In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
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Functions (part 1)
Skills You'll Learn
Critical Thinking, Data Analysis, Computer Programming, Rstudio, General Statistics, Programming Principles, Debugging, Problem Solving, Data Structures, Computer Programming Tools, Statistical Programming, R Programming
Reviews
4.5 (22,306 ratings)
- 5 stars68.05%
- 4 stars22.14%
- 3 stars5.82%
- 2 stars2.08%
- 1 star1.89%
DK
Feb 2, 2016
The content is superbly designer for a beginner. The Swirl assignments need to make compulsory. Infact they contributed more to the learning process. More Swirl contents will make the course richer.
YZ
Jul 16, 2020
This is course is probably my favorite out of the Data Science: Foundations using R specialization. There was plenty of opportunity to practice and further develop by burgeoning R programming skill.
From the lesson
Week 2: Programming with R
Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.
Taught By
Roger D. Peng, PhD
Professor of Statistics and Data Sciences
Jeff Leek, PhD
Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics in Public Health Sciences
Brian Caffo, PhD
Professor, Biostatistics