fluxible (version 1.3.6)

flux_plot: Plotting fluxes for visual evaluation

Description

Plots the fluxes, fit and slope in facets with color code indicating quality flags This function takes time to run and is optional in the workflow, but it is still highly recommended to use it to visually check the measurements. Note that 'flux_plot' is specific to the fluxible package and will work best with datasets produced following a fluxible workflow.

Usage

flux_plot(
  slopes_df,
  f_conc = f_conc,
  f_datetime = f_datetime,
  color_discard = "#D55E00",
  color_cut = "#D55E00",
  color_ok = "#009E73",
  color_zero = "#CC79A7",
  scale_x_datetime_args = list(date_breaks = "1 min", minor_breaks = "10 sec",
    date_labels = "%e/%m \n %H:%M"),
  f_ylim_upper = 800,
  f_ylim_lower = 400,
  f_plotname = "",
  f_facetid = "f_fluxid",
  facet_wrap_args = list(ncol = 4, nrow = 3, scales = "free"),
  longpdf_args = list(ncol = 4, width = 29.7, ratio = 1),
  y_text_position = 500,
  print_plot = "FALSE",
  output = "print_only",
  ggsave_args = list()
)

Value

plots of fluxes, with raw concentration data points, fit, slope, and color code indicating quality flags and cuts. The plots are organized in facets according to flux ID, and a text box display the quality flag and diagnostics of each measurement. The plots are returned as a ggplot object if print_plot = TRUE; if print_plot = FALSE it will not return anything but will produce a file according to the output argument.

Arguments

slopes_df

dataset containing slopes, with flags produced by flux_quality

f_conc

column with gas concentration

f_datetime

column with datetime of each data point

color_discard

color for fits with a discard quality flag

color_cut

color for the part of the flux that is cut

color_ok

color for fits with an ok quality flag

color_zero

color for fits with a zero quality flag

scale_x_datetime_args

list of arguments for scale_x_datetime

f_ylim_upper

y axis upper limit

f_ylim_lower

y axis lower limit

f_plotname

filename for the extracted pdf file; if empty, the name of slopes_df will be used

f_facetid

character vector of columns to use as facet IDs. Note that they will be united, and that has to result in a unique facet ID for each measurement. Default is f_fluxid

facet_wrap_args

list of arguments for facet_wrap, also used by facet_wrap_paginate in case output = "pdfpages

longpdf_args

arguments for longpdf in the form list(ncol, width (in cm), ratio)

y_text_position

position of the text box

print_plot

logical, if TRUE it prints the plot as a ggplot object but will take time depending on the size of the dataset

output

"pdfpages", the plots are saved as A4 landscape pdf pages; "ggsave", the plots can be saved with the ggsave function; "print_only" (default) prints the plot without creating a file (independently from print_plot being TRUE or FALSE); "longpdf", the plots are saved as a pdf file as long as needed (faster than "pdfpages")

ggsave_args

list of arguments for ggsave (in case output = "ggsave")

Details

output = "pdfpages" uses facet_wrap_paginate, which tends to be slow and heavy. With output = "longpdf, a long single page pdf is exported. Default width is 29.7 cm (A4 landscape) and is will be as long as it needs to be to fit all the facets. The arguments ncol and ratio in longpdf_args specify the number of columns and the ratio of the facet respectively. This method is considerably faster than pdfpages, because it bypasses facet_wrap_paginate, but is a bit less aesthetic.

Examples

data(co2_conc)
slopes <- flux_fitting(co2_conc, conc, datetime, fit_type = "exp_zhao18")
slopes_flag <- flux_quality(slopes, conc)
flux_plot(slopes_flag, conc, datetime)