Telemetry Reports for Array Operators

library(ggmap)


#first, what are our columns called?
names(teq_deploy)


#make a basemap for your stations, using the min/max deploy lat and longs as bounding box

base <- get_stamenmap(
  bbox = c(left = min(teq_deploy$DEPLOY_LONG), 
           bottom = min(teq_deploy$DEPLOY_LAT), 
           right = max(teq_deploy$DEPLOY_LONG), 
           top = max(teq_deploy$DEPLOY_LAT)),
  maptype = "terrain-background", 
  crop = FALSE,
  zoom = 8)

#filter for stations you want to plot

teq_deploy_plot <- teq_deploy %>% 
  mutate(deploy_date=ymd_hms(DEPLOY_DATE_TIME....yyyy.mm.ddThh.mm.ss.)) %>% #make a datetime
  mutate(recover_date=ymd_hms(RECOVER_DATE_TIME..yyyy.mm.ddThh.mm.ss.)) %>% #make a datetime
  filter(!is.na(deploy_date)) %>% #no null deploys
  filter(deploy_date > 2010-07-03) %>% #only looking at certain deployments!
  group_by(STATION_NO) %>% 
  summarise(MeanLat=mean(DEPLOY_LAT), MeanLong=mean(DEPLOY_LONG)) #get the mean location per station
  
# you could choose to plot stations which are within a certain bounding box!
# to do this you would add another filter to the above data, before passing to the map
# ex: add this line after the mutate() clauses:
	# filter(latitude >= 0.5 & latitude <= 24.5 & longitude >= 0.6 & longitude <= 34.9)


#add your stations onto your basemap

teq_map <- 
  ggmap(base, extent='panel') +
  ylab("Latitude") +
  xlab("Longitude") +
  geom_point(data = teq_deploy_plot, #filtering for recent deployments
             aes(x = MeanLong,y = MeanLat), #specify the data
             colour = 'blue', shape = 19, size = 2) #lots of aesthetic options here!

#view your receiver map!

teq_map

#save your receiver map into your working directory

ggsave(plot = teq_map, file = "code/day1/teq_map.tiff", units="in", width=15, height=8)

library(plotly)

#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

teq_map_plotly <- plot_geo(teq_deploy_plot, lat = ~MeanLat, lon = ~MeanLong)  

#add your markers for the interactive map

teq_map_plotly <- teq_map_plotly %>% add_markers(
  text = ~paste(STATION_NO, MeanLat, MeanLong, sep = "<br />"),
  symbol = I("square"), size = I(8), hoverinfo = "text" 
)

#Add layout (title + geo stying)

teq_map_plotly <- teq_map_plotly %>% layout(
  title = 'TEQ Deployments<br />(> 2010-07-03)', geo = geo_styling
)

#View map

teq_map_plotly

#You might see the following warning: it just means that the plotly package has some updating to do
  # Warning message:
  # `arrange_()` is deprecated as of dplyr 0.7.0.
  # Please use `arrange()` instead.
  # See vignette('programming') for more help
  # This warning is displayed once every 8 hours.
  # Call `lifecycle::last_warnings()` to see where this warning was generated.

#How many of each animals did we detect from each collaborator, by species

teq_qual_summary <- teq_qual_10_11 %>% 
  filter(datecollected > '2010-06-01') %>% #select timeframe, stations etc.
  group_by(trackercode, scientificname, tag_contact_pi, tag_contact_poc) %>% 
  summarize(count = n()) %>% 
  select(trackercode, tag_contact_pi, tag_contact_poc, scientificname, count)

#view our summary table

teq_qual_summary #remember, this is just the first 10,000 rows!

#export our summary table

write_csv(teq_qual_summary, "code/day1/teq_detection_summary_June2010_to_Dec2011.csv", col_names = TRUE)

# number of dets per month/year per station 

teq_det_summary  <- teq_qual_10_11  %>% 
  mutate(datecollected=ymd_hms(datecollected))  %>% 
  group_by(station, year = year(datecollected), month = month(datecollected)) %>% 
  summarize(count =n())

teq_det_summary #remember: this is a subset!

# number of dets per month/year per station & species

teq_anim_summary  <- teq_qual_10_11  %>% 
  mutate(datecollected=ymd_hms(datecollected))  %>% 
  group_by(station, year = year(datecollected), month = month(datecollected), scientificname) %>% 
  summarize(count =n())

teq_anim_summary # remember: this is a subset!

#try with teq_qual_10_11_full if you're feeling bold! takes about 1 min to run on a fast machine

teq_qual_10_11 %>% 
  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('TEQ Animal Detections by Month')+ #title
  labs(fill = "Year") #legend title