R/accGraph.R
In DBOSlab/expowo: An R package for mining global plant diversity and distribution data
Defines functions
accGraph
Documented in
accGraph
#' Create graphics of species discoveries accumulation and nomenclatural changes
#'
#' @author Debora Zuanny & Domingos Cardoso
#'
#' @description Produces graphics of the dynamics of species description and
#' historical nomenclature changes within any family or genus provided. It is
#' designed to create graphics based on the dataframe resulted from
#' \code{powoSpecies}. The graph type can be selected by two arguments
#' (spp_acc and spp_changes) and then multiple graphs for any different
#' taxonomic groups within the input data can be produced automatically in a
#' single run.
#'
#' @usage
#' accGraph(inputdf = NULL,
#' verbose = TRUE,
#' spp_acc = TRUE,
#' spp_changes = TRUE,
#' spp_changes_col = NULL,
#' genus_plots = TRUE,
#' save = FALSE,
#' dir = "results_accGraph",
#' filename = "cumulative_discovery_",
#' format = "pdf")
#'
#' @param inputdf A dataframe generated using \code{powoSpecies} function using
#' the argument synonyms set as \code{TRUE} and containing the genus and species
#' column and the associated information about the protologue of each species.
#' The species name must be binomial, i.e. must contain both the genus name and
#' specific epithet.
#'
#' @param verbose Logical, if \code{FALSE}, a message showing each step during
#' the POWO search will not be printed in the console in full.
#'
#' @param spp_acc Logical. Setting to \code{FALSE} means that you do not want to
#' create individual accumulation graphs for each genus present in your dataset.
#' The default is \code{TRUE}.
#'
#' @param spp_changes Logical. Setting to \code{FALSE} means that you do not want
#' to create a violin plot with all data provided. The default is \code{TRUE}.
#'
#' @param spp_changes_col Define the name of a column in the main input data
#' for which the species changes will be considered. For example, if densities
#' and jitters should be presented for each genus in the same graphic, then set
#' \code{spp_changes_col = "genus"}. If you are plotting the dynamics of
#' nomenclatural changes across the entire dataset in a single density graphic,
#' then you must add a new column in your input data containing the same character
#' inside all rows. In this case, ensure that you also keep \code{genus_plots = TRUE}.
#'
#' @param genus_plots Logical. If \code{FALSE}, a single density and jitter plot
#' graphic will be produced for the entire input data. The default is \code{TRUE},
#' which will result in a graphic where multiple densities and jitters will be
#' created inside the graphic, depending on the chosen column.
#'
#' @param save Logical, if \code{TRUE}, the search results will be saved on disk.
#'
#' @param dir Pathway to the computer's directory, where the file will be saved
#' provided that the argument \code{save} is set up in \code{TRUE}. The default
#' is to create a directory named **results_accGraph** and the search results
#' will be saved within a subfolder named after the current date.
#'
#' @param filename Name of the output file to be saved. The default is to create
#' a file entitled **cumulative_discovery_**.
#'
#' @param format A character vector related to the file format of the graph
#' to be saved. The default is "pdf" to save the output in Portable Document
#' Format (.pdf), but you can also choose "jpg" to save in Joint Photographic
#' Experts Group (.jpg), "tiff" to save in Tag Image File Format (.tiff) or
#' "png" to save in Portable Network Graphics (.png).
#'
#' @return Objects of class c("gg", "ggplot") and saves the output on disk.
