R/5_maxN_values_studied_species_functions.R
In CmlMagneville/SmaxNanalysis: What the Package Does (One Line, Title Case)
Defines functions
deltas.plot
clean.df.maxN
automat.maxN.spbysp
SmaxN.single.inp
Documented in
automat.maxN.spbysp
clean.df.maxN
deltas.plot
SmaxN.single.inp
###########################################################################
##
## Scripts to automatise the computation of maxN values for a set of species
## and build an informative df that will be use to plot deltas
##
## 5_maxN_values_studied_species_functions.R
##
## 03/03/2022
##
## Camille Magneville
##
###########################################################################
#' Computes SmaxN and other abundance metrics using a list which have
#' the abundance dataframe, the speed and the distance dataframe as input
#'
#' @param list_input is a list which contains the abundance dataframe of the
#' studied species, the distance dataframe and the speed for a given pose
#'
#' @return the result of the \code{SmaxN.computation} function
#'
#' @export
#'
SmaxN.single.inp <- function(i) {
SmaxN::SmaxN.computation(dist_df = paral_list[[i]]$dist_df,
speed = paral_list[[i]]$speed,
abund_df = paral_list[[i]]$abund_df)
}
# Don't use anymore because compute for only one species at a time:
#' #' Create a dataframe containing maxN values for the studied species and
#' #' the three poses
#' #'
#' #' This function computes a dataframes containing maxN,SmaxN, maxN_row,
#' #' for a given set of species and the three poses. This function uses
#' #' parallelisation.
#' #'
#' #' @param species_set a vector containing the latin name of the studied species
#' #' given with no space but underscore "_" between Genera and Species names
#' #'
#' #' @param abund_list a list gathering the abundance dataframes of the different
#' #' Poses (dataframes with cameras = columns and time = rows)
#' #'
#' #' @param dist_df a numerical dataframe containing the distance between each
#' #' pair of camera. There are as many rows as there are cameras and there are
#' #' as many columns as there are cameras, thus the dataframe is symmetrical
#' #' and the diagonal is filled with 0. \strong{Rows names and columns names
#' #' must be cameras names}. \strong{BE CAREFUL that the cameras are
#' #' the same and in the same order in the dist_df and in the abund_df!}
#' #'
#' #' @param fish_speed a numerical value refering to the maximal speed of the
#' #' studied species. \strong{Speed must be given in meters per second}. If the
#' #' computation of maxN values should not take into account fish speed (that is
#' #' to say if the camera pooling is done at the second level),
#' #' \code{fish_speed = NULL}
#' #'
#' #' @param os a chacaretr string refering to the operating system used as the
#' #' parallelisation process differs following the os. can either be "windows" or
#' #' "linux". (mac not done)
#' #'
#' #' @return a dataframe with five columns: maxN,SmaxN, maxN_row,
#' #' Species_nm, Poses
#' #'
#' #' @importFrom magrittr %>%
#' #'
#' #' @export
#' #'
#'
#'
#'
#'
#' automat.maxN.setsp <- function(species_set, abund_list, dist_df, fish_speed) {
#'
#'
#' # create a dataframe which will contain all data:
#' maxN_all <- as.data.frame(matrix(ncol = 5, nrow = 1))
#' colnames(maxN_all) <- c("species_nm", "pose_nb", "maxN", "SmaxN", "SmaxN_timestep")
#'
#'
#' # returns the number of available hardware threads, and if it is FALSE, returns the number of physical cores:
#' no_cores <- parallel::detectCores(logical = TRUE)
#'
#' # allocate this nb of available cores to R:
#' cl <- parallel::makeCluster(no_cores-1)
#' doParallel::registerDoParallel(cl)
#'
#'
#'
#' # restrict the abund_list to the studied species:
#' clean_abund_list <- abund_list[which(names(abund_list) %in% species_set)]
#'
#'
#' # loop on species:
#' for (i in (1:length(clean_abund_list))) {
#'
#' # create a dataframe which will contain maxN data for the studied sp:
#' maxN_sp <- as.data.frame(matrix(ncol = 5, nrow = 1))
#' colnames(maxN_sp) <- c("species_nm", "pose_nb", "maxN", "SmaxN", "SmaxN_timestep")
#'
#'
#' # create the list containing the objects on which to parallise:
#' # (be careful that dist_df has the right nb columns):
#'
#' paral_list <- list()
#'
#' # loop on the different poses:
#' for (j in (1:length(clean_abund_list[[i]]))) {
#'
#' paral_list <- append(paral_list, list(list(dist_df = dist_df[which(rownames(dist_df) %in% colnames(clean_abund_list[[i]][[j]])),
#' which(colnames(dist_df) %in% colnames(clean_abund_list[[i]][[j]]))],
#' speed = fish_speed,
#' abund_df = clean_abund_list[[i]][[j]])))
#'
#' } # end creating the list to parallelise
#'
#' if (os == "windows") {
