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Dan Tofan
Learn how to merge data with R. How do you merge values into vectors? How do you merge vectors into data frames? How do you join data frames? See how to use base R and dplyr to do left, right, and full outer joins with plenty of examples.
Read more
Learn how to merge data with R. How do you merge values into vectors? How do you merge vectors into data frames? How do you join data frames? See how to use base R and dplyr to do left, right, and full outer joins with plenty of examples.
Read more
Learn how to merge data with R. How do you merge values into vectors? How do you merge vectors into data frames? How do you join data frames? See how to use base R and dplyr to do left, right, and full outer joins with plenty of examples.
In your R data science projects, you need very often to work with data which is spread out across multiple data sources. For example, given two separate data sets on products and their sales, how can you merge them into a new data set? In this course, Merging Data Sources with R, you will gain the ability to merge data from different sources in a controlled way that enables you to keep only the data you need. First, you will learn to merge vectors, which includes using the paste() and append() methods. Next, you will discover how to join data sets with the merge() function, which includes left, inner, right and full outer joins, on data frames that can have one-to-one, one-to-many, or many-to-many relationships. Finally, you will explore how to join data sets with the dplyr package, which covers the previous joins plus anti and semi joins. When you are finished with this course, you will have the skills and knowledge of merging data from different sources, needed to do data wrangling with R.
Register for this course and see more details by visiting:
OpenCourser.com/course/ktxkfi/merging
What's inside
Syllabus
Course Overview
Managing Vectors
Joining Data Sets with the Merge() Function
Joining Data Sets with dplyr
Read more
Syllabus
Course Overview
Managing Vectors
Joining Data Sets with the Merge() Function
Joining Data Sets with dplyr
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Delves into the fundamentals of data merging with R, enhancing data manipulation skills
Provides a practical approach to merging data from various sources, addressing a common challenge in data science projects
Introduces both base R and the dplyr package for data merging, ensuring learners gain proficiency in industry-standard tools
Explicitly covers different join types, including left, right, and full outer joins, equipping learners with comprehensive data manipulation skills
Suitable for intermediate learners with a foundational understanding of R and data manipulation concepts
Save this course
Save Merging Data Sources with R 3 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 Merging Data Sources with R 3 with these
activities:
Data Science Meetup
Show steps
Attend a data science meetup to connect with professionals and discuss best practices for data merging in R, gaining valuable insights from experienced practitioners.
Browse courses on
Data Manipulation
Show steps
-
Identify and register for a data science meetup in your area.
-
Prepare a brief introduction about your interest in data merging.
-
Attend the meetup and actively engage in conversations.
-
Follow up with new connections on LinkedIn or other platforms.
Peer Study Group
Show steps
Join a peer study group to collaborate with fellow learners, discuss challenges, and reinforce your understanding of data merging techniques in R.
Browse courses on
Data Manipulation
Show steps
-
Identify or create a peer study group with individuals interested in data merging.
-
Establish a regular meeting schedule and agenda.
-
Share resources, ask questions, and work together to solve problems.
-
Present findings or insights to the group.
Merging Vectors
Show steps
Practice merging vectors using the paste() and append() methods to enhance your understanding of data manipulation in R.
Browse courses on
Data Manipulation
Show steps
-
Create two vectors of different lengths containing various elements.
-
Use the paste() method to concatenate the vectors into a single vector.
-
Utilize the append() method to add elements to the existing vector.
-
Print the resulting vector to verify the merging operation.
Five other activities
Expand to see all activities and additional details
Show all eight activities
R Data Manipulation Workshop
Show steps
Attend a workshop focused on data manipulation in R, allowing you to interact with experts and learn advanced techniques for merging data frames.
Browse courses on
Data Manipulation
Show steps
-
Identify and register for an R data manipulation workshop.
-
Prepare by reviewing basic data merging concepts.
-
Actively participate in the workshop, taking notes and asking questions.
