R Materials

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Below are R resources and tutorials for introductory statistics students.  *** This video gives an overview of the resources ***

1. R/RStudio Resources - Key resources needed to get started and progress with R.

   1. • • RStudio Master Plan: Overview of the R resources for course

        • Installing R and RStudio: How to install the software

        • R Scripts Index: List of all base R commands needed for Math318 and Math210

        • R Manual: Optional manual for those who want more detail

        • Base R Cheatsheet: Reference sheet with primary base R commands

        • Mosaic Cheatsheet: Reference sheet with mosaic package commands

        • www.rseek.org: Google search that removes non-R hits

        • RMarkdown: Explanation and resources for R code + word processing combo system

2. RStudio Introduction Videos, Data, and Scripts (for Math 210 IntroStat, Math 318 Biostats, Math 380 Stat Practicum & PHLT 515 Principles of Biostats)

   1. To learn R/RStudio, first download the IntroStats_Data_v3.RData datasets, as well as s.Introduction_to_R_21.R scripts (Note: If the dataset doesn't work, some older operating systems need the older version: IntroStats_Data_v2.RData).  Then begin the videos in consecutive order.

   2. Pedagogical Note: The best way to learn R is not by video, but rather by reading my scripts in s.Introduction_to_R_21.R and "playing with them" (running a line, changing it, running again to see what it does, etc.).  Nevertheless, beginning students often like the videos.  While videos exist for all of the elementary statistical methods covered in Math 210 & Math 318, I encourage you to wean yourself off of the videos and transition to fully using the sample scripts when you're ready.

   3. Technical Note: The videos are currently hosted by kaltura and may require your Biola NetID log-in (as if you were in Canvas).

• • Data: IntroStats_Data_v3.RData (data file of SAT data for US States and UCR data)

  • Scripts: s.Introduction_to_R_21.R (R Scripts for video series)

  • 2.1 Overview of RStudio (overview of some of R/RStudio capabilities)

  • 2.2 Installing R-RStudio (demonstration of key points of installation)

  • 2.3a Introduction to R/RStudio, part 1 (your first session - navigating RStudio)

  • 2.3b Introduction to R/RStudio, part 2 (your first session - navigating RStudio)

  • 2.4 Loading Data (five ways to load data into RStudio)

  • 2.5a Statistics for Numeric Variables, part 1 (basic statisics, two-way tables)

  • 2.5b Statistics for Numeric Variables, part 2

  • 3.1 Graphs for Numeric Variables, part 1 (histograms, density curves, boxplots)

  • 3.2 Graphs for Categorical Variables, part 1 (bar graphs, two-way tables, pie charts)

  • 3.3 Customizing Graphs (x,y labels, titles, legends, color, gridlines, etc.)

  • 4.1 Graphs for Numeric Variables, part 2 (scatterplot, scatterplot matrix)

  • 4.2 Graphs for Categorical Variables, part 2 (multiple bar graphs, mosaic plots)

  • 4.3 Graph Helper, mplot() (drop-down menu for plotting options)

  • 4.4 Simple Linear Regression (introduce simple linear regression and plotting lines)

3. Math 318 Biostatistics Materials

   2. • • WS_Ch1-172nd.RData (datasets for course)

        • s.318Biostat.R (prepared class demonstrations)

        • s.318LectureNotes.R_2nd (R scripts generating items in Lecture Notes)

        • WS_Text_Examples_2nd.R (R scripts to generate most figures, tables, and examples from WS 2nd Ed.)

        • s.318VisualizingData.R (R scripts for Visualizing Data Slides)

        • WS_HW_Solutions_Practice2ndEd.R (R scripts for HW assignment problems)

        • rtutor-SelectedTutorialSolutions.R (R scripts for assigned r-tutor.com tutorials)

4. Tutorials: www.r-tutor.com

5. RStudio Webcast Series, by Mike Marin:

https://sites.google.com/a/biola.edu/rtutorials/home/webcasts-by-mike-marin

6. Additional Tutorials

The additional tutorials are adapted with permission from the those created by Robert W. Hayden at http://statland.org/Software_Help/R/Rhome.htm.  Each tutorial works through the analysis of a dataset on a given topic.  The topics are divided according to the primary classifications within statistics: {Descriptive, Inferential}, {Categorical, Numeric Variables}, and {One, Two, More Than Two Variables}.  Some commentary is given on handling and interpreting data in tutorials, but the main focus is on getting the software to do the work. It is assumed that you understand the basic meaning of the statistical output from your prior statistical training or the statistics course you are currently taking.  Each tutorial will soon include one or more exercises at the end which are to be completed for Dr. Wilson's courses.

• 1. Introductory

     1. • • Getting Started With R

          • Transformations in R

  2. Descriptive Statistics

• • 1. Categorical Variables

       1. • • Mode, Tables, Bar Graphs and Pie Charts (Summarizing a Single Categorical Variable)

            • Two-Way Tables and Probability (Making and Interpreting Tables for Two Categorical Variables)

    2. Numeric Variables

• • • • Summary Stats, Multiple Boxplots, and Histograms (Summarizing Quantitative Data)

• 3. Inferential Statistics

• • • 1. One Variable

         1. Categorical

            • Proportion Test (Inference for One Proportion)

         2. Numeric

            • One Sample t-test (Inference for a Single Mean)

      2. Two Variables

• • • • 1. Categorical

           • Chi-Squared Tests of Independence 

        2. Numeric

           • Independent t-test (Inference for Two Means)

           • Paired t-test

           • Scatterplots and Correlation

           • Simple Linear Regression

        3. Categorical & Numeric

           • One-way ANOVA

• • • 3. More Than Two Variables

• • • • 1. Multiple Regression

        2. Two-Way ANOVA

        3. Difference of Proportions Test

        4. Survival Analysis