Wrangling Data in the Tidyverse

Johns Hopkins University via Coursera

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

Introduction

### Course Review: Wrangling Data in the Tidyverse In the modern landscape of data analysis, one of the pivotal skills that every data analyst or scientist must possess is the ability to effectively wrangle and prepare data for further analysis and visualization. If you're looking to develop this skill, the Coursera course "Wrangling Data in the Tidyverse" offers a comprehensive and practical introduction to this essential aspect of data science. #### Course Overview "Wrangling Data in the Tidyverse" is designed to address a common challenge faced by data professionals: raw data rarely arrives in a neat and organized format. The course walks you through the fundamental strategies for transforming unstructured datasets into tidy data—an organized format that is essential for effective analysis and visualization. The course focuses on the "tidy" approach to data, popularized by Hadley Wickham and the Tidyverse, which emphasizes creating datasets that are clean, efficient, and ready for analysis. Throughout the course, students learn how to reshape, re-arrange, and re-format their data to meet analysis requirements, especially for visualizations and machine learning algorithms. #### Syllabus Breakdown 1. **Working With Factors, Dates, and Times** This module introduces the concept of factors in R, which are used to handle categorical data. You'll learn how to work with these variables effectively, enhancing your ability to analyze data tied to specific categories, such as months of the year or different product types. 2. **Working With Strings and Text and Functional Programming** Text data is a cornerstone of contemporary data science, and this module teaches you how to manipulate strings efficiently. You will explore essential techniques for cleaning and transforming text data, which is crucial for making it usable for numerical measurements or for deriving insights from the text itself. 3. **Exploratory Data Analysis** Here, the focus shifts to examining datasets to recognize patterns and relationships. The module emphasizes the importance of exploratory data analysis (EDA) in uncovering correlations while reinforcing the principle that correlation does not imply causation. This foundational understanding is key as you dive deeper into data analysis. 4. **Case Studies** Real-world application is critical for truly engaging with the material. This module offers several case studies that allow you to apply your learned skills. You can choose between using your environment on RStudio or utilize Coursera’s provided lab space to conduct your analyses. 5. **Project: Wrangling Data in the Tidyverse** The culmination of the course is a hands-on project that focuses on consumer complaint data from the Consumer Financial Protection Bureau (CFPB). This project enables you to practice the full cycle of data wrangling and analysis, providing valuable experience with real-world data. #### Recommendation I highly recommend "Wrangling Data in the Tidyverse" for anyone who is either starting their journey in data science or looking to refine their data wrangling skills. The course provides a valuable blend of theoretical knowledge and practical application, all while utilizing the incredibly powerful Tidyverse suite in R. The instruction is clear, and the structure is well-organized, making it suitable for learners at various levels of expertise. Additionally, the use of case studies promotes an engaging learning experience, allowing you to see the application of your skills in real-world scenarios. By the end of the course, you’ll not only have a solid understanding of how to tidy data but also an appreciation for the nuance involved in exploratory data analysis. Whether you seek to enhance your analytical ability, prepare for a data-focused career, or simply improve your data skills, this course will equip you with the tools necessary to wrangle data effectively. Dive into "Wrangling Data in the Tidyverse" and take a significant step forward in your data analysis journey. Happy wrangling!

Syllabus

Wrangling Data in the Tidyverse

Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This module addresses the problem of wrangling your data so that you can bring them under control and analyze them effectively. The key goal in data wrangling is transforming non-tidy data into tidy data.

Working With Factors, Dates, and Times

In R, categorical data are handled as factors. By definition, categorical data are limited in that they have a set number of possible values they can take. For example, there are 12 months in a calendar year. In a month variable, each observation is limited to taking one of these twelve values. Thus, with a limited number of possible values, month is a categorical variable. Categorical data, which will be referred to as factors for the rest of this lesson, are regularly found in data. Learning how to work with this type of variable effectively will be incredibly helpful.

Working With Strings and Text and Functional Programming

Working with text data is increasingly common in data science projects. Text manipulation is often needed to clean up messy datasets and to create numerical measurements out of text input. In addition, often the text themselves are the data and this module covers tools to extract information from the text.

Exploratory Data Analysis

The goal of an exploratory analysis is to examine, or explore the data and find relationships that weren’t previously known. Exploratory analyses explore how different measures might be related to each other but do not confirm that relationship as causal, i.e., one variable causing another. You’ve probably heard the phrase “Correlation does not imply causation,” and exploratory analyses lie at the root of this saying. Just because you observe a relationship between two variables during exploratory analysis, it does not mean that one necessarily causes the other.

Case Studies

Now we will demonstrate how to import data using our case study examples. When working through the steps of the case studies, you can use either RStudio on your own computer or Coursera lab spaces provided for each case study.

Project: Wrangling data in the Tidyverse

In this project, you will practice data exploration and data wrangling with the tidyverse using consumer complaint data from the Consumer Financial Protection Bureau (CFPB).