Another communicate re-org
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hadley
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@ -35,8 +35,8 @@ rmd_files: [
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"communicate.Rmd",
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"rmarkdown.Rmd",
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"communicate-plots.Rmd",
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"workflow-rmarkdown.Rmd",
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"rmarkdown-exts.Rmd"
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"rmarkdown-formats.Rmd",
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"rmarkdown-workflow.Rmd",
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]
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before_chapter_script: "_common.R"
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@ -8,67 +8,24 @@ So far, you've learned the tools to get your data into R, tidy it into a form co
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knitr::include_graphics("diagrams/data-science-communicate.png")
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```
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Your audience might be:
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Communication is the theme of the following four chapters:
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* Fellow practioners who want to reproduce your work.
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* Decision makers want to understand your findings in plain English.
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* Future-you who will be thankful if you make your work easy to re-learn
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and recreate
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In this part of the book, you will learn about:
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* RMarkdown, a tool that allows you to easily combine prose and code to
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construct reproducible reports where the inputs and outputs are always
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in sync.
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* In [R Markdown], you will learn about R Markdown, a tool for integrating
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prose, code, and results. You can use R Markdown in notebook mode for
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analyst-to-analyst communication, and in report mode for
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analyst-to-decision-maker communication. Thanks to the power of R Markdown
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formats, you can even use the same document for both purposes.
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* Presentation techniques for visualsiation. When creating exploratory graphics,
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you want to be able to iterate as quickly as possible, so you'll typically
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stick to ggplot2's defaults. When you're communicating to others, you'll
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want to think through every aspect of your plot in order to make it as
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easy to understand as possible.
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## What you will not learn
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There are two important topics that you will not learn here: collaboration, and how to express yourself clearly.
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Collaboration is a vital part of modern data science, and you can make your life much easier by using version control tools, such as Git and Github. We recommend two free resources that will teach you about Git:
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1. "Happy Git with R": a user friendly introduction to Git and GitHub from
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R users, by Jenny Bryan. The book is freely available online:
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<http://happygitwithr.com>
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1. The "Git and GitHub" chapter of _R Packages_, by Hadley. You can also
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read it for free online: <http://r-pkgs.had.co.nz/git.html>.
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We're also not going to give you the human tools for communication. But here are a few recommendations:
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* If you want to improve your writing, I highly recommend reading either
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[_Style: Lessons in Clarity and Grace_](https://amzn.com/0134080416), or
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[_The Sense of Structure: Writing from the Reader's Perspective_](https://amzn.com/0205296327) by George Gopen. Both books will help you understand the structure of sentences and
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paragraphs and give you the tools to make your writing more clear. (These
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books are rather expensive if purchased new, but they're used by many English
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classes so there are plenty of cheap second-hand copies available.)
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* In [Graphics for communication], you will learn how to take your exploratory
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graphics and turn them into expository graphics, graphics that help the
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newcomer to your analysis understand what's going on as quickly and
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easily as possible.
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George Gopen also has a number of short articles on writing at
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<http://georgegopen.com/articles/litigation/>. They are aimed at lawyers,
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but almost everything applies to data scientists too.
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* In [R Markdown formats], you'll learn a little about the many other varieties
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of outputs you can produce using R Markdown, including dashboards, websites,
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and books.
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* To improve your presentation skills, I recommend
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[_Presentation Patterns_](https://amzn.com/0321820800), by Neal Ford,
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Matthew McCollough, and Nathaniel Schutta. It provides a set of effective
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patterns (both low- and high-level) that you can imply to improve your
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presentations.
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* If you give academic talks, I recommend reading the [_Leek group guide
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to giving talks_](https://github.com/jtleek/talkguide).
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* I haven't taken it personally, but I've heard good things about Matt
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McGarrity's online course on public speaking:
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<https://www.coursera.org/learn/public-speaking>.
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* Finally, effectively communicating your ideas often benefits from some
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knowledge of graphic design. [_The Non-Designer's Design
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Book_](http://amzn.com/0133966151) is a great place to start.
