View source: R/threshold_criteria_plot.R
| threshold_criteria_plot | R Documentation | 
Observed data compared against user-defined water quality thresholds
threshold_criteria_plot(swmpr_in, ...)
## S3 method for class 'swmpr'
threshold_criteria_plot(
  swmpr_in,
  param = NULL,
  rng = NULL,
  thresholds = NULL,
  threshold_labs = c("Good", "Fair", "Poor"),
  threshold_cols = c("#ABD9E9", "#FFFFCC", "#FEC596"),
  crit_threshold = NULL,
  log_trans = FALSE,
  monthly_smooth = FALSE,
  plot_title = FALSE,
  ...
)
| swmpr_in | input swmpr object | 
| ... | additional arguments passed to other methods. See  | 
| param | chr string of the variable to plot | 
| rng | num, years to include in the plot. This variable can either be one year (e.g.,  | 
| thresholds | numeric vector, numeric criteria that will be plotted in the background | 
| threshold_labs | chr vector of labels for categories created by  | 
| threshold_cols | chr vector of color values for categories created by  | 
| crit_threshold | num, value at which the critical threshold line should be plotted. Typically the same value used to establish the 'Poor' threshold. | 
| log_trans | logical, should y-axis be log? Defaults to  | 
| monthly_smooth | logical, calculate a monthly average? Defaults to  | 
| plot_title | logical, should the station name be included as the plot title? Defaults to  | 
This function visualizes exceedances of numeric criteria which are specified using thresholds. Suggested numeric criteria for several parameters (dissolved oxygen, dissolved inorganic phosphorus, dissolved inorganic nitrogen, and chlorophyll-a) can be found in the USEPA National Coastal Condition Report (2012).
If the parameter of interest does not have numeric criteria, then threshold_percentile_plot is recommended.
Returns a ggplot object
Julie Padilla
United States Environmental Protection Agency (USEPA). 2012. "National Coastal Condition Report IV." https://www.epa.gov/national-aquatic-resource-surveys/national-coastal-condition-report-iv-2012
ggplot,y_labeler
data(apacpwq)
dat_wq <- apacpwq
dat_wq <- qaqc(dat_wq, qaqc_keep = c(0, 3, 5))
## Due to the volume of instantaneous data, these plots are a bit slow
x <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , rng = 2012
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Poor', 'Fair', 'Good')
                 , monthly_smooth = TRUE
                 , threshold_cols = c('#FEC596', '#FFFFCC', '#ABD9E9'))
y <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Poor', 'Fair', 'Good')
                 , threshold_cols = c('#FEC596', '#FFFFCC', '#ABD9E9'))
z <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , rng = 2012
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Poor', 'Fair', 'Good')
                 , threshold_cols = c('#FEC596', '#FFFFCC', '#ABD9E9')
                 , monthly_smooth = TRUE)
## A few examples with only two thresholds
x1 <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , rng = 2012
                 , thresholds = c(2, 2)
                  # A dummy blank ('') value must be added as a threshold label
                 , threshold_labs = c('Poor', '', 'Good')
                 , threshold_cols = c('#FEC596', '#FFFFCC', '#ABD9E9')
                 , monthly_smooth = TRUE)
y1 <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , rng = 2012
                 , thresholds = c(5, 5)
                 # A dummy blank ('') value must be added as a threshold label
                 , threshold_labs = c('Poor', '', 'Good')
                 , threshold_cols = c('#FEC596', '#FEC596', '#ABD9E9')
                 , monthly_smooth = TRUE)
z1 <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , rng = 2012
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Poor', 'Good', 'Poor')
                 , threshold_cols = c('#FEC596', '#ABD9E9', '#FEC596')
                 , monthly_smooth = TRUE)
data(apacpnut)
dat_nut <- apacpnut
dat_nut <- qaqc(dat_nut, qaqc_keep = c(0, 3, 5))
dat_nut <- rem_reps(dat_nut)
x2 <-
  threshold_criteria_plot(dat_nut, param = 'chla_n'
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Good', 'Fair', 'Poor'))
y2 <-
  threshold_criteria_plot(dat_nut, param = 'chla_n'
                 , rng = 2012
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Good', 'Fair', 'Poor'))
## Nutrient plots are not capable of accidentally displaying any kind of smooth
z2 <-
  threshold_criteria_plot(dat_nut, param = 'chla_n'
                 , rng = 2012
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Good', 'Fair', 'Poor')
                 , monthly_smooth = TRUE)
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