#' start.time <- Sys.time()
#' maxN_data <- parallel::parLapply(cl, paral_list, fun = SmaxN.single.inp)
#' end.time <- Sys.time()
#' time.taken <- end.time - start.time
#' }
#'
#' if (os == "linux") {
#' start.time <- Sys.time()
#' maxN_data <- parallel::mclapply(paral_list, fun = SmaxN.single.inp)
#' end.time <- Sys.time()
#' time.taken <- end.time - start.time
#' }
#'
#' # put maxN values in the maxN_sp df for the three poses:
#' for (j in (1:length(maxN_data))) {
#'
#' new_row <- tibble::tibble(species_nm = names(clean_abund_list)[i],
#' pose_nb = j,
#' maxN = maxN_data[[j]]$maxN,
#' SmaxN = maxN_data[[j]]$SmaxN,
#' SmaxN_timestep = maxN_data[[j]]$SmaxN_timestep,
#' )
#' maxN_sp <- dplyr::add_row(maxN_sp, new_row)
#'
#' }
#'
#' # add species data to the global data:
#' maxN_all <- dplyr::bind_rows(maxN_all, maxN_sp)
#'
#' } # end loop on species
#'
#'
#' # remove NA rows:
#' maxN_all <- maxN_all[which(! is.na(maxN_all$species_nm)), ]
#'
#' # Close Cluster:
#' stopCluster(cl)
#'
#' # return global maxN:
#' return(list(maxN_all, time.taken))
#'
#' }
#' Create a dataframe containing maxN values for one studied species and
#' the three poses
#'
#' This function computes a dataframes containing maxN,SmaxN, maxN_row,
#' for a one species and the three poses. This function uses
#' parallelisation.
#'
#' @param species_nm a caracter string containing the latin name of the studied species
#' given with no space but underscore "_" between Genera and Species names
#'
#' @param abund_list a list gathering the abundance dataframes of the different
#' Poses (dataframes with cameras = columns and time = rows)
#'
#' @param dist_df a numerical dataframe containing the distance between each
#' pair of camera. There are as many rows as there are cameras and there are
#' as many columns as there are cameras, thus the dataframe is symmetrical
#' and the diagonal is filled with 0. \strong{Rows names and columns names
#' must be cameras names}. \strong{BE CAREFUL that the cameras are
#' the same and in the same order in the dist_df and in the abund_df!}
#'
#' @param fish_speed a numerical value refering to the maximal speed of the
#' studied species. \strong{Speed must be given in meters per second}. If the
#' computation of maxN values should not take into account fish speed (that is
#' to say if the camera pooling is done at the second level),
#' \code{fish_speed = NULL}
#'
#' @param os a chacaretr string refering to the operating system used as the
#' parallelisation process differs following the os. can either be "windows" or
#' "linux". (mac not done)
#'
#' @param nb_cores is a nuemric value refering to the number of cores on which
#' the process should be parallelised (do not need more than 3 cores as 3 poses)
#'
#' @return a dataframe with five columns: maxN,SmaxN, maxN_row,
#' Species_nm, Poses
#'
#' @importFrom magrittr %>%
#'
#' @export
#'
automat.maxN.spbysp <- function(species_nm, abund_list, dist_df, fish_speed, os, nb_cores) {
if (os == "windows") {
# allocate 3 cores to this process (server has 100 cores and only need 3 for the
# ... poses):
cl <- parallel::makeCluster(3)
doParallel::registerDoParallel(cl)
}
# restrict the abund_list to the studied species:
clean_abund_list <- list()
clean_abund_list <- abund_list[which(names(abund_list) == species_nm)]
paral_list <- list()
# loop on the different poses:
for (j in (1:length(clean_abund_list[[1]]))) {
paral_list <- append(paral_list, list(list(dist_df = dist_df[which(rownames(dist_df) %in% colnames(clean_abund_list[[1]][[j]])),
which(colnames(dist_df) %in% colnames(clean_abund_list[[1]][[j]]))],
speed = fish_speed,
abund_df = clean_abund_list[[1]][[j]])))
} # end creating the list to parallelise
if (os == "windows") {
start.time <- Sys.time()
maxN_data <- parallel::parLapply(cl, X = 1:length(paral_list), fun = function(i) {
SmaxN::SmaxN.computation(dist_df = paral_list[[i]]$dist_df,
speed = paral_list[[i]]$speed,
abund_df = paral_list[[i]]$abund_df)
})
end.time <- Sys.time()
time.taken <- end.time - start.time
}
if (os == "linux") {
start.time <- Sys.time()
maxN_data <- parallel::mclapply(X = 1:length(paral_list), FUN = function(i) {
SmaxN::SmaxN.computation(dist_df = paral_list[[i]]$dist_df,
speed = paral_list[[i]]$speed,
abund_df = paral_list[[i]]$abund_df)
},
mc.cores = nb_cores)
end.time <- Sys.time()
time.taken <- end.time - start.time
}
# return global maxN:
return(maxN_data)
}
#' Create a dataframe containing maxN values for one studied species and
#' the three poses
#'
#' This function computes a dataframes containing maxN,SmaxN, maxN_row,
#' for a one species and the three poses. This function uses
#' parallelisation.
