📝 Adds blog post on learning about mocking

.gitignore

@@ -18,5 +18,3 @@ site_libs/
 *.egg-info/
 **/*.quarto_ipynb
 **/*.ipynb_checkpoints
-
-**/*.quarto_ipynb

README.md

@@ -5,58 +5,90 @@
 
 # Data science team repo
 
-If you have any questions or need help, please contact anyone from [the Data Science team](https://the-strategy-unit.github.io/data_science/about.html).
+If you have any questions or need help, please contact anyone from [the Data
+Science team][about].
+
+[about]: https://the-strategy-unit.github.io/data_science/about.html
 
 This repo features:
 
-* Presentations the team have delivered
-* A website/ blog that the data science team can use to blog and compile other forms of resources
-* Guidance on good coding practices, also known as a "style guide"
+- Presentations the team have delivered
+- A website/ blog that the data science team can use to blog and compile other
+  forms of resources
+- Guidance on good coding practices, also known as a "style guide"
+
 
 ## Contributing to this repo
 
-All members of the Strategy Unit organisation on Github should be able to contribute to this repository.
+All members of the Strategy Unit organisation on Github should be able to
+contribute to this repository.
 
 1. Create an issue for the thing you want to add on GitHub
-2. Clone the repository (in RStudio, File > New Project > Checkout a project from a version controlled repository). Paste in the URL of this repository.
-3. Check out the main branch and check it's up to date in the RStudio Terminal  (type `git checkout main && git pull` in terminal)
-4. Check out a new branch  (`git checkout -b issue-number` in terminal)
+2. Clone the repository (in RStudio, File > New Project > Checkout a project
+   from a version controlled repository). Paste in the URL of this repository.
+3. Check out the main branch and check it's up to date in the RStudio Terminal
+   (type `git switch main && git pull` in terminal)
+4. Check out a new branch (`git switch -c issue-number` in terminal)
+
 
 ### How to create a new presentation
 
-1. Make the presentation with quarto, and put it in `presentations/` in a `YYYY-MM-DD_Talk-title` folder. Your presentation should conform to the SU branding. It should have the filename `index.qmd`
-2. Copy and edit the yaml header from another post to ensure you have the correct metadata (e.g. theming, author, date)
+1. Make the presentation with quarto, and put it in `presentations/` in a
+   `YYYY-MM-DD_Talk-title` folder. Your presentation should conform to the SU
+   branding. It should have the filename `index.qmd`
+2. Copy and edit the yaml header from another post to ensure you have the
+   correct metadata (e.g. theming, author, date)
 
 
-### How to create a new blogpost 
+### How to create a new blogpost
 
 1. Navigate to the `blogs/posts` folder
-2. Create a folder for your blogpost, following the naming convention `YYYY-MM-DD_title-of-post`
-3. Copy a previous blogpost index.qmd file into your folder and use that as your template
-4. Write your blogpost. To preview changes, run `quarto preview path/to/index.qmd` in terminal.
+2. Create a folder for your blogpost, following the naming convention
+   `YYYY-MM-DD_title-of-post`
+3. Copy a previous blogpost index.qmd file into your folder and use that as your
+   template
+4. Write your blogpost. To preview changes, run
+   `quarto preview path/to/index.qmd` in terminal.
 
 
 ### How to create/edit pages on the website
 
-1. Find the `.qmd` file that you wish to edit. For example, if you want to add to the Style Guide page, edit the `style/style_guide.qmd` file.
-2. If creating a new page, copy an existing `.qmd` file and use that as a template.
-3. If you want to see how it looks before pushing to GitHub, click Render in RStudio. The HTML version of your new post should open in your browser.
+1. Find the `.qmd` file that you wish to edit. For example, if you want to add
+   to the Style Guide page, edit the `style/style_guide.qmd` file.
+2. If creating a new page, copy an existing `.qmd` file and use that as a
+   template.
+3. If you want to see how it looks before pushing to GitHub, click "Render" in
+   RStudio. The HTML version of your new post should open in your browser.
 
 
 ### Pushing your blog post/presentation/page to GitHub
 
-1. Save your file, then add and commit it (`git add file.qmd` and `git commit -m "Add blog post/presentation about x"`). If you have any computed blocks in your content, ensure that you have run the code locally; this should generate files in the `_freeze` directory. You must ensure that these files are added to version control.
-2. Push your content to your branch in GitHub (`git push origin branchname`). 
-3. Then, on GitHub, make a pull request to main. Put any member of the Data Science team down as a reviewer. Link your pull request with your issue by typing `Closes #issuenumber` in the comment field of your pull request.
-4. When approved and merged to main, the Quarto page will automatically be rendered thanks to the GitHub action that has been set up.
+1. Save your file, then add and commit it (`git add file.qmd` and
+   `git commit -m "Add blog post/presentation about x"`). If you have any
+   computed blocks in your content, ensure that you have run the code locally;
+   this should generate files in the `_freeze` directory. You must ensure that
+   these files are added to version control.
+2. Push your content to your branch in GitHub (`git push origin branchname`).
+3. Then, on GitHub, make a pull request to `main`. Put any member of the Data
+   Science team down as a reviewer. Link your pull request with your issue by
+   adding `Closes #issuenumber` in the comment field of your pull request.
+4. When approved and merged to main, the Quarto page will automatically be
+   rendered thanks to the GitHub Action that has been set up.
+
 
