library(RSQLite)
data("mtcars")
mtcars$car_names <- rownames(mtcars)
rownames(mtcars) <- c()
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb car_names
## 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4
## 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
## 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
## 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
## 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
## 6 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 Valiant
conn <- dbConnect(RSQLite::SQLite(), "CarsDB.db")
dbWriteTable(conn, "cars_data", mtcars, append = TRUE)
dbListTables(conn)
## [1] "Cars_and_Makes" "cars_data"
car <- c("Camaro", "California", "Mustang", "Explorer")
make <- c("Chevrolet", "Ferrari", "Ford", "Ford")
df1 <- data.frame(car, make)
car <- c("Corolla", "Lancer", "Sportage", "XE")
make <- c("Toyota", "Mitsubishi", "Kia", "Jaguar")
df2 <- data.frame(car, make)
dfList <- list(df1, df2)
for(k in 1:length(dfList)){
dbWriteTable(conn, "Cars_and_Makes", dfList[[k]], append = TRUE)
}
dbListTables(conn)
## [1] "Cars_and_Makes" "cars_data"
dbGetQuery(conn, "SELECT * FROM Cars_and_Makes")
## car make
## 1 Camaro Chevrolet
## 2 California Ferrari
## 3 Mustang Ford
## 4 Explorer Ford
## 5 Corolla Toyota
## 6 Lancer Mitsubishi
## 7 Sportage Kia
## 8 XE Jaguar
## 9 Camaro Chevrolet
## 10 California Ferrari
## 11 Mustang Ford
## 12 Explorer Ford
## 13 Corolla Toyota
## 14 Lancer Mitsubishi
## 15 Sportage Kia
## 16 XE Jaguar
## 17 Camaro Chevrolet
## 18 California Ferrari
## 19 Mustang Ford
## 20 Explorer Ford
## 21 Corolla Toyota
## 22 Lancer Mitsubishi
## 23 Sportage Kia
## 24 XE Jaguar
dbGetQuery(conn, 'SELECT * FROM cars_data LIMIT 10')
## mpg cyl disp hp drat wt qsec vs am gear carb car_names
## 1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
## 2 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
## 3 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
## 4 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
## 5 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
## 6 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
## 7 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
## 8 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
## 9 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
## 10 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
dbGetQuery(conn, "SELECT car_names, hp, cyl FROM cars_data WHERE cyl = 8")
## car_names hp cyl
## 1 Hornet Sportabout 175 8
## 2 Duster 360 245 8
## 3 Merc 450SE 180 8
## 4 Merc 450SL 180 8
## 5 Merc 450SLC 180 8
## 6 Cadillac Fleetwood 205 8
## 7 Lincoln Continental 215 8
## 8 Chrysler Imperial 230 8
## 9 Dodge Challenger 150 8
## 10 AMC Javelin 150 8
## 11 Camaro Z28 245 8
## 12 Pontiac Firebird 175 8
## 13 Ford Pantera L 264 8
## 14 Maserati Bora 335 8
## 15 Hornet Sportabout 175 8
## 16 Duster 360 245 8
## 17 Merc 450SE 180 8
## 18 Merc 450SL 180 8
## 19 Merc 450SLC 180 8
## 20 Cadillac Fleetwood 205 8
## 21 Lincoln Continental 215 8
## 22 Chrysler Imperial 230 8
## 23 Dodge Challenger 150 8
## 24 AMC Javelin 150 8
## 25 Camaro Z28 245 8
## 26 Pontiac Firebird 175 8
## 27 Ford Pantera L 264 8
## 28 Maserati Bora 335 8
## 29 Hornet Sportabout 175 8
## 30 Duster 360 245 8
## 31 Merc 450SE 180 8
## 32 Merc 450SL 180 8
## 33 Merc 450SLC 180 8
## 34 Cadillac Fleetwood 205 8
## 35 Lincoln Continental 215 8
## 36 Chrysler Imperial 230 8
## 37 Dodge Challenger 150 