#'
#' @param all_sp_list a list containing for each species the output of the
#' automat.maxN.spbysp() function. Names must be given for each
#' species. See script 3.maxN_values_delta_analysis_spbysp.R part 2
#' "arrange data".
#'
#' @return a dataframe with five columns: species_nm, pose_nb, maxN, SmaxN
#' and maxN_timestep
#'
#' @importFrom magrittr %>%
#'
#' @export
#'
clean.df.maxN <- function(all_sp_list) {
# create a dataframe that will contains all value to plot:
maxN_all <- as.data.frame(matrix(ncol = 5, nrow = 1))
colnames(maxN_all) <- c("species_nm", "pose", "maxN", "SmaxN", "SmaxN_timestep")
# loop on the species list:
for (i in (1:length(all_sp_list))) {
sp_nm <- names(all_sp_list)[i]
# loop on the Poses:
for (j in (1:length(all_sp_list[[i]]))) {
pose_nb <- paste0("Pose", sep = "_", j)
# get the data for sp 1 and pose j:
data <- all_sp_list[[i]][[j]]
# get the values:
maxN <- data$maxN
SmaxN <- data$SmaxN
SmaxN_timestep <- data$SmaxN_timestep
# new row to add maxN:
new_row <- tibble::tibble(species_nm = sp_nm,
pose = pose_nb,
maxN = maxN,
SmaxN = SmaxN,
SmaxN_timestep = SmaxN_timestep)
# add:
maxN_all <- dplyr::add_row(maxN_all, new_row)
}
}
# remove first row with NA:
maxN_all <- maxN_all[-1, ]
return(maxN_all)
}
#' Create the plot showing delta between maxN values for a set of species
#'
#' This function computes a plot showing SmaxN, maxN, SmaxN_row (values on y)
#' for a given set of species (x) and the three poses (several colors)
#' and save the plot in outputs folder
#'
#' @param maxN_all a dataframe containing all maxN values, deltas, species
#' names and poses (obtained from automat.maxN.setsp() function)
#'
#' @param colors a vector containiing the different colors for the three poses
#'
#' @return a ggplot2 plot showing delta1 = SmaxN - maxN and
#' delta2 = SmaxN - SmaxN_row (values on y) for a set of species (x) and the
#' different poses and different facets for deltas
#'
#' @export
#'
deltas.plot <- function(maxN_all, colors) {
# factorise species:
maxN_all$species_nm <- as.factor(maxN_all$species_nm)
# if SmaxN and maxN equal 0 then remove rows because otherwise a difference of 0
# ... can be due to same SmaxN and maxN or to SmaxN = maxN = 0 (no individuals)
maxN_all <- maxN_all[which(maxN_all$SmaxN != 0), ]
