Index.Rmd

---
title: "IPBES Value Assessment"
author: 
  - name: Raïsa Carmen
    email: raisa.carmen@inbo.be
    orcid: 0000-0003-1025-8702
  - name: Sander Jacobs
    email: sander.jacobs@inbo.be
    orcid: 0000-0002-2196-4234
date: "21-10-2021"
output: 
  bookdown::gitbook: default
  bookdown::pdf_document2:
    toc: true
    fig_caption: yes
params:
    printfigures: FALSE
---

```{r setup, include=FALSE}
library(ggplot2)
library(scales)
library(stringr)
library(tidyverse)
library(grDevices)
library(likert)
library(writexl)
library(grid)
library(gridExtra)
require(gtable)
library(openxlsx)
knitr::opts_chunk$set(echo = TRUE)
#load the final dataset 
load('output/s3_single_WithDummies.Rdata')
#the columns in s3_single to use for method families
MFkeycol <- c('MF1.key','MF2.key','MF3.key','MF4.key')
#the columns in s3_single to use
MFSODcol <- c('MF1.SOD','MF2.SOD','MF3.SOD','MF4.SOD')
#the columns in s3_single to use for IPBEs method families
MFIPBEScol <- c("IPBES.econ_SOD", "IPBES.soccul_SOD", "IPBES.bioph_SOD", 
                "IPBES.health_SOD","IPBES.ILK_SOD")
#Labels for the method families
MFLabels <- c('Nature-based valuation','Statement-based valuation','Behaviour-based valuation','Integrated valuation', 'AllArticles') # change the names here if needed.
names(MFLabels) <- c('MF1','MF2','MF3','MF4', 'All articles')
MFIPBESLabels <- c("Economic valuation", "Socio-cultural valuation",
             "Biophysical valuation", "Health related", "ILK related")
names(MFIPBESLabels) <- c('MF1','MF2','MF3','MF4', 'MF5')
#define colors for the figures
IPbesdarkgreen <- rgb(92/255, 102/255, 93/255) #5c665d
IPbeslightgreen <- rgb(181/255, 212/255, 141/255) #b5d48d
colfunc <- colorRampPalette(c(rgb(92/255, 102/255, 93/255), 
                              rgb(181/255, 212/255, 141/255))
                            )
#Useful function if you want just one legend for several figures
g_legend <- function(a.gplot){
  tmp <- ggplot_gtable(ggplot_build(a.gplot))
  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
  legend <- tmp$grobs[[leg]]
  return(legend)
}
#define themes for clean figures
blank_theme <- theme_minimal() +
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.border = element_blank(),
    panel.grid = element_blank(),
    axis.ticks = element_blank(),
    plot.title = element_text(size = 14, face = "bold")
  )
minimal_theme_bars <- theme_bw() + 
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.line = element_line(colour = "black")
        )
```

# Introduction
This report will summarize all responses for the IPBES Value assessment. The data cleaning process can be found in the R script *'DataCleaning.R'*. Starting from the cleaned dataset, we develop data overviews for each of the themes in this report. 


