Telemetry Reports for Tag Owners

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

proj58_matched_full_no_release <- proj58_matched_full  %>% 
  filter(receiver != "release")

base <- get_stadiamap(
  bbox = c(left = min(proj58_matched_full_no_release$longitude),
           bottom = min(proj58_matched_full_no_release$latitude), 
           right = max(proj58_matched_full_no_release$longitude), 
           top = max(proj58_matched_full_no_release$latitude)),
  maptype = "stamen_terrain_background", 
  crop = FALSE,
  zoom = 5)


#add your releases and detections onto your basemap

proj58_map <- 
  ggmap(base, extent='panel') +
  ylab("Latitude") +
  xlab("Longitude") +
  geom_point(data = proj58_matched_full_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!

proj58_map

#set your basemap

geo_styling <- list(
  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")
)

#decide what data you're going to use

detections_map_plotly <- plot_geo(proj58_matched_full_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 = 'Project 58 Detections', geo = geo_styling
)

#View map
detections_map_plotly

# summary of animals you've tagged

proj58_tag_summary <- proj58_tag %>% 
  mutate(UTC_RELEASE_DATE_TIME = ymd_hms(UTC_RELEASE_DATE_TIME)) %>% 
  #filter(UTC_RELEASE_DATE_TIME > '2016-06-01') %>% #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))

#view our summary table

proj58_tag_summary

# Average location of each animal, without release records

proj58_matched_full_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.

proj58_tag <- proj58_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 = proj58_matched_full_no_release, y = proj58_tag, by = "tagname") #join!

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

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

View(tag_joined_dets)

#Avg length per location

proj58_tag_det_summary <- tag_joined_dets %>% 
  group_by(detectedby, station, latitude, longitude)  %>%  
  summarise(AvgSize = mean(`LENGTH (m)`, na.rm=TRUE))

proj58_tag_det_summary

#export our summary table as CSV
write_csv(proj58_tag_det_summary, "detections_summary.csv", col_names = TRUE)

# count detections per transmitter, per array

proj58_matched_full_no_release %>% 
  group_by(catalognumber, station, detectedby, commonname) %>% 
  summarize(count = n()) %>% 
  select(catalognumber, commonname, detectedby, station, count)

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

receivers <- proj58_matched_full_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 <- proj58_matched_full_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)

proj58_matched_full_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('Project 58 Detections by Month (2016-2017)')+ #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 = proj58_matched_full, aes(x = datecollected, y = catalognumber, col = detectedby)) +
  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 = proj58_matched_full,  aes(x = datecollected, y = detectedby, 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 #might be better with just a subset, huh??

# track movement using geom_path!!

proj58_subset <- proj58_matched_full %>%
  dplyr::filter(catalognumber %in% c('PROJ58-1191602-2014-07-24', 'PROJ58-1191606-2014-07-24', 
                               'PROJ58-1191612-2014-08-21', 'PROJ58-1218518-2015-09-16'))

View(proj58_subset)

movMap <- 
  ggmap(base, extent = 'panel') + #use the BASE we set up before
  ylab("Latitude") +
  xlab("Longitude") +
  geom_path(data = proj58_subset, aes(x = longitude, y = latitude, col = commonname)) + #connect the dots with lines
  geom_point(data = proj58_subset, aes(x = longitude, y = latitude, col = commonname)) + #layer the stations back on
  scale_colour_manual(values = c("red", "blue"), name = "Species")+ #
  facet_wrap(~catalognumber, nrow=2, ncol=2)+
  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)

proj58_matched_full %>%
  group_by(m=month(datecollected), catalognumber, scientificname) %>% #make our groups
  summarise(mean=mean(latitude)) %>% #mean lat
  ggplot(aes(m %>% factor, mean, colour=scientificname, fill=scientificname))+ #the data is supplied, but no info on how to show it!
  geom_point(size=3, position="jitter")+   # draw data as points, and use jitter to help see all points instead of superimposition
  #coord_flip()+   #flip x y, not needed here
  scale_colour_manual(values = "blue")+ #change the colour to represent the species better!
  scale_fill_manual(values = "grey")+ 
  geom_boxplot()+ #another layer
  geom_violin(colour="black") #and one more layer


