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Update intro.Rmd (#465) 

Typos fixed
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Jakub Nowosad 2016-10-12 10:38:04 -04:00 committed by Hadley Wickham
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@ -101,7 +101,7 @@ There are four things you need to run the code in this book: R, RStudio, a colle
### R
To download R, go to CRAN, the **comprehensive** **R** **a**rchive **network**. CRAN is composed of a set of mirror servers distributed around the world and is used to distribute R and R packages. Don't try and pick a mirror that's close to you: instead use the cloud mirror, <https://cloud.r-project.org>, which automatically figures it out for you.
To download R, go to CRAN, the **c**omprehensive **R** **a**rchive **n**etwork. CRAN is composed of a set of mirror servers distributed around the world and is used to distribute R and R packages. Don't try and pick a mirror that's close to you: instead use the cloud mirror, <https://cloud.r-project.org>, which automatically figures it out for you.
A new major version of R comes out once a year, and there are 2-3 minor releases each year. It's a good idea to update regularly. Upgrading can be a bit of a hassle, especially for major versions, which require you to reinstall all your packages, but putting it off only make it worse.
@ -141,7 +141,7 @@ Packages in the tidyverse change fairly frequently. You can see if updates are a
### Other packages
There are many other excellent packages that are not part of the tidyverse, because they solve problems in a different domain, are or designed with a different set of underlying principles. This doesn't make them better or worse, just different. In other words, the complement to the tidyverse is not the messyverse, but many other universes of interrelated packages. As you tackle more data science projects with R, you'll learn new packages and new ways of thinking about data.
There are many other excellent packages that are not part of the tidyverse, because they solve problems in a different domain, or are designed with a different set of underlying principles. This doesn't make them better or worse, just different. In other words, the complement to the tidyverse is not the messyverse, but many other universes of interrelated packages. As you tackle more data science projects with R, you'll learn new packages and new ways of thinking about data.
In this book we'll use three data packages from outside the tidyverse: