Dashboards for data visualization, such as R Shiny and Tableau, allow the interactive exploration of data by means of drop-down lists and checkboxes, with no coding for the user. The apps can be useful for both the data analyst and the public. See also: https://www.linkedin.com/pulse/shiny-data-presentation-added-value-pablo-bernabeu Visualization apps run on internet browsers. This allows for three options: private viewing (useful during analysis), selected sharing (used at institutions), or internet publication (all out). Among the available platforms for viz apps, R Shiny and Tableau stand out due to being relatively accessible to new users. Apps serve a broad variety of purposes (see here and here). For instance, at a time of open science in which publicly archiving data is becoming standard practice (cf. journals, osf.io, figshare.com, etc.), data presentation interfaces can be used to let anyone delve into data sets at once without any programming involved for them. It's the right tool for acknowledging all facets of the data (yet this doesn't replace archiving the data files).

1. Presenting data interactively online using R Shiny
Pablo Bernabeu

2. Presenting data interactively online using R Shiny
Pablo Bernabeu
Free
Reliable
Extensively supported

3. Presenting data interactively online using R Shiny
Pablo Bernabeu

4. Data visualization apps
What
They Add

5. 21st Century

9. How and where to present (research) data?

10. How and where to present (research) data?

11. How and where to present (research) data?

12. How and where to present (research) data?

13. How and where to present (research) data?

14. How and where to present (research) data?

16. https://www.elsevier.com/authors/author-services/data-visualization

19. Data visualization apps
Sneak
peek

22. Any text & links
on sidebar

23. https://shiny.rstudio.com/gallery/kmeans-example.html
https://shiny.rstudio.com/gallery/word-cloud.html
https://shiny.rstudio.com/gallery/movie-explorer.html
https://shiny.rstudio.com/gallery/lego-set.html
R Studio Gallery

25. Easy internet access to full code for
countless app of different levels.

26. + advanced

28. + advanced

31. What’s the catch?

32. Data visualization apps
How to

33. How long it takes (approx.)
What’s the catch?

34. How long it takes (approx.)
• Basics of R programming: around 3 weeks
What’s the catch?

35. How long it takes (approx.)
• Basics of R programming: around 3 weeks
• One week intensively to look into Shiny and create first basic app
What’s the catch?

36. How to Shiny
4.
1.
2.
1.
Data

37. How to Shiny
4.
1.
2.
1.
Data

38. How to Shiny
3.
1.
2.
1.
Data

39. Easy internet access to full code for
countless app of different levels.

41. server.R

42. ui.R

44. Full circle
back to data
repository

45. Data visualization apps
More

46. Tutorials
Thousands of blogs, videos,
and galleries with code.

47. Online visualisation apps in science