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Jane Wall
This course is a gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:
• you want to do data analysis but don’t know programming
• you know programming but aren’t familiar with R
• you know some R programming but want to learn the tidyverse verbs
Read more
This course is a gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:
• you want to do data analysis but don’t know programming
• you know programming but aren’t familiar with R
• you know some R programming but want to learn the tidyverse verbs
Read more
This course is a gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:
• you want to do data analysis but don’t know programming
• you know programming but aren’t familiar with R
• you know some R programming but want to learn the tidyverse verbs
You will learn to do data visualization and analysis in a reproducible manner and use functions that allow your code to be easily read and understood. You will use RMarkdown to create nice documents and reports that execute your code freshly every time it’s run and that capture your thoughts about the data along the way.
This course has been designed for learners from non-STEM backgrounds to help prepare them for more advanced data science courses by providing an introduction to programming and to the R language. I am excited for you to join me on the journey!
The course logo was created using images of stickers from the RStudio shop. Please visit https://swag.rstudio.com/s/shop.
Register for this course and see more details by visiting:
OpenCourser.com/course/nayclg/introduction
What's inside
Syllabus
Introduction to R, RStudio and RMarkdown
In the first module of this course, you will install and configure R and RStudio. You will review the fundamentals of R and reproducibility, install R packages required for the course, and input basic commands using the RStudio console. Finally, you will create an RMarkdown document - the deliverable for this module.
Read more
Syllabus
Introduction to R, RStudio and RMarkdown
In the first module of this course, you will install and configure R and RStudio. You will review the fundamentals of R and reproducibility, install R packages required for the course, and input basic commands using the RStudio console. Finally, you will create an RMarkdown document - the deliverable for this module.
Read more
Functions
In this module, we will explore functions in R. You will review the syntax of functions and best practices of function creation. You will also practice writing functions with default arguments and argument validation.
Data Visualization using ggplot2
In this module, you will be introduced to ggplot2 - an R package for data visualization. You will explore the different grammatical elements and aesthetic mappings (layers) that are essential to visualize data in ggplot2.
Data Analysis with dplyr
In the final module of this course, you will be introduced to data analysis using dplyr. You will learn and practice with the many dplyr verbs including select, filter, arrange, mutate, group_by, and summarize.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners with varied backgrounds, including those new to programming, R, and tidyverse verbs
Emphasizes reproducibility through RMarkdown, ensuring code freshness and clear documentation
Provides a solid foundation in programming fundamentals for learners from non-STEM backgrounds
Covers essential R packages and functions, such as dplyr and ggplot2, for data analysis and visualization
Save this course
Save Introduction to R Programming and Tidyverse 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 Introduction to R Programming and Tidyverse with these
activities:
R and RStudio Basics
Show steps
Review the fundamentals of R and RStudio to strengthen your foundational understanding and prepare for the course.
Browse courses on
R
Show steps
-
Install and configure R and RStudio
-
Review the R console
-
Practice writing basic R commands
R for Data Science
Show steps
Strengthen your understanding of R and data science principles by reviewing a foundational book on the topic.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read selected chapters
-
Summarize key concepts
-
Apply the concepts to your own data analysis projects
Ggplot2 Tutorial
Show steps
Enhance your data visualization skills by following a guided tutorial on using ggplot2, a powerful data visualization library in R.
Browse courses on
Ggplot2
Show steps
-
Explore the grammar of graphics
-
Create basic plots using ggplot2
-
Customize plots with themes and annotations
Five other activities
Expand to see all activities and additional details
Show all eight activities
Functions Practice
Show steps
Reinforce your understanding of functions in R through repetitive exercises and practice.
Browse courses on
Functions
Show steps
-
Create custom functions with default arguments
-
Validate function arguments
-
Practice writing functions for common tasks
Attend Data Science Meetup
Show steps
Connect with other professionals in the field and learn about current trends and best practices in data science.
Browse courses on
Data Science
Show steps
-
Identify and attend a local data science meetup
-
Network with attendees
-
Learn about industry trends and advancements
Data Analysis Project
Show steps
Apply the skills learned in the course to a practical data analysis project using dplyr, a powerful data manipulation library in R.
Browse courses on
Data Analysis
Show steps
-
Load and explore the dataset
-
Clean and prepare the data
-
Analyze the data using dplyr verbs
-
Visualize the results
-
Write a report summarizing the findings
R Programming Workshop
Show steps
Enhance your R programming skills and learn advanced techniques through a structured workshop environment.
