Sandbox

This repository holds scripts and notebooks for Steve's musings, investigations, case studies, animations, and slides.

Here's a high-level snapshot of each script.

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File	Language	Dataset	Package	Notes
`NB.R`	R	`NaiveBayes.csv`	`e1071`	Simple example of NB.
`arules.Rmd`	R	`arules::Groceries`	`arules`, `arulesViz`
`bigdata.Rmd`	R	N/A	`tidyverse`	Just some charts for the big data slides.
`classifiers.R`	R	`laheart.csv`	`rpart`, `e1071`, `MLmetrics`	Compares NB and DT.
`intro.Rmd`	R	`gapminder`	`tidyr`, `dplyr`, `ggplot2`	An intro to R and the tidyverse.
`recSys.R`	R	`recommenderlab::MovieLense`	`recommenderlab`	Recommendation system for Movie Lense data. Uses CF.
`slide_plots.Rmd`	R	`chirps.csv`, `Prestige.txt`, `clusters.csv`	`tidytext`, `tm`, `tidyverse`	Just a script to create some plots/charts I've used in slides.
`spark-sample.mdR`	R	`nycflights13`, `Lahman`	`sparklyr`	Simple of example of how to use `sparklyr`.
`sql.Rmd`	R	`customer.csv`, `transaction.csv`	`sqldf`	Shows how to use the `sqldf` package. Used for some of my slides on SQL.
`sqlChallenge.Rmd`	R	`Lahman`	`sqldf`	Used for creating the SQL challenge.
`titanic.Rmd`	R	`titanic`	`tidyverse`, `rpart`, `MLmetrics`	Titanic case study. Builds a DT to predict survival.

File	Language	Dataset	Package	Notes
`cluster_20.ipynb`	Python	`sklearn.datasets::20newsgroups`	`nltk`, `sklearn`	Clustering the 20 Newsgroup dataset.
`imdb.Rmd`	R	`all.imdb.pipe.csv`	`tidytext`, `cleanNLP`, `tm`	Classifying IMDB data.
`kiva.Rmd`	R	`kiva.csv`	`tidytext`, `topicmodels`, `rpart`, `MLmetrics`	Classifying KIVA loans. Used as a case study.
`nltk-cluster.py`	Python	`sklearn.datasets::20newsgroups`	`nltk`, `sklearn`	I'm not sure how this is different from `cluster_20.ipynb`
`sentiment-manning.Rmd`	R	`manning.csv`, `brady.csv`	`tidytext`	Sentiment analysis on tweets about Peyton Manning and Tom Brady.
`slides_sentiment.R`	R	N/A	`tidytext`	Just a script to do some simple tidy-based sentiment analysis on some made-up data.
`slides_text_amazon.Rmd`	R	`reviews_Grocery_and_Gourmet_Food_5_50000.csv`	`tidytext`, `tm`, `wordcloud`	Descriptive stats on Amazon Reviews (Food category).
`slides_text_amazon_classify.R`	R	`reviews_Grocery_and_Gourmet_Food_5_50000.csv`	`tidytext`, `tm`, `caret`	Classifying Amazon reviews.
`slides_text_reuters.Rmd`	R	`reutersCSV.csv`	`tidytext`, `tm`, `wordcloud`	Descriptive stats on Reuters dataset.

Note: the source isn't actually "Unknown" for most of the data files below. I just haven't done it yet.

File	Source
`HR_comma_sep.csv`	Unknown
`Master.csv`	Unknown
`NaiveBayes.csv`	Unknown
`Prestige.txt`	Unknown
`Salaries.csv`	Unknown
`all.imdb.pipe.csv`	Unknown
`alltweets.csv`	Unknown
`beta.csv`	Unknown
`beta_12.csv`	Unknown
`chirps.csv`	Unknown
`clusters.csv`	Unknown
`customer.csv`	Unknown
`gamma.csv`	Unknown
`gamma_12.csv`	Unknown
`jackastors.csv`	Unknown
`kiva..csv`	Unknown
`laheart.csv`	Unknown
`laheart2.csv`	Unknown
`site.csv`	Unknown
`student.csv`	Unknown
`survey.csv`	Unknown
`topicnames_12.csv`	Unknown
`transaction.csv`	Unknown
`visited.csv`	Unknown
`groceries.csv`	Unknown
`loan_small.csv`	Unknown
`all.imdb.pipe.csv`	Unknown
`brady.csv`	Unknown
`manning.csv`	Unknown
`reutersCSV.csv`	Unknown
`reviews_Grocery_and_Gourmet_Food_5_50000.csv`	Unknown