From a5d769e73db790c218503074d932de19790a934e Mon Sep 17 00:00:00 2001
From: "Alec L. Robitaille" <robit.alec@gmail.com>
Date: Mon, 6 Nov 2023 09:19:50 -0400
Subject: [PATCH 01/12] fst ignore

---
 .gitignore | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..5b6a065
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata

From ed648ef6c5588082e71ec646e2fd31a49d74d869 Mon Sep 17 00:00:00 2001
From: "Alec L. Robitaille" <robit.alec@gmail.com>
Date: Mon, 6 Nov 2023 09:19:53 -0400
Subject: [PATCH 02/12] fst proj

---
 wild_pigs.Rproj | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 wild_pigs.Rproj

diff --git a/wild_pigs.Rproj b/wild_pigs.Rproj
new file mode 100644
index 0000000..d063e8b
--- /dev/null
+++ b/wild_pigs.Rproj
@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: knitr
+LaTeX: pdfLaTeX

From 6ac1e4eb60437b2ce53feca30eae25011e7b48be Mon Sep 17 00:00:00 2001
From: "Alec L. Robitaille" <robit.alec@gmail.com>
Date: Mon, 6 Nov 2023 09:28:49 -0400
Subject: [PATCH 03/12] fix typos

---
 README.md                |  4 ++--
 scripts/activity_plots.R | 12 ++++++------
 scripts/hazard_script.R  | 14 +++++++-------
 3 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index 2e2f905..1567bc2 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 
 
 
-## Variation in temporal activity may reduce competetive interaction between native and introduced species
+## Variation in temporal activity may reduce competitive interaction between native and introduced species
 
 - Authors:
      - Brendan Carswell
@@ -11,6 +11,6 @@
      - [Eric Vander Wal](https://weel.gitlab.io/)
 
 
-This repository contains the code accompanying the paper ["Variation in temporal activity may reduce competetive interaction between native and introduced species"].
+This repository contains the code accompanying the paper ["Variation in temporal activity may reduce competitive interaction between native and introduced species"].
 Scripts are under `scripts/`.  
 
diff --git a/scripts/activity_plots.R b/scripts/activity_plots.R
index b00d4e5..9bbcd3b 100644
--- a/scripts/activity_plots.R
+++ b/scripts/activity_plots.R
@@ -1,8 +1,8 @@
-### This script is for the 24-hour activity patten graphs. 
+### This script is for the 24-hour activity pattern graphs. 
 #most of this code was adapted from Louvrier et al. 2021
 
 
-##lpackages req'd for figures 
+##packages req'd for figures 
 library(camtrapR)
 library(car)
 library(maptools)
@@ -48,14 +48,14 @@ Native <- c("white tailed deer", "coyote", "elk", "mule deer")
 color_list <- c("#E69F00", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#29AF7FFF", "#56B4E9")
 
 ###############################################################################
-#need to fix habiat types column here. typo in my OG data
+#need to fix habitat types column here. typo in my OG data
 unique(BoarDATA$Habitat_Ty)
 BoarDATA$Habitat_Ty[BoarDATA$Habitat_Ty == "Cropland/?"] <- "Cropland"
 
 habs <- unique(BoarDATA$Habitat_Ty)
 
 #first make a new column of "Native" or boar. 
-#just renaming becuase im very inconsistent with names apparently
+#just renaming because im very inconsistent with names apparently
 BoarDATA$native <- BoarDATA$Animal
 BoarDATA$native[BoarDATA$native == "Boar"] <- "wild pig"
 BoarDATA$Animal[BoarDATA$native == "White Tailed Deer"] <- "white tailed deer"
@@ -146,7 +146,7 @@ plot_overlap <- function(speciesA_for_activity, speciesB_for_activity, d_overlap
 boarnative <- plot_overlap(speciesA_for_activity,speciesB_for_activity, d_overlap_boarnative)
 boarnative
 ##############################################################################################################################################################
-# now i can crete circular plots for each sp. combination. and make multipannel 
+# now i can create circular plots for each sp. combination. and make multipanel 
 #Circular plots !! 
 #going to try to make one for boar v.s. moose
 
@@ -438,7 +438,7 @@ B_C <-  ggplot(data = ABCDEF, aes(x = x, y = value, colour = species)) +
 plot(B_C)
 
 ##############################################################################################################################################################
-#now, taking the 5 circular plots from above and making a multipannel figure
+#now, taking the 5 circular plots from above and making a multipanel figure
 library(cowplot)
 library(grid)
 library(ggplotify)
diff --git a/scripts/hazard_script.R b/scripts/hazard_script.R
index 56aae0d..9dc1179 100644
--- a/scripts/hazard_script.R
+++ b/scripts/hazard_script.R
@@ -2,7 +2,7 @@
 #Code for Carswell et al. wild pigs paper
 #Script 1: Contains code for proportional hazard models + plotting coef's
 
