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Carrie Wright, PhD, Shannon Ellis, PhD, Stephanie Hicks, PhD, and Roger D. Peng, PhD

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.

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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.

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What's inside

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.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches students how to import data into R, a fundamental skill for data analysis
Develops proficiency in using common data formats and sources, such as spreadsheets, databases, and web scraping
Assumes familiarity with R programming, making it suitable for intermediate learners who wish to enhance their data handling skills

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Reviews summary

Mastering data import with r's tidyverse

According to students, "Importing Data in the Tidyverse" is a highly practical course offering clear explanations and a strong focus on the Tidyverse, essential for modern R data analysis. Learners frequently highlight the hands-on exercises and real-world case studies as key strengths for solidifying understanding and immediate application. While the course is broadly praised, some reviewers suggest that specific sections, particularly web scraping examples, may feel a bit dated or contain broken links. It is crucial to have a solid R programming background before starting, as a few learners found the pace challenging without it.
Instructor provides clear explanations of complex topics.
"The instructor explained complex topics clearly, and the hands-on exercises really solidified my understanding."
"Excellent course! The detailed explanations of `readr` and `tibble` were a game-changer."
"The instructor's explanations are clear and easy to follow."
Solidifies skills through effective exercises and case studies.
"The hands-on exercises really solidified my understanding. I especially appreciated the real-world case studies."
"The detailed explanations of `readr` and `tibble` were a game-changer. The case studies helped connect the dots from theory to practice."
"This course covers a lot of ground in data importing. The hands-on projects were very helpful."
"This project will give you the opportunity to read in data from multiple sources and conduct some simple operations on those data."
Provides highly practical skills for data importing in R.
"This course was incredibly practical and directly applicable to my work. The modules on web scraping and APIs were particularly useful..."
"As a data analyst, this course was exactly what I needed. ...provides practical solutions using Tidyverse."
"I've already applied several techniques to my daily data tasks. Definitely one of the best I've taken on OpenCourser."
"I liked how it emphasized the Tidyverse way, which is essential for modern R."
Some sections may contain outdated methods or links.
"The content is okay, but some parts feel a bit dated now, especially the web scraping examples."
"Content is generally good, but some parts, especially web scraping, seemed to have broken links or outdated methods that required external research. This can be frustrating."
"I also felt that more advanced topics could have been covered, or at least provided as supplemental material."
Requires strong prior R programming knowledge for success.
"Prerequisites mentioned are truly necessary; I wouldn't recommend it without prior R knowledge."
"I struggled with this course. ...I also think the R programming prerequisite wasn't stressed enough; I came in with basic R and found myself constantly catching up."
"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..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Importing Data in the Tidyverse with these activities:
Review data science concepts
Reviewing data science concepts will help you with the course material.
Browse courses on Data Science
Show steps
  • Go over the data science concepts covered so far.
  • Complete a few practice exercises.
Review R programming basics
Reviewing R programming will help you prepare for the course.
Browse courses on R Programming
Show steps
  • Go over the R programming basics.
  • Complete a few practice exercises.
Join a study group or discuss with other students
Talking to other students will help reinforce the concepts you've learned.
Show steps
  • Join a study group for this course.
  • Discuss the course material with other students.
Three other activities
Expand to see all activities and additional details
Show all six activities
Read Data Science for Business
This book provides a comprehensive overview of the data science process, including data import techniques.
Show steps
  • Read the first three chapters of the book.
  • Complete the exercises in the first three chapters.
  • Summarize the key concepts from the first three chapters.
Complete the RStudio Data Import Tutorial
This tutorial will provide hands-on experience with importing data into R using RStudio.
Browse courses on RStudio
Show steps
  • Follow the steps in the RStudio Data Import Tutorial.
  • Import a dataset of your own.
Practice importing data into R using different formats
Practice will help you become proficient in importing data into R.
Browse courses on Data Formats
Show steps
  • Import a CSV file into R.
  • Import a JSON file into R.
  • Import an XML file into R.

