R/report_content.R
In abennici/faoBiofishr: Report compiler to biodiversity trent report of fisheries production
Defines functions
report_content
Documented in
report_content
#' Compute content of report
#'
#' This function compute all the output elements to include of report
#'
#' @param file Path to the input file
#' @param out Path to export destination
#' @return A list of element
#' @export
report_content<-function(file,out=NULL){
#Data cleaning
content<-list()
#Output repository creation
path<-if(is.null(out)) getwd() else out
report_folder<-file.path(path,"BIODIVERSITY_TREND_REPORT")
report_version<-file.path(report_folder,Sys.Date())
report_output<-file.path(report_version,"OUTPUT")
fig_path<-file.path(report_output,"FIGURES")
tab_path<-file.path(report_output,"TABLES")
report_path<-file.path(report_output,"REPORT")
content$path$fig<-fig_path
content$path$tab<-tab_path
content$path$report<-report_path
dir.create(report_output,recursive = T,showWarnings = F)
dir.create(fig_path,showWarnings = F)
dir.create(tab_path,showWarnings = F)
dir.create(report_path,showWarnings = F)
data<-data_formatting(file)
write.csv(data,file.path(tab_path,"structured_dataset.csv"),row.names = FALSE,quote=FALSE)
#Graphical rules
## ISSCAAP Group colors
ISSCAAPColors <- as.character(c("Fishes"="#AED6F1",
"Molluscs"="#D7BDE2",
"Crustaceans"="#F5B7B1",
"Aquatic Invertebrates"="#F9E79F",
"Aquatic Plants"="#ABEBC6"))
ISSCAAPScale <- scale_fill_manual(name="Major_Group_En", values=ISSCAAPColors)
ISSCAAPGroupLvl <- c("Freshwater fishes",
"Carps, barbels and other cyprinids",
"Tilapias and other cichlids",
"Miscellaneous freshwater fishes",
"Diadromous fishes",
"Sturgeons, paddlefishes",
"River eels",
"Salmons, trouts, smelts",
"Shads",
"Miscellaneous diadromous fishes",
"Marine fishes",
"Flounders, halibuts, soles",
"Cods, hakes, haddocks",
"Miscellaneous coastal fishes",
"Miscellaneous demersal fishes",
"Herrings, sardines, anchovies",
"Tunas, bonitos, billfishes",
"Miscellaneous pelagic fishes",
"Sharks, rays, chimaeras",
"Marine fishes not identified")
SubGrLvl <- c("Freshwater fishes"="white",
"Carps, barbels and other cyprinids"="#ffd400",
"Tilapias and other cichlids"="#ffb600",
"Miscellaneous freshwater fishes"="#ff9e00",
"Diadromous fishes"="white",
"Sturgeons, paddlefishes"="#aacc00",#C1FF1C",
"River eels"="#80b918",#9BE931",
"Salmons, trouts, smelts"="#55a630",#58BB43",
"Shads"="#2b9348",#3AA346",
"Miscellaneous diadromous fishes"="#007f5f",#007542",
"Marine fishes"="white",
"Flounders, halibuts, soles"="#80ffdb",
"Cods, hakes, haddocks"="#72efdd",
"Miscellaneous coastal fishes"="#64dfdf",
"Miscellaneous demersal fishes"="#56cfe1",
"Herrings, sardines, anchovies"="#48bfe3",
"Tunas, bonitos, billfishes"="#4ea8de",
"Miscellaneous pelagic fishes"="#5390d9",
"Sharks, rays, chimaeras"="#5e60ce",
"Marine fishes not identified"="#6930c3")
SubGrlabel <- c("Freshwater fishes"="<br/>**FRESHWATER FISHES**",
"Carps, barbels and other cyprinids"="Carps, barbels and other cyprinids",
"Tilapias and other cichlids"="Tilapias and other cichlids",
"Miscellaneous freshwater fishes"="Miscellaneous freshwater fishes",
"Diadromous fishes"="<br/>**DIADROMOUS FISHES**",
"Sturgeons, paddlefishes"="Sturgeons, paddlefishes",
"River eels"="River eels",
"Salmons, trouts, smelts"="Salmons, trouts, smelts",
"Shads"="Shads",
"Miscellaneous diadromous fishes"="Miscellaneous diadromous fishes",
"Marine fishes"="<br/>**MARINE FISHES**",
"Flounders, halibuts, soles"="Flounders, halibuts, soles",
"Cods, hakes, haddocks"="Cods, hakes, haddocks",
"Miscellaneous coastal fishes"="Miscellaneous coastal fishes",
"Miscellaneous demersal fishes"="Miscellaneous demersal fishes",
"Herrings, sardines, anchovies"="Herrings, sardines, anchovies",
"Tunas, bonitos, billfishes"="Tunas, bonitos, billfishes",
"Miscellaneous pelagic fishes"="Miscellaneous pelagic fishes",
"Sharks, rays, chimaeras"="Sharks, rays, chimaeras",
"Marine fishes not identified"="Marine fishes not identified")
SubGrScale <- scale_fill_manual(values=SubGrLvl,labels=SubGrlabel)
myColors <- scales::hue_pal()(17)
names(myColors) <- levels(as.factor(unique(data%>%filter(Major_Group_En=="Fishes")%>%pull(ISSCAAP_Group_En))))
colScale <- scale_fill_manual(name = "ISSCAAP_Group_En",values = myColors)
## Taxonomic line type
# TaxonomicLine <- as.character(c("species"="solid",
# "family"="longdash",
# "nei"="dotted"))
# TaxonomicScale <- scale_linetype_manual(name="level", values=TaxonomicLine)
TaxonomicColors <- as.character(c("species"="#516091",
"family"="#74bec1",
# "nei"="#adebbe")
"nei"="#ff4d4d"))
TaxonomicScale <- scale_color_manual(name="level", values=TaxonomicColors)
#generic indicator
period_start<-min(data$year)
period_end<-max(data$year)
source_text<-sprintf("SOURCE : FAO Global Capture Production (%s-%s)",period_start,period_end)
# Introduction
#content$text$intro_text<-Intro_text(data)
content$text$intro_text<-glue("The FAO Fisheries and Aquaculture Division (NFI) provides advice and objective information to FAO Members to help promote responsible fisheries and aquaculture. To fulfill this role, the Division compiles, analyses and disseminates fishery and aquaculture data structured within data collections. To ensure quality assurance, each collection is documented to highlight definitions and to specify the structure, sources, coverage, processes, intended use, etc.
The Global Capture Production Dataset is one of the data collections. Capture fisheries are activities of harvesting aquatic organisms from marine, coastal and inland areas. The reporting of capture production species implies biodiversity mainstreaming in fisheries management, assuming that more reported taxa imply a better mainstreaming. This mainstreaming can help understand biodiversity of the wild aquatic environment, including Commercially Exploited Aquatic Species (CEAS) and other species impacted by fishing. Aquaculture is the farming of aquatic organisms and thus is not included for the purpose of this report.
Current version of the Global Capture Production Dataset contains the volume of aquatic species caught from {period_start} to {period_end} by country or territory of capture, by species items, by FAO major fishing areas, and year, for all commercial, industrial, recreational and subsistence purposes. It provides a unique and complete record of countries’ efforts to report on species of importance to the management of their natural living resources.
The dataset can be used to highlight geographic and taxonomic biases and gaps in global reporting. The report contains analyses of reported species by geographic areas (continents and FAO areas), and aggregated taxonomic groups found in ISSCAAP, [reference to ASIFS and ISSCAAP]. The dataset has also significantly evolved over time, providing opportunities to investigate the time series in countries’ reporting to FAO Fisheries and Aquaculture Division.")
#Indicators
start_date_country <- data %>%
select(c("year","flag","flag_name","flag_iso")) %>%
distinct() %>%
mutate(flag=sprintf("%s[%s]",flag_name,flag_iso))%>%
group_by(flag)%>%
summarise(year = min(year)) %>%
group_by(year)%>%
summarise(flag = paste0(flag,collapse = ";"))
tab_name<-"flag_list_by_start_year.csv"
write.csv(start_date_country,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
area_in_ocean<-data %>%
filter(!is.na(ocean))%>%
select(c("ocean","f_area","f_area_label")) %>%
distinct() %>%
group_by(ocean)%>%
mutate(f_area=as.numeric(f_area))%>%
arrange(f_area)%>%
summarise(area = paste0(f_area_label,collapse = ";"))
tab_name<-"area_list_by_ocean.csv"
write.csv(area_in_ocean,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
# Part 1
## Commonness of Taxonomic Group records in FishStatJ
### Output :com_isscaap_global
name<-"com_isscaap_global"
#### Data
com_global <- data %>%
select(c("Major_Group_En","capture")) %>%
count(Major_Group_En) %>%
mutate(sum = sum(n)) %>%
mutate(pour = n/sum*100)%>%
mutate(n=n/1000)
###Table
com_global_table<-
com_global%>%
select(c("Major_Group_En","n","pour"))%>%
mutate(pour = round(pour,1))%>%
mutate(n = round(n,1))%>%
kable(format = "html",escape = FALSE,booktabs=T,align = "lcc",
col.names=c("", "(number in thousands)","(percentage)"))%>%
kable_styling("condensed")%>%
add_header_above(setNames(3,sprintf("Total records (%s-%s)",period_start,period_end)),include_empty=F,angle=0,line=T,escape = F,italic = T,align="c",color = "white", background = "#0091a6")%>%
row_spec(0, bold = T,italic = T, color = "white", background = "#0091a6")%>%
column_spec(1:3, width = "1in",extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
footnote(general='',general_title = paste0("<b>",source_text,"</b>"),escape=F)%>%
save_kable(file = file.path(fig_path,sprintf("table_global_%s_%s.png",period_start,period_end)), bs_theme = "flatly")
data %>%
filter(year%in%seq(period_end-2,period_end))%>%
select(c("Major_Group_En","capture")) %>%
count(Major_Group_En) %>%
mutate(sum = sum(n)) %>%
mutate(pour = n/sum*100)%>%
mutate(n=n/1000)%>%
select(c("Major_Group_En","n","pour"))%>%
mutate(pour = round(pour,1))%>%
mutate(n = round(n,1))%>%
kable(format = "html",escape = FALSE,booktabs=T,align = "lcc",
col.names=c("", "(number in thousands)","(percentage)"))%>%
kable_styling("condensed")%>%
add_header_above(setNames(3,sprintf("Total records (%s-%s)",period_end-2,period_end)),include_empty=F,angle=0,line=T,escape = F,italic = T,align="c",color = "white", background = "#0091a6")%>%
row_spec(0, bold = T,italic = T, color = "white", background = "#0091a6")%>%
column_spec(1:3, width = "1in",extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
footnote(general='',general_title = paste0("<b>",source_text,"</b>"),escape=F)%>%
save_kable(file = file.path(fig_path,sprintf("table_global_%s_%s.png",period_end-2,period_end)), bs_theme = "flatly")
test<-data %>%
mutate(year =ifelse(as.numeric(substring(as.character(year),4,4))%in%0:4,
sprintf('%s-%s',paste0(substring(as.character(year),1,3),0),paste0(substring(as.character(year),1,3),4)),
sprintf('%s-%s',paste0(substring(as.character(year),1,3),5),paste0(substring(as.character(year),1,3),9)))
)%>%
select(c("Major_Group_En","year","capture")) %>%
count(year,Major_Group_En) %>%
group_by(year)%>%
mutate(sum = sum(n)) %>%
group_by(year,Major_Group_En)%>%
mutate(pour = n/sum*100)%>%
mutate(n=n/1000)%>%
select(c("Major_Group_En","year","n","pour"))%>%
mutate(pour = round(pour,1))%>%
mutate(n = round(n,1))
test_table_v<- test%>%
rename(value=n)%>%
select(-pour)%>%
pivot_wider(names_from = year,values_from = value,values_fill = 0)%>%
ungroup()
test_table_p<- test%>%
mutate(value=paste0("(",round(pour,2),"%)"))%>%
select(-n,-pour)%>%
pivot_wider(names_from = year,values_from = value,values_fill = "(O%)")%>%
ungroup()
test_table<-rbind(test_table_v,test_table_p)
test_table$Major_Group_En<-factor(test_table$Major_Group_En,
levels = c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))
table<-test_table[order(test_table$Major_Group_En),]
table%>%
kable(format = "html",escape = FALSE,booktabs=T,align = "l",
col.names=c("", names(table)[2:ncol(table)]))%>%
kable_styling("condensed")%>%
row_spec(0, bold = T,italic = T, color = "white", background = "#0091a6")%>%
column_spec(1:ncol(table), width = "1in",extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
collapse_rows(columns = 1, valign = "m")%>%
footnote(general='',general_title = paste0("<b>",source_text,"</b>"),escape=F)%>%
save_kable(file = file.path(fig_path,sprintf("table_global_%s_%s_by_5.png",period_start,period_end)), bs_theme = "flatly")
#### Figure
fig<-ggplot(data=com_global,aes(x=Major_Group_En,y=n,fill=Major_Group_En))+
geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
labs(x=NULL,y="Number of records (thousands)",fill=NULL)+
geom_text(aes(label=scales::comma(n)), vjust=-0.3, color="grey40", size=9 / .pt)+
scale_y_continuous(labels = scales::comma)+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
ISSCAAPScale+
theme(text=element_text(size=9,family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9,angle = 0, vjust = 0.5, hjust=0.5),
panel.background = element_rect(fill="#f8f8f8"),#"#efeff0"
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width = 6.5,height=4)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_global%>%
select(Major_Group_En,n,pour)%>%
rename(ISSCAAP_Group = Major_Group_En)%>%
rename(Number_thousand = n)%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
content$title[name]<-list("Average number of Unique Records (Species Items) per Country reported 19XX to 201X — by Taxonomic Groups and Ocean Basin")
#glue("In FAO the word 'fish' is a term used as a collective term, that includes fish, molluscs, Crustaceans and any aquatic animal which is harvested. Definition source: FAO Fisheries and Aquaculture Department, FAO, 2014.
#FishStatJ capture production records (**{ min(as.numeric(data$year))}**-**{max(as.numeric(data$year))}** inclusive) contains **{round(subset(com_global,Major_Group_En=='Fishes')$pour,2)}**% vertebrate fish records more than records of molluscs **{round(subset(com_global,Major_Group_En=='Molluscs')$pour,2)}**% and Crustaceans **{round(subset(com_global,Major_Group_En=='Crustaceans')$pour,2)}**%. Aquatic plants has the least records in capture production dataset **{round(subset(com_global,Major_Group_En=='Aquatic Plants')$pour,2)}**%.")
####Global record by fao area
name<-"record_by_fao_area"
com_global_f_area <- data %>%
select(c("f_area_label","capture")) %>%
count(f_area_label) %>%
mutate(sum = sum(n)) %>%
mutate(pour = n/sum*100)%>%
mutate(n=n/1000)%>%
arrange(n)
com_global_f_area$f_area_label <- factor(com_global_f_area$f_area_label, levels=unique(as.character(com_global_f_area$f_area_label)) )
fig<-ggplot(data=com_global_f_area,aes(x=f_area_label,y=n))+
geom_bar(stat="identity",width = 0.6,show.legend = FALSE,fill="#5390d9")+
labs(x=NULL,y=NULL,fill=NULL)+
scale_y_continuous(limits=c(0,max(com_global_f_area$n)),breaks = seq(0, max(com_global_f_area$n), by = 10))+
coord_flip()+
theme(text=element_text(size=9,family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9,angle = 0, vjust = 0.5, hjust=0.5),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(linetype="dotted",color="grey60"),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width = 6.5,height=5)
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_global_f_area,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
###################
#Global by ISSCAAP group
#By default
com_global <- data %>%
select(c("ISSCAAP_Group_En","Major_Group_En","capture")) %>%
count(Major_Group_En,ISSCAAP_Group_En) %>%
mutate(sum = sum(n)) %>%
mutate(pour = n/sum*100)%>%
mutate(n=n/1000)
fig<-ggplot(data=com_global,aes(x=Major_Group_En,y=n,fill=ISSCAAP_Group_En))+
geom_bar(stat="identity",position="stack",width = 0.8,show.legend = T)+
labs(x=NULL,y="Number of records (thousands)",fill=NULL)+
#geom_text(aes(label=scales::comma(n)), vjust=-0.3, color="grey40", size=9 / .pt)+
scale_y_continuous(labels = scales::comma)+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
#ISSCAAPScale+
theme(text=element_text(size=9,family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9,angle = 0, vjust = 0.5, hjust=0.5),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
#By 5
com_global <- data %>%
select(c("ISSCAAP_Group_En","Major_Group_En","capture")) %>%
count(Major_Group_En,ISSCAAP_Group_En) %>%
mutate(n=n/1000)%>%
group_by(Major_Group_En)%>%
mutate(rank = rank(-n)) %>%
arrange(Major_Group_En,rank)%>%
ungroup()
com_global<-rbind(as.data.frame(com_global%>%filter(rank<=5)),
as.data.frame(com_global%>%filter(rank>5) %>% group_by(Major_Group_En)%>%summarise(ISSCAAP_Group_En=paste0("others ", tolower(unique(Major_Group_En))),n=sum(n),rank=6))
)
# library(ggalluvial)
#
# ggplot(com_global,aes(y = n, axis1 = ISSCAAP_Group_En, axis2 = Major_Group_En)) +
# geom_alluvium(aes(fill = ISSCAAP_Group_En), width = 1/12,show.legend = F) +
# geom_stratum(width = 1/12, fill = "grey", color = "grey") +
# geom_text(stat = "stratum", aes(label = after_stat(stratum)),angle=0,size=7 / .pt) +
# scale_x_discrete(limits = c("Group", "Major"), expand = c(.05, .05))+
# theme(text=element_text(size=9,family = 'sans'),
# axis.text.x = element_blank())
###############################################################################################################################
## Commonness of Marine and anadromous vs Inland records in FishStatJ
### Output :com_isscaap_area_type
name<-"com_isscaap_area_type"
#### Data
com_area_type <- data %>%
select(c("Major_Group_En","f_area_type","capture")) %>%
count(Major_Group_En,f_area_type)%>%
group_by(Major_Group_En)%>%
arrange(Major_Group_En,desc(f_area_type))%>%
mutate(n=n/1000)%>%
mutate(pour=(n/sum(n))*100)%>%
mutate(lab_ypos = ifelse(f_area_type=="marine",-10, cumsum(n)+10))
#### Figure
fig<-ggplot(data=com_area_type,aes(x=Major_Group_En,y=n,fill=f_area_type))+
geom_bar(stat="identity",position="stack")+
geom_text(aes(y=lab_ypos,label=sprintf("%s (%s%%)",scales::comma(n),format(round(pour,1), nsmall = 1)),colour=f_area_type), size=9 / .pt,show.legend = F)+
scale_fill_manual(values=c("#F7DC6F", "#85C1E9"))+
scale_color_manual(values=c("#C3A606", "#054478"))+
labs(x=NULL,y="Number of records (thousands)",fill=NULL)+
scale_y_continuous(labels = scales::comma)+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(size=9,family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9,angle = 0, vjust = 0.5, hjust=0.5),
# legend.background = element_rect(fill="transparent"),
# legend.position = c(.95, .95),
# legend.justification = c("right", "top"),
# legend.direction = "horizontal",
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
# fig_blue<-ggplot(data=com_area_type,aes(x=Major_Group_En,y=n,fill=f_area_type))+
# geom_bar(stat="identity",position="stack")+
# geom_text(aes(y=lab_ypos,label=scales::comma(n),colour=f_area_type), size=9 / .pt,show.legend = F)+
# scale_fill_manual(values=c("#80dfff", "#0099cc"))+
# scale_color_manual(values=c("#1ac6ff", "#0086b3"))+
# labs(x=NULL,y="Number of records in FishstatJ\n (in thousands)",caption="SOURCE : FAO Global Capture Production",fill=NULL)+
# scale_y_continuous(labels = scales::comma)+
# scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
# theme(axis.title.y = element_text(size=9),
# axis.text.y = element_text(size=9),
# axis.text.x = element_text(size=9,angle = 0, vjust = 0.5, hjust=0.5),
# legend.background = element_rect(fill="transparent"),
# legend.position = c(.95, .95),
# legend.justification = c("right", "top"),
# legend.direction = "horizontal",
# panel.background = element_rect(fill="#f8f8f8"),
# panel.grid.major.y = element_line(linetype="dotted",color="grey50"),
# panel.grid.major.x = element_blank(),
# panel.grid.minor = element_blank(),
# plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
# plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width = 6.5,height=4)
#ggsave(paste0(name,"_blue.png"),fig_blue,"png",path=fig_path,width=7,height=5)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_area_type%>%
select(Major_Group_En,f_area_type,n)%>%
rename(ISSCAAP_Group = Major_Group_En)%>%
rename(Number_thousand = n)%>%
rename(FAO_Area_Type = f_area_type)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness of Taxonomic Group records in FishStatJ — by Ocean Basin
### Output :com_isscaap_ocean
name<-"com_isscaap_ocean"
####
content$title[name]<-list(paste0("Average number of Unique Records (Species Items) per Country reported ",period_start," to ",period_end," — by Taxonomic Groups and Ocean Basin"))
#### Data
###Kim Method -
com_ocean_group<- data %>%
filter(!is.na(ocean))%>%
select(c("ocean","flag","year","Major_Group_En","capture","species")) %>%
group_by(ocean,flag,year,Major_Group_En)%>%
summarise(n=length(unique(species)))%>%
ungroup()%>%
complete(nesting(ocean,flag,year),Major_Group_En,fill=list(n=0))%>%
group_by(ocean,flag,year)%>%
mutate(sum=sum(n))%>%
group_by(ocean,flag,year,Major_Group_En)%>%
mutate(pourc=n/sum)%>%
group_by(ocean,year,Major_Group_En)%>%
summarise(meanOfFlag= mean(pourc))%>%
group_by(ocean,Major_Group_En)%>%
summarise(meanOfYear= mean(meanOfFlag),maxOfYear= max(meanOfFlag),minOfYear= min(meanOfFlag),nbOfYear= n(),sdOfYear=sd(meanOfFlag),seOfYear=sd(meanOfFlag)/sqrt(n()))%>%
ungroup()
com_ocean_group_isscaap2<- data %>%
filter(Major_Group_En=="Fishes")%>%
mutate(ISSCAAP_Group_En=factor(ISSCAAP_Group_En,levels = ISSCAAPGroupLvl))%>%
filter(!is.na(ocean))%>%
#filter(ocean!="Arctic Sea")%>%
select(c("ocean","flag","year","ISSCAAP_Group_En","capture","species")) %>%
group_by(ocean,flag,year,ISSCAAP_Group_En)%>%
summarise(n=length(unique(species)))%>%
group_by(ocean,year,ISSCAAP_Group_En)%>%
summarise(meanOfFlag= mean(n))%>%
group_by(ocean,ISSCAAP_Group_En)%>%
summarise(meanOfYear= mean(meanOfFlag))%>%
group_by(ocean)%>%
mutate(sum=sum(meanOfYear))%>%
ungroup()%>%
mutate(pour=meanOfYear/sum)
add<-com_ocean_group_isscaap2[1,]
add$ISSCAAP_Group_En<-"Diadromous fishes"
add$pour<-0
add2<-add
add2$ISSCAAP_Group_En<-"Marine fishes"
com_ocean_group_isscaap2<-rbind(com_ocean_group_isscaap2,add)
com_ocean_group_isscaap2<-rbind(com_ocean_group_isscaap2,add2)
#######################################################
nb_col<-2
nb_height<-ceiling(length(unique(com_ocean_group$ocean))/nb_col)
#### Figure
fig<-ggplot(data=com_ocean_group%>%filter(ocean!="Arctic Sea"),aes(x=Major_Group_En,y=meanOfYear,fill=Major_Group_En))+
geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
geom_errorbar(aes(ymin=meanOfYear-seOfYear, ymax=meanOfYear+seOfYear), width=.1,color="grey50",position=position_dodge(1))+
geom_text(aes(label=scales::percent(meanOfYear, accuracy=0.01,suffix =""),y=meanOfYear+seOfYear), vjust=-0.5, color="grey40", size=9 / .pt)+
facet_wrap(~ocean,ncol=2)+
ISSCAAPScale+
geom_label(data=com_ocean_group%>%filter(ocean!="Arctic Sea")%>%filter(Major_Group_En=="Fishes"),mapping=aes(x = Inf, y = Inf, label = ocean), fill="#f8f8f8", label.size=0,hjust=1.05, vjust=1.5, size= 9 / .pt)+
labs(x=NULL,y="Percentage",fill=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits=c(0,1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=7,angle =0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_isscaap2<-ggplot(data=com_ocean_group_isscaap2,aes(x=ocean,y=pour,fill=ISSCAAP_Group_En))+
geom_bar(stat="identity",position = 'stack',width = 0.8,show.legend = T)+
labs(x=NULL,y="Percentage",fill=NULL)+
SubGrScale+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits=c(0,1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9,angle =0, vjust = 0.5, hjust=0.5),
legend.key = element_rect(fill = "white"),
legend.key.size = unit(0.2, 'cm'),
legend.title = element_blank(),
legend.text=element_markdown(size=7),
legend.background = element_rect(fill="transparent"),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
ggsave(paste0(name,".png"),fig,"png",path=fig_path,dpi=900,width=6.5,height=4)
ggsave(paste0(name,"_subgroup.png"),fig_isscaap2,"png",path=fig_path,dpi=900,width=6.5,height=4)
# for(i in com_ocean_group%>%select(ocean)%>%filter(ocean!="Arctic Sea")%>%distinct()%>%pull()){
# fig<-ggplot(data=com_ocean_group%>%filter(ocean==i),aes(x=Major_Group_En,y=meanOfYear,fill=Major_Group_En))+
# geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
# geom_errorbar(aes(ymin=meanOfYear-sdOfYear, ymax=meanOfYear+sdOfYear), width=.1,color="grey40",position=position_dodge(1))+
# geom_text(aes(label=scales::percent(meanOfYear, accuracy=0.01)), vjust=-1.1, color="grey40", size=9 / .pt)+
# #facet_wrap(~ocean,ncol=nb_col)+
# ISSCAAPScale+
# geom_text(aes(x = Inf, y = Inf, label = i), hjust=1.05, vjust=1.5, size= 9 / .pt)+
# labs(x=NULL,y="Average number of annual unique records per country\n(in percent)",caption=source_text,fill=NULL)+
# scale_y_continuous(labels = scales::percent, limits=c(0,1))+
# scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
# theme(text=element_text(family = 'sans'),
# axis.title.y = element_text(size=6),
# axis.text.y = element_text(size=9),
# axis.text.x = element_text(size=6,angle = 0, vjust = 0.5, hjust=0.5),
# strip.background = element_blank(),
# strip.text.x = element_blank(),
# panel.background = element_rect(fill="#f8f8f8"),
# panel.grid.major.y = element_line(linetype="solid",color="grey60"),
# panel.grid.major.x = element_blank(),
# panel.grid.minor = element_blank(),
# plot.caption = element_text(face = "italic",hjust=0,size=6,color="grey20"),
# plot.margin = unit(c(0,0,0,0), "cm"))
#
# # fig_per_2<-ggplot(data=com_ocean_group_per%>%filter(ocean!="Arctic Sea"),aes(x=ocean,y=percent,fill=Major_Group_En))+
# # geom_bar(stat="identity",width = 0.8,position="stack",show.legend = TRUE)+
# # ISSCAAPScale+
# # labs(x=NULL,y="Average number of unique records per country (in percent)",caption="SOURCE : FAO Global Capture Production",fill=NULL)+
# # scale_y_continuous(labels = scales::percent)+
# # scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
# # coord_cartesian(clip = 'off') +
# # theme(axis.title.y = element_text(size=10*nb_col),
# # axis.text.y = element_text(size=12),
# # axis.text.x = element_text(size=12,angle = 0, vjust = 0.5, hjust=0.5),
# # panel.background = element_rect(fill="#f8f8f8"),
# # panel.grid.major.y = element_line(linetype="dotted",color="grey50"),
# # panel.grid.major.x = element_blank(),
# # panel.grid.minor = element_blank(),
# # plot.caption = element_text(face = "italic",hjust=0,size=7*nb_col,color="grey20"),
# # plot.margin = unit(c(0,0.5,0.5,0), "cm"))
#
# fig_name<-paste0(name,"_",i,".png")
# ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width=3.25,height=2)
#
# content$fig[name]<-list(file.path(fig_path,fig_name))
# }
####Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_ocean_group%>%
select(Major_Group_En,ocean,meanOfYear)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
tab_name<-paste0("ref_",name,"_subgroup",".csv")
write.csv(com_ocean_group_isscaap2,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness of Taxonomic Group records in FishStatJ — by FAO Fishery Areas
### Output :com_isccap_f_area
#### Data
# com_area_group <- data %>%
# select(c("f_area_type","f_area","f_area_label","flag","Major_Group_En","capture")) %>%
# count(f_area_type,f_area,f_area_label,flag,Major_Group_En)%>%
# group_by(f_area_type,f_area,f_area_label,Major_Group_En)%>%
# mutate(n=n/1000)%>%
# summarise(median = median(n))%>%
# group_by(f_area_type,f_area,f_area_label)%>%
# complete(Major_Group_En,fill=list(median=0))
com_area_group<- data %>%
select(c("f_area_type","f_area","f_area_label","flag","year","Major_Group_En","capture","species")) %>%
group_by(f_area_type,f_area,f_area_label,flag,year,Major_Group_En)%>%
summarise(n=length(unique(species)))%>%
ungroup()%>%
complete(nesting(f_area_type,f_area,f_area_label,flag,year),Major_Group_En,fill=list(n=0))%>%
group_by(f_area_type,f_area,f_area_label,flag,year)%>%
mutate(sum=sum(n))%>%
group_by(f_area_type,f_area,f_area_label,flag,year,Major_Group_En)%>%
mutate(pourc=n/sum)%>%
group_by(f_area_type,f_area,f_area_label,year,Major_Group_En)%>%
summarise(meanOfFlag= mean(pourc))%>%
group_by(f_area_type,f_area,f_area_label,Major_Group_En)%>%
summarise(meanOfYear= mean(meanOfFlag),maxOfYear= max(meanOfFlag),minOfYear= min(meanOfFlag),nbOfYear= n(),sdOfYear=sd(meanOfFlag),seOfYear=sd(meanOfFlag)/sqrt(n()))%>%
ungroup()
# for(i in unique(com_area_group$f_area_type)){
#
# name<-paste0("com_isccap_f_area_",i)
#
# com_area_type <- com_area_group %>%
# filter(f_area_type==i)
#
# nb_col<-3
# nb_height<-ceiling(length(unique(com_area_type$f_area_label))/nb_col)
#
# #### Figure
#
# fig<-ggplot(data=com_area_type%>%filter(f_area!="07"),aes(x=Major_Group_En,y=meanOfYear,fill=Major_Group_En))+
# geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
# geom_errorbar(aes(ymin=meanOfYear-sdOfYear, ymax=meanOfYear+sdOfYear), width=.1,color="grey40",position=position_dodge(1))+
# geom_text(aes(label=scales::percent(meanOfYear, accuracy=0.01)), vjust=-0.3, color="grey40", size=4)+
# facet_wrap(~f_area_label,ncol=nb_col)+
# ISSCAAPScale+
# geom_text(data=com_area_type%>%filter(f_area!="07")%>%filter(Major_Group_En=="Fishes"),mapping=aes(x = Inf, y = Inf, label = f_area_label), hjust=1.05, vjust=1.5, size= 5)+
# labs(x=NULL,y="Average number of unique records per country (in percent)",caption="SOURCE : FAO Global Capture Production",fill=NULL)+
# scale_y_continuous(labels = scales::percent)+
# scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
# theme(axis.title.y = element_text(size=10*nb_col),
# axis.text.y = element_text(size=10),
# axis.text.x = element_text(size=10,angle = 0, vjust = 0.5, hjust=0.5),
# strip.background = element_blank(),
# strip.text.x = element_blank(),
# panel.background = element_rect(fill="#f8f8f8"),
# panel.grid.major.y = element_line(linetype="dotted",color="grey50"),
# panel.grid.major.x = element_blank(),
# panel.grid.minor = element_blank(),
# plot.caption = element_text(face = "italic",hjust=0,size=8*nb_col,color="grey20"),
# plot.margin = unit(c(0,0.5,0.5,0), "cm"))
#
# fig_name<-paste0(name,".png")
# ggsave(fig_name,fig,"png",path=fig_path,width=7*nb_col,height=5*nb_height)
# content$fig[name]<-list(file.path(fig_path,fig_name))
#
# #### Table
#
# tab_name<-paste0("ref_",name,".csv")
#
# write.csv(com_area_type,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#
# content$tab[name]<-list(file.path(tab_path,tab_name))
#
# #### Text
# content$text[name]<-list("Text Place Holder")
# }
name<-paste0("com_isccap_f_area_inland")
com_area_type <- com_area_group %>%
filter(f_area_type=="inland")
# #### Figure
#
fig<-ggplot(data=com_area_type%>%filter(f_area!="07"),aes(x=Major_Group_En,y=meanOfYear,fill=Major_Group_En))+
geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
geom_errorbar(aes(ymin=meanOfYear-seOfYear, ymax=meanOfYear+seOfYear), width=.1,color="grey50",position=position_dodge(1))+
geom_text(aes(label=scales::percent(meanOfYear, accuracy=0.01,suffix=""),y=meanOfYear+seOfYear), vjust=-0.5, color="grey40", size=9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
ISSCAAPScale+
geom_label(data=com_area_type%>%filter(f_area!="07")%>%filter(Major_Group_En=="Fishes"),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
labs(x=NULL,y="Percentage",fill=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits =c(0,1.1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width=6.5,height=6)
#INLAND TABLE
tab_name<-paste0("ref_",name,".csv")
write.csv(com_area_type,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#ISSCAAP
com_area_group2<- data %>%
filter(f_area!="07")%>%
filter(Major_Group_En=="Fishes")%>%
mutate(ISSCAAP_Group_En=factor(ISSCAAP_Group_En,levels = ISSCAAPGroupLvl))%>%
select(c("ocean","f_area_type","f_area","f_area_label","flag","year","ISSCAAP_Group_En","capture","species")) %>%
