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

episodes/03-basics-factors-dataframes.Rmd

@@ -185,7 +185,7 @@
 of 29 variables (columns). Double-clicking on the name of the object will open
 a view of the data in a new tab.
 
 ![RStudio data frame view]("fig/rstudio_dataframeview.png")
 
 ## Summarizing, subsetting, and determining the structure of a data frame.
 
@@ -418,20 +418,19 @@
 ## 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).
+
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
 ## Subsetting data frames
@@ -813,12 +812,12 @@
 choose **From Excel...** (notice there are several other options you can
 explore).
 
 ![RStudio import menu]("fig/rstudio_import_menu.png")
 
 Next, under **File/Url:** click the <KBD>Browse</KBD> button and navigate to the **Ecoli\_metadata.xlsx** file located at `/home/dcuser/dc_sample_data/R`.
 You should now see a preview of the data to be imported:
 
 ![RStudio import screen]("fig/rstudio_import_screen.png")
 
 Notice that you have the option to change the data type of each variable by
 clicking arrow (drop-down menu) next to each column title. Under **Import