文章目錄 第1章 R語言概述 第2章 數(shù)據(jù)對象與數(shù)據(jù)讀寫 第3章 數(shù)據(jù)集基本處理 第4章 函數(shù)與控制流 第5章 初級繪圖 第6章 高級繪圖 第7章 可視化數(shù)據(jù)挖掘工具Rattle 資源 Reference
第1章 R語言概述
1.選擇題
（1)多行注釋的快捷鍵是（C）。
（2)以下函數(shù)不能直接查看plot函數(shù)的幫助文檔的是（B）。
（3)以下R包的加載方式正確的是（A）。
（4)以下R包中不能調(diào)用分類算法的是（D）。
2.操作題
（1)依據(jù)1.1節(jié)的R下載及安裝方法，在計算機上安裝R,通過熟悉基本操作的命令及操作界面，掌握軟件的使用方法。
圖1-29 iris數(shù)據(jù)集的交互式表格
第2章 數(shù)據(jù)對象與數(shù)據(jù)讀寫
1.選擇題
（1)下列可以判別字符型數(shù)據(jù)的函數(shù)是（A）。
（3)可將對象轉(zhuǎn)換為邏輯型數(shù)據(jù)的函數(shù)是（D）。
（4)下列選項不是邏輯型數(shù)據(jù)的是（C）。
（5)下列可以求矩陣的特征值和特征向量的函數(shù)是（B）。
（6)下列選項中可以使得列表轉(zhuǎn)換為向量的是（D）。
（7)下列用來轉(zhuǎn)換數(shù)據(jù)框的函數(shù)是（B）。
（8)下列用鍵盤導(dǎo)人數(shù)據(jù)的函數(shù)是（B）。
（9)RODBC包中向數(shù)據(jù)庫提交一個查詢，并返回結(jié)果的函數(shù)是（B）。
（10)抓取網(wǎng)頁上的表格，可使用XML包的是（D）函數(shù)。
2.操作題
（1)創(chuàng)建一個對象，并進行數(shù)據(jù)類型的轉(zhuǎn)換、判別等操作，步驟如下：
x
<- c
( 1 , 3 , 5 , 6 , 8 )
is.numeric
( x
)
x1
<- as.logical
( x
)
is.logical
( x1
)
（2)創(chuàng)建多種數(shù)據(jù)結(jié)構(gòu)，并進行數(shù)據(jù)結(jié)構(gòu)的轉(zhuǎn)換、索引、擴展等編輯操作，步驟如下：
setwd
( "./第2章 數(shù)據(jù)對象與數(shù)據(jù)讀寫/02-習(xí)題程序/code" )
x
<- c
( 11 , 23 , 25 , 46 , 38 , 30 , 59 , 47 , 21 , 67 )
x
[ c
( 2 , 4 ) ]
which
( x
> 35 & x
<= 50 )
Species
<- rep
( c
( "setosa" , "versicolor" , "virginica" ) , each
= 2 , length.out
= 5 ) a
<- matrix
( x
, 5 , 2 )
data_iris
<- data.frame
( Sepal.Length
= a
[ , 1 ] , Sepal.Width
= a
[ , 2 ] )
write.table
( data_iris
, " ./data_iris.txt" )
b
<- as.matrix
( data_iris
)
y
<- as.vector
( b
)
is.vector
( y
)
（3)讀取TXT文件，進行編輯操作，再寫入另外一個CSV文件中，步驟如下：
read.table
( "./第2章 數(shù)據(jù)對象與數(shù)據(jù)讀寫/02-習(xí)題程序/code/data_iris.txt" )
data_iris1
<- data.frame
( iris
[ 6 : 10 , ] )
data_iris2
<- cbind
( data_iris
, data_iris1
)
write.csv
( data_iris2
, "./第2章 數(shù)據(jù)對象與數(shù)據(jù)讀寫/02-習(xí)題程序/code/data_iris2.csv" )
第3章 數(shù)據(jù)集基本處理
1.選擇題
（1)下列不屬于用于修改變量名的函數(shù)是（C）。
（2)下列用于修改矩陣變量名的函數(shù)是（B）。
（3)下列屬于as.Date函數(shù)功能的選項是（A）。
（4)下列關(guān)于合并數(shù)據(jù)集不正確的選項是（C）。
（5)下列不屬于sample函數(shù)功能的是（B）。
（6)下列關(guān)于subset函數(shù)表達的錯誤選項是（D）。
（7)使用merge函數(shù)合并數(shù)據(jù)時，下列為默認值的是（B）。
（8)使用mell函數(shù)操作n維數(shù)組時，返回的結(jié)果有（A）列。
（9)元字符·的含義為（B）。
（10)下列關(guān)于paste函數(shù)表達錯誤的選項是（D）。
2.操作題
（1)創(chuàng)建一個矩陣，并使用交互式編輯器修改變量名；創(chuàng)建一個數(shù)據(jù)框，并在數(shù)據(jù)框中添加3個新變量，分別為原數(shù)據(jù)的差、乘積和余數(shù)。
a
<- matrix
( 1 : 12 , 3 , 4 )
fix
( a
)
a
<- c
( 1 , 2 , 3 , 4 )
b
<- c
( 11 , 22 , 33 , 44 )
x1
<- data.frame
( a
, b
, d
= a
- b
, e
= a
* b
, f
= a
%% b
)
x1
（2)構(gòu)建一個含有缺失值的數(shù)據(jù)框，檢測該數(shù)據(jù)框是否含有缺失值并刪除包含缺失值的行；創(chuàng)建一個字符串的日期值，分別使用as.Date 函數(shù)、as.POSIXIt 函數(shù)、strptime 函數(shù)轉(zhuǎn)換為日期變量；使用sort函數(shù)對score的Chinese列進行從大到小排列，并且把缺失值放在最后。
x2
<- data.frame
( id
= c
( 1 , 2 , 3 , 4 ) , name
= c
( "張三" , "李四" , "王五" , "趙六" ) , math
= c
( 70 , 89 , NA , 80 ) , English
= c
( 86 , 78 , 65 , 92 ) )
anyNA
( x2
)
na.omit
( x2
)
dates
<- c
( "10/27/2017" , "02/25/2017" , "01/14/2017" , "07/18/2017" , "04/01/2017" )
( date
<- as.Date
( dates
, "%m/%d/%Y" ) )
datas1
<- c
( "2017-09-08 11:17:52" , "2017-08-07 20:33:02" )
as.POSIXlt
( datas1
, tz
= "" , "%Y-%m-%d %H:%M:%S" )
( datas2
<- strptime
( datas1
, "%Y-%m-%d %H:%M:%S" ) )
score
<- data.frame
( student
= c
( "A" , "B" , "C" , "D" ) , gender
= c
( "M" , "M" , "F" , "F" ) , math
= c
( 90 , 70 , 80 , 60 ) , Eng
= c
( 88 , 78 , 69 , 98 ) , p1
= c