#There are other ways to present a summary of data like this that we might have chosen. 
#geom_density2d() will give us a KDE for our data points and give us some contours across our chosen plot axes.

proj58_matched_full %>% 
  group_by(month=month(datecollected), catalognumber, scientificname) %>%
  summarise(meanlat=mean(latitude)) %>%
  ggplot(aes(month, meanlat, colour=scientificname, fill=scientificname))+
  geom_point(size=3, position="jitter")+
  scale_colour_manual(values = "blue")+
  scale_fill_manual(values = "grey")+
  geom_density2d(linewidth=7, lty=1) #this is the only difference from the plot above 

#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!

proj58_matched_full %>%
  ggplot(aes(longitude, latitude))+
  facet_wrap(~catalognumber)+ #make one plot per individual
  geom_violin()
#Warnings going on above.

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

tqcs_matched_10_11_no_release <- tqcs_matched_10_11 %>% 
  filter(receiver != "release")

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

tqcs_matched_10_11_full_no_release <- tqcs_matched_10_11_full %>% 
  filter(receiver != "release")

base <- get_stadiamap(
  bbox = c(left = min(tqcs_matched_10_11$longitude),
           bottom = min(tqcs_matched_10_11$latitude), 
           right = max(tqcs_matched_10_11$longitude), 
           top = max(tqcs_matched_10_11$latitude)),
  maptype = "stamen_terrain_background", 
  crop = FALSE,
  zoom = 8)


#add your releases and detections onto your basemap

tqcs_map <- 
  ggmap(base, extent='panel') +
  ylab("Latitude") +
  xlab("Longitude") +
  geom_point(data = tqcs_matched_10_11, 
             aes(x = longitude,y = latitude), #specify the data
             colour = 'blue', shape = 19, size = 2) #lots of aesthetic options here!

#view your tagging map!

tqcs_map

#set your basemap

geo_styling <- list(
  fitbounds = "locations", visible = TRUE, #fits the bounds to your data!
  showland = TRUE,
  landcolor = toRGB("gray95"),
  subunitcolor = toRGB("gray85"),
  countrycolor = toRGB("gray85")
)

#decide what data you're going to use

tqcs_map_plotly <- plot_geo(tqcs_matched_10_11, lat = ~latitude, lon = ~longitude) 

#add your markers for the interactive map

tqcs_map_plotly <- tqcs_map_plotly %>% add_markers(
  text = ~paste(catalognumber, scientificname, paste("Date detected:", datecollected), 
                paste("Latitude:", latitude), paste("Longitude",longitude), 
                paste("Detected by:", detectedby), paste("Station:", station), 
                paste("Contact:", contact_poc, contact_pi), sep = "<br />"),
  symbol = I("square"), size = I(8), hoverinfo = "text" 
)

#Add layout (title + geo stying)

tqcs_map_plotly <- tqcs_map_plotly %>% layout(
  title = 'TQCS Detections<br />(2010-2011)', geo = geo_styling
)

#View map

tqcs_map_plotly

# summary of animals you've tagged

tqcs_tag_summary <- tqcs_tag %>% 
  mutate(UTC_RELEASE_DATE_TIME = ymd_hms(UTC_RELEASE_DATE_TIME)) %>% 
  #filter(UTC_RELEASE_DATE_TIME > '2019-06-01') %>% #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)) 
			
#view our summary table

tqcs_tag_summary

#Average location of each animal, without release records

tqcs_matched_10_11_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.

tqcs_tag <- tqcs_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 detections without the release information

tag_joined_dets <-  left_join(x = tqcs_matched_10_11_no_release, y = tqcs_tag, by = "tagname")

