R Markdown Advanced Tips to Become a Better Data Scientist & RStudio Connect | With Tom Mock

R Markdown is an incredible tool for being a more effective data scientist. It lets you share insights in ways that delight end users.

In this presentation, Tom Mock will teach you some advanced tips that will let you get the most out of R Markdown. Additionally, RStudio Connect will be highlighted, specifically how it works wonderfully with tools like R Markdown.

04:15 Literate Programming
09:00 - Rstudio Visual Editor Demo
15:44 - R and python in same document via {reticulate}
18:10 - Q&A: Options for collaborative editing (version control, shared drive etc.)
19:30 - Q&A: Multi-pane support in Rstudio
20:46 Data Product (reports, presentations, dashboards, websites etc.)
24:15 - Distill article
26:27 - Xaringan presentation (add three dashes --- for new slide)
28:58 - Flexdashboard (with shiny)
30:30 - Crosstalk (talk between different html widgets instead of {shiny} server)
35:03 - Q&A: Jobs panel -- parallelise render jobs in background
36:50 - Q&A: various data product packages, formats
39:35 Control Document (modularise data science tasks, control code flow)
39:58 - Knit with Parameters (YAML params: option)
41:20 - Reference named chunks from .R files (knitr::read_chunk())
43:00 - Child Documents (reuse content, conditional inclusion, {blastula} email)
47:07 Templating (don't repeat yourself)
47:38 - rmarkdown::render() with params, looping through different param combinations
49:30 - Loop templates within a single document
50:40 - 04-templating/ live code demo
54:37 - {whisker} vs {glue} -- {{logic-less}} vs {logic templating}
55:30 - {whisker} for generating markdown files that you can continue editing
57:49 RMarkdown + Rstudio Connect
1:00:41 Follow-up Reading and resources
1:04:49 Q&A - {shiny} apps, {webshot2} for screenshots of html, reading in multiple .R files, best practice for producing MSoffice files, {blastula}

[jerrytang]

This is The one tutorial that answered all my questions. Can't thank you enough.

[cynthiahuang]

04:15 Literate Programming
09:00 - Rstudio Visual Editor Demo
15:44 - R and python in same document via {reticulate}
18:10 - Q&A: Options for collaborative editing (version control, shared drive etc.)
19:30 - Q&A: Multi-pane support in Rstudio
20:46 Data Product (reports, presentations, dashboards, websites etc.)
24:15 - Distill article
26:27 - Xaringan presentation (add three dashes --- for new slide)
28:58 - Flexdashboard (with shiny)
30:30 - Crosstalk (talk between different html widgets instead of {shiny} server)
35:03 - Q&A: Jobs panel -- parallelise render jobs in background
36:50 - Q&A: various data product packages, formats
39:35 Control Document (modularise data science tasks, control code flow)
39:58 - Knit with Parameters (YAML params: option)
41:20 - Reference named chunks from .R files (knitr::read_chunk())
43:00 - Child Documents (reuse content, conditional inclusion, {blastula} email)
47:07 Templating (don't repeat yourself)
47:38 - rmarkdown::render() with params, looping through different param combinations
49:30 - loop templates within a single document
50:40 - 04-templating/ live code demo
54:37 - {whisker} vs {glue} -- {{logic-less}} vs {logic templating}
55:30 - {whisker} for generating markdown files that you can continue editing
57:49 RMarkdown + Rstudio Connect
1:00:41 Follow-up Reading and resources
1:04:49 Q&A - {shiny} apps, {webshot2} for screenshots of html, reading in multiple .R files, best practice for producing MSoffice files, {blastula}