( 66 , 59 , NA , 88 ) )
names
( score
) [ 5 ] = "Chinese"
score
sort
( score
$ Chinese
, decreasing
= TRUE , na.last
= TRUE )
（3)構(gòu)建一個數(shù)據(jù)框，并使用兩種方法來選取變量；使用sample函數(shù)實現(xiàn)放回隨機抽樣與不放回隨機抽樣。
data
<- data.frame
( a
= c
( 5.1 , 4.9 , 4.7 ) , b
= c
( 3.5 , 3.0 , 3.2 ) , c
= c
( 1.4 , 1.3 , 1.5 ) , d
= rep
( 0.2 , 3 ) )
newdata
<- data
[ , c
( 3 : 4 ) ]
newdata1
<- subset
( data
, a
== "4.9" , select
= c
( b
, d
) ) a
<- c
( 11 , 22 , 33 , 44 , 55 , 66 , 77 , 88 , 99 )
sample
( a
, 5 , replace
= TRUE )
sample
( a
, 5 , replace
= FALSE )
（4)使用SQL語句對文中數(shù)據(jù)框stuscore進行計算。
name
<- c
( rep
( "張三" , 1 , 3 ) , rep
( "李四" , 3 ) )
subject
<- c
( "數(shù)學(xué)" , "語文" , "英語" , "數(shù)學(xué)" , "語文" , "英語" )
score
<- c
( 89 , 80 , 70 , 90 , 70 , 80 )
stuid
<- c
( 1 , 1 , 1 , 2 , 2 , 2 )
stuscore
<- data.frame
( name
, subject
, score
, stuid
)
library
( sqldf
)
sqldf
( "select stuid
, sum
( score
) as allscore from stuscore group by stuid order by allscore"
)
sqldf
( "select stuid
, subject
, max
( score
) as maxscore from stuscore group by stuid"
)
sqldf
( "select stuid
, subject
, max
( score
) as maxscore from stuscore group by subject order by stuid"
)
sqldf
( "select stuid
, subject
, min
( score
) as minscore from stuscore group by subject order by stuid"
)
（5)創(chuàng)建一個列表，并使用melt函數(shù)將其融合。
data
<- list
( a
= c
( 11 , 22 , 33 , 44 ) , b
= matrix
( 1 : 10 , nrow
= 2 ) , c
= "one,two,three" , d
= c
( TRUE , FALSE ) )
data
library
( reshape2
)
melt
( data
, varnames
= c
( "X" , "Y" ) , value.name
= "value" , na.rm
= FALSE )
（6)構(gòu)建一個字符型向量，并使用sub函數(shù)和 gsub 函數(shù)完成字符串替換；使用paste
data1
<- c
( "we" , "are" , "family" , "you" , "good" )
sub
( "good" , "bad" , data1
)
gsub
( "we" , "student" , data1
) b
<- paste
( "ab" , 1 : 3 , sep
= "" )
x
<- list
( a
= "st" , b
= "nd" , c
= "yw" )
y
<- list
( d
= 1 , e
= 2 )
c
<- paste
( x
, y
, sep
= "-" , collapse
= "; " )
c
第4章 函數(shù)與控制流
1.選擇題
（1)下列能返回不小于x的最小整數(shù)的數(shù)學(xué)函數(shù)是（C）。
（2)下列不屬于apply函數(shù)使用對象的是（B）。
（3)在ifelse(condition,statementl,statement2)語句中，當condition為TRUE時，執(zhí)行的語句是（A）。
（4)在switch(expression,list)語句中，當list是有名定義、表達式等于變量名時，返回的結(jié)果是（B）。
（5)下列不屬于條件分支語句的數(shù)據(jù)分析的應(yīng)用場景的是（B）。
（6)下列關(guān)于cal(exprl,expr2…)函數(shù)表達不正確的是（A）。
（7)能讓while((i<=10){expr}語句停止循環(huán)的選項是（B）。
（8)使用自定義函數(shù)時可通過（A）調(diào)用。
（9)函數(shù)體不包括（C）部分。
（10)下列選項中表示返回值的函數(shù)是（D）。
2.操作題
（1)使用apply函數(shù)族中的函數(shù)計算列表x<-list(a=1:5,b=exp(0:3))中的各子列表的最大值、最小值與中位數(shù)。
x
<- list
( a
= 1 : 5 , b
= exp
( 0 : 3 ) )
lapply
( x
, max
)
lapply
( x
, min
)
lapply
( x
, median
)
（2)在區(qū)間［－5,5上繪制標準正態(tài)曲線，求位于z左側(cè)的標準正態(tài)曲線下方的面積。
x
<- pretty
( c
( - 5 , 5 ) , 50 )
y
<- dnorm
( x
)
plot
( x
, y
, type
= 'l' , xlab
= 'Normal Deviate' , ylab
= 'Density' , yaxs
= 'i' )
pnorm
( 1.96 )
（3)用條件分支語句將成績劃分為5個等級：A(大于等于90)、B(大于等于80)、（大于等于70)、D(大于等于60)、E(小于60).例如，對成績87分進行判斷。
x
<- sample
( 0 : 100 , 1 )
if ( x
>= 90 ) { grade
<- '成績等級：A'
} else if ( x
>= 80 ) { grade
<- '成績等級：B'
} else if ( x
>= 70 ) { grade
<- '成績等級：C'
} else if ( x
>= 60 ) { grade
<- '成績等級：D'
} else { grade
<- '成績等級：E'
}
grade
（4)判斷101-200之間有多少個素數(shù)，并輸出所有素數(shù)。
x
<- 101 : 200
y
<- matrix
( nrow
= 200 , ncol
= 100 )
count
<- 0
c
<- NULL
for ( i
in 1 : 100 ) { for ( j
in 1 : 200 ) { y
[ j
, i
] <- x
[ i
] %% j
} if ( length
( which
( y
[ , i