Browse courses on
R Programming
Show steps
-
Register and attend the workshop
-
Follow along with the instructor
-
Practice the concepts and techniques
-
Ask questions and engage with other participants
Course Materials Review
Show steps
Consolidate and review your notes, assignments, quizzes, and exams to reinforce your understanding of the course material.
Show steps
-
Organize and categorize your materials
-
Identify areas for further review
-
Summarize key concepts and ideas
R and RStudio Basics
Show steps
Review the fundamentals of R and RStudio to strengthen your foundational understanding and prepare for the course.
Browse courses on
R
Show steps
Show steps
Show steps
- Install and configure R and RStudio
- Review the R console
- Practice writing basic R commands
R for Data Science
Show steps
Strengthen your understanding of R and data science principles by reviewing a foundational book on the topic.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters
- Summarize key concepts
- Apply the concepts to your own data analysis projects
Ggplot2 Tutorial
Show steps
Enhance your data visualization skills by following a guided tutorial on using ggplot2, a powerful data visualization library in R.
Browse courses on
Ggplot2
Show steps
Show steps
Show steps
- Explore the grammar of graphics
- Create basic plots using ggplot2
- Customize plots with themes and annotations
Five other activities
Expand to see all activities and additional details
Show all eight activities
Functions Practice
Show steps
Reinforce your understanding of functions in R through repetitive exercises and practice.
Browse courses on
Functions
Show steps
Show steps
Show steps
- Create custom functions with default arguments
- Validate function arguments
- Practice writing functions for common tasks
Attend Data Science Meetup
Show steps
Connect with other professionals in the field and learn about current trends and best practices in data science.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify and attend a local data science meetup
- Network with attendees
- Learn about industry trends and advancements
Data Analysis Project
Show steps
Apply the skills learned in the course to a practical data analysis project using dplyr, a powerful data manipulation library in R.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Load and explore the dataset
- Clean and prepare the data
- Analyze the data using dplyr verbs
- Visualize the results
- Write a report summarizing the findings
R Programming Workshop
Show steps
Enhance your R programming skills and learn advanced techniques through a structured workshop environment.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Register and attend the workshop
- Follow along with the instructor
- Practice the concepts and techniques
- Ask questions and engage with other participants
Course Materials Review
Show steps
Consolidate and review your notes, assignments, quizzes, and exams to reinforce your understanding of the course material.
Show steps
Show steps
Show steps
- Organize and categorize your materials
- Identify areas for further review
- Summarize key concepts and ideas
Career center
Learners who complete Introduction to R Programming and Tidyverse will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to build models that can predict outcomes and make recommendations. They work in a variety of industries, including finance, healthcare, and retail. This course provides a strong foundation in R programming and the tidyverse, which are essential tools for data science. By learning how to use these tools, you will be well-equipped to succeed in a career as a Data Scientist.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to extract meaningful insights and communicate them to stakeholders. They use a variety of statistical and programming techniques to identify trends, patterns, and correlations in data. This course provides a solid foundation in R programming and the tidyverse, which are essential tools for data analysis. By learning how to use these tools, you will be well-equipped to succeed in a career as a Data Analyst.
Biostatistician
Biostatistician
Biostatisticians use their knowledge of statistics and biology to design and analyze studies that investigate the effects of treatments and interventions on human health. They work in a variety of settings, including academia, government, and industry. This course provides a strong foundation in R programming and the tidyverse, which are essential tools for biostatistical analysis. By learning how to use these tools, you will be well-equipped to succeed in a career as a Biostatistician.
Statistician
Statistician
Statisticians use their knowledge of statistics and probability to collect, analyze, and interpret data. They work in a variety of fields, including medicine, public health, and finance. This course provides a strong foundation in R programming and the tidyverse, which are essential tools for statistical analysis. By learning how to use these tools, you will be well-equipped to succeed in a career as a Statistician.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. They use their knowledge of statistics and epidemiology to design and conduct studies that identify risk factors for disease and develop strategies to prevent and control outbreaks. This course provides a strong foundation in R programming and the tidyverse, which are essential tools for epidemiological analysis. By learning how to use these tools, you will be well-equipped to succeed in a career as an Epidemiologist.
Data Engineer
Data Engineer
Data Engineers design and build the systems and infrastructure that store and manage data. They work with a variety of technologies, including databases, big data platforms, and cloud computing. This course provides a strong foundation in R programming and the tidyverse, which are essential tools for data engineering. By learning how to use these tools, you will be well-equipped to succeed in a career as a Data Engineer.
