analysis/about.md

In nateaff/legolda: Color Topic Models of the Lego Dat Set

title: "About" output: html_document: toc: false

Note

I've updated the repo so the dataset CSV files are in data. The load_csv method replaces the load_data method and loads data from the local CSV files.

Kaggle

There's also a Kaggle notebook that is nearly identical to the code. That notebook is here..

Installing and running the code

The code is organized as a package with some additional structure based on the workflowr package. Both this repo and the workflowr package need to be installed to rebuild the analysis. (This could also be done directly with rmarkdown)

install.packages('workflowr')
install.packages('devtools')
devtools::install_github('nateaff/legolda')

The analysis also uses the following packages:

purrr, tidyr, forcats, stringr, ggplot2, dplyr

# For text mining
topicmodels, SpeedReader, tidytext, ldatuning, clues, waffle

After installing required packages and setting your working directory to the legolda package you need to run:

library(legolda)
workflowr::wflow_build()

This builds the website version of the analysis you are reading now.

There are a few intermediate cached files that are produced by the LDA function. These take a long time to run and to re-run you will need to set from_cache to FALSE in the perplexity-cv.R and train-model.R files. (I might update how this option is changed.) This takes 8 hours or so to run on a larger AWS instance. You also have the option of passing the load_csv function the sample_data parameter to run on a subset of the data LEGO sets. The sample number refers to the number of LEGO sets used to build the models.

nateaff/legolda documentation built on May 18, 2019, 10:15 a.m.