Telemetry Reports for Tag Owners

#optional dataset to use: detections with releases filtered out!

gmr_matched_18_19_no_release <- gmr_matched_18_19  %>% 
  dplyr::filter(receiver != "release")

base <- get_stamenmap(
  bbox = c(left = min(gmr_matched_18_19_no_release$longitude),
           bottom = min(gmr_matched_18_19_no_release$latitude), 
           right = max(gmr_matched_18_19_no_release$longitude), 
           top = max(gmr_matched_18_19_no_release$latitude)),
  maptype = "terrain", 
  crop = FALSE,
  zoom = 12)


#add your releases and detections onto your basemap

gmr_tag_map <- 
  ggmap(base, extent='panel') +
  ylab("Latitude") +
  xlab("Longitude") +
  geom_point(data = gmr_matched_18_19_no_release, 
             aes(x = longitude,y = latitude), #specify the data
             colour = 'blue', shape = 19, size = 2) #lots of aesthetic options here!

#view your tagging map!

gmr_tag_map

#set your basemap

geo_styling <- list(
  scope = 'galapagos',
  #fitbounds = "locations", visible = TRUE, #fits the bounds to your data!
  showland = TRUE,
  showlakes = TRUE,
  lakecolor = toRGB("blue", alpha = 0.2), #make it transparent
  showcountries = TRUE,
  landcolor = toRGB("gray95"),
  countrycolor = toRGB("gray85"),
  lonaxis = list(
    showgrid = TRUE,
    range = c(-92.5, -90)),
  lataxis = list(
    showgrid = TRUE,
    range = c(0, 2)),
  resolution = 50
)

#decide what data you're going to use

detections_map_plotly <- plot_geo(gmr_matched_18_19_no_release, lat = ~latitude, lon = ~longitude) 

#add your markers for the interactive map
detections_map_plotly <- detections_map_plotly %>% add_markers(
  text = ~paste(catalognumber, commonname, paste("Date detected:", datecollected), 
                paste("Latitude:", latitude), paste("Longitude",longitude), 
                paste("Detected by:", detectedby), paste("Station:", station), 
                paste("Project:",collectioncode), sep = "<br />"),
  symbol = I("square"), size = I(8), hoverinfo = "text" 
)

#Add layout (title + geo stying)

detections_map_plotly <- detections_map_plotly %>% layout(
  title = 'GMR Tagged Animal Detections', geo = geo_styling
)

#View map
detections_map_plotly

# summary of animals you've tagged

gmr_tag_summary <- gmr_tag %>% 
  mutate(UTC_RELEASE_DATE_TIME = ymd_hms(UTC_RELEASE_DATE_TIME)) %>% 
  #dplyr::filter(UTC_RELEASE_DATE_TIME > '2018-06-01') %>% #select timeframe, specific animals etc.
  group_by(year = year(UTC_RELEASE_DATE_TIME), COMMON_NAME_E) %>% 
  summarize(count = n(), 
            Meanlength = mean(`LENGTH (m)`, na.rm=TRUE), 
            minlength= min(`LENGTH (m)`, na.rm=TRUE), 
            maxlength = max(`LENGTH (m)`, na.rm=TRUE), 
            MeanWeight = mean(`WEIGHT (kg)`, na.rm = TRUE)) 

# there are some species which don't have enough data to calculate a Min/Max value - these show `INF` instead in these fields.
#view our summary table

gmr_tag_summary

# Average location of each animal, without release records

gmr_matched_18_19_no_release %>% 
  group_by(catalognumber) %>% 
  summarize(NumberOfStations = n_distinct(station),
            AvgLat = mean(latitude),
            AvgLong =mean(longitude))

#First we need to make a tagname column in the tag metadata (to match the Detection Extract), and figure out the enddate of the tag battery.

gmr_tag <- gmr_tag %>% 
  mutate(enddatetime = (ymd_hms(UTC_RELEASE_DATE_TIME) + days(EST_TAG_LIFE))) %>% #adding enddate
  mutate(tagname = paste(TAG_CODE_SPACE,TAG_ID_CODE, sep = '-')) #adding tagname column

#Now we join by tagname, to the detection dataset (without the release information)

tag_joined_dets <- left_join(x = gmr_matched_18_19_no_release, y = gmr_tag, by = "tagname") #join!