group_by(ocean,f_area_type,f_area,f_area_label,flag,year,ISSCAAP_Group_En)%>%
summarise(n=length(unique(species)))%>%
group_by(ocean,f_area_type,f_area,f_area_label,year,ISSCAAP_Group_En)%>%
summarise(meanOfFlag= mean(n))%>%
group_by(ocean,f_area_type,f_area,f_area_label,ISSCAAP_Group_En)%>%
summarise(meanOfYear= mean(meanOfFlag))%>%
group_by(ocean,f_area_type,f_area,f_area_label)%>%
mutate(sum=sum(meanOfYear))%>%
ungroup()%>%
mutate(pour=meanOfYear/sum)
com_area_type2 <- com_area_group2 %>%
filter(f_area_type=="inland")
add<- com_area_type2[1,]
add$ISSCAAP_Group_En<-"Freshwater fishes"
add$pour<-0
add1<- add
add1$ISSCAAP_Group_En<-"Diadromous fishes"
#add2<-add
#add2$ISSCAAP_Group_En<-"Marine fishes"
com_area_type2 <-rbind(com_area_type2 ,add)
com_area_type2 <-rbind(com_area_type2 ,add1)
#com_area_type2 <-rbind(com_area_type2 ,add2)
fig_isscaap<-ggplot(data=com_area_type2,aes(x=f_area_label,y=pour,fill=ISSCAAP_Group_En))+
geom_bar(stat="identity",position='stack',width = 0.8,show.legend = T)+
labs(x=NULL,y="Percentage",fill=NULL)+
SubGrScale+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits =c(0,1.1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.key = element_rect(fill = "white"),
legend.key.size = unit(0.2, 'cm'),
legend.title = element_blank(),
legend.text=element_markdown(size=7),
legend.background = element_rect(fill="transparent"),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,"_subgroup.png")
ggsave(fig_name,fig_isscaap,"png",path=fig_path,dpi=900,width=6.5,height=4)
#INLAND TABLE SUBGROUP
tab_name<-paste0("ref_",name,"_subgroup",".csv")
write.csv(com_area_type2,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#Marine
name<-paste0("com_isccap_f_area_marine")
com_area_type <- com_area_group %>%
filter(f_area_type=="marine")
com_area_type$part<-ifelse(com_area_type$f_area_label%in%sort(unique(com_area_type$f_area_label))[1:10],1,2)
# #### Figure
for (i in unique(com_area_type$part)){
fig<-ggplot(data=com_area_type%>%filter(part==i),aes(x=Major_Group_En,y=meanOfYear,fill=Major_Group_En))+
geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
geom_errorbar(aes(ymin=meanOfYear-seOfYear, ymax=meanOfYear+seOfYear), width=.1,color="grey50",position=position_dodge(1))+
geom_text(aes(label=scales::percent(meanOfYear, accuracy=0.01,suffix=""),y=meanOfYear+seOfYear), vjust=-0.5, color="grey40", size=9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
ISSCAAPScale+
geom_label(data=com_area_type%>%filter(part==i)%>%filter(Major_Group_En=="Fishes"),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
labs(x=NULL,y="Percentage",fill=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits =c(0,1.1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,"_part",i,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width=6.5,height=9)
}
#MARINE TABLE
tab_name<-paste0("ref_",name,".csv")
write.csv(com_area_type,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
com_area_type2 <- com_area_group2 %>%
filter(f_area_type=="marine")
add<- com_area_type2[1,]
add$ISSCAAP_Group_En<-"Diadromous fishes"
add$pour<-0
add2<-add
add2$ISSCAAP_Group_En<-"Marine fishes"
com_area_type2 <-rbind(com_area_type2 ,add)
com_area_type2 <-rbind(com_area_type2 ,add2)
fig_issccap<-ggplot(data=com_area_type2,aes(x=f_area_label,y=pour,fill=ISSCAAP_Group_En))+
geom_bar(stat="identity",position='stack',width = 0.8,show.legend = TRUE)+
SubGrScale+
labs(x=NULL,y="Percentage",fill=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits =c(0,1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
coord_flip()+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.key = element_rect(fill = "white"),
legend.key.size = unit(0.2, 'cm'),
legend.title = element_blank(),
legend.text=element_markdown(size=7),
legend.background = element_rect(fill="transparent"),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,"_subgroup",".png")
ggsave(fig_name,fig_issccap,"png",path=fig_path,dpi=900,width=6.5,height=9)
#MARINE TABLE SUBGROUP
tab_name<-paste0("ref_",name,"_subgroup",".csv")
write.csv(com_area_type2,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
###############################################################################################################################
#Part 2.A Biodiversity reporting development in FAO Fisheries
## By Number of unique taxa
## Commonness among Fishes [vertebrate fish] records in FishStatJ — Global
### Output :com_sp_global
name<-"com_sp_nbtax_global"
#### Data
# com_sp_lvl <- data %>%
# filter(Major_Group_En %in% c("Fishes")) %>%
# select(c("species","scientific_name","family","order","Taxonomic_Code","year")) %>%
# mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
# distinct()%>%
# count(year,level)%>%
# group_by(year)%>%
# mutate(sum = sum(n))%>%
# mutate(pour = n/sum)%>%
# group_by(level) %>%
# group_by(level,grp = as.integer(gl(n(), 3, n()))) %>%
# summarise(year = last(year), pour = mean(pour),nb_sp=mean(sum)) %>%
# select(-grp)
com_sp_lvl <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
select(c("species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
distinct()%>%
count(year,level)%>%
group_by(year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_lvl$level<-factor(com_sp_lvl$level,levels = c("species","family","nei"))
#### Figure
# fig<-ggplot(data=com_sp_lvl,aes(x=year,y=pour,group=level,color=level))+
# geom_line(size =1.5,lineend = "round",alpha=0.7)+
# geom_text(data=com_sp_lvl%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
# TaxonomicScale+
# labs(x=NULL,y="Percentage",caption=source_text,color=NULL)+
# scale_y_continuous(labels = scales::percent,limits=c(0,1),breaks = seq(0, 1, by = 0.25))+
# scale_x_discrete(labels = as.character(com_sp_lvl$year), breaks = com_sp_lvl$year)+
# theme(text=element_text(family = 'sans'),
# axis.title.y = element_text(size=9),
# axis.text.y = element_text(size=9),
# axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
# legend.key = element_rect(fill = "white"),
# legend.title = element_blank(),
# legend.text=element_text(size=9),
# legend.background = element_rect(fill="transparent"),
# legend.position = "bottom",
# legend.justification = "left",
# legend.direction = "horizontal",
# panel.background = element_rect(fill="#efeff0"),
# panel.grid.major.y = element_line(linetype="dashed",color="grey50"),
# panel.grid.major.x = element_blank(),
# panel.grid.minor = element_blank(),
# plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
# plot.margin = unit(c(0,0,0,0), "cm"))
fig<-ggplot(data=com_sp_lvl,aes(x=year,group=level))+
geom_line(aes(y=pour,color=level),size =0.5,lineend = "round",alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_lvl%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_lvl$pour)/max(com_sp_lvl$nb_sp)), name="Total taxa (number)"))+
scale_x_continuous(breaks = seq(min(com_sp_lvl$year),max(com_sp_lvl$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width = 6.5,height=4)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_lvl%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness among fish records in FishStatJ — by Ocean Basins
### Output :com_sp_ocean
name<-"com_sp_nbtax_ocean"
#### Data
# com_sp_ocean <- data %>%
# filter(f_area_type=="marine") %>%
# filter(Major_Group_En %in% c("Fishes")) %>%
# select(c("flag","ocean","species","scientific_name","family","order","Taxonomic_Code","year",)) %>%
# mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
# distinct()%>%
# count(flag,ocean,year,level)%>%
# group_by(ocean,year,level)%>%
# summarise(n=median(n))%>%
# ungroup()%>%
# complete(ocean, nesting(year, level),fill=list(n=0))%>%
# group_by(ocean,year)%>%
# mutate(sum = sum(n))%>%
# mutate(pour = n/sum)%>%
# group_by(ocean,level) %>%
# group_by(ocean,level,grp = as.integer(gl(n(), 3, n()))) %>%
# summarise(year = as.numeric(first(year)), pour = mean(pour),nb_sp=mean(sum))%>%
# select(-grp)
com_sp_ocean <- data %>%
filter(f_area_type=="marine") %>%
filter(Major_Group_En %in% c("Fishes"))%>%
select(c("ocean","species","scientific_name","family","order","Taxonomic_Code","year",)) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
distinct()%>%
count(ocean,year,level)%>%
ungroup()%>%
complete(ocean, nesting(year, level),fill=list(n=0))%>%
group_by(ocean,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(ocean,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))%>%
filter(ocean!="Arctic Sea")
com_sp_ocean$level<-factor(com_sp_ocean$level,levels = c("species","family","nei"))
##### Figure
fig<-ggplot(data=com_sp_ocean,aes(x=year,group=level))+
geom_line(aes(y=pour,color=level),size =0.5,lineend = "round",alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_ocean%>%filter(ocean!="Arctic Sea")%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
geom_label(data=com_sp_ocean%>%filter(level=="species"),mapping=aes(x = Inf, y = Inf, label = ocean), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~ocean,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_ocean$pour)/max(com_sp_ocean$nb_sp)), name="Total taxa (number)"))+
scale_x_continuous(breaks = seq(min(com_sp_ocean$year),max(com_sp_ocean$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=4,dpi=900)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_ocean%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness among fish records in FishStatJ — By FAO Fisheries Areas
### Output :com_sp_f_area
#### Data
#inland
name<-paste0("com_sp_nbtax_f_area_inland")
com_sp_area_type <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
filter(f_area_type == "inland") %>%
filter(f_area!="07")%>%
select(c("f_area_label","species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
distinct()%>%
count(f_area_label,year,level)%>%
ungroup()%>%
complete(f_area_label, nesting(year, level),fill=list(n=0))%>%
group_by(f_area_label,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(f_area_label,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_area_type$level<-factor(com_sp_area_type$level,levels = c("species","family","nei"))
nb_col<-2
#### Figure
fig<-ggplot(data=com_sp_area_type,aes(x=year,group=level))+
geom_line(size =0.5,lineend = "round",aes(color=level,y=pour),alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_area_type%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
geom_label(data=com_sp_area_type%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_area_type$pour)/max(com_sp_area_type$nb_sp)), name="Total taxa (number)"))+
scale_x_continuous(breaks = seq(min(com_sp_area_type$year),max(com_sp_area_type$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=6,dpi=900)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_area_type%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#marine
name<-paste0("com_sp_nbtax_f_area_marine")
com_sp_area_type <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
filter(f_area_type == "marine") %>%
select(c("f_area_label","species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
distinct()%>%
count(f_area_label,year,level)%>%
ungroup()%>%
complete(f_area_label, nesting(year, level),fill=list(n=0))%>%
group_by(f_area_label,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(f_area_label,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_area_type$part<-ifelse(com_sp_area_type$f_area_label%in%sort(unique(com_sp_area_type$f_area_label))[1:10],1,2)
# #### Figure
for (i in unique(com_sp_area_type$part)){
com_sp_area_type_part<-com_sp_area_type%>%filter(part==i)
com_sp_area_type_part$level<-factor(com_sp_area_type_part$level,levels = c("species","family","nei"))
fig<-ggplot(data=com_sp_area_type_part,aes(x=year,group=level))+
geom_line(size =0.5,lineend = "round",aes(color=level,y=pour),alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour,na.rm=T)/max(nb_sp,na.rm=T))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
# geom_text(data=com_sp_area_type%>%filter(part==i)%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.7, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
#geom_text(data=com_sp_area_type_part%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), hjust=1.05, vjust=1.5, size= 9 /.pt)+
geom_label(data=com_sp_area_type_part%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_area_type_part$pour,na.rm=T)/max(com_sp_area_type_part$nb_sp,na.rm=T)), name="Total taxa (number)"))+
scale_x_continuous(breaks = seq(min(com_sp_area_type_part$year),max(com_sp_area_type_part$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,"_part",i,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=9,dpi=900)
}
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_area_type%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
#Part 2.B Biodiversity reporting development in FAO Fisheries
## By Number of unique record
## Summary Formated Table
#### Data
com_sp_rec <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
select(c("species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
count(level)%>%
mutate(sum = sum(n))%>%
mutate(pour = round(n/sum*100,1))%>%
mutate(n = round(n/1000,1))%>%
mutate(level=factor(level,levels = c("species","family","nei"),labels = c("Species level","Family level","Nei")))%>%
select(level,n,pour)%>%
arrange(level)%>%
kable(format = "html",escape = FALSE,booktabs=T,align = "lcc",
col.names=c("", "(number in thousands)","(percentage)"))%>%
kable_styling("condensed")%>%
add_header_above(c("Total records (1950-2019)" = 3),include_empty=F,angle=0,line=T,escape = F,italic = T,align="c",color = "white", background = "#0091a6")%>%
row_spec(0, bold = T,italic = T, color = "white", background = "#0091a6")%>%
column_spec(1:3, width = "1in",extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
footnote(general='',general_title = paste0("<b>",source_text,"</b>"),escape=F)%>%
save_kable(file = file.path(fig_path,"table_taxa_grp_rec_1950_2019.png"), bs_theme = "flatly")
## Commonness among Fishes [vertebrate fish] records in FishStatJ — Global
name<-"com_sp_nbrec_global"
#### Data
com_sp_lvl <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
select(c("species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
count(year,level)%>%
group_by(year)%>%
mutate(n=n/1000)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_lvl$level<-factor(com_sp_lvl$level,levels = c("species","family","nei"))
#### Figure
fig<-ggplot(data=com_sp_lvl,aes(x=year,group=level))+
geom_line(aes(y=pour,color=level),size =0.5,lineend = "round",alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_lvl%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_lvl$pour)/max(com_sp_lvl$nb_sp)), name="Total records (number in thousands)"))+
scale_x_continuous(breaks = seq(min(com_sp_lvl$year),max(com_sp_lvl$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width = 6.5,height=4)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_lvl%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness among fish records in FishStatJ — by Ocean Basins
### Output :com_sp_ocean
name<-"com_sp_nbrec_ocean"
#### Data
com_sp_ocean <- data %>%
filter(f_area_type=="marine") %>%
filter(Major_Group_En %in% c("Fishes"))%>%
select(c("ocean","species","scientific_name","family","order","Taxonomic_Code","year",)) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
count(ocean,year,level)%>%
mutate(n=n/1000)%>%
ungroup()%>%
complete(ocean, nesting(year, level),fill=list(n=0))%>%
group_by(ocean,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(ocean,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))%>%
filter(ocean!="Arctic Sea")
com_sp_ocean$level<-factor(com_sp_ocean$level,levels = c("species","family","nei"))
##### Figure
fig<-ggplot(data=com_sp_ocean,aes(x=year,group=level))+
geom_line(aes(y=pour,color=level),size =0.5,lineend = "round",alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_ocean%>%filter(ocean!="Arctic Sea")%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
geom_label(data=com_sp_ocean%>%filter(level=="species"),mapping=aes(x = Inf, y = Inf, label = ocean), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~ocean,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_ocean$pour)/max(com_sp_ocean$nb_sp)), name="Total records (number in thousands)"))+
scale_x_continuous(breaks = seq(min(com_sp_ocean$year),max(com_sp_ocean$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=4,dpi=900)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_ocean%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness among fish records in FishStatJ — By FAO Fisheries Areas
### Output :com_sp_f_area
#### Data
#inland
name<-paste0("com_sp_nbrec_f_area_inland")
com_sp_area_type <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
filter(f_area_type == "inland") %>%
filter(f_area!="07")%>%
select(c("f_area_label","species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
count(f_area_label,year,level)%>%
mutate(n=n/1000)%>%
ungroup()%>%
complete(f_area_label, nesting(year, level),fill=list(n=0))%>%
group_by(f_area_label,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(f_area_label,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_area_type$level<-factor(com_sp_area_type$level,levels = c("species","family","nei"))
nb_col<-2
#### Figure
fig<-ggplot(data=com_sp_area_type,aes(x=year,group=level))+
geom_line(size =0.5,lineend = "round",aes(color=level,y=pour),alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_area_type%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
