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Dr. Elijah Meyer
and
Mine Çetinkaya-Rundel
This course aims to better develop your statistical toolkit in the world of statistics and data science. You will learn how to collect, manipulate, and transform data in R into a more readily usable format using tidyverse data pipelines, primarily using verbs from the dplyr and tidyr packages. The topics covered provide you with the tools necessary to convert data to be better suited for data visualization (Course 1) and modeling; which is to come in this certificate program in a future course. Additionally, we discuss the topics of web scraping and the considerations one must take prior to scraping data from the web.
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What's inside
Syllabus
Tidy Data
Tidy datasets have a specific structure: each variable is a column, and each observation is a row. In this module, we use functional verbs from the dplyr package in R to transform data into a ready-to-use tidy data format. Additionally, we use functional verbs to manipulate data frames.
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Syllabus
Tidy Data
Tidy datasets have a specific structure: each variable is a column, and each observation is a row. In this module, we use functional verbs from the dplyr package in R to transform data into a ready-to-use tidy data format. Additionally, we use functional verbs to manipulate data frames.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Uses functional verbs from the dplyr package in R to transform data into a ready-to-use tidy data format, which is essential for data analysis
Teaches how to perform basic web scraping in R to make an abundance of data online more easily accessible, expanding data collection capabilities
Covers data manipulation and transformation using tidyverse data pipelines, which are standard tools in the field
Discusses how to recode variables in a data set to be different types, classes, or take on different values, which is a common task in data preparation
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Reviews summary
Data tidying and importing with r overview
According to learners, this course provides a solid foundation in data tidying and importing using R, with a strong focus on the tidyverse packages dplyr and tidyr. Students praise the instructor's clear explanations and the practical, hands-on exercises that reinforce concepts. While many find it a great introduction for beginners to R data manipulation, some note the pace can be fast for absolute newcomers and that there can be initial technical challenges with R environment setup. The web scraping module is seen as a useful, albeit basic, introduction. Overall, students feel the course effectively prepares them for subsequent courses in the certificate program and for practical data handling tasks.
Introduction is basic, not a deep dive.
"The web scraping part was interesting but only scratched the surface; don't expect to become a scraping expert."
"I liked the introduction to web scraping, but it felt a bit tacked on and not very practical for complex sites."
"It was a good demo of web scraping but I'd need another course for real-world application."
"The web scraping module provides a decent overview, but lacks the depth needed for serious projects."
Fits well within the certificate program.
"This course was essential for preparing me for the visualization and modeling courses that follow."
"It provides the necessary data manipulation skills needed for the rest of the certificate."
"Learning tidy data principles here made the subsequent courses much easier to follow."
"A vital building block for anyone doing the full data science certificate program."
Instructor explains concepts well and clearly.
"The instructor's explanations were very clear and easy to follow, even for complex topics."
"I found the lectures engaging and the instructor did a great job breaking down the material."
"His teaching style made learning data tidying concepts much less intimidating."
"The way the instructor walked through the code examples was excellent and easy to replicate."
Strong coverage of dplyr and tidyr is a plus.
"The modules on dplyr and tidyr were incredibly helpful for learning how to manipulate data efficiently in R."
"I really appreciated the focus on the tidyverse approach; it makes data handling much more intuitive."
"The hands-on exercises using real-world like data made applying dplyr and tidyr concepts easy to grasp."
"I now feel comfortable using dplyr and tidyr for most of my data manipulation needs after this course."
Some users faced setup challenges.
"Getting the R environment set up correctly with all the packages was a bit frustrating initially."
"I had some issues running the code in the labs due to package compatibility problems."
"The course assumes a certain level of technical familiarity that I didn't have, leading to setup woes."
"Make sure your R installation and packages are up-to-date before starting to avoid technical snags."
Good introduction, but pace can be fast.
"As a complete beginner to R, I found this course a bit challenging, especially the early setup parts."
"It's a great starting point, but be prepared to spend extra time on some concepts if you're new to programming."
"I felt the pace was good for someone with a little R background, maybe tough for absolute newbies."
"This course is perfect for someone with basic R familiarity looking to learn tidyverse."
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 Data Tidying and Importing with R with these
activities:
Review R Fundamentals
Show steps
Reinforce your understanding of R fundamentals to better grasp the dplyr and tidyr packages.
Browse courses on
R Programming
Show steps
-
Review basic R syntax and data types.
-
Practice writing simple R functions.
-
Familiarize yourself with common R data structures.
