10 vroom: Fast reading of delimited files
vroom(Hester and Wickham 2019)
file_path <- vroom_example("mtcars.csv")
vroom(file_path)
#> # A tibble: 32 x 12
#> model mpg cyl disp hp drat wt qsec vs am gear carb
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 Mazda RX4 W~ 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 Hornet 4 Dr~ 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 Hornet Spor~ 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> # ... with 26 more rowsspec(vroom(file_path))
#> cols(
#> model = col_character(),
#> mpg = col_double(),
#> cyl = col_double(),
#> disp = col_double(),
#> hp = col_double(),
#> drat = col_double(),
#> wt = col_double(),
#> qsec = col_double(),
#> vs = col_double(),
#> am = col_double(),
#> gear = col_double(),
#> carb = col_double()
#> )compressed <- vroom_example("mtcars.csv.zip")
vroom(compressed)
#> # A tibble: 32 x 12
#> model mpg cyl disp hp drat wt qsec vs am gear carb
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 Mazda RX4 W~ 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 Hornet 4 Dr~ 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 Hornet Spor~ 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> # ... with 26 more rowsvroom(compressed, col_select = c(model, cyl, gear))
#> # A tibble: 32 x 3
#> model cyl gear
#> <chr> <dbl> <dbl>
#> 1 Mazda RX4 6 4
#> 2 Mazda RX4 Wag 6 4
#> 3 Datsun 710 4 4
#> 4 Hornet 4 Drive 6 3
#> 5 Hornet Sportabout 8 3
#> 6 Valiant 6 3
#> # ... with 26 more rowsmtcars
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.62 16.5 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.88 17.0 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.32 18.6 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.21 19.4 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.44 17.0 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.46 20.2 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.57 15.8 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.19 20.0 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18.0 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.25 18.0 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.42 17.8 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.34 17.4 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.20 19.5 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.61 18.5 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.83 19.9 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.46 20.0 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.52 16.9 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.44 17.3 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.84 15.4 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.85 17.1 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.94 18.9 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.14 16.7 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.17 14.5 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.77 15.5 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.57 14.6 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.78 18.6 1 1 4 2