Maturing lifecycle

One of the main features of the tbl_df class is the printing:

  • Tibbles only print as many rows and columns as fit on one screen, supplemented by a summary of the remaining rows and columns.

  • Tibble reveals the type of each column, which keeps the user informed about whether a variable is, e.g., <chr> or <fct> (character versus factor).

Printing can be tweaked for a one-off call by calling print() explicitly and setting arguments like n and width. More persistent control is available by setting the options described below.

# S3 method for tbl_df
print(x, ..., n = NULL, width = NULL, n_extra = NULL)

# S3 method for tbl_df
format(x, ..., n = NULL, width = NULL, n_extra = NULL)

trunc_mat(x, n = NULL, width = NULL, n_extra = NULL)

Arguments

x

Object to format or print.

...

Other arguments passed on to individual methods.

n

Number of rows to show. If NULL, the default, will print all rows if less than option tibble.print_max. Otherwise, will print tibble.print_min rows.

width

Width of text output to generate. This defaults to NULL, which means use getOption("tibble.width") or (if also NULL) getOption("width"); the latter displays only the columns that fit on one screen. You can also set options(tibble.width = Inf) to override this default and always print all columns.

n_extra

Number of extra columns to print abbreviated information for, if the width is too small for the entire tibble. If NULL, the default, will print information about at most tibble.max_extra_cols extra columns.

Package options

The following options are used by the tibble and pillar packages to format and print tbl_df objects. Used by the formatting workhorse trunc_mat() and, therefore, indirectly, by print.tbl().

  • tibble.print_max: Row number threshold: Maximum number of rows printed. Set to Inf to always print all rows. Default: 20.

  • tibble.print_min: Number of rows printed if row number threshold is exceeded. Default: 10.

  • tibble.width: Output width. Default: NULL (use width option).

  • tibble.max_extra_cols: Number of extra columns printed in reduced form. Default: 100.

  • pillar.bold: Use bold font, e.g. for column headers? This currently defaults to FALSE, because many terminal fonts have poor support for bold fonts.

  • pillar.subtle: Use subtle style, e.g. for row numbers and data types? Default: TRUE.

  • pillar.subtle_num: Use subtle style for insignificant digits? Default: FALSE, is also affected by the pillar.subtle option.

  • pillar.neg: Highlight negative numbers? Default: TRUE.

  • pillar.sigfig: The number of significant digits that will be printed and highlighted, default: 3. Set the pillar.subtle option to FALSE to turn off highlighting of significant digits.

  • pillar.min_title_chars: The minimum number of characters for the column title, default: 15. Column titles may be truncated up to that width to save horizontal space. Set to Inf to turn off truncation of column titles.

  • pillar.min_chars: The minimum number of characters wide to display character columns, default: 0. Character columns may be truncated up to that width to save horizontal space. Set to Inf to turn off truncation of character columns.

