1. 조건에 맞는 데이터 찾기 (filter,slice 사용)
> table(df$Type)
Compact Large Midsize Small Sporty Van
16 11 22 21 14 9
> filter(df,Type=="Large" , Price<=20 , MPG.city >=20) ## ,는 and 개념
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain
1 Chrylser Concorde Large 18.4 18.4 18.4 20 28 Driver & Passenger Front
2 Eagle Vision Large 17.5 19.3 21.2 20 28 Driver & Passenger Front
Cylinders EngineSize Horsepower RPM Rev.per.mile Man.trans.avail Fuel.tank.capacity Passengers Length
1 6 3.3 153 5300 1990 No 18 6 203
2 6 3.5 214 5800 1980 No 18 6 202
Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make
1 113 74 40 31 15 3515 USA Chrylser Concorde
2 113 74 40 30 15 3490 USA Eagle Vision
> filter(df,Type=="Large" | Price<=20 | MPG.city >=20) ## |는 or 개념
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags
1 Acura Integra Small 12.9 15.9 18.8 25 31 None
2 Audi 90 Compact 25.9 29.1 32.3 20 26 Driver only
3 BMW 535i Midsize 23.7 30.0 36.2 22 30 Driver only
4 Buick Century Midsize 14.2 15.7 17.3 22 31 Driver only
5 Buick LeSabre Large 19.9 20.8 21.7 19 28 Driver only
6 Buick Roadmaster Large 22.6 23.7 24.9 16 25 Driver only
7 Cadillac DeVille Large 33.0 34.7 36.3 16 25 Driver only
8 Chevrolet Cavalier Compact 8.5 13.4 18.3 25 36 None
9 Chevrolet Corsica Compact 11.4 11.4 11.4 25 34 Driver only
10 Chevrolet Camaro Sporty 13.4 15.1 16.8 19 28 Driver & Passenger
11 Chevrolet Lumina Midsize 13.4 15.9 18.4 21 29 None
12 Chevrolet Lumina_APV Van 14.7 16.3 18.0 18 23 None
13 Chevrolet Astro Van 14.7 16.6 18.6 15 20 None
14 Chevrolet Caprice Large 18.0 18.8 19.6 17 26 Driver only
15 Chrylser Concorde Large 18.4 18.4 18.4 20 28 Driver & Passenger
16 Chrysler LeBaron Compact 14.5 15.8 17.1 23 28 Driver & Passenger
17 Chrysler Imperial Large 29.5 29.5 29.5 20 26 Driver only
18 Dodge Colt Small 7.9 9.2 10.6 29 33 None
19 Dodge Shadow Small 8.4 11.3 14.2 23 29 Driver only
20 Dodge Spirit Compact 11.9 13.3 14.7 22 27 Driver only
21 Dodge Caravan Van 13.6 19.0 24.4 17 21 Driver only
22 Dodge Dynasty Midsize 14.8 15.6 16.4 21 27 Driver only
23 Eagle Summit Small 7.9 12.2 16.5 29 33 None
24 Eagle Vision Large 17.5 19.3 21.2 20 28 Driver & Passenger
25 Ford Festiva Small 6.9 7.4 7.9 31 33 None
26 Ford Escort Small 8.4 10.1 11.9 23 30 None
27 Ford Tempo Compact 10.4 11.3 12.2 22 27 None
28 Ford Mustang Sporty 10.8 15.9 21.0 22 29 Driver only
29 Ford Probe Sporty 12.8 14.0 15.2 24 30 Driver only
30 Ford Aerostar Van 14.5 19.9 25.3 15 20 Driver only
31 Ford Taurus Midsize 15.6 20.2 24.8 21 30 Driver only
32 Ford Crown_Victoria Large 20.1 20.9 21.7 18 26 Driver only
33 Geo Metro Small 6.7 8.4 10.0 46 50 None
34 Geo Storm Sporty 11.5 12.5 13.5 30 36 Driver only
35 Honda Prelude Sporty 17.0 19.8 22.7 24 31 Driver & Passenger
36 Honda Civic Small 8.4 12.1 15.8 42 46 Driver only
37 Honda Accord Compact 13.8 17.5 21.2 24 31 Driver & Passenger
DriveTrain Cylinders EngineSize Horsepower RPM Rev.per.mile Man.trans.avail Fuel.tank.capacity Passengers