# compute deltas:
maxN_all$delta_SmaxN_maxN <- maxN_all$SmaxN / maxN_all$maxN
maxN_all$delta_SmaxN_SmaxNtimestep <- maxN_all$SmaxN / maxN_all$SmaxN_timestep
# rename columns with AcCtendark to be Ctenochaetus striatus ...
# ... so Chaetodon is the first species and it helps for the graph:
ac_cten <- maxN_all[which(maxN_all$species_nm == "AcCten_dark"), ]
ac_cten$species_nm <- rep("Ctenochaetus_striatus", 3)
maxN_all <- maxN_all[which(! maxN_all$species_nm == "AcCten_dark"), ]
maxN_all <- rbind(maxN_all, ac_cten)
maxN_all$species_nm <- forcats::fct_relevel(maxN_all$species_nm, c("Chaetodon_trifasciatus",
"Ctenochaetus_striatus",
"Gomphosus_caeruleus",
"Parapercis_hexophtalma",
"Parupeneus_macronemus",
"Thalassoma_hardwicke"))
# rename Pose -> Recording:
maxN_all[which(maxN_all$pose == "Pose_1"), "pose"] <- "Recording 1"
maxN_all[which(maxN_all$pose == "Pose_2"), "pose"] <- "Recording 2"
maxN_all[which(maxN_all$pose == "Pose_3"), "pose"] <- "Recording 3"
# factorise poses:
maxN_all$pose <- as.factor(maxN_all$pose)
# plot for delta 1:
plot_delta1 <- ggplot2::ggplot(data = maxN_all,
ggplot2::aes(x = species_nm, y = delta_SmaxN_maxN)) +
ggplot2::geom_bar(stat = "identity", position = ggplot2::position_dodge(),
ggplot2::aes(fill = pose, colour = pose),
width = 0.40) +
ggplot2::scale_fill_manual(values = colors,
name = "Recording number") +
ggplot2::scale_colour_manual(values = colors,
name = "Recording number") +
ggplot2::scale_alpha_manual(labels = NULL) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90,
face = "italic"),
axis.text = ggplot2::element_text(size = 10),
axis.title = ggplot2::element_text(size = 10),
panel.background = ggplot2::element_rect(fill = "white",
colour = "grey"),
panel.grid.major = ggplot2::element_line(colour = "grey"),
legend.title = ggplot2::element_text(size = 11),
legend.text = ggplot2::element_text(size = 10)) +
ggplot2::scale_y_continuous(limits = c(1, 5),
oob = scales::rescale_none,
breaks = c(1, 1.5, 2, 2.5, 3,
3.5, 4, 4.5, 5)) +
ggplot2::scale_x_discrete(labels= c("Chaetodon trifasciatus",
"Ctenochaetus striatus",
"Gomphosus caeruleus",
"Parapercis hexophtalma",
"Parupeneus macronemus",
"Thalassoma hardwicke")) +
ggplot2::xlab("") +
ggplot2::ylab("SmaxN / maxN")
# fishualize::add_fishape(family = "Acanthuridae",
# option = "Ctenochaetus_striatus",
# xmin = 1.5, xmax = 2.5, ymin = 2.1, ymax = 2.9,
# scaled = FALSE,
# fill = "grey38",
# alpha = 0.3) +
#
# fishualize::add_fishape(family = "Chaetodontidae",
# option = "Chaetodon_trifasciatus",
# xmin = 0.5, xmax = 1.5, ymin = 2.1, ymax = 2.9,
# scaled = FALSE,
# fill = "grey38",
# alpha = 0.3) +
#
# fishualize::add_fishape(family = "Labridae",
# option = "Gomphosus_caeruleus",
# xmin = 2.5, xmax = 3.5, ymin = 3.1, ymax = 3.9,
# scaled = FALSE,
# fill = "grey38",
# alpha = 0.3) +
#
# fishualize::add_fishape(family = "Mullidae",
# option = "Parupeneus_macronemus",
# xmin = 4.5, xmax = 5.5, ymin = 1.6, ymax = 2.4,
# scaled = FALSE,
# fill = "grey38",
# alpha = 0.3)
# plot for delta 2:
plot_delta2 <- ggplot2::ggplot(data = maxN_all,
ggplot2::aes(x = species_nm, y = delta_SmaxN_SmaxNtimestep)) +
ggplot2::geom_bar(stat = "identity", position = ggplot2::position_dodge(),
ggplot2::aes(fill = pose, colour = pose),
width = 0.40) +
ggplot2::scale_fill_manual(values = colors,
name = "Recording number") +
ggplot2::scale_colour_manual(values = colors,
name = "Recording number") +
ggplot2::scale_alpha_manual(labels = NULL) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90,
face = "italic"),
axis.text = ggplot2::element_text(size = 10),
axis.title = ggplot2::element_text(size = 10),
panel.background = ggplot2::element_rect(fill = "white",
colour = "grey"),
panel.grid.major = ggplot2::element_line(colour = "grey"),
legend.title = ggplot2::element_text(size = 11),
legend.text = ggplot2::element_text(size = 10)) +
ggplot2::scale_y_continuous(limits = c(1, 5),
oob = scales::rescale_none,
breaks = c(1, 1.5, 2, 2.5, 3,
3.5, 4, 4.5, 5)) +
ggplot2::scale_x_discrete(labels= c("Chaetodon trifasciatus",
"Ctenochaetus striatus",
"Gomphosus caeruleus",
"Parapercis hexophtalma",
"Parupeneus macronemus",
"Thalassoma hardwicke")) +
ggplot2::xlab("") +
ggplot2::ylab("SmaxN / iSmaxN")
# fishualize::add_fishape(family = "Acanthuridae",
# option = "Ctenochaetus_striatus",
# xmin = 0.5, xmax = 1.5, ymin = 3.1, ymax = 3.9,
# scaled = FALSE,
# fill = "grey38",
# alpha = 0.3)
# gather the two plots using patchwork:
patchwork_plot <- plot_delta1 + plot_delta2 +
patchwork::plot_layout(byrow = TRUE, ncol = 2, nrow = 1,
guides = "collect") +
patchwork::plot_annotation(tag_levels = 'A')
# save in outputs:
# save the plot in the outputs folder:
ggplot2::ggsave(filename = here::here("outputs/2_Deltas_maxN.pdf"),
plot = patchwork_plot,
device = "pdf",
scale = 0.8,
height = 4000,
width = 7500,
units = "px",
dpi = 600)
# return the plot:
return(patchwork_plot)
}
CmlMagneville/SmaxNanalysis documentation built on Jan. 28, 2024, 11:20 p.m.