-
Implement the techniques learned in your own projects.
Join Data Frames with merge()
Show steps
Follow guided tutorials to master data set joining with the merge() function, exploring different join types and handling one-to-one, one-to-many, and many-to-many relationships.
Browse courses on
Data Manipulation
Show steps
-
Identify two data frames with common columns for joining.
-
Use the merge() function to perform left, inner, right, and full outer joins.
-
Explore the results of each join operation and understand the differences.
-
Practice joining data frames with different key combinations.
-
Apply the merge() function to real-world data sets to solve practical problems.
Personal Data Merging Project
Show steps
Develop a personal project involving data merging in R, allowing you to apply and refine your skills while creating a valuable resource.
Browse courses on
Data Manipulation
Show steps
-
Identify a problem or topic that requires data merging.
-
Gather and clean the necessary data sets.
-
Merge the data using the techniques learned in the course.
-
Analyze the merged data and draw insights.
-
Present your project to others, showcasing your work and findings.
Advanced R
Show steps
Review the book 'Advanced R' by Wickham and Grolemund to reinforce your understanding of data manipulation techniques and expand your knowledge on working with complex data structures in R.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read the book thoroughly, focusing on chapters related to data merging.
-
Highlight and make notes on key concepts and techniques.
-
Attempt the exercises and problems provided in the book.
-
Create a summary or presentation based on what you learned.
Data Wrangling Project
Show steps
Create a data wrangling project that involves merging data from multiple sources using the techniques learned in the course, showcasing your ability to clean, transform, and prepare data for analysis.
Browse courses on
Data Manipulation
Show steps
-
Gather data from different sources relevant to a specific topic.
-
Clean and prepare the data for merging.
-
Apply data merging techniques to combine the data.
-
Explore and analyze the merged data to extract insights.
-
Present your findings in a clear and concise manner.
Data Science Meetup
Show steps
Attend a data science meetup to connect with professionals and discuss best practices for data merging in R, gaining valuable insights from experienced practitioners.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Identify and register for a data science meetup in your area.
- Prepare a brief introduction about your interest in data merging.
- Attend the meetup and actively engage in conversations.
- Follow up with new connections on LinkedIn or other platforms.
Peer Study Group
Show steps
Join a peer study group to collaborate with fellow learners, discuss challenges, and reinforce your understanding of data merging techniques in R.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Identify or create a peer study group with individuals interested in data merging.
- Establish a regular meeting schedule and agenda.
- Share resources, ask questions, and work together to solve problems.
- Present findings or insights to the group.
Merging Vectors
Show steps
Practice merging vectors using the paste() and append() methods to enhance your understanding of data manipulation in R.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Create two vectors of different lengths containing various elements.
- Use the paste() method to concatenate the vectors into a single vector.
- Utilize the append() method to add elements to the existing vector.
- Print the resulting vector to verify the merging operation.
Five other activities
Expand to see all activities and additional details
Show all eight activities
R Data Manipulation Workshop
Show steps
Attend a workshop focused on data manipulation in R, allowing you to interact with experts and learn advanced techniques for merging data frames.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Identify and register for an R data manipulation workshop.
- Prepare by reviewing basic data merging concepts.
- Actively participate in the workshop, taking notes and asking questions.
- Implement the techniques learned in your own projects.
Join Data Frames with merge()
Show steps
Follow guided tutorials to master data set joining with the merge() function, exploring different join types and handling one-to-one, one-to-many, and many-to-many relationships.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Identify two data frames with common columns for joining.
- Use the merge() function to perform left, inner, right, and full outer joins.
- Explore the results of each join operation and understand the differences.
- Practice joining data frames with different key combinations.
- Apply the merge() function to real-world data sets to solve practical problems.
Personal Data Merging Project
Show steps
Develop a personal project involving data merging in R, allowing you to apply and refine your skills while creating a valuable resource.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Identify a problem or topic that requires data merging.
