Author

George Roff

Published

March 18, 2025

Code 
library(ggplot2)
library(plotly)
library(scales)
library(RColorBrewer)
conflicts_prefer(plotly::layout)

library(tidyverse)
library(scholar)
library(DT)
library(magrittr)

scholar_id <- "g_m1R7IAAAAJ"

scholar_output <- scholar::get_publications(id = scholar_id) %>%
  rowwise() %>%
  mutate(
    author = case_when(
    str_ends(author, "\\.\\.\\.") ~ get_complete_authors(id = scholar_id, pubid = pubid),
    TRUE ~ author
    )
  ) %>%
ungroup()

profile <- scholar::get_profile(scholar_id)
                                
citation_history <- scholar::get_citation_history(id = scholar_id)

npapers <- scholar_output$title |> unique() |> length()
ncites <- scholar_output$cites |> sum()
hindex <- profile$h_index
i10 <- profile$i10_index
affiliation <- profile$affiliation

datatable(
  data.frame(
    a = c("Author", "Affiliation", "Total Citations", "i-10 index", "h-index", "Publications to date"),
    b = c("George Roff", affiliation, ncites, hindex, i10, npapers)
  ) |> t(),
  colnames = NULL,
  options = list(
    dom = 't',  # show only the table body
    paging = FALSE,
    ordering = FALSE
  ),
  rownames = FALSE
) %>%
  formatStyle(columns = c(1, 2, 3, 4, 5, 6, 7), fontSize = '80%')

All publication data generated dynamically R (scholar, wordcloud) and Plotly:

Summary statistics

Code 
library(ggplot2)
library(plotly)
library(scales)
library(RColorBrewer)
conflicts_prefer(plotly::layout)


pubs_per_year <- scholar_output %>%
  group_by(year) %>%
  summarise(publications = n(), .groups = 'drop')

scholar_output_prepped <- scholar_output %>%
  ungroup() %>%
  arrange(desc(year), cites) %>%
  mutate(
    n = seq(1:nrow(.)),
    author = gsub("(G Roff)", "<b>\\1</b>", author, ignore.case = TRUE),  # Use HTML for bold formatting
    cites = as.numeric(cites)  # Ensure cites is numeric
  ) %>%
  select(n, author, year, title, journal, number, cites) %>%
  rename(
    "Year" = "year",
    "Authors" = "author",
    "Journal" = "journal",
    "Issue/Number" = "number",
    "Citations" = "cites"
  )





###### Plot 1 – Publications per Year
pubs_per_year <- scholar_output %>%
  group_by(year) %>%
  summarise(publications = n(), .groups = 'drop')

# Generate Spectral palette for publications
num_years <- nrow(pubs_per_year)
colors1 <- colorRampPalette(brewer.pal(11, "Spectral"))(num_years)

pubs_plot <- plot_ly(
  pubs_per_year,
  x = ~year,
  y = ~publications,
  type = 'bar',
  marker = list(
    color = colors1,
    line = list(color = 'black', width = 1)  # Add thin black border
  ),
  hoverinfo = 'text',
  text = ~paste0("Year: ", year, "<br>Publications: ", publications),
  textfont = list(size = 6)
) %>%
  layout(
    title = "Publications per Year",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Number of Publications"),
    showlegend = FALSE
  )

####### Plot 2 – Cumulative Citations per Year
cumulative_citations <- citation_history %>%
  arrange(year) %>%
  mutate(cumulative_cites = cumsum(cites))

# Step 5: Plot 3 – Citations by Publication Rank
scholar_output_ranked <- scholar_output %>%
  arrange(desc(cites)) %>%
  mutate(n = row_number())


# Generate Spectral palette for publication rank plot
num_ranked <- nrow(scholar_output_ranked)
colors_ranked <- colorRampPalette(brewer.pal(8, "RdYlBu"))(num_ranked)

rank_plot <- plot_ly(
  scholar_output_ranked,
  x = ~n,
  y = ~cites,
  type = 'bar',
  marker = list(
    color = colors_ranked,
    line = list(color = 'black', width = 1)  # Add thin black border
  ),
  hoverinfo = 'text',                        # Only display hover text
  text = ~paste0("Title: ", title, "<br>Year: ", year, "<br>Citations: ", cites),
  textfont = list(size = 0)                  # No bar labels displayed
) %>%
  layout(
    title = NULL,
    xaxis = list(title = "Publication Rank"),
    yaxis = list(title = "Cumulative Citations"),
    showlegend = FALSE,
    shapes = list(
      list(
        type = 'line',
        x0 = 0,
        x1 = 1,
        xref = 'paper',
        y0 = 10,
        y1 = 10,
        line = list(
          color = 'black',
          width = 2,
          dash = 'dot'
        )
      )
    )
  )

