R/m_plot_all_micros.R
In sebmader/LizardsAndNiches: Handleing specific data frames of lizard morphology and reflectance data
Defines functions
m_plot_all_micros
Documented in
m_plot_all_micros
#' @title Plot all microclimates
#' @description This function plots the microclimate conditions from the output of
#' multiple micro_global models per location and scenario in one plot
#' @name m_plot_all_micros
#' @param multi_micro A tidy data frame of summarised output results of the ecotherm function
#' containing monthly (!) averaged microclimate variables per scenario and location
#' (for details see ?m_tidy_output).
#' @param save_plot Boolean whether the microclimate plot should be saved or not
#' (default = FALSE)
#' @return Plot
# @importFrom ggplot2 ggplot aes geom_line facet_grid vars scale_x_discrete labs
#' @importFrom grDevices png
#' @export
m_plot_all_micros <- function(multi_micro, save_plot = FALSE) {
# create directory of save path if applicable
save_path <- "./Plots/microclim_plots/"
if(save_plot) {
if(!dir.exists(save_path)) {
dir.create(save_path, recursive = T)
cat(paste0("Created folder ", save_path, "\n"))
}
}
# # vector of scenario names
# scenarios <- names(multi_micro)
#
# # vector of location names
# locations <- names(multi_micro[[1]])
#
# for(scen in scenarios) {
# for(loc in locations) {
#
# list_days <- split.data.frame(x = multi_micro[[scen]][[loc]]$metout[,3:6],
# f = multi_micro[[scen]][[loc]]$metout[,1])
#
# months <- seq(1, length(list_days), 1)
#
# # calculate average microclimate conditions per day and save as vector
# T_loc <- vector(mode = "numeric", length = length(months))
# T_ref <- vector(mode = "numeric", length = length(months))
# RH_loc <- vector(mode = "numeric", length = length(months))
# RH_ref <- vector(mode = "numeric", length = length(months))
#
#
# for(month in months) {
# T_loc[month] <- mean(list_days[[month]][,1])
# T_ref[month] <- mean(list_days[[month]][,2])
# RH_loc[month] <- mean(list_days[[month]][,3])
# RH_ref[month] <- mean(list_days[[month]][,4])
# }
#
# multi_micro[[scen]][[loc]]$months <- months
# multi_micro[[scen]][[loc]]$T_loc <- T_loc
# multi_micro[[scen]][[loc]]$T_ref <- T_ref
# multi_micro[[scen]][[loc]]$RH_loc <- RH_loc
# multi_micro[[scen]][[loc]]$RH_ref <- RH_ref
# }
# }
#
#
# for(scen in scenarios) {
# for(loc in locations) {
# # calculate and save total change
# multi_micro[[scen]][[loc]]$change_T_loc <- multi_micro[[scen]][[loc]]$T_loc - multi_micro[["present"]][[loc]]$T_loc
# multi_micro[[scen]][[loc]]$change_RH_loc <- multi_micro[[scen]][[loc]]$RH_loc - multi_micro[["present"]][[loc]]$RH_loc
# # calculate and save percentage change
# multi_micro[[scen]][[loc]]$perc_T_loc <- multi_micro[[scen]][[loc]]$T_loc / multi_micro[["present"]][[loc]]$T_loc
# multi_micro[[scen]][[loc]]$perc_RH_loc <- multi_micro[[scen]][[loc]]$RH_loc / multi_micro[["present"]][[loc]]$RH_loc
#
# multi_micro[[scen]][[loc]]$metout <- NULL
# multi_micro[[scen]][[loc]]$environ <- NULL # never needed this in the first place
# }
# }
#
# # unlist multi_ecto into a dataframe
# multi_micro_tab <- data.table::rbindlist(lapply(multi_micro,
# function(x) data.table::rbindlist(x)),
# idcol = "id")
#
# # make dataframe with 'present' being both rcp 4.5 and 8.5 instead of none
# present45 <- multi_micro_tab[which(stringr::str_detect(multi_micro_tab$timeper,
# "present")),]
# present85 <- present45
# present45$rcp <- "45"
# present85$rcp <- "85"
#
# multi_micro_tab_rcps <- rbind(multi_micro_tab[which(
# !stringr::str_detect(multi_micro_tab$timeper,
# "present")
# ),], present45, present85)
#
#
# # rename times and rcps
# multi_micro_tab_rcps$timeper <- gsub(pattern = "present.*",
# replacement = "pres",
# x = multi_micro_tab_rcps$timeper)
# multi_micro_tab_rcps$timeper <- gsub(pattern = "2040_2059",
# replacement = "40-59",
# x = multi_micro_tab_rcps$timeper)
# multi_micro_tab_rcps$timeper <- gsub(pattern = "2080_2099",
# replacement = "80-99",
# x = multi_micro_tab_rcps$timeper)
# multi_micro_tab_rcps$rcp <- gsub(pattern = "45",
# replacement = "4.5",
# x = multi_micro_tab_rcps$rcp)
# multi_micro_tab_rcps$rcp <- gsub(pattern = "85",
# replacement = "8.5",
# x = multi_micro_tab_rcps$rcp)
# # make id, time, day and rcp factors
# multi_micro_tab_rcps$id <- as.factor(multi_micro_tab_rcps$id)
# multi_micro_tab_rcps$timeper <- as.factor(multi_micro_tab_rcps$timeper)
# multi_micro_tab_rcps$rcp <- as.factor(multi_micro_tab_rcps$rcp)
assertthat::assert_that(is.data.frame(multi_micro))
multi_micro_tab_rcps <- multi_micro
#### plot data ####
# plot size
unit <- "cm"
width <- 22
height <- 13.7
