Package: madshapR 2.0.0

Guillaume Fabre

madshapR: Functions to Support Data Management and Processing Using the Maelstrom Research Approach

Functions to support data cleaning, evaluation, and description, developed for integration with Maelstrom Research software tools. 'madshapR' provides functions primarily to evaluate and manipulate datasets and data dictionaries in preparation for data harmonization with the package 'Rmonize' and to facilitate integration and transfer between RStudio servers and secure Opal environments. 'madshapR' functions can be used independently but are optimized in conjunction with ‘Rmonize’ functions for streamlined and coherent harmonization processing.

Authors:Guillaume Fabre [aut, cre], Maelstrom Research [aut, fnd, cph]

madshapR_2.0.0.tar.gz
madshapR_2.0.0.zip(r-4.7)madshapR_2.0.0.zip(r-4.6)madshapR_2.0.0.zip(r-4.5)
madshapR_2.0.0.tgz(r-4.6-any)madshapR_2.0.0.tgz(r-4.5-any)
madshapR_2.0.0.tar.gz(r-4.7-any)madshapR_2.0.0.tar.gz(r-4.6-any)
madshapR_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
madshapR/json (API)

# Install 'madshapR' in R:

install.packages('madshapR', repos = c('https://maelstrom-research.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/maelstrom-research/madshapr/issues

Datasets:

color_palette_maelstrom - Built-in data frame of colors used in the graphs and charts.
madshapR_examples - Built-in material allowing the user to test the package with example data
valueType_list - Built-in data frame of allowed valueType values

On CRAN:

5.45 score 2 stars 3 packages 31 scripts 274 downloads 70 exports 95 dependencies

Last updated from:73bd89c8f7. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	201
source / vignettes	OK	200
linux-release-x86_64	OK	207
macos-release-arm64	OK	189
macos-oldrel-arm64	OK	246
windows-devel	OK	183
windows-release	OK	182
windows-oldrel	OK	151
wasm-release	OK	121

Exports:as_category as_data_dict as_data_dict_mlstr as_data_dict_shape as_dataset as_dossier as_taxonomy as_valueType bookdown_open bookdown_render bookdown_template check_data_dict_categories check_data_dict_missing_categories check_data_dict_valueType check_data_dict_variables check_dataset_categories check_dataset_valueType check_dataset_variables check_name_standards col_id data_dict_apply data_dict_collapse data_dict_evaluate data_dict_expand data_dict_extract data_dict_filter data_dict_group_by data_dict_group_split data_dict_list_nest data_dict_match_dataset data_dict_pivot_longer data_dict_pivot_wider data_dict_trim_labels data_dict_ungroup data_dict_update data_extract dataset_cat_as_labels dataset_evaluate dataset_preprocess dataset_summarize dataset_visualize dataset_zap_data_dict dossier_create dossier_evaluate dossier_summarize drop_category first_label_get has_categories is_category is_data_dict is_data_dict_mlstr is_data_dict_shape is_dataset is_dossier is_taxonomy is_valueType madshapR_website summary_variables summary_variables_categorical summary_variables_date summary_variables_datetime summary_variables_numeric summary_variables_text typeof_convert_to_valueType valueType_adjust valueType_convert_to_typeof valueType_guess valueType_of valueType_self_adjust variable_visualize

Dependencies:askpass base64enc bit bit64 bookdown bslib cachem cellranger cli clipr cpp11 crayon credentials crosstalk curl desc digest dplyr DT evaluate fabR farver fastmap fontawesome forcats fs generics gert ggplot2 gitcreds glue gtable haven highr hms htmltools htmlwidgets httr2 ini isoband janitor jquerylib jsonlite knitr labeling later lazyeval lifecycle lubridate magrittr memoise mime openssl otel pillar pkgconfig prettyunits progress promises purrr R6 rappdirs RColorBrewer Rcpp readr readxl rematch rlang rmarkdown rprojroot rstudioapi S7 sass scales snakecase stringi stringr sys tibble tidyr tidyselect timechange tinytex tzdb usethis utf8 vctrs viridisLite vroom whisker withr writexl xfun yaml zip