Examples

print(as_tibble(mtcars))
#> # A tibble: 32 x 11 #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # … with 22 more rows
print(as_tibble(mtcars), n = 1)
#> # A tibble: 32 x 11 #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> # … with 31 more rows
print(as_tibble(mtcars), n = 3)
#> # A tibble: 32 x 11 #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> # … with 29 more rows
print(as_tibble(iris), n = 100)
#> # A tibble: 150 x 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> 11 5.4 3.7 1.5 0.2 setosa #> 12 4.8 3.4 1.6 0.2 setosa #> 13 4.8 3 1.4 0.1 setosa #> 14 4.3 3 1.1 0.1 setosa #> 15 5.8 4 1.2 0.2 setosa #> 16 5.7 4.4 1.5 0.4 setosa #> 17 5.4 3.9 1.3 0.4 setosa #> 18 5.1 3.5 1.4 0.3 setosa #> 19 5.7 3.8 1.7 0.3 setosa #> 20 5.1 3.8 1.5 0.3 setosa #> 21 5.4 3.4 1.7 0.2 setosa #> 22 5.1 3.7 1.5 0.4 setosa #> 23 4.6 3.6 1 0.2 setosa #> 24 5.1 3.3 1.7 0.5 setosa #> 25 4.8 3.4 1.9 0.2 setosa #> 26 5 3 1.6 0.2 setosa #> 27 5 3.4 1.6 0.4 setosa #> 28 5.2 3.5 1.5 0.2 setosa #> 29 5.2 3.4 1.4 0.2 setosa #> 30 4.7 3.2 1.6 0.2 setosa #> 31 4.8 3.1 1.6 0.2 setosa #> 32 5.4 3.4 1.5 0.4 setosa #> 33 5.2 4.1 1.5 0.1 setosa #> 34 5.5 4.2 1.4 0.2 setosa #> 35 4.9 3.1 1.5 0.2 setosa #> 36 5 3.2 1.2 0.2 setosa #> 37 5.5 3.5 1.3 0.2 setosa #> 38 4.9 3.6 1.4 0.1 setosa #> 39 4.4 3 1.3 0.2 setosa #> 40 5.1 3.4 1.5 0.2 setosa #> 41 5 3.5 1.3 0.3 setosa #> 42 4.5 2.3 1.3 0.3 setosa #> 43 4.4 3.2 1.3 0.2 setosa #> 44 5 3.5 1.6 0.6 setosa #> 45 5.1 3.8 1.9 0.4 setosa #> 46 4.8 3 1.4 0.3 setosa #> 47 5.1 3.8 1.6 0.2 setosa #> 48 4.6 3.2 1.4 0.2 setosa #> 49 5.3 3.7 1.5 0.2 setosa #> 50 5 3.3 1.4 0.2 setosa #> 51 7 3.2 4.7 1.4 versicolor #> 52 6.4 3.2 4.5 1.5 versicolor #> 53 6.9 3.1 4.9 1.5 versicolor #> 54 5.5 2.3 4 1.3 versicolor #> 55 6.5 2.8 4.6 1.5 versicolor #> 56 5.7 2.8 4.5 1.3 versicolor #> 57 6.3 3.3 4.7 1.6 versicolor #> 58 4.9 2.4 3.3 1 versicolor #> 59 6.6 2.9 4.6 1.3 versicolor #> 60 5.2 2.7 3.9 1.4 versicolor #> 61 5 2 3.5 1 versicolor #> 62 5.9 3 4.2 1.5 versicolor #> 63 6 2.2 4 1 versicolor #> 64 6.1 2.9 4.7 1.4 versicolor #> 65 5.6 2.9 3.6 1.3 versicolor #> 66 6.7 3.1 4.4 1.4 versicolor #> 67 5.6 3 4.5 1.5 versicolor #> 68 5.8 2.7 4.1 1 versicolor #> 69 6.2 2.2 4.5 1.5 versicolor #> 70 5.6 2.5 3.9 1.1 versicolor #> 71 5.9 3.2 4.8 1.8 versicolor #> 72 6.1 2.8 4 1.3 versicolor #> 73 6.3 2.5 4.9 1.5 versicolor #> 74 6.1 2.8 4.7 1.2 versicolor #> 75 6.4 2.9 4.3 1.3 versicolor #> 76 6.6 3 4.4 1.4 versicolor #> 77 6.8 2.8 4.8 1.4 versicolor #> 78 6.7 3 5 1.7 versicolor #> 79 6 2.9 4.5 1.5 versicolor #> 80 5.7 2.6 3.5 1 versicolor #> 81 5.5 2.4 3.8 1.1 versicolor #> 82 5.5 2.4 3.7 1 versicolor #> 83 5.8 2.7 3.9 1.2 versicolor #> 84 6 2.7 5.1 