O  BT_A  BT_O  BT_O  BT_A  BT_B

## Levels: BT_A BT_AB BT_B BT_O

Ordered factor

blood <- c("B", "AB", "O", "A", "O", "O", "A", "B")

blood_factor <- factor(blood)

blood_factor[1] < blood_factor[2] 

## Warning in Ops.factor(blood_factor[1], blood_factor[2]): '<' not meaningful

## for factors

## [1] NA

# 下面比较大小才是有意义的

tshirt <- c("M", "L", "S", "S", "L", "M", "L", "M")

tshirt_factor <- factor(tshirt, ordered = TRUE,

                          levels = c("S", "M", "L"))

tshirt_factor

## [1] M L S S L M L M

## Levels: S < M < L

tshirt_factor[1] < tshirt_factor[2]

## [1] TRUE

列表 List

Vector - Matrix - List

  • Vector: 1D, same type
  • Matrix: 2D, same type
  • List:
    • Different R objects
    • No coercion
    • Loss of some functionality

创建列表 list()

list("Rsome times", 190, 5)

## [[1]]

## [1] "Rsome times"

##

## [[2]]

## [1] 190

##

## [[3]]

## [1] 5

song <- list("Rsome times", 190, 5)

is.list(song)

## [1] TRUE

命名列表

#1

song <- list("Rsome times", 190, 5)

names(song) <- c("title", "duration", "track")

song

## $title

## [1] "Rsome times"

##

## $duration

## [1] 190

##

## $track

## [1] 5

#2

song <- list(title = "Rsome times",

               duration = 190,

               track = 5)

song

## $title

## [1] "Rsome times"

##

## $duration

## [1] 190

##

## $track

## [1] 5

str(song)

## List of 3

##  $ title   : chr "Rsome times"

##  $ duration: num 190

##  $ track   : num 5

列表嵌套

similar_song <- list(title = "R you on time?",

                       duration = 230)

song <- list(title = "Rsome times",

               duration = 190, track = 5,

               similar = similar_song)

str(song)

## List of 4

##  $ title   : chr "Rsome times"

##  $ duration: num 190

##  $ track   : num 5

##  $ similar :List of 2

##   ..$ title   : chr "R you on time?"

##   ..$ duration: num 230

子集运算

[ versus [[

similar_song <- list(title = "R you on time?",

                       duration = 230)

song <- list(title = "Rsome times",

               duration = 190, track = 5,

               similar = similar_song)

str(song)

## List of 4

##  $ title   : chr "Rsome times"

##  $ duration: num 190

##  $ track   : num 5

##  $ similar :List of 2

##   ..$ title   : chr "R you on time?"

##   ..$ duration: num 230

song[1]

## $title

## [1] "Rsome times"

song[[1]]

## [1] "Rsome times"

song[c(1, 3)] 

## $title

## [1] "Rsome times"

##

## $track

## [1] 5

#song[[c(1, 3)]] #can't work

#song[[1]][[3]] #can't work

song[["duration"]]

## [1] 190

song["duration"]

## $duration

## [1] 190

song[c(FALSE, TRUE, TRUE, FALSE)]

## $duration

## [1] 190

##

## $track

## [1] 5

#song[[c(FALSE, TRUE, TRUE, FALSE)]] # can't work

#song[[F]][[T]][[T]][[F]] #also

# list in list

song[[4]][[1]]

## [1] "R you on time?"

song[[c(4, 1)]]

## [1] "R you on time?"

song[c("duration", "similar")] 

## $duration

## [1] 190

##

## $similar

## $similar$title

## [1] "R you on time?"

##

## $similar$duration

## [1] 230

[[ or [ ? + [[ to select list element + [ results in

sublist + [[ and $ to subset and extend lists

列表扩展

这里引出了R中比较重要的一个符号$

similar_song <- list(title = "R you on time?",

                       duration = 230)

song <- list(title = "Rsome times",

               duration = 190, track = 5,

               similar = similar_song)

#$

song$duration

## [1] 190

#extending

friends <- c("Kurt", "Florence",

                "Patti", "Dave")

song$sent <- friends #或者 song[["sent"]] <- friends

song$similar$reason <- "too long"

song

## $title

## [1] "Rsome times"

##

## $duration

## [1] 190

##

## $track

## [1] 5

##

## $similar

## $similar$title

## [1] "R you on time?"

##

## $similar$duration

## [1] 230

##

## $similar$reason

## [1] "too long"

##

##

## $sent

## [1] "Kurt"     "Florence" "Patti"    "Dave"

数据框 Data Frame

  • Observations 观测值
  • Variables 变量
  • Example: people
    • each person = observation
    • properties (name, age …) = variables
  • Rows = observations (persons)
  • Columns = variables (age, name, …)

