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Affirm Class

Source: R/affirm_class.R, R/affirm_na.R
affirm_na.Rd

A wrapper for affirm_true(). Reports columns that do not inherit class, e.g. select(data, all_of(columns) && where(\(x) !inherits(x, class)))

A wrapper for affirm_true(). The columns argument is used to construct the affirm_true(condition = is.na(column)) argument.

Usage

affirm_class(
  data,
  label,
  columns,
  class,
  id = NA_integer_,
  priority = NA_integer_,
  data_frames = NA_character_,
  report_listing = NULL,
  data_action = NULL,
  error = getOption("affirm.error", default = FALSE)
)

affirm_na(
  data,
  label,
  column,
  id = NA_integer_,
  priority = NA_integer_,
  data_frames = NA_character_,
  report_listing = NULL,
  data_action = NULL,
  error = getOption("affirm.error", default = FALSE)
)

affirm_not_na(
  data,
  label,
  column,
  id = NA_integer_,
  priority = NA_integer_,
  data_frames = NA_character_,
  report_listing = NULL,
  data_action = NULL,
  error = getOption("affirm.error", default = FALSE)
)

Arguments

data

a data frame

label

a string used to describe the affirmation

columns

columns to check class

class

character class to affirm

id, priority, data_frames

Optional additional information that will be passed to affirmation report.

  • id must be an integer, e.g. id = 1L

  • priority must be an integer, e.g. priority = 1L

  • data_frames string of data frame names used in affirmation, e.g. data_frames = "RAND, DM"

report_listing

an expression selecting/filtering rows from data= to return in the issue listing report. The default is to return the result from create_report_listing(), which are the rows that do not met in condition= and columns included in the condition= expression along with any columns set in option('affirm.id_cols'). The 'affirm.id_cols' option must be a character vector of column names, where columns will be selected with select(any_of(getOption('affirm.id_cols'))).

data_action

this expression is executed at the end of the function call when supplied.

  • Default is NULL, and the passed data frame in data= is returned unaltered.

  • Perhaps you'll need to remove problematic rows: data_action = filter(., !(!!condition))

error

Logical indicating whether to throw an error when condition is not met. Default is FALSE.

column

column to check NA values against

Value

data frame

data frame

See also

Other Data Affirmations: affirm_false(), affirm_no_dupes(), affirm_range(), affirm_true(), affirm_values()

Other Data Affirmations: affirm_false(), affirm_no_dupes(), affirm_range(), affirm_true(), affirm_values()

Examples

affirm_init(replace = TRUE)
#> ✔ We're ready to make data affirmations...

affirm_class(
  as_tibble(iris),
  label = "all cols are numeric (but Species really isn't)",
  columns = everything(),
  class = "numeric"
)
#> • all cols are numeric (but Species really isn't)
#>   1 issue identified.
#> # A tibble: 150 × 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 
#> # ℹ 140 more rows

affirm_close()
affirm_init(replace = TRUE)
#> ✔ We're ready to make data affirmations...