10 documentation update

README.md

@@ -2,7 +2,13 @@
 
 # R for AMR Epidemiology adapted from R for Reproducible Scientific Analysis
 
+This lesson was commisioned by the EPSRC Digital Health Hub for AMR, for delivery
+to the UK HSA w/c 16th September 2024.
+
 An introduction to R for non-programmers using the [Gapminder][gapminder] data.
+In addition, this lesson makes use of open data from the [Centre for Consumer Research Data](https://data.cdrc.ac.uk/)
+and bespoke synthetic data provided by UKHSA.
+
 Please see [https://ucl-arc.github.io/r-amr-epidemiology](https://ucl-arc.github.io/r-amr-epidemiology) for a rendered version of this material,
 [the lesson template documentation][lesson-example]
 for instructions on formatting, building, and submitting material,
@@ -17,16 +23,17 @@ attendees a strong foundation in the fundamentals of R, and to teach best
 practices for scientific computing: breaking down analyses into modular units,
 task automation, and encapsulation.
 
-Note that this workshop focuses on the fundamentals of the programming
-language R, and not on statistical analysis.
-
-The lesson contains more material than can be taught in a day.  The [instructor notes page]({{ page.root }}/guide) has some suggested lesson plans suitable for a one or half day workshop.
+This lesson has been expanded to incorporate additional Software Carpentry content 
+on the use of Git for Version Control, navigating files and directories in a terminal, 
+SQL and new content on the creation and validation of regression models.
 
 A variety of third party packages are used throughout this workshop. These
 are not necessarily the best, nor are they comprehensive, but they are
 packages we find useful, and have been chosen primarily for their
 usability.
 
+## R for Reproducible Scientific Analysis
+
 Current Maintainers:
 
 - [Naupaka Zimmerman][zimmerman_naupaka]
@@ -45,5 +52,18 @@ Previous Maintainers:
 [mawdsley_david]: https://carpentries.org/maintainers/#mawds
 [oliver_jeffrey]: https://carpentries.org/maintainers/#jcoliver
 
+## Additional Carpentries Lessons incorporated
+- [SWC The Unix Shell: Navigating Files and Directories](https://swcarpentry.github.io/shell-novice/02-filedir.html)
+- [SWC Version Control with Git](https://swcarpentry.github.io/git-novice), modified for context.
+- [LC SQL](https://librarycarpentry.org/lc-sql/)
 
+## Additional non-Carpentries materials referenced
+- [https://libguides.princeton.edu/R-linear_regression](https://libguides.princeton.edu/R-linear_regression)
+- [https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/assumptions-of-linear-regression/](https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/assumptions-of-linear-regression/)
+- [https://cran.r-project.org/web/packages/broom/vignettes/broom.html](https://cran.r-project.org/web/packages/broom/vignettes/broom.html)
+- [https://jmsallan.netlify.app/blog/linear-regression-with-broom/](https://jmsallan.netlify.app/blog/linear-regression-with-broom/)
+- [https://stats.oarc.ucla.edu/r/dae/logit-regression/](https://stats.oarc.ucla.edu/r/dae/logit-regression/)
+- [https://github.com/jenineharris/logistic-regression-tutorial/blob/main/20211210-logistic-regression-tutorial-code.R](https://github.com/jenineharris/logistic-regression-tutorial/blob/main/20211210-logistic-regression-tutorial-code.R)
+- [https://dept.stat.lsa.umich.edu/~jerrick/courses/stat506_f23/08-sql.html](https://dept.stat.lsa.umich.edu/~jerrick/courses/stat506_f23/08-sql.html)
+- [https://www.quackit.com/sqlite/tutorial/about_sqlite.cfm](https://www.quackit.com/sqlite/tutorial/about_sqlite.cfm)
 

index.md

@@ -2,21 +2,16 @@
 site: sandpaper::sandpaper_site
 ---
 
-*an introduction to R for non-programmers using gapminder data*
+*This material was commisioned by the EPSRC Digital Health Hub for AMR, for delivery to the UK HSA w/c 16th September 2024.*
 
-The goal of this lesson is to teach novice programmers to write modular code
-and best practices for using R for data analysis. R is commonly used in many
-scientific disciplines for statistical analysis and its array of third-party
-packages. We find that many scientists who come to Software Carpentry workshops
-use R and want to learn more. The emphasis of these materials is to give
-attendees a strong foundation in the fundamentals of R, and to teach best
-practices for scientific computing: breaking down analyses into modular units,
-task automation, and encapsulation.
+An introduction to R for non-programmers using the Gapminder data. In addition, this lesson makes use of open data from the Centre for Consumer Research Data and bespoke synthetic data provided by UKHSA.
 
-Note that this workshop will focus on teaching the fundamentals of the
-programming language R, and will not teach statistical analysis.
+The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.
 
-The lesson contains more material than can be taught in a day.  The [instructor notes page](instructors/instructor-notes.md) has some suggested lesson plans suitable for a one or half day workshop.
+This lesson has been expanded to incorporate additional content on the use of Git for Version Control, navigating files and directories in a terminal, creation and validation of regression models and SQL.
+
+This content has been developed to form a five day course.  
+The [instructor notes page](instructors/instructor-notes.md) has some suggested lesson plans suitable for shorter workshops.
 
 A variety of third party packages are used throughout this workshop. These
 are not necessarily the best, nor are they comprehensive, but they are