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Rcpp: Integrating C++ into R

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The Rcpp package for R provides “seamless R and C++ integration”.  In this workshop, we will discuss the use of Rcpp to speed up existing R code by rewriting slow functions in C++.  

The workshop will be centered around a couple of case studies with an opportunity provided for participants to implement a few of their own C++ functions, compile, and call them from R.  Participants should be comfortable programming in R, but need not have any prior exposure to C++.

Mediation Models: A demonstration using multiple packages

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Mediation models are commonly applied in a variety of modeling settings, and people will typically flock to tools specific to structural equation modeling like Mplus or Amos for analysis.  However, not only are such tools not necessary for the more common implementations of mediation, they are often limiting and have various drawbacks.

Fortunately there are a variety of packages in R that can do mediation analysis, often using straightforward code and familiar models or other tools.  This presentation will demonstrate a variety of ways in which to do a standard mediation model in R (and Python), and discuss the available complexities that can be handled with the tools, as well as their corresponding strengths and weaknesses.
Note that this is not an introduction to mediation analysis, but is a demonstration of tools.  Some familiarity with R and mediation models will be assumed.

Back to a Future: Asynchronous Computing with futures in R

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Asynchronous computing is an umbrella term encompassing parallel and concurrent computational programs in which some tasks can be executed without a strict sequential order.  future is a programming abstraction for a value that may be available at some future point in time and allows.  Like other forms of parallelism, futures are a powerful tool for writing programs that efficiently make use of available computing resources.  At the same time, futures can also be used to make interactive data analyses more time efficient. 

 In this workshop, we’ll discuss futures as implemented in the R package “future” and provide example use cases for both interactive analysis and batch processing.  

Open Source GIS

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This workshop will provide a gentle introduction to open source GIS tools in R and QGIS. We will cover introductory GIS concepts and will explore the functionalities of R and QGIS for manipulating and analyzing vector GIS data. Familiarity with R is required.

Data management in R with data.table

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Matt Dowle, author of the data.table package, describes it as, “provid[ing] a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed.” In this workshop I will first introduce the data.table syntax using generic SQL and the dplyr R package as reference points.  Topics to be discussed include subsetting, aggregating, and merging data frames.  I will then discuss updating by reference and its role in efficiently working with large data sets.  Other advanced uses of the powerful data.table syntax will be covered as time permits.

If you have questions about this workshop, please send an email to jbhender@umich.edu

Statistical Analysis with R

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This is a two day workshop (March 4 and 5) in R which  is a free and open source environment for data analysis and statistical computing.  While R contains many built-in statistical procedures, a powerful feature of R is the facility for users to extend these procedures to suit their own needs.  Excellent graphing capability is another reason R is gaining wide popularity.

  • How to Obtain R
  • Help Tools
  • Importing / Exporting Data
  • Data Management
  • Descriptive and Exploratory Statistics
  • Common Statistical Analyses (t-test, Regression Modeling, ANOVA, etc.)
  • Graphics
  • Creating Functions

 

Statistical Analysis with R

By |

This is a two day workshop (February 4 and 5) in R which  is a free and open source environment for data analysis and statistical computing.  While R contains many built-in statistical procedures, a powerful feature of R is the facility for users to extend these procedures to suit their own needs.  Excellent graphing capability is another reason R is gaining wide popularity.

  • How to Obtain R
  • Help Tools
  • Importing / Exporting Data
  • Data Management
  • Descriptive and Exploratory Statistics
  • Common Statistical Analyses (t-test, Regression Modeling, ANOVA, etc.)
  • Graphics
  • Creating Functions

 

Matching with R

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An introduction to matching, such as propensity score matching, using R‘s “optmatch” package. Matching is used to improve balance between groups, typically in observational studies, by creating quasi-experimental strata of similar individuals. We will discuss the theory behind matching and propensity scores, followed by examples using R to perform the matching and judging the match quality, as well as speeding up the matching operation. Basic familiarity with R will be assumed.

Statistical Analysis with R

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This is a two day workshop (May 21 & 22) in R which  is a free and open source environment for data analysis and statistical computing.  While R contains many built-in statistical procedures, a powerful feature of R is the facility for users to extend these procedures to suit their own needs.  Excellent graphing capability is another reason R is gaining wide popularity.

  • How to Obtain R
  • Help Tools
  • Importing / Exporting Data
  • Data Management
  • Descriptive and Exploratory Statistics
  • Common Statistical Analyses (t-test, Regression Modeling, ANOVA, etc.)
  • Graphics
  • Creating Functions

Registration

To register for CSCAR Workshops, call the CSCAR front desk at (734) 764-7828 or come to the office in person with cash or check or a UM department shortcode:

OFFICE HOURS

9:00 a.m. – 5:00 p.m., Monday through Friday
Closed 12pm – 1:00 p.m. every Tuesday for staff meeting.
Voice: (734) 764-7828 (4-STAT from a campus phone)
Fax: (734) 647-2440

ADDRESS

Center for Statistical Consultation and Research (CSCAR)
The University of Michigan
3550 Rackham
915 E. Washington St.
Ann Arbor, MI 48109-1070

Regular Expressions II

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Regular expressions are perfectly suited for people who like puzzles. Regular expressions are a sequence of characters used to define a search pattern. They are commonly used to do “find” and “find and replace” string operations. They are also used to validate strings like phone numbers, passwords, etc. in data entry. Regular expression capabilities can be found in a variety of programming languages and software like ArcGIS, Java, Javascript, Matlab, Perl, PHP, Python, R, Visual Basic, etc. and some text editors. This workshop is part II of a two-part series and will cover more advanced topics like captured groups, backreferences and assertions. The workshop will consist of hands-on example problems. Basic understanding of regular expressions is required. You should be able to understand expressions like “\w{3,}-\d{1,2}-\d{4}“ and “des*ert?s?”. The tutorials will be conducted using Python. A basic programming background is helpful but not required for this workshop.