-#laoding 1st set of req. packages
+#loading 1st set of req. packages
 library(dplyr)
 library(readr)
 library(camtrapR)
@@ -21,12 +21,12 @@ BoarDATA <- read_csv("Data/Processed/BoarDATA.csv",
 
 ########################################################################################################
 #this section will identify the species of interest, and filter out the species recorded 
-## that are unliekly to be ecologically affected (or at least managers wont care by many interactions)
+## that are unlikely to be ecologically affected (or at least managers wont care by many interactions)
 
 #convert to data table to work with
 BoarDATA2 <- as.data.table(BoarDATA)
 
-#coverting boar --> invasive species and mammals of interest to native b/c we need to group to have enough 
+#converting boar --> invasive species and mammals of interest to native b/c we need to group to have enough 
 #observations 
 species_interest <- c("Native", "Invasive", "Human")
 
@@ -53,7 +53,7 @@ BoarDATA3 <- as.data.frame(BoarDATA3)
 
 ########################################################################################################
 ## Much of this code is adapted from Louvrier et al. 2021
-#I wasnt able to get the function (below) to run wihtout re-naming my variables to the same names they had.
+#I wasnt able to get the function (below) to run without re-naming my variables to the same names they had.
 #so just aligning our data so the function can run correctly.
 CT_act_MN <- BoarDATA3
 
@@ -96,7 +96,7 @@ calculate_delta <- function(species)
     
     #delta is going to contain the calculated difference of time between species A and B for each row of the detections of the CT
     #but only if the two species from row i and i+1 are in the specific order
-    #if the combination of the species names between the row i and i + 1 is equal to the names of the species of interest     stuck togetehr then we calcutalte difftime between the two lines,  otherwise, no
+    #if the combination of the species names between the row i and i + 1 is equal to the names of the species of interest     stuck together then we calculate difftime between the two lines,  otherwise, no
     
     delta <- rep(NA, nrow(B_ordered))
     for(i in 1: (nrow(B_ordered)))
@@ -143,7 +143,7 @@ delata_boar_boar <- calculate_delta(species)
 #putting this in a dataframe 
 df_time_diff_boar_boar = plyr::ldply(delata_boar_boar, rbind)
 
-##extracting events that were interuppted by humans (censor). First human --> native species
+##extracting events that were interupted by humans (censor). First human --> native species
 species = c(species_interest[3],species_interest[1])
 delata_human_native <- calculate_delta(species) 
 #putting this in a dataframe 
@@ -272,7 +272,7 @@ almostthere$Habitat_Ty[almostthere$Habitat_Ty == "Pasture"] <- "Apasture"
 
 #export this as a df ready to analyze. 
 
-cox_analyze <- write_csv(almostthere, "cox_analyze.csv"
+cox_analyze <- write_csv(almostthere, "cox_analyze.csv")
 ######################################################################################################################
 # Great, now we can actually run these hazard models
 ######################################################################################################################

From 2a31ed00f9221da6a7cd1d3724353084fca99911 Mon Sep 17 00:00:00 2001
From: "Alec L. Robitaille" <robit.alec@gmail.com>
Date: Mon, 6 Nov 2023 09:40:03 -0400
Subject: [PATCH 04/12] new title

---
 README.md | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 1567bc2..c9ef7d5 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,6 @@
+# Variation in spatiotemporal activity may reduce competitive interactions between invasive wild pigs (Sus scrofa) and native mammal species
 
 
-
-## Variation in temporal activity may reduce competitive interaction between native and introduced species
-
 - Authors:
      - Brendan Carswell
      - [Sean Boyle](https://sites.google.com/prod/view/seanboylephd)

From 259551176ec6550d03c94de5331baf0006e15ae6 Mon Sep 17 00:00:00 2001
From: "Alec L. Robitaille" <robit.alec@gmail.com>
Date: Mon, 6 Nov 2023 09:52:39 -0400
Subject: [PATCH 05/12] fst CITATION.cff

---
 CITATION.cff | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 CITATION.cff

diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..84db912
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,16 @@
+cff-version: 1.1.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: Carwsell
+  given-names: B.M.
+- family-names: Boyle
+  given-names: S.P.
+- family-names: Brook
+  given-names: R.K.
+- family-names: van Beest
+  given-names: F.M.
+- family-names: Vander Wal
+  given-names: E.
+title: Variation in spatiotemporal activity may reduce competitive interactions between invasive wild pigs (Sus scrofa) and native mammal species
+version: v0.0.0.9000
+date-released: 2023-11-06
\ No newline at end of file

From 97b51e94606b8b23d3a2dd6c307fb4a12c3a7622 Mon Sep 17 00:00:00 2001
From: "Alec L. Robitaille" <robit.alec@gmail.com>
Date: Mon, 6 Nov 2023 09:52:53 -0400
Subject: [PATCH 06/12] fst zenodo.json