8
## 38 AMC Javelin 150 8
## 39 Camaro Z28 245 8
## 40 Pontiac Firebird 175 8
## 41 Ford Pantera L 264 8
## 42 Maserati Bora 335 8
## 43 Hornet Sportabout 175 8
## 44 Duster 360 245 8
## 45 Merc 450SE 180 8
## 46 Merc 450SL 180 8
## 47 Merc 450SLC 180 8
## 48 Cadillac Fleetwood 205 8
## 49 Lincoln Continental 215 8
## 50 Chrysler Imperial 230 8
## 51 Dodge Challenger 150 8
## 52 AMC Javelin 150 8
## 53 Camaro Z28 245 8
## 54 Pontiac Firebird 175 8
## 55 Ford Pantera L 264 8
## 56 Maserati Bora 335 8
dbGetQuery(conn, "SELECT car_names, hp, cyl FROM cars_data WHERE car_names LIKE 'M%' AND cyl IN (6,8)")
## car_names hp cyl
## 1 Mazda RX4 Wag 110 6
## 2 Merc 280 123 6
## 3 Merc 280C 123 6
## 4 Merc 450SE 180 8
## 5 Merc 450SL 180 8
## 6 Merc 450SLC 180 8
## 7 Maserati Bora 335 8
## 8 Mazda RX4 Wag 110 6
## 9 Merc 280 123 6
## 10 Merc 280C 123 6
## 11 Merc 450SE 180 8
## 12 Merc 450SL 180 8
## 13 Merc 450SLC 180 8
## 14 Maserati Bora 335 8
## 15 Mazda RX4 Wag 110 6
## 16 Merc 280 123 6
## 17 Merc 280C 123 6
## 18 Merc 450SE 180 8
## 19 Merc 450SL 180 8
## 20 Merc 450SLC 180 8
## 21 Maserati Bora 335 8
## 22 Mazda RX4 110 6
## 23 Mazda RX4 110 6
## 24 Mazda RX4 Wag 110 6
## 25 Merc 280 123 6
## 26 Merc 280C 123 6
## 27 Merc 450SE 180 8
## 28 Merc 450SL 180 8
## 29 Merc 450SLC 180 8
## 30 Maserati Bora 335 8
dbGetQuery(conn, "SELECT cyl, AVG(hp) AS 'average_hp', AVG(mpg) AS 'average_mpg' FROM cars_data
GROUP BY cyl
ORDER BY average_hp")
## cyl average_hp average_mpg
## 1 4 82.63636 26.66364
## 2 6 123.23077 19.64615
## 3 8 209.21429 15.10000
avg_HpCyl <- dbGetQuery(conn, "SELECT cyl, AVG(hp) AS 'average_hp' FROM cars_data
GROUP BY cyl
ORDER BY average_hp")
avg_HpCyl
## cyl average_hp
## 1 4 82.63636
## 2 6 123.23077
## 3 8 209.21429
class(avg_HpCyl)
## [1] "data.frame"
mpg <- 18
cyl <- 6
Result <- dbGetQuery(conn, 'SELECT car_names, mpg, cyl FROM cars_data WHERE mpg >=? AND cyl >= ?', params = c(mpg, cyl))
Result
## car_names mpg cyl
## 1 Mazda RX4 Wag 21.0 6
## 2 Hornet 4 Drive 21.4 6
## 3 Hornet Sportabout 18.7 8
## 4 Valiant 18.1 6
## 5 Merc 280 19.2 6
## 6 Pontiac Firebird 19.2 8
## 7 Ferrari Dino 19.7 6
## 8 Mazda RX4 Wag 21.0 6
## 9 Hornet 4 Drive 21.4 6
## 10 Hornet Sportabout 18.7 8
## 11 Valiant 18.1 6
## 12 Merc 280 19.2 6
## 13 Pontiac Firebird 19.2 8
## 14 Ferrari Dino 19.7 6
## 15 Mazda RX4 Wag 21.0 6
## 16 Hornet 4 Drive 21.4 6
## 17 Hornet Sportabout 18.7 8
## 18 Valiant 18.1 6
## 19 Merc 280 19.2 6
## 20 Pontiac Firebird 19.2 8
## 21 Ferrari Dino 19.7 6
## 22 Mazda RX4 21.0 6
## 23 Mazda RX4 21.0 6
## 24 Mazda RX4 Wag 21.0 6
## 25 Hornet 4 Drive 21.4 6
## 26 Hornet Sportabout 18.7 8
## 27 Valiant 18.1 6
## 28 Merc 280 19.2 6
## 29 Pontiac Firebird 19.2 8
## 30 Ferrari Dino 19.7 6
assembleQuery <- function(conn, base, search_parameters){
parameter_names <- names(search_parameters)
partial_queries <- ""
for(k in 1:length(parameter_names)){
filter_k <- paste(parameter_names[k], ">=?")
if(k > 1){
filter_k <- paste("AND", parameter_names[k], ">=?")
}
partial_queries <- paste(partial_queries, filter_k)
}
final_paste <- paste(base, "WHERE", partial_queries)
print(final_paste)
values <- unlist(search_parameters, use.names = FALSE)
result <- dbGetQuery(conn, final_paste, params = values)
return(result)
}
base <- "SELECT car_names, mpg, hp, wt FROM cars_data"
search_parameters <- list("mpg" = 16, "hp" = 150, "wt" = 2.1)
result <- assembleQuery(conn, base, search_parameters)