不同的变量的观测值可以类型不同，但是变量自己的所有观测值类型一致。

多在导入数据时使用。

创建数据框

name <- c("Anne", "Pete", "Frank", "Julia", "Cath")

age <- c(28, 30, 21, 39, 35)

child <- c(FALSE, TRUE, TRUE, FALSE, TRUE)

df <- data.frame(name, age, child)

str(df)

## 'data.frame':    5 obs. of  3 variables:

##  $ name : Factor w/ 5 levels "Anne","Cath",..: 1 5 3 4 2

##  $ age  : num  28 30 21 39 35

##  $ child: logi  FALSE TRUE TRUE FALSE TRUE

命名数据框

name <- c("Anne", "Pete", "Frank", "Julia", "Cath")

age <- c(28, 30, 21, 39, 35)

child <- c(FALSE, TRUE, TRUE, FALSE, TRUE)

df <- data.frame(name, age, child)

names(df) <- c("Name", "Age", "Child")

str(df)

## 'data.frame':    5 obs. of  3 variables:

##  $ Name : Factor w/ 5 levels "Anne","Cath",..: 1 5 3 4 2

##  $ Age  : num  28 30 21 39 35

##  $ Child: logi  FALSE TRUE TRUE FALSE TRUE

df <- data.frame(Name = name, Age = age, Child = child) #also

str(df)

## 'data.frame':    5 obs. of  3 variables:

##  $ Name : Factor w/ 5 levels "Anne","Cath",..: 1 5 3 4 2

##  $ Age  : num  28 30 21 39 35

##  $ Child: logi  FALSE TRUE TRUE FALSE TRUE

可见，这里的字符串向量，被自动转化为因子类型，所以可以设置参数来避免此隐含行为。

name <- c("Anne", "Pete", "Frank", "Julia", "Cath")

age <- c(28, 30, 21, 39, 35)

child <- c(FALSE, TRUE, TRUE, FALSE, TRUE)

df <- data.frame(name, age, child,

                   stringsAsFactors = FALSE)

str(df)

## 'data.frame':    5 obs. of  3 variables:

##  $ name : chr  "Anne" "Pete" "Frank" "Julia" ...

##  $ age  : num  28 30 21 39 35

##  $ child: logi  FALSE TRUE TRUE FALSE TRUE

子集运算

Subset Data Frame * Subsetting syntax from matrices and lists * [

from matrices * [[ and $ from lists

name <- c("Anne", "Pete", "Frank", "Julia", "Cath")

age <- c(28, 30, 21, 39, 35)

child <- c(FALSE, TRUE, TRUE, FALSE, TRUE)

people <- data.frame(name, age, child,

               stringsAsFactors = FALSE)

# 类似矩阵的操作

people[3,2] 

## [1] 21

people[3,"age"]

## [1] 21

people[,"age"]

## [1] 28 30 21 39 35

people[3,] # 由于返回的是一个数据框，我的R notebook不显示数据框

##    name age child

## 3 Frank  21  TRUE

people[c(3, 5), c("age", "child")] # 同上

##   age child

## 3  21  TRUE

## 5  35  TRUE

# 类似列表的操作

people$age

## [1] 28 30 21 39 35

people[["age"]]

## [1] 28 30 21 39 35

people[[2]]

## [1] 28 30 21 39 35

## 由于返回的是一个数据框，我的R notebook不显示数据框

people["age"]

##   age

## 1  28

## 2  30

## 3  21

## 4  39

## 5  35

people[2]

##   age

## 1  28

## 2  30

## 3  21

## 4  39

## 5  35

扩展数据框

Extend Data Frame * Add columns = add variables * Add rows = add

observations

name <- c("Anne", "Pete", "Frank", "Julia", "Cath")

age <- c(28, 30, 21, 39, 35)

child <- c(FALSE, TRUE, TRUE, FALSE, TRUE)

people <- data.frame(name, age, child,

               stringsAsFactors = FALSE)

#Add column

height <- c(163, 177, 163, 162, 157)

people$height <- height

str(people)

## 'data.frame':    5 obs. of  4 variables:

##  $ name  : chr  "Anne" "Pete" "Frank" "Julia" ...

##  $ age   : num  28 30 21 39 35

##  $ child : logi  FALSE TRUE TRUE FALSE TRUE

##  $ height: num  163 177 163 162 157

##also

people[["height"]] <- height

str(people)

## 'data.frame':    5 obs. of  4 variables:

##  $ name  : chr  "Anne" "Pete" "Frank" "Julia" ...

##  $ age   : num  28 30 21 39 35

##  $ child : logi  FALSE TRUE TRUE FALSE TRUE

##  $ height: num  163 177 163 162 157

weight <- c(74, 63, 68, 55, 56)

cbind(people, weight)

##    name age child height weight

## 1  Anne  28 FALSE    163     74

## 2  Pete  30  TRUE    177     63

## 3 Frank  21  TRUE    163     68

## 4 Julia  39 FALSE    162     55

## 5  Cath  35  TRUE    157     56

#Add row 这里要注意，有时候会出错

tom <- data.frame("Tom", 37, FALSE, 183)

