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The package dplyr offers functions for: • Filtering of observations • Variable selection • Recoding • Grouping • Aggregation (in groups)
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data(Cars93, package = "MASS")
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library("dplyr")
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
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## ---- eval = TRUE, echo = TRUE-------------------------------------------
class (Cars93)
## ---- eval = TRUE, echo = TRUE-------------------------------------------
Cars93 <- tbl_df(Cars93)
class(Cars93)
'data.frame'
- 'tbl_df'
- 'tbl'
- 'data.frame'
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slice(Cars93, 1)
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain ... Passengers Length Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make Acura Integra Small 12.9 15.9 18.8 25 31 None Front ... 5 177 102 68 37 26.5 11 2705 non-USA Acura Integra
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slice (Cars93, c(1,4,10,15, n ()))
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain ... Passengers Length Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make Acura Integra Small 12.9 15.9 18.8 25 31 None Front ... 5 177 102 68 37 26.5 11 2705 non-USA Acura Integra Audi 100 Midsize 30.8 37.7 44.6 19 26 Driver & Passenger Front ... 6 193 106 70 37 31.0 17 3405 non-USA Audi 100 Cadillac DeVille Large 33.0 34.7 36.3 16 25 Driver only Front ... 6 206 114 73 43 35.0 18 3620 USA Cadillac DeVille Chevrolet Lumina Midsize 13.4 15.9 18.4 21 29 None Front ... 6 198 108 71 40 28.5 16 3195 USA Chevrolet Lumina Volvo 850 Midsize 24.8 26.7 28.5 20 28 Driver & Passenger Front ... 5 184 105 69 38 30.0 15 3245 non-USA Volvo 850
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filter(Cars93, Manufacturer == "Audi" & Min.Price > 25)
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain ... Passengers Length Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make Audi 90 Compact 25.9 29.1 32.3 20 26 Driver only Front ... 5 180 102 67 37 28 14 3375 non-USA Audi 90 Audi 100 Midsize 30.8 37.7 44.6 19 26 Driver & Passenger Front ... 6 193 106 70 37 31 17 3405 non-USA Audi 100
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subset(Cars93, Manufacturer == "Audi" & Min.Price > 25)
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain ... Passengers Length Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make Audi 90 Compact 25.9 29.1 32.3 20 26 Driver only Front ... 5 180 102 67 37 28 14 3375 non-USA Audi 90 Audi 100 Midsize 30.8 37.7 44.6 19 26 Driver & Passenger Front ... 6 193 106 70 37 31 17 3405 non-USA Audi 100
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head(arrange(Cars93, desc (MPG.city), Max.Price), 7)
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain ... Passengers Length Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make Geo Metro Small 6.7 8.4 10.0 46 50 None Front ... 4 151 93 63 34 27.5 10 1695 non-USA Geo Metro Honda Civic Small 8.4 12.1 15.8 42 46 Driver only Front ... 4 173 103 67 36 28.0 12 2350 non-USA Honda Civic Suzuki Swift Small 7.3 8.6 10.0 39 43 None Front ... 4 161 93 63 34 27.5 10 1965 non-USA Suzuki Swift Subaru Justy Small 7.3 8.4 9.5 33 37 None 4WD ... 4 146 90 60 32 23.5 10 2045 non-USA Subaru Justy Toyota Tercel Small 7.8 9.8 11.8 32 37 Driver only Front ... 5 162 94 65 36 24.0 11 2055 non-USA Toyota Tercel Ford Festiva Small 6.9 7.4 7.9 31 33 None Front ... 4 141 90 63 33 26.0 12 1845 USA Ford Festiva Pontiac LeMans Small 8.2 9.0 9.9 31 41 None Front ... 4 177 99 66 35 25.5 17 2350 USA Pontiac LeMans
