Validate Data Frame Arguments — dis

This function runs standard unit tests on data frame parameters for functions.

Usage

dis_df(x, valid_class = c("data.frame", "tibble", "data.table"), null_valid = TRUE,
    param = NULL, call = NULL, fact_check = "global")

Arguments

x: Required object; a parameter argument to test.
valid_class: Required character vector; list of possible variations of data frames. If a given type is passed to valid_class, it will not trigger a validation error. As of this time, dis_df() supports tibble and data.table objects in addition to base R data frames. If an object class is omitted from this argument, objects of that class will result in a validation error.
null_valid: Required logical scalar; whether the parameter can be NULL. If FALSE, the function will throw an error if x is NULL. Default is TRUE.
param: Optional character scalar; the parameter name. If NULL (default), the function will attempt to determine the parameter name from the calling environment. If nesting functions, it is recommended to provide the parameter name to ensure the correct parameter is referenced using rlang::caller_arg().
call: Optional environment; the environment in which the function was called. If NULL (default), the function will attempt to determine the calling environment. If nesting functions, it is recommended to provide the calling environment to ensure the correct environment is referenced using rlang::caller_env().
fact_check: Required character scalar; whether to override fact checking environment setting. If "global" (default), dis_character will follow the global setting. If "always", dis_character will ignore any global setting and will always check x. This argument is primarily intended for Shiny developers who wish to use disputeR in modules. See the vignette on vignette("developing", package = "disputeR") for details on how to use this function.

Value

This function will return either TRUE (if x passes all validation checks) or FALSE (if the validation checks are skipped). If x fails validation checks, an error message will be returned. Note that, if the input is NULL and null_valid is set to TRUE, the detailed unit tests are skipped and the function will return TRUE.

Details

See the vignette on vignette("developing", package = "disputeR") for details about internal validation of arguments for this function.

Examples

# create example function that uses dis_df()
example <- function(x){

  ## check inputs with disputeR
  dis_not_missing(.f = rlang::is_missing(x))
  dis_df(x, valid_class = "data.frame")

  ## return output
  return(x)

}

# test example function
example(x = mtcars)
#>                      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