---
 .zenodo.json | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 .zenodo.json

diff --git a/.zenodo.json b/.zenodo.json
new file mode 100644
index 0000000..a9721f0
--- /dev/null
+++ b/.zenodo.json
@@ -0,0 +1,27 @@
+{
+  "description": "<p>This repository contains the code accompanying the paper &ldquo;Variation in spatiotemporal activity may reduce competitive interactions between invasive wild pigs (Sus scrofa) and native mammal species&rdquo;.</p>", 
+  "title": "Code accompanying - Variation in spatiotemporal activity may reduce competitive interactions between invasive wild pigs (Sus scrofa) and native mammal species", 
+  "creators": [
+    {
+      "affiliation": "Memorial University", 
+      "name": "B.M. Carswell"
+    }, 
+    {
+      "affiliation": "Memorial University", 
+      "name": "S.P. Boyle"
+    }, 
+    {
+      "affiliation": "University of Saskatchewan", 
+      "name": "R.K. Brook"
+    }, 
+    {
+      "affiliation": "Aarhus University", 
+      "name": "F.M. van Beest"
+    }, 
+    {
+      "orcid": "0000-0002-8534-4317", 
+      "affiliation": "Memorial University", 
+      "name": "E. Vander Wal"
+    }
+  ]
+}

From 40a0ec4b252df09a8bf96f9a2fcf38f764cc13f9 Mon Sep 17 00:00:00 2001
From: "Alec L. Robitaille" <robit.alec@gmail.com>
Date: Mon, 6 Nov 2023 09:53:02 -0400
Subject: [PATCH 07/12] update title

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c9ef7d5..03e0c32 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# Variation in spatiotemporal activity may reduce competitive interactions between invasive wild pigs (Sus scrofa) and native mammal species
+# Variation in spatiotemporal activity may reduce competitive interactions between invasive wild pigs (*Sus scrofa*) and native mammal species
 
 
 - Authors:

From 4d019c7d4332c34de0b2aaa807d894cd4a4d9276 Mon Sep 17 00:00:00 2001
From: "Alec L. Robitaille" <robit.alec@gmail.com>
Date: Mon, 6 Nov 2023 09:53:30 -0400
Subject: [PATCH 08/12] journal

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 03e0c32..108f250 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,7 @@
      - Floris van Beest
      - [Eric Vander Wal](https://weel.gitlab.io/)
 
+Canadian Journal of Zoology. https://doi.org/10.1139.CJZ-2022-0145
 
 This repository contains the code accompanying the paper ["Variation in temporal activity may reduce competitive interaction between native and introduced species"].
 Scripts are under `scripts/`.  

From a43f7c3d941c5cb74d79d79f292ba2669202c949 Mon Sep 17 00:00:00 2001
From: "Alec L. Robitaille" <robit.alec@gmail.com>
Date: Mon, 6 Nov 2023 09:54:12 -0400
Subject: [PATCH 09/12] brief

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 108f250..1868f0b 100644
--- a/README.md
+++ b/README.md
@@ -8,8 +8,8 @@
      - Floris van Beest
      - [Eric Vander Wal](https://weel.gitlab.io/)
 
-Canadian Journal of Zoology. https://doi.org/10.1139.CJZ-2022-0145
 
-This repository contains the code accompanying the paper ["Variation in temporal activity may reduce competitive interaction between native and introduced species"].
-Scripts are under `scripts/`.  
 
+This repository contains the code accompanying the paper "Variation in spatiotemporal activity may reduce competitive interactions between invasive wild pigs (*Sus scrofa*) and native mammal species" Canadian Journal of Zoology. https://doi.org/10.1139.CJZ-2022-0145
+
+Scripts are under `scripts/`. 
\ No newline at end of file

From 53b28dc11a5e3a11feeba8c47b3cdbd2261a7e9c Mon Sep 17 00:00:00 2001
From: "Alec L. Robitaille" <robit.alec@gmail.com>
Date: Mon, 6 Nov 2023 09:56:36 -0400
Subject: [PATCH 10/12] badge

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 1868f0b..872e749 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,8 @@
 # Variation in spatiotemporal activity may reduce competitive interactions between invasive wild pigs (*Sus scrofa*) and native mammal species
 
 
+[![DOI](https://zenodo.org/badge/540079228.svg)](https://zenodo.org/doi/10.5281/zenodo.10075565)
+
 - Authors:
      - Brendan Carswell
      - [Sean Boyle](https://sites.google.com/prod/view/seanboylephd)

From 281f0136d45996eb06c201a76cfcbe9bdd42107f Mon Sep 17 00:00:00 2001
From: Brendan Carswell <91228884+carswellb@users.noreply.github.com>
Date: Mon, 6 Nov 2023 10:42:45 -0330
Subject: [PATCH 11/12] Update activity_plots.R

