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Martin Burger
Learn how to use and reshape data frames in R. The course also covers common alternatives to the data frame like tibble and data table.
Read more
Learn how to use and reshape data frames in R. The course also covers common alternatives to the data frame like tibble and data table.
Read more
Learn how to use and reshape data frames in R. The course also covers common alternatives to the data frame like tibble and data table.
Learn to work with very popular and versatile tabular data types in R. When using R, there is no way around the data frame and its alternatives. In this course, Managing Data in R Using Data Frames, you will learn that tabular data (data.frame, data.table, tibble) are the standard data type in R and most of the analyses performed in R use this data format. First, you will discover how to manipulate and reshape a raw data frame. Then, you will explore the common tasks such as table import, factor conversion, formatting of a table header, orientation in a table, column splits, column removal and addition, fusion of multiple tables, variable transformations, query, as well as export of a table. Finally, you will produce a clean data frame which can be used for detailed analysis or passed on to team members.
When you complete this course, you’ll have the skills and knowledge to perform standard tasks on data frames and can understand the many advantages of a data frame.
Register for this course and see more details by visiting:
OpenCourser.com/course/j7vnp3/managing
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What's inside
Syllabus
Course Overview
Understanding the Concepts Behind a Data Frame
Exploring a Data Frame
Making Changes to a Data Frame
Read more
Syllabus
Course Overview
Understanding the Concepts Behind a Data Frame
Exploring a Data Frame
Making Changes to a Data Frame
Read more
Continuing your Learning Path and Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for students looking to strengthen their existing foundation with managing data in R
Covers common alternatives to the data frame, like tibble and data table
Taught by an experienced instructor, Martin Burger
Explores fundamental data manipulation and reshaping techniques
Provides guidance on common tasks like table import and data formatting
Could benefit from hands-on exercises or interactive materials
Save this course
Save Managing Data in R Using Data Frames to your list so you can find it easily later:
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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 Managing Data in R Using Data Frames with these
activities:
Review tabular data structures
Show steps
Refresh your understanding of tabular data structures to build a stronger foundation for this course.
Browse courses on
Data Frames
Show steps
-
Read the documentation on data frames in R
Watch tutorials on data manipulation in R
Show steps
Enhance your data manipulation skills by following guided tutorials on the subject.
Browse courses on
Data Manipulation
Show steps
-
Search for tutorials on data manipulation in R
-
Select a tutorial that aligns with your skill level and learning preferences
-
Follow the tutorial step-by-step, practicing the techniques demonstrated
Contribute to an open-source data manipulation library
Show steps
Expand your knowledge by contributing to the development of a data manipulation library.
Show steps
-
Identify an open-source data manipulation library
-
Familiarize yourself with the library's codebase and documentation
-
Identify areas where you can contribute your skills and knowledge
-
Make a pull request with your contributions
Show all three activities
Review tabular data structures
Show steps
Refresh your understanding of tabular data structures to build a stronger foundation for this course.
Browse courses on
Data Frames
Show steps
Show steps
Show steps
- Read the documentation on data frames in R
Watch tutorials on data manipulation in R
Show steps
Enhance your data manipulation skills by following guided tutorials on the subject.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Search for tutorials on data manipulation in R
- Select a tutorial that aligns with your skill level and learning preferences
- Follow the tutorial step-by-step, practicing the techniques demonstrated
Contribute to an open-source data manipulation library
Show steps
Expand your knowledge by contributing to the development of a data manipulation library.
Show steps
Show steps
Show steps
- Identify an open-source data manipulation library
- Familiarize yourself with the library's codebase and documentation
- Identify areas where you can contribute your skills and knowledge
- Make a pull request with your contributions
Show all three activities
Career center
Learners who complete Managing Data in R Using Data Frames will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists combine programming, mathematics and statistics to extract knowledge from data. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Data Scientist as it introduces the concepts surrounding organizing and reshaping data. This course can help build a foundation for the roles and responsibilities typically expected of a Data Scientist.
Data Engineer
Data Engineer
Data Engineers build, maintain, and manage the infrastructure that processes large amounts of data. They use their expertise in data management and programming to develop solutions that can handle the complex challenges of big data. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Data Engineer as it teaches how to use and reshape data frames. It also introduces the common alternatives to the data frame such as tibble and data table.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data organization and data analysis to clean and interpret data. They turn raw data into useful information that can help their organization make better decisions. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Data Analyst as it teaches how to manipulate and reshape a raw data frame. Additionally, it provides a granular understanding of some of the challenges facing those who work with data including table import, factor conversion, and variable transformations.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to collect, analyze, interpret, and present data. They work with data from a variety of sources to help businesses, organizations, and governments make informed decisions. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Statistician as it provides a detailed overview of how to manipulate and reshape data frames. It also covers some of the tasks involved in working with data, such as table import, factor conversion, and variable transformations.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data to help businesses understand their customers and make informed decisions about their products and services. They use a variety of methods to collect data, including surveys, interviews, and focus groups. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Market Researcher as it teaches how to organize and reshape data. It also provides a granular understanding of some of the challenges facing those who work with data including table import, factor conversion, and variable transformations.
Business Analyst
Business Analyst
Business Analysts use their understanding of business processes and data analysis to help organizations improve their performance. They work with stakeholders to identify problems, analyze data, and develop solutions that can help the organization achieve its goals. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Business Analyst as it teaches how to organize and reshape data. It also provides a granular understanding of some of the challenges facing those who work with data including table import, factor conversion, and variable transformations.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their understanding of mathematics and statistics to help organizations improve their operations. They work with data from a variety of sources to identify problems, analyze data, and develop solutions that can help the organization achieve its goals. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Operations Research Analyst as it teaches how to organize and reshape data. It also provides a granular understanding of some of the challenges facing those who work with data including table import, factor conversion, and variable transformations.
