SWMPr (version 2.5.0)

plot_summary: Plot graphical summaries of SWMP data

Description

Plot graphical summaries of SWMP data for individual parameters, including seasonal/annual trends and anomalies

Usage

plot_summary(swmpr_in, ...)
# S3 method for swmpr
plot_summary(
  swmpr_in,
  param,
  colsleft = c("lightblue", "lightgreen"),
  colsmid = "lightblue",
  colsright = c("lightblue", "lightgreen", "tomato1"),
  base_size = 11,
  years = NULL,
  plt_sep = FALSE,
  sum_out = FALSE,
  fill = c("none", "monoclim", "interp"),
  ...
)

Value

A graphics object (Grob) of multiple ggplot objects, otherwise a list of individual ggplot objects if plt_sep = TRUE or a list with data frames of the summarized data if sum_out = TRUE.

Arguments

swmpr_in

input swmpr object

...

additional arguments passed to other methods

param

chr string of variable to plot

colsleft

chr string vector of length two indicating colors for left plots

colsmid

chr string vector of length one indicating colors for middle plots

colsright

chr string vector of length three indicating colors for right plots

base_size

numeric for text size

years

numeric vector of starting and ending years to plot, default all

plt_sep

logical if a list is returned with separate plot elements

sum_out

logical if summary data for the plots is returned

fill

chr string indicating if missing monthly values are left as is ('none', default), replaced by long term monthly averages ('monoclim'), or linearly interpolated using na.approx

Details

This function creates several graphics showing seasonal and annual trends for a given swmp parameter. Plots include monthly distributions, monthly anomalies, and annual anomalies in multiple formats. Anomalies are defined as the difference between the monthly or annual average from the grand mean. Monthly anomalies are in relation to the grand mean for the same month across all years. All data are aggregated for quicker plotting. Nutrient data are based on monthly averages, wheras weather and water quality data are based on daily averages. Cumulative precipitation data are based on the daily maximum.

Individual plots can be obtained if plt_sep = TRUE. Individual plots for elements one through six in the list correspond to those from top left to bottom right in the combined plot.

Summary data for the plots can be obtained if sum_out = TRUE. This returns a list with three data frames with names sum_mo, sum_moyr, and sum_mo. The data frames match the plots as follows: sum_mo for the top left, bottom left, and center plots, sum_moyr for the top right and middle right plots, and sum_yr for the bottom right plot.

Missing values can be filled using the long-term average across years for each month (fill = 'monoclim') or as a linear interpolation between missing values using na.approx (fill = 'interp'). The monthly average works well for long gaps, but may not be an accurate representation of long-term trends, i.e., real averages may differ early vs late in the time series if a trend exists. The linear interpolation option is preferred for small gaps.

See Also

ggplot

Examples

## import data
data(apacpnut)
dat <- qaqc(apacpnut)

## plot
plot_summary(dat, param = 'chla_n', years = c(2007, 2013))

## get individaul plots
plots <- plot_summary(dat, param = 'chla_n', years = c(2007, 2013), plt_sep = TRUE)

plots[[1]] # top left
plots[[3]] # middle
plots[[6]] # bottom right

## get summary data
plot_summary(dat, param = 'chla_n', year = c(2007, 2013), sum_out = TRUE)