---
 scripts/activity_plots.R | 45 ++++++++++++++++------------------------
 1 file changed, 18 insertions(+), 27 deletions(-)

diff --git a/scripts/activity_plots.R b/scripts/activity_plots.R
index 9bbcd3b..c4fa23b 100644
--- a/scripts/activity_plots.R
+++ b/scripts/activity_plots.R
@@ -19,11 +19,11 @@ library(maptools)
 library(data.table)
 library(ggplot2)
 
-###### Importing data 
+# Data import 
 BoarDATA <- read_csv("Data/Processed/BoarDATA.csv", 
                      col_types = cols(posixdate = col_datetime(format = "%m/%d/%Y %H:%M")))
 
-#renaming all names in the data sheet so I have consistent names for figures as in MS
+#species event renames
 unique(BoarDATA$Animal)
 BoarDATA$Animal[BoarDATA$Animal == "Boar"] <- "wild pig"
 BoarDATA$Animal[BoarDATA$Animal == "White Tailed Deer"] <- "white tailed deer"
@@ -32,30 +32,28 @@ BoarDATA$Animal[BoarDATA$Animal == "Moose"] <- "moose"
 BoarDATA$Animal[BoarDATA$Animal == "Mule Deer"] <- "mule deer"
 BoarDATA$Animal[BoarDATA$Animal == "Elk"] <- "elk"
 
-# everything to  character
-class(BoarDATA$Camera) #character
-class(BoarDATA$posixdate) #posix
-
+#data structuring
+class(BoarDATA$Camera) 
+class(BoarDATA$posixdate) 
 BoarDATA$posixdate <- as.character(BoarDATA$posixdate)
 
 ##############################################################################
-# create activity overlap plot
-# define species of interest
+# create activity overlap plots
+#define species of interest
 species_interest <- c("wild pig", "moose", "white tailed deer", "coyote", "elk", "mule deer", "native")
 Native <- c("white tailed deer", "coyote", "elk", "mule deer")
 
-#consistent colour patterns with other figures in other script
+#colour palate
 color_list <- c("#E69F00", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#29AF7FFF", "#56B4E9")
 
 ###############################################################################
-#need to fix habitat types column here. typo in my OG data
+#fix habitat name
 unique(BoarDATA$Habitat_Ty)
 BoarDATA$Habitat_Ty[BoarDATA$Habitat_Ty == "Cropland/?"] <- "Cropland"
 
 habs <- unique(BoarDATA$Habitat_Ty)
 
-#first make a new column of "Native" or boar. 
-#just renaming because im very inconsistent with names apparently
+#first make a new column of "native" or wild pig
 BoarDATA$native <- BoarDATA$Animal
 BoarDATA$native[BoarDATA$native == "Boar"] <- "wild pig"
 BoarDATA$Animal[BoarDATA$native == "White Tailed Deer"] <- "white tailed deer"
@@ -69,9 +67,7 @@ BoarDATA$native <-  mapvalues(BoarDATA$native, from =
                                   "mule deer", "moose"), to = c("native",
                                                                 "native", "native", "native", "native"))
 
-
-# 2D comparison of temporal overlap between wild pigs + all native species in one group
-
+# 2D comparison of temporal overlap between wild pigs + all native species 
 speciesA_for_activity <- "wild pig"    
 speciesB_for_activity <- "native"    
 dataf <- BoarDATA
@@ -88,7 +84,7 @@ d_overlap_boarnative <- activityOverlap(recordTable = dataf,
                                         #plotDirectory = getwd()
 )
 
-dhat <- "0.72" 
+dhat <- "0.72"  #coef of activtiy overlap. See Ridout and Linkie 2009.
 
 plot_overlap <- function(speciesA_for_activity, speciesB_for_activity, d_overlap)
 {
@@ -146,9 +142,8 @@ plot_overlap <- function(speciesA_for_activity, speciesB_for_activity, d_overlap
 boarnative <- plot_overlap(speciesA_for_activity,speciesB_for_activity, d_overlap_boarnative)
 boarnative
 ##############################################################################################################################################################
-# now i can create circular plots for each sp. combination. and make multipanel 
-#Circular plots !! 
-#going to try to make one for boar v.s. moose
+#Circular diel plots of wild pig to each native species 
+#wild pig / moose
 
 # create activity overlap plot
 # define species of interest
@@ -205,8 +200,7 @@ B_M <-  ggplot(data = ABCDEF, aes(x = x, y = value, colour = species)) +
 
 plot(B_M)
 ##############################################################################################################################################################
-# ROUND 2
-#boar v.s. moose
+#wild pig / white tail
 
