This is useful for benchmarking, but also for bug reports when you cannot share the real dataset.
gen_tbl(
rows,
cols = NULL,
col_types = NULL,
locale = default_locale(),
missing = 0
)Number of rows to generate
Number of columns to generate, if NULL this is derived from col_types.
One of NULL, a cols() specification, or
a string.
If NULL, all column types will be imputed from guess_max rows
on the input interspersed throughout the file. This is convenient (and
fast), but not robust. If the imputation fails, you'll need to increase
the guess_max or supply the correct types yourself.
Column specifications created by list() or cols() must contain
one column specification for each column. If you only want to read a
subset of the columns, use cols_only().
Alternatively, you can use a compact string representation where each character represents one column:
c = character
i = integer
n = number
d = double
l = logical
f = factor
D = date
T = date time
t = time
? = guess
_ or - = skip
By default, reading a file without a column specification will print a
message showing what readr guessed they were. To remove this message,
set show_col_types = FALSE or set options(readr.show_col_types = FALSE).
The locale controls defaults that vary from place to place.
The default locale is US-centric (like R), but you can use
locale() to create your own locale that controls things like
the default time zone, encoding, decimal mark, big mark, and day/month
names.
The percentage (from 0 to 1) of missing data to use
There is also a family of functions to generate individual vectors of each type.
generators to generate individual vectors.
# random 10 x 5 table with random column types
rand_tbl <- gen_tbl(10, 5)
rand_tbl
#> # A tibble: 10 × 5
#> X1 X2 X3 X4 X5
#> <dbl> <dbl> <date> <dttm> <time>
#> 1 0.971 0.629 2007-05-02 2016-08-02 20:23:17 22:44:46
#> 2 -1.01 2.07 2018-03-26 2005-01-31 12:32:49 13:01:10
#> 3 -0.0843 -1.63 2003-10-20 2015-04-09 08:48:39 13:04:13
#> 4 -0.554 0.512 2004-07-13 2002-04-22 09:41:19 06:41:10
#> 5 0.747 -1.86 2018-02-21 2008-02-01 11:32:09 10:43:15
#> 6 -0.935 -0.522 2003-07-19 2017-07-04 01:57:44 08:54:58
#> 7 -0.467 -0.0526 2016-06-19 2006-06-24 05:48:53 00:40:24
#> 8 -0.857 0.543 2012-09-15 2012-05-27 04:16:50 11:11:01
#> 9 -1.52 -0.914 2009-11-30 2007-09-19 10:16:09 09:21:38
#> 10 1.97 0.468 2006-09-01 2012-12-04 16:47:31 00:28:53
# all double 25 x 4 table
dbl_tbl <- gen_tbl(25, 4, col_types = "dddd")
dbl_tbl
#> # A tibble: 25 × 4
#> X1 X2 X3 X4
#> <dbl> <dbl> <dbl> <dbl>
#> 1 -0.313 -0.246 0.434 -0.442
#> 2 1.07 -1.18 -0.382 0.569
#> 3 0.0700 -0.976 0.424 2.13
#> 4 -0.639 1.07 1.06 0.425
#> 5 -0.0500 0.132 1.05 -1.68
#> 6 -0.251 0.489 -0.0381 0.249
#> 7 0.445 -1.70 0.486 1.07
#> 8 2.76 -1.47 1.67 2.04
#> 9 0.0465 0.284 -0.354 0.449
#> 10 0.578 1.34 0.946 1.39
#> # ℹ 15 more rows
# Use the dots in long form column types to change the random function and options
types <- rep(times = 4, list(col_double(f = stats::runif, min = -10, max = 25)))
types
#> [[1]]
#> <collector_double>
#>
#> [[2]]
#> <collector_double>
#>
#> [[3]]
#> <collector_double>
#>
#> [[4]]
#> <collector_double>
#>
dbl_tbl2 <- gen_tbl(25, 4, col_types = types)
dbl_tbl2
#> # A tibble: 25 × 4
#> X1 X2 X3 X4
#> <dbl> <dbl> <dbl> <dbl>
#> 1 18.5 -2.86 11.1 16.0
#> 2 -7.52 9.20 18.2 1.12
#> 3 8.44 19.0 -8.72 -6.09
#> 4 16.7 -5.54 15.7 -6.46
#> 5 5.24 -0.858 -2.47 18.0
#> 6 9.34 0.0946 -9.44 3.30
#> 7 -2.86 -9.48 -5.50 -8.16
#> 8 -8.91 19.9 14.0 24.5
#> 9 23.9 4.49 12.5 11.1
#> 10 -3.75 16.3 1.46 -4.79
#> # ℹ 15 more rows