R by Example: Analyzing RECS using data.table

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In the R by Example series of workshops, we’ll discuss example analyses in R as a vehicle for learning  commonly used tools and programming patterns.  The “Analyzing RECS using data.table” workshop will focus on analyzing winter home temperatures in the US using data from the Residential Energy Consumption Survey (https://www.eia.gov/consumption/residential/).  We’ll use the data.table package for data manipulations and ggplot2 for plotting.  The workshop will be organized in a parallel fashion, with participants given time to build an analysis from scratch by adapting presented examples step by step. In the process, participants will become familiar with core data.table functionality including its pivot methods.  This workshop is geared towards beginner to intermediate R users or those new to data.table.

R by Example: Analyzing RECS using tidyverse

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In the R by Example series of workshops, we’ll discuss example analyses in R as a vehicle for learning  commonly used tools and programming patterns.  The “Analyzing RECS using tidyverse” workshop will focus on analyzing winter home temperatures in the US using data from the Residential Energy Consumption Survey (https://www.eia.gov/consumption/residential/).  We’ll use the tidyverse (tidyverse.org) throughout, relying on the dplyr package for data manipulations and ggplot2 for plotting.  The workshop will be organized in a parallel fashion, with participants given time to build an analysis from scratch by adapting presented examples step by step. In the process, participants will become familiar with core dplyr functions, pivoting using tidyr, and a basic ggplot2 example.  This workshop is geared towards beginner to intermediate R users.

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++.

Programming with R

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People using R for applied research are often not taught basic programming practices such as writing functions, efficient iterative processing, vectorization, and other practices that would make their research far more efficient and reproducible.  Understandably, focus is on basic data manipulation and getting model results.  Unfortunately, this can mean the data isn’t as explored as it should be, or other opportunities are lost (e.g. feature engineering), because of the presumed effort that would be required to deal with the data more fully.

This workshop will help you get more out of R so that you can take your efforts to the next level.
Prereq: Some basic experience using R is required.  You should know how to create and manipulate objects, run basic analyses, etc.  This could also be useful to anyone with programming experience in another language like Python.

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 (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