R/visualizations.R

Defines functions sfn_plot

Documented in sfn_plot

#### Visualization functions ####

#' plot method for sfn_data class
#'
#' Plot the desired data from a site object
#'
#' @section ggplot plotting system:
#'   \code{\link{plot}} is a base R function which uses the base R plotting system
#'   to show the plot. We prefer the ggplot plotting system, which allow for
#'   storing the plots in objects and can be subjected to further modifications.
#'   This allow the package users to generate rather simple plots that can be
#'   fine tuned afterwards to the user taste. Generating a \code{\link{plot}}
#'   method for the \code{sfn_data} class returning a ggplot object is not
#'   desired (it change the way plot works and can be misleading about the plot
#'   general usage). So, instead, we offer this function, \code{sfn_plot}.
#'
#' @section Type:
#'   \code{type} argument controls what is going to be plotted. It accepts
#'   the following:
#'   \itemize{
#'     \item{"sapf": It will plot sapflow data vs. TIMESTAMP}
#'     \item{"env": It will plot environmental variables vs. TIMESTMAP}
#'     \item{"ta", "rh", "vpd", "ppfd_in", "netrad", "sw_in", "ext_rad",
#'           "ws", "precip", "swc_shallow" and "swc_deep": They will plot
#'           the corresponding variable vs. TIMESTAMP}
#'   }
#'
#' @section Formula:
#'   \code{formula} argument can be used to select an environmental variable to
#'   plot versus all the sapflow measurements. Any envirinmental variable is
#'   allowed, if it exist in the site provided.
#'
#' @section Geometry:
#'   By default \code{sfn_plot} generates plots using \code{\link{geom_point}}
#'   geometry, except in the case of \code{type = "ws"} and
#'   \code{type = "precip"} where \code{\link{geom_col}} is used. These
#'   geometries can be modified with the \code{...} argument.
#'
#' @param sfn_data sfn_data object to plot. It can be also an sfn_data_multi
#'   object.
#'
#' @param type Character indicating which data to plot. See Type section for
#'   detailed information about the available values. Ignored if formula is
#'   provided
#'
#' @param formula_env Right side formula indicating an environmental variable to
#'   plot vs. the sapflow values. If NULL (default), \code{sfn_plot} will use
#'   "type" to guess which plot show.
#'
#' @param solar Logical indicating if the solar timestamp must be used instead
#'   of the site timestamp
#'
#' @param ... Further arguments to be passed on \code{\link{geom_point}} or
#'   \code{\link{geom_col}} to modify geometry aesthetics.
#'
#' @examples
#' library(ggplot2)
#'
#' # data
#' data('ARG_TRE', package = 'sapfluxnetr')
#'
#' # plotting directly
#' sfn_plot(ARG_TRE, type = 'sapf')
#'
#' # this could be noisy, you can facet by "Tree" (for sapflow) or by
#' # "Variable" (for environmental data):
#' sfn_plot(ARG_TRE, type = 'sapf') +
#'   facet_wrap(~ Tree)
#'
#' sfn_plot(ARG_TRE, type = 'env') +
#'   facet_wrap(~ Variable, scales = 'free_y')
#'
#' # saving and modifying:
#' env_plot <- sfn_plot(ARG_TRE, type = 'env', solar = FALSE) +
#'   facet_wrap(~ Variable, scales = 'free_y')
#' env_plot + labs(title = 'Environmental variables facet plot')
#'
#' # formula
#' sfn_plot(ARG_TRE, formula_env = ~ vpd)
#'
#' @return A ggplot object that can be called to see the plot. If input is an
#'   sfn_data_multi object, a list with the plots
#'
#' @import ggplot2
#'
#' @export

sfn_plot <- function(
  sfn_data,
  type = c(
    'sapf', 'env',
    'ta', 'rh', 'vpd', 'ppfd_in', 'netrad', 'sw_in', 'ext_rad',
    'ws', 'precip', 'swc_shallow', 'swc_deep'
  ),
  formula_env = NULL,
  solar = TRUE,
  ...
) {

  # if sfn_data_multi, iterate over the elements
  if (class(sfn_data) == 'sfn_data_multi') {
    plot_list <- purrr::map(
      sfn_data,
      sfn_plot, type = type, formula_env = formula_env, solar = solar, ...
    )

    return(plot_list)
  }

  # if formula, lets do that plot
  if (rlang::is_formula(formula_env)) {
    data <- get_env_data(sfn_data, solar = solar) %>%
      dplyr::select(.data$TIMESTAMP, !!rlang::get_expr(formula_env)) %>%
      dplyr::inner_join(get_sapf_data(sfn_data, solar = solar), by = 'TIMESTAMP')

    units_char <- paste0(
      unique(
        get_plant_md(sfn_data)[['pl_sap_units']]
      ),
      sep = ' '
    )

    res_plot <- data %>%
      # tidyr::gather(
      #   key = 'Tree', value = 'Sapflow',
      #   -.data$TIMESTAMP, -!!rlang::get_expr(formula_env)
      # ) %>%
      tidyr::pivot_longer(
        !dplyr::contains(rlang::get_expr(formula_env) %>% as.character) &
          !dplyr::contains('TIMESTAMP'),
        names_to = 'Tree', values_to = 'Sapflow'
      ) %>%
      ggplot(aes_(x = formula_env, y = ~Sapflow, colour = ~Tree)) +
      geom_point(...) +
      labs(y = paste0('Sapflow [', units_char, ']'),
           subtitle = paste0('Sap flow vs. ', rlang::get_expr(formula_env)),
           title = get_si_code(sfn_data))
  } else {

