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In rWCVP: Generating Summaries, Reports and Plots from the World Checklist of Vascular Plants
knitr::opts_chunk$set(echo = FALSE)
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_chunk$set(message = FALSE)
taxa <- params$taxa
version <- params$version
input.area <- params$area_delim
df <- params$mydata
synonyms <- params$synonyms
cat("**Generated on: **", format(Sys.time(), '%d %B, %Y'),"\n")
cat("\n**WCVP version: **", version)
Licensing: [??]
Cite as:
| Govaerts, R., Nic Lughadha, E., Black, N., Turner, R., & Paton, A. (2021). The World Checklist of
| Vascular Plants, a continuously updated resource for exploring global plant diversity. Scientific
| Data, 8(1), 1-10.
|
| Brown, M.J.M, Black, N., Govaerts, R., Nic Lughadha, E., Ondo, I., Turner, R., Walker , B.E. (2022).
| rWCVP: Generating Summaries, Reports and Plots from the World Checklist of Vascular Plants. R
| package version 1.0.0. https://github.com/matildabrown/rWCVP
For compilers and reviewers of the dataset, see: https://wcsp.science.kew.org/compilersReviewers.do
if(!is.null(taxa)){
cat("### Taxa: ", paste(taxa, collapse = " + "), "\n")
} else {cat("### Taxa: All \n") }
cat("\n")
if(synonyms==TRUE) {
cat("**Including synonyms, illegitimate, invalid and other non-accepted names.**")
} else {cat("**Accepted names only.** ")}
cat("\n")
if(!identical(input.area, "global")) cat("### Geography: ", paste(get_area_name(input.area), collapse = " + "),"\n") else cat("### Geography: Global","\n")
shp <- wgsrpd3
shp$fillcol <- as.numeric(shp$LEVEL3_COD %in% input.area)+1
if(identical(input.area,"global")) shp$fillcol <- 2
suppressMessages({
suppressWarnings({
ggplot2::ggplot(shp) +
ggplot2::geom_sf(fill=c("gray80","#ad2b00")[shp$fillcol], col="gray80")+
ggplot2::stat_sf_coordinates(col=c("gray80","#ad2b00")[shp$fillcol])+
ggplot2::theme_void()+
ggplot2::coord_sf(expand=FALSE)
})
})
df <- params$mydata
if("X" %in% colnames(df)) df <- select(df, -X)
Symbols used in species list
data.frame(v1= c("E","e","x","i","?"),
v2=c("Endemic to a single WGSPRD Level 3 area",
"Regionally endemic (to the input geography, above)",
"Considered extinct or eradicated",
"Introduced and naturalised",
"Occurrence doubtful"
)) %>%
gt::gt() %>%
gt::tab_options(
data_row.padding = gt::px(1),
data_row.padding.horizontal = gt::px(15),
column_labels.hidden = TRUE,
table.align = "left")
Key to regions
df %>%
dplyr::select(area_code_l3, area, region, continent) %>%
{if(!identical(input.area, "global")) dplyr::filter(.,area_code_l3 %in% input.area) else unique(.)} %>%
unique() %>%
na.omit() %>%
dplyr::arrange(continent, region, area_code_l3) %>%
dplyr::group_by(continent, region) %>%
gt::gt() %>%
gt::tab_footnote(
footnote = "For codes outside the input geography, see: https://en.wikipedia.org/wiki/List_of_codes_used_in_the_World_Geographical_Scheme_for_Recording_Plant_Distributions",
