Introduction to the Tidyverse

Go to Course: https://www.coursera.org/learn/tidyverse

Introduction

### Course Review: Introduction to the Tidyverse on Coursera The "Introduction to the Tidyverse" course on Coursera is a must-take for anyone who is aspiring to delve into the world of data science, particularly using R. Developed within the framework of the growing popularity of the Tidyverse, this course provides the foundational skills needed to effectively manage, analyze, and visualize data. #### Overview At the forefront of modern data science, the Tidyverse encompasses a suite of R packages designed to simplify a variety of data-related tasks. This course introduces the core principles of tidy data, which organizes datasets to enhance manipulation and model-building efficiency. The course not only emphasizes understanding what tidy data is, but also guides learners on transforming messy datasets into tidy formats—an essential skill for aspiring data scientists. #### Syllabus Highlights 1. **Tidy Data**: The course begins with a clear explanation of what constitutes tidy data. The fundamental concept is that tidy datasets conform to a structured format that allows for easier analysis and visualization. The famous insight by Hadley Wickham that “tidy datasets are all alike but every messy dataset is messy in its own way” resonates throughout this section. Understanding this principle is crucial before diving into practical applications. 2. **From Non-Tidy to Tidy**: One of the most critical skills in data science is the ability to transform untidy or messy data into a tidy format. The course highlights common issues found in untidy data and provides insights on how to rectify them. This section has a practical focus, preparing learners for real-world data manipulation tasks they are likely to encounter. 3. **The Data Science Life Cycle & Tidyverse Ecosystem**: Here, the course explains the overall flow of a data science project and the role of various Tidyverse packages. While not every package will be covered in depth at this stage, learners will gain familiarity with the packages that fit into the broader data science landscape, preparing them for more complex analyses in subsequent modules. 4. **Data Science Project Organization & Workflows**: Understanding how to organize a data science project is critical for efficiency. This section tackles project file structures and customization, which are paramount whether you're working solo or as part of a larger team. 5. **Case Studies**: The course incorporates real-world examples from public health-related case studies, providing a hands-on opportunity to apply concepts learned. This approach ensures that the learning experience is both practical and engaging, as learners can see how theoretical concepts translate into actionable solutions. 6. **Project: Organizing a New Data Science Project**: The hands-on project at the end of the course allows learners to create a new data science project from scratch, focusing on proper organization for future analyses. This is an invaluable exercise for applying learned concepts and skills. #### Why You Should Enroll - **Comprehensive Framework**: The course offers a systematic approach to learning the essentials of tidy data and the Tidyverse ecosystem, making it ideal for beginners or those looking to deepen their knowledge. - **Hands-On Learning**: With real datasets and pertinent case studies, students get practical experience that enhances understanding and retention. - **Flexibility**: As an online course offered by Coursera, it allows for flexible pacing, accommodating learners with varying schedules. - **Excellent Instructors**: The course is designed by leading experts in the field, ensuring high-quality content and up-to-date practices. - **Career Advancement**: As companies increasingly rely on data for decision-making, the skills you acquire in this course can be invaluable for career progression in data science, analytics, and related fields. #### Recommendation I highly recommend the "Introduction to the Tidyverse" course on Coursera for anyone interested in data science, whether you are a student, a professional looking to upskill, or simply a curious individual wanting to understand data better. The structured curriculum, practical case studies, and strong emphasis on the principles of tidy data provide a robust foundation that will serve you well in any data-driven endeavor. Don’t miss the opportunity to learn how tidy data can transform your analytical practices!

Syllabus

Tidy Data

Before we can discuss all the ways in which R makes it easy to work with tidy data, we have to first be sure we know what tidy data are. Tidy datasets, by design, are easier to manipulate, model, and visualize because the tidy data principles that we’ll discuss in this course impose a general framework and a consistent set of rules on data. In fact, a well-known quote from Hadley Wickham is that “tidy datasets are all alike but every messy dataset is messy in its own way.” Utilizing a consistent tidy data format allows for tools to be built that work well within this framework, ultimately simplifying the data wrangling, visualization, and analysis processes. By starting with data that are already in a tidy format or by spending the time at the beginning of a project to get data into a tidy format, the remaining steps of your data science project will be easier.

The reason it’s important to discuss what tidy data are an what they look like is because out in the world, most data are untidy. If you are not the one entering the data but are instead handed the data from someone else to do a project, more often than not, those data will be untidy. Untidy data are often referred to simply as messy data. In order to work with these data easily, you’ll have to get them into a tidy data format. This means you’ll have to fully recognize untidy data and understand how to get data into a tidy format. The following common problems seen in messy datasets again come from Hadley Wickham’s paper on tidy data (http://vita.had.co.nz/papers/tidy-data.pdf). After briefly reviewing what each common problem is, we will then take a look at a few messy datasets. We’ll finally touch on the concepts of tidying untidy data, but we won’t actually do any practice yet. That’s coming soon!

With a solid understanding of tidy data and how tidy data fit into the data science life cycle, we’ll take a bit of time to introduce you to the tidyverse and tidyverse-adjacent packages that we’ll be teaching and using throughout this specialization. Taken together, these packages make up what we’re referring to as the tidyverse ecosystem. The purpose for the rest of this course is not for you to understand how to use each of these packages (that’s coming soon!), but rather to help you familiarize yourself with which packages fit into which part of the data science life cycle. Note that the official tidyverse packages below are bold. All other packages are tidyverse-adjacent, meaning they follow the same conventions as the official tidyverse packages and work well within the tidy framework and structure of data analysis.

Data science projects vary quite a lot so it can be difficult to give universal rules for how they should be organized. However, there are a few ways to organize projects that are commonly useful. In particular, almost all projects have to deal with files of various sorts—data files, code files, output files, etc. This section talks about how files work and how projects can be organized and customized.

Throughout this specialization, we’re going to make use of a number of case studies from Open Case Studies to demonstrate the concepts introduced in the course. We’ll generally make use of the same case studies throughout the specialization, providing continuity to allow you to focus on the concepts and skills being taught (rather than the context) while working with interesting data. These case studies aim to address a public-health question and all of them use real data.

This project will allow you to create a new project and organize the files that will be needed to engage in a future data analysis