1.6 versicolor #> 85 5.4 3 4.5 1.5 versicolor #> 86 6 3.4 4.5 1.6 versicolor #> 87 6.7 3.1 4.7 1.5 versicolor #> 88 6.3 2.3 4.4 1.3 versicolor #> 89 5.6 3 4.1 1.3 versicolor #> 90 5.5 2.5 4 1.3 versicolor #> 91 5.5 2.6 4.4 1.2 versicolor #> 92 6.1 3 4.6 1.4 versicolor #> 93 5.8 2.6 4 1.2 versicolor #> 94 5 2.3 3.3 1 versicolor #> 95 5.6 2.7 4.2 1.3 versicolor #> 96 5.7 3 4.2 1.2 versicolor #> 97 5.7 2.9 4.2 1.3 versicolor #> 98 6.2 2.9 4.3 1.3 versicolor #> 99 5.1 2.5 3 1.1 versicolor #> 100 5.7 2.8 4.1 1.3 versicolor #> # … with 50 more rows
print(mtcars, width = 10)
#> mpg #> Mazda RX4 21.0 #> Mazda RX4 Wag 21.0 #> Datsun 710 22.8 #> Hornet 4 Drive 21.4 #> Hornet Sportabout 18.7 #> Valiant 18.1 #> Duster 360 14.3 #> Merc 240D 24.4 #> Merc 230 22.8 #> Merc 280 19.2 #> Merc 280C 17.8 #> Merc 450SE 16.4 #> Merc 450SL 17.3 #> Merc 450SLC 15.2 #> Cadillac Fleetwood 10.4 #> Lincoln Continental 10.4 #> Chrysler Imperial 14.7 #> Fiat 128 32.4 #> Honda Civic 30.4 #> Toyota Corolla 33.9 #> Toyota Corona 21.5 #> Dodge Challenger 15.5 #> AMC Javelin 15.2 #> Camaro Z28 13.3 #> Pontiac Firebird 19.2 #> Fiat X1-9 27.3 #> Porsche 914-2 26.0 #> Lotus Europa 30.4 #> Ford Pantera L 15.8 #> Ferrari Dino 19.7 #> Maserati Bora 15.0 #> Volvo 142E 21.4 #> cyl #> Mazda RX4 6 #> Mazda RX4 Wag 6 #> Datsun 710 4 #> Hornet 4 Drive 6 #> Hornet Sportabout 8 #> Valiant 6 #> Duster 360 8 #> Merc 240D 4 #> Merc 230 4 #> Merc 280 6 #> Merc 280C 6 #> Merc 450SE 8 #> Merc 450SL 8 #> Merc 450SLC 8 #> Cadillac Fleetwood 8 #> Lincoln Continental 8 #> Chrysler Imperial 8 #> Fiat 128 4 #> Honda Civic 4 #> Toyota Corolla 4 #> Toyota Corona 4 #> Dodge Challenger 8 #> AMC Javelin 8 #> Camaro Z28 8 #> Pontiac Firebird 8 #> Fiat X1-9 4 #> Porsche 914-2 4 #> Lotus Europa 4 #> Ford Pantera L 8 #> Ferrari Dino 6 #> Maserati Bora 8 #> Volvo 142E 4 #> disp #> Mazda RX4 160.0 #> Mazda RX4 Wag 160.0 #> Datsun 710 108.0 #> Hornet 4 Drive 258.0 #> Hornet Sportabout 360.0 #> Valiant 225.0 #> Duster 360 360.0 #> Merc 240D 146.7 #> Merc 230 140.8 #> Merc 280 167.6 #> Merc 280C 167.6 #> Merc 450SE 275.8 #> Merc 450SL 275.8 #> Merc 450SLC 275.8 #> Cadillac Fleetwood 472.0 #> Lincoln Continental 460.0 #> Chrysler Imperial 440.0 #> Fiat 128 78.7 #> Honda Civic 75.7 #> Toyota Corolla 71.1 #> Toyota Corona 120.1 #> Dodge Challenger 318.0 #> AMC Javelin 304.0 #> Camaro Z28 350.0 #> Pontiac Firebird 400.0 #> Fiat X1-9 79.0 #> Porsche 914-2 120.3 #> Lotus Europa 95.1 #> Ford Pantera L 351.0 #> Ferrari Dino 145.0 #> Maserati Bora 301.0 #> Volvo 142E 121.0 #> hp #> Mazda RX4 110 #> Mazda RX4 Wag 110 #> Datsun 710 93 #> Hornet 4 Drive 110 #> Hornet Sportabout 175 #> Valiant 105 #> Duster 360 245 #> Merc 240D 62 #> Merc 230 95 #> Merc 280 123 #> Merc 280C 123 #> Merc 450SE 180 #> Merc 450SL 180 #> Merc 450SLC 180 #> Cadillac Fleetwood 205 #> Lincoln Continental 215 #> Chrysler Imperial 230 #> Fiat 128 66 #> Honda Civic 52 #> Toyota Corolla 65 #> Toyota Corona 97 #> Dodge Challenger 150 #> AMC Javelin 150 #> Camaro Z28 245 #> Pontiac Firebird 175 #> Fiat X1-9 66 #> Porsche 914-2 91 #> Lotus Europa 113 #> Ford Pantera L 264 #> Ferrari Dino 175 #> Maserati Bora 335 #> Volvo 142E 109 #> drat #> Mazda RX4 3.90 #> Mazda RX4 Wag 3.90 #> Datsun 710 3.85 #> Hornet 4 Drive 3.08 #> Hornet Sportabout 3.15 #> Valiant 2.76 #> Duster 360 3.21 #> Merc 240D 3.69 #> Merc 230 3.92 #> Merc 280 3.92 #> Merc 280C 3.92 #> Merc 450SE 3.07 #> Merc 450SL 3.07 #> Merc 450SLC 3.07 #> Cadillac Fleetwood 2.93 #> Lincoln Continental 3.00 #> Chrysler Imperial 3.23 #> Fiat 128 4.08 #> Honda Civic 4.93 #> Toyota Corolla 4.22 #> Toyota Corona 3.70 #> Dodge Challenger 2.76 #> AMC Javelin 3.15 #> Camaro Z28 3.73 #> Pontiac Firebird 3.08 #> Fiat X1-9 4.08 #> Porsche 914-2 4.43 #> Lotus Europa 3.77 #> Ford Pantera L 4.22 #> Ferrari Dino 3.62 #> Maserati Bora 3.54 #> Volvo 142E 4.11 #> wt #> Mazda RX4 2.620 #> Mazda RX4 Wag 2.875 #> Datsun 710 2.320 #> Hornet 4 Drive 3.215 #> Hornet Sportabout 3.440 #> Valiant 3.460 #> Duster 360 3.570 #> Merc 240D 3.190 #> Merc 230 3.150 #> Merc 280 3.440 #> Merc 280C 3.440 #> Merc 450SE 4.070 #> Merc 450SL 3.730 #> Merc 450SLC 3.780 #> Cadillac Fleetwood 5.250 #> Lincoln Continental 5.424 #> Chrysler Imperial 5.345 #> Fiat 128 2.200 #> Honda Civic 1.615 #> Toyota Corolla 1.835 #> Toyota Corona 2.465 #> Dodge Challenger 3.520 #> AMC Javelin 3.435 #> Camaro Z28 3.840 #> Pontiac Firebird 3.845 #> Fiat X1-9 1.935 #> Porsche 914-2 2.140 #> Lotus Europa 1.513 #> Ford Pantera L 3.170 #> Ferrari Dino 2.770 #> Maserati Bora 3.570 #> Volvo 142E 2.780 #> qsec #> Mazda RX4 16.46 #> Mazda RX4 Wag 17.02 #> Datsun 710 18.61 #> Hornet 4 Drive 19.44 #> Hornet Sportabout 17.02 #> Valiant 20.22 #> Duster 360 15.84 #> Merc 240D 20.00 #> Merc 230 22.90 #> Merc 280 18.30 #> Merc 280C 18.90 #> Merc 450SE 17.40 #> Merc 450SL 17.60 #> Merc 450SLC 18.00 #> Cadillac Fleetwood 17.98 #> Lincoln Continental 17.82 #> Chrysler Imperial 17.42 #> Fiat 128 19.47 #> Honda Civic 18.52 #> Toyota Corolla 19.90 #> Toyota Corona 20.01 #> Dodge Challenger 16.87 #> AMC Javelin 17.30 #> Camaro Z28 15.41 #> Pontiac Firebird 17.05 #> Fiat X1-9 18.90 #> Porsche 914-2 16.70 #> Lotus Europa 16.90 #> Ford Pantera L 14.50 #> Ferrari Dino 15.50 #> Maserati Bora 14.60 #> Volvo 142E 18.60 #> vs #> Mazda RX4 0 #> Mazda RX4 Wag 0 #> Datsun 710 1 #> Hornet 4 Drive 1 #> Hornet Sportabout 0 #> Valiant 1 #> Duster 360 0 #> Merc 240D 1 #> Merc 