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

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

View(tag_joined_dets)

# Avg length per location

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

tqcs_tag_det_summary

# count detections per transmitter, per array

tqcs_matched_10_11_no_release %>% 
  group_by(catalognumber, detectedby, commonname) %>% 
  summarize(count = n()) %>% 
  select(catalognumber, commonname, detectedby, count)

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

receivers <- tqcs_matched_10_11_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)

animal_id_summary <- tqcs_matched_10_11_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)

#try with tqcs_matched_10_11_full_no_release if you're feeling bold! takes ~30 secs

tqcs_matched_10_11_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('TQCS Detections by Month (2010-2011)')+ #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 = tqcs_matched_10_11_no_release, aes(x = datecollected, y = catalognumber, col = detectedby)) +
  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 = tqcs_matched_10_11_no_release,  aes(x = datecollected, y = station, col = catalognumber)) +
  geom_point() +
  ggtitle("Detections by station") +
  theme(plot.title = element_text(face = "bold", hjust = 0.5)) +
  scale_color_viridis(discrete = TRUE)

abacus_stations 

# track movement using geom_path!!

tqcs_subset <- tqcs_matched_10_11_no_release %>%
  dplyr::filter(catalognumber %in% 
                  c('TQCS-1049282-2008-02-28', 'TQCS-1049281-2008-02-28'))

View(tqcs_subset)

movMap <- 
  ggmap(base, extent = 'panel') + #use the BASE we set up before
  ylab("Latitude") +
  xlab("Longitude") +
  geom_path(data = tqcs_subset, aes(x = longitude, y = latitude, col = commonname)) + #connect the dots with lines
  geom_point(data = tqcs_subset, 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)

tqcs_matched_10_11 %>%
  group_by(m=month(datecollected), catalognumber, scientificname) %>% #make our groups
  summarise(mean=mean(latitude)) %>% #mean lat
  ggplot(aes(m %>% factor, mean, colour=scientificname, fill=scientificname))+ #the data is supplied, but no info on how to show it!
  geom_point(size=3, position="jitter")+   # draw data as points, and use jitter to help see all points instead of superimposition
  #coord_flip()+   #flip x y, not needed here
  scale_colour_manual(values = "blue")+ #change the colour to represent the species better!
  scale_fill_manual(values = "grey")+ 
  geom_boxplot()+ #another layer
  geom_violin(colour="black") #and one more layer


#There are other ways to present a summary of data like this that we might have chosen. 
#geom_density2d() will give us a KDE for our data points and give us some contours across our chosen plot axes.

tqcs_matched_10_11 %>% #doesnt work on the subsetted data, back to original dataset for this one
  group_by(month=month(datecollected), catalognumber, scientificname) %>%
  summarise(meanlat=mean(latitude)) %>%
  ggplot(aes(month, meanlat, colour=scientificname, fill=scientificname))+
  geom_point(size=3, position="jitter")+
  scale_colour_manual(values = "blue")+
  scale_fill_manual(values = "grey")+
  geom_density2d(linewidth=7, lty=1) #this is the only difference from the plot above 

#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!
tqcs_matched_10_11 %>%
  ggplot(aes(longitude, latitude))+
  facet_wrap(~catalognumber)+ #make one plot per individual
  geom_violin()

base <- get_stadiamap(
  bbox = c(left = min(all_dets$deploy_long),
           bottom = min(all_dets$deploy_lat), 
           right = max(all_dets$deploy_long), 
           top = max(all_dets$deploy_lat)),
  maptype = "stamen_terrain_background", 
  crop = FALSE,
  zoom = 8)


#add your detections onto your basemap
detections_map <- 
  ggmap(base, extent='panel') +
  ylab("Latitude") +
  xlab("Longitude") +
  geom_point(data = all_dets,
             aes(x = deploy_long,y = deploy_lat, colour = common_name_e), #specify the data
             shape = 19, size = 2) #lots of aesthetic options here!