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head (select (Cars93, Manufacturer, Price), 3)
Manufacturer Price Acura 15.9 Acura 33.9 Audi 29.1
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head (select (Cars93, contains ("Price")), 3)
Min.Price Price Max.Price 12.9 15.9 18.8 29.2 33.9 38.7 25.9 29.1 32.3
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head (select (Cars93, myPrize = Price, Min.Price))
myPrize Min.Price 15.9 12.9 33.9 29.2 29.1 25.9 37.7 30.8 30.0 23.7 15.7 14.2
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m <- mutate(Cars93, is_ford = Manufacturer == "Ford")
m[1:3, c(1,28)]
Manufacturer is_ford Acura FALSE Acura FALSE Audi FALSE
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by_type <- group_by (Cars93, Type)
summarize (by_type,
count = n(),min_es = min(EngineSize),
max_es = max(EngineSize)
)
Type count min_es max_es Compact 16 2.0 3.0 Large 11 3.3 5.7 Midsize 22 2.0 4.6 Small 21 1.0 2.2 Sporty 14 1.3 5.7 Van 9 2.4 4.3
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Cars93 %>%
group_by(Type) %>%
summarize(count = n(), min_es = min(EngineSize), max_es =
max(EngineSize) )
Type count min_es max_es Compact 16 2.0 3.0 Large 11 3.3 5.7 Midsize 22 2.0 4.6 Small 21 1.0 2.2 Sporty 14 1.3 5.7 Van 9 2.4 4.3
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Cars93 %>% group_by(Type) %>% slice(1:2)
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain ... Passengers Length Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make Audi 90 Compact 25.9 29.1 32.3 20 26 Driver only Front ... 5 180 102 67 37 28.0 14 3375 non-USA Audi 90 Chevrolet Cavalier Compact 8.5 13.4 18.3 25 36 None Front ... 5 182 101 66 38 25.0 13 2490 USA Chevrolet Cavalier Buick LeSabre Large 19.9 20.8 21.7 19 28 Driver only Front ... 6 200 111 74 42 30.5 17 3470 USA Buick LeSabre Buick Roadmaster Large 22.6 23.7 24.9 16 25 Driver only Rear ... 6 216 116 78 45 30.5 21 4105 USA Buick Roadmaster Acura Legend Midsize 29.2 33.9 38.7 18 25 Driver & Passenger Front ... 5 195 115 71 38 30.0 15 3560 non-USA Acura Legend Audi 100 Midsize 30.8 37.7 44.6 19 26 Driver & Passenger Front ... 6 193 106 70 37 31.0 17 3405 non-USA Audi 100 Acura Integra Small 12.9 15.9 18.8 25 31 None Front ... 5 177 102 68 37 26.5 11 2705 non-USA Acura Integra Dodge Colt Small 7.9 9.2 10.6 29 33 None Front ... 5 174 98 66 32 26.5 11 2270 USA Dodge Colt Chevrolet Camaro Sporty 13.4 15.1 16.8 19 28 Driver & Passenger Rear ... 4 193 101 74 43 25.0 13 3240 USA Chevrolet Camaro Chevrolet Corvette Sporty 34.6 38.0 41.5 17 25 Driver only Rear ... 2 179 96 74 43 NA NA 3380 USA Chevrolet Corvette Chevrolet Lumina_APV Van 14.7 16.3 18.0 18 23 None Front ... 7 178 110 74 44 30.5 NA 3715 USA Chevrolet Lumina_APV Chevrolet Astro Van 14.7 16.6 18.6 15 20 None 4WD ... 8 194 111 78 42 33.5 NA 4025 USA Chevrolet Astro
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Cars93 %>% mutate(ES2 = EngineSize^2) %>% group_by(Type) %>%
summarize(min.ES2 = min(ES2)) %>% arrange(desc(min.ES2))
Type min.ES2 Large 10.89 Van 5.76 Compact 4.00 Midsize 4.00 Sporty 1.69 Small 1.00
wINDOWS
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Cars93 %>%
group_by(Type) %>%
arrange(Type) %>%
select(Manufacturer:Price) %>%
mutate(cmean = cummean(Price), csum = cumsum(Price))