230 1 #> Merc 280 1 #> Merc 280C 1 #> Merc 450SE 0 #> Merc 450SL 0 #> Merc 450SLC 0 #> Cadillac Fleetwood 0 #> Lincoln Continental 0 #> Chrysler Imperial 0 #> Fiat 128 1 #> Honda Civic 1 #> Toyota Corolla 1 #> Toyota Corona 1 #> Dodge Challenger 0 #> AMC Javelin 0 #> Camaro Z28 0 #> Pontiac Firebird 0 #> Fiat X1-9 1 #> Porsche 914-2 0 #> Lotus Europa 1 #> Ford Pantera L 0 #> Ferrari Dino 0 #> Maserati Bora 0 #> Volvo 142E 1 #> am #> Mazda RX4 1 #> Mazda RX4 Wag 1 #> Datsun 710 1 #> Hornet 4 Drive 0 #> Hornet Sportabout 0 #> Valiant 0 #> Duster 360 0 #> Merc 240D 0 #> Merc 230 0 #> Merc 280 0 #> Merc 280C 0 #> Merc 450SE 0 #> Merc 450SL 0 #> Merc 450SLC 0 #> Cadillac Fleetwood 0 #> Lincoln Continental 0 #> Chrysler Imperial 0 #> Fiat 128 1 #> Honda Civic 1 #> Toyota Corolla 1 #> Toyota Corona 0 #> Dodge Challenger 0 #> AMC Javelin 0 #> Camaro Z28 0 #> Pontiac Firebird 0 #> Fiat X1-9 1 #> Porsche 914-2 1 #> Lotus Europa 1 #> Ford Pantera L 1 #> Ferrari Dino 1 #> Maserati Bora 1 #> Volvo 142E 1 #> gear #> Mazda RX4 4 #> Mazda RX4 Wag 4 #> Datsun 710 4 #> Hornet 4 Drive 3 #> Hornet Sportabout 3 #> Valiant 3 #> Duster 360 3 #> Merc 240D 4 #> Merc 230 4 #> Merc 280 4 #> Merc 280C 4 #> Merc 450SE 3 #> Merc 450SL 3 #> Merc 450SLC 3 #> Cadillac Fleetwood 3 #> Lincoln Continental 3 #> Chrysler Imperial 3 #> Fiat 128 4 #> Honda Civic 4 #> Toyota Corolla 4 #> Toyota Corona 3 #> Dodge Challenger 3 #> AMC Javelin 3 #> Camaro Z28 3 #> Pontiac Firebird 3 #> Fiat X1-9 4 #> Porsche 914-2 5 #> Lotus Europa 5 #> Ford Pantera L 5 #> Ferrari Dino 5 #> Maserati Bora 5 #> Volvo 142E 4 #> carb #> Mazda RX4 4 #> Mazda RX4 Wag 4 #> Datsun 710 1 #> Hornet 4 Drive 1 #> Hornet Sportabout 2 #> Valiant 1 #> Duster 360 4 #> Merc 240D 2 #> Merc 230 2 #> Merc 280 4 #> Merc 280C 4 #> Merc 450SE 3 #> Merc 450SL 3 #> Merc 450SLC 3 #> Cadillac Fleetwood 4 #> Lincoln Continental 4 #> Chrysler Imperial 4 #> Fiat 128 1 #> Honda Civic 2 #> Toyota Corolla 1 #> Toyota Corona 1 #> Dodge Challenger 2 #> AMC Javelin 2 #> Camaro Z28 4 #> Pontiac Firebird 2 #> Fiat X1-9 1 #> Porsche 914-2 2 #> Lotus Europa 2 #> Ford Pantera L 4 #> Ferrari Dino 6 #> Maserati Bora 8 #> Volvo 142E 2
mtcars2 <- as_tibble(cbind(mtcars, mtcars), .name_repair = "unique")
#> New names: #> * mpg -> mpg...1 #> * cyl -> cyl...2 #> * disp -> disp...3 #> * hp -> hp...4 #> * drat -> drat...5 #> * ...
print(mtcars2, n = 25, n_extra = 3)
