tweak

- last sentence in intro

Author: libjohn

docs/gis.html

@@ -325,9 +325,8 @@ <h1 class="title toc-ignore">Introduction to R – Workshop Home</h1>
 href="http://r4ds.had.co.nz/">R for Data Science</a> and <a
 href="http://socviz.co/">Data Visualization for Social Science</a>.
 Interactive primers and courses are available. Try the <a
-href="https://rstudio.cloud/learn/primers">Rstudio Cloud primers</a>, or
-register for workshops at <a
-href="https://www.datacamp.com/">DataCamp</a>.</p>
+href="https://rstudio.cloud/learn/primers">Rstudio Cloud primers</a> to
+reinforce your learning.</p>
 <ul>
 <li><a href="https://github.com/libjohn/intro2r-2019spring">practice
 code</a></li>

docs/index.html

@@ -7,7 +7,7 @@ output:
 
 The following learning materials support our *Introduction to R: Data Transformations, Analysis, and Data Structures* workshop.  Hosted by [Data & Visualization Services](http://library.duke.edu/) at Duke University Libraries, this is the first part of a multi-part learning series on R that we call [*R We Having Fun Yet‽*](https://rfun.library.duke.edu).  
 
-This gentle introduction provides a hands-on experience to learn the basics of modern R using RStudio and the Tidyverse method.  Learn about managing your R projects, data types, variable assignments, data structures, and visualization packages such as `ggplot2` and `mapview`.  The focus of these learning resources is as a code demonstrations supporting our in-person workshop.  Thus the explanatory prose is minimal, but you can watch an earlier recording below.  If you wish to read more about R, we recommend [R for Data Science](http://r4ds.had.co.nz/) and [Data Visualization for Social Science](http://socviz.co/).  Interactive primers and courses are available.  Try the [Rstudio Cloud primers](https://rstudio.cloud/learn/primers), or register for workshops at [DataCamp](https://www.datacamp.com/). 
+This gentle introduction provides a hands-on experience to learn the basics of modern R using RStudio and the Tidyverse method.  Learn about managing your R projects, data types, variable assignments, data structures, and visualization packages such as `ggplot2` and `mapview`.  The focus of these learning resources is as a code demonstrations supporting our in-person workshop.  Thus the explanatory prose is minimal, but you can watch an earlier recording below.  If you wish to read more about R, we recommend [R for Data Science](http://r4ds.had.co.nz/) and [Data Visualization for Social Science](http://socviz.co/).  Interactive primers and courses are available.  Try the [Rstudio Cloud primers](https://rstudio.cloud/learn/primers) to reinforce your learning. 
 
 - [practice code](https://github.com/libjohn/intro2r-2019spring)