Inauguration speeches

Acquiring inauguration speeches

Though not about Greenville especially, it might be interesting to quantitatively analyze inauguration speeches. This analysis will be done using two paradigms: the tm package and the tidytext package. We will read the speeches in such a way that we use the tidytext package; later on we will use some tools from that package to make analyses traditionally done by tm.

I looked around for inauguration speeches, and finally found them at www.bartelby.com. They are in a format more for human consumption, but with the use of the rvest (harvest?) package, we can read them in relatively easily. However, we need to do a mapping from speech IDs to speakers (newly inaugurated presidents), which is a little ugly and tedious.

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Greenville on Twitter

In this blogpost, we use R to use Twitter data to analyze topics of interest to Greenville, SC. We will describe obtaining, manipulating, and summarizing the data.

Twitter is a “microblogging” service where users can, usually publicly, share links, pictures, or short comments (up to 140 characters) onto a timeline. The public timeline consists of all public tweets, but people can build their own private timelines to narrow content to just what they want to see. (They do this by “following” users.) Over the years, many companies, news organizations, and users have considered the social media site essential for sharing news and other information. (Or cat memes.) Twitter has some organizational tools such as replies/conversation threads, mentions (i.e. naming other users using the @ notation), and hashtags (naming a topic using # notation). Twitter has encouraged the use of these organizational tools by automatically making mentions and hashtags clickable links.

These organizational tools can make for some interesting analysis. For instance, a game show may encourage viewers to vote on a winner using hashtags. On their end, they create a filter for a particular hashtag (e.g. #votemyplayer) and count votes. This also makes Twitter data ripe for text mining (which they use to identify trending topics).

Obtaining the Twitter data

Twitter makes it possible for software to obtain Twitter comments without having to resort to “web-scraping” techniques (i.e. downloading the data as a web page and then parsing the HTML). Instead, you can go through an Application Programming Interface (API) to obtain the comments directly. If you’re interested, Twitter has a whole subdomain related to accessing their data, including documentation. There are a lot of technical details, but for the casual user probably the only ones of interest are API key and rate limits. This post won’t fuss with rate limits, but more serious work may require some further understanding of these issues. However, you will need to create an API key. Follow these instructions, which are tailored for R users. It essentially consists of creating a token at Twitter’s app web site and running an R function with the token. I set variables consumer_secret, consumer_key, access_token, and access_secret in an R block just copying and pasting from the Twitter apps site, not echoed in this blog post for obvious reasons.

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Plotting GeoJSON polygons on a map with R

In a previous post we plotted some points, retrieved from a public dataset in GeoJSON format, on top of a Google Map of the area surrounding Greenville, SC. In this post we plot some public data in GeoJSON format as well, but instead of particular points, we plot polygons. Polygons describe an area rather than a single point. As before, to set up we do the following:

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Plotting GeoJSON data on a map with R

GeoJSON is a standard text-based data format for encoding geographical information, which relies on the JSON (Javascript object notation) standard. There are a number of public datasets for Greenville, SC that use this format, and, the R programming language makes working with these data easy. Install the rgeojson library, which is part of the ROpenSci family of packages.

In this post we plot some public data in GeoJSON format on top of a retrieved Google Map. To set up we do the following:

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