Manufacturer Model Type Min.Price Price cmean csum Audi 90 Compact 25.9 29.1 29.10000 29.1 Chevrolet Cavalier Compact 8.5 13.4 21.25000 42.5 Chevrolet Corsica Compact 11.4 11.4 17.96667 53.9 Chrysler LeBaron Compact 14.5 15.8 17.42500 69.7 Dodge Spirit Compact 11.9 13.3 16.60000 83.0 Ford Tempo Compact 10.4 11.3 15.71667 94.3 Honda Accord Compact 13.8 17.5 15.97143 111.8 Mazda 626 Compact 14.3 16.5 16.03750 128.3 Mercedes-Benz 190E Compact 29.0 31.9 17.80000 160.2 Nissan Altima Compact 13.0 15.7 17.59000 175.9 Oldsmobile Achieva Compact 13.0 13.5 17.21818 189.4 Pontiac Sunbird Compact 9.4 11.1 16.70833 200.5 Saab 900 Compact 20.3 28.7 17.63077 229.2 Subaru Legacy Compact 16.3 19.5 17.76429 248.7 Volkswagen Passat Compact 17.6 20.0 17.91333 268.7 Volvo 240 Compact 21.8 22.7 18.21250 291.4 Buick LeSabre Large 19.9 20.8 20.80000 20.8 Buick Roadmaster Large 22.6 23.7 22.25000 44.5 Cadillac DeVille Large 33.0 34.7 26.40000 79.2 Chevrolet Caprice Large 18.0 18.8 24.50000 98.0 Chrylser Concorde Large 18.4 18.4 23.28000 116.4 Chrysler Imperial Large 29.5 29.5 24.31667 145.9 Eagle Vision Large 17.5 19.3 23.60000 165.2 Ford Crown_Victoria Large 20.1 20.9 23.26250 186.1 Lincoln Town_Car Large 34.4 36.1 24.68889 222.2 Oldsmobile Eighty-Eight Large 19.5 20.7 24.29000 242.9 Pontiac Bonneville Large 19.4 24.4 24.30000 267.3 Acura Legend Midsize 29.2 33.9 33.90000 33.9 Audi 100 Midsize 30.8 37.7 35.80000 71.6 BMW 535i Midsize 23.7 30.0 33.86667 101.6 ... ... ... ... ... ... ... Pontiac LeMans Small 8.2 9.0 10.37333 155.6 Saturn SL Small 9.2 11.1 10.41875 166.7 Subaru Justy Small 7.3 8.4 10.30000 175.1 Subaru Loyale Small 10.5 10.9 10.33333 186.0 Suzuki Swift Small 7.3 8.6 10.24211 194.6 Toyota Tercel Small 7.8 9.8 10.22000 204.4 Volkswagen Fox Small 8.7 9.1 10.16667 213.5 Chevrolet Camaro Sporty 13.4 15.1 15.10000 15.1 Chevrolet Corvette Sporty 34.6 38.0 26.55000 53.1 Dodge Stealth Sporty 18.5 25.8 26.30000 78.9 Ford Mustang Sporty 10.8 15.9 23.70000 94.8 Ford Probe Sporty 12.8 14.0 21.76000 108.8 Geo Storm Sporty 11.5 12.5 20.21667 121.3 Honda Prelude Sporty 17.0 19.8 20.15714 141.1 Hyundai Scoupe Sporty 9.1 10.0 18.88750 151.1 Mazda RX-7 Sporty 32.5 32.5 20.40000 183.6 Mercury Capri Sporty 13.3 14.1 19.77000 197.7 Plymouth Laser Sporty 11.4 14.4 19.28182 212.1 Pontiac Firebird Sporty 14.0 17.7 19.15000 229.8 Toyota Celica Sporty 14.2 18.4 19.09231 248.2 Volkswagen Corrado Sporty 22.9 23.3 19.39286 271.5 Chevrolet Lumina_APV Van 14.7 16.3 16.30000 16.3 Chevrolet Astro Van 14.7 16.6 16.45000 32.9 Dodge Caravan Van 13.6 19.0 17.30000 51.9 Ford Aerostar Van 14.5 19.9 17.95000 71.8 Mazda MPV Van 16.6 19.1 18.18000 90.9 Nissan Quest Van 16.7 19.1 18.33333 110.0 Oldsmobile Silhouette Van 19.5 19.5 18.50000 129.5 Toyota Previa Van 18.9 22.7 19.02500 152.2 Volkswagen Eurovan Van 16.6 19.7 19.10000 171.9
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install.packages('data.table')
Installing package into 'C:/Users/admin/Documents/R/win-library/3.3'
(as 'lib' is unspecified)
also installing the dependency 'chron'
package 'chron' successfully unpacked and MD5 sums checked
package 'data.table' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\admin\AppData\Local\Temp\RtmpO0Tqzw\downloaded_packages
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require(data.table)
Cars93 <- data.table(Cars93)
Loading required package: data.table
------------------------------------------------------------------------------
data.table + dplyr code now lives in dtplyr.