#'
#' @seealso \code{\link{megaGen}}
#' @seealso \code{\link{topGen}}
#' @seealso \code{\link{powoSpecies}}
#' @seealso \code{\link{powoFam}}
#' @seealso \code{\link{powoGenera}}
#' @seealso \code{\link{powoMap}}
#'
#' @examples
#' \dontrun{
#'
#' library(expowo)
#'
#' accGraph(inputdf = "output",
#' verbose = TRUE,
#' spp_acc = TRUE,
#' spp_changes = TRUE,
#' spp_changes_col = "genus",
#' genus_plots = TRUE,
#' save = FALSE,
#' dir = "results_accGraph",
#' filename = "cumulative_discovery_Cyperaceae",
#' format = "pdf")
#' }
#'
#' @importFrom cowplot save_plot
#' @importFrom dplyr filter select
#' @importFrom magrittr "%>%"
#' @importFrom ggplot2 ggplot aes theme_bw theme stat_bin element_blank ylab
#' @importFrom ggplot2 geom_point scale_colour_viridis_d scale_fill_viridis_d
#' @importFrom ggplot2 scale_color_manual scale_y_continuous scale_x_continuous
#' @importFrom ggplot2 margin element_rect guides guide_legend annotate
#' @importFrom ggplot2 position_nudge position_jitter xlab scale_colour_manual
#' @importFrom PupillometryR geom_flat_violin
#' @importFrom viridisLite viridis
#' @importFrom plyr round_any
#'
#' @export
#'
accGraph <- function(inputdf = NULL,
verbose = TRUE,
spp_acc = TRUE,
spp_changes = TRUE,
spp_changes_col = NULL,
genus_plots = TRUE,
save = FALSE,
dir = "results_accGraph",
filename = "cumulative_discovery_",
format = "pdf") {
# format check
.arg_check_format(format)
# dir check
dir <- .arg_check_dir(dir)
# Get name of folder and file name if the results are intended to be saved
foldername <- paste0(dir, "/", format(Sys.time(), "%d%b%Y"))
fullname <- paste0(foldername, "/", filename, ".", format)
# inputdf check if it is a dataframe and if was generated by powoSpecies
.arg_check_inputdf(inputdf)
# Transform the input df to get year of publication and number of synonyms
# ff they do not have yet
df <- inputdf
tf <- names(df) %in% c("number_synonyms", "year", "year_basionym", "year_changed")
if (length(which(tf)) == 0) {
df <- get_year_pubs(inputdf = df,
verbose = FALSE,
save = FALSE)
}
# Plotting the graphs
if (spp_acc) {
# Input df with only the accepted species and associated basionym year
df_accepted <- df %>%
filter(df$status == "Accepted") %>%
select(-c("status", "accepted_name"))
# Plotting the accumulation figures
# Selecting colors
cols <- c("#180F3EFF", "#F1605DFF")
# Single plot for the entire input data
if (genus_plots == FALSE) {
tax_group <- "fully_plotted_data"
p <- list()
l <- length(df_accepted$species)
# p <- ggplot(df_accepted,
# aes(df_accepted$year,
# colour = df_accepted$year_changed)) +
p <- ggplot(df_accepted) +
stat_bin(aes(x = year, y = cumsum(..count..), colour = cols[1]),
geom = "step", bins = 80) +
stat_bin(aes(x = year_basionym, y = cumsum(..count..), colour = cols[2]),
geom = "step", bins=80) +
#stat_ecdf(linewidth = 1.5, alpha=0.6, geom = "step", pad = FALSE) +
theme_bw() +
xlab(expression(bold("Year of publication of name"))) +
ylab(expression(bold("Accumulation of species discovery"))) +
theme(legend.title = element_blank()) +
scale_colour_manual(values = cols, labels = c("accepted name", "basionym")) +
scale_y_continuous(breaks = scales::pretty_breaks(n=5)) +
scale_x_continuous(breaks = c(1753, 1800, 1850, 1900, 1950, 2000,
max(df$year[!is.na(df$year)]))) +
theme(axis.title.x = element_text(size = 14, margin = margin(12,0,0,0))) +
theme(axis.title.y = element_text(size = 14, margin = margin(0,12,0,0))) +
theme(axis.text.x = element_text(size = 14)) +
theme(axis.text.y = element_text(size = 14)) +
theme(legend.title = element_text(size = 14)) +
theme(plot.margin = unit(c(1, 1, 1, 1), "cm")) +
theme(legend.position = c(0.2, 0.8),
legend.key = element_rect(linewidth=10, linetype='blank'),
legend.text = element_text(size=14),
legend.key.size = unit(1, "cm"),
legend.title = element_text(size=14)) +
guides(alpha ='none', colour=guide_legend("publication year"),
size = 14) +
annotate("text",
x = max(df$year[!is.na(df$year)]),
y = plyr::round_any(l, 5, ceiling),
label = paste(l, " spp."),
colour = "black", alpha = 0.6)
if (save) {
if (!dir.exists(dir)) {
dir.create(dir)
}
if (!dir.exists(foldername)) {
dir.create(foldername)
}
if (verbose) {
message(paste0("Writing accumulation plot within '",
foldername, "' on disk."))