Financial Analyst
Financial Analyst
Financial Analysts use their understanding of finance and economics to analyze financial data and make recommendations for investment decisions. They work with a variety of financial data, including stock prices, bond yields, and economic indicators. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Financial Analyst as it teaches how to organize and reshape data. It also provides a granular understanding of some of the challenges facing those who work with data including table import, factor conversion, and variable transformations.
Actuary
Actuary
Actuaries use their understanding of mathematics, statistics, and finance to assess risk and uncertainty. They work with a variety of data to develop models that can help businesses and organizations make informed decisions about their future. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Actuary as it teaches how to organize and reshape data. It also provides a granular understanding of some of the challenges facing those who work with data including table import, factor conversion, and variable transformations.
Information Security Analyst
Information Security Analyst
Information Security Analysts design and implement security measures to protect data and information systems from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with a variety of security technologies and practices to ensure that data is kept safe and secure. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Information Security Analyst as it provides a detailed overview of how to manipulate and reshape data in R. While many Information Security Analysts may not work directly with R, it still may be helpful for them to have a basic understanding of the language.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies to create software that meets the needs of their users. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Software Engineer as it provides a detailed overview of how to manipulate and reshape data in R. While many Software Engineers may not work directly with data, they need to understand how to do so as their work will likely involve interacting with databases.
Data Architect
Data Architect
Data Architects design and manage the architecture of data systems. They work with a variety of stakeholders to ensure that data is organized and managed in a way that meets the needs of the organization. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Data Architect as it provides a detailed overview of how to manipulate and reshape data in R. While many Data Architects may not work directly with R, it still may be helpful for them to have a basic understanding of the language.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. They work with a variety of database technologies to ensure that data is stored and managed efficiently. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Database Administrator as it provides a detailed overview of how to manipulate and reshape data in R. While many Database Administrators may not work directly with R, it still may be helpful for them to have a basic understanding of the language.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and implement computer systems and applications. They work with a variety of programming languages and technologies to create solutions to real-world problems. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Computer Scientist as it provides a detailed overview of how to manipulate and reshape data frames in R. While many Computer Scientists may not work directly with data, they need to understand how to do so as their work may be tangentially related to data.
Web Developer
Web Developer
Web Developers design and develop websites and web applications. They work with a variety of programming languages and technologies to create websites that are both visually appealing and functional. This course, Managing Data in R Using Data Frames, may be useful for an aspiring Web Developer as it provides a detailed overview of how to manipulate and reshape data frames in R. While many Web Developers may not work directly with data, they need to understand how to do so as their work will likely involve interacting with databases.
For more career information including salaries, visit:
OpenCourser.com/course/j7vnp3/managing
Reading list
We've selected 11 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
Managing Data in R Using Data Frames.
Save
Comprehensive guide to using R for data science. It covers all the essential topics, from data import and cleaning to data analysis and visualization. It valuable resource for anyone who wants to learn how to use R for data science.
Comprehensive guide to R programming for data science. It covers all the essential topics, from data import and cleaning to data analysis and visualization. It valuable resource for anyone who wants to learn how to use R for data science.
Save
Comprehensive reference guide to the R programming language. It covers all the essential topics, from basic syntax to advanced statistical techniques. It valuable resource for anyone who wants to learn more about R.
Practical guide to effective R programming. It covers all the essential topics, from code style to debugging. It valuable resource for anyone who wants to learn how to write effective R code.
Save
Collection of recipes for common R tasks. It covers a wide range of topics, from data import and cleaning to data analysis and visualization. It valuable resource for anyone who wants to learn how to use R for common data science tasks.
Practical guide to data manipulation with R. It covers all the essential topics, from data import and cleaning to data transformation and reshaping. It valuable resource for anyone who wants to learn how to use R for data manipulation.
Save
Comprehensive guide to R for bioinformatics. It covers all the essential topics, from data import and cleaning to data analysis and visualization. It valuable resource for anyone who wants to learn how to use R for bioinformatics.
Practical guide to R for data science. It covers all the essential topics, from data import and cleaning to data analysis and visualization. It valuable resource for anyone who wants to learn how to use R for data science.
Comprehensive guide to modern statistics with R. It covers all the essential topics, from data visualization to statistical modeling. It valuable resource for anyone who wants to learn how to use R for modern statistics.
Comprehensive guide to machine learning with R. It covers all the essential topics, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn how to use R for machine learning.
Comprehensive guide to deep learning with R. It covers all the essential topics, from neural networks to deep learning models. It valuable resource for anyone who wants to learn how to use R for deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/j7vnp3/managing
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Time
121 hours
Level
Intermediate
Via
Pluralsight
Instructor
Martin Burger
Language
English
This course belongs to a
collection
called
Programming Data Structures in R. It's comprised of four courses:
- Managing Data in R Using Data Frames (viewing)
- Working with Data Types in R
- Programming R Vectors and Factors
- Programming R Matrices, Lists, and Arrays
Good to know
Suitable for students looking to strengthen their existing foundation with managing data in R
Covers common alternatives to the data frame, like tibble and data table
Taught by an experienced instructor, Martin Burger
Explores fundamental data manipulation and reshaping techniques
Provides guidance on common tasks like table import and data formatting
Could benefit from hands-on exercises or interactive materials
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