Caribou DataViz
Julian Avila-Jimenez
2/8/2020
Caribou
Import the data
library(tidyverse)
library(maps)
library(lubridate)
library(geosphere)
theme_set(theme_light())
caribou<- tidytuesdayR::tt_load("2020-06-23")##
## Downloading file 1 of 2: `locations.csv`
## Downloading file 2 of 2: `individuals.csv`Individuals
Ind<- caribou$individuals
Loc<- caribou$locations
Ind %>% summarize(across(sex:study_site,list(~mean(!is.na(.)))))## # A tibble: 1 x 6
## sex_1 life_stage_1 pregnant_1 with_calf_1 death_cause_1 study_site_1
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.234 0.0664 0.294 0.189 1Explore the spatial information
Ind %>%
count(study_site,
deploy_on_longitude,
deploy_on_latitude,
sort=T) %>%
ggplot(aes(deploy_on_longitude,
deploy_on_latitude,
size= n))+
geom_point(color="deepskyblue4")
Here we have an outlayer below 30 degree latitude
Ind %>%
filter(deploy_on_latitude>40) %>%
count(study_site,
deploy_on_longitude,
deploy_on_latitude,
sort=T) %>%
ggplot(aes(deploy_on_longitude,
deploy_on_latitude,
size= n,
color= study_site))+
geom_point(color="deepskyblue4")+
labs(x="Longitude",
y="Latitude")+
scale_size_continuous(guide=F)
much better!
let’s look to the Locations data
Loc %>%
ggplot(aes(longitude,
latitude,
color= study_site))+
geom_point()
A lot of points!!
Animal movement over time
Let’s take just one animal track and explore his seasonal movement
ExampleAnimal<-Loc %>%
arrange(timestamp) %>%
filter(animal_id== sample(unique(animal_id),1))
ExampleAnimal %>%
mutate(quarter= as.Date(floor_date(timestamp, "quarter"))) %>%
ggplot(aes(longitude,
latitude,
color= factor(floor_date(timestamp, "quarter"))))+
geom_path()+
labs(title = "Path of one caribou over time",
color="Quarter",
caption = "Source: BC Ministry of Environment (2014)")+
theme(legend.position="bottom")+
facet_wrap(~ quarter)
Total distance movement
locations_time<- Loc %>%
group_by(animal_id) %>%
mutate(last_lat= lag(latitude),
last_long= lag(longitude),
hours= as.numeric(difftime(timestamp,
lag(timestamp),
unit= "hours")),
km= distHaversine(cbind(longitude, latitude),
cbind(last_long, last_lat))/1000,
kph= km/ hours) %>%
ungroup()
locations_time %>%
filter(hours <=8,
hours >=.5) %>% # just take the data with tracks between 0.5 and 8 hours
ggplot(aes(kph))+
geom_histogram(fill="deepskyblue4")+
scale_x_log10(labels= scales::comma)+
labs(title= "On average, how fast caribou travel?",
caption = "Source: BC Ministry of Environment (2014)",
y="",
x="Km/h")
Caribou average speed
by_animal<-
locations_time %>%
filter(hours <=8,
hours >=.5) %>% # just take the data with tracks between 0.5 and 8 hours
group_by(animal_id, study_site) %>%
summarise(start= min(timestamp),
end= max(timestamp),
num_points= n(),
avg_speed= mean(kph[hours<=8], na.rm=T)) %>%
ungroup() %>%
arrange(desc(num_points)) %>%
filter(num_points>=10)
by_animal %>%
ggplot(aes(num_points, avg_speed))+
geom_point(color="deepskyblue4")+
scale_x_log10()+
expand_limits(y=0)+
labs(y = "Average speed",
x= "Observations by Caribou",
title = "Caribou Average Speed",
caption = "Source: BC Ministry of Environment (2014)")
Finaly the moveViz package!!
library(moveVis)
library(move)
data("move_data", package = "moveVis") # move class object
# if your tracks are present as data.frames, see df2move() for conversion
Loc2<- Loc %>%
filter(animal_id=="GR_C15") %>%
as.data.frame()
Locq<- df2move(Loc2,
proj = "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0",
x = "longitude", y = "latitude",
time = "timestamp", track_id = "animal_id")
# align move_data to a uniform time scale
m <- align_move(Locq, res = 1, unit = "days")
# create spatial frames with a OpenStreetMap watercolour map
frames <- frames_spatial(m, path_colours = c("red"),
map_service = "osm",
map_type = "watercolor",
alpha = 0.5) %>%
add_labels(x = "Longitude",
y = "Latitude") %>% # add some customizations, such as axis labels
add_northarrow() %>%
add_scalebar() %>%
add_timestamps(m,
type = "label")## Checking temporal alignment...
## Processing movement data...
## Approximated animation duration: ˜ 14.4s at 25 fps for 360 frames
## Retrieving and compositing basemap imagery...
## Assigning raster maps to frames...
## Creating frames...frames[[100]] # preview one of the frames, e.g. the 100th frame
# animate frames
animate_frames(frames, out_file = "moveVis.gif")## Rendering animation...
## Approximated animation duration: ˜ 14.4s at 25 fps for 360 frames##
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## Finalizing encoding... done!