# Overview

In this course we will review some of the tools of the trade, namely, R’s tidyverse - a collection of R packages designed with a common framework to aide in common data wrangling and data management tasks.

Data Wrangling is one subset set of skills within the Data Science Process. We will carefully investigate how decisions made while collecting and preparing the data have down-stream effects on model performance.

Analysis is worthless if it goes un-communicated. Stakeholders need regular up-to-the-date information to act upon. Luckily, RMarkdown , with the knitr (Xie 2021, 2015)and shiny packages can make R seamlessly integrate into reporting tasks including:

• HTML Web-pages
• Dashboards
• CSVs
• Power-Points
• Word-Documents
• Excel
• PDF reports
• Journal Submissions
• Books

RStudio V 1.4 Features a Visual Markdown Editor, which is very nice if you want to work on editing reports or documents and enjoy the “what-you-see-is-what-you-get” type interface over code. I do find flipping between both to be handy if I’m wondering what it might look like before rendering a document, it’s been a time saver in developing this course!

Finally, Combined with R’s modeling capabilities the entire data science process: from data ingestion to modeling to package development and version control can all be managed nicely with an RStudio Console.

We will go through what some might call a boilerplate pass - and walk through how to get started with these various tools to solve common data questions.

The goal here is to try to give that experience of connecting and working with a to a database with R. Collecting data from a potentially database, running statistical analyses, and making inferences as to which features would perform well when fitting predictive models.

## PART I - Welcome to R

In the first part of the text, we will cover getting started with R, we will install packages that we will utilize throughout the rest of the book, and we will introduce the tidyverse.

• 1 Welcome to R
• 2 - The Tidyverse

## PART II - Feature Engineering

In this part we will define a few features, targets, and other data-points of interest including: Gender, Age, Diabetic status, Age at Diabetes. We will breifly use these features to discuss ploting with ggplot2 in R.

• 3 - Feature Engineering
• 4 - The Anatomy of ggplot

## PART III - Exploratory Data Analysis

We use our data-set with the few features we defined in the last part and review statistical tests such as the t-test, ks-test, and chi-square test and ANOVA. We will showcase the relationship between p-values of statistical tests and corresponding and model accuracy we discuss two factor classification with:

• 6 - A single continuous feature
• 7 - categorical features and interactions

## PART IV - Data Analytics at Scale

It’s unrealistic that we will have only 3 or 4 features to review, we need to understand how to make R work for us.

We have provided features over three domains of interest, they include:

• 12.1 - Demographic - Feature Engineering
• 12.2 - Labs - Feature Engineering
• 12.3 - Examination - Feature Engineering

For the most part, these features are mapped in with similar methods we utilized in Part II; however, there are some issues when dealing with the Lab data.

We will utilize these domains to create a new analytic data-set with hundreds of columns and then discuss

• 9 - Functional dbplyr, purrr, and furrr
• 10 - Exploratory Data Analysis at Scale
• 11 - Packages for Automated Exploratory Data Analysis

## PART V - Factors, Time, & Text

• 13 - Diabetic monitoring; time-series classification

## PART VI - Communicate Results

A large aspect of Data Science is communication of results to stakeholders, in this Part we will introduce Shiny and flex_dashboards as well as discuss options when we knit an R markdown file.

• 14 - Shiny
• flex_dashboard

## PART VII - Package it up

• Make a package

### B Refrences

Mora, Pedro M. Valero. 2018. “Bookdown: Authoring Books and Technical Documents with R Markdown.” Journal of Statistical Software 87 (Book Review 1). https://doi.org/10.18637/jss.v087.b01.
Wickham, Hadley, and Garrett Grolemund. 2017. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 1st ed. O’Reilly Media, Inc.
Winter, Bodo. 2019. “The Tidyverse and Reproducible r Workflows.” In, 27–52. Routledge. https://doi.org/10.4324/9781315165547-2.
Xie, Yihui. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. https://yihui.org/knitr/.
———. 2016. Bookdown. Chapman; Hall/CRC. https://doi.org/10.1201/9781315204963.
———. 2021. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.
Xie, Yihui, J. J. Allaire, and Garrett Grolemund. 2018. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown.
Xie, Yihui, Christophe Dervieux, and Emily Riederer. 2020. R Markdown Cookbook. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown-cookbook.