19-other.Rmd

# (PART) ETL {.unnumbered}
# (PART) Statistical modelling {.unnumbered}
# (PART) Visualisation {.unnumbered}
# (PART) Appendix {.unnumbered}

# Other {#other}

This is a dumping ground for matters I'm yet to look in to properly, never mind write notes about.

## renv

[Introduction to renv](https://rstudio.github.io/renv/articles/renv.html) vignette

## Docker {#docker}

> Using docker containers means you don't have to deal with "works on my machine" problems.

*Currently, the set-up and reproducibility issues I've had with* `arcpy` *and Jupyter notebooks means I can't recommend others in the Council go down this path and therefore collaborate with these tools. Could Docker be a means to address these issues, or at the very least, allow officers to trial* `arcpy` *and Jupyter notebooks before investing in a complicated setup? For more typical R projects* `renv` *might be preferable.*

[Docker for Data Science --- A Step by Step Guide](https://towardsdatascience.com/docker-for-data-science-a-step-by-step-guide-1e5f7f3baf8e), Dean Pleban (18 October 2020)

[The Pros and Cons of Docker](https://blog.iron.io/docker-containers-the-pros-and-cons-of-docker/), Nick from Iron.io (28 August 2020)

[Play with Docker](https://www.docker.com/play-with-docker)

[rocker](https://github.com/rocker-org/rocker) R package

## reprex R package
[reprex.tidyverse.org](https://reprex.tidyverse.org/)

## Writing R packages

[R Packages](https://r-pkgs.org/index.html) by Hadley Wickham & Jenny Bryan

### roxygen2

[Introduction to roxygen2](https://cran.r-project.org/web/packages/roxygen2/vignettes/roxygen2.html) vignette  
[10.3 Roxygen comments](https://r-pkgs.org/man.html#roxygen-comments)  

## plumber R package

[rplumber.io](https://www.rplumber.io/)

## Data Visualisation  

[Data Visualization, A practical introduction](https://socviz.co/index.html) by Kieran Healy

 - grammer of graphics, R4DS, Graphics cookbook, stackexchange