#view your detections map!
detections_map

#set your basemap

geo_styling <- list(
  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")
)

#decide what data you're going to use

detections_map_plotly <- plot_geo(all_dets, lat = ~deploy_lat, lon = ~deploy_long) 

#add your markers for the interactive map

detections_map_plotly <- detections_map_plotly %>% add_markers(
  text = ~paste(animal_id, common_name_e, paste("Date detected:", detection_timestamp_utc), 
                paste("Latitude:", deploy_lat), paste("Longitude",deploy_long), 
                paste("Detected by:", glatos_array), paste("Station:", station), 
                paste("Project:",glatos_project_receiver), sep = "<br />"),
  symbol = I("square"), size = I(8), hoverinfo = "text" 
)
#Add layout (title + geo stying)

detections_map_plotly <- detections_map_plotly %>% layout(
  title = 'Lamprey and Walleye Detections<br />(2012-2013)', geo = geo_styling
)

#View map

detections_map_plotly

# summary of animals you've tagged
walleye_tag_summary <- walleye_tag %>% 
  mutate(GLATOS_RELEASE_DATE_TIME = ymd_hms(GLATOS_RELEASE_DATE_TIME)) %>% 
  #filter(GLATOS_RELEASE_DATE_TIME > '2012-06-01') %>% #select timeframe, specific animals etc.
  group_by(year = year(GLATOS_RELEASE_DATE_TIME), COMMON_NAME_E) %>% 
  summarize(count = n(), 
            Meanlength = mean(LENGTH, na.rm=TRUE), 
            minlength= min(LENGTH, na.rm=TRUE), 
            maxlength = max(LENGTH, na.rm=TRUE), 
            MeanWeight = mean(WEIGHT, na.rm = TRUE)) 
			

#view our summary table
walleye_tag_summary

#Average location of each animal!

all_dets %>% 
  group_by(animal_id) %>% 
  summarize(NumberOfStations = n_distinct(station),
            AvgLat = mean(deploy_lat),
            AvgLong =mean(deploy_long))


# Avg length per location

all_dets_summary <- all_dets %>% 
  mutate(detection_timestamp_utc = ymd_hms(detection_timestamp_utc)) %>% 
  group_by(glatos_array, station, deploy_lat, deploy_long, common_name_e)  %>%  
  summarise(AvgSize = mean(length, na.rm=TRUE))

all_dets_summary

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

# count detections per transmitter, per array

all_dets %>% 
  group_by(animal_id, glatos_array, common_name_e) %>% 
  summarize(count = n()) %>% 
  select(animal_id, common_name_e, glatos_array, count)
  
# list all GLATOS arrays each fish was seen on, and a number_of_arrays column too

arrays <- all_dets %>% 
  group_by(animal_id) %>% 
  mutate(arrays =  (list(unique(glatos_array)))) %>% #create a column with a list of the arrays
  dplyr::select(animal_id, arrays)  %>% #remove excess columns
  distinct_all() %>% #keep only one record of each
  mutate(number_of_arrays = sapply(arrays,length)) %>% #sapply: applies a function across a List - in this case we are applying length()
  as.data.frame() 
  
View(arrays)
  
#Full summary of each animal's track
animal_id_summary <- all_dets %>% 
  group_by(animal_id) %>%
  summarise(dets = length(animal_id),
            stations = length(unique(station)),
            min = min(detection_timestamp_utc), 
            max = max(detection_timestamp_utc), 
            tracklength = max(detection_timestamp_utc)-min(detection_timestamp_utc))

View(animal_id_summary)

all_dets  %>% 
  mutate(detection_timestamp_utc=ymd_hms(detection_timestamp_utc)) %>% #make datetime
  mutate(year_month = floor_date(detection_timestamp_utc, "months")) %>% #round to month
 filter(common_name_e == 'walleye') %>% #can filter for specific stations, dates etc. doesn't have to be species!
  group_by(year_month) %>% #can group by station, species et - doesn't have to be by date
  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('Walleye Detections by Month (2012-2013)')+ #title
  labs(fill = "Year") #legend title