] == 0 ) ) <= 2 ) { count
<- count
+ 1 c
[ count
] <- i
}
}
count
x
[ c
]
（5)編寫一個自定義函數(shù)求兩個矩陣的乘積，并找出乘積矩陣中的最大元素。
POM
<- function ( x
, y
)
{ maxer
<- function ( x.
) { print
( max
( x.
) ) } m1
<- ncol
( x
) n
<- nrow
( y
) if ( m1
!= n
) { print
( 'error dimension is not siutable' ) return
( 0 ) } m
<- nrow
( x
) n1
<- ncol
( y
) s
<- matrix
( 0 , m
, n1
) for ( i
in 1 : m
) for ( j
in 1 : n1
) s
[ i
, j
] <- sum
( x
[ i
, ] * y
[ , j
] ) maxer
( s
) return
( s
)
}
x
<- matrix
( c
( 1 : 6 ) , 2 , 3 , byrow
= TRUE )
y
<- matrix
( c
( 1 : 6 ) , 3 , 2 , byrow
= FALSE )
POM
( x
, y
)
第5章 初級繪圖
1.選擇題
（1)下列可用于繪制散點圖的是（A）。
（2)下列圖形中，不能分析數(shù)據(jù)分布情況的是（C）。
（3)數(shù)據(jù)維度較大時，為比較兩兩變量之間的相關(guān)關(guān)系，可以考慮繪制的圖形是（D）。
（4)為展示數(shù)據(jù)類別的占比情況，可以考慮繪制的圖形是（C）。
（5)下列繪制的圖形與R函數(shù)對應(yīng)關(guān)系不正確的是（B）。
（6)下列選項中，（D）不是R自帶的修改顏色函數(shù)。
（7)能修改點樣式的參數(shù)是（B）。
（8)可以在圖形中的任意位置添加文字說明的函數(shù)是（B）。
（9)為在現(xiàn)有圖形上添加擬合直線，可以考慮的函數(shù)是（B）。
（10)下列圖形參數(shù)與說明的對應(yīng)關(guān)系不正確的是（D）。
2.操作題
（1)表5-23是某銀行貸款拖欠率的數(shù)據(jù)bankloan.表5-23 銀行貸款拖欠率的數(shù)據(jù)
bankloan
<- read.csv
( "../bankloan.csv" )
summary
( bankloan
)
bankloan
$ age_group
<- cut
( bankloan
$ age
, breaks
= paste0
( 2 : 6 , 0 ) , include.lowest
= TRUE )
bankloan
$ seniority_group
<- cut
( bankloan
$ seniority
, breaks
= c
( 0 , 1 , 3 , 5 , 10 , 15 , 20 , 30 , 40 ) , include.lowest
= TRUE )
bankloan
$ education
<- factor
( bankloan
$ education
)
bankloan
$ debt
<- bankloan
$ debt_rate
/ 100 * bankloan
$ income
attach
( bankloan
)
pal
<- RColorBrewer
:: brewer.pal
( 8 , "Set1" )
de_e
<- ftable
( education
, default
)
barplot
( de_e
, col
= pal
[ 1 : 5 ] , beside
= TRUE , xlab
= "default" )
legend
( "topright" , levels
( education
) , pch
= 15 , col
= pal
, bty
= "n" )
text
( 11 , 270 , "education:" )
de_a
<- ftable
( age_group
, default
)
barplot
( de_a
, col
= pal
[ 1 : 4 ] , beside
= TRUE , xlab
= "default" )
legend
( "topright" , levels
( age_group
) , pch
= 15 , col
= pal
, bty
= "n" )
text
( 8.8 , 204 , "age:" )
de_s
<- ftable
( seniority_group
, default
)
barplot
( de_s
, col
= pal
[ 1 : 8 ] , beside
= TRUE , xlab
= "default" )
legend
( "topright" , levels
( seniority_group
) , pch
= 15 , col
= pal
, bty
= "n" )
text
( 15.3 , 140 , "seniority:" )
dotchart
( de_e
, bg
= pal
[ 1 : 5 ] , labels
= levels
( education
) )
dotchart
( de_a
, bg
= pal
[ 1 : 4 ] , labels
= levels
( age_group
) )
dotchart
( de_s
, bg
= pal
[ 1 : 8 ] , labels
= levels
( seniority_group
) )
set.seed
( 1234 )
norm_income
<- rnorm
( 1000 , mean
( income
) , sd
( income
) )
hist
( income
, freq
= FALSE , breaks
= 50 )
lines
( density
( income
) , col
= 2 )
lines
( density
( norm_income
) , lty
= 2 , col
= 3 )
legend
( "topright" , c
( "density" , "normal" ) , lty
= 1 : 2 , col
= 2 : 3 , bty
= "n" )
norm_debt
<- rnorm
( 1000 , mean
( debt
) , sd
( debt
) )
hist
( debt
, freq
= FALSE , breaks
= 50 )
lines
( density
( debt
) , col
= 2 )
lines
( density
( norm_debt
) , lty
= 2 , col
= 3 )
legend
( "topright" , c
( "density" , "normal" ) , lty
= 1 : 2 , col
= 2 : 3 , bty
= "n" )
library
( sm
)
sm.density.compare
( income
, factor
( education
) )
legend
( "topright" , levels
( education
) , lty
= 1 : 5 , col
= 2 : 6 , bty
= "n" )
text
( 450 , 0.015 , "education:" ) sm.density.compare
( income
, factor
( age_group
) )
legend
( "topright" , levels
