1、读取数据
> bank=read.table("bank-full.csv",header=TRUE,sep=";")
>
2、查看数据结构
> bank=read.table("bank-full.csv",header=TRUE,sep=",")
> str(bank)
'data.frame': 41188 obs. of 21 variables:
$ age : int 56 57 37 40 56 45 59 41 24 25 ...
$ job : Factor w/ 12 levels "admin.","blue-collar",..: 4 8 8 1 8 8 1 2 10 8 ...
$ marital : Factor w/ 4 levels "divorced","married",..: 2 2 2 2 2 2 2 2 3 3 ...
$ education : Factor w/ 8 levels "basic.4y","basic.6y",..: 1 4 4 2 4 3 6 8 6 4 ...
$ default : Factor w/ 3 levels "no","unknown",..: 1 2 1 1 1 2 1 2 1 1 ...
$ housing : Factor w/ 3 levels "no","unknown",..: 1 1 3 1 1 1 1 1 3 3 ...
$ loan : Factor w/ 3 levels "no","unknown",..: 1 1 1 1 3 1 1 1 1 1 ...
$ contact : Factor w/ 2 levels "cellular","telephone": 2 2 2 2 2 2 2 2 2 2 ...
$ month : Factor w/ 10 levels "apr","aug","dec",..: 7 7 7 7 7 7 7 7 7 7 ...
$ day_of_week : Factor w/ 5 levels "fri","mon","thu",..: 2 2 2 2 2 2 2 2 2 2 ...
$ duration : int 261 149 226 151 307 198 139 217 380 50 ...
$ campaign : int 1 1 1 1 1 1 1 1 1 1 ...
$ pdays : int 999 999 999 999 999 999 999 999 999 999 ...
$ previous : int 0 0 0 0 0 0 0 0 0 0 ...
$ poutcome : Factor w/ 3 levels "failure","nonexistent",..: 2 2 2 2 2 2 2 2 2 2 ...
$ emp.var.rate : num 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 ...
$ cons.price.idx: num 94 94 94 94 94 ...
$ cons.conf.idx : num -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 ...
$ euribor3m : num 4.86 4.86 4.86 4.86 4.86 ...
$ nr.employed : num 5191 5191 5191 5191 5191 ...
$ y : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
3、查看摘要统计量
> summary(bank)
age job marital education
Min. :17.00 admin. :10422 divorced: 4612 university.degree :12168
1st Qu.:32.00 blue-collar: 9254 married :24928 high.school : 9515
Median :38.00 technician : 6743 single :11568 basic.9y : 6045
Mean :40.02 services : 3969 unknown : 80 professional.course: 5243
3rd Qu.:47.00 management : 2924 basic.4y : 4176
Max. :98.00 retired : 1720 basic.6y : 2292
(Other) : 6156 (Other) : 1749
default housing loan contact month
no :32588 no :18622 no :33950 cellular :26144 may :13769
unknown: 8597 unknown: 990 unknown: 990 telephone:15044 jul : 7174
yes : 3 yes :21576 yes : 6248 aug : 6178
jun : 5318
nov : 4101
apr : 2632
(Other): 2016
day_of_week duration campaign pdays previous
fri:7827 Min. : 0.0 Min. : 1.000 Min. : 0.0 Min. :0.000
mon:8514 1st Qu.: 102.0 1st Qu.: 1.000 1st Qu.:999.0 1st Qu.:0.000
thu:8623 Median : 180.0 Median : 2.000 Median :999.0 Median :0.000
tue:8090 Mean : 258.3 Mean : 2.568 Mean :962.5 Mean :0.173
wed:8134 3rd Qu.: 319.0 3rd Qu.: 3.000 3rd Qu.:999.0 3rd Qu.:0.000
Max. :4918.0 Max. :56.000 Max. :999.0 Max. :7.000 poutcome emp.var.rate cons.price.idx cons.conf.idx
failure : 4252 Min. :-3.40000 Min. :92.20 Min. :-50.8
nonexistent:35563 1st Qu.:-1.80000 1st Qu.:93.08 1st Qu.:-42.7
success : 1373 Median : 1.10000 Median :93.75 Median :-41.8
Mean : 0.08189 Mean :93.58 Mean :-40.5
3rd Qu.: 1.40000 3rd Qu.:93.99 3rd Qu.:-36.4
Max. : 1.40000 Max. :94.77 Max. :-26.9 euribor3m nr.employed y
Min. :0.634 Min. :4964 no :36548
1st Qu.:1.344 1st Qu.:5099 yes: 4640
Median :4.857 Median :5191
Mean :3.621 Mean :5167
3rd Qu.:4.961 3rd Qu.:5228
Max. :5.045 Max. :5228
> psych::describe(bank)
vars n mean sd median trimmed mad min max
age 1 41188 40.02 10.42 38.00 39.30 10.38 17.00 98.00
job* 2 41188 4.72 3.59 3.00 4.48 2.97 1.00 12.00
marital* 3 41188 2.17 0.61 2.00 2.21 0.00 1.00 4.00
education* 4 41188 4.75 2.14 4.00 4.88 2.97 1.00 8.00
default* 5 41188 1.21 0.41 1.00 1.14 0.00 1.00 3.00