 ### Potential issues
 
-The GitHub action runner does not have R installed on it, so as mentioned above, all computations must be run locally and then added to the _freeze folder.
+The GitHub action runner does not have R installed on it so, as mentioned above,
+all computations must be run locally and then added to the `_freeze` folder.
+
 
 #### code-fold blocks
 
-If you have an `R` code block that has `#| code-fold: true`, then this can cause issues as quarto will need to run `R` with `{rmarkdown}` and `{knitr}` even when you have freeze'd the computations. You can get around this by using code-fold across the entire post (via the documents `format: html` options), or by doing something like:
+If you have an `R` code block that has `#| code-fold: true`, then this can cause
+issues as quarto will need to run `R` with `{rmarkdown}` and `{knitr}` even when
+you have freeze'd the computations.
+You can get around this by using code-fold across the entire post (via the
+document's `format: html` yaml options block), or by doing something like:
 
 ```
 <details>
@@ -68,7 +100,9 @@ If you have an `R` code block that has `#| code-fold: true`, then this can cause
 
 # Contributors ✨
 
-Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
+Thanks goes to these wonderful people ([emoji key][key]):
+
+[key]: https://allcontributors.org/docs/en/emoji-key
 
 <!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
 <!-- prettier-ignore-start -->
@@ -100,4 +134,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
 
 <!-- ALL-CONTRIBUTORS-LIST:END -->
 
-This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
+This project follows the [all-contributors][ac_spec] specification.
+Contributions of any kind are welcome!
+
+[ac_spec]: https://github.com/all-contributors/all-contributors

about.qmd

@@ -15,10 +15,38 @@ The Data Science team at the Strategy Unit comprises the following team members:
 - [Natasha Stephenson](https://github.com/Nat-Stephenson)
 - [Zoë Turner](https://github.com/Lextuga007)
 
-The team has a wealth of experience in deploying models and other products to the cloud for use by a wide range of users across health and care. This is particularly demonstrated in our work with the New Hospital Programme, where we built and deployed a sophisticated probabilistic demand and capacity model and supported the use of its outputs across the decision-making stages that lead to the construction of a new hospital. The data science team possesses expertise across the breadth of data science activity — for example, statistics, machine learning, natural language processing, and real-time evidence mapping. We also have significant experience in sharing our methods and code as open-source, as well as in training others to use these tools and understand foundational data science concepts and practices. Our experience in developing scalable data science solutions, and open-sourcing them for the benefit of users across health and care, enables us to contribute meaningfully at every stage of a project’s life cycle — from design through to deployment and adoption.
 
-Current and previous projects of note include:
+The team has a wealth of experience in deploying models and other products to
+the cloud for use by a wide range of users across health and care.
+This is particularly demonstrated in our work with the New Hospital Programme,
+where we built and deployed a sophisticated probabilistic demand and capacity
+model and supported the use of its outputs across the decision-making stages
+that lead to the construction of a new hospital.
 
-- Work supporting the New Hospitals Programme, including building [a model for predicting the demand and capacity requirements of hospitals in the future](https://connect.strategyunitwm.nhs.uk/nhp/project_information/), and [a tool for mapping the evidence on this topic](https://github.com/The-Strategy-Unit/nhp_evidence_maps).
-- The [Patient Experience Qualitative Data Categorisation project](https://the-strategy-unit.github.io/PatientExperience-QDC/) 
-- Work supporting the wider analytical community, through events/communities such as [NHS-R](https://nhsrcommunity.com/) and [HACA](https://haca-conference.nhs.uk/).
+The data science team possesses expertise across the breadth of data science
+activity — for example, statistics, machine learning, natural language
+processing, and real-time evidence mapping.
+We also have significant experience in sharing our methods and code as
+open-source, as well as in training others to use these tools and understand
+foundational data science concepts and practices.
+
+Our experience in developing scalable data science solutions, and
+open-sourcing them for the benefit of users across the health and care sector,
+enables us to contribute meaningfully at every stage of a project’s life cycle —
+from design through to deployment and adoption.
+
+Current and past projects of note include:
+
+- Work supporting the New Hospitals Programme, including building a
+  [model for predicting demand and capacity requirements for hospitals][nhpinfo]
+  in the future, and [a tool for mapping the evidence on this topic][ev_maps].
+- The [Patient Experience Qualitative Data Categorisation project][peqdc]
+- Work supporting the wider analytical community, through events/communities
+  such as [NHS-OA][nhsoa] and [HACA][haca].
+
+
+[nhpinfo]: https://connect.strategyunitwm.nhs.uk/nhp/project_information/
+[ev_maps]: https://github.com/The-Strategy-Unit/nhp_evidence_maps
+[peqdc]: https://the-strategy-unit.github.io/PatientExperience-QDC/
+[nhsoa]: https://nhsrcommunity.com/
+[haca]: https://haca-conference.nhs.uk/