0.3plot timeseries

[wlangera]

@shawndove I provide this PR to show how I would visualise time series (plot_ts()).
This is in my eyes more correct.
Let me know what you think.

There are 2 components:

1. Indicator estimates and intervals

• They are calculated per time point (e.g. per year)
• Estimates are indicated by a point
• Intervals are indicated by an error bar if upper and lower limits are available

2. Smooth trends (shown if smoothed_trend = TRUE)

• Calculated over complete time span as a Local Polynomial Regression Fitting (loess)
• for estimates (average trend)
• for upper and lower limits if they are available

The layer order is as follows:

1. Estimate points are most in the foreground
2. Error bars are second in foreground
3. Smooth trends are in background

Here is a reproducible example:

library(b3gbi)

denmark_cube <- process_cube(
  system.file("extdata", "denmark_mammals_cube_eqdgc.csv", package="b3gbi"))
denmark_observed_richness_ts <- pielou_evenness_ts(
  example_cube_1, first_year = 2011, level = "country", region = "Denmark")

plot(denmark_observed_richness_ts)

^{Created on 2024-12-03 with reprex v2.1.1}