 # create activity overlap plot
 # define species of interest
@@ -263,8 +257,7 @@ B_WTD <-  ggplot(data = ABCDEF, aes(x = x, y = value, colour = species)) +
 
 plot(B_WTD)
 ##############################################################################################################################################################
-# ROUND 3
-#boar v.s. Mule Deer
+#wild pig / Mule Deer
 
 # create activity overlap plot
 # define species of interest
@@ -321,8 +314,7 @@ B_MD <-  ggplot(data = ABCDEF, aes(x = x, y = value, colour = species)) +
 
 plot(B_MD)
 ##############################################################################################################################################################
-# ROUND 4
-#boar v.s. Elk
+#wild pig / Elk
 
 # create activity overlap plot
 # define species of interest
@@ -379,8 +371,7 @@ B_E <-  ggplot(data = ABCDEF, aes(x = x, y = value, colour = species)) +
 
 plot(B_E)
 ##############################################################################################################################################################
-# ROUND 5 
-#boar v.s. Coyote
+#wild pig / Coyote
 
 # create activity overlap plot
 # define species of interest

From 7bebbbd761cdf3ebe4010a1a0253b4633c5acaf4 Mon Sep 17 00:00:00 2001
From: Brendan Carswell <91228884+carswellb@users.noreply.github.com>
Date: Mon, 6 Nov 2023 10:58:09 -0330
Subject: [PATCH 12/12] Update hazard_script.R

---
 scripts/hazard_script.R | 117 +++++++++++++++++-----------------------
 1 file changed, 49 insertions(+), 68 deletions(-)

diff --git a/scripts/hazard_script.R b/scripts/hazard_script.R
index 9dc1179..9bc34aa 100644
--- a/scripts/hazard_script.R
+++ b/scripts/hazard_script.R
@@ -1,8 +1,6 @@
-###############################################################################
-#Code for Carswell et al. wild pigs paper
 #Script 1: Contains code for proportional hazard models + plotting coef's
 
-#loading 1st set of req. packages
+#packages
 library(dplyr)
 library(readr)
 library(camtrapR)
@@ -15,22 +13,20 @@ library(data.table)
 library(janitor)
 library(stats)
 
-#loading OG boar data from. 
+#data import
 BoarDATA <- read_csv("Data/Processed/BoarDATA.csv", 
                      col_types = cols(posixdate = col_datetime(format = "%m/%d/%Y %H:%M")))
 
 ########################################################################################################
 #this section will identify the species of interest, and filter out the species recorded 
-## that are unlikely to be ecologically affected (or at least managers wont care by many interactions)
 
 #convert to data table to work with
 BoarDATA2 <- as.data.table(BoarDATA)
 
-#converting boar --> invasive species and mammals of interest to native b/c we need to group to have enough 
-#observations 
+#converting boar --> invasive species and mammals of interest to native
 species_interest <- c("Native", "Invasive", "Human")
 
-#creating a new column so I have an OG reference to bounce off of 
+#new species column to work off as reference 
 BoarDATA2$Native <- BoarDATA2$Animal
 
 #then renaming the species to native / invasive 
@@ -44,15 +40,15 @@ BoarDATA2$Native[BoarDATA2$Native == "Mule Deer"] <- "Native"
 #importnat to include us at cameras for censoring  
 BoarDATA2$Native[BoarDATA2$Native == "Human"] <- "Human"
 
-#sorting DT by species of interest 
+#sorting by species of interest 
 BoarDATA2[, interest := Native %in% species_interest]
 
-# and creating a new DF of only species of interest
+#only species of interest
 BoarDATA3 <- filter(BoarDATA2, interest == TRUE)
 BoarDATA3 <- as.data.frame(BoarDATA3)
 
 ########################################################################################################
-## Much of this code is adapted from Louvrier et al. 2021
+##code adapted from Louvrier et al. 2021
 #I wasnt able to get the function (below) to run without re-naming my variables to the same names they had.
 #so just aligning our data so the function can run correctly.
 CT_act_MN <- BoarDATA3
@@ -76,14 +72,13 @@ class(d1_interest$DateTimeOriginal)
 id_CT <- unique(d1_interest$Station)
 
 ########################################################################################################
-## this is the function published in Louvrier et al. 2021 which calculates time 
-## between desired sequential events for a given camera trap. 
+#Louvrier et al. 2021 function which calculates time between sequential events for a given camera trap. 
 
 calculate_delta <- function(species)
 {
   #empty list 
   delta_species <- list(0)
-  ###loop starts here
+  #loop starts here
   
   for(j in id_CT)
   {
@@ -104,9 +99,7 @@ calculate_delta <- function(species)
       if (paste(B_ordered[c(i:(i+1)),"Species"], collapse ="") == paste(species, collapse=""))
       { 
         diff <- difftime(B_ordered[i,"DateTimeOriginal"], B_ordered[i+1,"DateTimeOriginal"])
-        if (diff < - 10000) #the only reason i left these in is b/c the f(x)n wouldn't run without it
-        {                   #and i dont know enough to re-write. I just extended the time to a massive # to make it useless tho.
-                            # you can adjust this time depending on your desired study
+        if (diff < - 10000) 
           delta[i] <- 0 
         }
         if (diff >= -10000)
@@ -123,21 +116,20 @@ calculate_delta <- function(species)
   return(delta_species)
   