locations = gt::cells_column_labels(
columns = area_code_l3)
) %>%
gt::cols_label(area_code_l3 = "Area (WGSRPD Level 3)",
area="") %>%
gt::tab_stubhead(label="CONTINENT - Region") %>%
gt::tab_options(
row_group.as_column= TRUE,
stub_row_group.font.weight = "bold",
column_labels.font.weight = "bold",
data_row.padding = gt::px(1),
table.font.size = 12,
table.align = "left",
footnotes.marks = "standard") %>%
gt::tab_style(
style = gt::cell_text(align = "left"),
locations = gt::cells_row_groups()
)
Basic statistics
``` {r results = 'asis', message=FALSE}
if(!"in_geography" %in% colnames(df)) {
df$in_geography <- 1
df$area_endemic <- 1
}
summ_df <- data.frame(area_code_l3="Region",
cat=c("Total", "Endemic (to region)"),
n.sp= c(nrow(unique(df %>%
dplyr::filter(taxon_status == "Accepted",
taxon_rank == "Species") %>%
dplyr::select(taxon_name))),
nrow(unique(df %>%
dplyr::filter(taxon_status == "Accepted",
taxon_rank == "Species",
area_endemic == 1) %>%
dplyr::select(taxon_name))))) %>%
tidyr::pivot_wider(names_from=cat, values_from = n.sp)
if(identical(input.area,"global")) {
cat("Total number of species: ", as.numeric(summ_df[1,2]))
input.area <- unique(df$area_code_l3)
} else {
cat("Total number of species: ", as.numeric(summ_df[1,2], "\n"))
cat('\n')
cat("\nNumber of regionally endemic species: ", as.numeric(summ_df[1,3]))
}
df_split <- df %>%
dplyr::filter(area_code_l3 %in% input.area) %>%
dplyr::group_by(area_code_l3) %>%
dplyr::group_split()
summ_split <- list()
for(i in seq_along(df_split)){
native <- nrow(unique(df_split[[i]] %>%
dplyr::filter(taxon_status == "Accepted",
taxon_rank == "Species",
introduced ==0 ) %>%
dplyr::select(taxon_name)))
endemic <- nrow(unique(df_split[[i]] %>%
dplyr::filter(taxon_status == "Accepted",
taxon_rank == "Species",
endemic ==1 ) %>%
dplyr::select(taxon_name)))
introduced <- nrow(unique(df_split[[i]] %>%
dplyr::filter(taxon_status == "Accepted",
taxon_rank == "Species",
introduced ==1 ) %>%
dplyr::select(taxon_name)))
extinct <- nrow(unique(df_split[[i]] %>%
dplyr::filter(taxon_status == "Accepted",
taxon_rank == "Species",
extinct ==1 ) %>%
dplyr::select(taxon_name)))
total <- nrow(unique(df_split[[i]] %>%
dplyr::filter(taxon_status == "Accepted",
taxon_rank == "Species") %>%
dplyr::select(taxon_name)))
summ_split[[i]] <- data.frame(l3=df_split[[i]][1,"area_code_l3"],
cat=c("Native",
"Endemic",
"Introduced",
"Extinct",
"Total"),
n.sp=c(native,
endemic,
introduced,
extinct,
total))
}
summ <- do.call(rbind, summ_split)
suppressMessages(summ %>%
tidyr::pivot_wider(names_from = cat, values_from = n.sp) %>%
dplyr::left_join(df %>% dplyr::select(continent, region, area_code_l3)) %>%
dplyr::arrange(continent, region, area_code_l3) %>%
dplyr::select(-continent) %>%
dplyr::group_by(region) %>%
unique() %>%
na.omit() %>%
gt::gt() %>%
gt::tab_options(
row_group.as_column= TRUE,
stub_row_group.font.weight = "bold",
column_labels.font.weight = "bold",
