The revaluation of Western European glass compositional data and the observation of patterns between different variables (chemical compositions, typology, chronology, etc…) is a key point of my MSCA project. Someone might even suggest that I had this idea so that I could FINALLY justify learning R and applying statistics to glass analysis… 😛
Months ago I started to put together a series of resources that could help learning both the basics of statistics and R-coding. So far I can do the really basics things such as plotting and some really crude PCA and hierarchal clustering analysis, but this clearly is not enough.
My approach was too naïve and disorganised and I clearly needed some help of some expert users to start taking seriously statistics and R-coding.
This is why I have asked for help to the R community through the #rstats hashtag on my twitter account:
There are some really good suggestions in the thread that I didn’t put here, so I highly recommend to check it out.
Moreover, there is nothing worse than keeping good resources to yourself. So, I will start to compile a list of R books, blogs, social media account that will be useful to beginner-users like me. This list does not want to be in any way comprehensive: just a small nudge in the right direction and a way for me to keep track of the most useful tools and books (or even the things I find interesting!). These resources are, of course, targeted specifically to other archaeometrists, but I really hope they will be useful to other researchers.
You are most welcome 🙂
Camilla
Books/articles:
Baxter, M.J., 2015. Notes on quantitative archaeology and R. Nottingham. https://www.academia.edu/12545743/Notes on Quantitative Archaeology and R
Baxter, M.J, Cool, H., 2016. Basic Statistical Graphics for Archaeology with R: Life Beyond Excel. Nottingham: Barbican Research Associates .
Baxter, M.J., Cool, H., Jackson, C.M., 2006. Comparing Glass Compositional Analyses. Archaeometry ,48, 399–414. https://doi.org/10.1111/j.1475-4754.2006.00263.x
Carlson, D.L., 2017. Quantitative methods in archaeology using R. New York, NY: Cambridge University Press. https://doi.org/10.1017/9781139628730
Drennan, R.D., 2009. Statistics for archaeologists: A Common Sense Approach. Boston, MA: Springer. http://doi.org/10.1007/978-1-4419-0413-3
Shennan, S., 1997. Quantifying archaeology. Edinburgh : Edinburgh University Press. https://doi.org/10.1016/C2009-0-21716-8
Wickham, H., Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. O’Reilly Media, Inc. Start here, this book is available online for free: https://r4ds.had.co.nz/
Wickham, Hadley, Navarro, Danielle, Lin Pedersen, Thomas, 2015. ggplot2: Elegant graphics for data analysis. Springer. Start here: https://ggplot2-book.org/
Websites:
https://www.r-bloggers.com/ – Incredible resource for R news and tutorials
RStudio education – RStudio (ultra-famous free integrated development environment for R) offers a series of resources based on your starting level (beginners, intermediates, and experts), Very very useful! Check also: cheatsheets and tutorials.
https://rladies.org/ – World-wide organisation to promote gender diversity in the R community. They are also have a blog: https://blog.rladies.org/post/
https://stackoverflow.com/ – question and answer site for professional and enthusiast programmers.
https://r-charts.com/ – Code examples of R graphics, ggplot2, and other packages.
https://rmarkdown.rstudio.com/ – Turn your analyses into high quality documents, reports, presentations and dashboards.
https://stat545.com/index.html – Data wrangling, exploration, and analysis with R by Jenny Bryan (excellent online course where you will learn to explore, visualize, and analyze data using R).
R-Packages:
https://www.tidyverse.org/ – The tidyverse is an opinionated collection of R packages designed for data science
Useful tags:
Twitter accounts:
