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Plot climatologies showing seasonal or interannual patterns

Source: R/plot_timeseries.R
pr_plot_Climatology.Rd

Create bar plots showing monthly or annual climatologies (mean values with error bars) to examine seasonal cycles or interannual variability in plankton indices. Data are aggregated across years (monthly) or within years (annual) to show typical patterns.

Usage

pr_plot_Climatology(df, Trend = "Month", trans = "identity")

Arguments

df

A dataframe from pr_get_Indices() containing timeseries data

Trend

The temporal aggregation for climatology:

  • "Month" - Monthly climatology averaged across all years (shows seasonal patterns)

  • "Year" - Annual means for each year (shows interannual variability)

trans

Transformation for the y-axis scale:

  • "identity" - No transformation (default)

  • "log10" - Log base 10 transformation (useful for abundance data)

  • "sqrt" - Square root transformation

  • Any other transformation accepted by ggplot2::scale_y_continuous()

Value

A ggplot2 bar plot with error bars showing climatology patterns

Details

Monthly Climatology (Trend = "Month")

Calculates the mean value for each month across all years in the dataset. This shows the typical seasonal cycle, useful for identifying:

  • Spring phytoplankton blooms

  • Summer stratification effects

  • Winter mixing impacts

  • Seasonal migration patterns in zooplankton

Annual Climatology (Trend = "Year")

Calculates the mean value for each calendar year. This shows year-to-year variability, useful for identifying:

  • Long-term trends (increasing or decreasing)

  • Regime shifts

  • Responses to climate oscillations (e.g., ENSO, SAM)

  • Extreme years (e.g., marine heatwaves)

Error bars represent standard error of the mean (±SE), calculated as SD/√N where N is the number of observations.

For NRS data, values are averaged across depths if present.

See also

pr_plot_Trends() for alternative climatology visualisation with trend lines, pr_plot_TimeSeries() for raw time series plots

Examples

df <- pr_get_Indices(Survey = "NRS", Type = "Phytoplankton") %>%
dplyr::filter(Parameters == "PhytoBiomassCarbon_pgL", StationCode %in% c("NSI", "PHB"))

monthly <- pr_plot_Climatology(df, Trend = "Month")

df <- pr_get_Indices(Survey = "CPR", Type = "Zooplankton") %>%
        dplyr::filter(Parameters == "ZoopAbundance_m3")
annual <- pr_plot_Climatology(df, Trend = "Year")