( age_group
) , lty
= 1 : 4 , col
= 2 : 5 , bty
= "n" )
text
( 430 , 0.026 , "age:" ) sm.density.compare
( income
, factor
( seniority_group
) )
legend
( "topright" , levels
( seniority_group
) , lty
= 1 : 8 , col
= 2 : 9 , bty
= "n" )
text
( 425 , 0.03 , "seniority:" )
sm.density.compare
( debt
, factor
( education
) )
legend
( "topright" , levels
( education
) , lty
= 1 : 5 , col
= 2 : 6 , bty
= "n" )
text
( 40 , 0.125 , "education:" ) sm.density.compare
( debt
, factor
( age_group
) )
legend
( "topright" , levels
( age_group
) , lty
= 1 : 4 , col
= 2 : 5 , bty
= "n" )
text
( 38 , 0.16 , "age:" ) sm.density.compare
( debt
, factor
( seniority_group
) )
legend
( "topright" , levels
( seniority_group
) , lty
= 1 : 8 , col
= 2 : 9 , bty
= "n" )
text
( 37 , 0.16 , "seniority:" )
boxplot
( income
~ education
, horizontal
= TRUE )
boxplot
( income
~ age_group
, horizontal
= TRUE )
boxplot
( income
~ seniority_group
, horizontal
= TRUE )
boxplot
( debt
~ education
, horizontal
= TRUE )
boxplot
( debt
~ age_group
, horizontal
= TRUE )
boxplot
( debt
~ seniority_group
, horizontal
= TRUE )
income_e
<- tapply
( income
, education
, function ( t
) t
)
income_a
<- tapply
( income
, age_group
, function ( t
) t
)
income_s
<- tapply
( income
, seniority_group
, function ( t
) t
)
library
( vioplot
)
vioplot
( income_e
$ `
1 `
, income_e
$ `
2 `
, income_e
$ `
3 `
, income_e
$ `
4 `
, income_e
$ `
5 `
, names
= levels
( education
) , border
= "black" , col
= "light green" , rectCol
= "blue" ) vioplot
( income_a
$ `
[ 20 , 30 ] `
, income_a
$ `
( 30 , 40 ] `
, income_a
$ `
( 40 , 50 ] `
, income_a
$ `
( 50 , 60 ] `
, names
= levels
( age_group
) , border
= "black" , col
= "light green" , rectCol
= "blue" ) vioplot
( income_s
$ `
[ 0 , 1 ] `
, income_s
$ `
( 1 , 3 ] `
, income_s
$ `
( 3 , 5 ] `
, income_s
$ `
( 5 , 10 ] `
, income_s
$ `
( 10 , 15 ] `
, income_s
$ `
( 15 , 20 ] `
, income_s
$ `
( 20 , 30 ] `
, income_s
$ `
( 30 , 40 ] `
, names
= levels
( seniority_group
) , border
= "black" , col
= "light green" , rectCol
= "blue" )
debt_e
<- tapply
( debt
, education
, function ( t
) t
)
debt_a
<- tapply
( debt
, age_group
, function ( t
) t
)
debt_s
<- tapply
( debt
, seniority_group
, function ( t
) t
)
vioplot
( debt_e
$ `
1 `
, debt_e
$ `
2 `
, debt_e
$ `
3 `
, debt_e
$ `
4 `
, debt_e
$ `
5 `
, names
= levels
( education
) , border
= "black" , col
= "light green" , rectCol
= "blue" ) vioplot
( debt_a
$ `
[ 20 , 30 ] `
, debt_a
$ `
( 30 , 40 ] `
, debt_a
$ `
( 40 , 50 ] `
, debt_a
$ `
( 50 , 60 ] `
, names
= levels
( age_group
) , border
= "black" , col
= "light green" , rectCol
= "blue" )
vioplot
( debt_s
$ `
[ 0 , 1 ] `
, debt_s
$ `
( 1 , 3 ] `
, debt_s
$ `
( 3 , 5 ] `
, debt_s
$ `
( 5 , 10 ] `
, debt_s
$ `
( 10 , 15 ] `
, debt_s
$ `
( 15 , 20 ] `
, debt_s
$ `
( 20 , 30 ] `
, debt_s
$ `
( 30 , 40 ] `
, names
= levels
( seniority_group
) , border
= "black" , col
= "light green" , rectCol
= "blue" )
le
<- levels
( education
)
op
<- par
( mfrow
= c
( 1 , 5 ) )
for ( i
in 1 : nlevels
( education
) ) { plot
( income
[ education
== le
[ i
] ] , debt
[ education
== le
[ i
] ] , main
= paste
( "education = " , le
[ i
] ) , xlab
= "income" , ylab
= "debt" ) abline
( lm
( debt
[ education
== le
[ i
] ] ~ income
[ education
== le
[ i
] ] ) , col
= "red" )
}
par
