Resources
This is a collection of the resources I’ve collected throughout my undergraduate studies. The traditionally low number of people with background in Statistics made academic support challenging. Thus I searched stuff online throughout the years and found these; I think these are helpful if you also:
- Are intimidated by statistical proofs and just want to generally grasp the logic behind the concept;
- Comes from a developing nation who needs to optimize time and resources due to poor internet connection, frequent power outage, etc and;
- Cares about reproducibility in science and valid inference so are considering making your own package!
Learning Statistical Methods
Doing Data Analysis in R
- R for Data Science (2e): Leveraging core Tidyverse paradigms— including advanced data transformation (
dplyr), functional programming (purrr), and exploratory data analysis— to maintain clean, reproducible, and standardized data pipelines before modeling.
- R for Data Science (2e): Leveraging core Tidyverse paradigms— including advanced data transformation (
Transforming R Functions into Package
If you have other questions about my experience navigating college life as a Statistics student, submitting to CRAN, making and maintaining your first package in the Global South, and anything we share in common, feel free to email me at ninotalingting77@gmail.com. Don’t forget to test your assumptions and map your residuals!