Package: cataScience 2.1.4

cataScience: Interactive Training App for Data Science and AI Skills

An interactive 'shiny' training companion for people who are new to data, developed for World Health Organization data trainings. Bundles hands-on modules for importing data, missing values, outliers, text cleaning, merging, visualization, and basic statistics, plus a set of AI-skills pages (prompting levels, a prompt gallery, AI safety rules, and a methodology case study) and an interactive quiz with per-session topic filters. Launch the app with run_cata().

Authors:Shanlong Ding [aut, cre]

cataScience_2.1.4.tar.gz
cataScience_2.1.4.zip(r-4.7-any)cataScience_2.1.4.zip(r-4.6-any)cataScience_2.1.4.zip(r-4.5-any)
cataScience_2.1.4.tgz(r-4.6-any)cataScience_2.1.4.tgz(r-4.5-any)
cataScience_2.1.4.tar.gz(r-4.7-any)cataScience_2.1.4.tar.gz(r-4.6-any)
cataScience_2.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cataScience/json (API)

# Install 'cataScience' in R:
install.packages('cataScience', repos = c('https://shanlong-who.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/shanlong-who/catascience/issues

Pkgdown/docs site:https://shanlong-who.github.io

On CRAN:

Conda:

5.43 score 3 stars 362 downloads 1 exports 146 dependencies

Last updated from:928930b7ed. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK271
source / vignettesOK263
linux-releaseOK209
macos-releaseOK172
macos-oldrelOK154
windows-develOK156
windows-releaseOK162
windows-oldrelOK161
wasm-releaseOK144

Exports:run_cata

Dependencies:askpassbackportsbase64encbitbit64bootbroombslibcachemcellrangerclassclicliprcodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tableDEoptimRdigestdplyrDTe1071evaluatefarverfastmapfontawesomeforcatsforeachforeignfsgenericsggplot2glmnetgluegridExtragtablehavenhighrhmshtmltoolshtmlwidgetshttpuvhttrisobanditeratorsjomojquerylibjsonliteknitrlabelinglaekenlaterlatticelazyevallifecyclelitedownlme4lmtestmagrittrmarkdownMASSMatrixmemoisemicemimeminqamitmlnaniarnlmenloptrnnetnormnumDerivopensslordinalotelpanpillarpkgconfigplotlyplyrprettyunitsprogresspromisesproxypurrrR.methodsS3R.ooR.utilsR6rangerrappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreadrreadxlreformulasrematchreprriorlangrmarkdownrobustbaserpartS7sassscalesshapeshinyskimrsourcetoolsspstringistringrsurvivalsystibbletidyrtidyselecttinytextzdbucminfUpSetRutf8vcdvctrsVIMviridisviridisLitevisdatvroomwithrwritexlxfunxgboostxtableyamlzoo

A guided data-quality workflow
1. Import and inspect | 2. Compare missing-data choices | 3. Investigate outliers | 4. Standardize text labels | 5. Check keys before merging | 6. Visualize a decision | 7. Debrief and test understanding
Rendered fromdata-quality-workflow.Rmdusingknitr::rmarkdown

Last update: 2026-10-06
Started: 2026-10-06

Getting started with cataScience
Install and launch | Your first learning session | The module map | Work with your own files | Use the AI pages | Common questions
Rendered fromcataScience.Rmdusingknitr::rmarkdown

Last update: 2026-10-06
Started: 2026-10-06

Planning a data science and AI training
Match the modules to a learning goal | A three-hour data science session | A two-hour AI session | Prepare the room and computers | Facilitate a 20–30 minute lab | Practise a prompt with explicit checks | Critique an AI-generated analysis | Use the quiz to support discussion | Close with an analysis record
Rendered fromtraining-guide.Rmdusingknitr::rmarkdown

Last update: 2026-10-06
Started: 2026-10-06