Chapter 1 Introduction to the Tidyverse
The data science life cycle begins with a question that can be answered with data and ends with an answer to that question. However, there are a lot of steps that happen after a question has been generated and before arriving at an answer. After generating their specific question, data scientists have to determine what data will be useful, import the data, tidy the data into a format that is easy to work with, explore the data, generate insightful visualizations, carry out the analysis, and communicate their findings. Throughout this process, it is often said that 50-80% of a data scientist’s time is spent wrangling data. It can be hard work to read the data in and get data into the format you need to ultimately answer the question. As a result, conceptual frameworks and software packages to make these steps easier have been developed.
Within the R community, R packages that have been developed for this very purpose are often referred to as the Tidyverse. According to their website, the tidyverse is "an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures." There are currently about a dozen packages that make up the official tidyverse; however, there are dozens of tidyverse-adjacent packages that follow this philosophy, grammar, and data structures and work well with the official tidyverse packages. It is this whole set of packages that we have set out to teach in this specialization.
In this course, we set out to introduce the conceptual framework behind tidy data and introduce the tidyverse and tidyverse-adjacent packages that we’ll be teaching throughout this specialization. Mastery of these fundamental concepts and familiarity with what can be accomplished using the tidyverse will be critical throughout the more technical courses ahead. So, be sure you are familiar with the vocabulary provided and have a clear understanding of the tidy data principles introduced here before moving forward.
In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this specialization. However, if you have some familiarity with R and want to learn how to work more efficiently with data, then you’ve come to the right place!
