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Chase DeHan
People coming from spreadsheets are blown away with how easy and powerful it is to manipulate tabular data with R. In this course, you will learn the fundamental building blocks of how to work with the R data frame.
Read more
People coming from spreadsheets are blown away with how easy and powerful it is to manipulate tabular data with R. In this course, you will learn the fundamental building blocks of how to work with the R data frame.
Read more
People coming from spreadsheets are blown away with how easy and powerful it is to manipulate tabular data with R. In this course, you will learn the fundamental building blocks of how to work with the R data frame.
Data preparation is one of the most difficult and time-consuming tasks for data professionals. In this course, Manipulating Dataframes in R, you will learn foundational knowledge of the R dataframe. First, you will explore the basics of the data frame. Next, you will discover how to access certain fields in your data. Finally, you will learn how to do these same tasks with the powerful dplyr package. When you’re finished with this course, you will have the skills and knowledge of data manipulation in R needed to succeed at getting your data into the proper form for analysis.
Register for this course and see more details by visiting:
OpenCourser.com/course/5jleci/manipulating
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What's inside
Syllabus
Course Overview
Understanding Dataframe Basics
Slicing Dataframes
Filtering Rows by Criteria
Read more
Syllabus
Course Overview
Understanding Dataframe Basics
Slicing Dataframes
Filtering Rows by Criteria
Read more
Manipulating Dataframes with dplyr
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces foundational knowledge of the R dataframe
Appropriate for those new to data manipulation in R
Provides a strong foundation in data manipulation, including slicing, filtering, and dplyr
Provides foundational knowledge of the R dataframe
Covers the basics of dataframes, data slicing, row filtering, and dplyr manipulation
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Save Manipulating Dataframes in R 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 Manipulating Dataframes in R with these
activities:
Review R Data Frame Basics
Show steps
Brush up on the fundamentals of R data frames, including their structure, indexing, and basic operations.
Browse courses on
Data Manipulation
Show steps
-
Review documentation or tutorials on R data frames.
-
Create a simple data frame and practice basic operations such as adding, deleting, and modifying rows and columns.
-
Complete exercises or quizzes to test your understanding.
Review Data Manipulation Basics
Show steps
Review the fundamental concepts of data manipulation, including data types, data structures, and basic operations.
Browse courses on
Data Manipulation
Show steps
-
Read through the course syllabus and identify key concepts related to data manipulation.
-
Review notes or materials from previous courses or online resources on data manipulation.
-
Complete practice exercises to reinforce your understanding.
Exercises: Filtering by Field and Multiple Criteria
Show steps
Complete practice exercises that focus on filtering and slicing data frames by field and multiple criteria.
Browse courses on
Data Manipulation
Show steps
-
Install and load the RStudio IDE.
-
Import a dataset into R.
-
Practice filtering the data by single and multiple fields.
-
Test your understanding by completing practice exercises.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Tutorial: Advanced Data Manipulation with dplyr
Show steps
Follow step-by-step tutorials to gain practical experience in using the dplyr package for advanced data manipulation tasks.
Browse courses on
Data Manipulation
Show steps
-
Install and load the dplyr package.
-
Follow a tutorial on dplyr's core functions, such as filter(), select(), and arrange().
-
Practice using dplyr functions to manipulate and transform data.
-
Complete exercises to test your understanding and apply your skills.
Blog Post: Data Manipulation Best Practices
Show steps
Write a blog post that shares your insights and best practices for effective data manipulation in R.
Browse courses on
Data Manipulation
Show steps
-
Research and gather information on best practices for data manipulation in R.
-
Organize your thoughts and structure your blog post.
-
Write clear and concise content, providing examples and code snippets.
-
Proofread and edit your post for clarity and accuracy.
-
Publish your blog post on a relevant platform or share it with others.
Data Manipulation Coding Challenges
Show steps
Challenge yourself with a series of coding challenges that involve complex data manipulation operations.
Browse courses on
Data Manipulation
Show steps
-
Find a website or platform that offers data manipulation coding challenges.
-
Select a challenge that aligns with your skill level and interests.
-
Code and execute your solution, ensuring that it meets the challenge requirements.
-
Review your solution and identify areas for improvement.
Project: Data Wrangling with R Data Frames
Show steps
Embark on a project that involves collecting, cleaning, and manipulating real-world data using R data frames.
Browse courses on
Data Manipulation
Show steps
-
Identify a dataset that aligns with your interests.
-
Import the data into R and explore its structure.
-
Clean and preprocess the data to handle missing values, outliers, and inconsistencies.
-
Use data manipulation techniques to transform and aggregate the data.
-
Visualize and analyze the results to gain insights from the data.
Mentor Junior Data Scientists
Show steps
Share your knowledge and skills by mentoring junior data scientists, guiding them in their journey to master data manipulation.
Browse courses on
Data Manipulation
Show steps
-
Identify opportunities to mentor junior data scientists, such as through volunteering or joining online communities.
-
Establish clear goals and expectations for the mentorship.
-
Provide guidance and support on data manipulation techniques and best practices.
-
Review their work, provide feedback, and encourage their professional growth.
