Plot data

Johannes Krietsch

This article shows different ways on how to plot WATLAS data. In this example we use a simple basemap with the extend of the data, but any other type could be used too (see article: Create a basemap).

It first presents some examples of how to plot data grouped by tag ID, then by species and then how to make single plots with different options to quickly check the data, and how to plot heatmaps of the data. These examples can be customized as desired and should jsut give a quick starting point.

Load packages

# packages
library(tools4watlas)
library(ggplot2)
library(viridis)
library(scales)

Plot grouped by tag ID

Simply colour points and the path with tag ID.

# load example data
data <- data_example

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

# plot points and tracks with standard ggplot colours
bm +
  geom_path(
    data = data, aes(x, y, colour = tag),
    linewidth = 0.5, alpha = 0.1, show.legend = FALSE
  ) +
  geom_point(
    data = data, aes(x, y, colour = tag),
    size = 0.5, alpha = 1, show.legend = FALSE
  )

# plot points and tracks with viridis colour scale
bm +
  geom_path(
    data = data, aes(x, y, colour = tag),
    linewidth = 0.5, alpha = 0.1, show.legend = FALSE
  ) +
  geom_point(
    data = data, aes(x, y, colour = tag),
    size = 0.5, alpha = 1, show.legend = FALSE
  ) +
  scale_color_viridis(discrete = TRUE)

Plot grouped by species

tools4watlas includes specific species colours in the atl_spec_cols() function and species labels in the atl_spec_labs() function.

# load example data
data <- data_example

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

# plot points and tracks
bm +
  geom_path(
    data = data, aes(x, y, colour = species),
    linewidth = 0.5, alpha = 0.5, show.legend = FALSE
  ) +
  geom_point(
    data = data, aes(x, y, color = species),
    size = 1, alpha = 1, show.legend = TRUE
  ) +
  scale_color_manual(
    values = atl_spec_cols(),
    labels = atl_spec_labs("multiline"),
    name = ""
  ) +
  guides(colour = guide_legend(
    nrow = 1, override.aes = list(size = 7, pch = 16, alpha = 1)
  )) +
  theme(
    legend.position = "top",
    legend.justification = "center",
    legend.key = element_blank(),
    legend.background = element_rect(fill = "transparent")
  )

Plot single plots by tag ID

Template for a customizable plot of one tag

# load example data
data <- data_example

# subset bar-tailed godwit
data_subset <- data[species == "bar-tailed godwit"]

# create basemap
bm <- atl_create_bm(data_subset, buffer = 800)

# plot points and tracks
bm +
  geom_path(
    data = data_subset, aes(x, y),
    linewidth = 0.5, alpha = 0.1, show.legend = FALSE
  ) +
  geom_point(
    data = data_subset, aes(x, y),
    size = 0.5, alpha = 1, show.legend = FALSE
  )

Simple preset plots to check the data

The atl_check_tag() function can be used to quickly check the data from one tag. It provides five different options for colouring the track:

• "datetime": Datetime along the track
• "nbs": Number of receiver (base) stations that contributed to the localization
• "var": Error as maximal variance of varx and vary
• "speed_in": Speed in m/s
• "gap": Gaps coloured by time and as point size

The scale can be coloured with all options from viridis (default: "A") by specifying the scale_option. The scale can also be transformed with scale_trans for example using "log" or "sqrt". First and last point can be highlighted (highlight_first and highlight_last) or just a specific number of points from the beginning or end of the track can be selected (first_n or last_n).

# path to csv with filtered data
data_path <- system.file(
  "extdata", "watlas_data_filtered.csv",
  package = "tools4watlas"
)

# load data
data <- fread(data_path, yaml = TRUE)

# subset bar-tailed godwit
data <- data[species == "bar-tailed godwit"]

# plot option datetime
atl_check_tag(
  data,
  option = "datetime",
  highlight_first = TRUE, highlight_last = TRUE
)

# plot option speed_in
atl_check_tag(data, option = "speed_in")

# plot option nbs
atl_check_tag(data, option = "nbs")

# plot option sd
atl_check_tag(data, option = "var")

# plot option gap
atl_check_tag(data, option = "gap", scale_trans = "log")

Plot heatmaps

Here we round the data (e.g. to 200 m) and then plot the number of positions per location.

All positions

This is a quick way to get an overview of the data.

# load example data
data <- data_example

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

# round data to 1 ha (100x100 meter) grid cells
data[, c("x_round", "y_round") := list(
  plyr::round_any(x, 100),
  plyr::round_any(y, 100)
)]

# N by location
data_subset <- data[, .N, by = c("x_round", "y_round")]

# plot heatmap
bm +
  geom_tile(
    data = data_subset, aes(x_round, y_round, fill = N),
    linewidth = 0.1, show.legend = TRUE
  ) +
  scale_fill_viridis(
    option = "A", discrete = FALSE, trans = "log10", name = "N positions",
    breaks = trans_breaks("log10", function(x) 10^x),
    labels = trans_format("log10", math_format(10^.x)),
    direction = -1
  )

Positions by species

We group the data by species (or whatever group we desire) and then make a plot for each of those groups.

# N by location and species
data_subset <- data[, .N, by = c("x_round", "y_round", "species")]

# plot heatmap by species
bm +
  geom_tile(
    data = data_subset, aes(x_round, y_round, fill = N),
    linewidth = 0.1, show.legend = TRUE
  ) +
  scale_fill_viridis(
    option = "A", discrete = FALSE, trans = "log10", name = "N positions",
    breaks = trans_breaks("log10", function(x) 10^x),
    labels = trans_format("log10", math_format(10^.x)),
    direction = -1
  ) +
  facet_wrap(~species, labeller = as_labeller(atl_spec_labs("singleline")))