Data Mining and R

[This article was first published on Jeromy Anglim's Blog: Psychology and Statistics, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

This post lists a few data mining resources in R. I also provide a few observations on the distinction between data mining, data analysis, and statistics as it pertains to the analysis work that I do in psychology.

Online Resources

Some Casual Observations
  • Data mining seems more concerned with prediction using observed variables than with understanding the causal system of latent variables; psychology is typically more concerned with the causal system of latent variables.
  • Data mining typically involves massive datasets (e.g. 10,000 + rows) collected for a purpose other than the purpose of the data mining. Psychological datasets are typically small (e.g., less than 1,000 or 100 rows) and collected explicitly to explore a research question.
  • Psychological analysis typically involves testing specific models. Automated model development approaches tend not to be theoretically interesting.

To leave a comment for the author, please follow the link and comment on their blog: Jeromy Anglim's Blog: Psychology and Statistics.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)