}
cowplot::save_plot(gsub(paste0(".", format),
paste0("_", tax_group, ".", format),
fullname),
p,
ncol = 1, nrow = 1,
base_height = 8.5,
base_aspect_ratio = 1.3,
base_width = NULL)
}
}
# Multiple individual plots for each genus
if (genus_plots) {
if (save) {
if (!dir.exists(dir)) {
dir.create(dir)
}
if (!dir.exists(foldername)) {
dir.create(foldername)
}
}
tax_group <- unique(df_accepted$genus)
p <- list()
for (i in seq_along(tax_group)) {
temp_df_accepted <- df_accepted[df_accepted$genus %in% tax_group[i], ]
l <- length(temp_df_accepted$species)
p[[i]] <- ggplot(temp_df_accepted) +
stat_bin(aes(x = year, y = cumsum(..count..), colour = cols[1]),
geom = "step", bins = 80) +
stat_bin(aes(x = year_basionym, y = cumsum(..count..), colour = cols[2]),
geom = "step", bins = 80) +
theme_bw() +
xlab(expression(bold("Year of publication of name"))) +
ylab(eval(bquote(expression(bold("Accumulation of species discovery in")
~bolditalic(.(tax_group[i])))))) +
theme(legend.title = element_blank()) +
scale_colour_manual(values = cols, labels = c("accepted name", "basionym")) +
scale_y_continuous(breaks = scales::pretty_breaks(n=5)) +
scale_x_continuous(breaks = c(1753, 1800, 1850, 1900, 1950, 2000,
max(temp_df_accepted$year[!is.na(temp_df_accepted$year)]))) +
theme(axis.title.x = element_text(size = 14, margin = margin(12,0,0,0))) +
theme(axis.title.y = element_text(size = 14, margin = margin(0,12,0,0))) +
theme(axis.text.x = element_text(size = 14)) +
theme(axis.text.y = element_text(size = 14)) +
theme(legend.title = element_text(size = 14)) +
theme(plot.margin = unit(c(1, 1, 1, 1), "cm")) +
theme(legend.position = c(0.2, 0.8),
legend.key = element_rect(linewidth = 10, linetype = 'blank'),
legend.text = element_text(size = 14),
legend.key.size = unit(1, "cm"),
legend.title = element_text(size = 14)) +
guides(alpha = 'none', colour=guide_legend("publication year"),
size = 14) +
annotate("text",
x = max(temp_df_accepted$year[!is.na(temp_df_accepted$year)]),
y = plyr::round_any(l, 5, ceiling),
label = paste(l, " spp."),
colour = "black", alpha=0.6)
if (save) {
if (verbose) {
message(paste0("Writing accumulation plot of '", tax_group[i],
"' within '", foldername, "' on disk."))
