R/07-data_summarize.R
dossier_summarize.RdAssesses and summarizes the content and structure of a dossier (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, and to summarize additional information about variable distributions and descriptive statistics.
dossier_summarize(
dossier,
group_by = NULL,
taxonomy = NULL,
valueType_guess = FALSE
)List of data frame(s), each of them being datasets.
A character string identifying the column in the dataset to use as a grouping variable. Elements will be grouped by this column.
An optional data frame identifying a variable classification schema.
Whether the output should include a more accurate valueType that could be applied to the dataset. FALSE by default.
A list of data frames containing overall assessment reports and summaries grouped by dataset.
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 valueType is a declared property of a variable that is required in certain functions to determine handling of the variables. Specifically, valueType refers to the OBiBa data type of a variable. The valueType is specified in a data dictionary in a column 'valueType' and can be associated with variables as attributes. Acceptable valueTypes include 'text', 'integer', 'decimal', 'boolean', datetime', 'date'. The full list of OBiBa valueType possibilities and their correspondence with R data types are available using valueType_list. The valueType can be used to coerce the variable to the corresponding data type.
{
# use madshapR_DEMO provided by the package
library(dplyr)
###### Example 1: Combine functions and summarize datasets.
dossier <- list(iris = tibble())
dossier_summary <- dossier_summarize(dossier)
glimpse(dossier_summary)
}
#> - DOSSIER SUMMARY: -----------------------------------------------------
#> - DATA DICTIONARY ASSESSMENT: data_dict --------------
#> Assess the standard adequacy of naming
#> 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
#> Assess the completion of `label(:xx)` column in 'Variables'
#> Assess the `valueType` column in 'Variables'
#> Generate report
#>
#> The data dictionary contains no error/warning.
#>
#> - WARNING MESSAGES (if any): --------------------------------------------
#>
#> - DATASET ASSESSMENT: iris (empty dataset) --------------------------
#> Assess the standard adequacy of naming
#> 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): -------------------------------------------------
#>
#> - DATASET SUMMARIZE: iris (empty dataset) --------------------------
#> Summarize the data type of each variable across the dataset
#> Summarize global information (Overview)
#> Generate report
#> List of 1
#> $ iris:List of 1
#> ..$ Overview: tibble [15 × 2] (S3: tbl_df/tbl/data.frame)