#make sure any redeployed tags have matched within their deployment period only

tag_joined_dets <- tag_joined_dets %>% 
  dplyr::filter(datecollected >= UTC_RELEASE_DATE_TIME & datecollected <= enddatetime)

View(tag_joined_dets)

#Avg length per location

gmr_tag_det_summary <- tag_joined_dets %>% 
  group_by(commonname, detectedby, station, latitude, longitude)  %>%  
  summarise(AvgSize = mean(`LENGTH (m)`, na.rm=TRUE))

gmr_tag_det_summary

#export our summary table as CSV
write_csv(gmr_tag_det_summary, "detections_summary.csv", col_names = TRUE)

# count detections per transmitter, per array

gmr_matched_18_19_no_release %>% 
  group_by(catalognumber, station, detectedby, commonname) %>% 
  summarize(count = n()) %>% 
  dplyr::select(catalognumber, commonname, detectedby, station, count)

# list all receivers each fish was seen on, and a number_of_receivers column too

receivers <- gmr_matched_18_19_no_release %>% 
  group_by(catalognumber) %>% 
  mutate(stations = (list(unique(station)))) %>% #create a column with a list of the stations
  dplyr::select(catalognumber, stations)  %>% #remove excess columns
  distinct_all() %>% #keep only one record of each
  mutate(number_of_stations = sapply(stations, length)) %>% #sapply: applies a function across a List - in this case we are applying length()
  as.data.frame()  

View(receivers)

# number of stations visited, start and end dates, and track length

animal_id_summary <- gmr_matched_18_19_no_release %>% 
  group_by(catalognumber) %>%
  summarise(dets = length(catalognumber),
            stations = length(unique(station)),
            min = min(datecollected), 
            max = max(datecollected), 
            tracklength = max(datecollected)-min(datecollected))

view(animal_id_summary)

gmr_matched_18_19_no_release  %>% 
  mutate(datecollected=ymd_hms(datecollected)) %>% #make datetime
  mutate(year_month = floor_date(datecollected, "months")) %>% #round to month
  group_by(year_month) %>% #can group by station, species etc.
  summarize(count =n()) %>% #how many dets per year_month
  ggplot(aes(x = (month(year_month) %>% as.factor()), 
             y = count, 
             fill = (year(year_month) %>% as.factor())
  )
  )+ 
  geom_bar(stat = "identity", position = "dodge2")+ 
  xlab("Month")+
  ylab("Total Detection Count")+
  ggtitle('GMR Tagged Animal Detections by Month (2018-2019)')+ #title
  labs(fill = "Year") #legend title

#Use the color scales in this package to make plots that are pretty, 
#better represent your data, easier to read by those with colorblindness, and print well in grey scale.
library(viridis)

# an easy abacus plot!

abacus_animals <- 
  ggplot(data = gmr_matched_18_19_no_release, aes(x = datecollected, y = catalognumber, col = station)) +
  geom_point() +
  ggtitle("Detections by animal") +
  theme(plot.title = element_text(face = "bold", hjust = 0.5)) +
  scale_color_viridis(discrete = TRUE)

abacus_animals

abacus_stations <- 
  ggplot(data = gmr_matched_18_19_no_release,  aes(x = datecollected, y = station, col = catalognumber)) +
  geom_point() +
  ggtitle("Detections by Array") +
  theme(plot.title = element_text(face = "bold", hjust = 0.5)) +
  scale_color_viridis(discrete = TRUE)

abacus_stations 

# track movement using geom_path!!

movMap <- 
  ggmap(base, extent = 'panel') + #use the BASE we set up before
  ylab("Latitude") +
  xlab("Longitude") +
  geom_path(data = gmr_matched_18_19_no_release, aes(x = longitude, y = latitude, col = commonname)) + #connect the dots with lines
  geom_point(data = gmr_matched_18_19_no_release, aes(x = longitude, y = latitude, col = commonname)) + #layer the stations back on
  scale_colour_manual(values = c("red", "blue"), name = "Species")+ #
  facet_wrap(~catalognumber)+
  ggtitle("Inferred Animal Paths")

#to size the dots by number of detections you could do something like: size = (log(length(animal)id))?

movMap


# monthly latitudinal distribution of your animals (works best w >1 species)
gmr_matched_18_19_no_release %>%
  group_by(month=month(datecollected), catalognumber, scientificname) %>% #make our groups
  summarise(meanLat=mean(latitude)) %>% #mean lat
  ggplot(aes(month %>% factor, meanLat, colour=scientificname, fill=scientificname))+ #the data is supplied, but no info on how to show it!
  geom_point(size=3, alpha = 0.5, position = "jitter")+   # draw data as points, and use jitter to help see all points instead of superimposition
  geom_boxplot()

#anything you specify in the aes() is applied to the actual data points/whole plot, 
#anything specified in geom() is applied to that layer only (colour, size...)

# per-individual density contours - lots of plots: called facets!

gmr_matched_18_19_no_release %>%
  ggplot(aes(longitude, latitude))+
  facet_wrap(~catalognumber)+ #make one plot per individual
  geom_violin()