geom_label(data=com_sp_area_type%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_area_type$pour)/max(com_sp_area_type$nb_sp)), name="Total records (number in thousands)"))+
scale_x_continuous(breaks = seq(min(com_sp_area_type$year),max(com_sp_area_type$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=6,dpi=900)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_area_type%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#marine
name<-paste0("com_sp_nbrec_f_area_marine")
com_sp_area_type <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
filter(f_area_type == "marine") %>%
select(c("f_area_label","species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
count(f_area_label,year,level)%>%
mutate(n=n/1000)%>%
ungroup()%>%
complete(f_area_label, nesting(year, level),fill=list(n=0))%>%
group_by(f_area_label,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(f_area_label,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_area_type$part<-ifelse(com_sp_area_type$f_area_label%in%sort(unique(com_sp_area_type$f_area_label))[1:10],1,2)
# #### Figure
for (i in unique(com_sp_area_type$part)){
com_sp_area_type_part<-com_sp_area_type%>%filter(part==i)
com_sp_area_type_part$level<-factor(com_sp_area_type_part$level,levels = c("species","family","nei"))
fig<-ggplot(data=com_sp_area_type_part,aes(x=year,group=level))+
geom_line(size =0.5,lineend = "round",aes(color=level,y=pour),alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour,na.rm=T)/max(nb_sp,na.rm=T))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
# geom_text(data=com_sp_area_type%>%filter(part==i)%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.7, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
#geom_text(data=com_sp_area_type_part%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), hjust=1.05, vjust=1.5, size= 9 /.pt)+
geom_label(data=com_sp_area_type_part%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_area_type_part$pour,na.rm=T)/max(com_sp_area_type_part$nb_sp,na.rm=T)), name="Total records (number in thousands)"))+
scale_x_continuous(breaks = seq(min(com_sp_area_type_part$year),max(com_sp_area_type_part$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,"_part",i,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=9,dpi=900)
}
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_area_type%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
#Biodiversity indexes
#2
#a
#ocean
name<-paste0("taxa_richess_","ocean")
ocean_nb_sp<- data %>%
filter(!is.na(ocean))%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(c("ocean","Major_Group_En","species")) %>%
group_by(ocean,Major_Group_En)%>%
summarise(n=length(unique(species)))%>%
ungroup()%>%
complete(ocean,Major_Group_En,fill=list(n=0))%>%
group_by(ocean)%>%
mutate(sum=sum(n))%>%
group_by(ocean,Major_Group_En)%>%
mutate(pourc=n/sum*100)%>%
select(-sum)%>%
ungroup()
ocean_nb_sp_table_v<- ocean_nb_sp%>%
rename(value=n)%>%
select(-pourc)%>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = 0)%>%
ungroup()
ocean_nb_sp_table_p<- ocean_nb_sp%>%
mutate(value=paste0("(",round(pourc,2),"%)"))%>%
select(-n,-pourc)%>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "(O%)")%>%
ungroup()
ocean_nb_sp_table<-rbind(ocean_nb_sp_table_v,ocean_nb_sp_table_p)
ocean_nb_sp_table<-ocean_nb_sp_table[order(ocean_nb_sp_table$ocean),]
# ocean_nb_sp_table<-as.data.frame(ocean_nb_sp_table)
#rownames(ocean_nb_sp_table)<-ocean_nb_sp_table$ocean
#ocean_nb_sp_table<-ocean_nb_sp_table[,-1]
names(ocean_nb_sp_table)[1]<-"Ocean/ISSCAAP groups"
kw0 <- paste0("(number of different taxa",footnote_marker_number(1, double_escape = T),"; percentage of total repartition)")
ocean_nb_sp_table%>%
kable(format = "html",escape = FALSE,booktabs=T,align = "lccccc",
col.names=paste0(substr(names(ocean_nb_sp_table),1,1),tolower(substr(names(ocean_nb_sp_table),2,nchar(names(ocean_nb_sp_table))))))%>%
kable_styling("condensed")%>%
add_header_above(c(setNames(6,kw0)),include_empty=F,angle=0,line=T,escape = F,italic = T,align="c",color = "white", background = "#0091a6")%>%
row_spec(0, bold = T, color = "white", background = "#0091a6")%>%
column_spec(1,extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
column_spec(2:6, width = "1in",extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
collapse_rows(columns = 1, valign = "m")%>%
footnote(number = c("Exclude aquatic mammals, amphibia and reptilia"))
tab_name<-paste0("ref_",name,".csv")
write.csv(ocean_nb_sp_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#fao major area
major_area_nb_sp<- data %>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(c("f_area_type","f_area","f_area_label","Major_Group_En","species")) %>%
group_by(f_area_type,f_area,f_area_label,Major_Group_En)%>%
summarise(n=length(unique(species)))%>%
ungroup()%>%
complete(nesting(f_area_type,f_area,f_area_label),Major_Group_En,fill=list(n=0))%>%
group_by(f_area_type,f_area,f_area_label)%>%
mutate(sum=sum(n))%>%
group_by(f_area_type,f_area,f_area_label,Major_Group_En)%>%
mutate(pourc=n/sum*100)%>%
select(-sum)%>%
ungroup()
for(i in unique(major_area_nb_sp$f_area_type)){
area_type_nb_sp <- major_area_nb_sp %>%
filter(f_area_type==i)%>%
mutate(value=paste0(n," [",round(pourc,2),"%]"))%>%
select(-n,-pourc)%>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "0 [O%]")%>%
ungroup()
print(area_type_nb_sp)
name<-paste0("taxa_richess_",i)
tab_name<-paste0("ref_",name,".csv")
write.csv(area_type_nb_sp,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
}
#b
##ocean
name<-paste0("taxa_inMoreThan2_","ocean")
multi_ocean<- data%>%
filter(!is.na(ocean))%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(ocean,Major_Group_En,species,capture)%>%
mutate(code=paste0(ocean,"-",Major_Group_En))%>%
select(-ocean,-Major_Group_En)%>%
mutate(capture=1)%>%
distinct()%>%
pivot_wider(names_from = code,values_from = capture,values_fill = 0)%>%
mutate(in_x_ocean=rowSums(across(where(is.numeric))))%>%
filter(in_x_ocean>=3)%>%
select(-in_x_ocean)%>%
pivot_longer(!species, names_to = "code", values_to = "count")%>%
group_by(code)%>%
summarise(morethan2=sum(count))%>%
separate(code, c("ocean", "Major_Group_En"), sep = "-")%>%
complete(ocean,Major_Group_En,fill=list(morethan2=0))%>%
ungroup()%>%
inner_join(select(ocean_nb_sp,ocean,Major_Group_En,n))%>%
group_by(ocean,Major_Group_En)%>%
mutate(pourc=morethan2/n*100)
multi_ocean_table<-multi_ocean%>%
group_by(ocean,Major_Group_En)%>%
mutate(value=if(!is.nan(pourc)){paste0(morethan2," [",round(pourc,2),"%]")}else{"_"})%>%
select(ocean,Major_Group_En,value)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "0 [O%]")%>%
ungroup()
tab_name<-paste0("ref_",name,".csv")
write.csv(multi_ocean_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
multi_area<- data%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(f_area_type,f_area_label,Major_Group_En,species,capture)%>%
mutate(code=paste0(f_area_type,"_",f_area_label,"_",Major_Group_En))%>%
select(-f_area_type,-f_area_label,-Major_Group_En)%>%
mutate(capture=1)%>%
distinct()%>%
pivot_wider(names_from = code,values_from = capture,values_fill = 0)%>%
mutate(in_x_ocean=rowSums(across(where(is.numeric))))%>%
filter(in_x_ocean>=3)%>%
select(-in_x_ocean)%>%
pivot_longer(!species, names_to = "code", values_to = "count")%>%
group_by(code)%>%
summarise(morethan2=sum(count))%>%
separate(code, c("f_area_type","f_area_label", "Major_Group_En"), sep = "_")%>%
complete(nesting(f_area_type,f_area_label),Major_Group_En,fill=list(morethan2=0))%>%
ungroup()%>%
inner_join(select(major_area_nb_sp,f_area_type,f_area_label,Major_Group_En,n))%>%
group_by(f_area_type,f_area_label,Major_Group_En)%>%
mutate(pourc=morethan2/n*100)
for(i in unique(multi_area$f_area_type)){
name<-paste0("taxa_inMoreThan2_",i)
multi_area_type <- multi_area %>%
filter(f_area_type==i)%>%
mutate(value=if(!is.nan(pourc)){paste0(morethan2," [",round(pourc,2),"%]")}else{"_"})%>%
select(f_area_label,Major_Group_En,value)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "0 [O%]")%>%
ungroup()
print(multi_area_type)
tab_name<-paste0("ref_",name,".csv")
write.csv(multi_area_type,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
}
#c
#ocean
name<-paste0("taxa_onlyIn1_","ocean")
unique_ocean<- data%>%
filter(!is.na(ocean))%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(ocean,Major_Group_En,species,capture)%>%
mutate(code=paste0(ocean,"-",Major_Group_En))%>%
select(-ocean,-Major_Group_En)%>%
mutate(capture=1)%>%
distinct()%>%
pivot_wider(names_from = code,values_from = capture,values_fill = 0)%>%
mutate(in_x_ocean=rowSums(across(where(is.numeric))))%>%
filter(in_x_ocean==1)%>%
select(-in_x_ocean)%>%
pivot_longer(!species, names_to = "code", values_to = "count")%>%
group_by(code)%>%
summarise(unique_nb=sum(count))%>%
separate(code, c("ocean", "Major_Group_En"), sep = "-")%>%
complete(ocean,Major_Group_En,fill=list(unique_nb=0))%>%
ungroup()%>%
inner_join(select(ocean_nb_sp,ocean,Major_Group_En,n))%>%
group_by(ocean,Major_Group_En)%>%
mutate(pourc=unique_nb/n*100)
unique_ocean_table<-unique_ocean%>%
group_by(ocean,Major_Group_En)%>%
mutate(value=if(!is.nan(pourc)){paste0(unique_nb," [",round(pourc,2),"%]")}else{"_"})%>%
select(ocean,Major_Group_En,value)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "0 [O%]")%>%
ungroup()
tab_name<-paste0("ref_",name,".csv")
write.csv(unique_ocean_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#f_area
unique_area<- data%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(f_area_type,f_area_label,Major_Group_En,species,capture)%>%
mutate(code=paste0(f_area_type,"_",f_area_label,"_",Major_Group_En))%>%
select(-f_area_type,-f_area_label,-Major_Group_En)%>%
mutate(capture=1)%>%
distinct()%>%
pivot_wider(names_from = code,values_from = capture,values_fill = 0)%>%
mutate(in_x_ocean=rowSums(across(where(is.numeric))))%>%
filter(in_x_ocean==1)%>%
select(-in_x_ocean)%>%
pivot_longer(!species, names_to = "code", values_to = "count")%>%
group_by(code)%>%
summarise(unique_nb=sum(count))%>%
separate(code, c("f_area_type","f_area_label", "Major_Group_En"), sep = "_")%>%
complete(nesting(f_area_type,f_area_label),Major_Group_En,fill=list(unique_nb=0))%>%
ungroup()%>%
inner_join(select(major_area_nb_sp,f_area_type,f_area_label,Major_Group_En,n))%>%
group_by(f_area_type,f_area_label,Major_Group_En)%>%
mutate(pourc=unique_nb/n*100)
for(i in unique(unique_area$f_area_type)){
name<-paste0("taxa_onlyIn1_",i)
unique_area_type <- unique_area %>%
filter(f_area_type==i)%>%
mutate(value=if(!is.nan(pourc)){paste0(unique_nb," [",round(pourc,2),"%]")}else{"_"})%>%
select(f_area_label,Major_Group_En,value)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "0 [O%]")%>%
ungroup()
print(unique_area_type)
tab_name<-paste0("ref_",name,".csv")
write.csv(unique_area_type,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
}
##
#3
#a
#ocean
name<-paste0("evenness_simpson_","ocean")
richess_ocean<- data%>%
filter(!is.na(ocean))%>%
select(ocean,Major_Group_En,flag,year,species,capture)%>%
mutate(code=paste0(ocean,"-",Major_Group_En))%>%
select(-ocean,-Major_Group_En)%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
mutate(capture=1)%>%
distinct()%>%
group_by(code,species)%>%
summarise(n=sum(capture))%>%
pivot_wider(names_from = species,values_from = n,values_fill = 0)%>%
group_by(code)%>%
summarise(simpson=diversity(across(where(is.numeric)),index="simpson"))%>%
separate(code, c("ocean","Major_Group_En"), sep = "-")%>%
ungroup()
richess_ocean_table<-richess_ocean%>%
group_by(ocean)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = simpson)
tab_name<-paste0("ref_",name,".csv")
write.csv(richess_ocean_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
richess_ocean$Major_Group_En<-factor(richess_ocean$Major_Group_En,
levels = c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))
fig<-ggplot(richess_ocean, aes(Major_Group_En, ocean, fill= simpson)) +
geom_tile(color = "white",lwd = 1.5,linetype = 1) +
geom_text(aes(label = scales::comma(simpson,accuracy=0.01)), color = "white", size = 4)+
scale_fill_gradient2(low="#caf0f8",mid="#48cae4",high="#023e8a",midpoint=0.5,limits=c(0,1))+
labs(x=NULL,y=NULL,caption="SOURCE : FAO Global Capture Production",fill="Simpson index")+
scale_y_discrete(limits=rev)+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9,vjust = 0.5),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
legend.title = element_text(size=8),
legend.text = element_text(size=8),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=10,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width=6.5,height=5)
#major area
richess_area<- data%>%
select(f_area_type,f_area,f_area_label,Major_Group_En,flag,year,species,capture)%>%
mutate(code=paste0(f_area_type,"_",f_area,"_",f_area_label,"_",Major_Group_En))%>%
select(-f_area_type,-f_area,-f_area_label,-Major_Group_En)%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
mutate(capture=1)%>%
distinct()%>%
group_by(code,species)%>%
summarise(n=sum(capture))%>%
pivot_wider(names_from = species,values_from = n,values_fill = 0)%>%
group_by(code)%>%
summarise(simpson=diversity(across(where(is.numeric)),index="simpson"))%>%
separate(code, c("f_area_type","f_area","f_area_label","Major_Group_En"), sep = "_")%>%
ungroup()
richess_area$Major_Group_En<-factor(richess_area$Major_Group_En,
levels = c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))
for(i in unique(richess_area$f_area_type)){
name<-paste0("evenness_simpson_",i)
richess_area_table<-richess_area%>%
filter(f_area_type==i)%>%
group_by(f_area_type,f_area,f_area_label)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = simpson)
print(richess_area_table)
tab_name<-paste0("ref_",name,".csv")
write.csv(richess_area_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
richess_area_type <- richess_area %>%
filter(f_area_type==i)
fig<-ggplot(richess_area_type%>%filter(f_area!="07"), aes(Major_Group_En, f_area_label, fill= simpson)) +
geom_tile(color = "white",lwd = 1.5,linetype = 1) +
geom_text(aes(label = scales::comma(simpson,accuracy=0.01)), color = "white", size = 4)+
scale_fill_gradient2(low="#caf0f8",mid="#48cae4",high="#023e8a",midpoint=0.5,limits=c(0,1))+
labs(x=NULL,y=NULL,caption="SOURCE : FAO Global Capture Production",fill="Simpson index")+
scale_y_discrete(limits=rev)+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9,vjust = 0.5),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
legend.title = element_text(size=8),
legend.text = element_text(size=8),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=10,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width=6.5,height=ifelse(i=="marine",6,5))
}
#4
#a
#ocean
name<-paste0("turnover_","ocean")
turn_ocean<- data %>%
filter(!is.na(ocean))%>%
select(ocean,flag,Major_Group_En,year,species) %>%
filter(year %in% seq(period_end-9,period_end,1))%>%
distinct()%>%
arrange(ocean,flag,Major_Group_En,year)%>%
group_by(ocean,flag,Major_Group_En,year)%>%
summarise(list_sp = paste0(unique(species),collapse = ","))%>%
group_by(ocean,flag,Major_Group_En)%>%
mutate(previous_year = lag(list_sp, order_by = year))%>%
filter(year!=2010)%>%
group_by(ocean,flag,Major_Group_En,year)%>%
mutate(previous_year = if(!is.na(previous_year)){previous_year}else{"_"})%>%
mutate(nb_diff={
x=sort(unlist(strsplit(list_sp,",")))==sort(unlist(strsplit(previous_year,",")))
length(x[x==FALSE])},nb_egal={
x=sort(unlist(strsplit(list_sp,",")))==sort(unlist(strsplit(previous_year,",")))
length(x[x==TRUE])})%>%
mutate(turnover=(nb_diff/(nb_diff+nb_egal))*100)%>%
group_by(ocean,flag,Major_Group_En)%>%
summarise(meanOfYear=mean(turnover))%>%
group_by(ocean,Major_Group_En)%>%
summarise(meanOfFlag=mean(meanOfYear))%>%
ungroup()
turn_ocean_table<-turn_ocean%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = meanOfFlag)
tab_name<-paste0("ref_",name,".csv")
write.csv(turn_ocean_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#fao area
turn_area<- data %>%
select(f_area_type,f_area_label,flag,Major_Group_En,year,species) %>%
filter(year %in% seq(period_end-9,period_end,1))%>%
distinct()%>%
arrange(f_area_type,f_area_label,flag,Major_Group_En,year)%>%
group_by(f_area_type,f_area_label,flag,Major_Group_En,year)%>%
summarise(list_sp = paste0(unique(species),collapse = ","))%>%
group_by(f_area_type,f_area_label,flag,Major_Group_En)%>%
mutate(previous_year = lag(list_sp, order_by = year))%>%
filter(year!=2010)%>%
group_by(f_area_type,f_area_label,flag,Major_Group_En,year)%>%
mutate(previous_year = if(!is.na(previous_year)){previous_year}else{"_"})%>%
mutate(nb_diff={
x=sort(unlist(strsplit(list_sp,",")))==sort(unlist(strsplit(previous_year,",")))
length(x[x==FALSE])},nb_egal={
x=sort(unlist(strsplit(list_sp,",")))==sort(unlist(strsplit(previous_year,",")))
length(x[x==TRUE])})%>%
mutate(turnover=(nb_diff/(nb_diff+nb_egal))*100)%>%
group_by(f_area_type,f_area_label,flag,Major_Group_En)%>%
summarise(meanOfYear=mean(turnover))%>%
group_by(f_area_type,f_area_label,Major_Group_En)%>%
summarise(meanOfFlag=mean(meanOfYear))%>%
ungroup()
for(i in unique(turn_area$f_area_type)){
name<-paste0("turnover_",i)
turn_area_table<-turn_area%>%
filter(f_area_type==i)%>%
group_by(f_area_type,f_area_label)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = meanOfFlag)
print(turn_area_table)
tab_name<-paste0("ref_",name,".csv")
write.csv(turn_area_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
}
return(content)
}
abennici/faoBiofishr documentation built on May 22, 2023, 10:28 p.m.
