【R】數據結構
以前一陣子，在EDX上學習了R語言的一門基礎課程，這裏作個總結。這門課程主要側重於R的數據結構的介紹，固然也介紹了它的基本的繪圖手段。數據結構
工做空間相關
ls() ## character(0) rm(a) ## Warning in rm(a): 找不到對象'a' ls() ## character(0)
基本數據類型
- logical
- TRUE/FALSE/NA/T/F(推薦使用完整形式)/某些時候的0與非0
- numeric
- integer is numeric
- numeric not always integer
- character
Other atomic types:app
- double: higher precision
- complex: complex numbers
- raw: store raw bytes
is.*()返回括號內內容是不是*對應類型。函數
# 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"
強制轉換
as.*()返回括號內內容轉化爲*對應類型後的結果，有些狀況沒法轉換。學習
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: 強制改變過程當中產生了NA
## [1] NA
向量 Vector
- Sequence of data elements
- Same basic type
- Automatic coercion if necessary
- character, numeric, logical
- Single value = Vector
建立 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
單值仍爲向量
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
強制變換
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"
基本運算
很天然的能夠由單數的運算推廣出來。ui
# 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
三種索引方式atom
- 序號（R從1開始）
- 名字 —— names()的利用
- 邏輯值
remain <- c(spades = 11, hearts = 12,
diamonds = 11, clubs = 13)
remain[1]
## spades
## 11
remain["spades"]
## spades
## 11
remain[c(4, 1)] # 此法能夠用來交換或者抽取特定位置的元素
## clubs spades
## 13 11
remain[c("clubs", "spades")]
## clubs spades
## 13 11
# 邏輯值索引，短的會被自動循環使用
remain[c(TRUE, FALSE)]
## spades diamonds
## 11 11
remain[c(TRUE, FALSE, TRUE, FALSE)]
## spades diamonds
## 11 11
# 負索引，「all but it」，返回除此以外的元素
remain[-1]
## hearts diamonds clubs
## 12 11 13
remain[-c(1, 2)]
## diamonds clubs
## 11 13
#remain[-"spades"] #can't work
矩陣 Matrix
- Vector: 1D array of data elements
- Matrix: 2D array of data elements
- Rows and columns
- One atomic vector type
建立 matrix()
默認按列填充spa
# 直接建立 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 # 循環建立 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): 數據長度[4]不是矩陣列數[3]的整 ## 倍數 ## [,1] [,2] [,3] ## [1,] 1 3 1 ## [2,] 2 4 2 # 組合建立 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()code
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
強制轉換
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"
子集運算
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] # 默認按列計數
## [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
矩陣運算
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
# 與數字 +-*/
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
# 與矩陣 +-*/ (這裏不是線性代數中的矩陣計算)
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
# 與向量
lotr_matrix - c(50, 80, 100) #按列循環計算
## 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
建立因子 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) # 默認等級按照字母順序定
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
# 自定義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
# 下面比較大小纔是有意義的
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對象
- Vector: 1D, same type
- Matrix: 2D, same type
- List:
- Different R objects
- No coercion
- Loss of some functionality
建立列表 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
子集運算
[ versus [[排序
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
列表擴展
這裏引出了R中比較重要的一個符號$
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"
數據框 Data Frame
- Observations 觀測值
- Variables 變量
- Example: people
- each person = observation
- properties (name, age …) = variables
- Rows = observations (persons)
- Columns = variables (age, name, …)
不一樣的變量的觀測值能夠類型不一樣，可是變量本身的全部觀測值類型一致。
多在導入數據時使用。
建立數據框
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
命名數據框
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
可見，這裏的字符串向量，被自動轉化爲因子類型，因此能夠設置參數來避免此隱含行爲。
