Introduction to R (The Course)
Course Info
This course includes 1 attempt.
Description
Data science is one of the most exciting and fastest growing careers in the world. The goal of this series is to help people with no background and limited resources transition into data science. It would be helpful to have already taken our Introduction, Organizing Data Science Projects, and Version Control courses. We guide you through the rest!
Learning objectives
After taking this course you will be able to:
- Describe what the R programming language is
- Log in to RStudio Cloud and use the R programming language
- Load and use R packages
- Perform the basic commands you will need to start using R
Things you need to do this course
This course is designed for people with no background with Chromebooks or with R. It would be helpful if you had already taken our Introduction, Organizing Data Science Projects, and Version Control courses. But we don't assume prior programming experience. This should be a great introduction to Rfor high-school students or people looking for a career change into the tech industry. The only requirements are:
- A computer with a web browser and an internet connection
- The ability to type and follow instructions.
- The appropriate accounts set up in our earlier courses.
How you will be graded
The course has a series of short quizzes, one for each chapter. You will get two attempts at each quiz and your best score for each quiz will count toward your final score. If you receive more than 70% of the points across all quizzes you will pass. If you receive more than 90% of the points across all quizzes you will pass with honors. You get two attempts at the class with each class purchase.
How to report an error
If you find a bug, typo, or issue in the material, feel free to contact us using this form.
Course Material
- 1 What is R?
- Welcome to the What is R? quiz1 attempt allowed
- 2 RStudio Cloud Tour
- RStudio Cloud Tour quiz1 attempt allowed
- 3 R Packages
- R Packages Quiz1 attempt allowed
- 4 Objects in R
- Objects in R quiz1 attempt allowed
- 5 Basic Commands in R
- Basic Commands in R quiz1 attempt allowed
- 6 Working with Logicals
- Working with Logicals quiz1 attempt allowed
- 7 Lists and Data Frames
- Lists and Data Frames quiz1 attempt allowed
- 8 Writing Functions in R
- Writing Functions in R quiz1 attempt allowed
- 9 R Markdown
- R Markdown quiz1 attempt allowed
- 10 Getting Help in R
- Getting Help in R quiz1 attempt allowed
- 11 Pushing Code from R to GitHub
- Pushing Code from R to GitHub quiz1 attempt allowed
- 12 Creating Websites with R
- Creating Websites with R quiz1 attempt allowed
- 13 References
- About this Course
- About the Authors
Instructors
Jeff is Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics at the Fred Hutchinson Cancer Center. Previously, he was a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. His group develops statistical methods, software, data resources, and data analyses that help people make sense of massive-scale genomic and biomedical data. As the co-director of the Johns Hopkins Data Science Lab he helped to develop massive online open programs that have enrolled more than 8 million individuals and partnered with community-based non-profits to use data science education for economic and public health development. He is a Fellow of the American Statistical Association and a recipient of the Mortimer Spiegelman Award and Committee of Presidents of Statistical Societies Presidential Award.
Shannon Ellis is an Associate Teaching Professor in the Cognitive Science Department at UC San Diego.
Leslie obtained her PhD in biostatistics from the Johns Hopkins Bloomberg School of Public Health and is currently an Assistant Professor in the Department of Mathematics, Statistics, and Computer Science at Macalester College.
Sarah is Human Genetics PhD student in the Institute of Genetic Medicine at Johns Hopkins. She studies the role of regulatory variation in neurodegenerative and neuropsychiatric diseases, like Parkinson disease and schizophrenia.
Aboozar Hadavand is a postdoctoral fellow at Johns Hopkins Bloomberg School of Public Health. His current research involves analyzing MOOC data. He has previously taught at Barnard College (Columbia University), Brooklyn College, and Yeshiva University.
Leonardo Collado-Torres is a Research Scientist at the Lieber Institute for Brain Development. He has been learning and using R & Bioconductor since 2008 and works analyzing genomic data. Leonardo occasionally writes blog posts, is a co-founder of the LIBD rstats club and the Community of Bioinformatics Software Developers (CDSB in Spanish), and is a member of the Bioconductor Community Advisory Board.
Community
This course has a private forum for learners who are taking this course.
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