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

batchanalytics

The goal of batch analytics is to Provide Functions That Represent Metrics For Analyzing Batching Behavior.

Installation

You can install the released version of batch analytics from CRAN with:

install.packages("batchanalytics")

Example

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.



NiklasCarlos1994/batchanalytics documentation built on Dec. 17, 2021, 5:25 a.m.