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Defines functions deltas.plot clean.df.maxN automat.maxN.spbysp SmaxN.single.inp
Documented in automat.maxN.spbysp clean.df.maxN deltas.plot SmaxN.single.inp
###########################################################################
##
## Scripts to automatise the computation of maxN values for a set of species
## and build an informative df that will be use to plot deltas
##
## 5_maxN_values_studied_species_functions.R
##
## 03/03/2022
##
## Camille Magneville
##
###########################################################################
#' Computes SmaxN and other abundance metrics using a list which have
#' the abundance dataframe, the speed and the distance dataframe as input
#'
#' @param list_input is a list which contains the abundance dataframe of the
#' studied species, the distance dataframe and the speed for a given pose
#'
#' @return the result of the \code{SmaxN.computation} function
#'
#' @export
#'
SmaxN.single.inp <- function(i) {
SmaxN::SmaxN.computation(dist_df = paral_list[[i]]$dist_df,
speed = paral_list[[i]]$speed,
abund_df = paral_list[[i]]$abund_df)
}
# Don't use anymore because compute for only one species at a time:
#' #' Create a dataframe containing maxN values for the studied species and
#' #' the three poses
#' #'
#' #' This function computes a dataframes containing maxN,SmaxN, maxN_row,
#' #' for a given set of species and the three poses. This function uses
#' #' parallelisation.
#' #'
#' #' @param species_set a vector containing the latin name of the studied species
#' #' given with no space but underscore "_" between Genera and Species names
#' #'
#' #' @param abund_list a list gathering the abundance dataframes of the different
#' #' Poses (dataframes with cameras = columns and time = rows)
#' #'
#' #' @param dist_df a numerical dataframe containing the distance between each
#' #' pair of camera. There are as many rows as there are cameras and there are
#' #' as many columns as there are cameras, thus the dataframe is symmetrical
#' #' and the diagonal is filled with 0. \strong{Rows names and columns names
#' #' must be cameras names}. \strong{BE CAREFUL that the cameras are
#' #' the same and in the same order in the dist_df and in the abund_df!}
#' #'
#' #' @param fish_speed a numerical value refering to the maximal speed of the
#' #' studied species. \strong{Speed must be given in meters per second}. If the
#' #' computation of maxN values should not take into account fish speed (that is
#' #' to say if the camera pooling is done at the second level),
#' #' \code{fish_speed = NULL}
#' #'
#' #' @param os a chacaretr string refering to the operating system used as the
#' #' parallelisation process differs following the os. can either be "windows" or
#' #' "linux". (mac not done)
#' #'
#' #' @return a dataframe with five columns: maxN,SmaxN, maxN_row,
#' #' Species_nm, Poses
#' #'
#' #' @importFrom magrittr %>%
#' #'
#' #' @export
#' #'
#'
#'
#'
#'
#' automat.maxN.setsp <- function(species_set, abund_list, dist_df, fish_speed) {
#'
#'
#' # create a dataframe which will contain all data:
#' maxN_all <- as.data.frame(matrix(ncol = 5, nrow = 1))
#' colnames(maxN_all) <- c("species_nm", "pose_nb", "maxN", "SmaxN", "SmaxN_timestep")
#'
#'
#' # returns the number of available hardware threads, and if it is FALSE, returns the number of physical cores:
#' no_cores <- parallel::detectCores(logical = TRUE)
#'
#' # allocate this nb of available cores to R:
#' cl <- parallel::makeCluster(no_cores-1)
#' doParallel::registerDoParallel(cl)
#'
#'
#'
#' # restrict the abund_list to the studied species:
#' clean_abund_list <- abund_list[which(names(abund_list) %in% species_set)]
#'
#'
#' # loop on species:
#' for (i in (1:length(clean_abund_list))) {
#'
#' # create a dataframe which will contain maxN data for the studied sp:
#' maxN_sp <- as.data.frame(matrix(ncol = 5, nrow = 1))
#' colnames(maxN_sp) <- c("species_nm", "pose_nb", "maxN", "SmaxN", "SmaxN_timestep")
#'
#'
#' # create the list containing the objects on which to parallise:
#' # (be careful that dist_df has the right nb columns):
#'
#' paral_list <- list()
#'
#' # loop on the different poses:
#' for (j in (1:length(clean_abund_list[[i]]))) {
#'
#' paral_list <- append(paral_list, list(list(dist_df = dist_df[which(rownames(dist_df) %in% colnames(clean_abund_list[[i]][[j]])),
#' which(colnames(dist_df) %in% colnames(clean_abund_list[[i]][[j]]))],
#' speed = fish_speed,
#' abund_df = clean_abund_list[[i]][[j]])))
#'
#' } # end creating the list to parallelise
#'
#' if (os == "windows") {
#' start.time <- Sys.time()