( op
) la
<- levels
( age_group
)
op
<- par
( mfrow
= c
( 1 , 4 ) )
for ( i
in 1 : nlevels
( age_group
) ) { plot
( income
[ age_group
== la
[ i
] ] , debt
[ age_group
== la
[ i
] ] , main
= paste
( "age_group = " , la
[ i
] ) , xlab
= "income" , ylab
= "debt" ) abline
( lm
( debt
[ age_group
== la
[ i
] ] ~ income
[ age_group
== la
[ i
] ] ) , col
= "red" )
}
par
( op
) ls
<- levels
( seniority_group
)
op
<- par
( mfrow
= c
( 1 , 4 ) )
for ( i
in 1 : nlevels
( seniority_group
) ) { plot
( income
[ seniority_group
== ls
[ i
] ] , debt
[ seniority_group
== ls
[ i
] ] , main
= paste
( "seniority_group = " , ls
[ i
] ) , xlab
= "income" , ylab
= "debt" ) abline
( lm
( debt
[ seniority_group
== ls
[ i
] ] ~ income
[ seniority_group
== ls
[ i
] ] ) , col
= "red" )
}
par
( op
)
（2)根據(jù)VADeaths數(shù)據(jù)集，分別繪制城鎮(zhèn)居民與農(nóng)村居民死亡情況的餅圖，添加標題及圖例說明，并分析圖表。
my_sum
<- colSums
( VADeaths
)
Rural
<- sum
( my_sum
[ grep
( 'Rural' , names
( my_sum
) ) ] )
Urban
<- sum
( my_sum
[ grep
( 'Urban' , names
( my_sum
) ) ] )
pie
( c
( Rural
, Urban
) , labels
= c
( 'Rural' , 'Urban' ) , col
= c
( 'blue' , 'green' ) )
title
( '城鎮(zhèn)居民與農(nóng)村居民死亡情況' )
legend
( 'topleft' , legend
= c
( 'Rural' , 'Urban' ) , fill
= c
( 'blue' , 'green' ) )
（3)在同一畫布上繪制iris數(shù)據(jù)集的4個屬性兩兩之間的散點圖，所得到的結(jié)果如圖5-36所示。圖5-36 屬性兩兩之間的散點圖
path
= paste0
( 'VADeaths' , '.png' )
png
( filename
= path
)
pie
( c
( Rural
, Urban
) , labels
= c
( 'Rural' , 'Urban' ) , col
= c
( 'blue' , 'green' ) )
title
( '城鎮(zhèn)居民與農(nóng)村居民死亡情況' )
legend
( 'topleft' , legend
= c
( 'Rural' , 'Urban' ) , fill
= c
( 'blue' , 'green' ) )
dev.off
( )
（4)將第（3)題的結(jié)果保存為PNG文件格式，并儲存到當前工作目錄下。
path
= paste
( 'ex5_4' , '.jpg' )
jpeg
( file
= path
)
par
( mfrow
= c
( 2 , 3 ) )
name
<- colnames
( iris
) [ 1 : 4 ]
for ( i
in 1 : 3 ) { for ( j
in ( i
+ 1 ) : 4 ) { plot
( iris
[ , i
] , iris
[ , j
] , xlab
= name
[ i
] , ylab
= name
[ j
] ) }
}
dev.off
( )
第6章 高級繪圖
1.選擇題
（1)下列不能作為lattice包中的繪圖函數(shù) formula輸人的是（B）。
（2)下列繪圖函數(shù)不屬于lattice包的是（C）。
（3)lattice包中可實現(xiàn)圖形組合的是（C）。
（4)下列繪圖函數(shù)與圖形對應(yīng)關(guān)系錯誤的是（A）。
（5)lattice包中的繪圖函數(shù)的條件變量不能輸入（A）。
（6)一個圖層不包含（D）。
（7)下列選項中不能描述坐標系轉(zhuǎn)換關(guān)系的是（C）。
（8)ggplot包中實現(xiàn)分面的函數(shù)是（D）。
（9)下列選項不能描述繪圖函數(shù)與圖形對應(yīng)關(guān)系的是（B）。
（10)下列不屬于圖形屬性的是（D）。
2.操作題
（1)表6-7所示是某銀行的貸款拖欠率的數(shù)據(jù) bankloan.要求使用lattice 包完成以下圖形的繪制。表6-7銀行貸款拖欠率數(shù)據(jù)
bankloan
= read.csv
( "./第6章 高級繪圖/02-習(xí)題程序/code/data/bankloan.csv" )
summary
( bankloan
)
bankloan
$ age_group
<- cut
( bankloan
$ age
, breaks
= paste0
( 2 : 6 , 0 ) , include.lowest
= TRUE )
bankloan
$ seniority_group
<- cut
( bankloan
$ seniority
, breaks
= c
( 0 , 1 , 3 , 5 , 10 , 15 , 20 , 30 , 40 ) , include.lowest
= TRUE )
bankloan
$ education
<- factor
( bankloan
$ education
)
bankloan
$ debt
<- bankloan
$ debt_rate
/ 100 * bankloan
$ income
library
( lattice
)
histogram_ie
<- histogram
( ~ income
| education
, data
= bankloan
, layout
= c
( 1 , 5 ) , nint
= 30 , type
= "count" )
densityplot_ie
<- densityplot
( ~ income
, groups
= education
, data
= bankloan
, plot.points
= FALSE , lty
= 1 : 5 , col
= 1 : 5 , key
= list
( title
= "education" , text
= list
( levels
( bankloan
$ education
) ) , column
= 2 , lines
= list
( lty
= 1 : 5 , col
= 1 : 5 ) ) )
plot
( histogram_ie
, position
= c
( 0 , 0 , 0.5 , 1 ) )
plot