}
cowplot::save_plot(gsub(paste0(".", format),
paste0("_", tax_group[i], ".", format),
fullname),
p[[i]],
ncol = 1, nrow = 1,
base_height = 8.5,
base_aspect_ratio = 1.3,
base_width = NULL)
}
}
}
}
if (spp_changes) {
# Plotting the species changes in a violin plot
tf <- !df$status %in% "Accepted"
df$genus[tf] <- gsub("\\s.*", "", df$accepted_name[tf])
#df$status[tf] <- "Synonym"
df$accepted_name[!tf] <- df$scientific_name[!tf]
df$number_synonyms[is.na(df$number_synonyms)] <- 0
# Plotting the graphic
p <- ggplot(df,
aes(x = .data[[spp_changes_col]], y = year,
size = number_synonyms,
fill = factor(status, levels = c("Homotypic Synonym",
"Heterotypic Synonym",
"Accepted")))) +
PupillometryR::geom_flat_violin(position = position_nudge(x = .25, y = 0),
trim = FALSE,
alpha = .6,
size = .1,
na.rm = TRUE) +
geom_point(aes(fill = status, size = number_synonyms),
position = position_jitter(width = .1, height = .05),
shape = 21,
alpha = .6,
stroke = .1,
colour = "black",
na.rm = TRUE,
show.legend = TRUE) +
scale_fill_viridis_d(option = "E") +
scale_colour_viridis_d(option = "E") +
theme_bw() +
ylab(expression(bold("Year of publication of name"))) +
xlab(expression(bold("Changes in species nomenclature over time"))) +
theme(legend.title = element_blank()) +
scale_y_continuous(breaks = c(1753, 1800, 1850, 1900, 1950, 2000,
max(df$year[!is.na(df$year)]))) +
theme(axis.text.x = element_text(size = 12, face = "italic")) +
theme(axis.title.x = element_text(size = 14, margin = margin(12,0,0,0))) +
theme(axis.title.y = element_text(size = 14, margin = margin(0,12,0,0))) +
theme(plot.margin = unit(c(1, 1, 1, 1), "cm")) +
guides(fill = guide_legend(order = 1),
size = guide_legend(order = 2))
if (save) {
if (!dir.exists(dir)) {
dir.create(dir)
}
if (!dir.exists(foldername)) {
dir.create(foldername)
}
if (verbose) {
message(paste0("Writing violin plot '", filename, ".", format,
"' within '", foldername, "' on disk."))
}
cowplot::save_plot(gsub(paste0(".", format),
paste0("all_data", ".", format),
fullname),
p,
ncol = 1, nrow = 1,
base_height = 8.5,
base_aspect_ratio = 1.3,
base_width = NULL)
}
}
return(p)
}
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Defines functions accGraph
Documented in accGraph
#' Create graphics of species discoveries accumulation and nomenclatural changes
#'
#' @author Debora Zuanny & Domingos Cardoso
#'
#' @description Produces graphics of the dynamics of species description and
#' historical nomenclature changes within any family or genus provided. It is
#' designed to create graphics based on the dataframe resulted from
#' \code{powoSpecies}. The graph type can be selected by two arguments
#' (spp_acc and spp_changes) and then multiple graphs for any different
#' taxonomic groups within the input data can be produced automatically in a
#' single run.
#'
#' @usage
#' accGraph(inputdf = NULL,
#' verbose = TRUE,
#' spp_acc = TRUE,
#' spp_changes = TRUE,
#' spp_changes_col = NULL,
#' genus_plots = TRUE,
#' save = FALSE,
#' dir = "results_accGraph",
#' filename = "cumulative_discovery_",
#' format = "pdf")
#'
#' @param inputdf A dataframe generated using \code{powoSpecies} function using
#' the argument synonyms set as \code{TRUE} and containing the genus and species
#' column and the associated information about the protologue of each species.
#' The species name must be binomial, i.e. must contain both the genus name and
#' specific epithet.
#'
#' @param verbose Logical, if \code{FALSE}, a message showing each step during
#' the POWO search will not be printed in the console in full.
#'
#' @param spp_acc Logical. Setting to \code{FALSE} means that you do not want to
#' create individual accumulation graphs for each genus present in your dataset.
#' The default is \code{TRUE}.
#'
#' @param spp_changes Logical. Setting to \code{FALSE} means that you do not want
#' to create a violin plot with all data provided. The default is \code{TRUE}.
#'
#' @param spp_changes_col Define the name of a column in the main input data
#' for which the species changes will be considered. For example, if densities
#' and jitters should be presented for each genus in the same graphic, then set
#' \code{spp_changes_col = "genus"}. If you are plotting the dynamics of
#' nomenclatural changes across the entire dataset in a single density graphic,
#' then you must add a new column in your input data containing the same character
#' inside all rows. In this case, ensure that you also keep \code{genus_plots = TRUE}.
