madshapR 1.1.0

madshapR 1.1.0 (release : 2024-04-22)

Bug fixes and improvements

deprecated functions

To avoid confusion with help(function), the function madshapR_help() has been renamed madshapR_website().

Dependency changes

madshapR 1.0.3

Bug fixes and improvements

Some of the tests were made with another package (Rmonize) which as “madshapR” as a dependence.

Enhance reports

suppress overwrite parameter in dataset_visualize().

in dataset_summary() minor issue (consistency in column names and content).

Correct Data dictionary functions

enhance the function check_data_dict_valueType(), which was too slow.

valueType_adjust() now works with empty column (all NAs)

New functions

Deprecated functions

madshapR 1.0.2

Creation of NEWS feed !!

Addition of for the development version use “(development version)”.

Bug fixes and improvements

Dependency changes

New Imports: haven, lifecycle

No longer in Imports: xfun

New functions

These functions are imported from fabR

This separation into 3 functions will allow future developments, such as render as a ppt or pdf.

deprecated functions

Due to another package development (see fabR), The function open_visual_report() has been deprecated in favor of bookdown_open() imported from fabR package.

madshapR 1.0.0

This package is a collection of wrapper functions used in data pipelines.

This is still a work in progress, so please let us know if you used a function before and is not working any longer.

Helper functions

functions to generate, shape and format meta data.

These functions allows to create, extract transform and apply meta data to a dataset.

data_dict_collapse(),data_dict_expand(),data_dict_filter(), data_dict_group_by(),data_dict_group_split(),data_dict_list_nest(), data_dict_pivot_longer(),data_dict_pivot_wider(),data_dict_ungroup()

data_dict_match_dataset(),data_dict_apply(), data_dict_extract()

as_data_dict(), as_data_dict_mlstr(),as_data_dict_shape(), is_data_dict(), is_data_dict_mlstr(), is_data_dict_shape() as_taxonomy(), is_taxonomy()

functions to generate, shape and format data.

These functions allows to create, extract transform data/meta data from a dataset. A dossier is a list of datasets.

as_dataset(), as_dossier() is_dataset(), is_dossier()

Functions to work with data types

These functions allow user to work with, extract or assign data type (valueType) to values and/or dataset.

as_valueType(), is_valueType(), valueType_adjust(), valueType_guess(), valueType_self_adjust(), valueType_of()

Unit tests and QA for datasets and data dictionaries

These helper functions evaluate content of a dataset and/or data dictionary to extract from them irregularities or potential errors. These informations are stored in a tibble that can be use to assess inputs.

check_data_dict_categories(), check_data_dict_missing_categories(), check_data_dict_taxonomy(), check_data_dict_variables(), check_data_dict_valueType(), check_dataset_categories(), check_dataset_valueType(), check_dataset_variables(), check_name_standards()

Summarize information in dataset and data dictionaries

These helper functions evaluate content of a dataset and/or data dictionary to extract from them summary statistics and elements such as missing values, NA, category names, etc. These informations are stored in a tibble that can be use to summary inputs.

dataset_preprocess(), summary_variables(), summary_variables_categorical(),summary_variables_date(), summary_variables_numeric(),summary_variables_text()

Write and read excel and csv

Plot and summary functions used in a visual report

plot_bar(), plot_box(), plot_date(), plot_density(), plot_histogram(), plot_main_word(), plot_pie_valid_value(), summary_category(), summary_numerical(),summary_text()

aggregate information and generate reports

data_dict_evaluate() dataset_evaluate() dossier_evaluate()

dataset_summarize() dossier_summarize()

dataset_visualize() variable_visualize() open_visual_report()