Data science: Data science tutorials

Hause Lin

What is this site about?

This site contains introduction tutorials as well as relatively advanced data analysis tips/tricks I have used to analyze data. Occasionally, I’ll also explain statistical procedures and concepts with code. I have used these tutorials and articles to teach data science in graduate school. In my tutorials and articles, I usually try my best to explain how things work.

tutorials: beginner R tutorials (R programming basics, tidyverse, data.table, etc.)
articles: more advanced analysis tricks/tips and statistical concepts illustrated with code

To learn more about me, visit my personal site.

Consider being a patron and supporting my work?

Donate and become a patron: If you find value in what I do and have learned something from my site, please consider becoming a patron. It takes me many hours to research, learn, and put together tutorials. Your support really matters.

Where to begin?

If you’re new to R, start with the tutorials. If you’re looking for tips/tricks to improve your existing R workflow or explanations of statistical concepts, check out my articles.

Questions or want to get in touch?

If you have any questions or comments, email me or create a new issue here or follow me on Twitter.

More resources?

data.table
- powerful, extremely fast, and simple way to manipulate dataframes/datatables
tidyverse: contains many packages (e.g., dplyr, tidyr, ggplot2, stringr)
- manipulate dataframes (and tibbles) easily with tidyr, dplyr; make beautiful plots with clean syntax (ggplot2)
DataCamp: mostly R and Python tutorials
my hausekeep R package: contains functions for experimental psychological research

Corrections

If you see mistakes or want to suggest changes, please create an issue on the source repository.

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/hauselin/rtutorialsite, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Lin (2019, Jan. 12). Data science: Data science tutorials. Retrieved from https://hausetutorials.netlify.com/

BibTeX citation

@misc{lin2019rdatascience,
  author = {Lin, Hause},
  title = {Data science: Data science tutorials},
  url = {https://hausetutorials.netlify.com/},
  year = {2019}
}