R语言边学边记6-数据组合拼接与合并

> mtcars

mpg cyl disp hp drat wt qsec vs am gear carb

Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4

Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4

Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1

Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1

Hornet Sportabout 18.7 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

> #重命名列名

> names(data)[1]<-"lll"#修改第一列列名

> library(reshape)

> mtcars

mpg cyl disp hp drat wt qsec vs am gear carb

Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4

Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4

Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1

Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1

Hornet Sportabout 18.7 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

> a<-mtcars

> rename(a,c(mpg="newmpg"))#但数据集不会改变，需要重新赋值

newmpg cyl disp hp drat wt qsec vs am gear carb

Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4

Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4

Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1

Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1

Hornet Sportabout 18.7 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

> a#还是等于mtcars

mpg cyl disp hp drat wt qsec vs am gear carb

Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4

Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4

Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1

Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1

Hornet Sportabout 18.7 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

> a<-rename(a,c(mpg="newmpg"))#重新赋值

> a

newmpg cyl disp hp drat wt qsec vs am gear carb

Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4

Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4

Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1

Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1

Hornet Sportabout 18.7 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

> a<-rename(a,c(mpg="newmpg",wt="op"))

> #组合数据---paste拼接函数

> v1<-c(10,20,30)

> v1

[1] 10 20 30

> v2<-"g"

> v2

[1] "g"

> v<-paste(v1,v2)

> v

[1] "10 g" "20 g" "30 g"

> v<-paste(v1,v2,sep="")#去掉空格

> v

[1] "10g" "20g" "30g"

> v<-paste(v1,v2,sep="++")#中间的连接符号

> v

[1] "10++g" "20++g" "30++g"

> #组合函数rbind-按照行合并，cbind按照列合并

> mtcars

mpg cyl disp hp drat wt qsec vs am gear carb

Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4

Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4

Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1

Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1

Hornet Sportabout 18.7 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

> df1<-mtcars[1:10]

> df1<-mtcars[1:10,]#注意2者区别，1：10是取的10列，[1:10,]取的是10行

> df1<-mtcars[1:10]

> df1

mpg cyl disp hp drat wt qsec vs am gear

Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4

Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4

Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4

Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3

Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3

Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3

Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3

Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4

Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4

Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4

Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4

Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3

Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3

Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3

Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3

Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3

Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3

Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4

Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4

Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4

Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3

Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3

AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3

Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3

Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3

Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4

Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5

Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5

Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5

Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5

Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5

Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4

> df1<-mtcars[1:10,]#注意2者区别，1：10是取的10列，[1:10,]取的是10行

> df1

mpg cyl disp hp drat wt qsec vs am gear carb

Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4

Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4

Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1

Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1

Hornet Sportabout 18.7 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

> df1<-mtcars[,1:10]#[1:10]=[,1:10]

> df1<-mtcars[1:10,]

> df2<-mtcars[11:20,]

> df<-rbind(df1,df2)#按照行重新组合

> df

mpg cyl disp hp drat wt qsec vs am gear carb

Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4

Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4

Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1

Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1

Hornet Sportabout 18.7 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

> df3<-mtcars[,1:4]#前四列

> df3

mpg cyl disp hp

Mazda RX4 21.0 6 160.0 110

Mazda RX4 Wag 21.0 6 160.0 110

Datsun 710 22.8 4 108.0 93

Hornet 4 Drive 21.4 6 258.0 110

Hornet Sportabout 18.7 8 360.0 175

Valiant 18.1 6 225.0 105

Duster 360 14.3 8 360.0 245

Merc 240D 24.4 4 146.7 62

Merc 230 22.8 4 140.8 95

Merc 280 19.2 6 167.6 123

Merc 280C 17.8 6 167.6 123

Merc 450SE 16.4 8 275.8 180

Merc 450SL 17.3 8 275.8 180

Merc 450SLC 15.2 8 275.8 180

Cadillac Fleetwood 10.4 8 472.0 205

Lincoln Continental 10.4 8 460.0 215

Chrysler Imperial 14.7 8 440.0 230

Fiat 128 32.4 4 78.7 66

Honda Civic 30.4 4 75.7 52

Toyota Corolla 33.9 4 71.1 65

Toyota Corona 21.5 4 120.1 97

Dodge Challenger 15.5 8 318.0 150

AMC Javelin 15.2 8 304.0 150

Camaro Z28 13.3 8 350.0 245

Pontiac Firebird 19.2 8 400.0 175

Fiat X1-9 27.3 4 79.0 66

Porsche 914-2 26.0 4 120.3 91

Lotus Europa 30.4 4 95.1 113

Ford Pantera L 15.8 8 351.0 264

Ferrari Dino 19.7 6 145.0 175

Maserati Bora 15.0 8 301.0 335

Volvo 142E 21.4 4 121.0 109

> df4<-mtcars[,5:8]

> df4

drat wt qsec vs

Mazda RX4 3.90 2.620 16.46 0

Mazda RX4 Wag 3.90 2.875 17.02 0

Datsun 710 3.85 2.320 18.61 1

Hornet 4 Drive 3.08 3.215 19.44 1

Hornet Sportabout 3.15 3.440 17.02 0

Valiant 2.76 3.460 20.22 1

Duster 360 3.21 3.570 15.84 0

Merc 240D 3.69 3.190 20.00 1

Merc 230 3.92 3.150 22.90 1

Merc 280 3.92 3.440 18.30 1

Merc 280C 3.92 3.440 18.90 1

Merc 450SE 3.07 4.070 17.40 0

Merc 450SL 3.07 3.730 17.60 0

Merc 450SLC 3.07 3.780 18.00 0

Cadillac Fleetwood 2.93 5.250 17.98 0

Lincoln Continental 3.00 5.424 17.82 0

Chrysler Imperial 3.23 5.345 17.42 0

Fiat 128 4.08 2.200 19.47 1

Honda Civic 4.93 1.615 18.52 1

Toyota Corolla 4.22 1.835 19.90 1

Toyota Corona 3.70 2.465 20.01 1

Dodge Challenger 2.76 3.520 16.87 0

AMC Javelin 3.15 3.435 17.30 0

Camaro Z28 3.73 3.840 15.41 0

Pontiac Firebird 3.08 3.845 17.05 0

Fiat X1-9 4.08 1.935 18.90 1

Porsche 914-2 4.43 2.140 16.70 0

Lotus Europa 3.77 1.513 16.90 1

Ford Pantera L 4.22 3.170 14.50 0

Ferrari Dino 3.62 2.770 15.50 0

Maserati Bora 3.54 3.570 14.60 0

Volvo 142E 4.11 2.780 18.60 1

> df5<-cbind(df3,df4)

> df5<-cbind(df3,df4)#按照列重新组合

> df5

mpg cyl disp hp drat wt qsec vs

Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0

Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0

Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1

Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1

Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0

Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1

Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0

Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1

Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1

Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1

Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1

Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0

Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0

Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0

Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0

Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0

Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0

Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1

Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1

Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1

Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1

Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0

AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0

Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0

Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0

Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1

Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0

Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1

Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0

Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0

Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0

Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1

> #组合数据，merge按照公有变量组合

> df6<-read.csv("R.csv")

> df6<-read.csv("id1.csv")

> df7<-read.csv("id2.csv")

> df8<-merge.data.frame(df6,df7,by.x ="ID1",by.y ="ID2")#变量不一样的情况，前面是X，后面是Y

> df6

ID1 gender age

1 101 f 10

2 123 m 11

3 124 f 12

4 125 m 11

5 127 m 10

> df7

ID2 income diary

1 101 1300 a

2 102 1300 b

3 123 20000 a

4 124 2300 b

5 125 2400 a

6 127 2500 b

7 133 2700 a

> df8

ID1 gender age income diary

1 101 f 10 1300 a

2 123 m 11 20000 a

3 124 f 12 2300 b

4 125 m 11 2400 a

5 127 m 10 2500 b

> #变量一样的情况

> names(df6)[1]<-"ID"

> names(df7)[1]<-"ID"

> df6

ID gender age

1 101 f 10

2 123 m 11

3 124 f 12

4 125 m 11

5 127 m 10

> df7

ID income diary

1 101 1300 a

2 102 1300 b

3 123 20000 a

4 124 2300 b

5 125 2400 a

6 127 2500 b

7 133 2700 a

> df9<-merge.data.frame(df6,df7,by="ID")

> df9

ID gender age income diary

1 101 f 10 1300 a

2 123 m 11 20000 a

3 124 f 12 2300 b

4 125 m 11 2400 a

5 127 m 10 2500 b

> df10<-merge.data.frame(df6,df7,by="ID",all.x = T)

