Assesses the content and structure of a dossier object (list of datasets) and generates reports of the results. This function can be used to evaluate data structure, presence of specific fields, coherence across elements, and data dictionary formats.
dossier_evaluate(dossier, taxonomy = NULL, as_data_dict_mlstr = TRUE)List of data frame, each of them being datasets.
An optional data frame identifying a variable classification schema.
Whether the input data dictionary should be coerced with specific format restrictions for compatibility with other Maelstrom Research software. TRUE by default.
A list of data frames containing assessment reports.
A dossier is a named list containing at least one data frame or more, each of them being datasets. The name of each data frame will be use as the reference name of the dataset.
A taxonomy is a classification schema that can be defined for variable
attributes. A taxonomy is usually extracted from an
Opal environment, and a
taxonomy object is a data frame that must contain at least the columns
taxonomy, vocabulary, and terms. Additional details about Opal
taxonomies are
available online.
The object may be specifically formatted to be compatible with additional Maelstrom Research software, in particular Opal environments.
{
# use madshapR_DEMO provided by the package
library(dplyr)
###### Example : a dataset list is a dossier by definition.
dataset <- as_dataset(
madshapR_DEMO$`dataset_TOKYO - errors with data`,
col_id = 'part_id') %>% slice(0)
dossier <- as_dossier(list(dataset = dataset))
glimpse(dossier_evaluate(dossier,as_data_dict_mlstr = FALSE))
}
#> - DOSSIER ASSESSMENT: ----------------------------------------------------
#> - DATA DICTIONARY ASSESSMENT: data_dict --------------
#> Assess the uniqueness of variable names
#> Assess the presence of possible duplicated columns
#> Assess the presence of empty rows in the data dictionary
#> Assess the presence of empty columns in the data dictionary
#> Generate report
#>
#> The data dictionary contains no error/warning.
#>
#> - WARNING MESSAGES (if any): --------------------------------------------
#>
#> - DATASET ASSESSMENT: dataset (empty dataset) --------------------------
#> Assess the presence of variable names both in dataset and data dictionary
#> Assess the presence of possible duplicated variable in the dataset
#> Assess the presence of duplicated participants in the dataset
#> Assess the presence of empty rows in the data dictionary
#> Assess the presence all NA(s) of columns in the data dictionary
#> Assess the presence of categories not in the data dictionary
#> Generate report
#>
#> The dataset contains no error/warning.
#>
#> - WARNING MESSAGES (if any): -------------------------------------------------
#>
#> List of 1
#> $ dataset:List of 1
#> ..$ Data dictionary summary: tibble [9 × 4] (S3: tbl_df/tbl/data.frame)