Read R for Data Science
Show steps
Gain a deeper understanding of the tidyverse and data manipulation techniques by reading this comprehensive guide.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read the chapters on data transformation and tidy data.
-
Work through the examples provided in the book.
Practice Data Manipulation with dplyr
Show steps
Solidify your understanding of dplyr verbs by completing practice exercises.
Show steps
-
Find online resources with dplyr exercises.
-
Complete exercises focusing on select, filter, mutate, and summarize.
-
Review solutions and identify areas for improvement.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a Data Cleaning Script
Show steps
Apply your knowledge by creating a script that cleans and transforms a messy dataset.
Show steps
-
Find a publicly available messy dataset.
-
Write an R script using dplyr and tidyr to clean the data.
-
Document your code with comments.
Tidy a Real-World Dataset
Show steps
Apply your skills to a real-world dataset to gain practical experience in data tidying.
Show steps
-
Identify a dataset of interest from a source like Kaggle.
-
Develop a plan for tidying and transforming the data.
-
Implement your plan using R and the tidyverse packages.
-
Document your process and findings.
Read Web Scraping with R
Show steps
Expand your web scraping skills by reading this practical guide.
View
Putting Knowledge to Work
on Amazon
Show steps
-
Read the chapters on rvest and handling different website structures.
-
Try the examples provided in the book.
Create a Data Visualization Dashboard
Show steps
Showcase your data tidying and manipulation skills by creating an interactive dashboard.
Show steps
-
Choose a dataset that you have tidied.
-
Use R and a visualization package like Shiny to create a dashboard.
-
Include interactive elements to explore the data.
-
Document your dashboard and its functionality.
Review R Fundamentals
Show steps
Reinforce your understanding of R fundamentals to better grasp the dplyr and tidyr packages.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Review basic R syntax and data types.
- Practice writing simple R functions.
- Familiarize yourself with common R data structures.
Read R for Data Science
Show steps
Gain a deeper understanding of the tidyverse and data manipulation techniques by reading this comprehensive guide.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters on data transformation and tidy data.
- Work through the examples provided in the book.
Practice Data Manipulation with dplyr
Show steps
Solidify your understanding of dplyr verbs by completing practice exercises.
Show steps
Show steps
Show steps
- Find online resources with dplyr exercises.
- Complete exercises focusing on select, filter, mutate, and summarize.
- Review solutions and identify areas for improvement.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a Data Cleaning Script
Show steps
Apply your knowledge by creating a script that cleans and transforms a messy dataset.
Show steps
Show steps
Show steps
- Find a publicly available messy dataset.
- Write an R script using dplyr and tidyr to clean the data.
- Document your code with comments.
Tidy a Real-World Dataset
Show steps
Apply your skills to a real-world dataset to gain practical experience in data tidying.
Show steps
Show steps
Show steps
- Identify a dataset of interest from a source like Kaggle.
- Develop a plan for tidying and transforming the data.
- Implement your plan using R and the tidyverse packages.
- Document your process and findings.
Read Web Scraping with R
Show steps
Expand your web scraping skills by reading this practical guide.
View
Putting Knowledge to Work
on Amazon
Show steps
Show steps
Show steps
- Read the chapters on rvest and handling different website structures.
- Try the examples provided in the book.
Create a Data Visualization Dashboard
Show steps
Showcase your data tidying and manipulation skills by creating an interactive dashboard.
Show steps
Show steps
Show steps
- Choose a dataset that you have tidied.
- Use R and a visualization package like Shiny to create a dashboard.
- Include interactive elements to explore the data.
- Document your dashboard and its functionality.
Career center
Learners who complete Data Tidying and Importing with R will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A data scientist often spends a significant amount of time preparing data for analysis, and this course is directly applicable to that work. The course teaches how to use R to collect, manipulate, and transform data. A data scientist needs to convert raw data, which can be in a variety of formats, into a tidy format ready for analysis. This course will help a data scientist be more efficient in their work.
Statistician
Statistician
The role of a statistician requires a strong understanding of how to prepare data. The course focuses on data tidying and importing with R which is central to the work of a statistician. The course introduces topics such as using functional verbs from the dplyr package in R to transform data. It also covers how to recode variables in a dataset. A statistician who takes this course may improve their ability to perform their work using R.
Data Analyst
Data Analyst
Data analysts frequently work with datasets that require cleaning and preparation prior to analysis. This course provides training in using R to tidy data, import data, and prepare it for analysis. A data analyst can use the skills developed in this course to manipulate data using tidyverse data pipelines, and also to recode variables. The role of a data analyst is made easier because of the material taught in this course.
