These are my initial notes from useR 2015. Will revise when I have time.
fbRads: Analyzing and managing Facebook ads from R (Gergely Daroczi)
Google/Amazon/Facebook use our information
Ad platforms: Google: RAdwords, facebook likes: fbRads. You can use the facebook API to get information from facebook. Get hashes of email address, not the actual address. In the last few years, the API has changed.
- Grab useR's email addresses from CRAN and R-help mailing list.
- Create a facebook app with API to get a token.
- Create a custom audience.
- Create lookalike audiences: get facebook users who are similar to my target list.
- Define audience, ad and budget.
- Upload an image and description.
- Run A/B testing.
The performance metrics API is still being developed.
Web scraping with R – A fast track overview. (Peter Meißner)
There are a number of R packages for web-scraping.
- Download: protocols/procedures, i.e. HTTP, cookies, POST, GET
- extraction: parsing/extraction/cleansing, i.e. XML, JSON, html into R
Reading text from the Web
The simplest solution is to use
readLines, then use some regular expressions (either with base R or
stringr or …).
rvest and use
xml_structure to view the structure of the XML scheme. To extract text, we need to use
XPath (still using
rvest there are a number of convenience functions, e.g.
html_table to get a list of tables.
jsonlite to translate JSON to a data frame.
HTML forms/HTTP methods
RSelenium for browser automation
- Don't use Windows for web scraping. Use Linux (or if you must, a Mac)
- Start with stringr, rvest, jsonlite
- Need to learn regular expression, file manipulation
- Before scraping, look for the download button
multiplex: Analysis of Multiple Social Networks with Algebra (Antonio Rivero Ostoic)
multiplex is a package designed to perform algebraic analyses of multiple networks (but isn't limited to algebra)
- The function
zbind creates multivariate network data from arrays
perm manipulates network data
Two-mode networks are represented in a Galois framework. This makes analysis easier(?)
What's new in igraph and networks (Gabor Csardi)
igraph is the premier R package for the analysis of network data and it went through major restructuring recently and has changed a lot since last time it was featured at useR! in 2008. This talk introduces the new/updated features of igraph: – Simplified ways of graph manipulation. – New methods community detection. – New layouts for graph visualization. – New statistical methods: graphlets, embeddings, graph matching, cohesive blocks, etc. – How to use igraph graphs with new visualization tools: DiagrammeR, D3, etc.
The igraph package deals mainly with infrastructure. It's actually a C library, with an R and python interface.
[ operator makes the graph behave like an adjacency matrix. For example, to check if an edge exists, use
air["BOS", "SFO"]. Can also use it to manipulate the network, e.g. to add or remove edges.
[[ can be used to get all adjacent vertices
What's new: consistent function names and manipulators
- Pipe friendly syntax
- Easier connection to other packages, e.g.
- Better connection to other packages
- Infrastructure cleanup
Please note that the notes/talks section of this post is merely my notes on the
presentation. I may have made mistakes: these notes are not guaranteed to be
correct. Unless explicitly stated, they represent neither my opinions nor the
opinions of my employers. Any errors you can assume to be mine and not the
speaker’s. I’m happy to correct any errors you may spot – just let me know!