#rbind(people, tom)

#会报错：

#Error : names do not match previous names

tom <- data.frame(name = "Tom", age = 37,

            child = FALSE, height = 183)

rbind(people, tom) 

##    name age child height

## 1  Anne  28 FALSE    163

## 2  Pete  30  TRUE    177

## 3 Frank  21  TRUE    163

## 4 Julia  39 FALSE    162

## 5  Cath  35  TRUE    157

## 6   Tom  37 FALSE    183

排序

这里主要介绍了sort()与order()，其中，order()更适合用来为数据框调整顺序。

str(people)

## 'data.frame':    5 obs. of  4 variables:

##  $ name  : chr  "Anne" "Pete" "Frank" "Julia" ...

##  $ age   : num  28 30 21 39 35

##  $ child : logi  FALSE TRUE TRUE FALSE TRUE

##  $ height: num  163 177 163 162 157

#sort()直接对于向量元素进行了排序

sort(people$age)

## [1] 21 28 30 35 39

#order()会返回对应大小等级所实际在的位置

ranks <- order(people$age)

ranks

## [1] 3 1 2 5 4

people$age

## [1] 28 30 21 39 35

people[ranks, ] #直接对行进行了排序

##    name age child height

## 3 Frank  21  TRUE    163

## 1  Anne  28 FALSE    163

## 2  Pete  30  TRUE    177

## 5  Cath  35  TRUE    157

## 4 Julia  39 FALSE    162

#或者如下可以实现降序排序

people[order(people$age, decreasing = TRUE), ] 

##    name age child height

## 4 Julia  39 FALSE    162

## 5  Cath  35  TRUE    157

## 2  Pete  30  TRUE    177

## 1  Anne  28 FALSE    163

## 3 Frank  21  TRUE    163

绘图 Graphics

这里主要介绍了graphics包的plot()与hist()

plot()会根据不同的数据类型，而画出不同的图像

  1. plot() (categorical) 条形图 例如：plot(countries$continent)
  2. plot() (numerical) 散点图 例如：plot(countries$population)
  3. plot() (2x numerical) 散点图

    例如：plot(countries$area, countries$population)、

    plot(log(countries$area), log(countries$population))
  4. plot() (2x categorical) 某种条形图的变形

    例如：plot(countries$continent, countries$religion)

hist()可以绘制直方图 例如： hist(africa$population)、

hist(africa$population, breaks = 10)

Other graphics functions * barplot() * boxplot() * pairs()

自定义绘图

这里就是修改参数了。无需多讲。

这里，引出了函数par()，这是一个绘图的公共参数列表，里面存放着常用的一些绘图的公共属性，可以实现绘制多幅图形时，基本属性的一次性确定。

例如：

par(col = "blue")

plot(mercury$temperature, mercury$pressure)

常用的plot的属性有：

plot(mercury$temperature, mercury$pressure,

       xlab = "Temperature",

       ylab = "Pressure",

       main = "T vs P for Mercury", #标题

       type = "o",

       col = "orange",

       col.main = "darkgray",

       cex.axis = 0.6, #cex系列属性表示缩放程度

       lty = 5, #Line Type

       pch = 4  #Plot Symbol

       )

多图绘制

mfrow与mfcol参数可以在一个图形框里，用来放置多个图像，区别是，前者是将后面plot语句生成的图像按行填充，而后者是按列填充。

#按行填充

par(mfrow = c(2,2))

plot(shop$ads, shop$sales)

plot(shop$comp, shop$sales)

plot(shop$inv, shop$sales)

plot(shop$size_dist, shop$sales)

#按列填充

par(mfcol = c(2,2))

plot(shop$ads, shop$sales)

plot(shop$comp, shop$sales)

plot(shop$inv, shop$sales)

plot(shop$size_dist, shop$sales)

Reset the grid

par(mfrow = c(1,1))

相较于这个，layout()函数设置的更为灵活。

grid <- matrix(c(1, 1, 2, 3), nrow = 2,

         ncol = 2, byrow = TRUE)

layout(grid)

plot(shop$ads, shop$sales) #放在grid的1号位置

plot(shop$comp, shop$sales) #放在grid的2号位置

plot(shop$inv, shop$sales) #放在grid的3号位置

Reset the grid

layout(1)

par(mfcol = c(1,1))

Reset all parameters

old_par <- par()

par(col = "red")

plot(shop$ads, shop$sales)

par(old_par)

plot(shop$ads, shop$sales)

线性拟合

引出函数lm() —— linear

model，**lm(a~b)就是对a=k*b+c进行线性拟合**

plot(shop$ads, shop$sales,

      pch = 16, col = 2,

      xlab = "advertisement",

      ylab = "net sales")

lm_sales <- lm(shop$sales ~ shop$ads)

abline(coef(lm_sales), lwd = 2) #取模型系数，线宽为2，画直线

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