R Package Documentation
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Defines functions report_content
Documented in report_content
#' Compute content of report
#'
#' This function compute all the output elements to include of report
#'
#' @param file Path to the input file
#' @param out Path to export destination
#' @return A list of element
#' @export
report_content<-function(file,out=NULL){
#Data cleaning
content<-list()
#Output repository creation
path<-if(is.null(out)) getwd() else out
report_folder<-file.path(path,"BIODIVERSITY_TREND_REPORT")
report_version<-file.path(report_folder,Sys.Date())
report_output<-file.path(report_version,"OUTPUT")
fig_path<-file.path(report_output,"FIGURES")
tab_path<-file.path(report_output,"TABLES")
report_path<-file.path(report_output,"REPORT")
content$path$fig<-fig_path
content$path$tab<-tab_path
content$path$report<-report_path
dir.create(report_output,recursive = T,showWarnings = F)
dir.create(fig_path,showWarnings = F)
dir.create(tab_path,showWarnings = F)
dir.create(report_path,showWarnings = F)
data<-data_formatting(file)
write.csv(data,file.path(tab_path,"structured_dataset.csv"),row.names = FALSE,quote=FALSE)
#Graphical rules
## ISSCAAP Group colors
ISSCAAPColors <- as.character(c("Fishes"="#AED6F1",
"Molluscs"="#D7BDE2",
"Crustaceans"="#F5B7B1",
"Aquatic Invertebrates"="#F9E79F",
"Aquatic Plants"="#ABEBC6"))
ISSCAAPScale <- scale_fill_manual(name="Major_Group_En", values=ISSCAAPColors)
ISSCAAPGroupLvl <- c("Freshwater fishes",
"Carps, barbels and other cyprinids",
"Tilapias and other cichlids",
"Miscellaneous freshwater fishes",
"Diadromous fishes",
"Sturgeons, paddlefishes",
"River eels",
"Salmons, trouts, smelts",
"Shads",
"Miscellaneous diadromous fishes",
"Marine fishes",
"Flounders, halibuts, soles",
"Cods, hakes, haddocks",
"Miscellaneous coastal fishes",
"Miscellaneous demersal fishes",
"Herrings, sardines, anchovies",
"Tunas, bonitos, billfishes",
"Miscellaneous pelagic fishes",
"Sharks, rays, chimaeras",
"Marine fishes not identified")
SubGrLvl <- c("Freshwater fishes"="white",
"Carps, barbels and other cyprinids"="#ffd400",
"Tilapias and other cichlids"="#ffb600",
"Miscellaneous freshwater fishes"="#ff9e00",
"Diadromous fishes"="white",
"Sturgeons, paddlefishes"="#aacc00",#C1FF1C",
"River eels"="#80b918",#9BE931",
"Salmons, trouts, smelts"="#55a630",#58BB43",
"Shads"="#2b9348",#3AA346",
"Miscellaneous diadromous fishes"="#007f5f",#007542",
"Marine fishes"="white",
"Flounders, halibuts, soles"="#80ffdb",
"Cods, hakes, haddocks"="#72efdd",
"Miscellaneous coastal fishes"="#64dfdf",
"Miscellaneous demersal fishes"="#56cfe1",
"Herrings, sardines, anchovies"="#48bfe3",
"Tunas, bonitos, billfishes"="#4ea8de",
"Miscellaneous pelagic fishes"="#5390d9",
"Sharks, rays, chimaeras"="#5e60ce",
"Marine fishes not identified"="#6930c3")
SubGrlabel <- c("Freshwater fishes"="<br/>**FRESHWATER FISHES**",
"Carps, barbels and other cyprinids"="Carps, barbels and other cyprinids",
"Tilapias and other cichlids"="Tilapias and other cichlids",
"Miscellaneous freshwater fishes"="Miscellaneous freshwater fishes",
"Diadromous fishes"="<br/>**DIADROMOUS FISHES**",
"Sturgeons, paddlefishes"="Sturgeons, paddlefishes",
"River eels"="River eels",
"Salmons, trouts, smelts"="Salmons, trouts, smelts",
"Shads"="Shads",
"Miscellaneous diadromous fishes"="Miscellaneous diadromous fishes",
"Marine fishes"="<br/>**MARINE FISHES**",
"Flounders, halibuts, soles"="Flounders, halibuts, soles",
"Cods, hakes, haddocks"="Cods, hakes, haddocks",
"Miscellaneous coastal fishes"="Miscellaneous coastal fishes",
"Miscellaneous demersal fishes"="Miscellaneous demersal fishes",
"Herrings, sardines, anchovies"="Herrings, sardines, anchovies",
"Tunas, bonitos, billfishes"="Tunas, bonitos, billfishes",
"Miscellaneous pelagic fishes"="Miscellaneous pelagic fishes",
"Sharks, rays, chimaeras"="Sharks, rays, chimaeras",
"Marine fishes not identified"="Marine fishes not identified")
SubGrScale <- scale_fill_manual(values=SubGrLvl,labels=SubGrlabel)
myColors <- scales::hue_pal()(17)
names(myColors) <- levels(as.factor(unique(data%>%filter(Major_Group_En=="Fishes")%>%pull(ISSCAAP_Group_En))))
colScale <- scale_fill_manual(name = "ISSCAAP_Group_En",values = myColors)
## Taxonomic line type
# TaxonomicLine <- as.character(c("species"="solid",
# "family"="longdash",
# "nei"="dotted"))
# TaxonomicScale <- scale_linetype_manual(name="level", values=TaxonomicLine)
TaxonomicColors <- as.character(c("species"="#516091",
"family"="#74bec1",
# "nei"="#adebbe")
"nei"="#ff4d4d"))
TaxonomicScale <- scale_color_manual(name="level", values=TaxonomicColors)
#generic indicator
period_start<-min(data$year)
period_end<-max(data$year)
source_text<-sprintf("SOURCE : FAO Global Capture Production (%s-%s)",period_start,period_end)
# Introduction
#content$text$intro_text<-Intro_text(data)
content$text$intro_text<-glue("The FAO Fisheries and Aquaculture Division (NFI) provides advice and objective information to FAO Members to help promote responsible fisheries and aquaculture. To fulfill this role, the Division compiles, analyses and disseminates fishery and aquaculture data structured within data collections. To ensure quality assurance, each collection is documented to highlight definitions and to specify the structure, sources, coverage, processes, intended use, etc.
The Global Capture Production Dataset is one of the data collections. Capture fisheries are activities of harvesting aquatic organisms from marine, coastal and inland areas. The reporting of capture production species implies biodiversity mainstreaming in fisheries management, assuming that more reported taxa imply a better mainstreaming. This mainstreaming can help understand biodiversity of the wild aquatic environment, including Commercially Exploited Aquatic Species (CEAS) and other species impacted by fishing. Aquaculture is the farming of aquatic organisms and thus is not included for the purpose of this report.
Current version of the Global Capture Production Dataset contains the volume of aquatic species caught from {period_start} to {period_end} by country or territory of capture, by species items, by FAO major fishing areas, and year, for all commercial, industrial, recreational and subsistence purposes. It provides a unique and complete record of countries’ efforts to report on species of importance to the management of their natural living resources.
The dataset can be used to highlight geographic and taxonomic biases and gaps in global reporting. The report contains analyses of reported species by geographic areas (continents and FAO areas), and aggregated taxonomic groups found in ISSCAAP, [reference to ASIFS and ISSCAAP]. The dataset has also significantly evolved over time, providing opportunities to investigate the time series in countries’ reporting to FAO Fisheries and Aquaculture Division.")
#Indicators
start_date_country <- data %>%
select(c("year","flag","flag_name","flag_iso")) %>%
distinct() %>%
mutate(flag=sprintf("%s[%s]",flag_name,flag_iso))%>%
group_by(flag)%>%
summarise(year = min(year)) %>%
group_by(year)%>%
summarise(flag = paste0(flag,collapse = ";"))
tab_name<-"flag_list_by_start_year.csv"
write.csv(start_date_country,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
area_in_ocean<-data %>%
filter(!is.na(ocean))%>%
select(c("ocean","f_area","f_area_label")) %>%
distinct() %>%
group_by(ocean)%>%
mutate(f_area=as.numeric(f_area))%>%
arrange(f_area)%>%
summarise(area = paste0(f_area_label,collapse = ";"))
tab_name<-"area_list_by_ocean.csv"
write.csv(area_in_ocean,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
# Part 1
## Commonness of Taxonomic Group records in FishStatJ
### Output :com_isscaap_global
name<-"com_isscaap_global"
#### Data
com_global <- data %>%
select(c("Major_Group_En","capture")) %>%
count(Major_Group_En) %>%
mutate(sum = sum(n)) %>%
mutate(pour = n/sum*100)%>%
mutate(n=n/1000)
###Table
com_global_table<-
com_global%>%
select(c("Major_Group_En","n","pour"))%>%
mutate(pour = round(pour,1))%>%
mutate(n = round(n,1))%>%
kable(format = "html",escape = FALSE,booktabs=T,align = "lcc",
col.names=c("", "(number in thousands)","(percentage)"))%>%
kable_styling("condensed")%>%
add_header_above(setNames(3,sprintf("Total records (%s-%s)",period_start,period_end)),include_empty=F,angle=0,line=T,escape = F,italic = T,align="c",color = "white", background = "#0091a6")%>%
row_spec(0, bold = T,italic = T, color = "white", background = "#0091a6")%>%
column_spec(1:3, width = "1in",extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
footnote(general='',general_title = paste0("<b>",source_text,"</b>"),escape=F)%>%
save_kable(file = file.path(fig_path,sprintf("table_global_%s_%s.png",period_start,period_end)), bs_theme = "flatly")
data %>%
filter(year%in%seq(period_end-2,period_end))%>%
select(c("Major_Group_En","capture")) %>%
count(Major_Group_En) %>%
mutate(sum = sum(n)) %>%
mutate(pour = n/sum*100)%>%
mutate(n=n/1000)%>%
select(c("Major_Group_En","n","pour"))%>%
mutate(pour = round(pour,1))%>%
mutate(n = round(n,1))%>%
kable(format = "html",escape = FALSE,booktabs=T,align = "lcc",
col.names=c("", "(number in thousands)","(percentage)"))%>%
kable_styling("condensed")%>%
add_header_above(setNames(3,sprintf("Total records (%s-%s)",period_end-2,period_end)),include_empty=F,angle=0,line=T,escape = F,italic = T,align="c",color = "white", background = "#0091a6")%>%
row_spec(0, bold = T,italic = T, color = "white", background = "#0091a6")%>%
column_spec(1:3, width = "1in",extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
footnote(general='',general_title = paste0("<b>",source_text,"</b>"),escape=F)%>%
save_kable(file = file.path(fig_path,sprintf("table_global_%s_%s.png",period_end-2,period_end)), bs_theme = "flatly")
test<-data %>%
mutate(year =ifelse(as.numeric(substring(as.character(year),4,4))%in%0:4,
sprintf('%s-%s',paste0(substring(as.character(year),1,3),0),paste0(substring(as.character(year),1,3),4)),
sprintf('%s-%s',paste0(substring(as.character(year),1,3),5),paste0(substring(as.character(year),1,3),9)))
)%>%
select(c("Major_Group_En","year","capture")) %>%
count(year,Major_Group_En) %>%
group_by(year)%>%
mutate(sum = sum(n)) %>%
group_by(year,Major_Group_En)%>%
mutate(pour = n/sum*100)%>%
mutate(n=n/1000)%>%
select(c("Major_Group_En","year","n","pour"))%>%
mutate(pour = round(pour,1))%>%
mutate(n = round(n,1))
test_table_v<- test%>%
rename(value=n)%>%
select(-pour)%>%
pivot_wider(names_from = year,values_from = value,values_fill = 0)%>%
ungroup()
test_table_p<- test%>%
mutate(value=paste0("(",round(pour,2),"%)"))%>%
select(-n,-pour)%>%
pivot_wider(names_from = year,values_from = value,values_fill = "(O%)")%>%
ungroup()
test_table<-rbind(test_table_v,test_table_p)
test_table$Major_Group_En<-factor(test_table$Major_Group_En,
levels = c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))
table<-test_table[order(test_table$Major_Group_En),]
table%>%
kable(format = "html",escape = FALSE,booktabs=T,align = "l",
col.names=c("", names(table)[2:ncol(table)]))%>%
kable_styling("condensed")%>%
row_spec(0, bold = T,italic = T, color = "white", background = "#0091a6")%>%
column_spec(1:ncol(table), width = "1in",extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
collapse_rows(columns = 1, valign = "m")%>%
footnote(general='',general_title = paste0("<b>",source_text,"</b>"),escape=F)%>%
save_kable(file = file.path(fig_path,sprintf("table_global_%s_%s_by_5.png",period_start,period_end)), bs_theme = "flatly")
#### Figure
fig<-ggplot(data=com_global,aes(x=Major_Group_En,y=n,fill=Major_Group_En))+
geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
labs(x=NULL,y="Number of records (thousands)",fill=NULL)+
geom_text(aes(label=scales::comma(n)), vjust=-0.3, color="grey40", size=9 / .pt)+
scale_y_continuous(labels = scales::comma)+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
ISSCAAPScale+
theme(text=element_text(size=9,family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9,angle = 0, vjust = 0.5, hjust=0.5),
panel.background = element_rect(fill="#f8f8f8"),#"#efeff0"
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width = 6.5,height=4)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_global%>%
select(Major_Group_En,n,pour)%>%
rename(ISSCAAP_Group = Major_Group_En)%>%
rename(Number_thousand = n)%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
content$title[name]<-list("Average number of Unique Records (Species Items) per Country reported 19XX to 201X — by Taxonomic Groups and Ocean Basin")
#glue("In FAO the word 'fish' is a term used as a collective term, that includes fish, molluscs, Crustaceans and any aquatic animal which is harvested. Definition source: FAO Fisheries and Aquaculture Department, FAO, 2014.
#FishStatJ capture production records (**{ min(as.numeric(data$year))}**-**{max(as.numeric(data$year))}** inclusive) contains **{round(subset(com_global,Major_Group_En=='Fishes')$pour,2)}**% vertebrate fish records more than records of molluscs **{round(subset(com_global,Major_Group_En=='Molluscs')$pour,2)}**% and Crustaceans **{round(subset(com_global,Major_Group_En=='Crustaceans')$pour,2)}**%. Aquatic plants has the least records in capture production dataset **{round(subset(com_global,Major_Group_En=='Aquatic Plants')$pour,2)}**%.")
