Data Analysis Using GLATOS

Overview

Teaching: 45 min
Exercises: 0 min

Questions

How does the GLATOS package facilitate data analysis?

How does the GLATOS package facilitate data visualization?

Objectives

Demonstrate how to clean and filter raw data with the GLATOS package.

Show how summarise() can be used to group data.

Demonstrate how grouped data can be plotted.

Demonstrate how grouped data can be plotted on a map.

Data Analysis Using GLATOS

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Basically, the point is to turn data into information and information into knowledge. There are many ways to look at and compare dependent and independent variables, find relations, and even create models and predict behaviours. But, we are only going to focus on analyzing time and location data in the simplest way.

First, clean and filter the data:

library(tidyverse)
library(glatos)


detections_path <- file.path('data', 'detections.csv')
detections <- glatos::read_glatos_detections(detections_path)

detections <- glatos::false_detections(detections, tf = 3600)
filtered_detections <- detections %>% filter(passed_filter != FALSE)

detection_events <- glatos::detection_events(filtered_detections, location_col = 'station')
detection_events  

Time Series Analysis & lubridate

Time series show the when, the before, and the after for data points. The lubridate package is especially useful for handling time calculations.

Date-time data can be frustrating to work with in R. R commands for date-times are generally unintuitive and change depending on the type of date-time object being used. Moreover, the methods we use with date-times must be robust to time zones, leap days, daylight savings times, and other time related quirks, and R lacks these capabilities in some situations. Lubridate makes it easier to do the things R does with date-times and possible to do the things R does not.

library(lubridate)

detection_events <- 
    detection_events %>% 
    mutate(detection_interval = lubridate::interval(first_detection, last_detection))

detection_events

Now that we have an interval column, we can go row by row and look at each location to figure out if more than one animal was seen at a station. This is useful to find co-located detections.

for(event in detection_events$event) {
    detection_events$overlaps_with[event] = paste( # We use paste to create a string of other events
        which(detection_events$location == detection_events$location[event] &  # Make sure that the location is the same
            detection_events$event != event &  # Make sure the event is not the same
            lubridate::int_overlaps(detection_events$detection_interval[event], detection_events$detection_interval) 
            # We can use lubridate's int_overlaps function to find the overlapping events
        ),
        collapse=",")
}

detection_events

We can then filter based on whether or not the overlaps_with string is empty

detection_events %>% 
    select(-one_of("detection_interval")) %>% 
    filter(detection_events$overlaps_with != '')  

Summarise

Summarise is a useful function implemented to create a new data frame from running functions on grouped data.

summarise() is typically used on grouped data created by group_by(). The output will have one row for each group.

summary_data <- 
    detection_events %>% 
    group_by(location) %>% 
    summarise(detection_count = sum(num_detections),
              num_unique_tags = n_distinct(animal_id),
              total_residence_time_in_seconds = sum(detection_interval),
              latitude = mean(mean_latitude),
              longitude = mean(mean_longitude))  

summary_data

Plotting

Plotting is necessary for most research and analysis. It’s just an easier way for our brains to digest the information.

We will be using Plotly, a plotting package that allows for exports and interactivity.

We will create an abacus plot first. An abacus plot will show us a timeline of what animal is seen when by the receivers. We put detection_timestamp_utc on the x axis and the animal_id on the y axis.

library(plotly)

abacus_plot <-
    filtered_detections %>% 
    filter(!str_detect(station, "lost")) %>% 
    ggplot(aes(x = detection_timestamp_utc, y = animal_id, color = deploy_lat)) +
    geom_point() +
    ylab("Animal ID") + xlab("Date") + labs(color = "Detection latitude") +
    theme_minimal()

## Static plot
abacus_plot

## Interactive plot using plotly
ggplotly(abacus_plot)

Maps

You can also plot your data against a map to give more geospatial context. Below is an example of a geospatial plot from plotly. The geo list sets the style and parameters for the map and will be passed into the layout function.

The scope of the map will determine what boundaries are drawn. You can also change the projection of the map. (Reference)

geo <- list(
  #   scope = 'north america',
  showland = TRUE,
  landcolor = toRGB("#7BB992"),
  showocean = TRUE,
  oceancolor = toRGB("#A0AAB4"),
  showrivers = TRUE,
  rivercolor = toRGB("#A0AAB4"),
  showlakes = TRUE,
  lakecolor = toRGB("#A0AAB4"),
  showcountries = TRUE,
  resolution = 50,
  center = list(lat = ~median(latitude),
                lon = ~median(longitude)),
  lonaxis = list(range=c(~min(longitude) - 4, ~max(longitude) + 4)),
  lataxis = list(range=c(~min(latitude) - 4, ~max(latitude) + 4))
)


map <- summary_data %>%
    filter(!str_detect(location, "lost")) %>%
    plot_geo(lat = ~latitude, lon = ~longitude, color = ~detection_count, height = 900 )%>%
    add_markers(
        text = ~paste(location, ': ', detection_count,'detections', ' & ', total_residence_time_in_seconds, ' seconds of residence time'),
        hoverinfo = "text",
        size = ~c(detection_count/10)#  + total_residence_time_in_seconds/3600)
    )%>%
    layout(title = "Detections in the Great Lakes", geo = geo)


map  

MapBox

Mapbox is a Live Location Platform that can serve up map tiles for use.

You can create a free account and get an access token at the Mapbox site

Below we set the access token as an environment variable that Plotly can call.

Sys.setenv('MAPBOX_TOKEN' = 'your token here')

From there, we can just call the plot_mapbox() function and pass whatever arguments we need for the map.

mapbox <- summary_data %>%
    filter(!str_detect(location, "lost")) %>%
    plot_mapbox(lat = ~latitude, lon = ~longitude, color = ~detection_count , height = 900) %>%
    add_markers(
        text = ~paste(location, ': ', detection_count,'detections', ' & ', total_residence_time_in_seconds, ' seconds of residence time'),
        hoverinfo = "text",
        size = ~c(detection_count/10  + total_residence_time_in_seconds/3600)
    )%>%
    layout( mapbox = list(zoom = 7,
                           center = list(lat = ~median(latitude),
                                         lon = ~median(longitude))
    ))

mapbox

Mapview

Lets replicate this using the mapview package

library(mapview)
library(sf)

map <-
  summary_data %>% 
  filter(!str_detect(location, "lost")) %>% 
  st_as_sf(coords = c("longitude", "latitude"), crs = 4326) %>% 
  mapview(zcol = "detection_count", cex = "detection_count")  
    
map

Key Points

Multiple tools agregated to allow for data ingestion and filtering.

Provides functionality for creating basic plots from GLATOS formatted data.

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Data Analysis Using GLATOS

Overview