BobMuenchen
I am an ASA Accredited Professional Statistician™ who focuses on helping organizations migrate from SAS, SPSS, and Stata to the R Language. I've taught workshops on research computing topics for more than 500 organizations.
Training
Introduction to R for SAS, SPSS, & Stata Users This workshop follows the format of my books, R for SAS and SPSS Users and R for Stata Users, introducing R in a way that takes advantage of what participants already know rather than starting from scratch. It emphasizes areas that are likely to confuse people coming from those packages. For example, SAS users are used to seeing "data=data_set_name", and R has a seemingly identical parameter. However, it doesn't always work the same way, introducing frustrating error messages. A detailed description of this 2-day hands-on workshop is here: http://r4stats.com/workshops/r-for-sas-spss-stata-users/.
Introduction to Modern R The R language is often initially taught focusing on its built-in functions. Later, beginners discover that there are add-on packages (e.g. tidyverse) that make R much easier to use. This workshop does just the opposite: it starts using the easiest and fastest R commands right from the start. For each topic, it then covers the built-in functions briefly, pointing out why we choose an alternative. There are, of course, situations in which some of the built-in functions are the best to use, and we’ll go over those as well. A detailed description of this 2-day hands-on workshop is here: http://r4stats.com/workshops/introduction-to-modern-r/
Managing Data with R Before you can analyze data, it must be in the right form. Getting it into that form is often where we spend most of our time. This workshop shows how to perform the most commonly used data management tasks in R. We will cover how to use R’s most popular add-on packages (dplyr, stringr, lubridate, tidyr, broom, compare, sqldf, etc.) and compare them to R’s older built-in functions. This full-day hands-on workshop covers everything you're likely to need to do to get your data in shape. A detailed description is here: http://r4stats.com/workshops/managing-data-with-r/.
Machine Learning with R R has a wide variety of machine learning (ML) models. While the many ML functions solve similar problems by predicting various outcomes, they use a confusing array of different command styles, making them hard to learn. Fortunately, the caret package provides a standard approach to dozens of ML functions, speeding learning and use. This full-day hands-on workshop starts with ML basics and takes you step-by-step through increasingly complex modeling styles. A detailed description is here: http://r4stats.com/workshops/machine-learning-with-r/.