This book aims to teach the following:
Getting started with your own R Markdown document
Improve workflow:
With rstudio projects
Using keyboard shortcuts
Export your R Markdown document to PDF, HTML, and Microsoft Word
Better manage figures and tables
Reference figures and tables in text so that they dynamically update
Create captions for figures and tables
Change the size and type of figures
Save the figures to disk when creating an rmarkdown document
Work with equations
inline and display
caption equations
reference equations
Manage bibliographies
Cite articles in text
generate bibliographies
Change bibliography styles
Debug and handle common errors with rmarkdown
Next steps in working with rmarkdown - how to extend yourself to other rmarkdown formats
The UK Data Service has prepared this costing tool and checklist to help formulate research data management costs in advance of research starting, for example for inclusion in a data management plan or in preparation for a funding application.
This tool considers the additional costs - above standard planned research procedures and practices - that are needed to preserve research data and make them shareable beyond the primary research team. The checklist indicates the activities to consider and cost to enable good data management. Such additional activities may require extra researcher or administrative staff time input, equipment, software, infrastructure or tools.
The first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages.
The Data Management Skillbuilding Hub contains resources for better data management and is open to community input and update. These resources are adaptable across a range of contexts and intended for use by researchers, teachers, librarians, or anyone who wants to learn better data management practices. Each tile below contains a lesson in slide format with annotations, a one page handout that distills the main message, and a hands-on exercise.
M. Atzmueller, J. Mueller, and M. Becker. Mining, Modeling and Recommending 'Things' in Social Media, volume 8940 of LNAI, Springer Verlag, Heidelberg, Germany, (2015)
M. Atzmueller, B. Fries, and N. Hayat. Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, New York, NY, USA, ACM Press, (2016)
M. Atzmueller, L. Thiele, G. Stumme, and S. Kauffeld. Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017), Eindhoven, The Netherlands, Eindhoven University of Technology, (2017)
M. Atzmueller, A. Schmidt, B. Kloepper, and D. Arnu. New Frontiers in Mining Complex Patterns. Postproceedings NFMCP 2016, volume 10312 of LNAI, Berlin/Heidelberg, Germany, Springer Verlag, (2017)
M. Atzmueller. Solving Large Scale Learning Tasks: Challenges and Algorithms. Festschrift in Honour of Prof. Dr. Katharina Morik, volume 9580 of LNCS, Springer Verlag, (2016)