inst/shiny_apps/MERA/Source/Reports/OM_report.R

In Blue-Matter/MERA: Method Evaluation and Risk Assessment

Qtab<<-function(Answer,selected){
  
  scol<-"#b9e8f9"
  Qtable<-data.frame(Answer)
  
  Qtable %>%
    mutate(
      Answer = ifelse(selected,
                      color_bar(color = scol)(Answer),
                      color_bar(color = "white")(Answer))
    )%>%
    select(everything())%>%
    knitr::kable("html", escape = F,align = "c") %>%
    kable_styling("hover", full_width = F)%>%
    column_spec(1, width = "3cm")
  
}

checkJs<-function(){
  
  Js<-unlist(Just)
  probJs<-grepl("No justification",Js)|Js==""
  
  probJs[1]<-(Js[1]=="1. Describe the history and current status of the fishery, including fleets, sectors, vessel types and practices/gear by vessel type, landing ports, economics/markets, whether targeted/bycatch, other stocks caught in the fishery.\n\n2. Describe the stocks ecosystem functions, dependencies, and habitat types.\n\n3. Provide all relevant reference materials, such as assessments, research, and other analysis.\n\n      ")
  probJs[20]<-(Js[20]=="1. Describe what, if any, current management measures are used to constrain catch/effort.\n\n2. Describe historical management measures, if any.\n\n3. Describe main strengths and weaknesses of current monitoring and enforcement capacity.\n\n4. Describe and reference any legal/policy requirements for management, monitoring and enforcement.\n\n       ") 
  probJs[27]<-(Js[27]=="1. Provide the time series (specify years, if possible) that exist for catch, effort, and CPUE/abundance indices.\n\n2. Describe how these data collected (e.g., log books, dealer reporting, observers).\n\n3. Describe what types of sampling programs and methodologies exist for data collection, including the time-series of available sampling data and quality.\n\n4. Describe all sources of uncertainty in the status, biology, life history and data sources of the fishery.\tInclude links to documentation, reports.\n\n      ")
  Qnams<-c(paste0("F",1:19),paste0("M",1:7),paste0("D",1:4))
  
  list(error=any(probJs),probJs=Qnams[probJs],nprob=sum(probJs))
  
}

plotJs<-function(){
  
  Jstats<-checkJs()
  dat<-matrix(c(Jstats$nprob,30-Jstats$nprob),ncol=1) #dat<-matrix(c(22,8),ncol=1)
  rownames(dat)<-c("Justified","Not justified")
  colfunc<-colorRampPalette(c("springgreen","yellow","red"))
  coly<-colfunc(1000)[ceiling(dat[1,1]/30*999)+0.01]
  if(Jstats$nprob==0)coly<-'springgreen'
  par(mai=c(0.01,0.01,0.3,0.01))
  singular=as.integer(Jstats$nprob!=29)+1
  barplot(dat,horiz=T,col=c('red','green'),border=F,axes=F,main=paste0(30-Jstats$nprob, " question",c(" was","s were")[singular], " provided with justification",c("","s")[singular],collapse=""),col.main=coly)  
  
}