####Global record by fao area
name<-"record_by_fao_area"
com_global_f_area <- data %>%
select(c("f_area_label","capture")) %>%
count(f_area_label) %>%
mutate(sum = sum(n)) %>%
mutate(pour = n/sum*100)%>%
mutate(n=n/1000)%>%
arrange(n)
com_global_f_area$f_area_label <- factor(com_global_f_area$f_area_label, levels=unique(as.character(com_global_f_area$f_area_label)) )
fig<-ggplot(data=com_global_f_area,aes(x=f_area_label,y=n))+
geom_bar(stat="identity",width = 0.6,show.legend = FALSE,fill="#5390d9")+
labs(x=NULL,y=NULL,fill=NULL)+
scale_y_continuous(limits=c(0,max(com_global_f_area$n)),breaks = seq(0, max(com_global_f_area$n), by = 10))+
coord_flip()+
theme(text=element_text(size=9,family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9,angle = 0, vjust = 0.5, hjust=0.5),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(linetype="dotted",color="grey60"),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width = 6.5,height=5)
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_global_f_area,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
###################
#Global by ISSCAAP group
#By default
com_global <- data %>%
select(c("ISSCAAP_Group_En","Major_Group_En","capture")) %>%
count(Major_Group_En,ISSCAAP_Group_En) %>%
mutate(sum = sum(n)) %>%
mutate(pour = n/sum*100)%>%
mutate(n=n/1000)
fig<-ggplot(data=com_global,aes(x=Major_Group_En,y=n,fill=ISSCAAP_Group_En))+
geom_bar(stat="identity",position="stack",width = 0.8,show.legend = T)+
labs(x=NULL,y="Number of records (thousands)",fill=NULL)+
#geom_text(aes(label=scales::comma(n)), vjust=-0.3, color="grey40", size=9 / .pt)+
scale_y_continuous(labels = scales::comma)+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
#ISSCAAPScale+
theme(text=element_text(size=9,family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9,angle = 0, vjust = 0.5, hjust=0.5),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
#By 5
com_global <- data %>%
select(c("ISSCAAP_Group_En","Major_Group_En","capture")) %>%
count(Major_Group_En,ISSCAAP_Group_En) %>%
mutate(n=n/1000)%>%
group_by(Major_Group_En)%>%
mutate(rank = rank(-n)) %>%
arrange(Major_Group_En,rank)%>%
ungroup()
com_global<-rbind(as.data.frame(com_global%>%filter(rank<=5)),
as.data.frame(com_global%>%filter(rank>5) %>% group_by(Major_Group_En)%>%summarise(ISSCAAP_Group_En=paste0("others ", tolower(unique(Major_Group_En))),n=sum(n),rank=6))
)
# library(ggalluvial)
#
# ggplot(com_global,aes(y = n, axis1 = ISSCAAP_Group_En, axis2 = Major_Group_En)) +
# geom_alluvium(aes(fill = ISSCAAP_Group_En), width = 1/12,show.legend = F) +
# geom_stratum(width = 1/12, fill = "grey", color = "grey") +
# geom_text(stat = "stratum", aes(label = after_stat(stratum)),angle=0,size=7 / .pt) +
# scale_x_discrete(limits = c("Group", "Major"), expand = c(.05, .05))+
# theme(text=element_text(size=9,family = 'sans'),
# axis.text.x = element_blank())
###############################################################################################################################
## Commonness of Marine and anadromous vs Inland records in FishStatJ
### Output :com_isscaap_area_type
name<-"com_isscaap_area_type"
#### Data
com_area_type <- data %>%
select(c("Major_Group_En","f_area_type","capture")) %>%
count(Major_Group_En,f_area_type)%>%
group_by(Major_Group_En)%>%
arrange(Major_Group_En,desc(f_area_type))%>%
mutate(n=n/1000)%>%
mutate(pour=(n/sum(n))*100)%>%
mutate(lab_ypos = ifelse(f_area_type=="marine",-10, cumsum(n)+10))
#### Figure
fig<-ggplot(data=com_area_type,aes(x=Major_Group_En,y=n,fill=f_area_type))+
geom_bar(stat="identity",position="stack")+
geom_text(aes(y=lab_ypos,label=sprintf("%s (%s%%)",scales::comma(n),format(round(pour,1), nsmall = 1)),colour=f_area_type), size=9 / .pt,show.legend = F)+
scale_fill_manual(values=c("#F7DC6F", "#85C1E9"))+
scale_color_manual(values=c("#C3A606", "#054478"))+
labs(x=NULL,y="Number of records (thousands)",fill=NULL)+
scale_y_continuous(labels = scales::comma)+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(size=9,family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9,angle = 0, vjust = 0.5, hjust=0.5),
# legend.background = element_rect(fill="transparent"),
# legend.position = c(.95, .95),
# legend.justification = c("right", "top"),
# legend.direction = "horizontal",
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
# fig_blue<-ggplot(data=com_area_type,aes(x=Major_Group_En,y=n,fill=f_area_type))+
# geom_bar(stat="identity",position="stack")+
# geom_text(aes(y=lab_ypos,label=scales::comma(n),colour=f_area_type), size=9 / .pt,show.legend = F)+
# scale_fill_manual(values=c("#80dfff", "#0099cc"))+
# scale_color_manual(values=c("#1ac6ff", "#0086b3"))+
# labs(x=NULL,y="Number of records in FishstatJ\n (in thousands)",caption="SOURCE : FAO Global Capture Production",fill=NULL)+
# scale_y_continuous(labels = scales::comma)+
# scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
# theme(axis.title.y = element_text(size=9),
# axis.text.y = element_text(size=9),
# axis.text.x = element_text(size=9,angle = 0, vjust = 0.5, hjust=0.5),
# legend.background = element_rect(fill="transparent"),
# legend.position = c(.95, .95),
# legend.justification = c("right", "top"),
# legend.direction = "horizontal",
# panel.background = element_rect(fill="#f8f8f8"),
# panel.grid.major.y = element_line(linetype="dotted",color="grey50"),
# panel.grid.major.x = element_blank(),
# panel.grid.minor = element_blank(),
# plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
# plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width = 6.5,height=4)
#ggsave(paste0(name,"_blue.png"),fig_blue,"png",path=fig_path,width=7,height=5)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_area_type%>%
select(Major_Group_En,f_area_type,n)%>%
rename(ISSCAAP_Group = Major_Group_En)%>%
rename(Number_thousand = n)%>%
rename(FAO_Area_Type = f_area_type)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness of Taxonomic Group records in FishStatJ — by Ocean Basin
### Output :com_isscaap_ocean
name<-"com_isscaap_ocean"
####
content$title[name]<-list(paste0("Average number of Unique Records (Species Items) per Country reported ",period_start," to ",period_end," — by Taxonomic Groups and Ocean Basin"))
#### Data
###Kim Method -
com_ocean_group<- data %>%
filter(!is.na(ocean))%>%
select(c("ocean","flag","year","Major_Group_En","capture","species")) %>%
group_by(ocean,flag,year,Major_Group_En)%>%
summarise(n=length(unique(species)))%>%
ungroup()%>%
complete(nesting(ocean,flag,year),Major_Group_En,fill=list(n=0))%>%
group_by(ocean,flag,year)%>%
mutate(sum=sum(n))%>%
group_by(ocean,flag,year,Major_Group_En)%>%
mutate(pourc=n/sum)%>%
group_by(ocean,year,Major_Group_En)%>%
summarise(meanOfFlag= mean(pourc))%>%
group_by(ocean,Major_Group_En)%>%
summarise(meanOfYear= mean(meanOfFlag),maxOfYear= max(meanOfFlag),minOfYear= min(meanOfFlag),nbOfYear= n(),sdOfYear=sd(meanOfFlag),seOfYear=sd(meanOfFlag)/sqrt(n()))%>%
ungroup()
com_ocean_group_isscaap2<- data %>%
filter(Major_Group_En=="Fishes")%>%
mutate(ISSCAAP_Group_En=factor(ISSCAAP_Group_En,levels = ISSCAAPGroupLvl))%>%
filter(!is.na(ocean))%>%
#filter(ocean!="Arctic Sea")%>%
select(c("ocean","flag","year","ISSCAAP_Group_En","capture","species")) %>%
group_by(ocean,flag,year,ISSCAAP_Group_En)%>%
summarise(n=length(unique(species)))%>%
group_by(ocean,year,ISSCAAP_Group_En)%>%
summarise(meanOfFlag= mean(n))%>%
group_by(ocean,ISSCAAP_Group_En)%>%
summarise(meanOfYear= mean(meanOfFlag))%>%
group_by(ocean)%>%
mutate(sum=sum(meanOfYear))%>%
ungroup()%>%
mutate(pour=meanOfYear/sum)
add<-com_ocean_group_isscaap2[1,]
add$ISSCAAP_Group_En<-"Diadromous fishes"
add$pour<-0
add2<-add
add2$ISSCAAP_Group_En<-"Marine fishes"
com_ocean_group_isscaap2<-rbind(com_ocean_group_isscaap2,add)
com_ocean_group_isscaap2<-rbind(com_ocean_group_isscaap2,add2)
#######################################################
nb_col<-2
nb_height<-ceiling(length(unique(com_ocean_group$ocean))/nb_col)
#### Figure
fig<-ggplot(data=com_ocean_group%>%filter(ocean!="Arctic Sea"),aes(x=Major_Group_En,y=meanOfYear,fill=Major_Group_En))+
geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
geom_errorbar(aes(ymin=meanOfYear-seOfYear, ymax=meanOfYear+seOfYear), width=.1,color="grey50",position=position_dodge(1))+
geom_text(aes(label=scales::percent(meanOfYear, accuracy=0.01,suffix =""),y=meanOfYear+seOfYear), vjust=-0.5, color="grey40", size=9 / .pt)+
facet_wrap(~ocean,ncol=2)+
ISSCAAPScale+
geom_label(data=com_ocean_group%>%filter(ocean!="Arctic Sea")%>%filter(Major_Group_En=="Fishes"),mapping=aes(x = Inf, y = Inf, label = ocean), fill="#f8f8f8", label.size=0,hjust=1.05, vjust=1.5, size= 9 / .pt)+
labs(x=NULL,y="Percentage",fill=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits=c(0,1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=7,angle =0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_isscaap2<-ggplot(data=com_ocean_group_isscaap2,aes(x=ocean,y=pour,fill=ISSCAAP_Group_En))+
geom_bar(stat="identity",position = 'stack',width = 0.8,show.legend = T)+
labs(x=NULL,y="Percentage",fill=NULL)+
SubGrScale+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits=c(0,1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9,angle =0, vjust = 0.5, hjust=0.5),
legend.key = element_rect(fill = "white"),
legend.key.size = unit(0.2, 'cm'),
legend.title = element_blank(),
legend.text=element_markdown(size=7),
legend.background = element_rect(fill="transparent"),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
ggsave(paste0(name,".png"),fig,"png",path=fig_path,dpi=900,width=6.5,height=4)
ggsave(paste0(name,"_subgroup.png"),fig_isscaap2,"png",path=fig_path,dpi=900,width=6.5,height=4)
# for(i in com_ocean_group%>%select(ocean)%>%filter(ocean!="Arctic Sea")%>%distinct()%>%pull()){
# fig<-ggplot(data=com_ocean_group%>%filter(ocean==i),aes(x=Major_Group_En,y=meanOfYear,fill=Major_Group_En))+
# geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
# geom_errorbar(aes(ymin=meanOfYear-sdOfYear, ymax=meanOfYear+sdOfYear), width=.1,color="grey40",position=position_dodge(1))+
# geom_text(aes(label=scales::percent(meanOfYear, accuracy=0.01)), vjust=-1.1, color="grey40", size=9 / .pt)+
# #facet_wrap(~ocean,ncol=nb_col)+
# ISSCAAPScale+
# geom_text(aes(x = Inf, y = Inf, label = i), hjust=1.05, vjust=1.5, size= 9 / .pt)+
# labs(x=NULL,y="Average number of annual unique records per country\n(in percent)",caption=source_text,fill=NULL)+
# scale_y_continuous(labels = scales::percent, limits=c(0,1))+
# scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
# theme(text=element_text(family = 'sans'),
# axis.title.y = element_text(size=6),
# axis.text.y = element_text(size=9),
# axis.text.x = element_text(size=6,angle = 0, vjust = 0.5, hjust=0.5),
# strip.background = element_blank(),
# strip.text.x = element_blank(),
# panel.background = element_rect(fill="#f8f8f8"),
# panel.grid.major.y = element_line(linetype="solid",color="grey60"),
# panel.grid.major.x = element_blank(),
# panel.grid.minor = element_blank(),
# plot.caption = element_text(face = "italic",hjust=0,size=6,color="grey20"),
# plot.margin = unit(c(0,0,0,0), "cm"))
#
# # fig_per_2<-ggplot(data=com_ocean_group_per%>%filter(ocean!="Arctic Sea"),aes(x=ocean,y=percent,fill=Major_Group_En))+
# # geom_bar(stat="identity",width = 0.8,position="stack",show.legend = TRUE)+
# # ISSCAAPScale+
# # labs(x=NULL,y="Average number of unique records per country (in percent)",caption="SOURCE : FAO Global Capture Production",fill=NULL)+
# # scale_y_continuous(labels = scales::percent)+
# # scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
# # coord_cartesian(clip = 'off') +
# # theme(axis.title.y = element_text(size=10*nb_col),
# # axis.text.y = element_text(size=12),
# # axis.text.x = element_text(size=12,angle = 0, vjust = 0.5, hjust=0.5),
# # panel.background = element_rect(fill="#f8f8f8"),
# # panel.grid.major.y = element_line(linetype="dotted",color="grey50"),
# # panel.grid.major.x = element_blank(),
# # panel.grid.minor = element_blank(),
# # plot.caption = element_text(face = "italic",hjust=0,size=7*nb_col,color="grey20"),
# # plot.margin = unit(c(0,0.5,0.5,0), "cm"))
#
# fig_name<-paste0(name,"_",i,".png")
# ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width=3.25,height=2)
#
# content$fig[name]<-list(file.path(fig_path,fig_name))
# }
####Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_ocean_group%>%
select(Major_Group_En,ocean,meanOfYear)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
tab_name<-paste0("ref_",name,"_subgroup",".csv")
write.csv(com_ocean_group_isscaap2,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness of Taxonomic Group records in FishStatJ — by FAO Fishery Areas
### Output :com_isccap_f_area
#### Data
# com_area_group <- data %>%
# select(c("f_area_type","f_area","f_area_label","flag","Major_Group_En","capture")) %>%
# count(f_area_type,f_area,f_area_label,flag,Major_Group_En)%>%
# group_by(f_area_type,f_area,f_area_label,Major_Group_En)%>%
# mutate(n=n/1000)%>%
# summarise(median = median(n))%>%
# group_by(f_area_type,f_area,f_area_label)%>%
# complete(Major_Group_En,fill=list(median=0))
com_area_group<- data %>%
select(c("f_area_type","f_area","f_area_label","flag","year","Major_Group_En","capture","species")) %>%
group_by(f_area_type,f_area,f_area_label,flag,year,Major_Group_En)%>%
summarise(n=length(unique(species)))%>%
ungroup()%>%
complete(nesting(f_area_type,f_area,f_area_label,flag,year),Major_Group_En,fill=list(n=0))%>%
group_by(f_area_type,f_area,f_area_label,flag,year)%>%
mutate(sum=sum(n))%>%
group_by(f_area_type,f_area,f_area_label,flag,year,Major_Group_En)%>%
mutate(pourc=n/sum)%>%
group_by(f_area_type,f_area,f_area_label,year,Major_Group_En)%>%
summarise(meanOfFlag= mean(pourc))%>%
group_by(f_area_type,f_area,f_area_label,Major_Group_En)%>%
summarise(meanOfYear= mean(meanOfFlag),maxOfYear= max(meanOfFlag),minOfYear= min(meanOfFlag),nbOfYear= n(),sdOfYear=sd(meanOfFlag),seOfYear=sd(meanOfFlag)/sqrt(n()))%>%
ungroup()
# for(i in unique(com_area_group$f_area_type)){
#
# name<-paste0("com_isccap_f_area_",i)
#
# com_area_type <- com_area_group %>%
# filter(f_area_type==i)
#
# nb_col<-3
# nb_height<-ceiling(length(unique(com_area_type$f_area_label))/nb_col)
#
# #### Figure
#
# fig<-ggplot(data=com_area_type%>%filter(f_area!="07"),aes(x=Major_Group_En,y=meanOfYear,fill=Major_Group_En))+
# geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
# geom_errorbar(aes(ymin=meanOfYear-sdOfYear, ymax=meanOfYear+sdOfYear), width=.1,color="grey40",position=position_dodge(1))+
# geom_text(aes(label=scales::percent(meanOfYear, accuracy=0.01)), vjust=-0.3, color="grey40", size=4)+
# facet_wrap(~f_area_label,ncol=nb_col)+
# ISSCAAPScale+
# geom_text(data=com_area_type%>%filter(f_area!="07")%>%filter(Major_Group_En=="Fishes"),mapping=aes(x = Inf, y = Inf, label = f_area_label), hjust=1.05, vjust=1.5, size= 5)+
# labs(x=NULL,y="Average number of unique records per country (in percent)",caption="SOURCE : FAO Global Capture Production",fill=NULL)+
# scale_y_continuous(labels = scales::percent)+
# scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
# theme(axis.title.y = element_text(size=10*nb_col),
# axis.text.y = element_text(size=10),
# axis.text.x = element_text(size=10,angle = 0, vjust = 0.5, hjust=0.5),
# strip.background = element_blank(),
# strip.text.x = element_blank(),
# panel.background = element_rect(fill="#f8f8f8"),
# panel.grid.major.y = element_line(linetype="dotted",color="grey50"),
# panel.grid.major.x = element_blank(),
# panel.grid.minor = element_blank(),
# plot.caption = element_text(face = "italic",hjust=0,size=8*nb_col,color="grey20"),
# plot.margin = unit(c(0,0.5,0.5,0), "cm"))
#
# fig_name<-paste0(name,".png")
# ggsave(fig_name,fig,"png",path=fig_path,width=7*nb_col,height=5*nb_height)
# content$fig[name]<-list(file.path(fig_path,fig_name))
#
# #### Table
#
# tab_name<-paste0("ref_",name,".csv")
#
# write.csv(com_area_type,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#
# content$tab[name]<-list(file.path(tab_path,tab_name))
#
# #### Text
# content$text[name]<-list("Text Place Holder")
# }
name<-paste0("com_isccap_f_area_inland")
com_area_type <- com_area_group %>%
filter(f_area_type=="inland")
# #### Figure
#
fig<-ggplot(data=com_area_type%>%filter(f_area!="07"),aes(x=Major_Group_En,y=meanOfYear,fill=Major_Group_En))+
geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
geom_errorbar(aes(ymin=meanOfYear-seOfYear, ymax=meanOfYear+seOfYear), width=.1,color="grey50",position=position_dodge(1))+
geom_text(aes(label=scales::percent(meanOfYear, accuracy=0.01,suffix=""),y=meanOfYear+seOfYear), vjust=-0.5, color="grey40", size=9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
ISSCAAPScale+
geom_label(data=com_area_type%>%filter(f_area!="07")%>%filter(Major_Group_En=="Fishes"),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
labs(x=NULL,y="Percentage",fill=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits =c(0,1.1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width=6.5,height=6)
#INLAND TABLE
tab_name<-paste0("ref_",name,".csv")
write.csv(com_area_type,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#ISSCAAP
com_area_group2<- data %>%
filter(f_area!="07")%>%
filter(Major_Group_En=="Fishes")%>%
mutate(ISSCAAP_Group_En=factor(ISSCAAP_Group_En,levels = ISSCAAPGroupLvl))%>%
select(c("ocean","f_area_type","f_area","f_area_label","flag","year","ISSCAAP_Group_En","capture","species")) %>%
group_by(ocean,f_area_type,f_area,f_area_label,flag,year,ISSCAAP_Group_En)%>%
summarise(n=length(unique(species)))%>%
group_by(ocean,f_area_type,f_area,f_area_label,year,ISSCAAP_Group_En)%>%
summarise(meanOfFlag= mean(n))%>%
group_by(ocean,f_area_type,f_area,f_area_label,ISSCAAP_Group_En)%>%
summarise(meanOfYear= mean(meanOfFlag))%>%
group_by(ocean,f_area_type,f_area,f_area_label)%>%
mutate(sum=sum(meanOfYear))%>%
ungroup()%>%
mutate(pour=meanOfYear/sum)
com_area_type2 <- com_area_group2 %>%
filter(f_area_type=="inland")
add<- com_area_type2[1,]
add$ISSCAAP_Group_En<-"Freshwater fishes"
add$pour<-0
add1<- add
add1$ISSCAAP_Group_En<-"Diadromous fishes"
#add2<-add
#add2$ISSCAAP_Group_En<-"Marine fishes"
com_area_type2 <-rbind(com_area_type2 ,add)
