Category: SAS

We’re pleased to announce that a special issue of the American Statistician on “Statistics and the Undergraduate Curriculum” (November, 2015) is available at http://amstat.tandfonline.com/toc/utas20/69/4.

Johanna Hardin (Pomona College) and Nick were the guest editors. There are a number of excellent and provocative papers that reinforce the importance of computing using tools such as R and SAS that are likely to be of widespread interest to the community.

Table of Contents

Teaching the Next Generation of Statistics Students to “Think With Data”: Special Issue on Statistics and the Undergraduate Curriculum : Nicholas J. Horton & Johanna S. Hardin, DOI:10.1080/00031305.2015.1094283 (freely available)

Mere Renovation is Too Little Too Late: We Need to Rethink our Undergraduate Curriculum from the Ground Up George Cobb, DOI:10.1080/00031305.2015.1093029 (freely available for a limited period)

Teaching Statistics at Google-Scale: Nicholas Chamandy, Omkar Muralidharan & Stefan Wager, DOI:10.1080/00031305.2015.1089790

Explorations in Statistics Research: An Approach to Expose Undergraduates to Authentic Data Analysis by Deborah Nolan & Duncan Temple Lang, DOI:10.1080/00031305.2015.1073624

Beyond Normal: Preparing Undergraduates for the Work Force in a Statistical Consulting Capstone by Byran J. Smucker & A. John Bailer, DOI:10.1080/00031305.2015.1077731

A Framework for Infusing Authentic Data Experiences Within Statistics Courses: Scott D. Grimshaw, DOI:10.1080/00031305.2015.1081106

Fostering Conceptual Understanding in Mathematical Statistics: Jennifer L. Green & Erin E. Blankenship, DOI:10.1080/00031305.2015.1069759

The Second Course in Statistics: Design and Analysis of Experiments? by Natalie J. Blades, G. Bruce Schaalje & William F. Christensen, DOI:10.1080/00031305.2015.1086437

A Data Science Course for Undergraduates: Thinking With Data: Ben Baumer, DOI:10.1080/00031305.2015.1081105

Data Science in Statistics Curricula: Preparing Students to “Think with Data” : J. Hardin, R. Hoerl, Nicholas J. Horton, D. Nolan, B. Baumer, O. Hall-Holt, P. Murrell, R. Peng, P. Roback, D. Temple Lang & M. D. Ward, DOI:10.1080/00031305.2015.1077729

Using Online Game-Based Simulations to Strengthen Students’ Understanding of Practical Statistical Issues in Real-World Data Analysis: Shonda Kuiper & Rodney X. Sturdivant, DOI:10.1080/00031305.2015.1075421

Combating Anti-Statistical Thinking Using Simulation-Based Methods Throughout the Undergraduate Curriculum: Nathan Tintle, Beth Chance, George Cobb, Soma Roy, Todd Swanson & Jill VanderStoep, DOI:10.1080/00031305.2015.1081619

What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum : Tim C. Hesterberg, DOI:10.1080/00031305.2015.1089789

Incorporating Statistical Consulting Case Studies in Introductory Time Series Courses: Davit Khachatryan, DOI:10.1080/00031305.2015.1026611

Developing a New Interdisciplinary Computational Analytics Undergraduate Program: A Qualitative-Quantitative-Qualitative Approach: Scotland Leman, Leanna House & Andrew Hoegh, DOI:10.1080/00031305.2015.1090337

From Curriculum Guidelines to Learning Outcomes: Assessment at the Program Level by Beth Chance & Roxy Peck, DOI:10.1080/00031305.2015.1077730

Program Assessment for an Undergraduate Statistics Major: Allison Amanda Moore & Jennifer J. Kaplan, DOI:10.1080/00031305.2015.1087331

The Cobb paper (“Mere Renovation is Too Little Too Late: We Need to Rethink Our Undergraduate Curriculum from the Ground Up (Cobb, 2015) “) has an associated discussion with 19 provocative responses plus George’s spirited rejoinder. These materials can be found on the TAS website or individually at http://www.amherst.edu/~nhorton/mererenovation/.