Research Analyst
Research Analyst
A research analyst often needs to gather and prepare data for analysis. This course focuses on data manipulation and transformation in R using tools such as the dplyr package. A research analyst must be able to work with diverse datasets and convert them to formats that are readily usable. This course provides a solid foundation for a research analyst by assisting with their ability to collect and process data.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use data to build models for decision making. The course is relevant to a quantitative analyst because it develops the ability to collect, manipulate, and transform data, using R and tidyverse. This course also shows how to work with different data types, which is crucial for a quantitative analyst. The content around web scraping also may be helpful to a quantitative analyst.
Business Intelligence Analyst
Business Intelligence Analyst
The role of a business intelligence analyst involves working with and preparing data so that it can be used to improve business operations. This course demonstrates how to collect, manipulate, and transform data using R and tidyverse data pipelines. A business intelligence analyst will benefit from this course which teaches how to transform data into readily usable formats and also about web scraping, which is a useful skill in this role.
Bioinformatician
Bioinformatician
A bioinformatician applies computational and statistical techniques to analyze biological data. This course may be useful to a bioinformatician because it teaches how to collect, manipulate, and transform data in R -- skills that are essential for anyone working with large biological datasets. The topics of data tidying, data recoding, and web scraping may be useful for a bioinformatician who uses R.
Market Research Analyst
Market Research Analyst
A market research analyst uses diverse datasets to understand consumer behavior and market trends. This course may be useful to the market research analyst because it provides training in R, which is an essential skill for many data roles. The market research analyst may also benefit from learning about web scraping, which is covered in this course. This course will help with their data preparation needs.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers build and deploy machine learning models, which requires the preparation of data. This course may be useful to a machine learning engineer because it provides skills in how to use R to tidy data, import and recode data, and perform web scraping. The data preparation skills taught in this course form the foundation for good training of machine learning models.
Financial Analyst
Financial Analyst
A financial analyst often works with large datasets, analyzing financial trends. This course may help a financial analyst with the data preparation component of their work. The course provides training in R, which a financial analyst will be able to leverage to perform their work. It also teaches concepts like data tidying, importing, and recoding, which are all relevant to the work of a financial analyst.
Operations Research Analyst
Operations Research Analyst
An operations research analyst uses mathematical and analytical methods to solve problems and improve operations, and this often involves working with data. This course may be useful to the operations research analyst as it trains them in how to use R to manipulate, collect, and transform data. An operations research analyst will be able to use their skills from this class to more easily work with data.
Business Analyst
Business Analyst
Business analysts work with data to help inform business decisions. This course may help a business analyst to more easily work with data. The course covers topics such as data manipulation, data transformation, and web scraping. A business analyst who takes this course may be able to more readily work with data using skills that they have learned from this course.
Economist
Economist
An economist often works with data. While this course does not directly deal with economics, it does provide important skills related to data handling. An economist who takes this course may be able to more readily work with data through the R programming language. The data manipulation and web scraping skills may be useful for an economist in gathering and cleaning data.
Actuary
Actuary
An actuary analyzes the financial costs of risk and uncertainty. This course may be useful to an actuary because it provides a foundation in working with data using R. An actuary can collect and manipulate data with skills from this course. In particular the skills related to data tidying and web scraping may be useful.
Database Administrator
Database Administrator
Database administrators are responsible for the performance and integrity of databases. This course may be useful to a database administrator because it introduces them to the process of data manipulation. Although the course focuses on R, it may be useful to a database administrator to understand how data is organized. In particular, data tidying using packages like dplyr may be relevant.
For more career information including salaries, visit:
OpenCourser.com/course/a1ec3c/data
Reading list
We've selected two 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
Data Tidying and Importing with R.
Save
Provides a comprehensive introduction to data science using R, with a strong focus on the tidyverse packages. It covers data manipulation, visualization, and modeling, making it an excellent resource for this course. It is commonly used as a textbook at academic institutions. Reading this book will give you a solid foundation in the tools and techniques used in the course.
Provides a practical guide to web scraping using R. It covers the basics of HTML and CSS, as well as the rvest package for extracting data from websites. This book is more valuable as additional reading than it is as a current reference. Reading this book will help you expand your knowledge of web scraping techniques and best practices.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/a1ec3c/data
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Effort
3 weeks of study, 2-4 hours/week
Level
Introductory
Via
Coursera
Institution
Duke University
Instructors
Dr. Elijah Meyer
Mine Çetinkaya-Rundel
Language
English
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