com_area_type2 <-rbind(com_area_type2 ,add1)
#com_area_type2 <-rbind(com_area_type2 ,add2)
fig_isscaap<-ggplot(data=com_area_type2,aes(x=f_area_label,y=pour,fill=ISSCAAP_Group_En))+
geom_bar(stat="identity",position='stack',width = 0.8,show.legend = T)+
labs(x=NULL,y="Percentage",fill=NULL)+
SubGrScale+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits =c(0,1.1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.key = element_rect(fill = "white"),
legend.key.size = unit(0.2, 'cm'),
legend.title = element_blank(),
legend.text=element_markdown(size=7),
legend.background = element_rect(fill="transparent"),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,"_subgroup.png")
ggsave(fig_name,fig_isscaap,"png",path=fig_path,dpi=900,width=6.5,height=4)
#INLAND TABLE SUBGROUP
tab_name<-paste0("ref_",name,"_subgroup",".csv")
write.csv(com_area_type2,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#Marine
name<-paste0("com_isccap_f_area_marine")
com_area_type <- com_area_group %>%
filter(f_area_type=="marine")
com_area_type$part<-ifelse(com_area_type$f_area_label%in%sort(unique(com_area_type$f_area_label))[1:10],1,2)
# #### Figure
for (i in unique(com_area_type$part)){
fig<-ggplot(data=com_area_type%>%filter(part==i),aes(x=Major_Group_En,y=meanOfYear,fill=Major_Group_En))+
geom_bar(stat="identity",width = 0.8,show.legend = FALSE)+
geom_errorbar(aes(ymin=meanOfYear-seOfYear, ymax=meanOfYear+seOfYear), width=.1,color="grey50",position=position_dodge(1))+
geom_text(aes(label=scales::percent(meanOfYear, accuracy=0.01,suffix=""),y=meanOfYear+seOfYear), vjust=-0.5, color="grey40", size=9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
ISSCAAPScale+
geom_label(data=com_area_type%>%filter(part==i)%>%filter(Major_Group_En=="Fishes"),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
labs(x=NULL,y="Percentage",fill=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits =c(0,1.1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,"_part",i,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width=6.5,height=9)
}
#MARINE TABLE
tab_name<-paste0("ref_",name,".csv")
write.csv(com_area_type,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
com_area_type2 <- com_area_group2 %>%
filter(f_area_type=="marine")
add<- com_area_type2[1,]
add$ISSCAAP_Group_En<-"Diadromous fishes"
add$pour<-0
add2<-add
add2$ISSCAAP_Group_En<-"Marine fishes"
com_area_type2 <-rbind(com_area_type2 ,add)
com_area_type2 <-rbind(com_area_type2 ,add2)
fig_issccap<-ggplot(data=com_area_type2,aes(x=f_area_label,y=pour,fill=ISSCAAP_Group_En))+
geom_bar(stat="identity",position='stack',width = 0.8,show.legend = TRUE)+
SubGrScale+
labs(x=NULL,y="Percentage",fill=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""), limits =c(0,1),breaks = seq(0, 1, by = 0.25))+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
coord_flip()+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.key = element_rect(fill = "white"),
legend.key.size = unit(0.2, 'cm'),
legend.title = element_blank(),
legend.text=element_markdown(size=7),
legend.background = element_rect(fill="transparent"),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,"_subgroup",".png")
ggsave(fig_name,fig_issccap,"png",path=fig_path,dpi=900,width=6.5,height=9)
#MARINE TABLE SUBGROUP
tab_name<-paste0("ref_",name,"_subgroup",".csv")
write.csv(com_area_type2,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
###############################################################################################################################
#Part 2.A Biodiversity reporting development in FAO Fisheries
## By Number of unique taxa
## Commonness among Fishes [vertebrate fish] records in FishStatJ — Global
### Output :com_sp_global
name<-"com_sp_nbtax_global"
#### Data
# com_sp_lvl <- data %>%
# filter(Major_Group_En %in% c("Fishes")) %>%
# select(c("species","scientific_name","family","order","Taxonomic_Code","year")) %>%
# mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
# distinct()%>%
# count(year,level)%>%
# group_by(year)%>%
# mutate(sum = sum(n))%>%
# mutate(pour = n/sum)%>%
# group_by(level) %>%
# group_by(level,grp = as.integer(gl(n(), 3, n()))) %>%
# summarise(year = last(year), pour = mean(pour),nb_sp=mean(sum)) %>%
# select(-grp)
com_sp_lvl <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
select(c("species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
distinct()%>%
count(year,level)%>%
group_by(year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_lvl$level<-factor(com_sp_lvl$level,levels = c("species","family","nei"))
#### Figure
# fig<-ggplot(data=com_sp_lvl,aes(x=year,y=pour,group=level,color=level))+
# geom_line(size =1.5,lineend = "round",alpha=0.7)+
# geom_text(data=com_sp_lvl%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
# TaxonomicScale+
# labs(x=NULL,y="Percentage",caption=source_text,color=NULL)+
# scale_y_continuous(labels = scales::percent,limits=c(0,1),breaks = seq(0, 1, by = 0.25))+
# scale_x_discrete(labels = as.character(com_sp_lvl$year), breaks = com_sp_lvl$year)+
# theme(text=element_text(family = 'sans'),
# axis.title.y = element_text(size=9),
# axis.text.y = element_text(size=9),
# axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
# legend.key = element_rect(fill = "white"),
# legend.title = element_blank(),
# legend.text=element_text(size=9),
# legend.background = element_rect(fill="transparent"),
# legend.position = "bottom",
# legend.justification = "left",
# legend.direction = "horizontal",
# panel.background = element_rect(fill="#efeff0"),
# panel.grid.major.y = element_line(linetype="dashed",color="grey50"),
# panel.grid.major.x = element_blank(),
# panel.grid.minor = element_blank(),
# plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
# plot.margin = unit(c(0,0,0,0), "cm"))
fig<-ggplot(data=com_sp_lvl,aes(x=year,group=level))+
geom_line(aes(y=pour,color=level),size =0.5,lineend = "round",alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_lvl%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_lvl$pour)/max(com_sp_lvl$nb_sp)), name="Total taxa (number)"))+
scale_x_continuous(breaks = seq(min(com_sp_lvl$year),max(com_sp_lvl$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width = 6.5,height=4)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_lvl%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness among fish records in FishStatJ — by Ocean Basins
### Output :com_sp_ocean
name<-"com_sp_nbtax_ocean"
#### Data
# com_sp_ocean <- data %>%
# filter(f_area_type=="marine") %>%
# filter(Major_Group_En %in% c("Fishes")) %>%
# select(c("flag","ocean","species","scientific_name","family","order","Taxonomic_Code","year",)) %>%
# mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
# distinct()%>%
# count(flag,ocean,year,level)%>%
# group_by(ocean,year,level)%>%
# summarise(n=median(n))%>%
# ungroup()%>%
# complete(ocean, nesting(year, level),fill=list(n=0))%>%
# group_by(ocean,year)%>%
# mutate(sum = sum(n))%>%
# mutate(pour = n/sum)%>%
# group_by(ocean,level) %>%
# group_by(ocean,level,grp = as.integer(gl(n(), 3, n()))) %>%
# summarise(year = as.numeric(first(year)), pour = mean(pour),nb_sp=mean(sum))%>%
# select(-grp)
com_sp_ocean <- data %>%
filter(f_area_type=="marine") %>%
filter(Major_Group_En %in% c("Fishes"))%>%
select(c("ocean","species","scientific_name","family","order","Taxonomic_Code","year",)) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
distinct()%>%
count(ocean,year,level)%>%
ungroup()%>%
complete(ocean, nesting(year, level),fill=list(n=0))%>%
group_by(ocean,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(ocean,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))%>%
filter(ocean!="Arctic Sea")
com_sp_ocean$level<-factor(com_sp_ocean$level,levels = c("species","family","nei"))
##### Figure
fig<-ggplot(data=com_sp_ocean,aes(x=year,group=level))+
geom_line(aes(y=pour,color=level),size =0.5,lineend = "round",alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_ocean%>%filter(ocean!="Arctic Sea")%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
geom_label(data=com_sp_ocean%>%filter(level=="species"),mapping=aes(x = Inf, y = Inf, label = ocean), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~ocean,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_ocean$pour)/max(com_sp_ocean$nb_sp)), name="Total taxa (number)"))+
scale_x_continuous(breaks = seq(min(com_sp_ocean$year),max(com_sp_ocean$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=4,dpi=900)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_ocean%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness among fish records in FishStatJ — By FAO Fisheries Areas
### Output :com_sp_f_area
#### Data
#inland
name<-paste0("com_sp_nbtax_f_area_inland")
com_sp_area_type <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
filter(f_area_type == "inland") %>%
filter(f_area!="07")%>%
select(c("f_area_label","species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
distinct()%>%
count(f_area_label,year,level)%>%
ungroup()%>%
complete(f_area_label, nesting(year, level),fill=list(n=0))%>%
group_by(f_area_label,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(f_area_label,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_area_type$level<-factor(com_sp_area_type$level,levels = c("species","family","nei"))
nb_col<-2
#### Figure
fig<-ggplot(data=com_sp_area_type,aes(x=year,group=level))+
geom_line(size =0.5,lineend = "round",aes(color=level,y=pour),alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_area_type%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
geom_label(data=com_sp_area_type%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_area_type$pour)/max(com_sp_area_type$nb_sp)), name="Total taxa (number)"))+
scale_x_continuous(breaks = seq(min(com_sp_area_type$year),max(com_sp_area_type$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=6,dpi=900)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_area_type%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#marine
name<-paste0("com_sp_nbtax_f_area_marine")
com_sp_area_type <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
filter(f_area_type == "marine") %>%
select(c("f_area_label","species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
distinct()%>%
count(f_area_label,year,level)%>%
ungroup()%>%
complete(f_area_label, nesting(year, level),fill=list(n=0))%>%
group_by(f_area_label,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(f_area_label,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_area_type$part<-ifelse(com_sp_area_type$f_area_label%in%sort(unique(com_sp_area_type$f_area_label))[1:10],1,2)
# #### Figure
for (i in unique(com_sp_area_type$part)){
com_sp_area_type_part<-com_sp_area_type%>%filter(part==i)
com_sp_area_type_part$level<-factor(com_sp_area_type_part$level,levels = c("species","family","nei"))
fig<-ggplot(data=com_sp_area_type_part,aes(x=year,group=level))+
geom_line(size =0.5,lineend = "round",aes(color=level,y=pour),alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour,na.rm=T)/max(nb_sp,na.rm=T))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
# geom_text(data=com_sp_area_type%>%filter(part==i)%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.7, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
#geom_text(data=com_sp_area_type_part%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), hjust=1.05, vjust=1.5, size= 9 /.pt)+
geom_label(data=com_sp_area_type_part%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_area_type_part$pour,na.rm=T)/max(com_sp_area_type_part$nb_sp,na.rm=T)), name="Total taxa (number)"))+
scale_x_continuous(breaks = seq(min(com_sp_area_type_part$year),max(com_sp_area_type_part$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,"_part",i,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=9,dpi=900)
}
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_area_type%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
#Part 2.B Biodiversity reporting development in FAO Fisheries
## By Number of unique record
## Summary Formated Table
#### Data
com_sp_rec <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
select(c("species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
count(level)%>%
mutate(sum = sum(n))%>%
mutate(pour = round(n/sum*100,1))%>%
mutate(n = round(n/1000,1))%>%
mutate(level=factor(level,levels = c("species","family","nei"),labels = c("Species level","Family level","Nei")))%>%
select(level,n,pour)%>%
arrange(level)%>%
kable(format = "html",escape = FALSE,booktabs=T,align = "lcc",
col.names=c("", "(number in thousands)","(percentage)"))%>%
kable_styling("condensed")%>%
add_header_above(c("Total records (1950-2019)" = 3),include_empty=F,angle=0,line=T,escape = F,italic = T,align="c",color = "white", background = "#0091a6")%>%
row_spec(0, bold = T,italic = T, color = "white", background = "#0091a6")%>%
column_spec(1:3, width = "1in",extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
footnote(general='',general_title = paste0("<b>",source_text,"</b>"),escape=F)%>%
save_kable(file = file.path(fig_path,"table_taxa_grp_rec_1950_2019.png"), bs_theme = "flatly")
## Commonness among Fishes [vertebrate fish] records in FishStatJ — Global
name<-"com_sp_nbrec_global"
#### Data
com_sp_lvl <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
select(c("species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
count(year,level)%>%
group_by(year)%>%
mutate(n=n/1000)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_lvl$level<-factor(com_sp_lvl$level,levels = c("species","family","nei"))
#### Figure
fig<-ggplot(data=com_sp_lvl,aes(x=year,group=level))+
geom_line(aes(y=pour,color=level),size =0.5,lineend = "round",alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_lvl%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_lvl$pour)/max(com_sp_lvl$nb_sp)), name="Total records (number in thousands)"))+
scale_x_continuous(breaks = seq(min(com_sp_lvl$year),max(com_sp_lvl$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width = 6.5,height=4)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_lvl%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness among fish records in FishStatJ — by Ocean Basins
### Output :com_sp_ocean
name<-"com_sp_nbrec_ocean"
#### Data
com_sp_ocean <- data %>%
filter(f_area_type=="marine") %>%
filter(Major_Group_En %in% c("Fishes"))%>%
select(c("ocean","species","scientific_name","family","order","Taxonomic_Code","year",)) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
count(ocean,year,level)%>%
mutate(n=n/1000)%>%
ungroup()%>%
complete(ocean, nesting(year, level),fill=list(n=0))%>%
group_by(ocean,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(ocean,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))%>%
filter(ocean!="Arctic Sea")
com_sp_ocean$level<-factor(com_sp_ocean$level,levels = c("species","family","nei"))
##### Figure
fig<-ggplot(data=com_sp_ocean,aes(x=year,group=level))+
geom_line(aes(y=pour,color=level),size =0.5,lineend = "round",alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_ocean%>%filter(ocean!="Arctic Sea")%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
geom_label(data=com_sp_ocean%>%filter(level=="species"),mapping=aes(x = Inf, y = Inf, label = ocean), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~ocean,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_ocean$pour)/max(com_sp_ocean$nb_sp)), name="Total records (number in thousands)"))+
scale_x_continuous(breaks = seq(min(com_sp_ocean$year),max(com_sp_ocean$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=4,dpi=900)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_ocean%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
## Commonness among fish records in FishStatJ — By FAO Fisheries Areas
### Output :com_sp_f_area
#### Data
#inland
name<-paste0("com_sp_nbrec_f_area_inland")
com_sp_area_type <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
filter(f_area_type == "inland") %>%
filter(f_area!="07")%>%
select(c("f_area_label","species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
count(f_area_label,year,level)%>%
mutate(n=n/1000)%>%
ungroup()%>%
complete(f_area_label, nesting(year, level),fill=list(n=0))%>%
group_by(f_area_label,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(f_area_label,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_area_type$level<-factor(com_sp_area_type$level,levels = c("species","family","nei"))
nb_col<-2
#### Figure
fig<-ggplot(data=com_sp_area_type,aes(x=year,group=level))+
geom_line(size =0.5,lineend = "round",aes(color=level,y=pour),alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour)/max(nb_sp))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
#geom_text(data=com_sp_area_type%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.5, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
geom_label(data=com_sp_area_type%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_area_type$pour)/max(com_sp_area_type$nb_sp)), name="Total records (number in thousands)"))+
scale_x_continuous(breaks = seq(min(com_sp_area_type$year),max(com_sp_area_type$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=6,dpi=900)
content$fig[name]<-list(file.path(fig_path,fig_name))
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_area_type%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#marine
name<-paste0("com_sp_nbrec_f_area_marine")
com_sp_area_type <- data %>%
filter(Major_Group_En %in% c("Fishes")) %>%
filter(f_area_type == "marine") %>%
select(c("f_area_label","species","scientific_name","family","order","Taxonomic_Code","year")) %>%
mutate(level=ifelse(stringr::str_detect(Taxonomic_Code,"X")==FALSE,"species",ifelse(is.na(family),"nei","family")))%>%
count(f_area_label,year,level)%>%
mutate(n=n/1000)%>%
ungroup()%>%
complete(f_area_label, nesting(year, level),fill=list(n=0))%>%
group_by(f_area_label,year)%>%
mutate(sum = sum(n))%>%
mutate(pour = n/sum)%>%
group_by(f_area_label,level) %>%
mutate(pour = zoo::rollapplyr(pour, 3, mean, partial=TRUE),nb_sp=zoo::rollapplyr(sum, 3, mean, partial=TRUE))
com_sp_area_type$part<-ifelse(com_sp_area_type$f_area_label%in%sort(unique(com_sp_area_type$f_area_label))[1:10],1,2)
# #### Figure
for (i in unique(com_sp_area_type$part)){