-
Foster a positive and supportive learning environment.
Review R Data Frame Basics
Show steps
Brush up on the fundamentals of R data frames, including their structure, indexing, and basic operations.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Review documentation or tutorials on R data frames.
- Create a simple data frame and practice basic operations such as adding, deleting, and modifying rows and columns.
- Complete exercises or quizzes to test your understanding.
Review Data Manipulation Basics
Show steps
Review the fundamental concepts of data manipulation, including data types, data structures, and basic operations.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Read through the course syllabus and identify key concepts related to data manipulation.
- Review notes or materials from previous courses or online resources on data manipulation.
- Complete practice exercises to reinforce your understanding.
Exercises: Filtering by Field and Multiple Criteria
Show steps
Complete practice exercises that focus on filtering and slicing data frames by field and multiple criteria.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Install and load the RStudio IDE.
- Import a dataset into R.
- Practice filtering the data by single and multiple fields.
- Test your understanding by completing practice exercises.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Tutorial: Advanced Data Manipulation with dplyr
Show steps
Follow step-by-step tutorials to gain practical experience in using the dplyr package for advanced data manipulation tasks.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Install and load the dplyr package.
- Follow a tutorial on dplyr's core functions, such as filter(), select(), and arrange().
- Practice using dplyr functions to manipulate and transform data.
- Complete exercises to test your understanding and apply your skills.
Blog Post: Data Manipulation Best Practices
Show steps
Write a blog post that shares your insights and best practices for effective data manipulation in R.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Research and gather information on best practices for data manipulation in R.
- Organize your thoughts and structure your blog post.
- Write clear and concise content, providing examples and code snippets.
- Proofread and edit your post for clarity and accuracy.
- Publish your blog post on a relevant platform or share it with others.
Data Manipulation Coding Challenges
Show steps
Challenge yourself with a series of coding challenges that involve complex data manipulation operations.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Find a website or platform that offers data manipulation coding challenges.
- Select a challenge that aligns with your skill level and interests.
- Code and execute your solution, ensuring that it meets the challenge requirements.
- Review your solution and identify areas for improvement.
Project: Data Wrangling with R Data Frames
Show steps
Embark on a project that involves collecting, cleaning, and manipulating real-world data using R data frames.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Identify a dataset that aligns with your interests.
- Import the data into R and explore its structure.
- Clean and preprocess the data to handle missing values, outliers, and inconsistencies.
- Use data manipulation techniques to transform and aggregate the data.
- Visualize and analyze the results to gain insights from the data.
Mentor Junior Data Scientists
Show steps
Share your knowledge and skills by mentoring junior data scientists, guiding them in their journey to master data manipulation.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior data scientists, such as through volunteering or joining online communities.
- Establish clear goals and expectations for the mentorship.
- Provide guidance and support on data manipulation techniques and best practices.
- Review their work, provide feedback, and encourage their professional growth.
- Foster a positive and supportive learning environment.
Career center
Learners who complete Manipulating Dataframes in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts play a crucial role in organizations by transforming raw data into actionable insights. They are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. Taking this course can augment a Data Analyst's skillset. The content covered in the course is directly applicable to the day-to-day tasks of a Data Analyst.
Health Data Analyst
Health Data Analyst
Health Data Analysts use statistical and analytical techniques to analyze healthcare data. They help healthcare organizations improve patient care, reduce costs, and identify trends. This course can be beneficial for aspiring Health Data Analysts as it provides a foundation in data manipulation in R. This skillset is essential for managing and analyzing large healthcare datasets.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They help investment firms make informed decisions about investment strategies. This course can be helpful for Quantitative Analysts, as it provides a deep dive into data manipulation in R. This skillset is essential for managing and analyzing large datasets used in financial modeling.
Data Scientist
Data Scientist
Data Scientists use their skills in data analysis to solve complex business problems. They leverage statistical techniques to derive valuable insights from structured and unstructured data. This course in Manipulating Dataframes in R is a solid first step for aspiring Data Scientists as it provides a strong foundation in how to organize and access tabular data. Data manipulation is a key skillset for the manipulation of complex datasets, a task that Data Scientists frequently perform.
Epidemiologist
Epidemiologist
Epidemiologists investigate the distribution and determinants of health-related states or events in specified populations. They use statistical methods to analyze data and identify risk factors for diseases. This course may be useful for aspiring Epidemiologists, as it provides a foundation in data manipulation in R. This skillset is valuable for managing and analyzing large datasets used in epidemiological studies.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in the biomedical sciences. They work closely with medical researchers to design and analyze clinical trials and other studies. This course may be useful for aspiring Biostatisticians, as it provides a foundation in data manipulation in R. This skillset is valuable for managing and analyzing complex medical data.
Data Visualization Engineer
Data Visualization Engineer
Data Visualization Engineers design and develop data visualizations to communicate insights from data. They use their technical skills to create clear and visually appealing representations of data. This course can be useful for Data Visualization Engineers as it provides a foundation in data manipulation in R. This skillset is essential for preparing and transforming data for visualization purposes.