Please library(dtplyr)!
------------------------------------------------------------------------------
Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':
between, last
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install.packages('microbenchmark')
Installing package into 'C:/Users/admin/Documents/R/win-library/3.3'
(as 'lib' is unspecified)
also installing the dependencies 'colorspace', 'RColorBrewer', 'dichromat', 'munsell', 'labeling', 'gtable', 'plyr', 'reshape2', 'scales', 'ggplot2'
package 'colorspace' successfully unpacked and MD5 sums checked
package 'RColorBrewer' successfully unpacked and MD5 sums checked
package 'dichromat' successfully unpacked and MD5 sums checked
package 'munsell' successfully unpacked and MD5 sums checked
package 'labeling' successfully unpacked and MD5 sums checked
package 'gtable' successfully unpacked and MD5 sums checked
package 'plyr' successfully unpacked and MD5 sums checked
package 'reshape2' successfully unpacked and MD5 sums checked
package 'scales' successfully unpacked and MD5 sums checked
package 'ggplot2' successfully unpacked and MD5 sums checked
package 'microbenchmark' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\admin\AppData\Local\Temp\RtmpO0Tqzw\downloaded_packages
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require(microbenchmark)
N <- 1000000
dat<- data.table(
x=sample(LETTERS[1:20], N, replace=TRUE),
y=sample(letters[1:5], N, replace=TRUE))
head(dat, 3)
setkey(dat, x,y)
microbenchmark(
data.table = dat[list(c("B", "D"), c("b", "d"))],
dplyr = dat %>% slice(x %in% c("B", "D") & y %in% c("b", "d")),
baseR = dat[x %in% c("B", "D") & y %in% c("b", "d")]
)
Loading required package: microbenchmark
x y K a Q d G c
expr time data.table 1950543 data.table 1006286 baseR 55832576 data.table 1303486 baseR 58823766 baseR 54660286 baseR 58205715 baseR 53069864 data.table 1062067 baseR 57616669 dplyr 119652071 dplyr 105449384 data.table 1177644 baseR 53854365 data.table 1207543 baseR 102857584 data.table 1069206 data.table 936671 dplyr 59487781 baseR 57939306 data.table 1091519 dplyr 61305343 data.table 1316428 dplyr 62308058 baseR 110325966 data.table 1057158 dplyr 108256720 data.table 1256631 dplyr 55326532 data.table 1355251 ... ... data.table 1024136 data.table 948720 baseR 53621424 data.table 1086610 data.table 1006286 baseR 57465838 baseR 51934167 baseR 100115846 dplyr 57611761 data.table 1193709 baseR 53329133 dplyr 56833953 dplyr 56365394 baseR 58661332 baseR 53363940 baseR 57207907 baseR 54375134 baseR 58050421 data.table 1087503 baseR 59920194 baseR 53611161 baseR 58632773 baseR 54360409 baseR 58321293 dplyr 104288250 data.table 1274034 dplyr 55624179 dplyr 60385183 dplyr 63381728 baseR 57344905
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Content source: JEstebanMejiaV/Mineria.de.Datos.Aplicada
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