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Splitting and Combining Data with R
Mariah Weatherford
Mariah Weatherford
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.
Read more
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.
Read more
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. .
Register for this course and see more details by visiting:
OpenCourser.com/course/say79m/splitting
What's inside
Syllabus
Course Overview
Creating Variables to Combine/Summarize Data
Grouping and Summarizing Data
Plotting Combined Data
Read more
Syllabus
Course Overview
Creating Variables to Combine/Summarize Data
Grouping and Summarizing Data
Plotting Combined Data
Read more
Good to know
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
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Splitting and Combining Data with R.
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Career center
Learners who complete Splitting and Combining Data with R will develop knowledge and skills
that may be useful to these careers:
Statistician
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.
Data Analyst
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.
Machine Learning Engineer
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.
Data Scientist
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.
Market Researcher
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.
Business Analyst
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.
Financial 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.
Data Engineer
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.
Actuary
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.
Risk Analyst
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.
Operations Research 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.
Data Visualization Specialist
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.
Software Engineer
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.
Database Administrator
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.
Quantitative Analyst
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.
For more career information including salaries, visit:
OpenCourser.com/course/say79m/splitting
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
Splitting and Combining Data with R.
Provides a comprehensive overview of data manipulation in R, covering topics such as data cleaning, transformation, and reshaping. It valuable resource for learners who want to gain a deeper understanding of data manipulation techniques and their application in R.
Save
Classic introduction to data science using R. It covers a wide range of topics, including data manipulation, visualization, and modeling. It valuable resource for learners who are new to data science or who want to learn more about R.
Save
Provides a comprehensive overview of ggplot2, a popular R package for creating visualizations. It covers a wide range of topics, including data exploration, plot customization, and advanced graphics. It valuable resource for learners who want to create beautiful and informative visualizations in R.
Save
Provides an introduction to data mining techniques using R. It covers a wide range of topics, including data preprocessing, feature selection, and model evaluation. It valuable resource for learners who want to learn more about data mining and its application in R.
Provides an introduction to machine learning techniques using R. It covers a wide range of topics, including supervised learning, unsupervised learning, and model evaluation. It valuable resource for learners who want to learn more about machine learning and its application in R.
Provides an introduction to deep learning techniques using R. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for learners who want to learn more about deep learning and its application in R.
Save
Provides a collection of recipes for common tasks in R. It covers a wide range of topics, including data manipulation, visualization, and statistical analysis. It valuable resource for learners who want to quickly find solutions to common problems in R.
Provides a comprehensive overview of R programming. It covers a wide range of topics, including data structures, control flow, and object-oriented programming. It valuable resource for learners who want to learn more about the fundamentals of R programming.
Provides a practical introduction to R programming. It covers a wide range of topics, including data manipulation, visualization, and statistical analysis. It valuable resource for learners who want to quickly get started with R.
Provides a comprehensive overview of statistical inference techniques using R. It covers a wide range of topics, including data exploration, hypothesis testing, and regression analysis. It valuable resource for learners who want to learn more about statistical inference and its application in R.
Save
Provides a comprehensive overview of time series analysis techniques using R. It covers a wide range of topics, including data exploration, forecasting, and model selection. It valuable resource for learners who want to learn more about time series analysis and its application in R.
Provides a comprehensive overview of statistical learning techniques. It covers a wide range of topics, including supervised learning, unsupervised learning, and model evaluation. It valuable resource for learners who want to learn more about statistical learning and its application in R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/say79m/splitting
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Time
117 hours
Level
Introductory
Via
Pluralsight
Instructor
Mariah Weatherford
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
- Splitting and Combining Data with R (viewing)
Good to know
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
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