Materials for workshop on preparing data for modeling and analysis using R
Switch branches/tags
Nothing to show
Clone or download
Pull request Compare This branch is 7 commits ahead of jesbot:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
BadModels
BasicTreatment
KDD2009
NestedModels
Slides
TestingForSignal
.gitignore
LICENSE
README.Rmd
README.html
README.md

README.md

https://github.com/WinVector/PreparingDataWorkshop

These are instructions how to prepare to work along with the examples from the workshop:

"Preparing Data for Analysis using R: Basic through Advanced Techniques (WORKSHOP)" John Mount & Nina Zumel, Win-Vector, LLC ODSC 2015.

Sabstract for "Preparing data for analysis using R: basic through advanced techniques" John Mount / Nina Zumel.

Data quality is the biggest determiner of data science project success or failure. Preparing data for analysis is one of the most important, laborious, and yet neglected aspects of data science. Many of the routine steps can be automated in a principled manner. This workshop will lay out the fundamentals of preparing data and provide interactive demonstrations in the open source R analysis environment. We will cover what commonly goes wrong, and how to detect and fix it. Participants can download materials from https://github.com/WinVector/PreparingDataWorkshop and either follow along during the workshop, or at their leisure. We will work examples using R ( https://cran.r-project.org ), RStudio ( https://www.rstudio.com ), and a few packages (named in README.md on the Github repository). Participants can re-run all the demonstrations whenever they want.

You will want to download all files in this Github repository, and prepare your machine before trying the exampels. Please be patient as we are putting these instructions together as we finish and polish our workshop, so there may be some changes prior to the workshop.

To run all of the examples you will need a machine with a current version of R, and RStudio installed.

To install some of the additional packages you will need your system's compiler tools installed (often c,c++, and fortran). How to do this varies by system and is beyond the scope of the worksop.

The additonal R packages you want installed are the following:

install.packages(c('caret',
                   'devtools',
                   'e1071',
                   'gbm',
                   'glmnet',
                   'ggplot2',
                   'kernlab',
                   'knitr',
                   'plyr',
                   'pROC',
                   'randomForest',
                   'reshape2',
                   'rpart',
                   'snow',
                   'vtreat'))
devtools::install_github('WinVector/WVPlots')