hm_plot: Methods to easily use 'ggplot2' or 'plotly' (interactive)

In hydrotoolbox: Hydrological Tools for Handling Hydro-Meteorological Data Records

Methods to easily use ggplot2 or plotly (interactive)

Description

This method allows you to make plots (using simple and expressive arguments) of the variables contained inside an hydromet_XXX class object. The plot outputs can be static (ggplot2) or dynamic (plotly).

Usage

hm_plot(
  obj,
  slot_name,
  col_name,
  interactive = FALSE,
  line_type = NULL,
  line_color = NULL,
  line_size = NULL,
  line_alpha = NULL,
  x_lab = "date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  dual_yaxis = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)

## S4 method for signature 'hydromet_station'
hm_plot(
  obj,
  slot_name,
  col_name,
  interactive = FALSE,
  line_type = NULL,
  line_color = NULL,
  line_size = NULL,
  line_alpha = NULL,
  x_lab = "date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  dual_yaxis = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)

## S4 method for signature 'hydromet_compact'
hm_plot(
  obj,
  slot_name,
  col_name,
  interactive = FALSE,
  line_type = NULL,
  line_color = NULL,
  line_size = NULL,
  line_alpha = NULL,
  x_lab = "date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  dual_yaxis = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)

Arguments

obj	a valid hydromet_XXX class object.
slot_name	string vector with the name of the slot(s) to use in plotting.
col_name	list containing the column name of the variables to plot. Every element inside the list belongs to the previous defined slot(s).
interactive	logical. Default value, FALSE, will return a ggplot2 class object. Otherwise you will get a plotly one.
line_type	string with the name of the line dash type (ggplot2) or mode in the plotly case. ggplot2: 'solid' (default value), 'twodash', 'longdash', 'dotted', 'dotdash', 'dashed' or 'blank'. plotly: 'lines' (default value), 'lines+markers' or 'markers'. NOTE: when using scatter plot this arguments goes through the shape argument (in geom_point()) as numeric.
line_color	string with a valid color name. See 'colors()' or Rcolor document.
line_size	numeric vector containing the size of every line to plot. If you use the NULL value it will return the plots with default(s) for either ggplot2 or plotly.
line_alpha	numeric vector with line(s) transparency. From 0 (invisible) to 1.
x_lab	string with x axis label. Default is 'Date'.
y_lab	string with y axis label. In case you use dual_yaxis argument you must supply both c('ylab', 'y2lab').
title_lab	string with the title of the plot. Default is a plot without title.
legend_lab	string vector with plot label(s) name(s).
dual_yaxis	string vector suggesting which variables are assign either to the 'left' or 'right' y axis.
from	string value for 'Date' class or POSIXct(lt) class for date-time data with the starting Date. You can use 'from' without 'to'. In this case you will subset your data 'from' till the end.
to	string value for 'Date' class or POSIXct(lt) class for date-time data with the ending Date. You can use 'to' without 'from'. In this case you will subset your data from the beginning till 'to'.
scatter	string vector (of length two) suggesting which variables goes in the 'x' and 'y' axis respectively. Valid character entries are 'x' and 'y'.

Value

A ggplot2 or plotly object.

Functions

• hm_plot(hydromet_station): plot method for station class

• hm_plot(hydromet_compact): plot method for compact class

Examples

## Not run: 
# lets work with the cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
           )

# let's start by making a single variable static plot
hm_plot(obj = hm_cuevas, slot_name = 'tair',
        col_name = list('tair(°C)') )

# we add labels, change color, line type and we focus
# on specific date range
hm_plot(obj = hm_cuevas, slot_name = 'tair',
        col_name = list('tair(°C)'),
        line_type = 'longdash',
        line_color = 'dodgerblue',
        x_lab = 'Date time', y_lab = 'T(°C)',
        title_lab = 'Hourly temperature at Cuevas',
        legend_lab = 'Tair',
        from = ISOdate(2020, 7, 1),
        to = ISOdate(2020, 7, 5))

# compare air with soil temperature
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'tsoil'),
        col_name = list('tair(°C)', 'tsoil(°C)'),
        line_type = c('longdash', 'solid'),
        line_color = c('dodgerblue', 'tan4'),
        x_lab = 'Date time', y_lab = 'T(°C)',
        title_lab = 'Hourly temperature at Cuevas',
        legend_lab = c('Tair', 'Tsoil'),
        from = ISOdate(2020, 7, 1),
        to = ISOdate(2020, 7, 5))

# let's add relative humidity on the right y-axis
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'tsoil', 'rh'),
        col_name = list('tair(°C)', 'tsoil(°C)', 'rh(%)'),
        line_type = c('longdash', 'solid', 'solid'),
        line_color = c('dodgerblue', 'tan4', 'red'),
        x_lab = 'Date time', y_lab = c('T(°C)', 'RH(%)'),
        title_lab = 'Hourly meteo data at Cuevas',
        legend_lab = c('Tair', 'Tsoil', 'RH'),
        dual_yaxis = c('left', 'left', 'right'),
        from = ISOdate(2020, 7, 1),
        to = ISOdate(2020, 7, 5))

# we decide to analize the previous variables in detail
# with a dynamic plot
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'tsoil', 'rh'),
        col_name = list('tair(°C)', 'tsoil(°C)', 'rh(%)'),
        line_color = c('dodgerblue', 'tan4', 'red'),
        x_lab = 'Date time', y_lab = c('T(°C)', 'RH(%)'),
        title_lab = 'Hourly meteo data at Cuevas',
        legend_lab = c('Tair', 'Tsoil', 'RH'),
        dual_yaxis = c('left', 'left', 'right'),
        interactive = TRUE)
# click on the Zoom icon and play a little...


# suppose now that we want to make a scatter plot to show
# the negative correlation between air temperature and
# relative humidity
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'rh'),
        col_name = list('tair(°C)', 'rh(%)'),
        line_color = 'dodgerblue',
        x_lab = 'Tair', y_lab = 'RH',
        scatter = c('x', 'y') )

## End(Not run)

hydrotoolbox documentation built on April 14, 2023, 12:34 a.m.