- Gather and clean the necessary data sets.
- Merge the data using the techniques learned in the course.
- Analyze the merged data and draw insights.
- Present your project to others, showcasing your work and findings.
Advanced R
Show steps
Review the book 'Advanced R' by Wickham and Grolemund to reinforce your understanding of data manipulation techniques and expand your knowledge on working with complex data structures in R.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly, focusing on chapters related to data merging.
- Highlight and make notes on key concepts and techniques.
- Attempt the exercises and problems provided in the book.
- Create a summary or presentation based on what you learned.
Data Wrangling Project
Show steps
Create a data wrangling project that involves merging data from multiple sources using the techniques learned in the course, showcasing your ability to clean, transform, and prepare data for analysis.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Gather data from different sources relevant to a specific topic.
- Clean and prepare the data for merging.
- Apply data merging techniques to combine the data.
- Explore and analyze the merged data to extract insights.
- Present your findings in a clear and concise manner.
Career center
Learners who complete Merging Data Sources with R 3 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to solve business problems and develop new products and services. This course will help you build a foundation in data merging, which is a key skill for Data Scientists. By learning how to merge data from different sources, you can gain a more complete understanding of your data and make more accurate predictions. Additionally, this course will help you develop the skills you need to use R, a powerful programming language that is widely used in data science.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course will help you build a foundation in data merging, which is a key skill for Data Analysts. By learning how to merge data from different sources, you can gain a more complete understanding of your data and make more accurate predictions. Additionally, this course will help you develop the skills you need to use R, a powerful programming language that is widely used in data analysis.
Business Analyst
Business Analyst
Business Analysts help businesses understand their data and make better decisions. This course will help you build a foundation in data merging, which is a key skill for Business Analysts. By learning how to merge data from different sources, you can gain a more complete understanding of your business and make more informed decisions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses make informed decisions. This course will help you build a foundation in data merging, which is a key skill for Statisticians. By learning how to merge data from different sources, you can gain a more complete understanding of your data and make more accurate predictions.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course will help you build a foundation in data merging, which is a key skill for Database Administrators. By learning how to merge data from different sources, you can ensure that your databases are accurate and up-to-date.
Data Engineer
Data Engineer
Data Engineers design and build the systems that store and process data. This course will help you build a foundation in data merging, which is a key skill for Data Engineers. By learning how to merge data from different sources, you can help your organization gain a more complete understanding of its data and make better decisions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course will help you build a foundation in data merging, which is a key skill for Software Engineers. By learning how to merge data from different sources, you can help your team develop more robust and reliable applications.
Data Warehouse Analyst
Data Warehouse Analyst
Data Warehouse Analysts design and build data warehouses, which are large databases that store data from multiple sources. This course will help you build a foundation in data merging, which is a key skill for Data Warehouse Analysts. By learning how to merge data from different sources, you can help your organization gain a more complete understanding of its data and make better decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make predictions about financial markets. This course will help you build a foundation in data merging, which is a key skill for Quantitative Analysts. By learning how to merge data from different sources, you can gain a more complete understanding of the financial markets and make more accurate predictions.
Market Researcher
Market Researcher
Market Researchers study consumer behavior and market trends. This course will help you build a foundation in data merging, which is a key skill for Market Researchers. By learning how to merge data from different sources, you can gain a more complete understanding of your target market and develop more effective marketing campaigns.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency of operations. This course will help you build a foundation in data merging, which is a key skill for Operations Research Analysts. By learning how to merge data from different sources, you can gain a more complete understanding of your operations and make more informed decisions.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations. This course will help you build a foundation in data merging, which is a key skill for Financial Analysts. By learning how to merge data from different sources, you can gain a more complete understanding of the financial markets and make more informed investment recommendations.
Actuary
Actuary
Actuaries use data to assess risk and determine insurance premiums. This course will help you build a foundation in data merging, which is a key skill for Actuaries. By learning how to merge data from different sources, you can gain a more complete understanding of the risks involved in different insurance policies and set more accurate premiums.
