Splitting and Combining Data with R from Pluralsight

If you’ve struggled to categorize dates, clean strings, or order bars in ggplot, this course is for you. Learn the basics of splitting and combining data, variable cleaning and creation, grouping and summarizing data, and creating visualizations.

Summarizing statistics across groups is invaluable for comparing categories of observations. In this course, Splitting and Combining Data with R, you'll explore splitting data into groups based on some criteria, applying functions or calculations to each group independently, and combining the results into a data structure. To begin, you’ll learn how to create custom categorical variables for grouping, and custom numeric variables to which you can apply functions. Next, with the criteria for grouping created, you will split the data, apply functions, and combine the data into a data structure. Finally, with the raw data transformed, you’ll discover how a grouped dataframe can then be ungrouped with summary statistics maintained, or keep the grouped dataframe intact with plotting functions for visualizing variation between groups. By the end of this course, you’ll have a better understanding of how to use R to build data pipelines with dplyr, manipulate strings and dates for feature engineering, and create customized ggplot charts. .

What's inside

Syllabus

Course Overview

Creating Variables to Combine/Summarize Data

Grouping and Summarizing Data

Plotting Combined Data

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills in creating custom categorical and numeric variables for grouping and summarizing data, which are core skills for data analysis and visualization

Introduces techniques for splitting data into groups, applying functions or calculations to each group independently, and combining the results into a data structure, which are essential skills for data manipulation

Demonstrates how to create customized ggplot charts to visualize variation between groups, which is valuable for exploratory data analysis and data storytelling

Taught by Mariah Weatherford, who is recognized for their work in data science and data visualization

Examines advanced topics in data manipulation, including string and date manipulation, which are highly relevant to data scientists and data analysts

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 Splitting and Combining Data with R with these activities:

Join a study group or participate in online forums to discuss data manipulation challenges

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Engaging with peers will provide you with different perspectives and help you overcome challenges in data manipulation.

Browse courses on Data Manipulation

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Identify a study group or online forum that aligns with your learning goals.
Participate in discussions and ask questions related to data manipulation.
Share your knowledge and experiences to help others.

Review a book on advanced data manipulation techniques in R

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Reading a book will provide you with a comprehensive understanding of advanced data manipulation techniques and best practices.

View R for Data Science: Import, Tidy, Transform,... on Amazon

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Identify a book that covers advanced data manipulation techniques in R.
Read the book thoroughly and take notes on key concepts.
Apply the techniques learned to your own data analysis projects.

Follow an online tutorial on advanced data manipulation techniques in R

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Following a tutorial will provide you with structured guidance and examples to enhance your skills in data manipulation.

Browse courses on Data Manipulation

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Identify a tutorial that covers advanced data manipulation techniques in R.
Follow the tutorial step-by-step and complete the exercises provided.
Apply the techniques learned to your own data analysis projects.

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Practice splitting and combining data frames using dplyr

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Practice these skills to solidify your understanding of data manipulation and preparation techniques in R.

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Create a sample data frame with various data types.
Split the data frame into multiple data frames based on a categorical variable.
Combine the data frames back together using different functions (e.g., bind_rows(), bind_cols())

Create a collection of resources and tools for data manipulation in R

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Creating a compilation will provide you with a valuable reference for future data manipulation tasks.

Browse courses on Data Manipulation

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Gather resources such as tutorials, articles, and code snippets on data manipulation in R.
Organize the resources into a logical structure.
Share the compilation with others to benefit the community.

Attend a workshop on advanced data manipulation techniques in R

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Attending a workshop will provide you with hands-on experience and expert guidance in advanced data manipulation techniques.

Browse courses on Data Manipulation

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Identify a workshop that aligns with your learning goals.
Register for the workshop and prepare any necessary materials.
Attend the workshop and actively participate in the exercises and discussions.

Create a visualization that summarizes the findings from your data analysis

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Creating a visualization will help you communicate your findings effectively and reinforce your understanding of the data.

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Choose an appropriate visualization type (e.g., bar chart, line chart, scatterplot).
Use ggplot2 to create the visualization.
Customize the visualization to improve its readability and impact.

Mentor a junior data analyst or student who is learning data manipulation in R

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Mentoring others will reinforce your understanding of data manipulation and help you develop leadership skills.

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Identify a junior data analyst or student who needs guidance in data manipulation.
Provide regular support and guidance on data manipulation techniques and best practices.
Review their work and provide constructive feedback.
Encourage them to ask questions and participate in discussions.

Career center

Learners who complete Splitting and Combining Data with R will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians use data to make inferences about the world. They design and conduct studies, collect data, and analyze the results to draw conclusions. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Statistician.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts use data to solve business problems. They collect, clean, analyze, and visualize data to help businesses make informed decisions. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Data Analyst.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers build and deploy machine learning models. They work with data scientists to develop algorithms and then use their engineering skills to build systems that can learn from data. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use data to build models and make predictions. They work with large datasets and use statistical techniques to identify patterns and trends. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Data Scientist.

See salaries and explore the career path for Data Scientist

Market Researcher

Market Researchers use data to understand the needs of a market. They conduct surveys, focus groups, and other research methods to collect data on consumer behavior. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Market Researcher.

See salaries and explore the career path for Market Researcher

Business Analyst

Business Analysts use data to understand the needs of a business and recommend solutions. They work with stakeholders to gather requirements, analyze data, and develop recommendations. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Business Analyst.

See salaries and explore the career path for Business Analyst

Financial Analyst

Financial Analysts use data to make investment recommendations. They analyze financial statements, market data, and other information to evaluate the performance of companies and make recommendations on whether to buy, sell, or hold stocks. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Data Engineer

Data Engineers build and maintain the data infrastructure that powers data-driven organizations. They work with data scientists and other stakeholders to ensure that data is accessible, reliable, and secure. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Data Engineer.

See salaries and explore the career path for Data Engineer

Actuary

Actuaries use data to assess risk and uncertainty. They work with insurance companies and other financial institutions to develop products and pricing strategies. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for an Actuary.

See salaries and explore the career path for Actuary

Risk Analyst

Risk Analysts use data to assess and mitigate risk. They work with businesses to identify and manage risks that could impact their operations. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Risk Analyst.

See salaries and explore the career path for Risk Analyst

Operations Research Analyst

Operations Research Analysts use data to improve the efficiency and effectiveness of operations. They work with businesses to identify and solve problems using mathematical and statistical techniques. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Data Visualization Specialist

Data Visualization Specialists use data to create visual representations of data. They work with data analysts and other stakeholders to communicate insights and findings. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Data Visualization Specialist.

See salaries and explore the career path for Data Visualization Specialist

Software Engineer

Software Engineers design, develop, and maintain software applications. They work with data to build features and functionality into software products. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Software Engineer.

See salaries and explore the career path for Software Engineer

Database Administrator

Database Administrators manage and maintain databases. They work with data to ensure that it is stored, organized, and accessible. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Database Administrator.

See salaries and explore the career path for Database Administrator

Quantitative Analyst

Quantitative Analysts use data to make investment decisions. They work with financial data to develop models and strategies for investing. This course on Splitting and Combining Data with R can help you build a strong foundation in data manipulation and analysis, which are essential skills for a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst