Summarising and Plotting Data

Overview

Teaching: 45 min
Exercises: 0 min

Questions

What are some of the ways to summarise my data?

How can I make plots of the summarised data?

Objectives

Show how to summarise data by FishID.

Show how to summarise data by station.

Use ggplot and ggmap to display plots and maps of the data.

Use ggmap to track individual fish movement.

Summarise by FishID

We can group our data by animal ID, letting us isolate individuals for summary stats, plotting and further analysis.

str(dets_with_stations)

animal_id_summary <- 
  dets_with_stations %>% 
  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)) %>% 
  as.data.frame()

animal_id_summary

Summarise by station:

To group and summarise our data by station, we first need to find all unique station names and locations:

head(Rxdeploy)

stations <- 
  Rxdeploy %>% 
  select(station, deploy_date_time, recover_date_time, lat=deploy_lat, lon=deploy_long)

head(stations)

Summarise detections:

Create a new data product, det_days, that give you the unique dates that an animal was seen by a station. This summary can be used to calculate a residence index as in Kessel et al. 2017

stationsum <- 
  dets_with_stations %>% 
  group_by(station) %>%
  summarise(detections = length(animal_id),
            start = min(detection_timestamp_utc),
            end = max(detection_timestamp_utc),
            uniqueID = length(unique(animal_id)), det_days=length(unique(as.Date(detection_timestamp_utc)))) %>% 
  as.data.frame()

head(stationsum)

Merge with station list:

We can then re-attach station summary information to the list of stations we made earlier.

stations2 <-
  stations %>% 
  full_join(stationsum, by = "station") %>% 
  filter(detections > 0) %>% # Filter out stations with no detections
  filter(deploy_date_time <= start & recover_date_time >= end) %>% 
  select(-start, -end)

head(stations2)  

Plotting data

Abacus plots:

Simple plots using the glatos library.

library(glatos)

glatos::abacus_plot(dets_with_stations, location_col =  "station") # Plot by station
glatos::abacus_plot(dets_with_stations, location_col =  "animal_id") # Plot by animal

Nicer plots using the ggplot library.

library(tidyverse)
library(viridis)

plot_data <- 
  dets %>% 
  dplyr::select(animal_id, station, detection_timestamp_utc)

abacus_animals <- 
  ggplot(data = plot_data, aes(x = detection_timestamp_utc, y = animal_id, 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 = plot_data,  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

Spatial Plots:

Summarise data by station and individual ID, and then plot a map of each animal path.

library(ggmap)

# examine by station and FishID:
stationFishID <- 
  dets_with_stations %>% 
  group_by(station, animal_id) %>%
  summarise(lat = mean(deploy_lat), 
            lon = mean(deploy_long), 
            dets = length(animal_id), 
            logdets = log(dets))

# Peek at the first few rows
head(stationFishID)

base <- 
  get_stamenmap(
    bbox = c(left = min(dets_with_stations$deploy_long),
             bottom = min(dets_with_stations$deploy_lat ), 
             right = max(dets_with_stations$deploy_long), 
             top = max(dets_with_stations$deploy_lat)),
    maptype = "terrain-background", 
    crop = FALSE,
    zoom = 8)
    
perm_map <- 
  ggmap(base, extent='panel') +
  ylab("Latitude") +
  xlab("Longitude") +
  labs(size = "log(detections)")+
  geom_point(data = stationFishID, aes(x = lon,y = lat, size = logdets, col = animal_id))

perm_map

# can simply save plots using output window, or to save high res plots:
ggsave(plot = perm_map, file = "Key_permit_map.tiff", units="in", width=15, height=8)

Using maps to track movement patterns

Let’s look at maps by FishID to see individual fish movement patterns.

movMap <- 
  ggmap(base, extent = 'panel') +
  ylab("Latitude") +
  xlab("Longitude") +
  labs(size = "log(detections)") +
  geom_path(data = dets_with_stations, aes(x = deploy_long, y = deploy_lat, col = animal_id))+
  geom_point(data = stationFishID, aes(x = lon, y = lat, size = logdets, col = animal_id))+
  facet_wrap(~animal_id)

movMap

Key Points

Data can be summarised by fish ID, station or detection.

Data can be plotted into an abacus plot by day, or can be mapped based on the station information.

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