Here I will aggregate sources related to programming tutorials, gathering data through different mediums and processing that data. This page is under construction.
- Udacity Tutorial
- Excellent tutorial that Jon Cohen used for his cognitive psychology course.
- TwitteR is an R package that allows you to collect tweets based on a number of parameters (key words, dates, etc).
- I have a friend at UChicago who does text scraping on twitter and showed me the twitteR package, so I figured I would add some info on that here since it seems it could be useful in the future for questions related to social networks.
- A different but similar package is rtweet
- actually has a cool tutorial
- Here is a a page with tutorials on Facebook and Twitter scraping with R code.
- Text mining with R: A tidy approach
- Social Media Mining with R
- Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining
- Citations: Link
- Blog on the Turk: Link
- Tim Brady Tips: Link
- Princeton resources: Link
- Overview of HITs/Turkers: Link Link2 Link3
- Command line tutorials: Link Link2 Link3
- Examples: Link
- Tips: Link Link2 Link3
- PsiTurk: Link Link2 Link3 Link4
- JsPysch (I use this): Link Link2
- If you collect data from Qualtrics but analyze in R, you will find that the format you download the data in from Qualtrics is not R friendly. I found this website that gives some great instructions to make the preprocessing of the data that is pretty simple.
- I use Rmarkdown in Rstudio to create my analysis scripts in a reproducible way, and in a way that allows easy documentation into pdf or html form.
- Links on how to use it. Cheat sheet. Documentation.
- I realized that it is possible to write python code in Rmarkdown! Check the deets here. I have been making tutorials for the lab so this will come in very handy. I’m probably behind the times on this, but I figured I would share.
- Papaya – to write APA style papers
- Skimr – get the most out of summary statistics of data frames.
- Write thesis in R markdown
- If you gather data on online databases, it’s possible it will come in JSON format, which is very not R friendly. However there are some ways to get around this.
- R package: jsonlite, Link (I use this method)
- R package: tidyjson, Link
- R tutorials on hacking through the json data: Link, Link
- Sentiment analysis
- Sentiment analysis allows one to quantify valence/emotion in text
- R package “sentiment”
- Sentiment Analysis on Donald Trump using R and Tableau
- TACIT – text analysis