Skip to main content

Simple Text Web Crawler

I put together a simple web crawler for R. It's useful if you are doing any text analysis and need to make .txt files from webpages. If you have a data frame of URLs it will cycle through them and grab all the websites. It strips out the HTML code. Then it saves each webpage as an individual text file.

Thanks to Rex Douglass, also.

 Enjoy (and please feel free to improve)

Comments

Nice piece of code. Does what it is supposed to.

Do you have any suggestions to how one can delay the code with x seconds? When using the code for retrieving many pages from same server I am overloading the server giving me "bad" files with no text, and probably some angry hosts, which is not my intention.

I solved the problem by taking 5% of total n of pages at the time. Therefore i believe a solution would be if one could make count total number of pages in the input file and tell the code to only send like 50 requests or 5% of total n at the time.

Best
Kasper
That's a good suggestion. I think I like it better than the approach I took later here.
Salim KHALIL said…
This comment has been removed by the author.
Salim KHALIL said…
You can use an R web crawler and scraper called RCrawler, it's designed to crawl, parse, store and extract contents of web page automatically.
install.packages("Rcrawler")
see manual for more detail here R web scraper

Popular posts from this blog

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 blogposts. 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, 1997. ExamplesNot…

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 use it follow …

Showing results from Cox Proportional Hazard Models in R with simPH

Update 2 February 2014: A new version of simPH (Version 1.0) will soon be available for download from CRAN. It allows you to plot using points, ribbons, and (new) lines. See the updated package description paper for examples. Note that the ribbons argument will no longer work as in the examples below. Please use type = 'ribbons' (or 'points' or 'lines'). Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models, especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time interactions, interactions between variables, and nonlinear effects without the reader further calculating quantities of interest for a variety of fitted values.So, I’ve been putting together the simPH R p…