### Libraries required for this analysis
- tidyverse
- brms
- ggpubr
- scales
- car
- ggmcmc
- tidybayes
- ggrepel
### Files contained in this analysis
**README**
- `README.md` (current document)
**Data**
- `data/all_numbers.csv` (before preprocessing: all numbers identified)
- `data/master.csv`: (after preprocessing: frequencies for numbers 0 to a billion)
- `data/for_model.csv` (frequencies for numbers 1 to a million with extra columns for statistical model)
- `data/residuals.csv` (residuals for numbers 1 to a million from statistical model)
**Code**
- `code/scrape_BNC.py` (Python: number identification)
- `code/preprocessing.Rmd` (R: preprocessing, Markdown)
- `code/preprocessing.html` (R: preprocessing, HTML)
- `code/analysis.Rmd` (R: analysis, minus statistical models, Markdown)
- `code/analysis.html` (R: analysis, minus statistical models, HTML)
- `code/model.Rmd` (R: statistical model, Markdown)
- `code/model.html` (R: statistical model, HTML)
**Figures**
- `figures/fig1.jpg`
- `figures/fig2.jpg`
- `figures/fig3.jpg`
- `figures/fig4.jpg`
- `figures/tab1.pdf`
**Manuscript**
- `manuscript.pdf`
**Presentations**
- `presentations/LACAB.pdf`
- `presentations/LxMeeting.pdf`