Discussion (and rejoinder):

• Response from Albert and Glickman: Attracting undergraduates to statistics through data science
• Response from Chance, Peck, and Rossman: Response to mere renovation is too little too late
• Response from De Veaux and Velleman: Teaching statistics algorithmically or stochastically misses the point: why not teach holistically?
• Response from Fisher and Bailar: Who, what, when and how: changing the undergraduate statistics curriculum
• Response from Franklin: We need to rethink the way we teach statistics at K-12
• Response from Gelman and Loken: Moving forward in statistics education while avoiding overconfidence
• Response from Gould: Augmenting the vocabulary used to describe data
• Response from Holcomb, Quinn, and Short: Seeking the niche for traditional mathematics within undergraduate statistics and data science curricula
• Response from Kass: The gap between statistics education and statistical practice
• Response from King: Training the next generation of statistical scientist
• Response from Lane-Getaz: Stirring the curricular pot once again
• Response from Notz: Vision or bad dream?
• Response from Ridgway: Data Cowboys and Statistical Indians
• Response from Temple Lang: Authentic data analysis experience
• Response from Utts: Challenges, changes and choices in the undergraduate statistics curriculum
• Response from Ward: Learning communities and the undergraduate statistics curriculum
• Response from Wickham: Teaching Safe-Stats, not statistical abstinence
• Response from Wild: Further, faster, wider
• Response from Zieffler: Teardowns, historical renovation, and paint-and-patch: curricular changes and faculty development
• Rejoinder by Cobb

An unrelated note about aggregators:We love aggregators! Aggregators collect blogs that have similar coverage for the convenience of readers, and for blog authors they offer a way to reach new audiences. SAS and R is aggregated by R-bloggers, PROC-X, and statsblogs with our permission, and by at least 2 other aggregating services which have never contacted us. If you read this on an aggregator that does not credit the blogs it incorporates, please come visit us at SAS and R. We answer comments there and offer direct subscriptions if you like our content. In addition, no one is allowed to profit by this work under our license; if you see advertisements on this page, the aggregator is violating the terms by which we publish our work.

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I’ve been working on a Shiny app and wanted to display some math equations. It’s possible to use LaTeX to show math using MathJax, as shown in this example from the makers of Shiny. However, by default, MathJax does not allow in-line equations, because the dollar sign is used so frequently. But I needed to use in-line math in my application. Fortunately, the folks who make MathJax show how to enable the in-line equation mode, and the Shiny documentation shows how to write raw HTML. Here’s how to do it.

R

Here I replicated the code from the official Shiny example linked above. The magic code is inserted into ui.R, just below withMathJax().
## ui.R

library(shiny)

shinyUI(fluidPage(
  title = 'MathJax Examples with in-line equations',
  withMathJax(),
  # section below allows in-line LaTeX via $ in mathjax.
  tags$div(HTML("
")),
  helpText('An irrational number $\sqrt{2}$
           and a fraction $1-\frac{1}{2}$'),
  helpText('and a fact about $\pi$:$\frac2\pi = \frac{\sqrt2}2 \cdot
           \frac{\sqrt{2+\sqrt2}}2 \cdot
           \frac{\sqrt{2+\sqrt{2+\sqrt2}}}2 \cdots$'),
  uiOutput('ex1'),
  uiOutput('ex2'),
  uiOutput('ex3'),
  uiOutput('ex4'),
  checkboxInput('ex5_visible', 'Show Example 5', FALSE),
  uiOutput('ex5')
))



## server.R
library(shiny)

shinyServer(function(input, output, session) {
  output$ex1     withMathJax(helpText('Dynamic output 1:  $\alpha^2$'))
  })
  output$ex2     withMathJax(
      helpText('and output 2 $3^2+4^2=5^2$'),
      helpText('and output 3 $\sin^2(\theta)+\cos^2(\theta)=1$')
    )
  })
  output$ex3     withMathJax(
      helpText('The busy Cauchy distribution
               $\frac{1}{\pi\gamma\,\left[1 +
               \left(\frac{x-x_0}{\gamma}\right)^2\right]}\!$'))
  })
  output$ex4     invalidateLater(5000, session)
    x     withMathJax(sprintf("If $X$ is a Cauchy random variable, then
                        $P(X \leq %.03f ) = %.03f$", x, pcauchy(x)))
  })
  output$ex5     if (!input$ex5_visible) return()
    withMathJax(
      helpText('You do not see me initially: $e^{i \pi} + 1 = 0$')
    )
  })
  })

Give it a try (or check out the Shiny app at https://r.amherst.edu/apps/nhorton/mathjax/)! One caveat is that the other means of in-line display, as shown in the official example, doesn’t work when the MathJax HTML is inserted as above.

An unrelated note about aggregators:We love aggregators! Aggregators collect blogs that have similar coverage for the convenience of readers, and for blog authors they offer a way to reach new audiences. SAS and R is aggregated by R-bloggers, PROC-X, and statsblogs with our permission, and by at least 2 other aggregating services which have never contacted us. If you read this on an aggregator that does not credit the blogs it incorporates, please come visit us at SAS and R. We answer comments there and offer direct subscriptions if you like our content. In addition, no one is allowed to profit by this work under our license; if you see advertisements on this page, the aggregator is violating the terms by which we publish our work.

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