Michael's Code
Table of Contents
1 R Statistical Computing
1.1 Survival Plot
This is a simple example of a survival plot. You can do quite a few interesting things with Survival plots - for instance, in a lot of human factors simulation research, you can examine time to event detection or system failure.
install.packages("survminer") install.packages("ggthemes") library(survminer) library(survival) survdats <- read.csv("C:/Users/micha/Downloads/survival.csv") autoplot.survfit(surv) sfit <- (survfit(Surv(survdats$survival)~1)) ggsurvplot(sfit, conf.int=TRUE, pval=FALSE, risk.table=FALSE, color=c("dodgerblue2"), main="Kaplan-Meier Curve for Automation Failure Detection", ylab = "Survival Probability", font.tickslab = 16, conf.int.style = "step", break.x.by = 50, ggtheme = theme_bw(), font.x = c(16, "bold"), font.y = c(16))
1.1.1 TODO Add the data for this
1.2 Populating a Tibble dplyr
fill.tibb <- tibble( subjectid = rep(unique(props.offonextra$subjectid),each=6), period = rep(c("Offwatch", "Onwatch"), 48), offonextra = rep(c("ON-REST", "ON-WORK", "OFF-WORK", "OFF-REST", "OFF-NA", "ON-NA"), length(unique(props.offonextra$subjectid))), time = 0, prop = 0 )