Web Developer
Web Developer
Web Developers design and develop websites and web applications. They work with a variety of technologies, including HTML, CSS, and JavaScript. This course provides a strong foundation in R programming, which can be used for data analysis and visualization. By learning how to use R, you will be well-equipped to succeed in a career as a Web Developer.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and programming to develop and implement financial models. They work in a variety of industries, including investment banking, hedge funds, and asset management. This course provides a strong foundation in R programming, which is a popular language for financial modeling. By learning how to use R, you will be well-equipped to succeed in a career as a Quantitative Analyst.
Actuary
Actuary
Actuaries use their knowledge of mathematics, statistics, and finance to assess and manage risk. They work in a variety of industries, including insurance, pensions, and healthcare. This course provides a strong foundation in R programming, which is a popular language for actuarial modeling. By learning how to use R, you will be well-equipped to succeed in a career as an Actuary.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including technology, finance, and healthcare. This course provides a strong foundation in R programming, which is a popular language for data analysis and visualization. By learning how to use R, you will be well-equipped to succeed in a career as a Software Engineer.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with a variety of stakeholders, including engineers, designers, and marketers. This course provides a strong foundation in R programming and the tidyverse, which are essential tools for product management. By learning how to use these tools, you will be well-equipped to succeed in a career as a Product Manager.
Data Journalist
Data Journalist
Data Journalists use their knowledge of journalism and data analysis to tell stories and communicate information to the public. They work for a variety of media outlets, including newspapers, magazines, and websites. This course provides a strong foundation in R programming and the tidyverse, which are essential tools for data journalism. By learning how to use these tools, you will be well-equipped to succeed in a career as a Data Journalist.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to identify and solve business problems. They work with a variety of stakeholders, including executives, managers, and employees. This course provides a strong foundation in R programming and the tidyverse, which are essential tools for business analysis. By learning how to use these tools, you will be well-equipped to succeed in a career as a Business Analyst.
UX Researcher
UX Researcher
UX Researchers use their knowledge of human-computer interaction to design and evaluate user experiences. They work with a variety of stakeholders, including designers, engineers, and product managers. This course provides a strong foundation in R programming and the tidyverse, which are essential tools for UX research. By learning how to use these tools, you will be well-equipped to succeed in a career as a UX Researcher.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and trends. They use their findings to develop marketing strategies and campaigns. This course provides a strong foundation in R programming and the tidyverse, which are essential tools for market research. By learning how to use these tools, you will be well-equipped to succeed in a career as a Market Researcher.
For more career information including salaries, visit:
OpenCourser.com/course/nayclg/introduction
Reading list
We've selected ten 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
Introduction to R Programming and Tidyverse.
Save
Provides a comprehensive introduction to the R programming language and its use in data science. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning.
Provides a comprehensive introduction to the R programming language and its use in data science. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning.
Provides a comprehensive introduction to machine learning in R. It covers a wide range of topics, from linear regression to support vector machines to deep learning.
Save
Provides a comprehensive introduction to big data analytics in R and Hadoop. It covers a wide range of topics, from data ingestion and processing to data analysis and visualization.
Save
Provides a comprehensive introduction to time series analysis in R. It covers a wide range of topics, from time series decomposition to forecasting to statistical inference.
Provides a comprehensive introduction to the ggplot2 package for data visualization. It covers a wide range of topics, from basic chart types to advanced visualization techniques.
Save
Provides a comprehensive introduction to the dplyr package for data manipulation. It covers a wide range of topics, from basic data manipulation tasks to advanced techniques such as joins and aggregations.
Provides a comprehensive introduction to deep learning in R. It covers a wide range of topics, from neural networks to convolutional neural networks to recurrent neural networks.
Save
Provides a comprehensive introduction to the R Markdown language. It covers a wide range of topics, from basic syntax to advanced techniques such as creating interactive documents and presentations.
Save
Provides a more advanced treatment of the R programming language. It covers topics such as object-oriented programming, high-performance computing, and developing R packages.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nayclg/introduction
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Effort
5 to 7 hours per week for four weeks
Level
Introductory
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Jane Wall
Language
English
Transcripts
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This course belongs to a
collection
called
Expressway to Data Science: R Programming and Tidyverse. It's comprised of three courses:
- Introduction to R Programming and Tidyverse (viewing)
- Data Analysis with Tidyverse
- R Programming and Tidyverse Capstone Project
Good to know
Suitable for learners with varied backgrounds, including those new to programming, R, and tidyverse verbs
Emphasizes reproducibility through RMarkdown, ensuring code freshness and clear documentation
Provides a solid foundation in programming fundamentals for learners from non-STEM backgrounds
Covers essential R packages and functions, such as dplyr and ggplot2, for data analysis and visualization
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