In PaolaCognigni/CTraxHelper: CTrax Helper

knitr::opts_chunk$set(echo = FALSE)
require(CTraxHelper)

raw<-readRDS('raw.RDS')
t<-readRDS('t.RDS')

The raw data is decorated with:

• spatial reference variables (distance from edge, centre, quadrant edges)
• frame-by-frame derivatives (speed, yaw, body rotation)
• time protocol values (experimental sections and repeats, with incremental timers)

For protocol sections 4 and 5, quadrant preference is computed on the basis of time and location.

garn<-list()
for (i in 1: length(raw)) {
  garn[[i]]<-trxgarnish(raw[[i]],t)
}
names(garn)<-names(raw)
vartype<-lapply(garn[[1]]$trx,class)
rawvars<-colnames(raw[[1]]$trx)
garnvars<-setdiff(names(vartype),rawvars)

The data is structured into a r length(garn)-element garn list that contains:

• native variables from CTrax

  • categorical: r names(vartype[rawvars][vartype[rawvars]=="factor"])
  • integer: r names(vartype[rawvars][vartype[rawvars]=="integer"])
  • numeric: r names(vartype[rawvars][vartype[rawvars]=="numeric"])

• calculated variables:

  • categorical: r names(vartype[garnvars][vartype[garnvars]=="factor"])
  • integer: r names(vartype[garnvars][vartype[garnvars]=="integer"])
  • numeric: r names(vartype[garnvars][vartype[garnvars]=="numeric"])

The list is saved to r paste(getwd(),"garn.RDS",sep="/").

saveRDS(garn,file="garn.RDS")

PaolaCognigni/CTraxHelper documentation built on May 7, 2019, 11:57 p.m.