#read data
data <- read.csv("Data_time.csv")
data$Time <- as.numeric(data$Time)
data$Cat <- as.character(data$Cat)
data$Cat2 <- as.character(data$Cat2)
#detections of python and other snakes 
table (data$Cat)
#detections of large snakes 
table (data$Cat2)
# By treatment and control
table (data$treat)

head (data)
#libraries we may use 
library(tidyverse)
library(ggpubr)
library (glmmTMB)
library(data.table)
library("plotrix")
library(DHARMa)
library(sjPlot)
library(sjmisc)
library(sjlabelled)

#subset - note that names have a space after them 
other <- data[which(data$Cat=="Other "),]
pyt <- data[which(data$Cat=="Python "),]
pyt_T <- pyt %>%
  filter(treat == "trt") 

#summerize pythons at treatment pens
median (pyt_T$Time/60)

# test time at pens for other snakes, first plot 
hist (other$Time)
#non normal use wilcox to text if treatment and control vary for time other snakes spent at pens
res <- wilcox.test(Time ~ treat, data = other,
                   exact = FALSE)
res
### subset for large snakes
large <- data[which(data$Cat2=="Large"),]

## summary stats of other snakes at treatment and other pens
Large_T <- large %>%
  filter(treat == "trt") 
Large_C <- large %>%
  filter(treat == "ctrl") 
median (Large_T$Time/60)
median (Large_C$Time/60)


## summary stats of Large snakes at treatment and other pens
other_T <- other %>%
  filter(treat == "trt") 
other_C <- other %>%
  filter(treat == "ctrl") 
median (other_T$Time/60)
median (other_C$Time/60)


# test time at pens for large snakes, first plot 
hist (large$Time)
#non normal use wilcox to text if treatment and control vary for time other snakes spent at pens
res2 <- wilcox.test(Time ~ treat, data = large,
                   exact = FALSE)


res2

# Compare other snakes between treatments. Load data 
snakes3 <-read.csv("snakes3.csv",header = TRUE)
#Count data use a poisson model for other snakes and large snakes
Others_glmm <-glmmTMB(S ~ Treatment + (1|Pair), family=poisson,  data=snakes3)
summary(Others_glmm)

Large_glmm <-glmmTMB(L ~ Treatment + (1|Pair), family=poisson,  data=snakes3)
summary(Large_glmm)
#Incidence Rate Ratios
tab_model(Others_glmm)
tab_model(Large_glmm)

#plot residuals to check model fit
others_glmm_simres<-simulateResiduals(Others_glmm)
plot(others_glmm_simres)

Large_glmm_simres<-simulateResiduals(Large_glmm)
plot(Large_glmm_simres)

#chi square pythons to test treatment and control of pythons 
chisq.test(x = table(pyt$treat))

# Histogram
dat = read_csv("timestamp.csv")

dat$hours = as.numeric(dat$Time)/(60*60)


library (ggtext)
library (ggplot2)
ggplot(dat) +
  geom_histogram(aes(x = hours),
                 bins = 24,
                 boundary = 0,
                 color = "white") +
  scale_x_continuous(name = "Time (hours)",
                     breaks = c(seq(0, 24, 4)),
                     labels = paste0(c(seq(0, 24, 4)), ":00")) +
  ylab("Pictures") +
  theme_bw() +
  theme(panel.grid = element_blank(),
        axis.text = element_text(size = 18),
        axis.title = element_text(size = 24)) +
  geom_vline(xintercept = 7, linetype = 2) +
  geom_vline(xintercept = 15, linetype = 2) +
  labs(x='Time of day (hours)', y = 'Number of photos')+
  geom_curve(aes(x = 7, y = 230,
                 xend = 9, yend = 250), curvature = -0.2, colour = "grey25") +
  geom_curve(aes(x = 13, y = 250,
                 xend = 15, yend = 230), curvature = -0.2, colour = "grey25") +
  annotate("richtext", x = 11, y = 250, hjust = 0.5, lineheight = 0.85,size = 4,
           colour = "grey25",
           label = "Timespan of<br>visual sampling",
           label.padding = grid::unit(rep(0, 4), "pt"),
           fill = NA, label.color = NA)+
  geom_vline(xintercept = 18, linetype = 3) +
  geom_vline(xintercept = 20.25, linetype = 3) +
  geom_curve(aes(x = 18, y = 230,
                 xend = 18.5, yend = 250), curvature = -0.2, colour = "grey25") +
  geom_curve(aes(x = 19.75, y = 250,
                 xend = 20.25, yend = 230), curvature = -0.2, colour = "grey25") +
  annotate("richtext", x = 19.125, y = 250, hjust = 0.5, lineheight = 0.85, size = 4,
           colour = "grey25",
           label = "Timespan of<br>canine<br>sampling",
           label.padding = grid::unit(rep(0, 4), "pt"),
           fill = NA, label.color = NA)


ggsave("Histogram.png")