Merging Data Sources with R 3 from Pluralsight

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.

What's inside

Syllabus

Course Overview

Managing Vectors

Joining Data Sets with the Merge() Function

Joining Data Sets with dplyr

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

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

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Attend a data science meetup to connect with professionals and discuss best practices for data merging in R, gaining valuable insights from experienced practitioners.

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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

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Join a peer study group to collaborate with fellow learners, discuss challenges, and reinforce your understanding of data merging techniques in R.

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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

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Practice merging vectors using the paste() and append() methods to enhance your understanding of data manipulation in R.

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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

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R Data Manipulation Workshop

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Attend a workshop focused on data manipulation in R, allowing you to interact with experts and learn advanced techniques for merging data frames.

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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()

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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.

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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

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Develop a personal project involving data merging in R, allowing you to apply and refine your skills while creating a valuable resource.

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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

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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

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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

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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.

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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 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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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