The unprecedented advance in digital technology during the second half of the 20th century has produced a measurement revolution that is transforming the world. However, interpreting information extracted from these massive and complex datasets requires sophisticated statistical skills as one can easily be fooled by patterns arising by chance. This has greatly elevated the importance of statistics and data analysis.

In this programming course, we will be using the R software environment for all our analysis. You will learn R and data analysis techniques simultaneously. We will introduce basic R syntax to get you going. However, rather than cover every R skill you need, we introduce just enough so that you can follow along. Upcoming courses in this series will provide more in depth coverage, building upon what you learn here. We believe that you can better retain R knowledge when you learn it to solve a specific problem.

Using a motivating case study, we ask specific questions related to crime in the United States and provide a relevant dataset. You will learn some basic R skills to permit us to answer these questions.

What you'll learn:

  • Introduction to the R programming language including basic R syntax
  • How to employ different structures for variables, vectors, and arithmetic functions in R
  • How to perform operations in R including coercion, sorting, data frame creation, and the pipe command
  • How to use R to manipulate data, create plots, and perform basic programming

Meet The Faculty

Rafael Irizarry

Rafael Irizarry

Professor of Biostatistics, T.H. Chan School of Public Health

Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. For the past 15 years, Dr. Irizarry’s research has focused on the analysis of genomics data. During this time, he has also has taught several classes, all related to applied statistics. Dr. Irizarry is one of the founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data. His publications related to these topics have been highly cited and his software implementations widely downloaded.

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