R Markdown is an authoring format that enables easy creation of dynamic documents, presentations, and reports that use R plots and data analysis. It combines the core syntax of markdown (an easy-to-write plain text format) with embedded R code chunks that are run so their output can be included in the final document. R Markdown documents are fully reproducible (they can be automatically regenerated whenever underlying R code or data changes). Continue reading “RMarkdown Tutorial”
Month: April 2015
Getting Started: Scraping Twitter Data
Introduction
The aim of this blogpost is for a beginner level user to be able to scrap data from Twitter. In this example, I’ll scrap the 20 most recent statuses from @PureMichigan‘s Twitter feed. My end goal of scraping these posts is to find out quickly who has been talking about @PureMichigan on Twitter most recently and what they are saying. You can also use the count feature to pull up to 200 statuses at a time and analyze the content.
Continue reading “Getting Started: Scraping Twitter Data”
Working with Large Data Sets
This tutorial provides a walk-through of managing a large data set in R. The sample data set used is on Precipitation in the Great Lakes Region retrieved from GLERL. It is a multi-tab excel file that needs to be cleaned up in R before it can be used efficiently. General methods of dealing with large datasets and the problems one can run into are included so that information in this tutorial can be applied to various types of data.