#' maxN_data <- parallel::parLapply(cl, paral_list, fun = SmaxN.single.inp)
#' end.time <- Sys.time()
#' time.taken <- end.time - start.time
#' }
#'
#' if (os == "linux") {
#' start.time <- Sys.time()
#' maxN_data <- parallel::mclapply(paral_list, fun = SmaxN.single.inp)
#' end.time <- Sys.time()
#' time.taken <- end.time - start.time
#' }
#'
#' # put maxN values in the maxN_sp df for the three poses:
#' for (j in (1:length(maxN_data))) {
#'
#' new_row <- tibble::tibble(species_nm = names(clean_abund_list)[i],
#' pose_nb = j,
#' maxN = maxN_data[[j]]$maxN,
#' SmaxN = maxN_data[[j]]$SmaxN,
#' SmaxN_timestep = maxN_data[[j]]$SmaxN_timestep,
#' )
#' maxN_sp <- dplyr::add_row(maxN_sp, new_row)
#'
#' }
#'
#' # add species data to the global data:
#' maxN_all <- dplyr::bind_rows(maxN_all, maxN_sp)
#'
#' } # end loop on species
#'
#'
#' # remove NA rows:
#' maxN_all <- maxN_all[which(! is.na(maxN_all$species_nm)), ]
#'
#' # Close Cluster:
#' stopCluster(cl)
#'
#' # return global maxN:
#' return(list(maxN_all, time.taken))
#'
#' }
#' Create a dataframe containing maxN values for one studied species and
#' the three poses
#'
#' This function computes a dataframes containing maxN,SmaxN, maxN_row,
#' for a one species and the three poses. This function uses
#' parallelisation.
#'
#' @param species_nm a caracter string containing the latin name of the studied species
#' given with no space but underscore "_" between Genera and Species names
#'
#' @param abund_list a list gathering the abundance dataframes of the different
#' Poses (dataframes with cameras = columns and time = rows)
#'
#' @param dist_df a numerical dataframe containing the distance between each
#' pair of camera. There are as many rows as there are cameras and there are
#' as many columns as there are cameras, thus the dataframe is symmetrical
#' and the diagonal is filled with 0. \strong{Rows names and columns names
#' must be cameras names}. \strong{BE CAREFUL that the cameras are
#' the same and in the same order in the dist_df and in the abund_df!}
#'
#' @param fish_speed a numerical value refering to the maximal speed of the
#' studied species. \strong{Speed must be given in meters per second}. If the
#' computation of maxN values should not take into account fish speed (that is
#' to say if the camera pooling is done at the second level),
#' \code{fish_speed = NULL}
#'
#' @param os a chacaretr string refering to the operating system used as the
#' parallelisation process differs following the os. can either be "windows" or
#' "linux". (mac not done)
#'
#' @param nb_cores is a nuemric value refering to the number of cores on which
#' the process should be parallelised (do not need more than 3 cores as 3 poses)
#'
#' @return a dataframe with five columns: maxN,SmaxN, maxN_row,
#' Species_nm, Poses
#'
#' @importFrom magrittr %>%
#'
#' @export
#'
automat.maxN.spbysp <- function(species_nm, abund_list, dist_df, fish_speed, os, nb_cores) {
if (os == "windows") {
# allocate 3 cores to this process (server has 100 cores and only need 3 for the
# ... poses):
cl <- parallel::makeCluster(3)
doParallel::registerDoParallel(cl)
}
# restrict the abund_list to the studied species:
clean_abund_list <- list()
clean_abund_list <- abund_list[which(names(abund_list) == species_nm)]
paral_list <- list()
# loop on the different poses:
for (j in (1:length(clean_abund_list[[1]]))) {
paral_list <- append(paral_list, list(list(dist_df = dist_df[which(rownames(dist_df) %in% colnames(clean_abund_list[[1]][[j]])),
which(colnames(dist_df) %in% colnames(clean_abund_list[[1]][[j]]))],
speed = fish_speed,
abund_df = clean_abund_list[[1]][[j]])))
} # end creating the list to parallelise
if (os == "windows") {
start.time <- Sys.time()
maxN_data <- parallel::parLapply(cl, X = 1:length(paral_list), fun = function(i) {
SmaxN::SmaxN.computation(dist_df = paral_list[[i]]$dist_df,
speed = paral_list[[i]]$speed,
abund_df = paral_list[[i]]$abund_df)
})
end.time <- Sys.time()
time.taken <- end.time - start.time
}
if (os == "linux") {
start.time <- Sys.time()
maxN_data <- parallel::mclapply(X = 1:length(paral_list), FUN = function(i) {
SmaxN::SmaxN.computation(dist_df = paral_list[[i]]$dist_df,
speed = paral_list[[i]]$speed,
abund_df = paral_list[[i]]$abund_df)
},
mc.cores = nb_cores)
end.time <- Sys.time()
time.taken <- end.time - start.time
}
# return global maxN:
return(maxN_data)
}
#' Create a dataframe containing maxN values for one studied species and
#' the three poses
#'
#' This function computes a dataframes containing maxN,SmaxN, maxN_row,
#' for a one species and the three poses. This function uses
#' parallelisation.
