{'fraction_non_alphanumeric': 0.011363636363636364, 'fraction_numerical': 0.0011363636363636363, 'mean_word_length': 4.796052631578948, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Creating Python Algorithms to Mimic R Functions from the R Stats Package

While R and Python are the most used languages within Data Analytics, the differences between them are becoming less polarized as it is now possible to complete the same tasks using both. A Python library developed from analyzing original R source code would help users cross-reference between both platforms. To design the Python algorithms, pseudocode was produced from registering the source code of different R stats functions. A Python test case was generated that would input result textfiles from both scripts and strive to output 0 differences to check for compatibility. So far, several R functions were able to be mimicked and there is still continued work being done to expand the scripts. We hope to later integrate the R and Python scripts into a web application to better display the results.