Update introduction.Rmd

Author: sibusiso16

introduction.Rmd

@@ -4,7 +4,7 @@
 
 ## Why interactive web graphics *from R*?
 
-As @r4ds argue, the exploratory phase of a data science workflow (Figure \@ref(fig:workflow)) requires lots of iteration between data manipulation, visualization, and modeling. Acheiving these tasks through a programming language like `R` offers the opportunity to scale and automate tasks, document and track them, and reliably reproduce their output. That power, however, typically comes at the cost of increasing the amount of cognitive load involved relative to a GUI-based system.^[For more on the benefits of using code over a GUI to perform data analysis, see @data-science-gui.] `R` packages like the **tidyverse** have been incredibly successful due to their ability to limit cognitive load without removing the benefits of performing analysis via code. Moreover, the **tidyverse**'s unifying principles of designing for humans, consistency, and composabilty makes iteration within and between these stages seamless -- an important but often overlooked challenge in exploratory data analysis (EDA) [@tidy-principles].
+As @r4ds argue, the exploratory phase of a data science workflow (Figure \@ref(fig:workflow)) requires lots of iteration between data manipulation, visualization, and modeling. Achieving these tasks through a programming language like `R` offers the opportunity to scale and automate tasks, document and track them, and reliably reproduce their output. That power, however, typically comes at the cost of increasing the amount of cognitive load involved relative to a GUI-based system.^[For more on the benefits of using code over a GUI to perform data analysis, see @data-science-gui.] `R` packages like the **tidyverse** have been incredibly successful due to their ability to limit cognitive load without removing the benefits of performing analysis via code. Moreover, the **tidyverse**'s unifying principles of designing for humans, consistency, and composabilty makes iteration within and between these stages seamless -- an important but often overlooked challenge in exploratory data analysis (EDA) [@tidy-principles].
 
 ```{r workflow, echo = FALSE, fig.cap="(ref:workflow)"}
 knitr::include_graphics("images/workflow.svg")
@@ -86,7 +86,7 @@ To advanced server-side linking with **shiny** to implement responsive and scala
 include_vimeo("318129502")
 ```
 
-By going through the code behind these examples, you'll that many of them leverage other `R` packages in their implementation. To highlight a few of the R packages that you'll see:
+By going through the code behind these examples, you'll see that many of them leverage other `R` packages in their implementation. To highlight a few of the R packages that you'll see:
 
 * __dplyr__ and __tidyr__
   * For transforming data into a form suitable for the visualization method.
@@ -111,7 +111,7 @@ This book contains six parts and each part contains numerous chapters. A summary
 
 4. _Linking multiple views:_ provides an overview of the two models for linking **plotly** graph(s) to other data views. The first model, covered in Chapter \@ref(graphical-queries), outlines **plotly**'s support for linking views purely client-side, meaning the resulting graphs render in any web browser on any machine without requiring external software. The second model, covered in Chapter \@ref(linking-views-with-shiny), demonstrates how to link **plotly** with other views via **shiny**, a reactive web application framework for `R`. Relatively speaking, the second model grants the `R` user way more power and flexbility, but comes at the cost of requiring more computational infrastructure. That being said, RStudio provides accessible resources for deploying **shiny** apps <https://shiny.rstudio.com/articles/#deployment>.
 
-5. _Custom behavior with JavaScript:_ demonstrates various ways to customize **plotly** graphs by writing custom JavaScript to handle certain user events. This part of the book is designed to be approachable for `R` users that want learn just enough JavaScript to **plotly** to do something it doesn't "natively" support. 
+5. _Custom behavior with JavaScript:_ demonstrates various ways to customize **plotly** graphs by writing custom JavaScript to handle certain user events. This part of the book is designed to be approachable for `R` users that want to learn just enough JavaScript to **plotly** to do something it doesn't "natively" support. 
 
 6. _Various special topics_: offers a grab-bag of topics that address common questions, mostly related to the customization of **plotly** graphs in `R`. 
 
@@ -208,4 +208,4 @@ devtools::session_info("plotly")
     * Two approaches, one object
   * Works well with the tidyverse
 -->
-    
+