 }
-
 #################################################################################
-##selecting the combo of invasive boar --> any native species 
+#selecting the combo of invasive wild pig --> any native species 
 species = c(species_interest[2],species_interest[1])
 delata_boar_any <- calculate_delta(species)
 #putting this in a dataframe 
 df_time_diff_boar_any = plyr::ldply(delata_boar_any, rbind)
 
-##next interactions.... selecting the combo of any native --> any native sp of interest 
+##next interactions.... combo of any native --> any native sp of interest 
 species = c(species_interest[1],species_interest[1])
 delata_any_any <- calculate_delta(species) 
 #putting this in a dataframe 
 df_time_diff_any_any = plyr::ldply(delata_any_any, rbind)
 
-##final interaction of interest... boar --> boar 
+##final interaction of interest... wild pig --> wild pig 
 species = c(species_interest[2],species_interest[2])
 delata_boar_boar <- calculate_delta(species) 
 #putting this in a dataframe 
@@ -156,46 +148,43 @@ delata_human_boar <- calculate_delta(species)
 df_time_diff_human_boar = plyr::ldply(delata_human_boar, rbind)
 
 #################################################################################
-#The 'df_time_diff' dataframes are large and very jumbled 
-# below i extract the TTE's in a weird way... but it works 
+#need to extract time-to-event values from dataframes 
 
-#first step is to pivot longer... then last two columns in the DF end up the TTE's + the camera associated with 
-#boar --> native species combo
+#pivot longer,  last two columns in the DF = TTE's + the camera associated with 
+#wild pig --> native species combo
 boar_any_extract <- tidyr::pivot_longer(df_time_diff_boar_any, c(2:50), values_drop_na = TRUE)
-#and then extracting the two columns at the end..
+# extracting the two columns
 boar_any_extracted <- cbind.data.frame(boar_any_extract$.id, boar_any_extract$value)
 #create a new column labelling that this is the interaction
 boar_any_extracted$Type <- rep(c("Boar_native"), times = 7)
-#also need to fix the names. ARGH.There must be an easier way to do this but I'm not sure how. 
 boar_any_extracted$ID <- boar_any_extracted$`boar_any_extract$.id`
 
-#repeating for the next interaction (boar --> boar). same steps as above.
+#repeat for wild pig to wild pig
 boar_boar_extract <- tidyr::pivot_longer(df_time_diff_boar_boar, c(2:50), values_drop_na = TRUE)
 boar_boar_extracted <- cbind.data.frame(boar_boar_extract$.id, boar_boar_extract$value)
 boar_boar_extracted$Type <- rep(c("Boar_boar"), times = 11)
 boar_boar_extracted$ID <- boar_boar_extracted$`boar_boar_extract$.id`
 
-#repeating for the next interaction (native --> native). same steps as above.
+#repeat for native to native
 any_any_extract <- tidyr::pivot_longer(df_time_diff_any_any, c(2:50), values_drop_na = TRUE)
 any_any_extracted <- cbind.data.frame(any_any_extract$.id, any_any_extract$value)
 any_any_extracted$Type <- rep(c("Native_native"), times = 517)
 any_any_extracted$ID <- any_any_extracted$`any_any_extract$.id`
 
-#repeating for the next interaction (human --> native). same steps as above. CENSOR
+#repeat for human to native species  
 human_native_extract <- tidyr::pivot_longer(df_time_diff_human_native, c(2:50), values_drop_na = TRUE)
 human_native_extracted <- cbind.data.frame(human_native_extract$.id, human_native_extract$value)
 human_native_extracted$Type <- rep(c("Human_native"), times = 27)
 human_native_extracted$ID <- human_native_extracted$`human_native_extract$.id`
 
-#repeating for the final interaction (human --> native). same steps as above. CENSOR
+#repeat for human to wild pig
 human_boar_extract <- tidyr::pivot_longer(df_time_diff_human_boar, c(2:50), values_drop_na = TRUE)
 human_boar_extracted <- cbind.data.frame(human_boar_extract$.id, human_boar_extract$value)
 human_boar_extracted$Type <- rep(c("Human_boar"), times = 1)
 human_boar_extracted$ID <- human_boar_extracted$`human_boar_extract$.id`
 
