Importing Data in the Tidyverse

Johns Hopkins University via Coursera

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

Introduction

### Course Review: Importing Data in the Tidyverse on Coursera If you're a data enthusiast or a budding data scientist, you understand that the journey into data analysis often begins with a crucial skill: importing data. The course **"Importing Data in the Tidyverse"** on Coursera offers a comprehensive introduction to this essential aspect of data science using R, one of the most popular programming languages in the field. #### Overview Getting data into your statistical analysis framework can often feel like an uphill battle. Whether you're working with datasets from various departments within an organization or gathering data from disparate sources, understanding how to harmonize and import data effectively is key to unlocking insights. This course aims to equip you with the necessary skills to import data from a wide array of sources efficiently and seamlessly. #### Syllabus Breakdown The course is structured into several modules, each focusing on different facets of importing data into R, specifically utilizing the Tidyverse—a collection of R packages designed for data science. Let’s delve into each module: 1. **Importing (and Exporting) Data in R**: This foundational module introduces you to tibbles, a modern way to store tabular data in R. You will learn about various formats, including Excel, CSV, and TSV files. This part of the course is crucial for anyone looking to manipulate and analyze structured data efficiently. 2. **JSON, XML, and Databases**: Data isn’t always structured neatly in tables. This module covers the importation of JSON and XML, two prevalent formats for storing semi-structured data. You’ll also learn how to connect to relational databases using SQLite, an essential skill for working with large datasets that need to be queried rather than fully loaded into memory. 3. **Web Scraping and APIs**: With the explosion of data on the internet, knowing how to pull in web data is vital. This module teaches you how to use the `rvest` and `httr` packages to scrape data from websites and interact with web APIs. This is a practical skill set for those who aim to conduct real-time data analysis. 4. **Foreign Formats, Images, and Google Drive**: The course doesn’t stop at traditional formats; it also addresses challenges you might face when collaborating with teams using different software. This module covers importing data from various foreign formats and even how to handle images and data stored in Google Drive. 5. **Case Studies**: Practical application is critical for reinforcing learning. The case studies section provides you with real-world examples where you will apply all the skills you've acquired throughout the course, either using RStudio or the provided Coursera lab spaces. 6. **Project: Importing Data into R**: To solidify your learning, you’ll undertake a project where you'll import data from multiple sources and conduct operations on that data—perfect for showcasing your skills to potential employers or collaborators. #### Recommendations **"Importing Data in the Tidyverse"** is highly recommended for anyone working in data science, data analysis, or related fields. Whether you're a beginner wanting to learn the basics or an intermediate user looking to brush up on specifics, this course provides a well-rounded education in data importing. Its modular approach allows you to learn at your own pace, ensuring strong comprehension of each area before moving on to more complex topics. The real-world applicability of these skills will undoubtedly enhance your data science toolkit, making it easier to tackle various projects as they arise. I would encourage prospective students to take advantage of the practical components, such as case studies and the final project, to deepen their understanding and confidence in data handling. In conclusion, this course stands out not only for its content but also for its practical focus which aligns well with the needs of today's data-driven world. Embrace the opportunity to master data importing in R—your future data science projects will thank you!

Syllabus

Importing (and Exporting) Data in R

A basic data type in the tidyverse is the tibble. Tibbles store tabular data and are a modern take on the standard R data frame. They have many user-friendly features that are an improvement over standard data frames when doing interactive data analysis. The remainder of this module covers tabular data in spreadsheet formats like Excel, CSV, TSV, and other delimited files.

JSON, XML, and Databases

Data can come in non-tabular formats, especially unstructured data or data that otherwise would not fit into a table. JSON and XML are common formats for storing arbitrarily structured data and this module covers the packages used to read in those data formats. In addition, relational databases are common for storing very large collections of tables where you do not need to read in the entire dataset at once. There are many relational database formats and we will cover the SQLite format, which is a compact and simple to use format.

Web Scraping and APIs

Reading in data from various Internet sources can be a useful way to build analyses that need to be regularly updated. The rvest and httr packages are useful for connecting to web sites, web APIs and other online sources of data.

Foreign Formats, Images, and googledrive

Working with others in a data science project often involves reading output or data produced using other statistical analysis packages or other software. This module covers packages for reading in these foreign formats, as well as images and data from Google Drive.

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: Importing Data into R

This project will give you the opportunity to read in data from multiple sources and conduct some simple operations on those data.