# an easy abacus plot!

#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)

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

abacus_animals

#another way to vizualize

abacus_stations <- 
  ggplot(data = all_dets,  aes(x = detection_timestamp_utc, y = station, col = animal_id)) +
  geom_point() +
  ggtitle("Detections by station") +
  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 = all_dets, aes(x = deploy_long, y = deploy_lat, col = common_name_e)) + #connect the dots with lines
  geom_point(data = all_dets, aes(x = deploy_long, y = deploy_lat, col = common_name_e)) + #layer the stations back on
  scale_colour_manual(values = c("red", "blue"), name = "Species")+ #
  facet_wrap(~animal_id, ncol = 6, nrow=1)+
  ggtitle("Inferred Animal Paths")


movMap


# monthly latitudinal distribution of your animals (works best w >1 species)

all_dets %>%
  group_by(month=month(detection_timestamp_utc), animal_id, common_name_e) %>% #make our groups
  summarise(meanlat=mean(deploy_lat)) %>% #mean lat
  ggplot(aes(month %>% factor, meanlat, colour=common_name_e, fill=common_name_e))+ #the data is supplied, but no info on how to show it!
  geom_point(size=3, position="jitter")+   # draw data as points, and use jitter to help see all points instead of superimposition
  #coord_flip()+   #flip x y, not needed here
  scale_colour_manual(values = c("brown", "green"))+ #change the colour to represent the species better!
  scale_fill_manual(values = c("brown", "green"))+  #colour of the boxplot
  geom_boxplot()+ #another layer
  geom_violin(colour="black") #and one more layer


# per-individual contours - lots of plots: called facets!
all_dets %>%
  ggplot(aes(deploy_long, deploy_lat))+
  facet_wrap(~animal_id)+ #make one plot per individual
  geom_violin()

#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_stadiamap(
  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 = "stamen_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()

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

nsbs_matched_full_no_release <- nsbs_matched_full  %>% 
  filter(receiver != "release")

base <- get_stadiamap(
  bbox = c(left = min(nsbs_matched_full_no_release$longitude),
           bottom = min(nsbs_matched_full_no_release$latitude), 
           right = max(nsbs_matched_full_no_release$longitude), 
           top = max(nsbs_matched_full_no_release$latitude)),
  maptype = "stamen_toner_lite", 
  crop = FALSE,
  zoom = 5)


#add your releases and detections onto your basemap

nsbs_map <- 
  ggmap(base, extent='panel') +
  ylab("Latitude") +
  xlab("Longitude") +
  geom_point(data = nsbs_matched_full_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!

nsbs_map

#set your basemap

geo_styling <- list(
  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")
)

#decide what data you're going to use

detections_map_plotly <- plot_geo(nsbs_matched_full_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 = 'NSBS Detections', geo = geo_styling
)

#View map
detections_map_plotly

# summary of animals you've tagged

nsbs_tag_summary <- nsbs_tag %>% 
  mutate(UTC_RELEASE_DATE_TIME = ymd_hms(UTC_RELEASE_DATE_TIME)) %>% 
  #filter(UTC_RELEASE_DATE_TIME > '2016-06-01') %>% #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)) 

#view our summary table

View(nsbs_tag_summary)

# Average location of each animal, without release records

nsbs_matched_full_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.

nsbs_tag <- nsbs_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 = nsbs_matched_full_no_release, y = nsbs_tag, by = "tagname") #join!

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

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

View(tag_joined_dets)

#Avg length per location

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

View(nsbs_tag_det_summary)

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

# count detections per transmitter, per array

nsbs_matched_full_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

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

View(arrays)

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

animal_id_summary <- nsbs_matched_full_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)

nsbs_matched_full_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('NSBS Detections by Month (2021-2022)')+ #title
  labs(fill = "Year") #legend title