Basic Animation
Overview
Teaching: 20 min
Exercises: 0 minQuestions
How do I set up my data extract for animation?
How do I animate my animal movements?
Objectives
OTN Node
Static plots are excellent tools and are appropriate a lot of the time, but there are instances where something extra is needed to demonstrate your interesting fish movements. This is where plotting animated tracks can be a useful tool. In this lesson we will explore how to take data from your OTN-style detection extract documents and animate the journey of one fish between stations.
Getting our Packages
If not done already, we will first need to ensure we have all the required packages activated in our R session.
library(glatos)
library(sf)
library(mapview)
library(plotly)
library(gganimate)
library(ggmap)
library(tidyverse)
Preparing our Dataset
Before we can animate, we need to do some preprocessing on our dataset. For this animation we will be using detection events (a format we learned about in the glatos lessons) so we will need to first create that variable. To do this, we will read in our data using the read_otn_detections function from glatos and check for false detections with the false_detections function.
For the purposes of this lesson we will assume that any detection that did not pass the filter is a false detection, and will filter them out using filter(passed_filter != FALSE). It is important to note that for real data you will need to look over these detections to be sure they are truly false.
Finally, we use the detection_events function with station as the location_col argument to get our detection events.
unzip('nsbs_matched_detections_2022.zip', overwrite = TRUE)
detection_events <- #create detections event variable
read_otn_detections('nsbs_matched_detections_2022/nsbs_matched_detections_2022.csv') %>%
false_detections(tf = 3600) %>% #find false detections
dplyr::filter(passed_filter != FALSE) %>%
detection_events(location_col = 'station', time_sep=3600)
There is extra information in detection_events (such as the number of detections per event and the residence time in seconds) that can make some interesting plots, but for our visualization we only need the animal_id, mean_longitude, mean_latitude, and first_detection columns. So we will use the dplyr select function to create a dataframe with just those columns.
plot_data <- detection_events %>%
dplyr::select(animal_id, mean_longitude,mean_latitude, first_detection)
Additionally, animating many animal tracks can be computationally intensive as well as create a potentially confusing plot, so for this lesson we will only be plotting one fish. We well subset our data by filtering where the animal_id is equal to NSBS-1393342-2021-08-10.
one_fish <- plot_data[plot_data$animal_id == "NSBS-1393342-2021-08-10",]
Preparing a Static Plot
Now that we have our data we can begin to create our plot. We will start with creating a static plot and then once happy with that, we will animate it.
The first thing we will do for our plot is download the basemap. This will provide the background for our plot. To do this we will use the get_stadiamap function from ggmap. This function gets a Stamen Map based off a bounding box that we provide. “Stamen” is the name of the service that provides the map tiles, but it was recently bought by Stadia, so the name of the function has changed. To create the bounding box we will pass a vector of four values to the argument bbox ; those four values represent the left, bottom, right, and top boundaries of the map.
To determine which values are needed we will use the min and max function on the mean_longitude and mean_latitude columns of our one_fish variable. min(one_fish$mean_longitude) will be our left-most bound, min(one_fish$mean_latitude) will be our bottom bound, max(one_fish$mean_longitude) will be our right-most bound, and max(one_fish$mean_latitude) will be our top bound. This gives most of what we need for our basemap but we can further customize our plot with maptype which will change what type of map we use, crop which will crop raw map tiles to the specified bounding box, and zoom which will adjust the zoom level of the map.
A note on maptype
The different values you can put for maptype: “terrain”, “terrain-background”, “terrain-labels”, “terrain-lines”, “toner”, “toner-2010”, “toner-2011”, “toner-background”, “toner-hybrid”, “toner-labels”, “toner-lines”, “toner-lite”, “watercolor”
basemap <-
get_stadiamap(
bbox = c(left = min(one_fish$mean_longitude),
bottom = min(one_fish$mean_latitude),
right = max(one_fish$mean_longitude),
top = max(one_fish$mean_latitude)),
maptype = "stamen_toner_lite",
crop = FALSE,
zoom = 7)
ggmap(basemap)
Now that we have our basemap ready we can create our static plot. We will store our plot in a variable called otn.plot so we can access it later on.
