In MicaelaP24/R_Package_Pineda: An R package for smart science!

Installation

Load in common libraries used by this package

library(ggplot2)
library(tidyverse)
library(lubridate)

You might need to install

install.packages("momentuHMM")
install.packages("conicfit")
install.packages("dplyr")
devtools::install_git('https://gitlab.com/bartk/move.git')

And load the libraries:

library(move)
library(momentuHMM)
library(conicfit)
library(dplyr)

Now, we install my R Package

#devtools::install_github("MicaelaP24/R_Package_Pineda")

library(PinedaRPackage)

Load these files

download.file("https://raw.githubusercontent.com/MicaelaP24/R_Package_Pineda/master/data/Nyctalus2.csv", destfile = "/cloud/project/data/Nyctalus2.csv")

download.file("https://raw.githubusercontent.com/MicaelaP24/R_Package_Pineda/master/data/spring.csv", destfile = "/cloud/project/data/SP.csv")

download.file("https://raw.githubusercontent.com/MicaelaP24/R_Package_Pineda/master/data/B212_2.csv", destfile = "/cloud/project/data/B212_2.csv")

Working Examples

An example of each function and how it works
Say something about why it is a good thing to do (goal)
Expected output

one individual filtered out of the data set

product data frame of one individual

noctules <- read.csv("/cloud/project/data/Nyctalus2.csv")

noc <- fil(noctules, individual.local.identifier, "60F1")

plot of height vs speed

product plot of speed & height above sea level

noc$timestamp <- ymd_hms(noc$timestamp)


f1_m <- move(x = noc$location.long,
             y = noc$location.lat,
             time = noc$timestamp,
             data = noc,
             proj = CRS("+proj=longlat +ellps=WGS84"),
             animal = "noc")

f1_m$speed <- c(move::speed(f1_m), NA)
f1_m$speed
f1_m$turnAngle <- c(move::angle(f1_m), NA)
f1_m$timeLag <- c(move::timeLag(f1_m, units="mins"), NA)
f1_m$timeLag
f1_m$stepLength <- c(move::distance(f1_m), NA)
f1_m$stepLength
plot(f1_m$stepLength)

point_plot(f1_m@data, height.above.msl, speed)

data frame with added column timelag and histogram of timelag intervals

spring <- move("/cloud/project/data/SP.csv")

timeLags(spring)

Data frame of data organized by individual and date of tagging so individuals tagged in both years will be grouped together

data_ordered(noctules, individual.local.identifier, timestamp)

best fit model with lowest AIC value

B212_2 <- read.csv("/cloud/project/data/B212_2.csv")
B212_2$timestamp <- ymd_hms(B212_2$timestamp)
B212_2_re <- B212_2 %>% dplyr::select(ID=batIDday, time=timestamp, x=utm.x, y=utm.y)

#fitting crwPredict
crwOut_2 <- crawlWrap(obsData= B212_2_re, 
                      Time.name="time", 
                      timeStep="30 secs",
                      retryFits = 5)

plot(crwOut_2,ask=FALSE)
B212_2_processed <- prepData(crwOut_2, type= c("UTM"), coordNames = c("x", "y"))

plot(B212_2_processed)
par(mfrow=c(2,1))
hist(B212_2_processed$step)
hist(B212_2_processed$angle)
set.seed(12345)
niter <- 5
vals <- seq(1, niter, by = 1) 
allm4 <- split(vals, f = vals)
##for loop four states
for(i in 1:niter) {
  tryCatch({
    #Step length mean 
     #Step length mean 
    ES_Step0 <- runif(1, min = c(0.001, 1), max = c(1, 150))
    forageStep0 <- runif(1, min = c(150, 200), max = c(200, 250))
    moveStep0 <- runif(1, min = c(250, 300), max = c(300, 350))
    commuteStep0 <- runif(1, min = c(350, 450), max = c(450, 500))
    stepMean0 <- c(ES_Step0, forageStep0, moveStep0, commuteStep0)
    #Step length standard deviation
    stepSD0 <- runif(4,
                     min = c(1, 10),
                     max = c(10, 25)) # Turning angle mean
    anglePar0 <- c(2, 1.5, 1, 0.001)
    #Turning angle concentration
    dist <- list(step = "gamma", angle = "vm")
    # Fit model
    stepPar0 <- c(stepMean0, stepSD0)
    allm4[[i]] <- momentuHMM::fitHMM(data = B212_2_processed, 
                                     nbStates = 4, 
                                     dist= dist, 
                                     Par0=list(step=stepPar0, angle=anglePar0))
  }, error=function(e) NULL)
}



best_fit_model(allm4)