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.
Subsetting - Partial Matching
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Kompetenzen, die Sie erwerben
Data Analysis, Debugging, R Programming, Rstudio
Bewertungen
11. Mai 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.
2. Feb. 2016
"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!
Aus der Unterrichtseinheit
Week 1: Background, Getting Started, and Nuts & Bolts
This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
Unterrichtet von
Roger D. Peng, PhD
Associate Professor, Biostatistics
Jeff Leek, PhD
Associate Professor, Biostatistics
Brian Caffo, PhD
Professor, Biostatistics
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