r4ds/communicate.Rmd

# (PART) Communicate {-}

# Introduction {#communicate-intro}

So far, you've learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand you data through transformation, visualisation and modelling. However, it doesn't matter how great your analysis is unless you can explain it to others: you need to __communicate__ your results.

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/data-science-communicate.png")
```

Your audience might be:

* Fellow practioners who want to reproduce your work.

* Decision makers want to understand your findings in plain English.

* Future-you who will be thankful if you make your work easy to re-learn 
  and recreate 

In this part of the book, you will learn about:

* RMarkdown, a tool that allows you to easily combine prose and code to 
  construct reproducible reports where the inputs and outputs are always
  in sync.
  
* Presentation techniques for visualsiation. When creating exploratory graphics,
  you want to be able to iterate as quickly as possible, so you'll typically
  stick to ggplot2's defaults. When you're communicating to others, you'll
  want to think through every aspect of your plot in order to make it as 
  easy to understand as possible.

## What you will not learn

There are two important topics that you will not learn here: collaboration, and how to express yourself clearly. 

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:

1.  "Happy Git with R": a user friendly introduction to Git and GitHub from 
    R users, by Jenny Bryan. The book is freely available online:
    <http://happygitwithr.com>
    
1.  The "Git and GitHub" chapter of _R Packages_, by Hadley. You can also 
    read it for free online: <http://r-pkgs.had.co.nz/git.html>.

We're also not going to give you the human tools for communication. But here are a few recommendations:

* If you want 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 available.)
  
    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.
  
* To improve your presentation skills, I recommend 
  [_Presentation Patterns_](https://amzn.com/0321820800), by Neal Ford,
  Matthew McCollough, and Nathaniel Schutta. It provides a set of effective
  patterns (both low- and high-level) that you can imply to improve your 
  presentations.
  
* If you give academic talks, I recommend reading the [_Leek group guide
  to giving talks_](https://github.com/jtleek/talkguide).
  
* I haven't taken it personally, but I've heard good things about Matt 
  McGarrity's online course on public speaking: 
  <https://www.coursera.org/learn/public-speaking>.

* Finally, effectively communicating your ideas often benefits from some
  knowledge of graphic design. [_The Non-Designer's Design
  Book_](http://amzn.com/0133966151) is a great place to start.