Printing tibbles — formatting (2024)

Source: R/print.R

formatting.Rd

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 aboutwhether a variable is, e.g., <chr> or <fct> (character versus factor).See vignette("types") for an overview of commontype abbreviations.

Printing can be tweaked for a one-off call by calling print() explicitlyand setting arguments like n and width. More persistent control isavailable by setting the options described in pillar::pillar_options.See also vignette("digits") for a comparison to base options,and vignette("numbers") that showcases num() and char()for creating columns with custom formatting options.

As of tibble 3.1.0, printing is handled entirely by the pillar package.If you implement a package that extends tibble,the printed output can be customized in various ways.See vignette("extending", package = "pillar") for details,and pillar::pillar_options for options that control the display in the console.

Usage

# S3 method for tbl_dfprint( x, width = NULL, ..., n = NULL, max_extra_cols = NULL, max_footer_lines = NULL)# S3 method for tbl_dfformat( x, width = NULL, ..., n = NULL, max_extra_cols = NULL, max_footer_lines = NULL)

Arguments

x: Object to format or print.
width: Width of text output to generate. This defaults to NULL, whichmeans use the width option.
...: Passed on to tbl_format_setup().
n: Number of rows to show. If NULL, the default, will print all rowsif less than the print_max option.Otherwise, will print as many rows as specified by theprint_min option.
max_extra_cols: Number of extra columns to print abbreviated information for,if the width is too small for the entire tibble. If NULL,the max_extra_cols option is used.The previously defined n_extra argument is soft-deprecated.
max_footer_lines: Maximum number of footer lines. If NULL,the max_footer_lines option is used.

Examples

print(as_tibble(mtcars))#> # A tibble: 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 rowsprint(as_tibble(mtcars), n = 1)#> # A tibble: 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#> # … with 31 more rowsprint(as_tibble(mtcars), n = 3)#> # A tibble: 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#> # … with 29 more rowsprint(as_tibble(trees), n = 100)#> # A tibble: 31 × 3#> Girth Height Volume#> <dbl> <dbl> <dbl>#>  1 8.3 70 10.3#>  2 8.6 65 10.3#>  3 8.8 63 10.2#>  4 10.5 72 16.4#>  5 10.7 81 18.8#>  6 10.8 83 19.7#>  7 11 66 15.6#>  8 11 75 18.2#>  9 11.1 80 22.6#> 10 11.2 75 19.9#> 11 11.3 79 24.2#> 12 11.4 76 21 #> 13 11.4 76 21.4#> 14 11.7 69 21.3#> 15 12 75 19.1#> 16 12.9 74 22.2#> 17 12.9 85 33.8#> 18 13.3 86 27.4#> 19 13.7 71 25.7#> 20 13.8 64 24.9#> 21 14 78 34.5#> 22 14.2 80 31.7#> 23 14.5 74 36.3#> 24 16 72 38.3#> 25 16.3 77 42.6#> 26 17.3 81 55.4#> 27 17.5 82 55.7#> 28 17.9 80 58.3#> 29 18 80 51.5#> 30 18 80 51 #> 31 20.6 87 77 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 2mtcars2 <- 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`#> • `wt` -> `wt...6`#> • `qsec` -> `qsec...7`#> • `vs` -> `vs...8`#> • `am` -> `am...9`#> • `gear` -> `gear...10`#> • `carb` -> `carb...11`#> • `mpg` -> `mpg...12`#> • `cyl` -> `cyl...13`#> • `disp` -> `disp...14`#> • `hp` -> `hp...15`#> • `drat` -> `drat...16`#> • `wt` -> `wt...17`#> • `qsec` -> `qsec...18`#> • `vs` -> `vs...19`#> • `am` -> `am...20`#> • `gear` -> `gear...21`#> • `carb` -> `carb...22`print(mtcars2, n = 25, max_extra_cols = 3)#> # A tibble: 32 × 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>, …print(nycflights13::flights, max_footer_lines = 1)#> # A tibble: 336,776 × 19#> year month day dep_time sched_dep_…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵#> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl>#>  1 2013 1 1 517 515 2 830 819 11#>  2 2013 1 1 533 529 4 850 830 20#>  3 2013 1 1 542 540 2 923 850 33#>  4 2013 1 1 544 545 -1 1004 1022 -18#>  5 2013 1 1 554 600 -6 812 837 -25#>  6 2013 1 1 554 558 -4 740 728 12#>  7 2013 1 1 555 600 -5 913 854 19#>  8 2013 1 1 557 600 -3 709 723 -14#>  9 2013 1 1 557 600 -3 838 846 -8#> 10 2013 1 1 558 600 -2 753 745 8#> # … with 336,766 more rows, 10 more variables: carrier <chr>, …print(nycflights13::flights, width = Inf)#> # A tibble: 336,776 × 19#> year month day dep_time sched_dep_time dep_delay arr_time#> <int> <int> <int> <int> <int> <dbl> <int>#>  1 2013 1 1 517 515 2 830#>  2 2013 1 1 533 529 4 850#>  3 2013 1 1 542 540 2 923#>  4 2013 1 1 544 545 -1 1004#>  5 2013 1 1 554 600 -6 812#>  6 2013 1 1 554 558 -4 740#>  7 2013 1 1 555 600 -5 913#>  8 2013 1 1 557 600 -3 709#>  9 2013 1 1 557 600 -3 838#> 10 2013 1 1 558 600 -2 753#> sched_arr_time arr_delay carrier flight tailnum origin dest air_time#> <int> <dbl> <chr> <int> <chr> <chr> <chr> <dbl>#>  1 819 11 UA 1545 N14228 EWR IAH 227#>  2 830 20 UA 1714 N24211 LGA IAH 227#>  3 850 33 AA 1141 N619AA JFK MIA 160#>  4 1022 -18 B6 725 N804JB JFK BQN 183#>  5 837 -25 DL 461 N668DN LGA ATL 116#>  6 728 12 UA 1696 N39463 EWR ORD 150#>  7 854 19 B6 507 N516JB EWR FLL 158#>  8 723 -14 EV 5708 N829AS LGA IAD 53#>  9 846 -8 B6 79 N593JB JFK MCO 140#> 10 745 8 AA 301 N3ALAA LGA ORD 138#> distance hour minute time_hour #> <dbl> <dbl> <dbl> <dttm> #>  1 1400 5 15 2013-01-01 05:00:00#>  2 1416 5 29 2013-01-01 05:00:00#>  3 1089 5 40 2013-01-01 05:00:00#>  4 1576 5 45 2013-01-01 05:00:00#>  5 762 6 0 2013-01-01 06:00:00#>  6 719 5 58 2013-01-01 05:00:00#>  7 1065 6 0 2013-01-01 06:00:00#>  8 229 6 0 2013-01-01 06:00:00#>  9 944 6 0 2013-01-01 06:00:00#> 10 733 6 0 2013-01-01 06:00:00#> # … with 336,766 more rows