#'
#' @param all_sp_list a list containing for each species the output of the
#' automat.maxN.spbysp() function. Names must be given for each
#' species. See script 3.maxN_values_delta_analysis_spbysp.R part 2
#' "arrange data".
#'
#' @return a dataframe with five columns: species_nm, pose_nb, maxN, SmaxN
#' and maxN_timestep
#'
#' @importFrom magrittr %>%
#'
#' @export
#'
clean.df.maxN <- function(all_sp_list) {
# create a dataframe that will contains all value to plot:
maxN_all <- as.data.frame(matrix(ncol = 5, nrow = 1))
colnames(maxN_all) <- c("species_nm", "pose", "maxN", "SmaxN", "SmaxN_timestep")
# loop on the species list:
for (i in (1:length(all_sp_list))) {
sp_nm <- names(all_sp_list)[i]
# loop on the Poses:
for (j in (1:length(all_sp_list[[i]]))) {
pose_nb <- paste0("Pose", sep = "_", j)
# get the data for sp 1 and pose j:
data <- all_sp_list[[i]][[j]]
# get the values:
maxN <- data$maxN
SmaxN <- data$SmaxN
SmaxN_timestep <- data$SmaxN_timestep
# new row to add maxN:
new_row <- tibble::tibble(species_nm = sp_nm,
pose = pose_nb,
maxN = maxN,
SmaxN = SmaxN,
SmaxN_timestep = SmaxN_timestep)
# add:
maxN_all <- dplyr::add_row(maxN_all, new_row)
}
}
# remove first row with NA:
maxN_all <- maxN_all[-1, ]
return(maxN_all)
}
#' Create the plot showing delta between maxN values for a set of species
#'
#' This function computes a plot showing SmaxN, maxN, SmaxN_row (values on y)
#' for a given set of species (x) and the three poses (several colors)
#' and save the plot in outputs folder
#'
#' @param maxN_all a dataframe containing all maxN values, deltas, species
#' names and poses (obtained from automat.maxN.setsp() function)
#'
#' @param colors a vector containiing the different colors for the three poses
#'
#' @return a ggplot2 plot showing delta1 = SmaxN - maxN and
#' delta2 = SmaxN - SmaxN_row (values on y) for a set of species (x) and the
#' different poses and different facets for deltas
#'
#' @export
#'
deltas.plot <- function(maxN_all, colors) {
# factorise species:
maxN_all$species_nm <- as.factor(maxN_all$species_nm)
# if SmaxN and maxN equal 0 then remove rows because otherwise a difference of 0
# ... can be due to same SmaxN and maxN or to SmaxN = maxN = 0 (no individuals)
maxN_all <- maxN_all[which(maxN_all$SmaxN != 0), ]