data_row.padding = gt::px(1),
table.font.size = 12,
table.align = "left") %>%
gt::tab_style(
style = gt::cell_text(align = "left"),
locations = gt::cells_row_groups()
) %>%
gt::cols_label(area_code_l3 = "Area") %>%
gt::tab_style(
style=gt::cell_text(weight="bold"),
locations = gt::cells_body(
columns= Total
)
) %>%
gt::text_transform(
locations = gt::cells_body(),
fn = function(x){
ifelse(x == 0, "", x)
}
)
)
### List of species
```r
textkey <- data.frame(introduced=c(0,1,1,0,0,1,1,0), extinct=c(0,1,0,1,0,1,0,1), location_doubtful=c(0,1,0,0,1,0,1,1), text=c("","(i,x,d)","(i)","(x)","(d)","(i,x)","(i,d)","(x,d)"))
suppressMessages({
df <- dplyr::left_join(df, textkey)
df$area_text <- paste0(df$area_code_l3, df$text)
df_area_key <- unique(df[, c("continent_code_l1","continent","region_code_l2", "region", "area_code_l3", "area")])
distvars <- c("continent_code_l1","continent","region_code_l2", "region", "area_code_l3", "area","text")
df2 <- df %>% dplyr::arrange(continent, region, area_code_l3, taxon_name) %>%
dplyr::select(-dplyr::any_of(distvars)) %>%
dplyr::group_by(plant_name_id) %>%
dplyr::summarise(
area_text = paste(area_text, collapse=", "))
df3 <- df %>%
dplyr::select(-any_of(c(distvars, "area_text"))) %>%
dplyr::left_join(df2) %>%
unique()
df3$area_text[which(df3$area_text %in% c("","NANA"))] <- NA
df3$genus_text <- df3$genus
df3$genus_text[which(df3$taxon_status=="Accepted")] <- paste("<b><em>", df3$genus[which(df3$taxon_status=="Accepted")], "</b></em>")
df3$species_text <- df3$species
df3$species_text[which(df3$taxon_status=="Accepted")] <- paste("<b><em>", df3$species[which(df3$taxon_status=="Accepted")], "</b></em>")
df3$species_hybrid_text <- df3$species_hybrid
df3$species_hybrid_text[which(df3$taxon_status=="Accepted" & !is.na(df3$species_hybrid))] <- paste("<b>", df3$species_hybrid[which(df3$taxon_status=="Accepted"& !is.na(df3$species_hybrid))], "</b>")
df3$infraspecific_rank_text <- df3$infraspecific_rank
df3$infraspecific_rank_text[which(df3$taxon_status=="Accepted" & !is.na(df3$infraspecific_rank))] <- paste(" <b>", df3$infraspecific_rank_text[which(df3$taxon_status=="Accepted" & !is.na(df3$infraspecific_rank))], "</b>")
df3$infraspecies_text <- df3$infraspecies
df3$infraspecies_text[which(df3$taxon_status=="Accepted" & !is.na(df3$infraspecies))] <- paste(" <b><em>", df3$infraspecies_text[which(df3$taxon_status=="Accepted" & !is.na(df3$infraspecies))], "</b></em>")
rowgroup <- data.frame(
plant_name_id = df3$plant_name_id,
rowgrouptext = paste(df3$genus_hybrid, df3$genus, " ", df3$species_hybrid, df3$species, df3$infraspecific_rank, df3$infraspecies)
)
df3 <- left_join(df3, rowgroup, by=c("accepted_plant_name_id"="plant_name_id"))
df3$maintext <- paste(" = ", df3$genus_hybrid,"<em>", df3$genus_text,"</em>", " ",
df3$species_hybrid_text,"<em>", df3$species_text,"</em>"," ",
df3$infraspecific_rank_text," ","<em>", df3$infraspecies_text,"</em>",
