The R Programming Environment
This course is part of Mastering Software Development in R.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Mastering Software Development in R Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.3
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English
پښتو, বাংলা, اردو, 5 more
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What you'll learn
Master R syntax and basic programming concepts
Work with tidy data principles and tidyverse tools
Process and manipulate complex datasets
Handle textual data and regular expressions
Manage memory and large-scale data efficiently
Skills you'll gain
This course includes:
2.3 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This comprehensive course provides a rigorous introduction to R programming with a focus on software development in data science. Students learn R language fundamentals, tidy data principles, tidyverse tools, data manipulation techniques, text processing with regular expressions, and handling large datasets. The curriculum emphasizes practical application through hands-on programming assignments and covers essential concepts for contributing to data science teams.
Basic R Language
Module 1 · 1 Hours to complete
Basic R Language: Lesson Choices
Module 2 · 6 Hours to complete
Data Manipulation
Module 3 · 1 Hours to complete
Data Manipulation: Lesson Choices
Module 4 · 6 Hours to complete
Text Processing, Regular Expression, & Physical Memory
Module 5 · 1 Hours to complete
Text Processing, Regular Expression, & Physical Memory: Lesson Choices
Module 6 · 6 Hours to complete
Large Datasets
Module 7 · 5 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Mastering Software Development in R
Instructors
Pioneering Research in Environmental Health
Dr. Anderson is an Assistant Professor at Colorado State University in the Department of Environmental & Radiological Health Sciences and a Faculty Associate in the Department of Statistics. She is actively involved in the university’s Partnership of Air Quality, Climate, and Health and serves on the editorial boards of Epidemiology and Environmental Health Perspectives. Previously, she completed a postdoctoral appointment in Biostatistics at Johns Hopkins Bloomberg School of Public Health and earned her PhD in Engineering from Yale University. Her research focuses on health risks related to climate-related exposures, such as heat waves and air pollution, and she has conducted several national-level studies. Dr. Anderson has also developed open-source R software packages to enhance environmental epidemiologic research.
Professor of Biostatistics at Johns Hopkins University
Dr. Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and serves as a Co-Editor of the Simply Statistics blog. He earned his PhD in Statistics from the University of California, Los Angeles, and is recognized for his research in air pollution, health risk assessment, and statistical methods for environmental data. In 2016, he received the Mortimer Spiegelman Award from the American Public Health Association, honoring his significant contributions to health statistics. Dr. Peng developed the Statistical Programming course at Johns Hopkins to equip students with essential computational tools for data analysis. Additionally, he is a national leader in promoting reproducible research practices and serves as the Reproducible Research editor for the journal Biostatistics. His interdisciplinary research has been published in prestigious journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. He has authored over a dozen software packages that implement statistical methods for environmental studies and reproducible research, and he regularly conducts workshops and tutorials on statistical computing and data analysis.
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4.3 course rating
1,160 ratings
Frequently asked questions
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