###### Plot 3 Cumulative citations

# Generate Spectral palette for cumulative citations
num_years2 <- nrow(cumulative_citations)
colors2 <- colorRampPalette((brewer.pal(11, "RdBu")))(num_years2)

citations_plot <- plot_ly(
  cumulative_citations,
  x = ~year,
  y = ~cumulative_cites,
  type = 'scatter',
  mode = 'lines+markers',
  line = list(color = 'black', width = 1),  # Narrow black line
   marker = list(
    color = colors2,
    size = 9,       
    line = list(color = 'black', width = 1)  # Add thin black border
  ),
  hoverinfo = 'text',
  text = ~paste0("Year: ", year, "<br>Cumulative Citations: ", cumulative_cites)
) %>%
  layout(
    title = "",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Cumulative Citations"),
    showlegend = FALSE
  )
# 
# # Step 6: Display all three plots in a multi-panel layout
# subplot(
#   pubs_plot, rank_plot, citations_plot,
#   nrows = 1,
#   titleX = TRUE,
#   titleY = TRUE,
#   widths = c(0.3, 0.3, 0.3),  # Adjust plot widths if necessary
#   margin = 0.04               # Set spacing between each plot
# ) %>%
# layout(
#   xaxis = list(title = "Year / Rank", domain = c(0, 0.3), titlefont = list(size = 9), tickfont = list(size = 9)),
#   xaxis2 = list(title = "Rank", domain = c(0.35, 0.65), titlefont = list(size = 9), tickfont = list(size = 9)),
#   xaxis3 = list(title = "Citations", domain = c(0.7, 1), titlefont = list(size = 9), tickfont = list(size = 9)),
#   yaxis = list(titlefont = list(size = 9), tickfont = list(size = 9)),
#   yaxis2 = list(titlefont = list(size = 9), tickfont = list(size = 9)),
#   yaxis3 = list(titlefont = list(size = 9), tickfont = list(size = 9)),
#   #uniformtext = list(minsize = 6, mode = 'show'),
#   margin = list(l = 50, r = 50, t = 50, b = 50, pad = 5)
# )

subplot(
  pubs_plot, citations_plot, rank_plot,
  nrows = 3,
  titleX = TRUE,
  titleY = TRUE,
  margin = 0.04
) %>%
layout(
  xaxis = list(title = "Year / Rank", titlefont = list(size = 9), tickfont = list(size = 9)),
  xaxis2 = list(title = "Rank", titlefont = list(size = 9), tickfont = list(size = 9)),
  xaxis3 = list(title = "Citations", titlefont = list(size = 9), tickfont = list(size = 9)),
  yaxis = list(titlefont = list(size = 9), tickfont = list(size = 9)),
  yaxis2 = list(titlefont = list(size = 9), tickfont = list(size = 9)),
  yaxis3 = list(titlefont = list(size = 9), tickfont = list(size = 9)),
  margin = list(l = 50, r = 50, t = 50, b = 50, pad = 5)
)

Publication journals

Code 
library(wordcloud)
library(grid)
library(cowplot)
library(gridGraphics)
library(png)
library(gtable)

# Define keyword lookup
subject_keywords <- list(
  Ecology = c("ecology", "oikos", "oecologia", "ecological", "Ecosystems"),
  MarineBiology = c("coral reefs", "marine biology", "PLoS ONE", "proceedings", "Scientific", "Fish", "total", "Oceanography", "sharks"),
  Geology = c("palaeo",  "geology", "earth", "Quaternary", "Paleoceanography"),
  Microbiology = c("microbiology", "Molecular", "isme"),
  Climate = c("National", "Open", "Climate", "bioRxiv"),
  Geochemistry = c("geochem", "Geophysical", "Geochronology", "Geochimica", "Biogeosciences"),
  Conservation = c("pollution", "peer", "global", "conservation","Communications", "endangered", "Biology"),
  Evolution = c("bioscience", "Biogeography")
)

# Assign subject(s)
journal_subjects <- scholar_output_prepped %>%
  mutate(
    subject = sapply(Journal, function(j) {
      matches <- names(subject_keywords)[
        sapply(subject_keywords, function(keywords) {
          any(grepl(paste(keywords, collapse = "|"), j, ignore.case = TRUE))
        })
      ]
      paste(matches, collapse = ";")
    })
  )

# Count frequency of each journal per subject
subject_freq <- journal_subjects %>%
  filter(subject != "") %>%
  separate_rows(subject, sep = ";") %>%
  group_by(subject, Journal) %>%
  summarise(n = n(), .groups = "drop") %>%
  arrange(subject, desc(n)) %>%
  mutate(freq_scaled = log1p(n))


set.seed(1)
wordcloud(
  words = subject_freq$Journal,
  freq = subject_freq$n,
  min.freq = 1,
  max.words = Inf,
  color="#202020",
 # colors = brewer.pal(4, "Spectral"),
  scale = c(5,0.5),
  random.order = FALSE,
  use.r.layout = TRUE,
  rot.per=0.3
)

Code 
set.seed(NULL)

Selected publications

Code 
## All publications to bib
# Convert to BibTeX format
scholar_bib <- scholar_output %>%
  mutate(
    bibtex_entry = paste0(
      "@article{", pubid, ",\n",
      "  title = {", title, "},\n",
      "  author = {", author, "},\n",
      "  journal = {", journal, "},\n",
      "  year = {", year, "},\n",
      "  volume = {", sub(" \\(.*", "", number), "},\n", # Extract volume before '('
      "  number = {", sub(".*\\((.*)\\).*", "\\1", number), "},\n", # Extract issue inside '()'
      "  pages = {", cites, "},\n",
      "  cid = {", cid, "},\n",
      "  pubid = {", pubid, "}\n",
      "}"
    )
  ) %>%
  pull(bibtex_entry)