housing* 6 41188 2.07 0.99 3.00 2.09 0.00 1.00 3.00
loan* 7 41188 1.33 0.72 1.00 1.16 0.00 1.00 3.00
contact* 8 41188 1.37 0.48 1.00 1.33 0.00 1.00 2.00
month* 9 41188 5.23 2.32 5.00 5.31 2.97 1.00 10.00
day_of_week* 10 41188 3.00 1.40 3.00 3.01 1.48 1.00 5.00
duration 11 41188 258.29 259.28 180.00 210.61 139.36 0.00 4918.00
campaign 12 41188 2.57 2.77 2.00 1.99 1.48 1.00 56.00
pdays 13 41188 962.48 186.91 999.00 999.00 0.00 0.00 999.00
previous 14 41188 0.17 0.49 0.00 0.05 0.00 0.00 7.00
poutcome* 15 41188 1.93 0.36 2.00 2.00 0.00 1.00 3.00
emp.var.rate 16 41188 0.08 1.57 1.10 0.27 0.44 -3.40 1.40
cons.price.idx 17 41188 93.58 0.58 93.75 93.58 0.56 92.20 94.77
cons.conf.idx 18 41188 -40.50 4.63 -41.80 -40.60 6.52 -50.80 -26.90
euribor3m 19 41188 3.62 1.73 4.86 3.81 0.16 0.63 5.04
nr.employed 20 41188 5167.04 72.25 5191.00 5178.43 55.00 4963.60 5228.10
y* 21 41188 1.11 0.32 1.00 1.02 0.00 1.00 2.00
range skew kurtosis se
age 81.00 0.78 0.79 0.05
job* 11.00 0.45 -1.39 0.02
marital* 3.00 -0.06 -0.34 0.00
education* 7.00 -0.24 -1.21 0.01
default* 2.00 1.44 0.07 0.00
housing* 2.00 -0.14 -1.95 0.00
loan* 2.00 1.82 1.38 0.00
contact* 1.00 0.56 -1.69 0.00
month* 9.00 -0.31 -1.03 0.01
day_of_week* 4.00 0.01 -1.27 0.01
duration 4918.00 3.26 20.24 1.28
campaign 55.00 4.76 36.97 0.01
pdays 999.00 -4.92 22.23 0.92
previous 7.00 3.83 20.11 0.00
poutcome* 2.00 -0.88 3.98 0.00
emp.var.rate 4.80 -0.72 -1.06 0.01
cons.price.idx 2.57 -0.23 -0.83 0.00
cons.conf.idx 23.90 0.30 -0.36 0.02
euribor3m 4.41 -0.71 -1.41 0.01
nr.employed 264.50 -1.04 0.00 0.36
y* 1.00 2.45 4.00 0.00
4、查看数据是否有缺失
> sapply(bank,anyNA)
age job marital education default
FALSE FALSE FALSE FALSE FALSE
housing loan contact month day_of_week
FALSE FALSE FALSE FALSE FALSE
duration campaign pdays previous poutcome
FALSE FALSE FALSE FALSE FALSE
emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed
FALSE FALSE FALSE FALSE FALSE
y
FALSE
>
5、单变量频数分析
> table(bank$y) no yes
36548 4640
>
6、两个变量的交叉列联表
> table(bank$y,bank$marital)
divorced married single unknown
no 4136 22396 9948 68
yes 476 2532 1620 12
>
> xtabs(~y+marital,data=bank)
marital
y divorced married single unknown
no 4136 22396 9948 68
yes 476 2532 1620 12
>
7、
> prop.table(tab,1)
divorced married single unknown
no 0.113166247 0.612783189 0.272189997 0.001860567
yes 0.102586207 0.545689655 0.349137931 0.002586207
> prop.table(tab,2)
divorced married single unknown
no 0.8967910 0.8984275 0.8599585 0.8500000
yes 0.1032090 0.1015725 0.1400415 0.1500000
>
8、构建更复杂的Table
> ftable(bank[,c(3,4,21)],row.vars = c(1,2),col.vars = "y")
y no yes
marital education
divorced basic.4y 406 83
basic.6y 169 13
basic.9y 534 31
high.school 1086 107
illiterate 1 1
professional.course 596 61
university.degree 1177 160
unknown 167 20
married basic.4y 2915 313
basic.6y 1628 139
basic.9y 3858 298
high.school 4683 475
illiterate 12 3
professional.course 2799 357
university.degree 5573 821
unknown 928 126
single basic.4y 422 31
basic.6y 301 36
basic.9y 1174 142
high.school 2702 448
illiterate 1 0
professional.course 1247 177
university.degree 3723 683
unknown 378 103
unknown basic.4y 5 1
basic.6y 6 0
basic.9y 6 2
high.school 13 1
illiterate 0 0
professional.course 6 0
university.degree 25 6
unknown 7 2
>
9、卡方检验
> tab
divorced married single unknown
no 4136 22396 9948 68
yes 476 2532 1620 12
> chisq.test(tab)
Pearson's Chi-squared test
data: tab
X-squared = 122.66, df = 3, p-value < 2.2e-16
>
10、连续数据可视化
> hist(bank$age)
>
11、连续变量的分布
> library(lattice)
> densityplot(~age,groups=y,data=bank,plot.point=FALSE,auto.key = TRUE)
>