This course is part of the Tidyverse Skills for Data Science in R Specialization

Carrie Wright, PhD +3 more instructors

Offered By

Tidyverse Skills for Data Science in R SpecializationJohns Hopkins University

About this Course

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Getting data into your statistical analysis system can be one of the most challenging parts of any data science project. Data must be imported and harmonized into a coherent format before any insights can be obtained. You will learn how to get data into R from commonly used formats and harmonizing different kinds of datasets from different sources. If you work in an organization where different departments collect data using different systems and different storage formats, then this course will provide essential tools for bringing those datasets together and making sense of the wealth of information in your organization.
This course introduces the Tidyverse tools for importing data into R so that it can be prepared for analysis, visualization, and modeling. Common data formats are introduced, including delimited files, spreadsheets and relational databases, and techniques for obtaining data from the web are demonstrated, such as web scraping and web APIs.

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 course.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Course 2 of 5 in the

Tidyverse Skills for Data Science in R Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

Familiarity with the R programming language

Approx. 15 hours to complete

English

Subtitles: English

What you will learn

Describe different data formats

Apply Tidyverse functions to import data into R from external formats

Obtain data from a web API

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 5 in the

Tidyverse Skills for Data Science in R Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Beginner Level

Familiarity with the R programming language

Approx. 15 hours to complete

English

Subtitles: English

Instructors

Offered by

Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Syllabus - What you will learn from this course

Week

Week 1

3 hours to complete

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.

3 hours to complete

15 readings

15 readings

About This Course5m

Tibbles10m

Creating a tibble20m

Subsetting10m

Spreadsheets1m

Excel files30m

Google Sheets45m

CSVs10m

Downloading CSV files5m

Reading CSVs into R10m

TSVs2m

Reading TSVs Files into R5m

Delimited Files3m

Reading Delimited Files into R5m

Exporting Data from R5m

1 practice exercise

Importing and Exporting Data Quiz30m

Week

Week 2

3 hours to complete

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.

3 hours to complete

10 readings

10 readings

JSON30m

XML15m

Databases2m

Relational Data15m

Relational Databases: SQL5m

Connecting to Databases: RSQLite10m

Working with Relational Data: dplyr & dbplyr5m

Mutating Joins30m

Filtering Joins10m

How to Connect to a Database Online10m

1 practice exercise

JSON, XML, and Databases Quiz30m

Week

Week 3

2 hours to complete

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.

2 hours to complete

11 readings

11 readings

Web Scraping10m

rvest Basics

SelectorGadget10m

Web Scraping Example10m

A final note: SelectorGadget2m

API5m

Getting Data: httr5m

Example 1: GitHub’s API30m

Example 2: Obtaining a CSV20m

read_csv() from a URL3m

API keys10m

1 practice exercise

Getting Data from the Internet Quiz30m

2 hours to complete

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.

2 hours to complete

3 readings

3 readings

haven15m

Images30m

googledrive20m

1 practice exercise

Foreign Formats, Images and googledrive Quiz30m

Week

Week 4

4 hours to complete

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.

4 hours to complete

11 readings

11 readings

Case Study #1: Health Expenditures5m

Healthcare Coverage Data45m

Healthcare Spending Data30m

New Case Study #2: Firearms2m

Census Data5m

Counted Data5m

Suicide Data10m

Brady Data10m

Crime Data10m

Land Area Data10m

Unemployment Data10m

1 hour to complete

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.

1 hour to complete

2 readings

2 readings

Introduction and Background10m

Datasets10m

1 practice exercise

Importing Data into R Project30m

About the Tidyverse Skills for Data Science in R Specialization

This Specialization 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 courses, you will cover importing, wrangling, visualizing, and modeling data using the powerful Tidyverse 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 course covers the entire life cycle of a data science project and presents specific tidy tools for each stage.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.

Importing Data in the Tidyverse