#writeLines(scholar_bib, "scholar_output.bib")

## select publications to bib
search_terms <- c("Global disparity", "sharks on coral reefs", "evolutionary history", "seascapes", "decline of branching", "decoupled in Holocene")

scholar_bib <- scholar_output %>%
  filter(sapply(title, function(x) any(grepl(paste(search_terms, collapse = "|"), x, ignore.case = TRUE)))) |> 
  mutate(
    bibtex_entry = paste0(
      "@article{", pubid, ",\n",
      "  title = {", title, "},\n",
      "  author = {", author, "},\n",
      "  journal = {", journal, "},\n",
      "  year = {", year, "},\n",
      "  volume = {", sub(" \\(.*", "", number), "},\n", # Extract volume before '('
      "  number = {", sub(".*\\((.*)\\).*", "\\1", number), "},\n", # Extract issue inside '()'
      "  pages = {", cites, "},\n",
      "  cid = {", cid, "},\n",
      "  pubid = {", pubid, "}\n",
      "}"
    )
  ) %>%
  pull(bibtex_entry)

writeLines(scholar_bib, "select_scholar_output.bib")

# Function to lighten a vector of colors
lighten_colors <- function(colors, factor = 0.5) {
  if (factor < 0 || factor > 1) stop("Factor must be between 0 and 1")
  col2rgb(colors) %>%
    t() %>%
    apply(1, function(col) {
      rgb(
        red = col[1] + (255 - col[1]) * factor,
        green = col[2] + (255 - col[2]) * factor,
        blue = col[3] + (255 - col[3]) * factor,
        maxColorValue = 255
      )
    })
}




pubs_per_year <- scholar_output %>%
  group_by(year) %>%
  summarise(publications = n(), .groups = 'drop')

scholar_output_prepped <- scholar_output %>%
  ungroup() %>%
  arrange(desc(year), cites) %>%
  mutate(
    n = seq(1:nrow(.)),
    author = gsub("(G Roff)", "<b>\\1</b>", author, ignore.case = TRUE),  # Use HTML for bold formatting
    cites = as.numeric(cites)  # Ensure cites is numeric
  ) %>%
  select(n, author, year, title, journal, number, cites) %>%
  rename(
    "Year" = "year",
    "Authors" = "author",
    "Journal" = "journal",
    "Issue/Number" = "number",
    "Citations" = "cites"
  )



# Generate colors using RColorBrewer with adjusted alpha
num_colors <- nrow(scholar_output_prepped)
color_palette <- colorRampPalette(rev(RColorBrewer::brewer.pal(9, "RdYlBu")))(num_colors)
color_palette <- lighten_colors(color_palette, factor = 0.4)  # Set alpha to 0.7

# Map colors to the Citations column
scholar_output_prepped <- scholar_output_prepped %>%
                                 filter(grepl("Palaeoecological evidence", title) | 
                                 grepl("Global disparity", title) |
                                 grepl("Decadal‐scale rates of reef erosion", title) |
                                 grepl("Seascape", title) |
                                 grepl("Rapid accretion of inshore reef", title) |
                                 grepl("Reassessing Shark-Driven Trophic", title) |
                                 grepl("Decline of coastal apex shark populations", title) |
                                 grepl("Reef accretion and coral growth rates", title) |
                                 grepl("Evolutionary history drives", title) |
                                 grepl("Revisiting the paradigm of shark‐driven", title) |
                                 grepl("Climate change impacts on mesophotic regions", title) |
                                 grepl("Reef accretion and coral growth rates", title) |
                                 grepl("Cryptic coral recruits as dormant “seed banks”", title)
                                  ) |> 
  mutate(
    cite_color = color_palette[rank(Citations, ties.method = "first")]
  )


datatable(
  scholar_output_prepped %>% select(-cite_color) %>% mutate(n=seq(1:n())),
  escape = FALSE,
  options = list(
    search = list(regex = TRUE, caseInsensitive = FALSE),
    autoWidth = FALSE,
    dom = 't<"bottom"ip>',
    pageLength = 100,
    columnDefs = list(
      list(className = 'dt-center', targets = "_all"),
      list(width = '10%', targets = 0),
      list(width = '30%', targets = 1),
      list(width = '10%', targets = 2),
      list(width = '30%', targets = 3),
      list(width = '10%', targets = 4),
      list(width = '10%', targets = 5),
      list(width = '10%', targets = 6)
    )
  ),
  rownames = FALSE,
  class = "cell-border stripe"
) %>% 
  formatStyle( 'Citations',
  backgroundColor = styleEqual(scholar_output_prepped$Citations, scholar_output_prepped$cite_color),
  color = "black", fontSize = '60%'
) %>%
  formatStyle(columns = c(1, 2, 3, 4, 5, 6, 7), fontSize = '80%')