In levisc8/ipmr: Integral Projection Models

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Figure S1

library(dplyr)
library(purrr)
library(grid)
library(gridExtra)
library(ggplot2)

ipm_pubs <- read.csv('../../../data-raw/padr-pubs-feb-2021.csv',
                     stringsAsFactors = FALSE)

names(ipm_pubs) <- gsub("\\.\\.", "_", names(ipm_pubs))
names(ipm_pubs) <- gsub("\\.$", "", names(ipm_pubs))
names(ipm_pubs) <- gsub("\\.", "_", names(ipm_pubs))

ipm_id <- pmap_chr(.l = data.frame(a = ipm_pubs$Authors,
                                   b = ipm_pubs$Journal, 
                                   c = ipm_pubs$Year),
               .f = function(a, b, c) paste(a, b, c, sep = "_")) 

ipm_pubs <- cbind(ipm_pubs, ipm_id)
ipm_pubs$Year <- as.integer(ipm_pubs$Year)

pub_tot  <- length(unique(ipm_id))

spec_tot <- ipm_pubs %>%
  group_by(Kingdom) %>%
  summarise(length(unique(Species)))

cdb_fetch <- function(cdb) {
  # get url or path
  if (tolower(cdb) == 'comadre') {
    path <- url('https://compadre-db.org/Data/ComadreDownload')
  } else if (tolower(cdb) == 'compadre') {
    path <- url('https://compadre-db.org/Data/CompadreDownload')
  } else {
    path <- path.expand(cdb)
  }

  # fetch and load
  env <- new.env()
  x <- load(path, env)[1]
  dbFetch <- env[[x]]

  # Deal with differences between s4 and s3 versions of database

  if(inherits(dbFetch, 'list')) {
    dbFetch <- dbFetch[[1]]
  } else if(inherits(dbFetch, "CompadreDB")) {
    dbFetch <- dbFetch@data
  } else {
    stop("Cannot recognize class of currently fetched com(p)adre object",
         call. = FALSE)
  }


  return(dbFetch)

}

cpd <- cdb_fetch('compadre')

mpm_id<- pmap_chr(.l = data.frame(a = cpd$Authors,
                                  b = cpd$Journal, 
                                  c = cpd$YearPublication),
               .f = function(a, b, c) paste(a, b, c, sep = "_")) 

cpd <- cbind(cpd, mpm_id)

cpd$YearPublication <- as.integer(cpd$YearPublication)

cpd_tot_pubs <- cpd %>% 
  filter(!duplicated(mpm_id)) %>% 
  group_by(YearPublication) %>%
  summarise(n_tot = n()) %>%
  ungroup() %>%
  arrange(YearPublication) %>%
  filter(!is.na(YearPublication))

cpd_tot_pubs <- mutate(cpd_tot_pubs, run_sum = cumsum(n_tot),
                       Database = "Compadre_MPM_Database") %>%
  setNames(c(
    "Year",
    "Number_per_Year",
    "Cumulative_Publications",
    "Database"
  ))

pdr_tot_pubs <- ipm_pubs %>%
  filter(!duplicated(ipm_id)) %>%
  group_by(Year) %>%
  summarise(n_tot = n()) %>%
  ungroup() %>%
  arrange(Year)

pdr_tot_pubs <- mutate(pdr_tot_pubs, run_sum = cumsum(n_tot),
                       Database = "Padrino_IPM_Database") %>%
  setNames(c(
    "Year",
    "Number_per_Year",
    "Cumulative_Publications",
    "Database"
  ))  %>%
  filter(!is.na(Year))

all_pubs <- rbind(pdr_tot_pubs, cpd_tot_pubs)
all_pubs$Year <- as.integer(all_pubs$Year)

library(gridExtra)
library(grid)

all_pubs <- filter(all_pubs, Year < 2020)

