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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# spccharter <img src="man/figures/logo.png" width="160px" align="right" />
<!-- badges: start -->
[](https://www.repostatus.org/#active)
[](https://ci.appveyor.com/project/johnmackintosh/spccharter)
[](https://github.com/johnmackintosh/spccharter/actions)
<!-- badges: end -->
The goal of spccharter is rapid analysis of multiple statistical process control charts.
The package will create charts, detect signals of improvement, and revise control limits each time a signal occurs.
Currently, 'C', 'P' and 'U' charts can be produced.
The package supports non standard evaluation - you can pass bare variable names.
## Installation
spccharter is not on [CRAN](https://CRAN.R-project.org) yet
*But* the development version is available on [GitHub](https://github.com/) with:
``` r
# install.packages("remotes") # if not already installed
remotes::install_github("johnmackintosh/spccharter")
```
## Example
One grouping variable:
```{r example, eval=FALSE}
library(spccharter)
spccharter(data, numerator = counts, datecol = date,
by = category_1, plot_type = 'c', direction = "both")
```
Two grouping variables:
```{r, eval=FALSE}
library(spccharter)
spccharter(data, numerator = counts, denominator = attends, datecol = date,
by = c('ward','hospital'), plot_type = 'p', direction = "both")
```
## Example plots
