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.
Data Types - Missing Values
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Skills You'll Learn
Data Analysis, Debugging, R Programming, Rstudio
Reviews
4.5 (22,263 ratings)
- 5 stars68.05%
- 4 stars22.13%
- 3 stars5.83%
- 2 stars2.08%
- 1 star1.89%
MR
May 11, 2020
Really interesting course. The interactive coding sessions with swirl are especially useful. Would be great, if you provided sample solutions for the programming assignments, in particular for week 4.
JM
Aug 11, 2019
Very challenging, but good course. I've been programming in R for over a year, but there were still some things for me to pick up in this class. Assignments were a challenge, but satisfying to tackle.
From the lesson
Week 1: Background, Getting Started, and Nuts & Bolts
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