Plot data interactively

This article shows different ways to plot WATLAS-data interactively using plotly and mapview. This is useful when manually checking specific data. plotly allows for a quick interactive overview of the data from any ggplot2, and mapview is specifically designed for spatial data and provides more options for exploration (e.g. specification of what should be shown and different base maps). Check the specific tutorials for more details the plotly or mapview.

Load packages

# packages
library(tools4watlas)
library(ggplot2)
library(plotly)
library(mapview)

Using plotly

Note that the scale bar is not supported at the moment, and plotly will give a warning if the base map contains a scale bar.

# load example data
data <- data_example

# create basemap
bm <- atl_create_bm(data, buffer = 800, scalebar = FALSE)

# plot points and tracks with standard ggplot colours
p <- bm +
  geom_path(
    data = data, aes(x, y, colour = tag),
    linewidth = 0.5, alpha = 0.1, show.legend = TRUE
  ) +
  geom_point(
    data = data, aes(x, y, colour = tag),
    size = 0.5, alpha = 1, show.legend = TRUE
  ) +
  scale_color_discrete(name = paste("N = ", length(unique(data$tag)))) +
  theme(legend.position = "top")

# plot interactively
ggplotly(p, tooltip = c("tag", "x", "y"))

Using mapview

Note that one can change the base map to, for example, a satellite image, by clicking in the layer symbol. Each chunk of code only requires the first chunk with loading the data to be run before and is otherwise independent.

One individual

Subset the individual of choice and transform it into a sf with the additional columns of your choice (can be seen when clicking on the point). Colour the track by selecting the desired parameter as zcol.

# load example data from one tide
data <- data_example[tideID == "2023513"]

# subset data
data_subset <- data[tag == "3063"]

# make data spatial
d_sf <- atl_as_sf(
  data_subset,
  additional_cols = c("species", "datetime", "speed_in", "nbs", "waterlevel")
)

# add track
d_sf_lines <- atl_as_sf(
  data_subset,
  additional_cols = c("species", "datetime", "speed_in", "nbs", "waterlevel"),
  option = "lines"
)

# plot interactive map
mapview(d_sf_lines, zcol = "speed_in", legend = FALSE) +
  mapview(d_sf, zcol = "speed_in")

Multiple individuals

If one wants to plot a lot of data, it is recommended to thin the data first.

# load example data
data <- data_example

# thin the data by subsampling with a 60-second interval
data <- atl_thin_data(
  data = data,
  interval = 60,
  id_columns = c("tag", "species"),
  method = "subsample"
)

# make data spatial
d_sf <- atl_as_sf(data, additional_cols = c("datetime", "species"))

# add track
d_sf_lines <- atl_as_sf(
  data,
  additional_cols = c("species"),
  option = "lines"
)

# plot interactive map
mapview(d_sf_lines, zcol = "tag", legend = FALSE) +
  mapview(d_sf, zcol = "tag")

Multiple species

# load example data from one tide
data <- data_example[tideID == "2023513"]

# thin the data by subsampling with a 60-second interval
data <- atl_thin_data(
  data = data,
  interval = 60,
  id_columns = c("tag", "species"),
  method = "subsample"
)

# make data spatial
d_sf <- atl_as_sf(data, additional_cols = c("datetime", "species"))

# add track
d_sf_lines <- atl_as_sf(
  data,
  additional_cols = c("species"),
  option = "lines"
)

# plot interactive map
mapview(d_sf_lines, zcol = "species", legend = FALSE) +
  mapview(d_sf, zcol = "species")