#'
#' @param genus_plots Logical. If \code{FALSE}, a single density and jitter plot
#' graphic will be produced for the entire input data. The default is \code{TRUE},
#' which will result in a graphic where multiple densities and jitters will be
#' created inside the graphic, depending on the chosen column.
#'
#' @param save Logical, if \code{TRUE}, the search results will be saved on disk.
#'
#' @param dir Pathway to the computer's directory, where the file will be saved
#' provided that the argument \code{save} is set up in \code{TRUE}. The default
#' is to create a directory named **results_accGraph** and the search results
#' will be saved within a subfolder named after the current date.
#'
#' @param filename Name of the output file to be saved. The default is to create
#' a file entitled **cumulative_discovery_**.
#'
#' @param format A character vector related to the file format of the graph
#' to be saved. The default is "pdf" to save the output in Portable Document
#' Format (.pdf), but you can also choose "jpg" to save in Joint Photographic
#' Experts Group (.jpg), "tiff" to save in Tag Image File Format (.tiff) or
#' "png" to save in Portable Network Graphics (.png).
#'
#' @return Objects of class c("gg", "ggplot") and saves the output on disk.
#'
#' @seealso \code{\link{megaGen}}
#' @seealso \code{\link{topGen}}
#' @seealso \code{\link{powoSpecies}}
#' @seealso \code{\link{powoFam}}
#' @seealso \code{\link{powoGenera}}
#' @seealso \code{\link{powoMap}}
#'
#' @examples
#' \dontrun{
#'
#' library(expowo)
#'
#' accGraph(inputdf = "output",
#' verbose = TRUE,
#' spp_acc = TRUE,
#' spp_changes = TRUE,
#' spp_changes_col = "genus",
#' genus_plots = TRUE,
#' save = FALSE,
#' dir = "results_accGraph",
#' filename = "cumulative_discovery_Cyperaceae",
#' format = "pdf")
#' }
#'
#' @importFrom cowplot save_plot
#' @importFrom dplyr filter select
#' @importFrom magrittr "%>%"
#' @importFrom ggplot2 ggplot aes theme_bw theme stat_bin element_blank ylab
#' @importFrom ggplot2 geom_point scale_colour_viridis_d scale_fill_viridis_d
#' @importFrom ggplot2 scale_color_manual scale_y_continuous scale_x_continuous
#' @importFrom ggplot2 margin element_rect guides guide_legend annotate
#' @importFrom ggplot2 position_nudge position_jitter xlab scale_colour_manual
#' @importFrom PupillometryR geom_flat_violin
#' @importFrom viridisLite viridis
#' @importFrom plyr round_any
#'
#' @export
#'
accGraph <- function(inputdf = NULL,
verbose = TRUE,
spp_acc = TRUE,
spp_changes = TRUE,
spp_changes_col = NULL,
genus_plots = TRUE,
save = FALSE,
dir = "results_accGraph",
filename = "cumulative_discovery_",
format = "pdf") {
# format check
.arg_check_format(format)
# dir check
dir <- .arg_check_dir(dir)
# Get name of folder and file name if the results are intended to be saved
foldername <- paste0(dir, "/", format(Sys.time(), "%d%b%Y"))
fullname <- paste0(foldername, "/", filename, ".", format)
# inputdf check if it is a dataframe and if was generated by powoSpecies
.arg_check_inputdf(inputdf)
# Transform the input df to get year of publication and number of synonyms
# ff they do not have yet
df <- inputdf
tf <- names(df) %in% c("number_synonyms", "year", "year_basionym", "year_changed")
if (length(which(tf)) == 0) {
df <- get_year_pubs(inputdf = df,
verbose = FALSE,