com_sp_area_type_part<-com_sp_area_type%>%filter(part==i)
com_sp_area_type_part$level<-factor(com_sp_area_type_part$level,levels = c("species","family","nei"))
fig<-ggplot(data=com_sp_area_type_part,aes(x=year,group=level))+
geom_line(size =0.5,lineend = "round",aes(color=level,y=pour),alpha=0.7)+
geom_line(aes(y=nb_sp*(max(pour,na.rm=T)/max(nb_sp,na.rm=T))),size =0.5,lineend = "round",color="#c6a67a",linetype="11")+
# geom_text(data=com_sp_area_type%>%filter(part==i)%>%filter(level=='species'),mapping=aes(y =0,label=paste0("n\n",round(nb_sp,0))),lineheight = .7,vjust=0.7, color="#c6a67a",size=8 /.pt)+
TaxonomicScale+
labs(x=NULL,y="Aggregated groups (percentages)",color=NULL)+
#geom_text(data=com_sp_area_type_part%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), hjust=1.05, vjust=1.5, size= 9 /.pt)+
geom_label(data=com_sp_area_type_part%>%filter(level=='species'),mapping=aes(x = Inf, y = Inf, label = f_area_label), fill="#f8f8f8", label.size=0, hjust=1.05, vjust=1.5, size= 9 /.pt)+
facet_wrap(~f_area_label,ncol=2)+
scale_y_continuous(labels = function(x) scales::percent(x,suffix=""),limits=c(0,1.2),breaks = seq(0, 1, by = 0.25),
sec.axis = sec_axis(~./(max(com_sp_area_type_part$pour,na.rm=T)/max(com_sp_area_type_part$nb_sp,na.rm=T)), name="Total records (number in thousands)"))+
scale_x_continuous(breaks = seq(min(com_sp_area_type_part$year),max(com_sp_area_type_part$year),10))+
theme(text=element_text(family = 'sans'),
axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=8,angle = 90, vjust = 0.5, hjust=0.5),
axis.title.y.right = element_text(color = "#c6a67a" , size=9),
axis.text.y.right = element_text(color = "#c6a67a" , size=9),
#axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
#axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
legend.key = element_rect(fill = "white"),
legend.title = element_blank(),
legend.text=element_text(size=9),
legend.background = element_rect(fill="transparent"),
legend.position = "bottom",
legend.justification = "left",
legend.direction = "horizontal",
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=9,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,"_part",i,".png")
ggsave(fig_name,fig,"png",path=fig_path,width=6.5,height=9,dpi=900)
}
#### Table
tab_name<-paste0("ref_",name,".csv")
write.csv(com_sp_area_type%>%
rename(Pourcentage = pour)
,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
content$tab[name]<-list(file.path(tab_path,tab_name))
#### Text
content$text[name]<-list("Text Place Holder")
###############################################################################################################################
#Biodiversity indexes
#2
#a
#ocean
name<-paste0("taxa_richess_","ocean")
ocean_nb_sp<- data %>%
filter(!is.na(ocean))%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(c("ocean","Major_Group_En","species")) %>%
group_by(ocean,Major_Group_En)%>%
summarise(n=length(unique(species)))%>%
ungroup()%>%
complete(ocean,Major_Group_En,fill=list(n=0))%>%
group_by(ocean)%>%
mutate(sum=sum(n))%>%
group_by(ocean,Major_Group_En)%>%
mutate(pourc=n/sum*100)%>%
select(-sum)%>%
ungroup()
ocean_nb_sp_table_v<- ocean_nb_sp%>%
rename(value=n)%>%
select(-pourc)%>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = 0)%>%
ungroup()
ocean_nb_sp_table_p<- ocean_nb_sp%>%
mutate(value=paste0("(",round(pourc,2),"%)"))%>%
select(-n,-pourc)%>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "(O%)")%>%
ungroup()
ocean_nb_sp_table<-rbind(ocean_nb_sp_table_v,ocean_nb_sp_table_p)
ocean_nb_sp_table<-ocean_nb_sp_table[order(ocean_nb_sp_table$ocean),]
# ocean_nb_sp_table<-as.data.frame(ocean_nb_sp_table)
#rownames(ocean_nb_sp_table)<-ocean_nb_sp_table$ocean
#ocean_nb_sp_table<-ocean_nb_sp_table[,-1]
names(ocean_nb_sp_table)[1]<-"Ocean/ISSCAAP groups"
kw0 <- paste0("(number of different taxa",footnote_marker_number(1, double_escape = T),"; percentage of total repartition)")
ocean_nb_sp_table%>%
kable(format = "html",escape = FALSE,booktabs=T,align = "lccccc",
col.names=paste0(substr(names(ocean_nb_sp_table),1,1),tolower(substr(names(ocean_nb_sp_table),2,nchar(names(ocean_nb_sp_table))))))%>%
kable_styling("condensed")%>%
add_header_above(c(setNames(6,kw0)),include_empty=F,angle=0,line=T,escape = F,italic = T,align="c",color = "white", background = "#0091a6")%>%
row_spec(0, bold = T, color = "white", background = "#0091a6")%>%
column_spec(1,extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
column_spec(2:6, width = "1in",extra_css = "border-bottom: solid;border-bottom-width: 2px;")%>%
collapse_rows(columns = 1, valign = "m")%>%
footnote(number = c("Exclude aquatic mammals, amphibia and reptilia"))
tab_name<-paste0("ref_",name,".csv")
write.csv(ocean_nb_sp_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#fao major area
major_area_nb_sp<- data %>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(c("f_area_type","f_area","f_area_label","Major_Group_En","species")) %>%
group_by(f_area_type,f_area,f_area_label,Major_Group_En)%>%
summarise(n=length(unique(species)))%>%
ungroup()%>%
complete(nesting(f_area_type,f_area,f_area_label),Major_Group_En,fill=list(n=0))%>%
group_by(f_area_type,f_area,f_area_label)%>%
mutate(sum=sum(n))%>%
group_by(f_area_type,f_area,f_area_label,Major_Group_En)%>%
mutate(pourc=n/sum*100)%>%
select(-sum)%>%
ungroup()
for(i in unique(major_area_nb_sp$f_area_type)){
area_type_nb_sp <- major_area_nb_sp %>%
filter(f_area_type==i)%>%
mutate(value=paste0(n," [",round(pourc,2),"%]"))%>%
select(-n,-pourc)%>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "0 [O%]")%>%
ungroup()
print(area_type_nb_sp)
name<-paste0("taxa_richess_",i)
tab_name<-paste0("ref_",name,".csv")
write.csv(area_type_nb_sp,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
}
#b
##ocean
name<-paste0("taxa_inMoreThan2_","ocean")
multi_ocean<- data%>%
filter(!is.na(ocean))%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(ocean,Major_Group_En,species,capture)%>%
mutate(code=paste0(ocean,"-",Major_Group_En))%>%
select(-ocean,-Major_Group_En)%>%
mutate(capture=1)%>%
distinct()%>%
pivot_wider(names_from = code,values_from = capture,values_fill = 0)%>%
mutate(in_x_ocean=rowSums(across(where(is.numeric))))%>%
filter(in_x_ocean>=3)%>%
select(-in_x_ocean)%>%
pivot_longer(!species, names_to = "code", values_to = "count")%>%
group_by(code)%>%
summarise(morethan2=sum(count))%>%
separate(code, c("ocean", "Major_Group_En"), sep = "-")%>%
complete(ocean,Major_Group_En,fill=list(morethan2=0))%>%
ungroup()%>%
inner_join(select(ocean_nb_sp,ocean,Major_Group_En,n))%>%
group_by(ocean,Major_Group_En)%>%
mutate(pourc=morethan2/n*100)
multi_ocean_table<-multi_ocean%>%
group_by(ocean,Major_Group_En)%>%
mutate(value=if(!is.nan(pourc)){paste0(morethan2," [",round(pourc,2),"%]")}else{"_"})%>%
select(ocean,Major_Group_En,value)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "0 [O%]")%>%
ungroup()
tab_name<-paste0("ref_",name,".csv")
write.csv(multi_ocean_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
multi_area<- data%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(f_area_type,f_area_label,Major_Group_En,species,capture)%>%
mutate(code=paste0(f_area_type,"_",f_area_label,"_",Major_Group_En))%>%
select(-f_area_type,-f_area_label,-Major_Group_En)%>%
mutate(capture=1)%>%
distinct()%>%
pivot_wider(names_from = code,values_from = capture,values_fill = 0)%>%
mutate(in_x_ocean=rowSums(across(where(is.numeric))))%>%
filter(in_x_ocean>=3)%>%
select(-in_x_ocean)%>%
pivot_longer(!species, names_to = "code", values_to = "count")%>%
group_by(code)%>%
summarise(morethan2=sum(count))%>%
separate(code, c("f_area_type","f_area_label", "Major_Group_En"), sep = "_")%>%
complete(nesting(f_area_type,f_area_label),Major_Group_En,fill=list(morethan2=0))%>%
ungroup()%>%
inner_join(select(major_area_nb_sp,f_area_type,f_area_label,Major_Group_En,n))%>%
group_by(f_area_type,f_area_label,Major_Group_En)%>%
mutate(pourc=morethan2/n*100)
for(i in unique(multi_area$f_area_type)){
name<-paste0("taxa_inMoreThan2_",i)
multi_area_type <- multi_area %>%
filter(f_area_type==i)%>%
mutate(value=if(!is.nan(pourc)){paste0(morethan2," [",round(pourc,2),"%]")}else{"_"})%>%
select(f_area_label,Major_Group_En,value)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "0 [O%]")%>%
ungroup()
print(multi_area_type)
tab_name<-paste0("ref_",name,".csv")
write.csv(multi_area_type,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
}
#c
#ocean
name<-paste0("taxa_onlyIn1_","ocean")
unique_ocean<- data%>%
filter(!is.na(ocean))%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(ocean,Major_Group_En,species,capture)%>%
mutate(code=paste0(ocean,"-",Major_Group_En))%>%
select(-ocean,-Major_Group_En)%>%
mutate(capture=1)%>%
distinct()%>%
pivot_wider(names_from = code,values_from = capture,values_fill = 0)%>%
mutate(in_x_ocean=rowSums(across(where(is.numeric))))%>%
filter(in_x_ocean==1)%>%
select(-in_x_ocean)%>%
pivot_longer(!species, names_to = "code", values_to = "count")%>%
group_by(code)%>%
summarise(unique_nb=sum(count))%>%
separate(code, c("ocean", "Major_Group_En"), sep = "-")%>%
complete(ocean,Major_Group_En,fill=list(unique_nb=0))%>%
ungroup()%>%
inner_join(select(ocean_nb_sp,ocean,Major_Group_En,n))%>%
group_by(ocean,Major_Group_En)%>%
mutate(pourc=unique_nb/n*100)
unique_ocean_table<-unique_ocean%>%
group_by(ocean,Major_Group_En)%>%
mutate(value=if(!is.nan(pourc)){paste0(unique_nb," [",round(pourc,2),"%]")}else{"_"})%>%
select(ocean,Major_Group_En,value)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "0 [O%]")%>%
ungroup()
tab_name<-paste0("ref_",name,".csv")
write.csv(unique_ocean_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#f_area
unique_area<- data%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
select(f_area_type,f_area_label,Major_Group_En,species,capture)%>%
mutate(code=paste0(f_area_type,"_",f_area_label,"_",Major_Group_En))%>%
select(-f_area_type,-f_area_label,-Major_Group_En)%>%
mutate(capture=1)%>%
distinct()%>%
pivot_wider(names_from = code,values_from = capture,values_fill = 0)%>%
mutate(in_x_ocean=rowSums(across(where(is.numeric))))%>%
filter(in_x_ocean==1)%>%
select(-in_x_ocean)%>%
pivot_longer(!species, names_to = "code", values_to = "count")%>%
group_by(code)%>%
summarise(unique_nb=sum(count))%>%
separate(code, c("f_area_type","f_area_label", "Major_Group_En"), sep = "_")%>%
complete(nesting(f_area_type,f_area_label),Major_Group_En,fill=list(unique_nb=0))%>%
ungroup()%>%
inner_join(select(major_area_nb_sp,f_area_type,f_area_label,Major_Group_En,n))%>%
group_by(f_area_type,f_area_label,Major_Group_En)%>%
mutate(pourc=unique_nb/n*100)
for(i in unique(unique_area$f_area_type)){
name<-paste0("taxa_onlyIn1_",i)
unique_area_type <- unique_area %>%
filter(f_area_type==i)%>%
mutate(value=if(!is.nan(pourc)){paste0(unique_nb," [",round(pourc,2),"%]")}else{"_"})%>%
select(f_area_label,Major_Group_En,value)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = value,values_fill = "0 [O%]")%>%
ungroup()
print(unique_area_type)
tab_name<-paste0("ref_",name,".csv")
write.csv(unique_area_type,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
}
##
#3
#a
#ocean
name<-paste0("evenness_simpson_","ocean")
richess_ocean<- data%>%
filter(!is.na(ocean))%>%
select(ocean,Major_Group_En,flag,year,species,capture)%>%
mutate(code=paste0(ocean,"-",Major_Group_En))%>%
select(-ocean,-Major_Group_En)%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
mutate(capture=1)%>%
distinct()%>%
group_by(code,species)%>%
summarise(n=sum(capture))%>%
pivot_wider(names_from = species,values_from = n,values_fill = 0)%>%
group_by(code)%>%
summarise(simpson=diversity(across(where(is.numeric)),index="simpson"))%>%
separate(code, c("ocean","Major_Group_En"), sep = "-")%>%
ungroup()
richess_ocean_table<-richess_ocean%>%
group_by(ocean)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = simpson)
tab_name<-paste0("ref_",name,".csv")
write.csv(richess_ocean_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
richess_ocean$Major_Group_En<-factor(richess_ocean$Major_Group_En,
levels = c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))
fig<-ggplot(richess_ocean, aes(Major_Group_En, ocean, fill= simpson)) +
geom_tile(color = "white",lwd = 1.5,linetype = 1) +
geom_text(aes(label = scales::comma(simpson,accuracy=0.01)), color = "white", size = 4)+
scale_fill_gradient2(low="#caf0f8",mid="#48cae4",high="#023e8a",midpoint=0.5,limits=c(0,1))+
labs(x=NULL,y=NULL,caption="SOURCE : FAO Global Capture Production",fill="Simpson index")+
scale_y_discrete(limits=rev)+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9,vjust = 0.5),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
legend.title = element_text(size=8),
legend.text = element_text(size=8),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=10,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width=6.5,height=5)
#major area
richess_area<- data%>%
select(f_area_type,f_area,f_area_label,Major_Group_En,flag,year,species,capture)%>%
mutate(code=paste0(f_area_type,"_",f_area,"_",f_area_label,"_",Major_Group_En))%>%
select(-f_area_type,-f_area,-f_area_label,-Major_Group_En)%>%
filter(year %in% seq(period_end-9,period_end,1))%>%
mutate(capture=1)%>%
distinct()%>%
group_by(code,species)%>%
summarise(n=sum(capture))%>%
pivot_wider(names_from = species,values_from = n,values_fill = 0)%>%
group_by(code)%>%
summarise(simpson=diversity(across(where(is.numeric)),index="simpson"))%>%
separate(code, c("f_area_type","f_area","f_area_label","Major_Group_En"), sep = "_")%>%
ungroup()
richess_area$Major_Group_En<-factor(richess_area$Major_Group_En,
levels = c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))
for(i in unique(richess_area$f_area_type)){
name<-paste0("evenness_simpson_",i)
richess_area_table<-richess_area%>%
filter(f_area_type==i)%>%
group_by(f_area_type,f_area,f_area_label)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = simpson)
print(richess_area_table)
tab_name<-paste0("ref_",name,".csv")
write.csv(richess_area_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
richess_area_type <- richess_area %>%
filter(f_area_type==i)
fig<-ggplot(richess_area_type%>%filter(f_area!="07"), aes(Major_Group_En, f_area_label, fill= simpson)) +
geom_tile(color = "white",lwd = 1.5,linetype = 1) +
geom_text(aes(label = scales::comma(simpson,accuracy=0.01)), color = "white", size = 4)+
scale_fill_gradient2(low="#caf0f8",mid="#48cae4",high="#023e8a",midpoint=0.5,limits=c(0,1))+
labs(x=NULL,y=NULL,caption="SOURCE : FAO Global Capture Production",fill="Simpson index")+
scale_y_discrete(limits=rev)+
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 15))+
theme(axis.title.y = element_text(size=9),
axis.text.y = element_text(size=9,vjust = 0.5),
axis.text.x = element_text(size=7,angle = 0, vjust = 0.5, hjust=0.5),
strip.background = element_blank(),
strip.text.x = element_blank(),
panel.background = element_rect(fill="#f8f8f8"),
legend.title = element_text(size=8),
legend.text = element_text(size=8),
panel.grid.major.y = element_line(linetype="dotted",color="grey60"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(face = "italic",hjust=0,size=10,color="grey20"),
plot.margin = unit(c(0,0,0,0), "cm"))
fig_name<-paste0(name,".png")
ggsave(fig_name,fig,"png",path=fig_path,dpi=900,width=6.5,height=ifelse(i=="marine",6,5))
}
#4
#a
#ocean
name<-paste0("turnover_","ocean")
turn_ocean<- data %>%
filter(!is.na(ocean))%>%
select(ocean,flag,Major_Group_En,year,species) %>%
filter(year %in% seq(period_end-9,period_end,1))%>%
distinct()%>%
arrange(ocean,flag,Major_Group_En,year)%>%
group_by(ocean,flag,Major_Group_En,year)%>%
summarise(list_sp = paste0(unique(species),collapse = ","))%>%
group_by(ocean,flag,Major_Group_En)%>%
mutate(previous_year = lag(list_sp, order_by = year))%>%
filter(year!=2010)%>%
group_by(ocean,flag,Major_Group_En,year)%>%
mutate(previous_year = if(!is.na(previous_year)){previous_year}else{"_"})%>%
mutate(nb_diff={
x=sort(unlist(strsplit(list_sp,",")))==sort(unlist(strsplit(previous_year,",")))
length(x[x==FALSE])},nb_egal={
x=sort(unlist(strsplit(list_sp,",")))==sort(unlist(strsplit(previous_year,",")))
length(x[x==TRUE])})%>%
mutate(turnover=(nb_diff/(nb_diff+nb_egal))*100)%>%
group_by(ocean,flag,Major_Group_En)%>%
summarise(meanOfYear=mean(turnover))%>%
group_by(ocean,Major_Group_En)%>%
summarise(meanOfFlag=mean(meanOfYear))%>%
ungroup()
turn_ocean_table<-turn_ocean%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = meanOfFlag)
tab_name<-paste0("ref_",name,".csv")
write.csv(turn_ocean_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
#fao area
turn_area<- data %>%
select(f_area_type,f_area_label,flag,Major_Group_En,year,species) %>%
filter(year %in% seq(period_end-9,period_end,1))%>%
distinct()%>%
arrange(f_area_type,f_area_label,flag,Major_Group_En,year)%>%
group_by(f_area_type,f_area_label,flag,Major_Group_En,year)%>%
summarise(list_sp = paste0(unique(species),collapse = ","))%>%
group_by(f_area_type,f_area_label,flag,Major_Group_En)%>%
mutate(previous_year = lag(list_sp, order_by = year))%>%
filter(year!=2010)%>%
group_by(f_area_type,f_area_label,flag,Major_Group_En,year)%>%
mutate(previous_year = if(!is.na(previous_year)){previous_year}else{"_"})%>%
mutate(nb_diff={
x=sort(unlist(strsplit(list_sp,",")))==sort(unlist(strsplit(previous_year,",")))
length(x[x==FALSE])},nb_egal={
x=sort(unlist(strsplit(list_sp,",")))==sort(unlist(strsplit(previous_year,",")))
length(x[x==TRUE])})%>%
mutate(turnover=(nb_diff/(nb_diff+nb_egal))*100)%>%
group_by(f_area_type,f_area_label,flag,Major_Group_En)%>%
summarise(meanOfYear=mean(turnover))%>%
group_by(f_area_type,f_area_label,Major_Group_En)%>%
summarise(meanOfFlag=mean(meanOfYear))%>%
ungroup()
for(i in unique(turn_area$f_area_type)){
name<-paste0("turnover_",i)
turn_area_table<-turn_area%>%
filter(f_area_type==i)%>%
group_by(f_area_type,f_area_label)%>%
arrange(match(Major_Group_En, c("Fishes", "Molluscs", "Crustaceans", "Aquatic Invertebrates","Aquatic Plants"))) %>%
pivot_wider(names_from = Major_Group_En,values_from = meanOfFlag)
print(turn_area_table)
tab_name<-paste0("ref_",name,".csv")
write.csv(turn_area_table,file.path(tab_path,tab_name),row.names = FALSE,quote=FALSE)
}
return(content)
}
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