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.

Non-text Analytics

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.

Text Analytics

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.

Data

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