#> # A tibble: 32 x 22 #> mpg...1 cyl...2 disp...3 hp...4 drat...5 wt...6 qsec...7 vs...8 am...9 #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 #> 11 17.8 6 168. 123 3.92 3.44 18.9 1 0 #> 12 16.4 8 276. 180 3.07 4.07 17.4 0 0 #> 13 17.3 8 276. 180 3.07 3.73 17.6 0 0 #> 14 15.2 8 276. 180 3.07 3.78 18 0 0 #> 15 10.4 8 472 205 2.93 5.25 18.0 0 0 #> 16 10.4 8 460 215 3 5.42 17.8 0 0 #> 17 14.7 8 440 230 3.23 5.34 17.4 0 0 #> 18 32.4 4 78.7 66 4.08 2.2 19.5 1 1 #> 19 30.4 4 75.7 52 4.93 1.62 18.5 1 1 #> 20 33.9 4 71.1 65 4.22 1.84 19.9 1 1 #> 21 21.5 4 120. 97 3.7 2.46 20.0 1 0 #> 22 15.5 8 318 150 2.76 3.52 16.9 0 0 #> 23 15.2 8 304 150 3.15 3.44 17.3 0 0 #> 24 13.3 8 350 245 3.73 3.84 15.4 0 0 #> 25 19.2 8 400 175 3.08 3.84 17.0 0 0 #> # … with 7 more rows, and 13 more variables: gear...10 <dbl>, carb...11 <dbl>, #> # mpg...12 <dbl>, …
trunc_mat(mtcars)
#> # Description: df[,11] [32 × 11] #> mpg cyl disp hp drat wt qsec vs am gear carb #> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # … with 22 more rows
print(nycflights13::flights, n_extra = 2)
#> # A tibble: 336,776 x 19 #> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time #> <int> <int> <int> <int> <int> <dbl> <int> <int> #> 1 2013 1 1 517 515 2 830 819 #> 2 2013 1 1 533 529 4 850 830 #> 3 2013 1 1 542 540 2 923 850 #> 4 2013 1 1 544 545 -1 1004 1022 #> 5 2013 1 1 554 600 -6 812 837 #> 6 2013 1 1 554 558 -4 740 728 #> 7 2013 1 1 555 600 -5 913 854 #> 8 2013 1 1 557 600 -3 709 723 #> 9 2013 1 1 557 600 -3 838 846 #> 10 2013 1 1 558 600 -2 753 745 #> # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>, #> # carrier <chr>, …
print(nycflights13::flights, width = Inf)
#> # A tibble: 336,776 x 19 #> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time #> <int> <int> <int> <int> <int> <dbl> <int> <int> #> 1 2013 1 1 517 515 2 830 819 #> 2 2013 1 1 533 529 4 850 830 #> 3 2013 1 1 542 540 2 923 850 #> 4 2013 1 1 544 545 -1 1004 1022 #> 5 2013 1 1 554 600 -6 812 837 #> 6 2013 1 1 554 558 -4 740 728 #> 7 2013 1 1 555 600 -5 913 854 #> 8 2013 1 1 557 600 -3 709 723 #> 9 2013 1 1 557 600 -3 838 846 #> 10 2013 1 1 558 600 -2 753 745 #> arr_delay carrier flight tailnum origin dest air_time distance hour minute #> <dbl> <chr> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 11 UA 1545 N14228 EWR IAH 227 1400 5 15 #> 2 20 UA 1714 N24211 LGA IAH 227 1416 5 29 #> 3 33 AA 1141 N619AA JFK MIA 160 1089 5 40 #> 4 -18 B6 725 N804JB JFK BQN 183 1576 5 45 #> 5 -25 DL 461 N668DN LGA ATL 116 762 6 0 #> 6 12 UA 1696 N39463 EWR ORD 150 719 5 58 #> 7 19 B6 507 N516JB EWR FLL 158 1065 6 0 #> 8 -14 EV 5708 N829AS LGA IAD 53 229 6 0 #> 9 -8 B6 79 N593JB JFK MCO 140 944 6 0 #> 10 8 AA 301 N3ALAA LGA ORD 138 733 6 0 #> time_hour #> <dttm> #> 1 2013-01-01 05:00:00 #> 2 2013-01-01 05:00:00 #> 3 2013-01-01 05:00:00 #> 4 2013-01-01 05:00:00 #> 5 2013-01-01 06:00:00 #> 6 2013-01-01 05:00:00 #> 7 2013-01-01 06:00:00 #> 8 2013-01-01 06:00:00 #> 9 2013-01-01 06:00:00 #> 10 2013-01-01 06:00:00 #> # … with 336,766 more rows