R Programming Fundamentals
Learn to master R programming for statistical computing and data analysis with Stanford's comprehensive beginner-friendly course.
Learn to master R programming for statistical computing and data analysis with Stanford's comprehensive beginner-friendly course.
This comprehensive course introduces students to R programming, a powerful language for statistical computing and data analysis. Starting from installation basics, learners progress through key concepts including data structures, variable manipulation, and function writing. The course covers practical skills like working with external datasets, handling missing values, and creating visualizations using ggplot2. Students benefit from insights shared by Robert Gentleman, one of R's co-creators, making this an authoritative introduction to R programming. Designed for beginners, the course emphasizes hands-on learning with interactive exercises and real-world applications.
4.4
(47 ratings)
Instructors:
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What you'll learn
Master R installation and basic setup
Work with R's interactive environment effectively
Manipulate key R objects and understand their storage
Work with essential data structures including data frames, lists, and matrices
Import and preprocess data efficiently
Handle missing values in datasets
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a comprehensive introduction to R programming, covering essential concepts from installation to advanced data manipulation. Students learn about fundamental data structures, file operations, and statistical functions. The curriculum emphasizes practical skills through hands-on exercises, including data visualization with ggplot2 and working with external datasets. The course features insights from R language co-creator Robert Gentleman, offering unique perspectives on R's capabilities and applications.
Fee Structure
Instructor
1 Course
A Pioneer in Statistical Biology and Data Science
Susan Holmes has revolutionized the application of statistics to biological research as Professor Emerita at Stanford University. After receiving her Ph.D. in 1985 from Université Montpellier II, she built her career through positions at INRA, MIT, Harvard, and Cornell before joining Stanford in 1998. Trained in the French school of Data Analysis, she specializes in nonparametric multivariate statistics applied to complex biological systems. Her research spans immunology, cancer biology, and microbial ecology, with particular focus on developing statistical methods for analyzing microbiome data. She co-authored the influential textbook "Modern Statistics for Modern Biology" with Wolfgang Huber and created Stanford's "Breaking Codes and Finding Patterns" course. Her theoretical work encompasses applied probability, Monte Carlo Markov chains, Graph Limit Theory, and the topology of phylogenetic trees. As a Bass Fellow in Undergraduate Education and member of BioX, she continues to advance the field through her focus on improving statistical analyses and reproducibility in human microbiome studies while maintaining her commitment to innovative teaching methods.
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4.4 course rating
47 ratings
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