SPCreporter/README.Rmd at main · ThomUK/SPCreporter · GitHub

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---
output: github_document
---

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<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r setup}
#| include: false

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# {SPCreporter}

{SPCreporter} is a simple way to add value to your performance reporting using statistical process control.  It produces reports similar to this [**example report**](report_examples/My_Example_Report.html).

**Help sort signals from noise, and ensure your leadership are talking about signals that matter.**

The layout and method are heavily inspired by the NHS England & Improvement ["Making Data Count" programme](https://bmjleader.bmj.com/content/5/4/252), which encourages the use of SPC and time-series charts to replace "red, amber, green" threshold performance reporting.
The SPC calculations are made using the {NHSRplotthedots} package, from the [NHS-R Community](https://nhsrcommunity.com/).

This is a link to my [2022 NHS-R Conference](https://nhsrcommunity.com/events/nhs-r-online-speaker-conference-2022-9th-november-2022/) talk, where I presented an overview of the package:
<iframe class="youtube-video" src="https://www.youtube.com/embed/fWYehE5U6Vs?si=lNEoHaXxEVUB5Bk2&amp;start=2862" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

## Installation

You can install the development version of SPCreporter from [GitHub](https://github.com/ThomUK/SPCreporter) with:

```{r install}
#| eval: false

# install.packages("remotes")
remotes::install_github("ThomUK/SPCreporter", build_vignettes = TRUE)
```

## Package concept

Two main functions are used to make a report.
The first creates a "data bundle" which contains all the metric-level information needed by the report.  The second takes this bundle as raw input, and converts it into the html report.

```{r}
#| eval: false

library(SPCreporter)

# create a data_bundle, using 3 arguments
data_bundle <- spcr_make_data_bundle(
  measure_data, # your data to plot
  report_config, # config for the report (section titles, etc)
  measure_config  # config for the measures (details required by SPC)
)

# pass the bundle into the make_report function
spcr_make_report(
  data_bundle = data_bundle,
  ... # various report arguments - see full docs for details
)

```

### See the vignettes for additional examples:

[Get Started vignette](https://thomuk.github.io/SPCreporter/articles/get_started.html) - Start here to produce your first report using data that is bundled into this package.

[Creating multiple reports](https://thomuk.github.io/SPCreporter/articles/multiple_reports.html) - An example of how to use {purrr} to automate groups of reports.