Please join us for two exciting speakers!

Kevin Wright and Hui Lin will each give  a 30 minute talk. 

Kevin will discuss one or two of his R packages.  Here are the vignettes :

https://cran.r-project.org/web/packages/agridat/index.html

https://cran.r-project.org/web/packages/corrgram/index.html

https://cran.r-project.org/web/packages/lucid/index.html

https://cran.r-project.org/web/packages/mountainplot/index.html

Hui will talk about using R to preprocess data for predictive analytics.  Data pre-processing generally refer to the addition, deletion or transformation of training set stat. Data preparation can make or break a model’s predictive ability. Different models have different sensitivities to the type of predictors in the model, how the predictors enter the model. It is necessary to exam and preprocess the data before you start to build predictive model.  R is a very powerful tool to accomplish the task.

Space is limited to the first 28 that register, so please sign up soon!

We look forward to seeing everyone!

The Central Iowa R User Group Is proud to present: Xihui Xie!

Yihui Xie (http://yihui.name) is a software engineer at RStudio. He earned
his PhD from the Department of Statistics, Iowa State University. He is
interested in interactive statistical graphics, statistical computing, and web
applications. He is an active R user and the author of several R packages, such
as animation, formatR, and knitr. He is also the author of the book "Dynamic
Documents with R and knitr". In 2006, he founded the "Capital of Statistics",
which has grown into a large online community on statistics in China. He
initiated the first Chinese R conference in 2008 and has been organizing R
conferences in China since then.

Escape the Land of LaTeX/Word for Statistical Reporting: The Ecosystem of
R Markdown

Abstract: It is time to consider the web. Most people may naturally open a LaTeX
or Word document to write a statistical report. I will show what you have
possibly missed, which can be basically summarized into 1) your report is less
reproducible and requires more work, and 2) your report contains nothing
interactive and can be less appealing to readers. In the ecosystem of R
Markdown v2, there are tools for generating reproducible and interactive
statistical reports. In this talk, I will introduce a series of R packages
including knitr, rmarkdown, htmlwidgets, DT, leaflet, and shiny, etc, to show
how easy it is to enhance your reports with the power of web technologies.