madshapR-vignette

madshapR | Get started | Install the package

Rendered frommadshapR-vignette.Rmdusingknitr::rmarkdown

Last update: 2025-07-25
Started: 2023-03-13

Help page	Topics
Validate and coerce any object as a categorical variable.	as_category
Validate and coerce any object as a data dictionary	as_data_dict
Validate and coerce any object as an Opal data dictionary format	as_data_dict_mlstr
Validate and coerce any object as a workable data dictionary structure	as_data_dict_shape
Validate and coerce any object as a dataset	as_dataset
Validate and coerce any object as a dossier (list of dataset(s))	as_dossier
Validate and coerce any object as a taxonomy	as_taxonomy
Validate and coerce any object according to a given valueType	as_valueType
Objects exported from other packages	bookdown_open
Objects exported from other packages	bookdown_render
Objects exported from other packages	bookdown_template
Assess a data dictionary for potential issues in categories	check_data_dict_categories
Assess categorical variables for non-Boolean values in 'missing' column	check_data_dict_missing_categories
Assess a data dictionary for non-valid valueType values	check_data_dict_valueType
Assess a data dictionary for potential issues in variables	check_data_dict_variables
Assess a data dictionary and associated dataset for category differences	check_dataset_categories
Assess a data dictionary and associated dataset for valueType differences	check_dataset_valueType
Assess a data dictionary and associated dataset for undeclared variables	check_dataset_variables
Assess variable names in a data dictionary for non-standard formats	check_name_standards
Return the id column names(s) of a dataset	col_id
Built-in data frame of colors used in the graphs and charts.	color_palette_maelstrom
Apply a data dictionary to a dataset	data_dict_apply
Transform multi-row category column(s) to single rows and join to "Variables"	data_dict_collapse
Generate an assessment report for a data dictionary	data_dict_evaluate
Transform single-row category information to multiple rows as element	data_dict_expand
Generate a data dictionary from a dataset	data_dict_extract
Subset data dictionary by row values	data_dict_filter
Group listed data dictionaries by specified column names	data_dict_group_by
Split grouped data dictionaries into a named list	data_dict_group_split
Bind listed data dictionaries	data_dict_list_nest
Inner join between a dataset and its associated data dictionary	data_dict_match_dataset
Transform column(s) of a data dictionary from wide format to long format	data_dict_pivot_longer
Transform column(s) of a data dictionary from long format to wide format	data_dict_pivot_wider
Add shortened labels to data dictionary	data_dict_trim_labels
Ungroup data dictionary	data_dict_ungroup
Update a data dictionary from a dataset	data_dict_update
Create an empty dataset from a data dictionary	data_extract
Apply data dictionary category labels to the associated dataset variables	dataset_cat_as_labels
Generate an assessment report for a dataset	dataset_evaluate
Generate an evaluation of all variable values in a dataset	dataset_preprocess
Generate an assessment report and summary of a dataset	dataset_summarize
Generate a web-based visual report for a dataset	dataset_visualize
Remove labels (attributes) from a data frame, leaving its unlabelled columns	dataset_zap_data_dict
Generate a dossier from a list of one or more datasets	dossier_create
Generate an assessment report of a dossier	dossier_evaluate
Generate an assessment report and summary of a dossier	dossier_summarize
Validate and coerce any object as a non-categorical variable.	drop_category
Get the first label from a data dictionary	first_label_get
Test if an object has categorical variables.	has_categories
Test and validate if an object is a categorical variable.	is_category
Test if an object is a valid data dictionary	is_data_dict
Test if an object is a valid Maelstrom data dictionary	is_data_dict_mlstr
Test if an object is a workable data dictionary structure	is_data_dict_shape
Test if an object is a valid dataset	is_dataset
Test if an object is a valid dossier (list of dataset(s))	is_dossier
Test if an object is a valid taxonomy	is_taxonomy
Test if a character object is one of the valid valueType values	is_valueType
Built-in material allowing the user to test the package with example data	madshapR_examples
Call to online documentation	madshapR_website
Provide descriptive statistics for variables in a dataset	summary_variables
Provide descriptive statistics for variables of categorical in a dataset	summary_variables_categorical
Provide descriptive statistics for variables of type 'date' in a dataset	summary_variables_date
Provide descriptive statistics for variables of type 'datetime' in a dataset	summary_variables_datetime
Provide descriptive statistics for variables of type 'numeric' in a dataset	summary_variables_numeric
Provide descriptive statistics for variables of type 'text' in a dataset	summary_variables_text
Convert typeof (and class if any) into its corresponding valueType	typeof_convert_to_valueType
Attribute the valueType from a data dictionary to a dataset, or vice versa	valueType_adjust
Convert valueType into its corresponding typeof and class in R representation	valueType_convert_to_typeof
Guess the first possible valueType of an object (Can be a vector)	valueType_guess
Built-in data frame of allowed valueType values	valueType_list
Return the valueType of an object	valueType_of
Self-adjust the valueType from a data dictionary or a dataset.	valueType_self_adjust
Generate a list of charts, figures and summary tables of a variable	variable_visualize

Package: madshapR 2.0.0

madshapR: Functions to Support Data Management and Processing Using the Maelstrom Research Approach

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