save = FALSE)
}
# Plotting the graphs
if (spp_acc) {
# Input df with only the accepted species and associated basionym year
df_accepted <- df %>%
filter(df$status == "Accepted") %>%
select(-c("status", "accepted_name"))
# Plotting the accumulation figures
# Selecting colors
cols <- c("#180F3EFF", "#F1605DFF")
# Single plot for the entire input data
if (genus_plots == FALSE) {
tax_group <- "fully_plotted_data"
p <- list()
l <- length(df_accepted$species)
# p <- ggplot(df_accepted,
# aes(df_accepted$year,
# colour = df_accepted$year_changed)) +
p <- ggplot(df_accepted) +
stat_bin(aes(x = year, y = cumsum(..count..), colour = cols[1]),
geom = "step", bins = 80) +
stat_bin(aes(x = year_basionym, y = cumsum(..count..), colour = cols[2]),
geom = "step", bins=80) +
#stat_ecdf(linewidth = 1.5, alpha=0.6, geom = "step", pad = FALSE) +
theme_bw() +
xlab(expression(bold("Year of publication of name"))) +
ylab(expression(bold("Accumulation of species discovery"))) +
theme(legend.title = element_blank()) +
scale_colour_manual(values = cols, labels = c("accepted name", "basionym")) +
scale_y_continuous(breaks = scales::pretty_breaks(n=5)) +
scale_x_continuous(breaks = c(1753, 1800, 1850, 1900, 1950, 2000,
max(df$year[!is.na(df$year)]))) +
theme(axis.title.x = element_text(size = 14, margin = margin(12,0,0,0))) +
theme(axis.title.y = element_text(size = 14, margin = margin(0,12,0,0))) +
theme(axis.text.x = element_text(size = 14)) +
theme(axis.text.y = element_text(size = 14)) +
theme(legend.title = element_text(size = 14)) +
theme(plot.margin = unit(c(1, 1, 1, 1), "cm")) +
theme(legend.position = c(0.2, 0.8),
legend.key = element_rect(linewidth=10, linetype='blank'),
legend.text = element_text(size=14),
legend.key.size = unit(1, "cm"),
legend.title = element_text(size=14)) +
guides(alpha ='none', colour=guide_legend("publication year"),
size = 14) +
annotate("text",
x = max(df$year[!is.na(df$year)]),
y = plyr::round_any(l, 5, ceiling),
label = paste(l, " spp."),
colour = "black", alpha = 0.6)
if (save) {
if (!dir.exists(dir)) {
dir.create(dir)
}
if (!dir.exists(foldername)) {
dir.create(foldername)
}
if (verbose) {
message(paste0("Writing accumulation plot within '",
foldername, "' on disk."))
}
cowplot::save_plot(gsub(paste0(".", format),
paste0("_", tax_group, ".", format),
fullname),
p,
ncol = 1, nrow = 1,
base_height = 8.5,
base_aspect_ratio = 1.3,
base_width = NULL)
}
}
# Multiple individual plots for each genus
if (genus_plots) {
if (save) {
if (!dir.exists(dir)) {
dir.create(dir)
}
if (!dir.exists(foldername)) {
dir.create(foldername)
}
}
tax_group <- unique(df_accepted$genus)
p <- list()
for (i in seq_along(tax_group)) {
temp_df_accepted <- df_accepted[df_accepted$genus %in% tax_group[i], ]
l <- length(temp_df_accepted$species)
p[[i]] <- ggplot(temp_df_accepted) +
stat_bin(aes(x = year, y = cumsum(..count..), colour = cols[1]),
geom = "step", bins = 80) +
stat_bin(aes(x = year_basionym, y = cumsum(..count..), colour = cols[2]),
geom = "step", bins = 80) +
theme_bw() +
xlab(expression(bold("Year of publication of name"))) +
ylab(eval(bquote(expression(bold("Accumulation of species discovery in")
~bolditalic(.(tax_group[i])))))) +
theme(legend.title = element_blank()) +
scale_colour_manual(values = cols, labels = c("accepted name", "basionym")) +