> df10<-merge.data.frame(df6,df7,by="ID",all.y = T)#df7是Y，其中数据全部得到保留，没有的项以NA呈现

> df10<-merge.data.frame(df6,df7,by="ID",all= T)#两个数据集其中数据全部都保留

> df10

ID gender age income diary

1 101 f 10 1300 a

2 102 <NA> NA 1300 b

3 123 m 11 20000 a

4 124 f 12 2300 b

5 125 m 11 2400 a

6 127 m 10 2500 b

7 133 <NA> NA 2700 a

人面猴
序言：七十年代末，一起剥皮案震惊了整个滨河市，随后出现的几起案子，更是在滨河造成了极大的恐慌，老刑警刘岩，带你破解...
沈念sama阅读 203,937评论 6赞 478
死咒
序言：滨河连续发生了三起死亡事件，死亡现场离奇诡异，居然都是意外死亡，警方通过查阅死者的电脑和手机，发现死者居然都...
沈念sama阅读 85,503评论 2赞 381
救了他两次的神仙让他今天三更去死
文/潘晓璐我一进店门，熙熙楼的掌柜王于贵愁眉苦脸地迎上来，“玉大人，你说我怎么就摊上这事。” “怎么了？”我有些...
开封第一讲书人阅读 150,712评论 0赞 337
道士缉凶录：失踪的卖姜人
文/不坏的土叔我叫张陵，是天一观的道长。经常有香客问我，道长，这世上最难降的妖魔是什么？我笑而不...
开封第一讲书人阅读 54,668评论 1赞 276
港岛之恋（遗憾婚礼）
正文为了忘掉前任，我火速办了婚礼，结果婚礼上，老公的妹妹穿的比我还像新娘。我一直安慰自己，他们只是感情好，可当我...
茶点故事阅读 63,677评论 5赞 366
恶毒庶女顶嫁案：这布局不是一般人想出来的
文/花漫我一把揭开白布。她就那样静静地躺着，像睡着了一般。火红的嫁衣衬着肌肤如雪。梳的纹丝不乱的头发上，一...
开封第一讲书人阅读 48,601评论 1赞 281
城市分裂传说
那天，我揣着相机与录音，去河边找鬼。笑死，一个胖子当着我的面吹牛，可吹牛的内容都是我干的。我是一名探鬼主播，决...
沈念sama阅读 37,975评论 3赞 396
双鸳鸯连环套：你想象不到人心有多黑
文/苍兰香墨我猛地睁开眼，长吁一口气：“原来是场噩梦啊……” “哼！你这毒妇竟也来了？” 一声冷哼从身侧响起，我...
开封第一讲书人阅读 36,637评论 0赞 258
万荣杀人案实录
序言：老挝万荣一对情侣失踪，失踪者是张志新（化名）和其女友刘颖，没想到半个月后，有当地人在树林里发现了一具尸体，经...
沈念sama阅读 40,881评论 1赞 298
护林员之死
正文独居荒郊野岭守林人离奇死亡，尸身上长有42处带血的脓包…… 初始之章·张勋以下内容为张勋视角年9月15日...
茶点故事阅读 35,621评论 2赞 321
白月光启示录
正文我和宋清朗相恋三年，在试婚纱的时候发现自己被绿了。大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
茶点故事阅读 37,710评论 1赞 329
活死人
序言：一个原本活蹦乱跳的男人离奇死亡，死状恐怖，灵堂内的尸体忽然破棺而出，到底是诈尸还是另有隐情，我是刑警宁泽，带...
沈念sama阅读 33,387评论 4赞 319
日本核电站爆炸内幕
正文年R本政府宣布，位于F岛的核电站，受9级特大地震影响，放射性物质发生泄漏。R本人自食恶果不足惜，却给世界环境...
茶点故事阅读 38,971评论 3赞 307
男人毒药：我在死后第九天来索命
文/蒙蒙一、第九天我趴在偏房一处隐蔽的房顶上张望。院中可真热闹，春花似锦、人声如沸。这庄子的主人今日做“春日...
开封第一讲书人阅读 29,947评论 0赞 19
一桩弑父案，背后竟有这般阴谋
文/苍兰香墨我抬头看了看天上的太阳。三九已至，却和暖如春，着一层夹袄步出监牢的瞬间，已是汗流浃背。一阵脚步声响...
开封第一讲书人阅读 31,189评论 1赞 260
情欲美人皮
我被黑心中介骗来泰国打工，没想到刚下飞机就差点儿被人妖公主榨干…… 1. 我叫王不留，地道东北人。一个月前我还...
沈念sama阅读 44,805评论 2赞 349
代替公主和亲
正文我出身青楼，却偏偏与公主长得像，于是被迫代替她去往敌国和亲。传闻我的和亲对象是个残疾皇子，可洞房花烛夜当晚...
茶点故事阅读 42,449评论 2赞 342

R语言边学边记6-数据组合拼接与合并

推荐阅读更多精彩内容