for assessment, summary and visualization, the character columns in dataset are put to lower to avoid duplicated informations in outputs. https://github.com/maelstrom-research/madshapR/issues/63
bug in the function variable_visualize() when the column was empty after removing internally stopwords. https://github.com/maelstrom-research/Rmonize/issues/53 https://github.com/maelstrom-research/Rmonize/issues/49
Some elements were missing in the reports in dataset_evaluate() https://github.com/maelstrom-research/madshapR/issues/66
Problem with names containing underscores in variables when visualized fixed. https://github.com/maelstrom-research/madshapR/issues/62
Functions involving valueType (such as data_dict_apply(),valueType_guess() and valueType_adujst()) have been corrected to be more consistent in the usage of these functions. https://github.com/maelstrom-research/madshapR/issues/61
The bug affecting tibbles which contain a variable named “test” has been corrected in the package fabR. https://github.com/maelstrom-research/madshapR/issues/60
functions such as data_dict_summarize() and dataset_evaluate() have cells in tibble generated that can have more than accepted characters in a cell in Excel. the function truncates the cells in tibbles to a maximum of 10000 characters. https://github.com/maelstrom-research/madshapR/issues/59
Problem with dataType in the function dataset_cat_as_labels() when the values found in the dataset are not in the data dictionary, and the valueType is text, and the dataType is “integer” has been fixed. https://github.com/maelstrom-research/madshapR/issues/58
Functions involving date formatted variables have been corrected in the package fabR. https://github.com/maelstrom-research/madshapR/issues/57
The inconsistent error in dataset_evaluate() has been corrected in the package fabR. https://github.com/maelstrom-research/madshapR/issues/46
To avoid confusion with help(function), the function madshapR_help() has been renamed madshapR_website().
Some of the tests were made with another package (Rmonize) which as “madshapR” as a dependence.
in visual reports, void confusing changes in color scheme in visual reports.
Histograms for date variables display valid ranges.
in reports, change % NA as proportion in reports.
dossier_visualize() report shows variable labels in the same lang.
in visual reports, the bar plot only appears when there are multiple missing value types, otherwise only the pie chart is shown.
in reports, all of the percentages are now included under “Other values (non categorical)”, which gives a single value.
suppress overwrite parameter in dataset_visualize().
in dataset_summary() minor issue (consistency in column names and content).
variable_visualize() when valueType_guess = TRUEenhance the function check_data_dict_valueType(), which was too slow.
valueType_adjust() now works with empty column (all NAs)
col_id() function which is a short cut for calling the attribute madshapR::col_id of a dataset.
as_category(),is_category(),drop_category() function which coerces a vector as a categorical object. Typically a column in a dataset that needs to be coerced into a categorical variable (The data dictionary is updated accordingly).
DEMO_files into madshapR_DEMO for consistency across our other packages.Addition of NEWS.md for the development version use “(development version)”.
Some improvements in the documentation of the package has been made.
internal call of libraries (using ::) has been replaced by proper import in the declaration function.
get functions in fabR have been changed in its last release. the functions using them as dependencies ( check_xxx()) have been updated accordingly.
DEMO files no longer include harmonization files that are now in the package harmonizR
New Imports: haven, lifecycle
No longer in Imports: xfun
These functions are imported from fabR
bookdown_template() replaces the deprecated function bookdown_template().
bookdown_render() which renders a Rmd collection of files into a docs/index.html website.
bookdown_open() Which allows to open a docs/index.html document when the bookdown is rendered
This separation into 3 functions will allow future developments, such as render as a ppt or pdf.
Due to another package development (see fabR), The function open_visual_report() has been deprecated in favor of bookdown_open() imported from fabR package.
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.
madshapR_help() Call the help center for full documentationThese 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()
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()
data_extract(), dossier_create(), dataset_zap_data_dict(), dataset_cat_as_labels()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()
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()
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()
read_csv_any_formats() The csv file is read twice to detect the number of lines to use in attributing the column type (guess_max parameter of read_csv). This avoids common errors when reading csv files.
read_excel_allsheets() The Excel file is read and the values are placed in a list of tibbles, with each sheet in a separate element in the list. If the Excel file has only one sheet, the output is a single tibble.
write_excel_allsheets() Write all Excel sheets using xlsx::write.xlsx() recursively.
plot_bar(), plot_box(), plot_date(), plot_density(), plot_histogram(), plot_main_word(), plot_pie_valid_value(), summary_category(), summary_numerical(),summary_text()
data_dict_evaluate() dataset_evaluate() dossier_evaluate()
dataset_summarize() dossier_summarize()
dataset_visualize() variable_visualize() open_visual_report()