### Temperatures ###
# local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_rcps)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_localTemp.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# data frame without 'present' --> don't want it for displaying change in temp or RH
multi_micro_tab_nopres <- multi_micro_tab_rcps[which(multi_micro_tab_rcps$timeper != "pres"),]
# change in local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_nopres)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'change_T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'change_T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_change_localTemp.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# % of change in local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_nopres)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::geom_hline(ggplot2::aes(yintercept = 1, linetype = "present"),
colour = "black")+
ggplot2::scale_linetype_manual(name = "Reference", values = 2,
guide = ggplot2::guide_legend(override.aes = list(color = "black")))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_percchange_localTemp.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
### just RCP 8.5 ###
multi_micro_tab_85 <- multi_micro_tab_nopres[which(multi_micro_tab_nopres$rcp != "4.5"),]
# local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_85)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_localTemp_85.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# change in local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_85)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'change_T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'change_T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_change_localTemp_85.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# % of change in local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_85)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::geom_hline(ggplot2::aes(yintercept = 1, linetype = "present"),
colour = "black")+
ggplot2::scale_linetype_manual(name = "Reference", values = 2,
guide = ggplot2::guide_legend(override.aes = list(color = "black")))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_percchange_localTemp_85.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# # reference temperature over the year; facet grid locations and RCPs
# p <- ggplot2::ggplot(data = multi_micro_tab_rcps)+
# ggplot2::geom_point(size = 2,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'T_ref',
# colour = 'timeper',
# shape = 'timeper'))+
# ggplot2::geom_line(size = 1,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'T_ref',
# colour = 'timeper',
# group = 'timeper'))+
# ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# # ggplot2::facet_wrap(~LID)+
# ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
# ggplot2::theme_bw()
#
# # save plot
# if(save_plot) {
# file_name <- "microclimates_refTemp.png"
# ggplot2::ggsave(filename = file_name, plot = p, device = png(),
# path = save_path, units = unit,
# width = width, height = height, dpi = 500)
#
# message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# # unlink(file_name)
# } else { print(p) }
### Rel. humidity ###
# local relative humidity over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_rcps)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'RH_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'RH_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_localRH.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# # total change in local RH over the year; facet grid locations and RCPs
# p <- ggplot2::ggplot(data = multi_micro_tab_rcps[which(multi_micro_tab_rcps$timeper != "pres"),])+
# ggplot2::geom_point(size = 2,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'change_RH_loc',
# colour = 'timeper',
# shape = 'timeper'))+
# ggplot2::geom_line(size = 1,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'change_RH_loc',
# colour = 'timeper',
# group = 'timeper'))+
# # ggplot2::facet_wrap(~LID)+
# ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
# ggplot2::theme_bw()
#
# # save plot
# if(save_plot) {
# file_name <- "microclimates_change_localRH.png"
# ggplot2::ggsave(filename = file_name, plot = p, device = png(),
# path = save_path, units = unit,
# width = width, height = height, dpi = 500)
#
# message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# # unlink(file_name)
# } else { print(p) }
# % of change in local RH over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_rcps[which(multi_micro_tab_rcps$timeper != "pres"),])+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_RH_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_RH_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::geom_hline(ggplot2::aes(yintercept = 1, linetype = "present"),
colour = "black")+
ggplot2::scale_linetype_manual(name = "Reference", values = 2,
guide = ggplot2::guide_legend(override.aes = list(color = "black")))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_percchange_localRH.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# just RCP 8.5
# % of change in local RH over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_85)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_RH_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_RH_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::geom_hline(ggplot2::aes(yintercept = 1, linetype = "present"),
colour = "black")+
ggplot2::scale_linetype_manual(name = "Reference", values = 2,
guide = ggplot2::guide_legend(override.aes = list(color = "black")))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_percchange_localRH_85.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# # reference relative humidity over the year; facet grid locations and RCPs
# p <- ggplot2::ggplot(data = multi_micro_tab_rcps)+
# ggplot2::geom_point(size = 2,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'RH_ref',
# colour = 'timeper',
# shape = 'timeper'))+
# ggplot2::geom_line(size = 1,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'RH_ref',
# colour = 'timeper',
# group = 'timeper'))+
# ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# # ggplot2::facet_wrap(~LID)+
# ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
# ggplot2::theme_bw()
#
# # save plot
# if(save_plot) {
# file_name <- "microclimates_refRH.png"
# ggplot2::ggsave(filename = file_name, plot = p, device = png(),
# path = save_path, units = unit,
# width = width, height = height, dpi = 500)
#
# message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# # unlink(file_name)
# } else { print(p) }
}
sebmader/LizardsAndNiches documentation built on March 11, 2021, 6:08 p.m.
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Defines functions m_plot_all_micros
Documented in m_plot_all_micros
#' @title Plot all microclimates
#' @description This function plots the microclimate conditions from the output of
#' multiple micro_global models per location and scenario in one plot
#' @name m_plot_all_micros
#' @param multi_micro A tidy data frame of summarised output results of the ecotherm function
#' containing monthly (!) averaged microclimate variables per scenario and location
#' (for details see ?m_tidy_output).
#' @param save_plot Boolean whether the microclimate plot should be saved or not
#' (default = FALSE)
#' @return Plot
# @importFrom ggplot2 ggplot aes geom_line facet_grid vars scale_x_discrete labs
#' @importFrom grDevices png
#' @export
m_plot_all_micros <- function(multi_micro, save_plot = FALSE) {
# create directory of save path if applicable
save_path <- "./Plots/microclim_plots/"
if(save_plot) {
if(!dir.exists(save_path)) {
dir.create(save_path, recursive = T)
cat(paste0("Created folder ", save_path, "\n"))
}
}
# # vector of scenario names
# scenarios <- names(multi_micro)
#
# # vector of location names
# locations <- names(multi_micro[[1]])
#
# for(scen in scenarios) {
# for(loc in locations) {
#
# list_days <- split.data.frame(x = multi_micro[[scen]][[loc]]$metout[,3:6],
# f = multi_micro[[scen]][[loc]]$metout[,1])
#
# months <- seq(1, length(list_days), 1)
#
# # calculate average microclimate conditions per day and save as vector
# T_loc <- vector(mode = "numeric", length = length(months))
# T_ref <- vector(mode = "numeric", length = length(months))
# RH_loc <- vector(mode = "numeric", length = length(months))
# RH_ref <- vector(mode = "numeric", length = length(months))
#
#
# for(month in months) {
# T_loc[month] <- mean(list_days[[month]][,1])
# T_ref[month] <- mean(list_days[[month]][,2])
# RH_loc[month] <- mean(list_days[[month]][,3])
# RH_ref[month] <- mean(list_days[[month]][,4])
# }
#
# multi_micro[[scen]][[loc]]$months <- months
# multi_micro[[scen]][[loc]]$T_loc <- T_loc
# multi_micro[[scen]][[loc]]$T_ref <- T_ref
# multi_micro[[scen]][[loc]]$RH_loc <- RH_loc
# multi_micro[[scen]][[loc]]$RH_ref <- RH_ref
# }
# }
#
#
# for(scen in scenarios) {
# for(loc in locations) {
# # calculate and save total change
# multi_micro[[scen]][[loc]]$change_T_loc <- multi_micro[[scen]][[loc]]$T_loc - multi_micro[["present"]][[loc]]$T_loc
# multi_micro[[scen]][[loc]]$change_RH_loc <- multi_micro[[scen]][[loc]]$RH_loc - multi_micro[["present"]][[loc]]$RH_loc
# # calculate and save percentage change
# multi_micro[[scen]][[loc]]$perc_T_loc <- multi_micro[[scen]][[loc]]$T_loc / multi_micro[["present"]][[loc]]$T_loc
# multi_micro[[scen]][[loc]]$perc_RH_loc <- multi_micro[[scen]][[loc]]$RH_loc / multi_micro[["present"]][[loc]]$RH_loc
#
# multi_micro[[scen]][[loc]]$metout <- NULL
# multi_micro[[scen]][[loc]]$environ <- NULL # never needed this in the first place
# }
# }
#
# # unlist multi_ecto into a dataframe
# multi_micro_tab <- data.table::rbindlist(lapply(multi_micro,
# function(x) data.table::rbindlist(x)),
# idcol = "id")
#
# # make dataframe with 'present' being both rcp 4.5 and 8.5 instead of none
# present45 <- multi_micro_tab[which(stringr::str_detect(multi_micro_tab$timeper,
# "present")),]
# present85 <- present45
# present45$rcp <- "45"
# present85$rcp <- "85"
#
# multi_micro_tab_rcps <- rbind(multi_micro_tab[which(
# !stringr::str_detect(multi_micro_tab$timeper,
# "present")
# ),], present45, present85)
#
#
# # rename times and rcps
# multi_micro_tab_rcps$timeper <- gsub(pattern = "present.*",
# replacement = "pres",
# x = multi_micro_tab_rcps$timeper)
# multi_micro_tab_rcps$timeper <- gsub(pattern = "2040_2059",
# replacement = "40-59",
# x = multi_micro_tab_rcps$timeper)
# multi_micro_tab_rcps$timeper <- gsub(pattern = "2080_2099",
# replacement = "80-99",
# x = multi_micro_tab_rcps$timeper)
# multi_micro_tab_rcps$rcp <- gsub(pattern = "45",
# replacement = "4.5",
# x = multi_micro_tab_rcps$rcp)
# multi_micro_tab_rcps$rcp <- gsub(pattern = "85",
# replacement = "8.5",
# x = multi_micro_tab_rcps$rcp)
# # make id, time, day and rcp factors
# multi_micro_tab_rcps$id <- as.factor(multi_micro_tab_rcps$id)
# multi_micro_tab_rcps$timeper <- as.factor(multi_micro_tab_rcps$timeper)
# multi_micro_tab_rcps$rcp <- as.factor(multi_micro_tab_rcps$rcp)
assertthat::assert_that(is.data.frame(multi_micro))
multi_micro_tab_rcps <- multi_micro
#### plot data ####
# plot size
unit <- "cm"
width <- 22
height <- 13.7
### Temperatures ###
# local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_rcps)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_localTemp.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# data frame without 'present' --> don't want it for displaying change in temp or RH
multi_micro_tab_nopres <- multi_micro_tab_rcps[which(multi_micro_tab_rcps$timeper != "pres"),]
# change in local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_nopres)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'change_T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'change_T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_change_localTemp.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# % of change in local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_nopres)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::geom_hline(ggplot2::aes(yintercept = 1, linetype = "present"),
colour = "black")+
ggplot2::scale_linetype_manual(name = "Reference", values = 2,
guide = ggplot2::guide_legend(override.aes = list(color = "black")))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_percchange_localTemp.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
### just RCP 8.5 ###
multi_micro_tab_85 <- multi_micro_tab_nopres[which(multi_micro_tab_nopres$rcp != "4.5"),]
# local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_85)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_localTemp_85.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# change in local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_85)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'change_T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'change_T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_change_localTemp_85.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# % of change in local temperature over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_85)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_T_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_T_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::geom_hline(ggplot2::aes(yintercept = 1, linetype = "present"),
colour = "black")+
ggplot2::scale_linetype_manual(name = "Reference", values = 2,
guide = ggplot2::guide_legend(override.aes = list(color = "black")))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_percchange_localTemp_85.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# # reference temperature over the year; facet grid locations and RCPs