1 Front 4 1.8 140 6300 2890 Yes 13.2 5
2 Front 6 2.8 172 5500 2280 Yes 16.9 5
3 Rear 4 3.5 208 5700 2545 Yes 21.1 4
4 Front 4 2.2 110 5200 2565 No 16.4 6
5 Front 6 3.8 170 4800 1570 No 18.0 6
6 Rear 6 5.7 180 4000 1320 No 23.0 6
7 Front 8 4.9 200 4100 1510 No 18.0 6
8 Front 4 2.2 110 5200 2380 Yes 15.2 5
9 Front 4 2.2 110 5200 2665 Yes 15.6 5
10 Rear 6 3.4 160 4600 1805 Yes 15.5 4
11 Front 4 2.2 110 5200 2595 No 16.5 6
12 Front 6 3.8 170 4800 1690 No 20.0 7
13 4WD 6 4.3 165 4000 1790 No 27.0 8
14 Rear 8 5.0 170 4200 1350 No 23.0 6
15 Front 6 3.3 153 5300 1990 No 18.0 6
16 Front 4 3.0 141 5000 2090 No 16.0 6
17 Front 6 3.3 147 4800 1785 No 16.0 6
18 Front 4 1.5 92 6000 3285 Yes 13.2 5
19 Front 4 2.2 93 4800 2595 Yes 14.0 5
20 Front 4 2.5 100 4800 2535 Yes 16.0 6
21 4WD 6 3.0 142 5000 1970 No 20.0 7
22 Front 4 2.5 100 4800 2465 No 16.0 6
23 Front 4 1.5 92 6000 2505 Yes 13.2 5
24 Front 6 3.5 214 5800 1980 No 18.0 6
25 Front 4 1.3 63 5000 3150 Yes 10.0 4
26 Front 4 1.8 127 6500 2410 Yes 13.2 5
27 Front 4 2.3 96 4200 2805 Yes 15.9 5
28 Rear 4 2.3 105 4600 2285 Yes 15.4 4
29 Front 4 2.0 115 5500 2340 Yes 15.5 4
30 4WD 6 3.0 145 4800 2080 Yes 21.0 7
31 Front 6 3.0 140 4800 1885 No 16.0 5
32 Rear 8 4.6 190 4200 1415 No 20.0 6
33 Front 3 1.0 55 5700 3755 Yes 10.6 4
34 Front 4 1.6 90 5400 3250 Yes 12.4 4
35 Front 4 2.3 160 5800 2855 Yes 15.9 4
36 Front 4 1.5 102 5900 2650 Yes 11.9 4
37 Front 4 2.2 140 5600 2610 Yes 17.0 4
Length Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make
1 177 102 68 37 26.5 11 2705 non-USA Acura Integra
2 180 102 67 37 28.0 14 3375 non-USA Audi 90
3 186 109 69 39 27.0 13 3640 non-USA BMW 535i
4 189 105 69 41 28.0 16 2880 USA Buick Century
5 200 111 74 42 30.5 17 3470 USA Buick LeSabre
6 216 116 78 45 30.5 21 4105 USA Buick Roadmaster
7 206 114 73 43 35.0 18 3620 USA Cadillac DeVille
8 182 101 66 38 25.0 13 2490 USA Chevrolet Cavalier
9 184 103 68 39 26.0 14 2785 USA Chevrolet Corsica
10 193 101 74 43 25.0 13 3240 USA Chevrolet Camaro
11 198 108 71 40 28.5 16 3195 USA Chevrolet Lumina
12 178 110 74 44 30.5 NA 3715 USA Chevrolet Lumina_APV
13 194 111 78 42 33.5 NA 4025 USA Chevrolet Astro
14 214 116 77 42 29.5 20 3910 USA Chevrolet Caprice
15 203 113 74 40 31.0 15 3515 USA Chrylser Concorde
16 183 104 68 41 30.5 14 3085 USA Chrysler LeBaron
17 203 110 69 44 36.0 17 3570 USA Chrysler Imperial
18 174 98 66 32 26.5 11 2270 USA Dodge Colt
19 172 97 67 38 26.5 13 2670 USA Dodge Shadow
20 181 104 68 39 30.5 14 2970 USA Dodge Spirit
21 175 112 72 42 26.5 NA 3705 USA Dodge Caravan
22 192 105 69 42 30.5 16 3080 USA Dodge Dynasty
23 174 98 66 36 26.5 11 2295 USA Eagle Summit
24 202 113 74 40 30.0 15 3490 USA Eagle Vision
25 141 90 63 33 26.0 12 1845 USA Ford Festiva
26 171 98 67 36 28.0 12 2530 USA Ford Escort
27 177 100 68 39 27.5 13 2690 USA Ford Tempo
28 180 101 68 40 24.0 12 2850 USA Ford Mustang
29 179 103 70 38 23.0 18 2710 USA Ford Probe
30 176 119 72 45 30.0 NA 3735 USA Ford Aerostar
31 192 106 71 40 27.5 18 3325 USA Ford Taurus
