hydrotoolbox (version 1.1.2)

hm_plot: 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 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 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
)

Value

A ggplot2 or plotly object.

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'.

Functions

• hm_plot(hydromet_station): plot method for station class

• hm_plot(hydromet_compact): plot method for compact class

Examples

if (FALSE) {
# 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') )
}