# compute deltas:
maxN_all$delta_SmaxN_maxN <- maxN_all$SmaxN / maxN_all$maxN
maxN_all$delta_SmaxN_SmaxNtimestep <- maxN_all$SmaxN / maxN_all$SmaxN_timestep
# rename columns with AcCtendark to be Ctenochaetus striatus ...
# ... so Chaetodon is the first species and it helps for the graph:
ac_cten <- maxN_all[which(maxN_all$species_nm == "AcCten_dark"), ]
ac_cten$species_nm <- rep("Ctenochaetus_striatus", 3)
maxN_all <- maxN_all[which(! maxN_all$species_nm == "AcCten_dark"), ]
maxN_all <- rbind(maxN_all, ac_cten)
maxN_all$species_nm <- forcats::fct_relevel(maxN_all$species_nm, c("Chaetodon_trifasciatus",
"Ctenochaetus_striatus",
"Gomphosus_caeruleus",
"Parapercis_hexophtalma",
"Parupeneus_macronemus",
"Thalassoma_hardwicke"))
# rename Pose -> Recording:
maxN_all[which(maxN_all$pose == "Pose_1"), "pose"] <- "Recording 1"
maxN_all[which(maxN_all$pose == "Pose_2"), "pose"] <- "Recording 2"
maxN_all[which(maxN_all$pose == "Pose_3"), "pose"] <- "Recording 3"
# factorise poses:
maxN_all$pose <- as.factor(maxN_all$pose)
# plot for delta 1:
plot_delta1 <- ggplot2::ggplot(data = maxN_all,
ggplot2::aes(x = species_nm, y = delta_SmaxN_maxN)) +
ggplot2::geom_bar(stat = "identity", position = ggplot2::position_dodge(),
ggplot2::aes(fill = pose, colour = pose),
width = 0.40) +
ggplot2::scale_fill_manual(values = colors,
name = "Recording number") +
ggplot2::scale_colour_manual(values = colors,
name = "Recording number") +
ggplot2::scale_alpha_manual(labels = NULL) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90,
face = "italic"),
axis.text = ggplot2::element_text(size = 10),
axis.title = ggplot2::element_text(size = 10),
panel.background = ggplot2::element_rect(fill = "white",
colour = "grey"),
panel.grid.major = ggplot2::element_line(colour = "grey"),
legend.title = ggplot2::element_text(size = 11),
legend.text = ggplot2::element_text(size = 10)) +
ggplot2::scale_y_continuous(limits = c(1, 5),
oob = scales::rescale_none,
breaks = c(1, 1.5, 2, 2.5, 3,
3.5, 4, 4.5, 5)) +
ggplot2::scale_x_discrete(labels= c("Chaetodon trifasciatus",
"Ctenochaetus striatus",
"Gomphosus caeruleus",
"Parapercis hexophtalma",
"Parupeneus macronemus",
"Thalassoma hardwicke")) +
ggplot2::xlab("") +
ggplot2::ylab("SmaxN / maxN")
# fishualize::add_fishape(family = "Acanthuridae",
# option = "Ctenochaetus_striatus",
# xmin = 1.5, xmax = 2.5, ymin = 2.1, ymax = 2.9,
# scaled = FALSE,
# fill = "grey38",
# alpha = 0.3) +
#
# fishualize::add_fishape(family = "Chaetodontidae",
# option = "Chaetodon_trifasciatus",
# xmin = 0.5, xmax = 1.5, ymin = 2.1, ymax = 2.9,
# scaled = FALSE,
# fill = "grey38",
# alpha = 0.3) +
#
# fishualize::add_fishape(family = "Labridae",
# option = "Gomphosus_caeruleus",
# xmin = 2.5, xmax = 3.5, ymin = 3.1, ymax = 3.9,
# scaled = FALSE,
# fill = "grey38",
# alpha = 0.3) +
#
# fishualize::add_fishape(family = "Mullidae",
# option = "Parupeneus_macronemus",
# xmin = 4.5, xmax = 5.5, ymin = 1.6, ymax = 2.4,
# scaled = FALSE,
# fill = "grey38",
# alpha = 0.3)
# plot for delta 2:
plot_delta2 <- ggplot2::ggplot(data = maxN_all,
ggplot2::aes(x = species_nm, y = delta_SmaxN_SmaxNtimestep)) +
ggplot2::geom_bar(stat = "identity", position = ggplot2::position_dodge(),
ggplot2::aes(fill = pose, colour = pose),
width = 0.40) +
ggplot2::scale_fill_manual(values = colors,
name = "Recording number") +
ggplot2::scale_colour_manual(values = colors,
name = "Recording number") +
ggplot2::scale_alpha_manual(labels = NULL) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90,
face = "italic"),
axis.text = ggplot2::element_text(size = 10),
axis.title = ggplot2::element_text(size = 10),
panel.background = ggplot2::element_rect(fill = "white",
colour = "grey"),
panel.grid.major = ggplot2::element_line(colour = "grey"),
legend.title = ggplot2::element_text(size = 11),
legend.text = ggplot2::element_text(size = 10)) +
ggplot2::scale_y_continuous(limits = c(1, 5),
oob = scales::rescale_none,
breaks = c(1, 1.5, 2, 2.5, 3,
3.5, 4, 4.5, 5)) +
ggplot2::scale_x_discrete(labels= c("Chaetodon trifasciatus",
"Ctenochaetus striatus",
"Gomphosus caeruleus",
"Parapercis hexophtalma",
"Parupeneus macronemus",
"Thalassoma hardwicke")) +
ggplot2::xlab("") +
ggplot2::ylab("SmaxN / iSmaxN")
# fishualize::add_fishape(family = "Acanthuridae",
# option = "Ctenochaetus_striatus",
# xmin = 0.5, xmax = 1.5, ymin = 3.1, ymax = 3.9,
# scaled = FALSE,
# fill = "grey38",
# alpha = 0.3)
# gather the two plots using patchwork:
patchwork_plot <- plot_delta1 + plot_delta2 +
patchwork::plot_layout(byrow = TRUE, ncol = 2, nrow = 1,
guides = "collect") +
patchwork::plot_annotation(tag_levels = 'A')
# save in outputs:
# save the plot in the outputs folder:
ggplot2::ggsave(filename = here::here("outputs/2_Deltas_maxN.pdf"),
plot = patchwork_plot,
device = "pdf",
scale = 0.8,
height = 4000,
width = 7500,
units = "px",
dpi = 600)
# return the plot:
return(patchwork_plot)
}
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