## [1] "SELECT car_names, mpg, hp, wt FROM cars_data WHERE mpg >=? AND hp >=? AND wt >=?"
result
## car_names mpg hp wt
## 1 Hornet Sportabout 18.7 175 3.440
## 2 Merc 450SE 16.4 180 4.070
## 3 Merc 450SL 17.3 180 3.730
## 4 Pontiac Firebird 19.2 175 3.845
## 5 Ferrari Dino 19.7 175 2.770
## 6 Hornet Sportabout 18.7 175 3.440
## 7 Merc 450SE 16.4 180 4.070
## 8 Merc 450SL 17.3 180 3.730
## 9 Pontiac Firebird 19.2 175 3.845
## 10 Ferrari Dino 19.7 175 2.770
## 11 Hornet Sportabout 18.7 175 3.440
## 12 Merc 450SE 16.4 180 4.070
## 13 Merc 450SL 17.3 180 3.730
## 14 Pontiac Firebird 19.2 175 3.845
## 15 Ferrari Dino 19.7 175 2.770
## 16 Hornet Sportabout 18.7 175 3.440
## 17 Merc 450SE 16.4 180 4.070
## 18 Merc 450SL 17.3 180 3.730
## 19 Pontiac Firebird 19.2 175 3.845
## 20 Ferrari Dino 19.7 175 2.770
dbGetQuery(conn, "SELECT * FROM cars_data LIMIT 10")
## mpg cyl disp hp drat wt qsec vs am gear carb car_names
## 1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
## 2 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
## 3 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
## 4 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
## 5 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
## 6 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
## 7 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
## 8 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
## 9 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
## 10 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
dbExecute(conn, "DELETE FROM cars_data WHERE car_names = 'Mazda RX4'")
## [1] 2
dbGetQuery(conn, "SELECT * FROM cars_data LIMIT 10")
## mpg cyl disp hp drat wt qsec vs am gear carb car_names
## 1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
## 2 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
## 3 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
## 4 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
## 5 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
## 6 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
## 7 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
## 8 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
## 9 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
## 10 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
dbExecute(conn, "INSERT INTO cars_data VALUES (21.0, 6, 160.0, 110, 3.90, 2.260, 16.46, 0, 1, 4, 4, 'Mazda RX4')")
## [1] 1
dbGetQuery(conn, "SELECT * FROM cars_data")
## mpg cyl disp hp drat wt qsec vs am gear carb car_names
## 1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
## 2 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
## 3 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
## 4 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
## 5 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
## 6 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
## 7 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
## 8 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
## 9 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
## 10 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
## 11 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SE
## 12 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SL
## 13 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Merc 450SLC
## 14 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Cadillac Fleetwood
## 15 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Lincoln Continental
## 16 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Chrysler Imperial
## 17 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Fiat 128
## 18 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Honda Civic
## 19 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
## 20 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Toyota Corona
## 21 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 Dodge Challenger
## 22 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 AMC Javelin
## 23 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Camaro Z28
## 24 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Pontiac Firebird
## 25 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Fiat X1-9
## 26 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Porsche 914-2
## 27 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Lotus Europa
## 28 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ford Pantera L
## 29 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Ferrari Dino
## 30 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 Maserati Bora
## 31 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 Volvo 142E
## 32 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
## 33 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
## 34 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
## 35 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
## 36 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
## 37 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
## 38 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
## 39 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
## 40 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
## 41 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
## 42 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SE
## 43 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SL
## 44 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Merc 450SLC
## 45 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Cadillac Fleetwood
## 46 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Lincoln Continental
## 47 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Chrysler Imperial
## 48 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Fiat 128
## 49 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Honda Civic
## 50 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
## 51 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Toyota Corona
## 52 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 Dodge Challenger
## 53 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 AMC Javelin
## 54 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Camaro Z28
## 55 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Pontiac Firebird
## 56 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Fiat X1-9
## 57 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Porsche 914-2
## 58 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Lotus Europa
## 59 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ford Pantera L
## 60 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Ferrari Dino
## 61 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 Maserati Bora
## 62 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 Volvo 142E
## 63 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
## 64 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
## 65 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
## 66 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
## 67 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
## 68 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
## 69 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
## 70 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
## 71 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
## 72 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
## 73 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SE
## 74 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SL
## 75 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Merc 450SLC
## 76 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Cadillac Fleetwood
## 77 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Lincoln Continental
## 78 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Chrysler Imperial
## 79 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Fiat 128
## 80 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Honda Civic
## 81 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
## 82 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Toyota Corona
## 83 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 Dodge Challenger
## 84 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 AMC Javelin
## 85 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Camaro Z28
## 86 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Pontiac Firebird
## 87 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Fiat X1-9
## 88 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Porsche 914-2
## 89 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Lotus Europa
## 90 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ford Pantera L
## 91 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Ferrari Dino
## 92 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 Maserati Bora
## 93 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 Volvo 142E
## 94 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
## 95 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
## 96 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
## 97 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
## 98 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
## 99 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
## 100 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
## 101 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
## 102 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
## 103 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
## 104 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SE
## 105 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SL
## 106 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Merc 450SLC
## 107 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Cadillac Fleetwood
## 108 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Lincoln Continental
## 109 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Chrysler Imperial
## 110 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Fiat 128
## 111 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Honda Civic
## 112 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
## 113 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Toyota Corona
## 114 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 Dodge Challenger
## 115 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 AMC Javelin
## 116 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Camaro Z28
## 117 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Pontiac Firebird
## 118 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Fiat X1-9
## 119 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Porsche 914-2
## 120 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Lotus Europa
## 121 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ford Pantera L
## 122 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Ferrari Dino
## 123 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 Maserati Bora
## 124 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 Volvo 142E
## 125 21.0 6 160.0 110 3.90 2.260 16.46 0 1 4 4 Mazda RX4
dbDisconnect(conn)