Algebra level symbolic math

  • This software makes it easier to use the R language. It includes a code debugger, editing, and visualization tools.

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  • This is a web application framework for R, in which you can write and publish web apps without knowing HTML, Java, etc. You create two .R files: one that controls the user interface, and one that controls what the app does. The site contains examples of Shiny apps, a tutorial on how to get started, and information on how to have your apps hosted, if you don't have a server.

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  • These slides from the 2014 ICOTS workshop describe a minimal set of R commands for Introductory Statistics. Also, it describes the best way to teach them to students. There are 61 slides that start with plotting, move through modeling, and finish with randomization.
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  • This online booklet, Start Teaching with R, by Randall Pruim, Nicholas J. Horton, and Daniel T. Kaplan comes out of the Mosaic project. It describes how to get started teaching Statistics using R, and gives teaching tips for many ideas in the course, using R commands.

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  • This online booklet comes out of the Mosaic project. It is a guide aimed at students in an introductory statistics class. After a chapter on getting started, the chapters are grouped around what kind of variable is being analyzed. One quantitative variable; one categorical variable; two quantitative variables; two categorical variables; quantitative response, categorical predictor; categorical response, quantitative predictor; and survival time outcomes.
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  • This site shows the code you would use to replicate the examples in Applied Survival Analysis, by Hosmer and Lemeshow. It has code in Stata, R, and SAS.
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  • This site has the data and shows the code you would use to replicate the examples in Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, by Judith D. Singer and John B. Willett. It has code in SAS, R, Stata, SPSS, HLM, MLwiN, and Mplus.
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  • This site is an interactive, online tutorial for R. It asks the user to type in commands at an R prompt, which are then evaluated. Typing the right thing allows the user to continue on, typing the wrong thing yields an error. The user cannot skip the easier lessons. Lessons are: Using R; Vectors; Matrices; Summary Statistics; Factors; Data Frames; Real-World Data; and What’s Next.
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  • This is an e-book tutorial for R. It is organized according to the topics usually taught in an Introductory Statistics course. Topics include: Qualitative Data; Quantitative Data; Numerical Measures; Probability Distributions; Interval Estimation; Hypothesis Testing; Type II Error; Inference about Two Populations; Goodness of Fit; Analysis of Variance; Non-parametric methods; Linear Regression; and Logistic Regression.
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  • The Comprehensive Epidemiologic Data Resource is a collection of data sets. It includes definitions of each variable in the data set. It requires a login to retrieve the data sets. Registering involves giving your name and address and the name of the study and a detailed description of the intended use of the data.
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