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* We'll finish up with [R Markdown workflow], where you'll learn a fourth
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workflow in which R Markdown takes center stage.
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Unfortunately these chapters focus mostly on the technical mechanics of communication, not the really hard problems of communicating your thoughts to other humans. However, there are lot of other great books about communication, which we'll point you to at the end of each chapter.
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@ -1,4 +1,4 @@
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# Extensions
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# R Markdown formats
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```{r setup, include = FALSE}
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chunk <- "```"
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@ -178,3 +178,25 @@ Learn more about Shiny at the [Shiny Development Center](http://shiny.rstudio.co
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## Books
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The bookdown package extends R Markdown to create book length documents, like *R for Data Science*, which was written with R Markdown and bookdown. To learn more about bookdown, see the free ebook [Authoring Books with R Markdown](https://bookdown.org/yihui/bookdown/) or [www.bookdown.org](www.bookdown.org).
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## Learning more
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* To improve your presentation skills, I recommend
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[_Presentation Patterns_](https://amzn.com/0321820800), by Neal Ford,
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Matthew McCollough, and Nathaniel Schutta. It provides a set of effective
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patterns (both low- and high-level) that you can imply to improve your
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presentations.
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* If you give academic talks, I recommend reading the [_Leek group guide
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to giving talks_](https://github.com/jtleek/talkguide).
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* I haven't taken it personally, but I've heard good things about Matt
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McGarrity's online course on public speaking:
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<https://www.coursera.org/learn/public-speaking>.
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* Finally, effectively communicating your ideas often benefits from some
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knowledge of graphic design. [_The Non-Designer's Design
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Book_](http://amzn.com/0133966151) is a great place to start.
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@ -1,4 +1,4 @@
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# Workflow: RMarkdown
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# R Markdown workflow
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You have seen three basic workflows:
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@ -4,6 +4,11 @@
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R Markdown provides an unified authoring framework for data science, combining your code, its results, and your prose commentary. R Markdown documents are fully reproducible and support dozens of output formats, like PDFs, Word files, slideshows, and more. RMarkdown files are designed to be used in two ways:
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You can use R Markdown in notebook mode for
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analyst-to-analyst communication, and in report mode for
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analyst-to-decision-maker communication. Thanks to the power of R Markdown
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formats, you can even use the same document for both purposes.
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1. To produce a final report that you can share with others.
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1. As a notebook, which allows yout to mingle prose, code, and output during
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@ -373,3 +378,17 @@ csl: apa.csl
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```
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As with the bibliography field, your csl file should contain a path to the file. Here I assume that the csl file is in the same directory as the .Rmd file. <http://github.com/citation-style-language/styles> contains many useful CSL style files.
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## Learning more
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There are two important topics that we haven't covered here: collaboration, and the details of accurately communicating your ideas to other humans. Collaboration is a vital part of modern data science, and you can make your life much easier by using version control tools, such as Git and Github. We recommend two free resources that will teach you about Git:
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1. "Happy Git with R": a user friendly introduction to Git and GitHub from
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R users, by Jenny Bryan. The book is freely available online:
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<http://happygitwithr.com>
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1. The "Git and GitHub" chapter of _R Packages_, by Hadley. You can also
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read it for free online: <http://r-pkgs.had.co.nz/git.html>.
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I have also not touched about what you should actually write in the prose in order to clearly communicate the results of your analysis. To improve your writing, I highly recommend reading either [_Style: Lessons in Clarity and Grace_](https://amzn.com/0134080416), or [_The Sense of Structure: Writing from the Reader's Perspective_](https://amzn.com/0205296327) by George Gopen. Both books will help you understand the structure of sentences and paragraphs, and give you the tools to make your writing more clear. (These books are rather expensive if purchased new, but they're used by many English classes so there are plenty of cheap second-hand copies.) George Gopen also has a number of short articles on writing at <http://georgegopen.com/articles/litigation/>. They are aimed at lawyers, but almost everything applies to data scientists too.
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