This is an R package for analyzing and visualizing data from [Microsoft Workplace Analytics](https://www.microsoft.com/microsoft-365/partners/workplaceanalytics).
- **Simple**: the functions ought to be simple and intuitive, to maximise adoption.
- **Practical**: the functions should prioritise delivering against the most frequently used outputs and analyses.
- **Consistency**: functions should share a broadly consistent set of input arguments and naming conventions. This will help minimise unexpected results and errors when using the package.
- **Parsimony**: in creating the package, as much of the existing code should be re-used if possible to minimise duplication of work and to make analysis reproducible.
The package comes shipped with a sample Standard Query dataset (`sq_data`), so you can start exploring functions without having to read in any data. Most functions in **wpa** share a consistent API, and enable you to return results for both a **plot** or a **table** (data frame):
**Standard Analysis** functions are the most common type of functions in **wpa**. They typically accept a data frame as an input (usually requiring a Standard Person Query), and can return either a pre-designed graph as a ggplot object, or a summary data table as a data frame.
For the standard functions, there are six basic **plot types** which could be paired with six different **key metrics**. The six plot types are:
1.`_summary()`: produces a summary bar plot of the metric.
2.`_dist()`: produces a stacked bar plot of the metric.
3.`_fizz()`: produces a jittered, 'fizzy drink' plot of the metric.
4.`_line()`: produces a time-series line plot of the metric, with organizational attributes shown as facets.
5.`_trend()`: produces heatmap bars of the metric to show intensity over time.
6.`_rank()`: produces a rank table of all sub-groups (as per a set of organizational attributes) for a given metric. This is the only exception where the function returns a data frame by default, rather than a plot.
The six key metrics are:
1.`collab`: stands for Collaboration Hours, and uses the metric `Collaboration_hours`.
2.`email`: stands for Email Hours, and uses the metric `Email_hours`.
3.`meeting`: stands for Meeting Hours, and uses the metric `Meeting_hours`.
4.`afterhours`: stands for After-hours Collaboration Hours, and uses the metric `After_hours_collaboration_hours`.
5.`one2one`: stands for one-to-one collaboration hours with direct manager. Uses the metric `Meeting_hours_with_manager_1_on_1`.
6.`workloads`: stands for Work Week Span, and uses the metric `Workweek_span`.
You can combine the **plot types** and the **key metrics** (as prefixes and suffixes) to generate the desired output, e.g. `email_` and `dist` for `email_dist()`.
For more advanced users, there are also a number of **flexible analysis** functions which allow you to generate the plots with _any_ Workplace Analytics metric, where the metric name needs to be supplied in addition to the function. For instance,
```R
create_bar(sq_data, metric = "Email_hours")
```
would return a similar result as `email_summary(sq_data)`, but where you can replace the metric with one of your own choice. Here are some of the available flexible analysis functions, which are typically prefixed with `create_`:
-`create_bar()`
-`create_bar_asis()`
-`create_boxplot()`
-`create_dist()`
-`create_fizz()`
-`create_line()`
-`create_line_asis()`
-`create_plot_scatter()`
-`create_rank()`
-`create_stacked()`
You can find out more about the feature of each individual function by running `?function` once you have the package loaded.
**Report Generation** functions are a special class of functions within **wpa** which outputs an interactive HTML report on a specific area based on the data you supply.
This group consists of miscellaneous functions which either perform a specific piece of analysis (e.g. computing the Information Value score), or are designed to be used with Standard Analysis functions.
There are several pre-loaded demo Workplace Analytics datasets that you can use straight away from the package, to help you explore the functions more easily. Here is a list of them:
-`sq_data`: Standard Person Query
-`mt_data`: Standard Meeting Query
-`em_data`: Hourly Collaboration Query
-`g2g_data`: Group-to-group Query
You can explore the structure of these datasets by running `?sq_data` or `dplyr::glimpse(sq_data)`, for instance.
If you would like contribute code to the repo, please read our [Contributor Guide](CONTRIBUTING.md) and [Developer Guide](.github/developer_guide.md). This documentation should provide you all the information you will need to get started.
If you would like to log an issue or submit a feature request, please create a new issue or comment on an existing issue on [GitHub Issues](https://github.com/microsoft/wpa/issues) on this repo.
Please do not report security vulnerabilities through public GitHub issues. Please read our Security document [for more details](.github/reporting_security_issues.md).
We would ask you to please read the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct) prior to engaging with this package.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
