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 print(x, ..., n = NULL, width = NULL, n_extra = NULL) # S3 method for tbl 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 |
| width | Width of text output to generate. This defaults to |
| n_extra | Number of extra columns to print abbreviated information for,
if the width is too small for the entire tibble. If |
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 toInfto 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(usewidthoption).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 toFALSE, 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 thepillar.subtleoption.pillar.neg: Highlight negative numbers? Default:TRUE.pillar.sigfig: The number of significant digits that will be printed and highlighted, default:3. Set thepillar.subtleoption toFALSEto 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 toInfto turn off truncation of column titles.
Examples
#> # 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#> # 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#> # 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#> # 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#> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2#> #> #> #> #> #> #>#> # 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 rowsif (requireNamespace("nycflights13", quietly = TRUE)) { print(nycflights13::flights, n_extra = 2) 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 #> # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>, #> # carrier <chr>, … #> # 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