" ", df3$taxon_authors, " [", df3$taxon_status, "]",
sep="")
df3$maintext[which(df3$taxon_status == "Accepted")] <- df3$taxon_authors[which(df3$taxon_status == "Accepted")]
df3$maintext <- paste(df3$maintext, df3$place_of_publication, df3$volume_and_page, df3$first_published)
# df3$maintext[which(df3$taxon_name != df3$accepted_name & !is.na(df3$accepted_name))] <- paste0(df3$maintext[which(df3$taxon_name != df3$accepted_name & !is.na(df3$accepted_name))],
# " = <b><em>", df3$accepted_name[which(df3$taxon_name != df3$accepted_name & !is.na(df3$accepted_name))], "</em></b>")
#
df3$maintext <- gsub("NA ", "", df3$maintext)
df3$maintext <- gsub(" NA", "", df3$maintext)
df3$maintext[which(!is.na(df3$area_text))] <- paste0(df3$maintext[which(!is.na(df3$area_text))], "<p> "," ", df3$area_text[which(!is.na(df3$area_text))] )
df3 <- df3 %>% dplyr::mutate(endemic_flag=dplyr::case_when(endemic==1 ~ "E",
area_endemic==1 & endemic==0 ~ "e",
TRUE ~ ""))
df3[is.na(df3$accepted_name),"rowgrouptext"] <- "Unplaced"
df3 %>%
dplyr::filter(taxon_rank!= "Genus") %>%
dplyr::arrange(family, taxon_status) %>%
dplyr::select(maintext, plant_name_id, rowgrouptext, ipni_id) %>%
dplyr::group_by(rowgrouptext) %>%
unique() %>%
gt::gt() %>%
gt::text_transform(
locations = gt::cells_body(columns = maintext),
fn = function(x){
purrr::map(x, html, replace_sign_entities = TRUE)
}
) %>%
gt::tab_options(
row_group.font.weight = "bold",
row_group.font.size = 12,
table.font.size = 12,
data_row.padding = gt::px(1),
column_labels.hidden = TRUE,
table_body.hlines.color = "transparent",
table_body.border.top.color = "transparent"
) %>%
gt::tab_style(
style = gt::cell_borders(sides = "all", color = "transparent"),
locations = gt::cells_row_groups(
))%>%
gt::tab_style(
style = gt::cell_text(style="italic"),
locations = gt::cells_row_groups(
)) %>%
gt::tab_style(
style = gt::cell_text(align = "left", indent = gt::px(20)),
locations = gt::cells_body(columns = maintext)
) %>%
gt::tab_style(
style = gt::cell_text(size = 8),
locations = gt::cells_body(columns = plant_name_id)
)
})
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rWCVP documentation built on Feb. 16, 2023, 8:30 p.m.
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knitr::opts_chunk$set(echo = FALSE) knitr::opts_chunk$set(warning = FALSE) knitr::opts_chunk$set(message = FALSE) taxa <- params$taxa version <- params$version input.area <- params$area_delim df <- params$mydata synonyms <- params$synonyms
cat("**Generated on: **", format(Sys.time(), '%d %B, %Y'),"\n") cat("\n**WCVP version: **", version)
Licensing: [??]
Cite as:
| Govaerts, R., Nic Lughadha, E., Black, N., Turner, R., & Paton, A. (2021). The World Checklist of
| Vascular Plants, a continuously updated resource for exploring global plant diversity. Scientific
| Data, 8(1), 1-10.
|
| Brown, M.J.M, Black, N., Govaerts, R., Nic Lughadha, E., Ondo, I., Turner, R., Walker , B.E. (2022).
