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More intro tweaks

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@ -164,23 +164,25 @@ You will need to reload the package every time you start a new R session.
### Code conventions
* In text, we refer to functions in a code font followed by parentheses,
for example, `sum()`, or `mean()`.
Throughout the book we use a consistent set of conventions to refer to code:
* We refer to other R objects (like data or function arguments) without
parentheses: `flights`, `x`, ...
* Functions are in a code font and followed by parentheses, like `sum()`,
or `mean()`.
* Other R objects (like data or function arguments) are in a code font,
without parentheses, like `flights` or `x`.
* If we want to make it clear which package an object comes from, we'll use
the package name followed by two colons: `dplyr::mutate()`, or
`nycflights13::flights`. This is the same convention that R uses.
* If we want to make it clear what package an object comes from, we'll use
the package name followed by two colons, like `dplyr::mutate()`, or
`nycflights13::flights`. This is valid R code.
## Getting help and learning more
This book is not an island: there is no single resource that will allow you to master R. As you start to apply the techniques described in this book to your own data you will soon find questions that we do not answer. This section describes a few tips to help you get help, and to help you keep learning.
This book is not an island: there is no single resource that will allow you to master R. As you start to apply the techniques described in this book to your own data you will soon find questions that I do not answer. This section describes a few tips to help you get help, and to help you keep learning.
If you get stuck, start with google. Typically adding "R" to a query is enough to restrict it to relevant results: if the search isn't useful, it often means that there aren't any R specific results available. Google is particuarly useful for error messages. If you get an error message and you have no idea what it means, try googling it! Chances are that someone else has been confused by it in the past, and there will be help somewhere on the web. (If the error message isn't in English, run `Sys.setenv(LANGUAGE = "en")` and re-run the code; you're more likely to find help for English error messages.)
If google doesn't help, try [stackoverflow](http://stackoverflow.com). Start by spending a little time searching for an existing answer (including `[R]` to restrict your search). If you don't find anything useful, next prepare a minimal reproducible example or __reprex__. A good reprex makes it easier for other people to help you, and often you'll figure out the problem yourself in the course of making it.
If google doesn't help, try [stackoverflow](http://stackoverflow.com). Start by spending a little time searching for an existing answer (including `[R]` to restrict your search to questions about R). If you don't find anything useful, prepare a minimal reproducible example or __reprex__. A good reprex makes it easier for other people to help you, and often you'll figure out the problem yourself in the course of making it.
There are three things you need to include to make your example reproducible: required packages, data, and code.
@ -212,15 +214,15 @@ There are three things you need to include to make your example reproducible: re
The shorter your code is, the easier it is to understand, and the
easier it is to fix.
Finish by checking that you have actually made a reproducible example by starting a fresh R session and copying and pasting your script in.
Finish by checking that you have actually made a reproducible example by starting a fresh R session and copying and pasting your script in.
You should also spend some time preparing yourself to solve problems before they occur, and investing a little time in learning R each day will pay off handsomely in the long run. One way to is follow what Hadley, Garrett, and everyone else at RStudio are doing on the [RStudio blog](https://blog.rstudio.org). This is where we post announcements about new packages, new IDE features, and in-person courses. You might also want to follow Hadley ([@hadleywickham](https://twitter.com/hadleywickham)) or Garrett ([@statgarrett](https://twitter.com/statgarrett)) on twitter, or follow [@rstudiotips](https://twitter.com/rstudiotips) to keep up with new features in the IDE.
You should also spend some time preparing yourself to solve problems before they occur. Investing a little time in learning R each day will pay off handsomely in the long run. One way to is follow what Hadley, Garrett, and everyone else at RStudio are doing on the [RStudio blog](https://blog.rstudio.org). This is where we post announcements about new packages, new IDE features, and in-person courses. You might also want to follow Hadley ([@hadleywickham](https://twitter.com/hadleywickham)) or Garrett ([@statgarrett](https://twitter.com/statgarrett)) on twitter, or follow [@rstudiotips](https://twitter.com/rstudiotips) to keep up with new features in the IDE.
To keep up with the R community more broadly, we recommend reading <http://www.r-bloggers.com>: it aggregates over 500 blogs about R from around the world. If you're an active twitter user, following the `#rstats` hashtag on twitter is also a great way to keep up with the latest and greatest. That's one of the key tools that Hadley uses to keep up with new developments in the community.
To keep up with the R community more broadly, we recommend reading <http://www.r-bloggers.com>: it aggregates over 500 blogs about R from around the world. If you're an active twitter user, follow the `#rstats` hashtag. Twitter is one the key tools that Hadley uses to keep up with new developments in the community.
## Acknowledgements
This book isn't just the product of Hadley and Garrett, but is the result of many conversations (in person and online) that we've had with the R community. There are few people we'd like to thank specifically because they have spent many hours answering our dumb questions and helping us to better think about data science:
This book isn't just the product of Hadley and Garrett, but is the result of many conversations (in person and online) that we've had with the many people in the R community. There are few people we'd like to thank in particularly, because they have spent many hoursanswering our dumb questions and helping us to better think about data science:
* Jenny Bryan and Lionel Henry for many helpful discussions around working
with lists and list-columns.
@ -229,13 +231,13 @@ This book isn't just the product of Hadley and Garrett, but is the result of man
learning perspective, and the difference between hypothesis generation and
hypothesis confirmation.
* Yihui Xie for his work on the bookdown package, and for tireless satisfying
all my feature request.
* Yihui Xie for his work on the bookdown package, and for tirelessly
responding to my feature requests.
* Bill Behrman for thoughtfully reading the entire book and trying it out
with his data science class at Stanford.
* Bill Behrman for his thoughtful reading of the entinre book, and for trying
it out with his data science class at Stanford.
This book was written in the open, so a special thanks goes to everyone who contributed via GitHub:
This book was written in the open, and many people contributed pull requests to fix minor problems. I special thanks goes to everyone who contributed via GitHub:
```{r, results = "asis", echo = FALSE, message = FALSE}
library(dplyr)