Provide descriptive statistics for variables in a dataset

Source: R/07-data_summarize.R

Summarizes (in a data frame) the columns in a dataset and its data dictionary (if any). The summary provides information about quality, type, composition, and descriptive statistics of variables. Statistics are generated by valueType.

summary_variables(
  dataset_preprocess,
  dataset = NULL,
  data_dict = NULL,
  group_by = NULL
)

Arguments

dataset_preprocess

A data frame which provides summary of the variables (used for internal processes and programming).

dataset

A dataset object.

data_dict

A list of data frame(s) representing metadata of the input dataset. Automatically generated if not provided.

group_by

A character string identifying the column in the dataset to use as a grouping variable. Elements will be grouped by this column.

Value

A data frame providing statistical description of variables present in a dataset.

Details

A data dictionary contains the list of variables in a dataset and metadata about the variables and can be associated with a dataset. A data dictionary object is a list of data frame(s) named 'Variables' (required) and 'Categories' (if any). To be usable in any function, the data frame 'Variables' must contain at least the name column, with all unique and non-missing entries, and the data frame 'Categories' must contain at least the variable and name columns, with unique combination of variable and name.

A dataset is a data table containing variables. A dataset object is a data frame and can be associated with a data dictionary. If no data dictionary is provided with a dataset, a minimum workable data dictionary will be generated as needed within relevant functions. Identifier variable(s) for indexing can be specified by the user. The id values must be non-missing and will be used in functions that require it. If no identifier variable is specified, indexing is handled automatically by the function.

Examples

{

# use madshapR_examples provided by the package
dataset <- madshapR_examples$`dataset_example`
dataset_preprocess <- dataset_preprocess(dataset)
summary_prep <- summary_variables(dataset_preprocess = dataset_preprocess)
head(summary_prep)

}
#> $`(all)`
#> # A tibble: 9 × 12
#>   `Variable name` Quality assessment c…¹ `Categorical variable` `Variable class`
#>   <chr>           <chr>                  <chr>                  <chr>           
#> 1 part_id         [INFO] - All rows are… no                     no              
#> 2 gndr            NA                     no                     no              
#> 3 height          NA                     no                     no              
#> 4 weight_ms       NA                     no                     no              
#> 5 weight_dc       NA                     no                     no              
#> 6 dob             NA                     no                     no              
#> 7 prg_ever        NA                     no                     no              
#> 8 empty           [INFO] - Empty variab… no                     no              
#> 9 opentext        NA                     no                     no              
#> # ℹ abbreviated name: ¹​`Quality assessment comment`
#> # ℹ 8 more variables: `Number of rows` <dbl>, `Number of valid values` <dbl>,
#> #   `Number of non-valid values` <dbl>, `Number of empty values` <dbl>,
#> #   `% Valid values` <dbl>, `% Non-valid values` <dbl>, `% Empty values` <dbl>,
#> #   `Number of distinct values` <dbl>
#>