| rWCVP: Generating Summaries, Reports and Plots from the World Checklist of Vascular Plants. R
| package version 1.0.0. https://github.com/matildabrown/rWCVP
For compilers and reviewers of the dataset, see: https://wcsp.science.kew.org/compilersReviewers.do
if(!is.null(taxa)){ cat("### Taxa: ", paste(taxa, collapse = " + "), "\n") } else {cat("### Taxa: All \n") } cat("\n") if(synonyms==TRUE) { cat("**Including synonyms, illegitimate, invalid and other non-accepted names.**") } else {cat("**Accepted names only.** ")}
cat("\n") if(!identical(input.area, "global")) cat("### Geography: ", paste(get_area_name(input.area), collapse = " + "),"\n") else cat("### Geography: Global","\n")
shp <- wgsrpd3 shp$fillcol <- as.numeric(shp$LEVEL3_COD %in% input.area)+1 if(identical(input.area,"global")) shp$fillcol <- 2 suppressMessages({ suppressWarnings({ ggplot2::ggplot(shp) + ggplot2::geom_sf(fill=c("gray80","#ad2b00")[shp$fillcol], col="gray80")+ ggplot2::stat_sf_coordinates(col=c("gray80","#ad2b00")[shp$fillcol])+ ggplot2::theme_void()+ ggplot2::coord_sf(expand=FALSE) }) })
df <- params$mydata if("X" %in% colnames(df)) df <- select(df, -X)
Symbols used in species list
data.frame(v1= c("E","e","x","i","?"), v2=c("Endemic to a single WGSPRD Level 3 area", "Regionally endemic (to the input geography, above)", "Considered extinct or eradicated", "Introduced and naturalised", "Occurrence doubtful" )) %>% gt::gt() %>% gt::tab_options( data_row.padding = gt::px(1), data_row.padding.horizontal = gt::px(15), column_labels.hidden = TRUE, table.align = "left")
Key to regions
df %>% dplyr::select(area_code_l3, area, region, continent) %>% {if(!identical(input.area, "global")) dplyr::filter(.,area_code_l3 %in% input.area) else unique(.)} %>% unique() %>% na.omit() %>% dplyr::arrange(continent, region, area_code_l3) %>% dplyr::group_by(continent, region) %>% gt::gt() %>% gt::tab_footnote( footnote = "For codes outside the input geography, see: https://en.wikipedia.org/wiki/List_of_codes_used_in_the_World_Geographical_Scheme_for_Recording_Plant_Distributions", locations = gt::cells_column_labels( columns = area_code_l3) ) %>% gt::cols_label(area_code_l3 = "Area (WGSRPD Level 3)", area="") %>% gt::tab_stubhead(label="CONTINENT - Region") %>% gt::tab_options( row_group.as_column= TRUE, stub_row_group.font.weight = "bold", column_labels.font.weight = "bold", data_row.padding = gt::px(1), table.font.size = 12, table.align = "left", footnotes.marks = "standard") %>% gt::tab_style( style = gt::cell_text(align = "left"), locations = gt::cells_row_groups() )
Basic statistics
``` {r results = 'asis', message=FALSE} if(!"in_geography" %in% colnames(df)) { df$in_geography <- 1 df$area_endemic <- 1 }
summ_df <- data.frame(area_code_l3="Region", cat=c("Total", "Endemic (to region)"), n.sp= c(nrow(unique(df %>% dplyr::filter(taxon_status == "Accepted", taxon_rank == "Species") %>% dplyr::select(taxon_name))), nrow(unique(df %>% dplyr::filter(taxon_status == "Accepted", taxon_rank == "Species", area_endemic == 1) %>% dplyr::select(taxon_name))))) %>% tidyr::pivot_wider(names_from=cat, values_from = n.sp)
if(identical(input.area,"global")) { cat("Total number of species: ", as.numeric(summ_df[1,2])) input.area <- unique(df$area_code_l3) } else { cat("Total number of species: ", as.numeric(summ_df[1,2], "\n")) cat('\n') cat("\nNumber of regionally endemic species: ", as.numeric(summ_df[1,3])) }