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
子集運算
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)
# 相似矩陣的操做
people[3,2]
## [1] 21
people[3,"age"]
## [1] 21
people[,"age"]
## [1] 28 30 21 39 35
people[3,] # 因爲返回的是一個數據框，個人R notebook不顯示數據框
## name age child
## 3 Frank 21 TRUE
people[c(3, 5), c("age", "child")] # 同上
## age child
## 3 21 TRUE
## 5 35 TRUE
# 相似列表的操做
people$age
## [1] 28 30 21 39 35
people[["age"]]
## [1] 28 30 21 39 35
people[[2]]
## [1] 28 30 21 39 35
## 因爲返回的是一個數據框，個人R notebook不顯示數據框
people["age"]
## age
## 1 28
## 2 30
## 3 21
## 4 39
## 5 35
people[2]
## age
## 1 28
## 2 30
## 3 21
## 4 39
## 5 35
擴展數據框
Extend Data Frame * Add columns = add variables * Add rows = add
observations
name <- c("Anne", "Pete", "Frank", "Julia", "Cath")
age <- c(28, 30, 21, 39, 35)
child <- c(FALSE, TRUE, TRUE, FALSE, TRUE)
people <- data.frame(name, age, child,
stringsAsFactors = FALSE)
#Add column
height <- c(163, 177, 163, 162, 157)
people$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 這裏要注意，有時候會出錯
tom <- data.frame("Tom", 37, FALSE, 183)
#rbind(people, tom)
#會報錯：
#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
這裏主要介紹了sort()與order()，其中，order()更適合用來爲數據框調整順序。
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()直接對於向量元素進行了排序 sort(people$age) ## [1] 21 28 30 35 39 #order()會返回對應大小等級所實際在的位置 ranks <- order(people$age) ranks ## [1] 3 1 2 5 4 people$age ## [1] 28 30 21 39 35 people[ranks, ] #直接對行進行了排序 ## name age child height ## 3 Frank 21 TRUE 163 ## 1 Anne 28 FALSE 163 ## 2 Pete 30 TRUE 177 ## 5 Cath 35 TRUE 157 ## 4 Julia 39 FALSE 162 #或者以下能夠實現降序排序 people[order(people$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
繪圖 Graphics
這裏主要介紹了graphics包的plot()與hist()
plot()會根據不一樣的數據類型，而畫出不一樣的圖像
- plot() (categorical) 條形圖 例如：
plot(countries$continent) - plot() (numerical) 散點圖 例如：
plot(countries$population) - plot() (2x numerical) 散點圖
例如：plot(countries$area, countries$population)、
plot(log(countries$area), log(countries$population)) - plot() (2x categorical) 某種條形圖的變形
例如：plot(countries$continent, countries$religion)
hist()能夠繪製直方圖 例如： hist(africa$population)、
hist(africa$population, breaks = 10)
Other graphics functions * barplot() * boxplot() * pairs()
自定義繪圖
這裏就是修改參數了。無需多講。
這裏，引出了函數par()，這是一個繪圖的公共參數列表，裏面存放着經常使用的一些繪圖的公共屬性，能夠實現繪製多幅圖形時，基本屬性的一次性肯定。
例如：
par(col = "blue") plot(mercury$temperature, mercury$pressure)
經常使用的plot的屬性有：
plot(mercury$temperature, mercury$pressure,
xlab = "Temperature",
ylab = "Pressure",
main = "T vs P for Mercury", #標題
type = "o",
col = "orange",
col.main = "darkgray",
cex.axis = 0.6, #cex系列屬性表示縮放程度
lty = 5, #Line Type
pch = 4 #Plot Symbol
)
多圖繪製
mfrow與mfcol參數能夠在一個圖形框裏，用來放置多個圖像，區別是，前者是將後面plot語句生成的圖像按行填充，然後者是按列填充。
#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))
相較於這個，layout()函數設置的更爲靈活。
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號位置
plot(shop$comp, shop$sales) #放在grid的2號位置
plot(shop$inv, shop$sales) #放在grid的3號位置
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)
線性擬合
引出函數lm() —— linear
model，**lm(a~b)就是對a=k*b+c進行線性擬合**
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) #取模型係數，線寬爲2，畫直線
相關文章
- 1. R數據結構
- 2. Advanced R -- 數據結構
- 3. R 基礎--數據結構
- 4. R語言-數據結構
- 5. R語言數據結構
- 6. R語言的基本數據結構
- 7. R語言數據結構一
- 8. R學習筆記（一）數據結構
- 9. R語言數據結構二
- 10. R-數據結構(綜述)-3
- 更多相關文章...
- • Rust 結構體 - RUST 教程
- • XML 樹結構 - XML 教程
- • Flink 數據傳輸及反壓詳解
- • TiDB 在摩拜單車在線數據業務的應用和實踐
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. R數據結構
- 2. Advanced R -- 數據結構
- 3. R 基礎--數據結構
- 4. R語言-數據結構
- 5. R語言數據結構
- 6. R語言的基本數據結構
- 7. R語言數據結構一
- 8. R學習筆記（一）數據結構
- 9. R語言數據結構二
- 10. R-數據結構(綜述)-3