# p <- ggplot2::ggplot(data = multi_micro_tab_rcps)+
# ggplot2::geom_point(size = 2,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'T_ref',
# colour = 'timeper',
# shape = 'timeper'))+
# ggplot2::geom_line(size = 1,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'T_ref',
# colour = 'timeper',
# group = 'timeper'))+
# ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# # ggplot2::facet_wrap(~LID)+
# ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
# ggplot2::theme_bw()
#
# # save plot
# if(save_plot) {
# file_name <- "microclimates_refTemp.png"
# ggplot2::ggsave(filename = file_name, plot = p, device = png(),
# path = save_path, units = unit,
# width = width, height = height, dpi = 500)
#
# message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# # unlink(file_name)
# } else { print(p) }
### Rel. humidity ###
# local relative humidity over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_rcps)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'RH_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'RH_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_localRH.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# # total change in local RH over the year; facet grid locations and RCPs
# p <- ggplot2::ggplot(data = multi_micro_tab_rcps[which(multi_micro_tab_rcps$timeper != "pres"),])+
# ggplot2::geom_point(size = 2,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'change_RH_loc',
# colour = 'timeper',
# shape = 'timeper'))+
# ggplot2::geom_line(size = 1,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'change_RH_loc',
# colour = 'timeper',
# group = 'timeper'))+
# # ggplot2::facet_wrap(~LID)+
# ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
# ggplot2::theme_bw()
#
# # save plot
# if(save_plot) {
# file_name <- "microclimates_change_localRH.png"
# ggplot2::ggsave(filename = file_name, plot = p, device = png(),
# path = save_path, units = unit,
# width = width, height = height, dpi = 500)
#
# message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# # unlink(file_name)
# } else { print(p) }
# % of change in local RH over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_rcps[which(multi_micro_tab_rcps$timeper != "pres"),])+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_RH_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_RH_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::geom_hline(ggplot2::aes(yintercept = 1, linetype = "present"),
colour = "black")+
ggplot2::scale_linetype_manual(name = "Reference", values = 2,
guide = ggplot2::guide_legend(override.aes = list(color = "black")))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_percchange_localRH.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# just RCP 8.5
# % of change in local RH over the year; facet grid locations and RCPs
p <- ggplot2::ggplot(data = multi_micro_tab_85)+
ggplot2::geom_point(size = 2,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_RH_loc',
colour = 'timeper',
shape = 'timeper'))+
ggplot2::geom_line(size = 1,
mapping = ggplot2::aes_string(x = 'months',
y = 'perc_RH_loc',
colour = 'timeper',
group = 'timeper'))+
ggplot2::geom_hline(ggplot2::aes(yintercept = 1, linetype = "present"),
colour = "black")+
ggplot2::scale_linetype_manual(name = "Reference", values = 2,
guide = ggplot2::guide_legend(override.aes = list(color = "black")))+
ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# ggplot2::facet_wrap(~LID)+
ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
ggplot2::theme_bw()
# save plot
if(save_plot) {
file_name <- "microclimates_percchange_localRH_85.png"
ggplot2::ggsave(filename = file_name, plot = p, device = png(),
path = save_path, units = unit,
width = width, height = height, dpi = 500)
message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# unlink(file_name)
} else { print(p) }
# # reference relative humidity over the year; facet grid locations and RCPs
# p <- ggplot2::ggplot(data = multi_micro_tab_rcps)+
# ggplot2::geom_point(size = 2,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'RH_ref',
# colour = 'timeper',
# shape = 'timeper'))+
# ggplot2::geom_line(size = 1,
# mapping = ggplot2::aes_string(x = 'months',
# y = 'RH_ref',
# colour = 'timeper',
# group = 'timeper'))+
# ggplot2::scale_x_continuous(breaks = seq(2, 12, 2), limits = c(1,12))+
# # ggplot2::facet_wrap(~LID)+
# ggplot2::facet_grid(cols = ggplot2::vars(LID), rows = ggplot2::vars(rcp))+
# ggplot2::theme_bw()
#
# # save plot
# if(save_plot) {
# file_name <- "microclimates_refRH.png"
# ggplot2::ggsave(filename = file_name, plot = p, device = png(),
# path = save_path, units = unit,
# width = width, height = height, dpi = 500)
#
# message(paste0("Plot ", file_name, " has been saved in ", save_path, "\n"))
# # unlink(file_name)
# } else { print(p) }
}
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