To start our plot we will call the ggmap function and pass it our basemap as an argument. To make our detection locations we will then call geom_point, supplying one_fish as the data argument. For the aesthetic we will make the x argument equal to mean_longitude and the y argument will be mean_latitude. This will orient our map and data properly.
We will then call geom_path to connect those detections supplying one_fish as the data argument. For the aesthetic x will again be mean_longitude and y will be mean_latitude.
Lastly, we will use the labs function to add context to our plot including a title, a label for the x axis, and a label for the y axis. We are then ready to view our graph by calling ggplotly with otn.plot as the argument!
otn.plot <-
ggmap(basemap) +
geom_point(data = one_fish, aes(x = mean_longitude, y = mean_latitude), size = 2) +
geom_path(data = one_fish, aes(x = mean_longitude, y = mean_latitude)) +
labs(title = "NSBS Animation",
x = "Longitude", y = "Latitude", color = "Tag ID")
ggplotly(otn.plot)
Animating our Static Plot
Once we have a static plot we are happy with, we are ready for the final step of animating it! We will use the gganimate package for this, since it integrates nicely with ggmap.
To animate our plot we update our otn.plot variable by using it as our base, then add a label for the dates to go along with the animation. We then call transition_reveal, which is a function from gganimate that determines how to create the transitions for the animations. There are many transitions you can use for animations with gganimate but transition_reveal will calculate intermediary values between time observations. For our plot we will pass transition_reveal the first_detection information. We will finally use the functions shadow_mark with the arguments of past equal to TRUE and future equal to FALSE. This makes the animation continually show the previous data (a track) but not the future data yet to be seen (allowing it to be revealed as the animation progresses).
Finally, to see our new animation we call the animate function with otn.plot as the argument.
otn.plot <-
otn.plot +
labs(subtitle = 'Date: {format(frame_along, "%d %b %Y")}') +
transition_reveal(first_detection) +
shadow_mark(past = TRUE, future = FALSE)
animate(otn.plot)
ACT Node
Static plots are excellent tools and are appropriate a lot of the time, but there are instances where something extra is needed to demonstrate your interesting fish movements. This is where plotting animated tracks can be a useful tool. In this lesson we will explore how to take data from your OTN-style detection extract documents and animate the journey of one fish between stations.
Getting our Packages
If not done already, we will first need to ensure we have all the required packages activated in our R session.
library(glatos)
library(sf)
library(mapview)
library(plotly)
library(gganimate)
library(ggmap)
library(tidyverse)
Preparing our Dataset
Before we can animate, we need to do some preprocessing on our dataset. For this animation we will be using detection events (a format we learned about in the glatos lessons) so we will need to first create that variable. To do this, we will read in our data using the read_otn_detections function from glatos and check for false detections with the false_detections function.
For the purposes of this lesson we will assume that any detection that did not pass the filter is a false detection, and will filter them out using filter(passed_filter != FALSE). It is important to note that for real data you will need to look over these detections to be sure they are truly false.
Finally, we use the detection_events function with station as the location_col argument to get our detection events.
detection_events <- #create detections event variable
read_otn_detections('proj58_matched_detections_2016.csv') %>% # reading detections
false_detections(tf = 3600) %>% #find false detections
filter(passed_filter != FALSE) %>%
detection_events(location_col = 'station', time_sep=3600)
There is extra information in detection_events (such as the number of detections per event and the residence time in seconds) that can make some interesting plots, but for our visualization we only need the animal_id, mean_longitude, mean_latitude, and first_detection columns. So we will use the dplyr select function to create a dataframe with just those columns.
plot_data <- detection_events %>%
dplyr::select(animal_id, mean_longitude,mean_latitude, first_detection)
Additionally, animating many animal tracks can be computationally intensive as well as create a potentially confusing plot, so for this lesson we will only be plotting one fish. We well subset our data by filtering where the animal_id is equal to PROJ58-1218508-2015-10-13.
one_fish <- plot_data[plot_data$animal_id == "PROJ58-1218508-2015-10-13",]
Preparing a Static Plot
Now that we have our data we can begin to create our plot. We will start with creating a static plot and then once happy with that, we will animate it.
