主要内容：
- 什么是缺失值
- 缺失值的识别
- 缺失模式探索
- 缺失值处理
1. 什么是缺失值
1.1查看R内置数据集
data()
mydata <- mtcars
data() mydata <- mtcars #创造1个有空值的data frame #给mydata的第1列的第1-5行赋值为NA mydata[(1:5),1] <- NA mydata
结果：
> data()
> mydata <- mtcars
> mydata[(1:5),1] <- NA
> mydata
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 NA 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag NA 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 NA 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive NA 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
手动改数据框
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 NA 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag NA 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 NA 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive NA 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Cadillac Fleetwood 10.4 8 NA 205 2.93 5.250 17.98 0 0 3 4 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Toyota Corolla 33.9 4 71.1 65 4.22 NA 19.90 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 AMC Javelin 15.2 8 304.0 150 3.15 NA 17.30 0 0 3 2 Camaro Z28 13.3 8 350.0 NA 3.73 3.840 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
1.2 识别缺失值
1）is.na( ) 针对对象，包括向量和数据框
#查看第一列缺失值的比例 sum(isnadata[,1])/nrow(isnadata) #查看第一列缺失值的比例,也可用mean mean(isnadata[,1]) #查看第一hang缺失值的比例 mean(isnadata[1,]) #查看每一列的缺失情况 #apply(array, margin, ...)中的margin:1代表行，2代表列 apply(isnadata, 2, mean)
结果：
> #查看第一列缺失值的比例
> sum(isnadata[,1])/nrow(isnadata)
[1] 0.15625
> #查看第一列缺失值的比例,也可用mean
> mean(isnadata[,1])
[1] 0.15625
> #查看第一hang缺失值的比例
> mean(isnadata[1,])
[1] 0.09090909
>
> #查看每一列的缺失情况
> #apply(array, margin, ...)中的margin:1代表行，2代表列
> apply(isnadata, 2, mean)
mpg cyl disp hp drat wt qsec vs am gear carb
0.15625 0.00000 0.03125 0.03125 0.00000 0.06250 0.00000 0.00000 0.00000 0.00000 0.00000
2）which 查看缺失值位置，适用向量
#查看缺失值的位置 which(isnadata,arr.ind = T)
结果：
> #查看缺失值的位置
> which(isnadata,arr.ind = T)
row col
Mazda RX4 1 1
Mazda RX4 Wag 2 1
Datsun 710 3 1
Hornet 4 Drive 4 1
Hornet Sportabout 5 1
Cadillac Fleetwood 15 3
Camaro Z28 24 4
Toyota Corolla 20 6
AMC Javelin 23 6
3）complete.cases()
#查看行是否有缺失值 complete.cases(mydata) length(complete.cases(mydata)) #显示有缺失值的行 mydata[!complete.cases(mydata),]
结果：
> #查看行是否有缺失值
> complete.cases(mydata)
[1] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[17] TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
> length(complete.cases(mydata))
[1] 32
> #显示有缺失值的行
> mydata[!complete.cases(mydata),]
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 NA 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag NA 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 NA 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive NA 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Cadillac Fleetwood 10.4 8 NA 205 2.93 5.250 17.98 0 0 3 4
Toyota Corolla 33.9 4 71.1 65 4.22 NA 19.90 1 1 4 1
AMC Javelin 15.2 8 304.0 150 3.15 NA 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 NA 3.73 3.840 15.41 0 0 3 4
4) mice 包
安装：
install.packages("mice")
library(mice) md.pattern(mydata) mydata
结果：
> md.pattern(mydata)
cyl drat qsec vs am gear carb disp hp wt mpg
23 1 1 1 1 1 1 1 1 1 1 1 0
5 1 1 1 1 1 1 1 1 1 1 0 1
2 1 1 1 1 1 1 1 1 1 0 1 1
1 1 1 1 1 1 1 1 1 0 1 1 1
1 1 1 1 1 1 1 1 0 1 1 1 1
0 0 0 0 0 0 0 1 1 2 5 9
> mydata
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 NA 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag NA 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 NA 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive NA 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 NA 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 NA 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 NA 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 NA 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
2.缺失值的处理
2.1 删除缺失值多的变量
#删除第一列 mydata_1 <- mydata[,-1] #逗号前表示取所有的行，减号代表删除 mydata_1
结果：
> #删除第一列
> mydata_1 <- mydata[,-1] #逗号前表示取所有的行，减号代表删除
> mydata_1
cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 8 NA 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 4 71.1 65 4.22 NA 19.90 1 1 4 1
Toyota Corona 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 8 304.0 150 3.15 NA 17.30 0 0 3 2
Camaro Z28 8 350.0 NA 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#删除缺失值的行 mydata_2 <- na.omit(mydata_1) mydata_2
complete.cases(mydata_1)
mydata_2_1 <- mydata_1[complete.cases(mydata_1),]
mydata_2_1
结果：
> #删除缺失值的行
> mydata_2 <- na.omit(mydata_1)
> mydata_2
cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Lincoln Continental 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corona 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Pontiac Firebird 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 4 121.0 109 4.11 2.780 18.60 1 1 4 2
也可用：
complete.cases(mydata_1) mydata_2_1 <- mydata_1[complete.cases(mydata_1),] mydata_2_1
结果：
> complete.cases(mydata_1)
[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[17] TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
> mydata_2_1 <- mydata_1[complete.cases(mydata_1),]
> mydata_2_1
cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Lincoln Continental 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corona 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Pontiac Firebird 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 4 121.0 109 4.11 2.780 18.60 1 1 4 2
2.2 均值插补缺失值
#查看缺失值的位置 which(is.na(mydata),arr.ind = T) which(is.na(mydata)) #求某一列剔除缺失值的均值 mydata[is.na(mydata[,1]),1] <- mean(mydata[,1],na.rm = T) mydata
结果：
> #查看缺失值的位置
> which(is.na(mydata),arr.ind = T)
row col
Mazda RX4 1 1
Mazda RX4 Wag 2 1
Datsun 710 3 1
Hornet 4 Drive 4 1
Hornet Sportabout 5 1
Cadillac Fleetwood 15 3
Camaro Z28 24 4
Toyota Corolla 20 6
AMC Javelin 23 6
> which(is.na(mydata))
[1] 1 2 3 4 5 79 120 180 183
> #求某一列剔除缺失值的均值
> mydata[is.na(mydata[,1]),1] <- mean(mydata[,1],na.rm = T)
> mydata
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 19.92593 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 19.92593 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 19.92593 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 19.92593 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 19.92593 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.10000 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.30000 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.40000 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.80000 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.20000 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.80000 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.40000 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.30000 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.20000 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.40000 8 NA 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.40000 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.70000 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.40000 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.40000 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.90000 4 71.1 65 4.22 NA 19.90 1 1 4 1
Toyota Corona 21.50000 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.50000 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.20000 8 304.0 150 3.15 NA 17.30 0 0 3 2
Camaro Z28 13.30000 8 350.0 NA 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.20000 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.30000 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.00000 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.40000 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.80000 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.70000 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.00000 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.40000 4 121.0 109 4.11 2.780 18.60 1 1 4 2
标签：No.10,mydata,NA,识别,110,Merc,TRUE,缺失 From： https://www.cnblogs.com/bltstop/p/18683908