Career center

Learners who complete Importing Data in the Tidyverse will develop knowledge and skills that may be useful to these careers:
Data Engineer
Data Engineers build and maintain the infrastructure that supports data analysis. They work with data scientists and other stakeholders to design and implement data pipelines, ensuring that data is clean, consistent, and accessible. This course provides a strong foundation in data import and harmonization, essential skills for Data Engineers. By learning how to import data from various sources and formats, you will be better equipped to build and maintain data pipelines that meet the needs of your organization.
Data Analyst
Data Analysts use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course provides a strong foundation in data import and harmonization, essential skills for Data Analysts. By learning how to import data from various sources and formats, you will be better equipped to collect and clean data for analysis.
Data Scientist
Data Scientists use data to build predictive models and make recommendations. They work with data engineers and other stakeholders to design and implement data pipelines, and they develop and deploy models to solve business problems. This course provides a strong foundation in data import and harmonization, essential skills for Data Scientists. By learning how to import data from various sources and formats, you will be better equipped to build and maintain data pipelines and develop models that meet the needs of your organization.
Statistician
Statisticians use data to design and conduct studies, analyze data, and draw conclusions. They work in a variety of settings, including academia, government, and industry. This course provides a strong foundation in data import and harmonization, essential skills for Statisticians. By learning how to import data from various sources and formats, you will be better equipped to collect and clean data for analysis.
Quantitative Researcher
Quantitative Researchers use data to make investment decisions. They work with data scientists and other stakeholders to develop and implement investment models. This course provides a strong foundation in data import and harmonization, essential skills for Quantitative Researchers. By learning how to import data from various sources and formats, you will be better equipped to build and maintain data pipelines and develop models that meet the needs of your organization.
Business Analyst
Business Analysts use data to improve business processes. They work with stakeholders to identify and analyze problems, and they develop and implement solutions. This course provides a strong foundation in data import and harmonization, essential skills for Business Analysts. By learning how to import data from various sources and formats, you will be better equipped to collect and clean data for analysis.
Market Researcher
Market Researchers use data to understand consumer behavior. They work with marketing teams to develop and implement marketing campaigns. This course provides a strong foundation in data import and harmonization, essential skills for Market Researchers. By learning how to import data from various sources and formats, you will be better equipped to collect and clean data for analysis.
Web Developer
Web Developers design and build websites. They work with web designers and other stakeholders to create websites that are user-friendly and meet the needs of the organization. This course may be useful for Web Developers who need to import data from various sources and formats into their websites.
Database Administrator
Database Administrators manage and maintain databases. They work with database designers and other stakeholders to ensure that databases are performant and secure. This course may be useful for Database Administrators who need to import data from various sources and formats into their databases.
Data Journalist
Data Journalists use data to tell stories. They work with journalists and other stakeholders to create data-driven articles and visualizations. This course may be useful for Data Journalists who need to import data from various sources and formats into their articles and visualizations.
Information Security Analyst
Information Security Analysts protect organizations from cyberattacks. They work with security teams and other stakeholders to identify and mitigate security risks. This course may be useful for Information Security Analysts who need to import data from various sources and formats into their security systems.
Product Manager
Product Managers develop and manage products. They work with engineers, designers, and other stakeholders to create products that meet the needs of users. This course may be useful for Product Managers who need to import data from various sources and formats into their product development process.
Project Manager
Project Managers plan and execute projects. They work with project teams and other stakeholders to ensure that projects are completed on time and within budget. This course may be useful for Project Managers who need to import data from various sources and formats into their project management tools.
Marketing Manager
Marketing Managers develop and implement marketing campaigns. They work with marketing teams and other stakeholders to create marketing campaigns that reach the target audience and achieve the desired results. This course may be useful for Marketing Managers who need to import data from various sources and formats into their marketing campaigns.
Sales Manager
Sales Managers lead and manage sales teams. They work with sales teams and other stakeholders to develop and implement sales strategies. This course may be useful for Sales Managers who need to import data from various sources and formats into their sales management tools.

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Importing Data in the Tidyverse.
Reference guide to the ggplot2 package for data visualization in R, and would be a useful reference for students in this course.
Is not a general introduction to the R programming language, but it is one of the leading references for Hadley Wickham's tidyverse tools and as such would be a valuable reference text for students taking this course.
Reference guide to the R Markdown language for creating dynamic reports, and would be a useful reference for students in this course.
Practical guide to data manipulation in R, and would be a useful reference for students in this course.
Collection of recipes for solving common problems in R, and is written to be useful as a reference for R users at all levels.
Provides an introduction to the use of R in machine learning and statistical modeling, which would complement this course nicely.
Is primarily intended as an introduction to the R programming language and thus will serve as a good prerequisite or additional reference for this course.
Is an introduction to the use of R in data science, and is another good reference for the tidyverse tools used in this course.
Reference guide to the RStudio integrated development environment, and would be useful as a reference for students using RStudio for this course.
This textbook good introduction to linear algebra, and would provide a good foundation for understanding some of the concepts used in data science and machine learning.
Although this textbook uses Python rather than R, it provides a nice introduction to exploratory data analysis which would be useful for students in this course.

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