文件列表:

.Rbuildignore (110, 2023-03-14)
DESCRIPTION (625, 2023-03-14)
LICENSE.md (34896, 2023-03-14)
NAMESPACE (619, 2023-03-14)
R/ (0, 2023-03-14)
R/data.R (512, 2023-03-14)
R/globals.R (484, 2023-03-14)
R/spccharter.R (27695, 2023-03-14)
R/utilities.R (5890, 2023-03-14)
R/utils-data-table.R (632, 2023-03-14)
appveyor.yml (1085, 2023-03-14)
data-raw/ (0, 2023-03-14)
data-raw/testdata.R (4606, 2023-03-14)
data/ (0, 2023-03-14)
data/testdata.rda (791, 2023-03-14)
man/ (0, 2023-03-14)
man/figures/ (0, 2023-03-14)
man/figures/logo.jpg (6069, 2023-03-14)
man/figures/logo.png (43351, 2023-03-14)
man/spccharter.Rd (4327, 2023-03-14)
man/testdata.Rd (650, 2023-03-14)
spccharter.Rproj (343, 2023-03-14)

# spccharter [](https://www.repostatus.org/#active) [](https://ci.appveyor.com/project/johnmackintosh/spccharter) [](https://github.com/johnmackintosh/spccharter/actions) 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 library(spccharter) spccharter(data, numerator = counts, datecol = date, by = category_1, plot_type = 'c', direction = "both") ``` Two grouping variables: ``` r library(spccharter) spccharter(data, numerator = counts, denominator = attends, datecol = date, by = c('ward','hospital'), plot_type = 'p', direction = "both") ``` ## Example plots   

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：