Data Governance Specialist
Data Governance Specialist
Data Governance Specialists develop and implement policies and procedures for managing data. This course will help you build a foundation in data merging, which is a key skill for Data Governance Specialists. By learning how to merge data from different sources, you can help your organization ensure that its data is accurate, consistent, and secure.
Risk Manager
Risk Manager
Risk Managers use data to assess and manage risk. This course will help you build a foundation in data merging, which is a key skill for Risk Managers. By learning how to merge data from different sources, you can gain a more complete understanding of the risks involved in different activities and make more informed decisions.
For more career information including salaries, visit:
OpenCourser.com/course/ktxkfi/merging
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
Merging Data Sources with R 3.
Save
Comprehensive introduction to data science with R, covering a wide range of topics from data import and cleaning to data visualization and modeling. It valuable resource for both beginners and experienced R users who want to learn more about data science.
Provides a comprehensive overview of data manipulation in R, covering a wide range of topics from data import and cleaning to data transformation and aggregation. It valuable resource for both beginners and experienced R users who want to improve their data wrangling skills.
Provides a practical introduction to data science with R. It covers a wide range of topics from data import and cleaning to data visualization and statistical modeling. It valuable resource for both beginners and experienced R users who want to learn more about data science.
Provides a practical introduction to the R programming language. It covers a wide range of topics from basic syntax to advanced data analysis techniques. It valuable resource for both beginners and experienced R users who want to learn more about the language and its capabilities.
Save
Provides a collection of recipes for solving common problems in R. It covers a wide range of topics from data import and cleaning to data analysis and visualization. It valuable resource for both beginners and experienced R users who want to learn more about the language.
Save
Comprehensive reference guide to the R programming language. It covers a wide range of topics from basic syntax to advanced statistical modeling. It valuable resource for both beginners and experienced R users who want to learn more about the language.
Save
Provides a comprehensive guide to the ggplot2 R package. It covers a wide range of topics from creating basic ggplot2 graphs to developing more complex and interactive visualizations. It valuable resource for both beginners and experienced R users who want to learn more about ggplot2.
Provides a comprehensive guide to the R programming language. It covers a wide range of topics from basic syntax to advanced programming techniques. It valuable resource for both beginners and experienced R users who want to learn more about the language and its capabilities.
Provides a comprehensive introduction to modern statistics with R. It covers a wide range of topics from probability and inference to linear and nonlinear modeling. It valuable resource for both beginners and experienced R users who want to learn more about statistics.
Provides a concise and accessible introduction to the R programming language. It covers a wide range of topics from basic syntax to data analysis and visualization. It valuable resource for both beginners and experienced R users who want to learn more about the language.
Save
Provides a comprehensive guide to the Shiny R package. It covers a wide range of topics from creating basic Shiny apps to developing more complex and interactive applications. It valuable resource for both beginners and experienced R users who want to learn more about Shiny.
Save
Provides a gentle introduction to the R programming language. It covers a wide range of topics from basic syntax to data analysis and visualization. It valuable resource for beginners who want to learn more about R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ktxkfi/merging
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Time
89 hours
Level
Introductory
Via
Pluralsight
Instructor
Dan Tofan
Language
English
This course belongs to a
collection
called
Data Wrangling with R. It's comprised of two courses:
- Merging Data Sources with R 3 (viewing)
- Splitting and Combining Data with R
Good to know
Delves into the fundamentals of data merging with R, enhancing data manipulation skills
Provides a practical approach to merging data from various sources, addressing a common challenge in data science projects
Introduces both base R and the dplyr package for data merging, ensuring learners gain proficiency in industry-standard tools
Explicitly covers different join types, including left, right, and full outer joins, equipping learners with comprehensive data manipulation skills
Suitable for intermediate learners with a foundational understanding of R and data manipulation concepts
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