scale_y_continuous(breaks = scales::pretty_breaks(n=5)) +
scale_x_continuous(breaks = c(1753, 1800, 1850, 1900, 1950, 2000,
max(temp_df_accepted$year[!is.na(temp_df_accepted$year)]))) +
theme(axis.title.x = element_text(size = 14, margin = margin(12,0,0,0))) +
theme(axis.title.y = element_text(size = 14, margin = margin(0,12,0,0))) +
theme(axis.text.x = element_text(size = 14)) +
theme(axis.text.y = element_text(size = 14)) +
theme(legend.title = element_text(size = 14)) +
theme(plot.margin = unit(c(1, 1, 1, 1), "cm")) +
theme(legend.position = c(0.2, 0.8),
legend.key = element_rect(linewidth = 10, linetype = 'blank'),
legend.text = element_text(size = 14),
legend.key.size = unit(1, "cm"),
legend.title = element_text(size = 14)) +
guides(alpha = 'none', colour=guide_legend("publication year"),
size = 14) +
annotate("text",
x = max(temp_df_accepted$year[!is.na(temp_df_accepted$year)]),
y = plyr::round_any(l, 5, ceiling),
label = paste(l, " spp."),
colour = "black", alpha=0.6)
if (save) {
if (verbose) {
message(paste0("Writing accumulation plot of '", tax_group[i],
"' within '", foldername, "' on disk."))
}
cowplot::save_plot(gsub(paste0(".", format),
paste0("_", tax_group[i], ".", format),
fullname),
p[[i]],
ncol = 1, nrow = 1,
base_height = 8.5,
base_aspect_ratio = 1.3,
base_width = NULL)
}
}
}
}
if (spp_changes) {
# Plotting the species changes in a violin plot
tf <- !df$status %in% "Accepted"
df$genus[tf] <- gsub("\\s.*", "", df$accepted_name[tf])
#df$status[tf] <- "Synonym"
df$accepted_name[!tf] <- df$scientific_name[!tf]
df$number_synonyms[is.na(df$number_synonyms)] <- 0
# Plotting the graphic
p <- ggplot(df,
aes(x = .data[[spp_changes_col]], y = year,
size = number_synonyms,
fill = factor(status, levels = c("Homotypic Synonym",
"Heterotypic Synonym",
"Accepted")))) +
PupillometryR::geom_flat_violin(position = position_nudge(x = .25, y = 0),
trim = FALSE,
alpha = .6,
size = .1,
na.rm = TRUE) +
geom_point(aes(fill = status, size = number_synonyms),
position = position_jitter(width = .1, height = .05),
shape = 21,
alpha = .6,
stroke = .1,
colour = "black",
na.rm = TRUE,
show.legend = TRUE) +
scale_fill_viridis_d(option = "E") +
scale_colour_viridis_d(option = "E") +
theme_bw() +
ylab(expression(bold("Year of publication of name"))) +
xlab(expression(bold("Changes in species nomenclature over time"))) +
theme(legend.title = element_blank()) +
scale_y_continuous(breaks = c(1753, 1800, 1850, 1900, 1950, 2000,
max(df$year[!is.na(df$year)]))) +
theme(axis.text.x = element_text(size = 12, face = "italic")) +
theme(axis.title.x = element_text(size = 14, margin = margin(12,0,0,0))) +
theme(axis.title.y = element_text(size = 14, margin = margin(0,12,0,0))) +
theme(plot.margin = unit(c(1, 1, 1, 1), "cm")) +
guides(fill = guide_legend(order = 1),
size = guide_legend(order = 2))
if (save) {
if (!dir.exists(dir)) {
dir.create(dir)
}
if (!dir.exists(foldername)) {
dir.create(foldername)
}
if (verbose) {
message(paste0("Writing violin plot '", filename, ".", format,
"' within '", foldername, "' on disk."))
}
cowplot::save_plot(gsub(paste0(".", format),
paste0("all_data", ".", format),
fullname),
p,
ncol = 1, nrow = 1,
base_height = 8.5,
base_aspect_ratio = 1.3,
base_width = NULL)
}
}
return(p)
}
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