 #################################################################################
-#now i need to merge these three TTE dataframes to I can analyze it
-#but first, correcting the columns so all names line up
+#clean up and merge 3 dataframes 
 boar_boar_extracted$TTE <- boar_boar_extracted$`boar_boar_extract$value`
 boar_any_extracted$TTE <- boar_any_extracted$`boar_any_extract$value`
 any_any_extracted$TTE <- any_any_extracted$`any_any_extract$value`
@@ -203,7 +192,7 @@ any_any_extracted$TTE <- any_any_extracted$`any_any_extract$value`
 human_boar_extracted$TTE <- human_boar_extracted$`human_boar_extract$value`
 human_native_extracted$TTE <- human_native_extracted$`human_native_extract$value`
 
-#removing the old columns with the bad names. Sorry, this is not efficient but.. worked for me
+#clean up unnecessary columns 
 boar_boar_clean <- dplyr::select(boar_boar_extracted, -1,-2)
 boar_any_clean <- dplyr::select(boar_any_extracted, -1,-2)
 any_any_clean <- dplyr::select(any_any_extracted, -1,-2)
@@ -212,34 +201,32 @@ human_boar_clean <- dplyr::select(human_boar_extracted, -1,-2)
 human_native_clean <- dplyr::select(human_native_extracted, -1,-2)
 
 #################################################################################
-#merging the DF's into one to analyze
+#merge to an analysis dataframe
 cox_1.0 <- rbind(boar_any_clean, boar_boar_clean, any_any_clean, 
                  human_boar_clean, human_native_clean)
 
 #save as a CSV
 cox_1.0 <- write_csv(cox_1.0, "cox_1.0.csv")
 ##############################################################################
-# okay now that I have my DF set up... I need to attach the habitat covars +  time etc. 
+# attaching habitat covariates to TTE's
 
-#re-loading the dataset where the co vars are attached for each camera location
+#load habitat covar data 
 BoarDATA <- read_csv("Data/Processed/BoarDATA.csv", 
                      col_types = cols(posixdate = col_datetime(format = "%m/%d/%Y %H:%M")))
 
-#re-loading the processed TTE dataframe
+#TTE data
 cox_1.0 <- read_csv("Data/Processed/cox_1.0.csv")
 
-
-#sub set the first data set so that i have what i need from big DF
+#subset habitat data to covars of intetest 
 sub <- BoarDATA[c("Camera", "posixdate", "Habitat_Ty" , "ElevationC", "DIST_WATER", "DIST_ROAD")]
 
-#rename ID --> camera in the other DF
+#rename 
 colnames(cox_1.0)[2] <- "Camera"
 
 #creating a column of unique values in cox_1.0 to sort out later after merge. 
 cox_1.0$Unique <- c(1:530)
 
-#changing names in column to match up for merge. b/c of the weird names this is the best 
-## way i know how to do it. in hindsight.. would have purely numeric names. 
+#renaming numerical to camera_id names
 cox_1.0$Camera[cox_1.0$Camera == "1"] <- "Cam01"
 cox_1.0$Camera[cox_1.0$Camera == "2"] <- "Cam02"
 cox_1.0$Camera[cox_1.0$Camera == "3"] <- "Cam03"
@@ -258,24 +245,23 @@ cox_1.0$Camera[cox_1.0$Camera == "15"] <- "Cam16"
 cox_1.0$Camera[cox_1.0$Camera == "16"] <- "Cam17"
 cox_1.0$Camera[cox_1.0$Camera == "17"] <- "Cam18"
 
-#okay cool. now i can finally merge all cox_1.0 + sub set covars. b/c this dont work yet 
+#merge 
 intermediate <- merge(cox_1.0, sub, by = "Camera", all.x = FALSE, all.y = FALSE, no.dups = TRUE)
 
-#now can i filter out by unique values?
+#filter by unique values
 almostthere <-  intermediate %>% 
   group_by(Unique) %>%
   filter(row_number()==1)
 
-#final step is to rename that weird cropland/? to normal cropland so it can be a category.
+#fix habitat names for plotting later 
 almostthere$Habitat_Ty[almostthere$Habitat_Ty == "Cropland/?"] <- "Cropland"
 almostthere$Habitat_Ty[almostthere$Habitat_Ty == "Pasture"] <- "Apasture"
 
-#export this as a df ready to analyze. 
-
+# save analysis dataframe
 cox_analyze <- write_csv(almostthere, "cox_analyze.csv")
+
 ######################################################################################################################
-# Great, now we can actually run these hazard models
-######################################################################################################################
+# Hazard models
 #packages req'd for hazard models
 library(readr)
 library(data.table)
@@ -289,21 +275,18 @@ library(plotrix)
 #data 
 cox_analyze <- read_csv("Data/Processed/cox_analyze.csv")
 
-#remove the human observations, just wanted to not include them inturrupting 
+#remove the human inturrupted observations  
 cox_analyze <- subset(cox_analyze$Type, select = -c("Human_native", "Human_boar"))  
 
