Tidyverse Skills for Data Science in R

Description

Develop insights from data with tidy tools. Import, wrangle, visualize, and model data with the Tidyverse R packages. This book is intended for data scientists with some familiarity with the R programming language who are seeking to do Data Science using the Tidyverse family of packages. Through 5 chapters, you will cover importing, wrangling, visualizing, and modeling data using the powerful Tidyverse packages, including the new Tidymodels framework. The Tidyverse packages provide a simple but powerful approach to Data Science which scales from the most basic analyses to massive data deployments. This book covers the entire life cycle of a Data Science project and presents specific tidy tools for each stage. This course introduces a powerful set of Data Science tools known as the Tidyverse. The Tidyverse has revolutionized the way in which data scientists do almost every aspect of their job. We will cover the simple idea of “tidy data” and how this idea serves to organize data for analysis and modeling. We will also cover how non-tidy data can be transformed to tidy data, the Data Science project life cycle, and the ecosystem of Tidyverse R packages that can be used to execute a Data Science project. Functional programming is an approach to programming in which the code evaluated is treated as a mathematical function. It is declarative, so expressions (or declarations) are used instead of statements. Functional programming is often touted and used due to the fact that cleaner, shorter code can be written. In this shorter code, functional programming allows for code that is elegant but also understandable. Ultimately, the goal is to have simpler code that minimizes time required for debugging, testing, and maintaining. R at its core is a functional programming language. If you’re familiar with the apply() family of functions in base R, you’ve carried out some functional programming! Here, we’ll discuss functional programming and utilize the purrr package, designed to enhance functional programming in R. By utilizing functional programming, you’ll be able to minimize redundancy within your code. The way this happens in reality is by determining what small building blocks your code needs. These will each be a function. These small building block functions are then combined into more complex structures to be your final program. Table of Contents Introduction to the Tidyverse About This Course Tidy Data From Non-Tidy –> Tidy The Data Science Life Cycle The Tidyverse Ecosystem Data Science Project Organization Data Science Workflows Case Studies Importing Data in the Tidyverse About This Course Tibbles Spreadsheets CSVs TSVs Delimited Files Exporting Data from R JSON XML Databases Web Scraping APIs Foreign Formats Images googledrive Case Studies Wrangling Data in the Tidyverse About This Course Tidy Data Review Reshaping Data Data Wrangling Working With Factors Working With Dates and Times Working With Strings Working With Text Functional Programming Exploratory Data Analysis Case Studies Visualizing Data in the Tidyverse About This Course Data Visualization Background Plot Types Making Good Plots Plot Generation Process ggplot2: Basics ggplot2: Customization Tables ggplot2: Extensions Case Studies Modeling Data in the Tidyverse About This Course The Purpose of Data Science Types of Data Science Questions Data Needs Descriptive and Exploratory Analysis Inference Linear Modeling Multiple Linear Regression Beyond Linear Regression More Statistical Tests Hypothesis Testing Prediction Modeling The tidymodels Ecosystem Case Studies Summary of tidymodels About the Authors