df_split <- df %>% dplyr::filter(area_code_l3 %in% input.area) %>% dplyr::group_by(area_code_l3) %>% dplyr::group_split() summ_split <- list() for(i in seq_along(df_split)){ native <- nrow(unique(df_split[[i]] %>% dplyr::filter(taxon_status == "Accepted", taxon_rank == "Species", introduced ==0 ) %>% dplyr::select(taxon_name))) endemic <- nrow(unique(df_split[[i]] %>% dplyr::filter(taxon_status == "Accepted", taxon_rank == "Species", endemic ==1 ) %>% dplyr::select(taxon_name))) introduced <- nrow(unique(df_split[[i]] %>% dplyr::filter(taxon_status == "Accepted", taxon_rank == "Species", introduced ==1 ) %>% dplyr::select(taxon_name))) extinct <- nrow(unique(df_split[[i]] %>% dplyr::filter(taxon_status == "Accepted", taxon_rank == "Species", extinct ==1 ) %>% dplyr::select(taxon_name))) total <- nrow(unique(df_split[[i]] %>% dplyr::filter(taxon_status == "Accepted", taxon_rank == "Species") %>% dplyr::select(taxon_name)))
summ_split[[i]] <- data.frame(l3=df_split[[i]][1,"area_code_l3"], cat=c("Native", "Endemic", "Introduced", "Extinct", "Total"), n.sp=c(native, endemic, introduced, extinct, total))
}
summ <- do.call(rbind, summ_split)
suppressMessages(summ %>% tidyr::pivot_wider(names_from = cat, values_from = n.sp) %>% dplyr::left_join(df %>% dplyr::select(continent, region, area_code_l3)) %>% dplyr::arrange(continent, region, area_code_l3) %>% dplyr::select(-continent) %>% dplyr::group_by(region) %>% unique() %>% na.omit() %>% gt::gt() %>% gt::tab_options( row_group.as_column= TRUE, stub_row_group.font.weight = "bold", column_labels.font.weight = "bold", data_row.padding = gt::px(1), table.font.size = 12, table.align = "left") %>% gt::tab_style( style = gt::cell_text(align = "left"), locations = gt::cells_row_groups() ) %>% gt::cols_label(area_code_l3 = "Area") %>% gt::tab_style( style=gt::cell_text(weight="bold"), locations = gt::cells_body( columns= Total ) ) %>% gt::text_transform( locations = gt::cells_body(), fn = function(x){ ifelse(x == 0, "", x) } ) )
### List of species ```r textkey <- data.frame(introduced=c(0,1,1,0,0,1,1,0), extinct=c(0,1,0,1,0,1,0,1), location_doubtful=c(0,1,0,0,1,0,1,1), text=c("","(i,x,d)","(i)","(x)","(d)","(i,x)","(i,d)","(x,d)")) suppressMessages({ df <- dplyr::left_join(df, textkey) df$area_text <- paste0(df$area_code_l3, df$text) df_area_key <- unique(df[, c("continent_code_l1","continent","region_code_l2", "region", "area_code_l3", "area")]) distvars <- c("continent_code_l1","continent","region_code_l2", "region", "area_code_l3", "area","text") df2 <- df %>% dplyr::arrange(continent, region, area_code_l3, taxon_name) %>% dplyr::select(-dplyr::any_of(distvars)) %>% dplyr::group_by(plant_name_id) %>% dplyr::summarise( area_text = paste(area_text, collapse=", ")) df3 <- df %>% dplyr::select(-any_of(c(distvars, "area_text"))) %>% dplyr::left_join(df2) %>% unique() df3$area_text[which(df3$area_text %in% c("","NANA"))] <- NA df3$genus_text <- df3$genus df3$genus_text[which(df3$taxon_status=="Accepted")] <- paste("<b><em>", df3$genus[which(df3$taxon_status=="Accepted")], "</b></em>") df3$species_text <- df3$species df3$species_text[which(df3$taxon_status=="Accepted")] <- paste("<b><em>", df3$species[which(df3$taxon_status=="Accepted")], "</b></em>") df3$species_hybrid_text <- df3$species_hybrid df3$species_hybrid_text[which(df3$taxon_status=="Accepted" & !is.na(df3$species_hybrid))] <- paste("<b>", df3$species_hybrid[which(df3$taxon_status=="Accepted"& !is.na(df3$species_hybrid))], "</b>") df3$infraspecific_rank_text <- df3$infraspecific_rank df3$infraspecific_rank_text[which(df3$taxon_status=="Accepted" & !is.na(df3$infraspecific_rank))] <- paste(" <b>", df3$infraspecific_rank_text[which(df3$taxon_status=="Accepted" & !is.na(df3$infraspecific_rank))], "</b>") df3$infraspecies_text <- df3$infraspecies df3$infraspecies_text[which(df3$taxon_status=="Accepted" & !is.na(df3$infraspecies))] <- paste(" <b><em>", df3$infraspecies_text[which(df3$taxon_status=="Accepted" & !is.na(df3$infraspecies))], "</b></em>") rowgroup <- data.frame( plant_name_id = df3$plant_name_id, rowgrouptext = paste(df3$genus_hybrid, df3$genus, " ", df3$species_hybrid, df3$species, df3$infraspecific_rank, df3$infraspecies) ) df3 <- left_join(df3, rowgroup, by=c("accepted_plant_name_id"="plant_name_id")) df3$maintext <- paste(" = ", df3$genus_hybrid,"<em>", df3$genus_text,"</em>", " ", df3$species_hybrid_text,"<em>", df3$species_text,"</em>"," ", df3$infraspecific_rank_text," ","<em>", df3$infraspecies_text,"</em>", " ", df3$taxon_authors, " [", df3$taxon_status, "]", sep="") df3$maintext[which(df3$taxon_status == "Accepted")] <- df3$taxon_authors[which(df3$taxon_status == "Accepted")] df3$maintext <- paste(df3$maintext, df3$place_of_publication, df3$volume_and_page, df3$first_published) # df3$maintext[which(df3$taxon_name != df3$accepted_name & !is.na(df3$accepted_name))] <- paste0(df3$maintext[which(df3$taxon_name != df3$accepted_name & !is.na(df3$accepted_name))], # " = <b><em>", df3$accepted_name[which(df3$taxon_name != df3$accepted_name & !is.na(df3$accepted_name))], "</em></b>") # df3$maintext <- gsub("NA ", "", df3$maintext) df3$maintext <- gsub(" NA", "", df3$maintext) df3$maintext[which(!is.na(df3$area_text))] <- paste0(df3$maintext[which(!is.na(df3$area_text))], "<p> "," ", df3$area_text[which(!is.na(df3$area_text))] ) df3 <- df3 %>% dplyr::mutate(endemic_flag=dplyr::case_when(endemic==1 ~ "E", area_endemic==1 & endemic==0 ~ "e", TRUE ~ "")) df3[is.na(df3$accepted_name),"rowgrouptext"] <- "Unplaced" df3 %>% dplyr::filter(taxon_rank!= "Genus") %>% dplyr::arrange(family, taxon_status) %>% dplyr::select(maintext, plant_name_id, rowgrouptext, ipni_id) %>% dplyr::group_by(rowgrouptext) %>% unique() %>% gt::gt() %>% gt::text_transform( locations = gt::cells_body(columns = maintext), fn = function(x){ purrr::map(x, html, replace_sign_entities = TRUE) } ) %>% gt::tab_options( row_group.font.weight = "bold", row_group.font.size = 12, table.font.size = 12, data_row.padding = gt::px(1), column_labels.hidden = TRUE, table_body.hlines.color = "transparent", table_body.border.top.color = "transparent" ) %>% gt::tab_style( style = gt::cell_borders(sides = "all", color = "transparent"), locations = gt::cells_row_groups( ))%>% gt::tab_style( style = gt::cell_text(style="italic"), locations = gt::cells_row_groups( )) %>% gt::tab_style( style = gt::cell_text(align = "left", indent = gt::px(20)), locations = gt::cells_body(columns = maintext) ) %>% gt::tab_style( style = gt::cell_text(size = 8), locations = gt::cells_body(columns = plant_name_id) ) })
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