Interactive Exploration of Data / Animating ggplot with gganimate and gifski

Overview

Teaching: 10 min
Exercises: 0 min

Questions

• How can I explore my data interactively

• How can I avoid ‘overplotting’ my data when performance is a factor?

• How do I animate my tracks?

Objectives

• Isolate one individual and animate his paths between receivers using gganimate and gifski.

Interactive Data exploration with mapview

with spatial data subsetting via spdplyr!

A couple of quick and exciting packages for exploring and manipulating spatial datasets I wanted to mention quickly so you could explore on your own data. First, mapview, which launches a tiled slippy-map with your data objects projected onto it for you. Here we will hand it the spatial data object we made earlier, stS, and without telling mapview anything about it, we’ll get a map we can interact with in our browser.

# Exploring spatial data interactively

library(mapview)  # generates 'slippy' maps from spatial data frames
library(spdplyr)  # this is a bit of a new package, 
                  # this will let us keep our spatial data frame 
                  # and still explore the data in the tidyverse way!



# How long would plotting all of this take? 
# A long time! And the resulting browser window will be overloaded and
# non-functional. So don't pass -too- many points to mapview!

# don't run this!
# mapview(stS)

But wait, our browser won’t be very happy with us throwing 1.5m points at it. We’d better subset! but how to subset a spatial data object? With a new package called spdplyr, author Michael Sumner is implementing dplyr ‘verbs’ on spatial dataframes. So we’ll use both mapview and spdplyr together for a quick example.

# Instead, how could we look at a single animal? 
mapview(stS %>% filter(tag.ID == "A69-1601-30617"))
# 18,000 rows of data
# Quick and snappy.

That isn’t too much data yet. let’s see about all the data points over a given time period, say a month.

# A single month?  # 100,000 rows of data, at the edge of what mapview can do comfortably
# Plotting this one takes a little longer, and the plot may be very slow to interact!
mapview(stS %>% mutate(DateTime = ymd_hms(DateTime)) %>% 
                filter(DateTime > as.POSIXct("2012-05-01") & DateTime < as.POSIXct("2012-06-01")))

Q: Since we don’t have the ability to represent time, what are some optimal subsetting strategies for presenting data to mapview()?

Q: How could you confirm how big a subset of your data will be before you pass it to a plotting or analysis function?

Don’t forget: Investigate how big a dataset you’re going to pass to a tool like mapview! Try not to ‘over-plot’ too many data points on top of one another in a static plot!

Animating Plots

You can extend ggplot with gganimate to generate multiple plots, and stitch them together into an animation. In the glatos package, we’ll use ffmpeg to make videos out of these static images, but you can also generate a gif using gifski.

## Animating plots ####

# Let's pick one animal to follow
st1<-st_summary %>% filter(tag.ID=="A69-1601-30617") 

an1<-bgo %>%
  fortify %>%
  ggplot(aes(x, y, fill=z))+
  geom_raster()+
  scale_fill_etopo()+
  labs(x="Longitude", y="Latitude", fill="Depth")+
  theme_classic()+
  theme(legend.key.width=unit(5, "cm"), legend.position="top")+
  theme(legend.position="top")+
  geom_point(data=st_summary %>%
               as_tibble() %>%
               distinct(lon, lat),
             aes(lon, lat), inherit.aes=F, pch=21, fill="red", size=2)+
  geom_point(data=st1 %>% filter(tag.ID=="A69-1601-30617"),
             aes(lon, lat), inherit.aes=F, colour="purple", size=5)+ # from here, this plot is not an animation yet. an1
  transition_time(date(st1$dt))+
  labs(title = 'Date: {frame_time}')  # Variables supplied to change with animation.

Now that we have the plots in an1, we can animate them by handing them to gganimate::animate()

# an1 is now a list of plot objects but we haven't plotted them.

?gganimate::animate  # To go deeper into gganimate's animate function and its features.

gganimate::animate(an1)

Notably: our fish is doing a lot of portage! The perils of working in a winding river system, or around land masses is that our straight-line interpolations plain look silly when you animate them this way.

Later we’ll use the glatos package to help us dodge land masses better in our transitions.

Key Points