Merge pull request #289 from naupaka/fix-280

Revise base vs add-on package text to address #280

episodes/03-basics-factors-dataframes.Rmd

@@ -418,20 +418,19 @@ to understand how to manipulate factors.
 ## Tip: Packages in R -- what are they and why do we use them?
 
 Packages are simply collections of functions and/or data that can be used to extend the
-capabilities of R beyond the core functionality that comes with it by default. There are
-useful R packages available that span all types of statistical analysis, data visualization,
-and more. The main place that R packages are installed from is a website called
-[CRAN](https://cran.r-project.org/) (the Comprehensive R Archive Network). Many thousands
-of R packages are available there, and when you use the built-in R function `install.packages()`,
-it will look for a CRAN repository to install from. So, for example, to install
-[tidyverse](https://www.tidyverse.org) packages such as `dplyr` and `ggplot2`
+capabilities of R beyond the core functionality that comes with it by default. The default set of functions and packages that come 'in the box' when you install R for the first time on a given computer are called 'base R'. However, one of the major benefits of using an open source programming language is that there are thousands of useful R packages freely available that span all types of statistical analysis, data visualization,
+and more. The main place that these additional R packages are made available is from a website called the Comprehensive R Archive Network ([CRAN](https://cran.r-project.org/)). When you use the built-in R function `install.packages()`,
+it will look on CRAN for the package and install it on your computer. So, for example, to install packages such as `dplyr` and `ggplot2`
 (which you'll do in the next few lessons), you would use the following command:
 
 ```{r}
 # install a package from CRAN
 install.packages("ggplot2")
+install.packages("dplyr")
 ```
 
+These two packages are among the most popular add on packages used in R, and they are part of a large set of very useful packages called the [tidyverse](https://www.tidyverse.org). Packages in the tidyverse are designed to work well together and are made to work with tidy data (which we described earlier in this lesson).
+
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 ## Subsetting data frames