( densityplot_ie
, position
= c
( 0.5 , 0 , 1 , 1 ) , newpage
= FALSE )
histogram_ia
<- histogram
( ~ income
| age_group
, data
= bankloan
, layout
= c
( 1 , 4 ) , nint
= 30 , type
= "count" )
densityplot_ia
<- densityplot
( ~ income
, groups
= age_group
, data
= bankloan
, plot.points
= FALSE , lty
= 1 : 4 , col
= 1 : 4 , key
= list
( title
= "age" , text
= list
( levels
( bankloan
$ age_group
) ) , column
= 2 , lines
= list
( lty
= 1 : 4 , col
= 1 : 4 ) ) )
plot
( histogram_ia
, position
= c
( 0 , 0 , 0.5 , 1 ) )
plot
( densityplot_ia
, position
= c
( 0.5 , 0 , 1 , 1 ) , newpage
= FALSE )
histogram_is
<- histogram
( ~ income
| seniority_group
, data
= bankloan
, layout
= c
( 1 , 8 ) , nint
= 30 , type
= "count" )
densityplot_is
<- densityplot
( ~ income
, groups
= seniority_group
, data
= bankloan
, plot.points
= FALSE , lty
= 1 : 8 , col
= 1 : 8 , key
= list
( title
= "seniority" , text
= list
( levels
( bankloan
$ seniority_group
) ) , column
= 2 , lines
= list
( lty
= 1 : 8 , col
= 1 : 8 ) ) )
plot
( histogram_is
, position
= c
( 0 , 0 , 0.5 , 1 ) )
plot
( densityplot_is
, position
= c
( 0.5 , 0 , 1 , 1 ) , newpage
= FALSE )
histogram_de
<- histogram
( ~ debt_rate
| education
, data
= bankloan
, layout
= c
( 1 , 5 ) , nint
= 30 , type
= "count" )
densityplot_de
<- densityplot
( ~ debt_rate
, groups
= education
, data
= bankloan
, plot.points
= FALSE , lty
= 1 : 5 , col
= 1 : 5 , key
= list
( title
= "education" , text
= list
( levels
( bankloan
$ education
) ) , column
= 2 , lines
= list
( lty
= 1 : 5 , col
= 1 : 5 ) ) )
plot
( histogram_de
, position
= c
( 0 , 0 , 0.5 , 1 ) )
plot
( densityplot_de
, position
= c
( 0.5 , 0 , 1 , 1 ) , newpage
= FALSE )
histogram_da
<- histogram
( ~ debt_rate
| age_group
, data
= bankloan
, layout
= c
( 1 , 4 ) , nint
= 30 , type
= "count" )
densityplot_da
<- densityplot
( ~ debt_rate
, groups
= age_group
, data
= bankloan
, plot.points
= FALSE , lty
= 1 : 4 , col
= 1 : 4 , key
= list
( title
= "age" , text
= list
( levels
( bankloan
$ age_group
) ) , column
= 2 , lines
= list
( lty
= 1 : 4 , col
= 1 : 4 ) ) )
plot
( histogram_da
, position
= c
( 0 , 0 , 0.5 , 1 ) )
plot
( densityplot_da
, position
= c
( 0.5 , 0 , 1 , 1 ) , newpage
= FALSE )
histogram_ds
<- histogram
( ~ debt_rate
| seniority_group
, data
= bankloan
, layout
= c
( 1 , 8 ) , nint
= 30 , type
= "count" )
densityplot_ds
<- densityplot
( ~ debt_rate
, groups
= seniority_group
, data
= bankloan
, plot.points
= FALSE , lty
= 1 : 8 , col
= 1 : 8 , key
= list
( title
= "seniority" , text
= list
( levels
( bankloan
$ seniority_group
) ) , column
= 2 , lines
= list
( lty
= 1 : 8 , col
= 1 : 8 ) ) )
plot
( histogram_ds
, position
= c
( 0 , 0 , 0.5 , 1 ) )
plot
( densityplot_ds
, position
= c
( 0.5 , 0 , 1 , 1 ) , newpage
= FALSE )
id_e
<- xyplot
( debt
~ income
| education
, data
= bankloan
, layout
= c
( 1 , 5 ) , panel
= function ( ... ) { panel.lmline
( ... ) panel.xyplot
( ... ) } )
id_a
<- xyplot
( debt
~ income
| age_group
, data
= bankloan
, layout
= c
( 1 , 4 ) , panel
= function ( ... ) { panel.lmline
( ... ) panel.xyplot
( ... ) } )
id_s
<- xyplot
( debt
~ income
| seniority_group
, data
= bankloan
, layout
= c
( 1 , 8 ) , panel
= function ( ... ) { panel.lmline
( ... ) panel.xyplot
( ... ) } )
plot
( id_e
, split
= c
( 1 , 1 , 3 , 1 ) )
plot
( id_a
, split
= c
( 2 , 1 , 3 , 1 ) , newpage
= FALSE )
plot
( id_s
, split
= c
( 3 , 1 , 3 , 1 ) , newpage
= FALSE )
glmdei
<- as.vector
( glm
( default
~ income
, data
= bankloan
, family
= binomial
( link
= "logit" ) ) $ coefficients
)
glmded
<- as.vector
( glm
( default
~ debt_rate
, data
= bankloan
, family
= binomial
( link
= "logit" ) ) $ coefficients
) paneli
<- function ( x
, y
) { panel.curve
( exp
( glmdei
[ 1 ] + glmdei
[ 2 ] * x
) / ( 1 + exp
( glmdei
[ 1 ] + glmdei
[ 2 ] * x
) ) , col
= "red" , lwd
= 1 , lty
= 2 ) panel.xyplot
( x
, y
)
}
paneld
<- function ( x
, y
) { panel.curve
( exp
( glmded
[ 1 ] + glmded
[ 2 ] * x
) / ( 1 + exp
( glmded
[ 1 ] + glmded
[ 2 ] * x
) ) , col
= "red" , lwd
= 1 , lty
= 2 ) panel.xyplot
( x
, y
)
}
glmla_dei
<- xyplot
( default
~ income
, data
= bankloan
, panel
= paneli
)
glmla_ded
<- xyplot
( default
~ income
, data
= bankloan
, panel
= paneld
)
plot
( glmla_dei
, split
= c
( 1 , 1 , 2 , 1 ) )
plot
( glmla_ded
, split
= c
( 2 , 1 , 2 , 1 ) , newpage
= FALSE )
tbankloan
<- table
( bankloan
$ education
, bankloan
$ seniority_group
, bankloan
$ age_group
, bankloan
$ default
)
barchart
( tbankloan
, auto.key
= list
( title
= "default" , columns
= 2 ) )
（2)針對（1)中提及的bankloan的示例表，使用ggplo12包完成以下圖形的繪制。
bankloan
= read.csv
( "./第6章 高級繪圖/02-習(xí)題程序/code/data/bankloan.csv" )
summary
( bankloan
)
bankloan
$ age_group
<- cut
( bankloan
$ age
, breaks
= paste0
( 2 : 6 , 0 ) , include.lowest
= TRUE )
bankloan
$ seniority_group
<- cut
( bankloan
$ seniority
, breaks
= c
( 0 , 1 , 3 , 5 , 10 , 15 , 20 , 30 , 40 ) , include.lowest
= TRUE )
bankloan
$ education
<- factor
( bankloan
$ education
)
bankloan
$ debt
<- bankloan
$ debt_rate
/ 100 * bankloan
$ income
library
( ggplot2
)
library
( grid
)
page
<- function ( x
, y
) viewport
( layout.pos.row
= x
, layout.pos.col
= y
)
hist_ie
<- ggplot
( data
= bankloan
, aes
( x
= income
) ) + geom_histogram
( bins
= 30 , fill
= "#0080ff" ) + facet_grid
( education
~ .
)
density_ie
<- ggplot
( data
= bankloan
, aes
( x
= income
, colour
= education
) ) + geom_density
( ) + theme
( legend.position
= "top" )
grid.newpage
( )
pushViewport
( viewport
( layout
= grid.layout
( 1 , 2 ) ) )
print
( hist_ie
, vp
= page
( 1 , 1 ) )
print
( density_ie
, vp
= page
( 1 , 2 ) )
hist_ia
<- ggplot
( data
= bankloan
, aes
( x
= income
) ) + geom_histogram
( bins
= 30 , fill
= "#0080ff" ) + facet_grid
( age_group
~ .
)
density_ia
<- ggplot
( data
= bankloan
, aes
( x
= income
, colour
= age_group
) ) + geom_density
( ) + theme
( legend.position
= "top" )
grid.newpage
( )
pushViewport
( viewport
( layout
= grid.layout
( 1 , 2 ) ) )
print
( hist_ia
, vp
= page
( 1 , 1 ) )
print
( density_ia
, vp
= page
( 1 , 2 ) )
hist_is
<- ggplot
( data
= bankloan
, aes
( x
= income
) ) + geom_histogram
( bins
= 30 , fill
= "#0080ff" ) + facet_grid
( seniority_group
~ .
)
density_is
<- ggplot
( data
= bankloan
, aes
( x
= income
, colour
= seniority_group
) ) + geom_density
( ) + theme
( legend.position
= "top" )
grid.newpage
( )
pushViewport
( viewport
( layout
= grid.layout
( 1 , 2 ) ) )
print
( hist_is
, vp
= page
( 1 , 1 ) )
print
( density_is
, vp
= page
( 1 , 2 ) )
hist_de
<- ggplot
( data
= bankloan
, aes
( x
= debt_rate
) ) + geom_histogram
( bins
= 30 , fill
= "#0080ff" ) + facet_grid
( education
~ .