-#looking at structure 
-str(cox_analyze)
-
-#all characters as factors
+#structuring
 cox_analyze$Camera <- as.factor(cox_analyze$Camera)
 cox_analyze$Type <- as.factor(cox_analyze$Type)
 cox_analyze$Habitat_Ty <- as.factor(cox_analyze$Habitat_Ty)
 
-#need TTE's as positive not negative numbers. Function calculates TTE as a negative 
+#TTE's as positive numbers  
 cox_analyze$TTE <- abs(cox_analyze$TTE)
 #####################################################################################################
-#model set for ranking based on the few imp. covars were interested in
+#model set for ranking based on the few important covars
 
 #just a test model to make sure structure works. 
 test <- coxph(Surv(TTE) ~ 1, data = cox_analyze)
@@ -330,9 +313,8 @@ model <- coxme(Surv(TTE) ~ Habitat_Ty + Type  + (1|Camera), data = cox_analyze)
 summary(model)
 AIC3 <- AICc(model)
 
-##############3
-#creating an AICc table for model ranking
 
+#creating an AICc table for model ranking
 #putting AICc values into a vector with models attached
 Model_names <- c("Base", "Type", "Habitat", "Habitat + Type")
 AICvector <- c(AICbase, AIC1, AIC2, AIC3)
@@ -353,13 +335,14 @@ exp(mean(log(cox_analyze$TTE[cox_analyze$Type == "Boar_native"])))
 
 exp(mean(log(cox_analyze$TTE[cox_analyze$Type == "Anative_native"])))
 # mean = 11.18876 hrs   median = 6.471181
+
 ################################################################################################
 #Below is predicting risk based on the top suppored model. 
 
 #creating new column of predicted risk from model
 cox_analyze$risk <- coxme:::predict.coxme(model, type="risk")
 
-#sub setting the predicted risk by the interaction type and then generating summary stats 
+#subsetting the predicted risk by the interaction type and then generating summary stats 
 b_b <- filter(cox_analyze, Type == "Boar_boar")
 mean(b_b$risk)  
 median(b_b$risk)
@@ -382,12 +365,12 @@ std.error(n_n$risk)
 library(ggplot2)
 library(ggpubr)
 
-#create a new DF dropping the censor data so I can make a boxplot of predicted risk. 
+#new data just to plot 
 boxplot_data <- cox_analyze
 
 boxplot_data$Type <- as.character(boxplot_data$Type)
 
-#just getting names consistent with MS
+#consistent names 
 boxplot_data$Type[boxplot_data$Type == "Boar_boar"] <- "wild pig:wild pig"
 boxplot_data$Type[boxplot_data$Type == "Boar_native"] <- "wild pig:native"
 boxplot_data$Type[boxplot_data$Type == "Anative_native"] <- "native:native species"
@@ -428,7 +411,7 @@ ggplot(data = boxplot_data, mapping = aes(x = Type, y = risk, fill=Type)) +
 ###############################################################################
 #plotting the coefficient values from top supported hazard model.
 
-#only new req'd package. 
+#package call  
 library(tibble)
 
 #coe'f names, values, and error
@@ -446,7 +429,7 @@ model.summary$Confid <- (model.summary$SE * 1.96)
 bold.hab <- c("Wild pig:wild pig")
 bold.labels <- ifelse(levels(model.summary$Coefs) %in% bold.hab, yes = "bold", no = "plain")
 
-#the plot of coef values for the appendix.
+#the plot of coef values for the suppliment
 ggplot(model.summary, aes(x=Coefs, y=Values)) +
   geom_point(col="black", size=3) +
   xlab("Coefficients from top supported model") + ylab("Standardized coefficient value") +
@@ -463,8 +446,8 @@ ggplot(model.summary, aes(x=Coefs, y=Values)) +
            fontface = "bold")
 
 ################################################################################
-#Figure to show that we have confidence in difference between species to species distributions
-#new packages required                
+#Bootstrapping distributions of each event type to show we have confidence in trends exhibited (Fig 4)
+#packages required                
 library(png)
 library(jpeg)
 library(grid)
@@ -490,7 +473,6 @@ median(b_b_sub$TTE) #11
 box_sub <- subset(cox_analyze, !Type == "Native_native")
 
 #bootstrapping native-to-native species dist'n to n=7 (smallest sample in data), and reiterating 1000x
-#highlighting that despite small sample, trends still hold
 set.seed(08092023)
 TTE <- replicate(1000, median(sample(n_n_sub$TTE, 7, replace = TRUE)))
 
@@ -518,7 +500,6 @@ b_n_boxplot_img <- ggplot(box_sub) +
         axis.title = element_blank())
 
 #saving blank version of the boxplot to be imposed with probabilty distribution
-
 ggsave("/Output/Box_insert.png",
        plot = b_n_boxplot_img, 
        device = "png")