    # We need to go type by type checking and plotting if type matchs
    type <- match.arg(type)

    # sapf
    if (type == 'sapf') {
      data <- get_sapf_data(sfn_data, solar = solar)
      units_char <- paste0(
        unique(
          get_plant_md(sfn_data)[['pl_sap_units']]
        ),
        sep = ' '
      )

      # actual plot
      res_plot <- data %>%
        # tidyr::gather(key = 'Tree', value = 'Sapflow', -.data$TIMESTAMP) %>%
        tidyr::pivot_longer(
          -.data$TIMESTAMP, names_to = 'Tree', values_to = 'Sapflow'
        ) %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~Sapflow, colour = ~Tree)) +
        geom_point(...) +
        labs(y = paste0('Sapflow [', units_char, ']')) +
        scale_x_datetime()
    }

    # env
    if (type == 'env') {
      data <- get_env_data(sfn_data, solar = solar)

      # actual plot
      res_plot <- data %>%
        # tidyr::gather(key = 'Variable', value = 'Value', -.data$TIMESTAMP) %>%
        tidyr::pivot_longer(
          -.data$TIMESTAMP,
          names_to = 'Variable', values_to = 'Value'
        ) %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~Value, colour = ~Variable)) +
        geom_point(...) +
        scale_x_datetime()
    }

    # ta
    if (type == 'ta') {
      data <- get_env_data(sfn_data, solar)

      # we need to check if environmental variable exists
      if (is.null(data[['ta']])) {
        stop('Site has not ta data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~ta)) +
        geom_point(...) +
        labs(y = 'Air Temperature [C]') +
        scale_x_datetime()
    }

    # rh
    if (type == 'rh') {
      data <- get_env_data(sfn_data, solar)

      if (is.null(data[['rh']])) {
        stop('Site has not rh data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~rh)) +
        geom_point(...) +
        labs(y = 'Relative Humidity [%]') +
        scale_x_datetime()
    }

    # vpd
    if (type == 'vpd') {
      data <- get_env_data(sfn_data, solar)

      if (is.null(data[['vpd']])) {
        stop('Site has not vpd data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~vpd)) +
        geom_point(...) +
        labs(y = 'VPD [kPa]') +
        scale_x_datetime()
    }

    # ppfd_in
    if (type == 'ppfd_in') {
      data <- get_env_data(sfn_data, solar)

      if (is.null(data[['ppfd_in']])) {
        stop('Site has not ppfd_in data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~ppfd_in)) +
        geom_point(...) +
        labs(y = 'PPFD [?]') +
        scale_x_datetime()
    }

    # sw_in
    if (type == 'sw_in') {
      data <- get_env_data(sfn_data, solar)

      if (is.null(data[['sw_in']])) {
        stop('Site has not sw_in data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~sw_in)) +
        geom_point(...) +
        labs(y = 'sw [?]') +
        scale_x_datetime()
    }

    # netrad
    if (type == 'netrad') {
      data <- get_env_data(sfn_data, solar)

      if (is.null(data[['netrad']])) {
        stop('Site has not netrad data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~netrad)) +
        geom_point(...) +
        labs(y = 'Net Radiation [?]') +
        scale_x_datetime()
    }

    # ext_rad
    if (type == 'ext_rad') {
      data <- get_env_data(sfn_data, solar)

      if (is.null(data[['ext_rad']])) {
        stop('Site has not ext_rad data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~ext_rad)) +
        geom_point(...) +
        labs(y = 'Extraterrestrial Radiation [?]') +
        scale_x_datetime()
    }

    # ws
    if (type == 'ws') {
      data <- get_env_data(sfn_data, solar)

      if (is.null(data[['ws']])) {
        stop('Site has not ws data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~ws)) +
        geom_col(...) +
        labs(y = 'Wind Speed [m/s]') +
        scale_x_datetime()
    }

    # precip
    if (type == 'precip') {
      data <- get_env_data(sfn_data, solar)

      if (is.null(data[['precip']])) {
        stop('Site has not precip data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~precip)) +
        geom_col(...) +
        labs(y = 'Precipitation [?]') +
        scale_x_datetime()
    }

    # swc_shallow
    if (type == 'swc_shallow') {
      data <- get_env_data(sfn_data, solar)

      if (is.null(data[['swc_shallow']])) {
        stop('Site has not swc_shallow data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~swc_shallow)) +
        geom_point(...) +
        labs(y = 'SWC Shallow [cm3/cm3]') +
        scale_x_datetime()
    }

    # swc_deep
    if (type == 'swc_deep') {
      data <- get_env_data(sfn_data, solar)

      if (is.null(data[['swc_deep']])) {
        stop('Site has not swc_deep data')
      }

      # actual plot
      res_plot <- data %>%
        ggplot(aes_(x = ~TIMESTAMP, y = ~swc_deep)) +
        geom_point(...) +
        labs(y = 'SWC Deep [cm3/cm3]') +
        scale_x_datetime()
    }

  }

  return(res_plot)

}

# TODO add explanation for the formula argument in the function doc

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sapfluxnetr documentation built on Oct. 4, 2021, 9:06 a.m.