Market Researcher
Market Researcher
Market Researchers study consumer behavior and trends to help businesses make informed decisions. They use various techniques to collect and analyze data, including surveys, interviews, and focus groups. This course can be beneficial for aspiring Market Researchers as it covers the basics of data manipulation in R. This skillset is valuable for understanding and analyzing market research data to identify consumer insights.
Statistician
Statistician
Statisticians use statistical methods to analyze data and make informed decisions. They work in diverse fields, including healthcare, finance, and marketing. This course can aid the professional development of a Statistician as it covers the foundational concepts of data manipulation in R. This is a fundamental skillset for the statistical analysis of complex datasets.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models to solve complex problems. They work closely with data scientists to develop and implement algorithms that learn from data. This course can be beneficial for Machine Learning Engineers as it provides a foundation in data manipulation in R. This skillset is essential for preparing data for machine learning models.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines that ensure the availability and quality of data for analysis. They possess strong programming skills and a deep understanding of data management technologies. This course can be useful for individuals interested in becoming Data Engineers as it provides a solid foundation in data manipulation using R, a skillset required for this role.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work in various industries, including insurance, finance, and healthcare. This course may be useful for aspiring Actuaries as it provides a foundation in data manipulation in R. This skillset is valuable for analyzing and interpreting data to make informed decisions about risk and uncertainty.
Business Analyst
Business Analyst
Business Analysts bridge the gap between business stakeholders and technical teams by translating business requirements into technical specifications. They help organizations make informed decisions by analyzing data and identifying improvement opportunities. This course may be useful for aspiring Business Analysts as it covers the basics of data manipulation in R. This skillset is valuable for understanding and analyzing data to support decision-making.
Database Administrator
Database Administrator
Database Administrators ensure the smooth functioning and maintenance of an organization's databases. They implement data security measures to protect sensitive information while also optimizing database performance. This course will be helpful as the work of a Database Administrator often involves manipulating data in tabular form, a skill that this course teaches.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their programming skills to create solutions that meet the needs of users. This course may be useful for Software Engineers interested in working with data. The course provides a foundation in data manipulation in R, a skillset that can be applied to software development projects involving data analysis and visualization.
For more career information including salaries, visit:
OpenCourser.com/course/5jleci/manipulating
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
Manipulating Dataframes in R.
Save
Provides a comprehensive introduction to the R programming language, with a focus on data science applications. It covers the basics of data manipulation, visualization, and statistical modeling, and includes many practical examples and exercises.
Save
Provides a collection of recipes for solving common problems with R, covering topics such as data manipulation, visualization, and statistical modeling. It good resource for those who want to learn how to use R for specific tasks.
Provides a comprehensive introduction to machine learning with R, covering topics such as supervised and unsupervised learning, and model evaluation. It includes many examples and exercises, and good resource for those who want to learn how to use R for machine learning.
Provides a comprehensive introduction to the ggplot2 package for R, which is used for creating visualizations. It covers topics such as data visualization, chart types, and customization. It good resource for those who want to learn how to create beautiful and informative visualizations with R.
Provides a collection of recipes for solving common problems with RStudio, covering topics such as data manipulation, visualization, and package management. It good resource for those who want to learn how to use RStudio more efficiently.
Provides an introduction to Bayesian statistics using R, covering topics such as probability, inference, and model checking. It includes many examples and exercises, and good resource for those who want to learn how to use R for Bayesian analysis.
Provides a practical guide to data manipulation with R, covering topics such as data cleaning, transformation, and aggregation. It includes many examples and exercises, and good resource for those who want to learn how to use R for data analysis.
Provides a practical guide to using R for data analysis, covering topics such as data manipulation, visualization, and statistical modeling. It includes many examples and exercises, and good resource for those who want to learn how to use R for data analysis.
Save
Provides a comprehensive overview of the R programming language, from its history and development to its use in various fields. It covers a wide range of topics, including data manipulation, visualization, statistical modeling, and programming techniques.
Provides a comprehensive overview of the R programming language, with a focus on good programming practices. It covers topics such as data structures, functions, and object-oriented programming.
Save
Provides a gentle introduction to the R programming language, with a focus on data analysis. It covers the basics of data manipulation, visualization, and statistical modeling, and good resource for those who are new to R.
Provides an introduction to statistical methods using R, covering topics such as probability, inference, and regression. It includes many examples and exercises, and good resource for those who want to learn how to use R for statistical analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5jleci/manipulating
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Time
84 hours
Level
Introductory
Via
Pluralsight
Instructor
Chase DeHan
Language
English
This course belongs to a
collection
called
Cleaning Data with R. It's comprised of four courses:
- Querying and Converting Data Types in R
- Manipulating Dataframes in R (viewing)
- Coping with Missing, Invalid, and Duplicate Data in R
- Manipulating String Data in R
Good to know
Introduces foundational knowledge of the R dataframe
Appropriate for those new to data manipulation in R
Provides a strong foundation in data manipulation, including slicing, filtering, and dplyr
Provides foundational knowledge of the R dataframe
Covers the basics of dataframes, data slicing, row filtering, and dplyr manipulation
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