32 212 114 78 43 30.0 21 3950 USA Ford Crown_Victoria
33 151 93 63 34 27.5 10 1695 non-USA Geo Metro
34 164 97 67 37 24.5 11 2475 non-USA Geo Storm
35 175 100 70 39 23.5 8 2865 non-USA Honda Prelude
36 173 103 67 36 28.0 12 2350 non-USA Honda Civic
37 185 107 67 41 28.0 14 3040 non-USA Honda Accord
[ reached 'max' / getOption("max.print") -- omitted 41 rows ]
> slice(df,1:3) #원하는 행의 범위를 지정해서 추출해줌
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain
1 Acura Integra Small 12.9 15.9 18.8 25 31 None Front
2 Acura Legend Midsize 29.2 33.9 38.7 18 25 Driver & Passenger Front
3 Audi 90 Compact 25.9 29.1 32.3 20 26 Driver only Front
Cylinders EngineSize Horsepower RPM Rev.per.mile Man.trans.avail Fuel.tank.capacity Passengers Length
1 4 1.8 140 6300 2890 Yes 13.2 5 177
2 6 3.2 200 5500 2335 Yes 18.0 5 195
3 6 2.8 172 5500 2280 Yes 16.9 5 180
Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make
1 102 68 37 26.5 11 2705 non-USA Acura Integra
2 115 71 38 30.0 15 3560 non-USA Acura Legend
3 102 67 37 28.0 14 3375 non-USA Audi 90
2.특정 열 추출하기(select)
원래 따로 dplyr를 사용하지않은상태에서 특정열을 추출하는 방법은 다음과 같습니다.
> df[,c("Model","Type","Price")] ##dplyr 사용x 기본사용o
Model Type Price
1 Integra Small 15.9
2 Legend Midsize 33.9
3 90 Compact 29.1
4 100 Midsize 37.7
5 535i Midsize 30.0
하지만 dplyr의 select를 사용하면 좀더 편하게 컬럼을 추출할수있습니다.
> select(df,1:5) #1열~5열 추출
Manufacturer Model Type Min.Price Price
1 Acura Integra Small 12.9 15.9
2 Acura Legend Midsize 29.2 33.9
3 Audi 90 Compact 25.9 29.1
4 Audi 100 Midsize 30.8 37.7
5 BMW 535i Midsize 23.7 30.0
> select(df,Type,Price,Model) #type, price,model열 추출
Type Price Model
1 Small 15.9 Integra
2 Midsize 33.9 Legend
3 Compact 29.1 90
4 Midsize 37.7 100
5 Midsize 30.0 535i
> select(df,Model:Price) #model~price열 추출
Model Type Min.Price Price
1 Integra Small 12.9 15.9
2 Legend Midsize 29.2 33.9
3 90 Compact 25.9 29.1
4 100 Midsize 30.8 37.7
5 535i Midsize 23.7 30.0
> select(df,-(Model:Price)) #model~price열 제외하고 추출
Manufacturer Max.Price MPG.city MPG.highway AirBags DriveTrain Cylinders EngineSize Horsepower RPM Rev.per.mile Man.trans.avail Fuel.tank.capacity Passengers
1 Acura 18.8 25 31 None Front 4 1.8 140 6300 2890 Yes 13.2 5
2 Acura 38.7 18 25 Driver & Passenger Front 6 3.2 200 5500 2335 Yes 18.0 5
3 Audi 32.3 20 26 Driver only Front 6 2.8 172 5500 2280 Yes 16.9 5
4 Audi 44.6 19 26 Driver & Passenger Front 6 2.8 172 5500 2535 Yes 21.1 6
5 BMW 36.2 22 30 Driver only Rear 4 3.5 208 5700 2545 Yes 21.1 4
> select(df,starts_with("M")) #M으로 시작하는 열 추출
Manufacturer Model Min.Price Max.Price MPG.city MPG.highway Man.trans.avail Make
1 Acura Integra 12.9 18.8 25 31 Yes Acura Integra
2 Acura Legend 29.2 38.7 18 25 Yes Acura Legend
3 Audi 90 25.9 32.3 20 26 Yes Audi 90
4 Audi 100 30.8 44.6 19 26 Yes Audi 100
5 BMW 535i 23.7 36.2 22 30 Yes BMW 535i
> select(df,ends_with("e")) #e로 끝나는 열 추출출
Type Min.Price Price Max.Price EngineSize Rev.per.mile Wheelbase Turn.circle Make
1 Small 12.9 15.9 18.8 1.8 2890 102 37 Acura Integra