The first thing we will do for our plot is download the basemap. This will provide the background for our plot. To do this we will use the get_stadiamap function from ggmap. This function gets a Stamen Map based off a bounding box that we provide. “Stamen” is the name of the service that provides the map tiles, but it was recently bought by Stadia, so the name of the function has changed. To create the bounding box we will pass a vector of four values to the argument bbox ; those four values represent the left, bottom, right, and top boundaries of the map.
To determine which values are needed we will use the min and max function on the mean_longitude and mean_latitude columns of our one_fish variable. min(one_fish$mean_longitude) will be our left-most bound, min(one_fish$mean_latitude) will be our bottom bound, max(one_fish$mean_longitude) will be our right-most bound, and max(one_fish$mean_latitude) will be our top bound. This gives most of what we need for our basemap but we can further customize our plot with maptype which will change what type of map we use, crop which will crop raw map tiles to the specified bounding box, and zoom which will adjust the zoom level of the map.
basemap <-
get_stadiamap(
bbox = c(left = min(one_fish$mean_longitude),
bottom = min(one_fish$mean_latitude),
right = max(one_fish$mean_longitude),
top = max(one_fish$mean_latitude)),
maptype = "stamen_toner_lite",
crop = FALSE,
zoom = 8)
ggmap(basemap)
Now that we have our basemap ready we can create our static plot. We will store our plot in a variable called act.plot so we can access it later on.
To start our plot we will call the ggmap function and pass it our basemap as an argument. To make our detection locations we will then call geom_point, supplying one_fish as the data argument. For the aesthetic we will make the x argument equal to mean_longitude and the y argument will be mean_latitude. This will orient our data properly.
We will then call geom_path to connect those detections supplying one_fish as the data argument. For the aesthetic x will again be mean_longitude and y will be mean_latitude.
Lastly, we will use the labs function to add context to our plot including a title, a label for the x axis, and a label for the y axis. We are then ready to view our graph by calling ggplotly with act.plot as the argument!
act.plot <-
ggmap(base) +
geom_point(data = one_fish, aes(x = mean_longitude, y = mean_latitude, group = animal_id, color = animal_id), size = 2) +
geom_path(data = one_fish, aes(x = mean_longitude, y = mean_latitude, group = animal_id, color = animal_id)) +
labs(title = "ACT animation",
x = "Longitude", y = "Latitude", color = "Tag ID")
ggplotly(act.plot)
Animating our Static Plot
Once we have a static plot we are happy with, we are ready for the final step of animating it! We will use the gganimate package for this, since it integrates nicely with ggmap.
To animate our plot we update our act.plot variable by using it as our base, then add a label for the dates to go along with the animation. We then call transition_reveal, which is a function from gganimate that determines how to create the transitions for the animations. There are many transitions you can use for animations with gganimate but transition_reveal will calculate intermediary values between time observations. For our plot we will pass transition_reveal the first_detection information. We will finally use the functions shadow_mark with the arguments of past equal to TRUE and future equal to FALSE. This makes the animation continually show the previous data (a track) but not the future data yet to be seen (allowing it to be revealed as the animation progresses).
Finally, to see our new animation we call the animate function with act.plot as the argument.
act.plot <-
act.plot +
labs(subtitle = 'Date: {format(frame_along, "%d %b %Y")}') +
transition_reveal(first_detection) +
shadow_mark(past = TRUE, future = FALSE)
animate(act.plot)
Key Points