)
density_de
<- ggplot
( data
= bankloan
, aes
( x
= debt_rate
, colour
= education
) ) + geom_density
( ) + theme
( legend.position
= "top" )
grid.newpage
( )
pushViewport
( viewport
( layout
= grid.layout
( 1 , 2 ) ) )
print
( hist_de
, vp
= page
( 1 , 1 ) )
print
( density_de
, vp
= page
( 1 , 2 ) )
hist_da
<- ggplot
( data
= bankloan
, aes
( x
= debt_rate
) ) + geom_histogram
( bins
= 30 , fill
= "#0080ff" ) + facet_grid
( age_group
~ .
)
density_da
<- ggplot
( data
= bankloan
, aes
( x
= debt_rate
, colour
= age_group
) ) + geom_density
( ) + theme
( legend.position
= "top" )
grid.newpage
( )
pushViewport
( viewport
( layout
= grid.layout
( 1 , 2 ) ) )
print
( hist_da
, vp
= page
( 1 , 1 ) )
print
( density_da
, vp
= page
( 1 , 2 ) )
hist_ds
<- ggplot
( data
= bankloan
, aes
( x
= debt_rate
) ) + geom_histogram
( bins
= 30 , fill
= "#0080ff" ) + facet_grid
( seniority_group
~ .
)
density_ds
<- ggplot
( data
= bankloan
, aes
( x
= debt_rate
, colour
= seniority_group
) ) + geom_density
( ) + theme
( legend.position
= "top" )
grid.newpage
( )
pushViewport
( viewport
( layout
= grid.layout
( 1 , 2 ) ) )
print
( hist_ds
, vp
= page
( 1 , 1 ) )
print
( density_ds
, vp
= page
( 1 , 2 ) )
ggid_e
<- ggplot
( data
= bankloan
, aes
( x
= income
, y
= debt
) ) + geom_point
( colour
= "#0080ff" ) + stat_smooth
( method
= lm
, lwd
= 0.5 , se
= FALSE ) + facet_grid
( education
~ .
)
ggid_a
<- ggplot
( data
= bankloan
, aes
( x
= income
, y
= debt
) ) + geom_point
( colour
= "#0080ff" ) + stat_smooth
( method
= lm
, lwd
= 0.5 , se
= FALSE ) + facet_grid
( age_group
~ .
)
ggid_s
<- ggplot
( data
= bankloan
, aes
( x
= income
, y
= debt
) ) + geom_point
( colour
= "#0080ff" ) + stat_smooth
( method
= lm
, lwd
= 0.5 , se
= FALSE ) + facet_grid
( seniority_group
~ .
) grid.newpage
( )
pushViewport
( viewport
( layout
= grid.layout
( 1 , 3 ) ) )
print
( ggid_e
, vp
= page
( 1 , 1 ) )
print
( ggid_a
, vp
= page
( 1 , 2 ) )
print
( ggid_s
, vp
= page
( 1 , 3 ) )
glmgg_dei
<- ggplot
( data
= bankloan
, aes
( x
= income
, y
= default
) ) + geom_point
( ) + stat_smooth
( method
= glm
, method.args
= list
( family
= "binomial" ) , se
= FALSE )
glmgg_ded
<- ggplot
( data
= bankloan
, aes
( x
= debt_rate
, y
= default
) ) + geom_point
( ) + stat_smooth
( method
= glm
, method.args
= list
( family
= "binomial" ) , se
= FALSE )
grid.newpage
( )
pushViewport
( viewport
( layout
= grid.layout
( 1 , 2 ) ) )
print
( glmgg_dei
, vp
= page
( 1 , 1 ) )
print
( glmgg_ded
, vp
= page
( 1 , 2 ) )
ggplot
( bankloan
, aes
( x
= education
, fill
= factor
( default
) ) ) + geom_bar
( position
= "stack" ) + facet_grid
( age_group
~ seniority_group
)
（3)結(jié)合（1)與（2)的操作題，用以上兩個操作題中所繪制的圖形創(chuàng)建腳本ui.R和server.R,利用shiny包搭建數(shù)據(jù)可視化平臺demo.
第7章 可視化數(shù)據(jù)挖掘工具Rattle
1.選擇題
（1)下列不屬于Rattle工具功能的是（C）。
（2)Rattle工具不能導(dǎo)入（D）。
（3)如果想要查看數(shù)據(jù)總體的概況，那么應(yīng)該運用（A）功能。
（4)Rattle的交互圖GGobi包不可以實現(xiàn)（D）的綜合使用。
（5)數(shù)據(jù)建模中，聚類分析不能得到的結(jié)果是（C）。
（6)關(guān)聯(lián)規(guī)則的Apriori算法不會默認設(shè)置的是（A）。
（7)在Model選項中，分類算法需要設(shè)置Min Bucket的值，如果要分成70、15、15的占比，則應(yīng)該設(shè)置為（B）。
（8)Rattle工具的隨機森林模型不能得到（D）。
（9)下列不屬于Rattle工具的模型評估方法的是（B）。
（10)下列數(shù)據(jù)挖掘算法中，適合用Rale工具的是（C）。
2.操作題
打開Rattle工具的圖形界面，導(dǎo)人Telephone.csv數(shù)據(jù)，并將數(shù)據(jù)按照70:15:15的比例分成訓(xùn)練集、驗證集和測試集。然后對數(shù)據(jù)進行探索，完成描述性統(tǒng)計分析、圖形探索等操作。提示：在Data選項卡中選擇合適的變量構(gòu)建模型，在Model選項卡中選擇合適的分類模型，并對模型進行評估。
資源
百度網(wǎng)盤–課后習(xí)題答案&源代碼&數(shù)據(jù)
Reference
R語言編程基礎(chǔ)-圖書-人郵教育社區(qū) →\rightarrow →
總結(jié)
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