2 Midsize 29.2 33.9 38.7 3.2 2335 115 38 Acura Legend
3 Compact 25.9 29.1 32.3 2.8 2280 102 37 Audi 90
4 Midsize 30.8 37.7 44.6 2.8 2535 106 37 Audi 100
5 Midsize 23.7 30.0 36.2 3.5 2545 109 39 BMW 535i
> select(df,contains("x")) #x를 포함하는 열 추출 대소문자 구분x
Max.Price
1 18.8
2 38.7
3 32.3
4 44.6
5 36.2
> select(df,matches(".P.")) #문자열 가운데 P가 와야함
Type Min.Price Max.Price MPG.city MPG.highway Horsepower RPM Rev.per.mile Fuel.tank.capacity
1 Small 12.9 18.8 25 31 140 6300 2890 13.2
2 Midsize 29.2 38.7 18 25 200 5500 2335 18.0
3 Compact 25.9 32.3 20 26 172 5500 2280 16.9
4 Midsize 30.8 44.6 19 26 172 5500 2535 21.1
5 Midsize 23.7 36.2 22 30 208 5700 2545 21.1
> select(df,matches("P")) # 그냥 P가 들어가기만 하면 됨
Type Min.Price Price Max.Price MPG.city MPG.highway Horsepower RPM Rev.per.mile Fuel.tank.capacity Passengers
1 Small 12.9 15.9 18.8 25 31 140 6300 2890 13.2 5
2 Midsize 29.2 33.9 38.7 18 25 200 5500 2335 18.0 5
3 Compact 25.9 29.1 32.3 20 26 172 5500 2280 16.9 5
4 Midsize 30.8 37.7 44.6 19 26 172 5500 2535 21.1 6
5 Midsize 23.7 30.0 36.2 22 30 208 5700 2545 21.1 4
> select(df,one_of(c("Price","Type","chicken"))) #data에 있는 열만 추출함 chicken이라는 열은 없으므로 추출x
Price Type
1 15.9 Small
2 33.9 Midsize
3 29.1 Compact
4 37.7 Midsize
5 30.0 Midsize
여기서 주의할점이 MASS와 DPLYR에는 각각 select함수가 있어 이런식으로 에러가 뜨는데 이때는 MASS 패키지를 detach해주면 됩니다.
> select(df,1:5)
Error in select(df, 1:5) : 사용되지 않은 인자 (1:5)
## detach 사용
detach("package:MASS", unload = TRUE)
다음으로 사용할 함수를 이용하기 위하여 임의의 data를 생성하고 num_range와 rename을 사용해보겠습니다.
A1 <- c(rep(1:10))
A2 <- c(rep(11:20))
A3 <- c(rep(21:30))
A4 <- c(rep(31:40))
df_1 <- cbind(A1,A2,A3,A4)
df_1 <- data.frame(df_1)
select(df_1,num_range("A",2:3)) #A1,A2,A3,A4열중에서 A로 시작하는 A2,A3을 추출
> ## 이름 변경하기
> names(df_1)
[1] "A1" "A2" "A3" "A4"
> df_1 <- rename(df_1,
+ one=A1,
+ two=A2,
+ three=A3,
+ four=A4)
> names(df_1)
[1] "one" "two" "three" "four"
3. 오름차순, 내림차순 정렬(arrange)
> arrange(df, Price) # default값은 오름차순
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain Cylinders EngineSize Horsepower RPM Rev.per.mile
1 Ford Festiva Small 6.9 7.4 7.9 31 33 None Front 4 1.3 63 5000 3150
2 Hyundai Excel Small 6.8 8.0 9.2 29 33 None Front 4 1.5 81 5500 2710
3 Mazda 323 Small 7.4 8.3 9.1 29 37 None Front 4 1.6 82 5000 2370
4 Geo Metro Small 6.7 8.4 10.0 46 50 None Front 3 1.0 55 5700 3755
5 Subaru Justy Small 7.3 8.4 9.5 33 37 None 4WD 3 1.2 73 5600 2875
> arrange(df, Price, desc(Max.Price)) # 첫번째로 price로 오름차순정렬하고 2번째로 Max.price로 내림차순정렬
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain Cylinders EngineSize Horsepower RPM Rev.per.mile
1 Ford Festiva Small 6.9 7.4 7.9 31 33 None Front 4 1.3 63 5000 3150
2 Hyundai Excel Small 6.8 8.0 9.2 29 33 None Front 4 1.5 81 5500 2710
3 Mazda 323 Small 7.4 8.3 9.1 29 37 None Front 4 1.6 82 5000 2370
4 Geo Metro Small 6.7 8.4 10.0 46 50 None Front 3 1.0 55 5700 3755
5 Subaru Justy Small 7.3 8.4 9.5 33 37 None 4WD 3 1.2 73 5600 2875
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