run_sum_plot <- ggplot(all_pubs,
       aes(x = Year,
           y = Cumulative_Publications)) +
  geom_line(aes(color = Database),
            size = 1.25) +
  theme_bw() +
  scale_color_manual(breaks = c("Compadre_MPM_Database",
                                "Padrino_IPM_Database"),
                     labels = c("COMPADRE MPM Database",
                                "PADRINO IPM Database"),
                     values = viridis::inferno(2,
                                               begin = 0,
                                               end = 0.5,
                                               direction = -1)) +
  annotate("text", x = 1965, y = 600, label = "A", size = 10) +
  theme(legend.position = "bottom",
        axis.title = element_text(size = 17),
        axis.text =  element_text(size = 16),
        legend.text = element_text(size = 16),
        legend.title = element_text(size = 18),) +
  ylab("Cumulative Publications") + 
  xlab("")

ann_pubs <- ggplot(all_pubs,
                   aes(x = Year)) +
  geom_col(aes(y = Number_per_Year,
               fill = Database)) +
  theme_bw() +
  scale_fill_manual(breaks = c("Compadre_MPM_Database",
                               "Padrino_IPM_Database"),
                    labels = c("COMPADRE MPM Database",
                               "PADRINO IPM Database"),
                    values = viridis::inferno(2,
                                               begin = 0,
                                               end = 0.5,
                                               direction = -1)) +
  annotate("text", x = 1965, y = 50, label = "B", size = 10) +
  theme(legend.position = "none",
        axis.title = element_text(size = 18),
        axis.text =  element_text(size = 16),
        plot.margin = margin(b = 30, l = 0, r = 2)
        ) +
  ylab("Number per Year") + 
  annotation_custom(grob = textGrob(label = "IPMpack Introduced",
                                    gp = gpar(fontsize = 16)), 
                    xmin = 2000,
                    xmax = 2000,
                    ymin = -16,
                    ymax = -16) +
  geom_segment(x = 2005, xend = 2013,
               y = -12, yend = -4,
               size = 1.5,
               color = "black") +
  annotation_custom(grob = textGrob("Ellner, Childs, & Rees,\nMerow et al. Published",
                                    gp = gpar(fontsize = 16)),
                    xmin = 2010,
                    xmax = 2020,
                    ymin = -16,
                    ymax = -16) + 
  geom_segment(x = 2016, 
               xend = 2016,
               y = -9,
               yend = -4,
               size = 1.5,
               color = "black") + 
  xlab("")


gt <- ggplot_gtable(ggplot_build(ann_pubs))
gt$layout$clip[gt$layout$name == "panel"] <- "off"

grid.arrange(run_sum_plot, gt)

\newpage \pagenumbering{gobble} \blandscape

Table S1

library(dplyr)
library(kableExtra)

studies <- read.csv('../../../data-raw/padr-pubs-feb-2021.csv',
                    stringsAsFactors = FALSE) %>%
  select(Species, Kingdom, `Full.citation..APA.`) %>%
  setNames(c("Species", "Kingdom", "Full Citation")) %>%
  filter(!is.na(Species) &
         !is.na(`Full Citation`) &
         `Full Citation` != "NA" &
         `Full Citation` != "") %>% 
  arrange(`Full Citation`)

studies$Species <- gsub("_", " ", studies$Species)
studies$Species <- gsub("\\.", "", studies$Species)
studies$Species <- gsub(" sp$", " spp", studies$Species)


sum_tab <- studies %>%
  group_by(Kingdom) %>%
  summarise(`Number of Species` = length(unique(Species)),
            `Number of Studies` = length(unique(`Full Citation`)))


tab_cap <- "Table S1: Table of species, kingdom, and publications for papers that contain Integral Projection Models. This table is not exhaustive and does not include some papers for which species and kingdom information is not relevant (e.g. papers developing new theory relying on simulated data)."

kbl(studies,
    align = c("l", "c", "r"),
    format = "latex", 
    longtable = TRUE,
    caption = tab_cap) %>%
  column_spec(1, width = "1.25in", border_left = TRUE) %>%
  column_spec(2, width = "1in") %>% 
  column_spec(3, width = "5in", border_right = TRUE) %>%
  kable_styling(latex_options = "scale_down")