knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of batch analytics is to Provide Functions That Represent Metrics For Analyzing Batching Behavior.
You can install the released version of batch analytics from CRAN with:
install.packages("batchanalytics")
This is a basic example which shows you how to solve a common problem:
library(batchanalytics) library(bupaR) library(tidyr) library(lubridate) ## basic example code
What is special about using README.Rmd
instead of just README.md
? You can include R chunks like so:
csv_log = read.csv(system.file("exdata", "sample_data_1.csv", package = "batchanalytics")) #time converstion - why sometimes read correctly sometimes not? csv_log$arrival <- as.POSIXct(csv_log$arrival, format = "%Y-%m-%d %H:%M:%S", tz = "GMT") csv_log$start <- as.POSIXct(csv_log$start, format = "%Y-%m-%d %H:%M:%S", tz = "GMT") csv_log$complete <- as.POSIXct(csv_log$complete, format = "%Y-%m-%d %H:%M:%S", tz = "GMT") #creating event_Log elog <- csv_log %>% mutate(activity_instance = 1:nrow(.)) %>% gather(status, timestamp, arrival, start, complete) %>% eventlog( case_id = "case_id", activity_id = "activity", activity_instance_id = "instance_id", lifecycle_id = "status", timestamp = "timestamp", resource_id = "resource" )
You'll still need to render README.Rmd
regularly, to keep README.md
up-to-date. devtools::build_readme()
is handy for this. You could also use GitHub Actions to re-render README.Rmd
every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/master/examples.
You can also embed plots, for example:
elog %>% processing_time("activity") %>% plot()
In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.
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