Skip to main content

Scrappy Scapers

In an earlier post I presented some R code for a basic way of collecting text from websites. This is a good place to start for collecting text for use in text analysis. 


However, it clearly has some limitations; 
  • You need to have all of the URLs already stored in a .csv file.
  • The method of extracting the text from the downloaded HTML code using <gsub> is a bit imprecise. It doesn't remove the text from common links such as "Home" or "About".
Both of these problems can be solved in R with a bit of work. But I think for bigger scrapping projects it is probably a good idea to use other languages such as Python or Ruby. 

ProPublica has an excellent little series on scraping that covers how to gather data from online databases and PDFs. This is a really good public service and enables something sadly unusual in journalism: reproducibility. Their chapter on using Ruby and Nokogiri for scraping the Phizer's doctor payments disclosure database is particularly helpful. 

Building on this, I'm thinking of putting together a slideshow for how to use Ruby, Nokogiri, and Mechanize to scrap the Congressional Records database. It will be similar to the slideshow I made for how to use the googleVis and WDI packages to make Google Motion Charts. 

Bit busy over the next few weeks, but now that I've blogged it, it's in my "Must-Do" list.
Labels:

Comments

Post a Comment

Popular posts from this blog

Dropbox & R Data

I'm always looking for ways to download data from the internet into R. Though I prefer to host and access plain-text data sets (CSV is my personal favourite) from GitHub (see my short paper on the topic) sometimes it's convenient to get data stored on Dropbox . There has been a change in the way Dropbox URLs work and I just added some functionality to the repmis R package. So I though that I'ld write a quick post on how to directly download data from Dropbox into R. The download method is different depending on whether or not your plain-text data is in a Dropbox Public folder or not. Dropbox Public Folder Dropbox is trying to do away with its public folders. New users need to actively create a Public folder. Regardless, sometimes you may want to download data from one. It used to be that files in Public folders were accessible through non-secure (http) URLs. It's easy to download these into R, just use the read.table command, where the URL is the file name
51 comments
Read more

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame. I've found the various R methods for doing this hard to remember and usually need to look at old blog posts . Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command– slide –to the DataCombine R package (v0.1.5). Building on the shift function TszKin Julian posted on his blog , slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data. Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996
46 comments
Read more

A Link Between topicmodels LDA and LDAvis

Carson Sievert and Kenny Shirley have put together the really nice LDAvis R package. It provides a Shiny-based interactive interface for exploring the output from Latent Dirichlet Allocation topic models. If you've never used it, I highly recommend checking out their XKCD example (this paper also has some nice background). LDAvis doesn't fit topic models, it just visualises the output. As such it is agnostic about what package you use to fit your LDA topic model. They have a useful example of how to use output from the lda package. I wanted to use LDAvis with output from the topicmodels package. It works really nicely with texts preprocessed using the tm package. The trick is extracting the information LDAvis requires from the model and placing it into a specifically structured JSON formatted object. To make the conversion from topicmodels output to LDAvis JSON input easier, I created a linking function called topicmodels_json_ldavis . The full function is below. To
22 comments
Read more