# Data overviews
```{r Figurefunctions, echo=FALSE}
lgnd <- read.xlsx("data/LegendListForDummyColumns.xlsx")
### this function generates a figure with bars and a pie-chart
# type can be either "piebar" or "pie" such that either the pie or a barchart 
# ith a pie chart is shown. Default is piebar.
PieBar <- function(Q, title, short, ordered, save, type = "piebar"){
  legend <- lgnd[lgnd$Question == Q,]
  sbst <- s3_single[,c('paperID',MFkeycol,MFSODcol,as.character(legend$code))]
  #from wide to long format
  sbst %>%
    gather(question, value, -paperID,-as.character(MFkeycol),
           -as.character(MFSODcol)) -> 
    sbst_long
  if (sum(str_detect(colnames(sbst),'_unclear')) > 0) {
    fillCol <- as.factor(1*(sbst[,str_detect(colnames(sbst),'_unclear')] == 1) + 
                           2*(pmax(sbst[,str_detect(colnames(sbst),
                                                    '_none|_irrelevant')]) == 1)
                         )
    fillCol[fillCol == '3'] <- '2'
    pie <- ggplot() +
      geom_bar(aes(x = factor(1), fill = fillCol), width = 1, color = 'black') + 
      blank_theme + 
      theme(axis.text.x = element_blank(), axis.text.y = element_blank()) + 
      scale_fill_manual(name = '', 
                        breaks = c('0','1','2'), 
                        labels = c('It is assessed','Unclear','It is assessed'),
                        values  = c('black', 'grey','white')) +
      xlab('') + ylab('') + 
      coord_polar("y", start = 0,direction = 1) + 
      theme(legend.position = 'bottom' )
  }else if (sum(str_detect(colnames(sbst),'_none|_irrelevant')) > 0) {
    pie <- ggplot() +
      geom_bar(aes(x = factor(1), fill = (pmax(sbst[,str_detect(
        colnames(sbst), '_none|_irrelevant')]) == 1)), 
        width = 1, 
        color = 'black') +
      blank_theme + 
      theme(axis.text.x = element_blank(), axis.text.y = element_blank()) +
      scale_fill_manual(name = '', 
                        breaks = c(TRUE, FALSE),
                        labels = c('Not assessed','It is assessed'), 
                        values  = c('white', 'grey')) +
      xlab('') + ylab('') + 
      coord_polar("y", start = 0,direction = 1) + 
      theme(legend.position = 'bottom' )
  }

  if (ordered) {#make ordered bar charts
    cnt <- aggregate(1*(sbst_long$value), by = list(sbst_long$question), 
                   FUN = function(x) sum(x))
    sbst_long$question <- factor(sbst_long$question, 
                                 levels = cnt[order(cnt$x, decreasing = FALSE),
                                              'Group.1'])
    selection <- !(str_detect(sbst_long$question,
                              regex('_none|_irrelevant|_unclear')))
  }else{
      selection <- !(str_detect(sbst_long$question,
                                regex('_irrelevant|_unclear')))
      }
  bar <- ggplot(sbst_long[selection,]) + 
    geom_bar(alpha = 1, size = 1,color = 'grey', fill = 'grey',
             aes(x = question, y = 1*value), 
             stat = "identity") + 
    coord_flip() + 
    minimal_theme_bars + 
    scale_x_discrete(breaks = legend$code, 
                     labels = str_wrap(as.character(legend$txt),50)) + 
    ylab('Number of papers') + xlab('') + ggtitle(title)
  library(cowplot)
  piebar <- ggdraw() +
    draw_plot(bar) +
    draw_plot(pie, x = 0.65, y = 0.09, width = .3, height = .4)
  if (type == "piebar") {
    return(piebar)
  } else if (type == "pie") {
    return(pie)
  }
  
  
  if (save) {
    ggsave(pie, 
           filename = sprintf('output/Q%s%sPie.pdf',Q,short), 
           width = 4, height = 4)
    ggsave(bar, filename = sprintf('output/Q%s%sBar.pdf',Q,short), 
           width = 10, height = 4)
    ggsave(piebar, filename = sprintf('output/Q%s%sPieBar.pdf', Q, short), 
           width = 10, height = 4)
    ggsave(pie, filename = sprintf('output/Q%s%sPie.jpg', Q, short), 
           width = 4, height = 4)
    ggsave(bar, filename = sprintf('output/Q%s%sBar.jpg', Q, short), 
           width = 10, height = 4)
    ggsave(piebar, filename = sprintf('output/Q%s%sPieBar.jpg', Q, short),
           width = 10, height = 4)
  }
}

# type can be either "mfkey", "mfsod" or "ipbes" such that either the 
# keyword search based, SOD, or ipbes method families are used. Default is mfkey.
MFBar <- function(Q, short, save, type = "mfkey"){
  legend <- lgnd[lgnd$Question == Q,]
  sbst <- s3_single[,c('paperID',MFkeycol,MFSODcol,MFIPBEScol, as.character(legend$code))]
  familysummary <- data.frame(MF = c('MF1','MF1','MF2','MF2','MF3','MF3','MF4','MF4'),
                                Answer = c('Yes','No','Yes','No','Yes','No','Yes','No'),
                                N = NA,
                                Percentage = NA)
  if (type == "mfkey") {
    familysummary$N <- sapply(1:nrow(familysummary), FUN = function(x)
      ifelse(familysummary$Answer[x] == 'No',
             sum(sbst[,(1 + as.numeric(str_remove(familysummary[x,'MF'], "MF")))]
                 == 1 &
                   pmax(sbst[,str_detect(colnames(sbst),'none|irrelevant')]) == 1,
                 na.rm = T),
             sum(sbst[,(1 + as.numeric(str_remove(familysummary[x,'MF'], "MF")))] 
                 == 1 &
                   pmax(sbst[,str_detect(colnames(sbst),'none|irrelevant')]) == 0,
                 na.rm = T))
      )
     familysummary$Percentage <- familysummary$N /
      sapply(familysummary$MF,
             FUN = function(x) sum(familysummary[familysummary$MF == x, 'N']) 
             )
    } else if (type == "mfsod") {
      familysummary$N <- sapply(1:nrow(familysummary), 
                                FUN = function(x) ifelse(
                                  familysummary$Answer[x] == 'No',
                                  sum(
                                    sbst[pmax(
                                      sbst[,str_detect(colnames(sbst),
                                                       'none|irrelevant')]) == 1,
                                      (5 + as.numeric(str_remove(
                                        familysummary[x,'MF'], "MF")))],
                                    na.rm = T),
                                  sum(
                                    sbst[pmax(
                                      sbst[,str_detect(colnames(sbst),
                                                       'none|irrelevant')]) == 0,
                                      (5 + as.numeric(str_remove(
                                        familysummary[x,'MF'], "MF")))], 
                                    na.rm = T)
                                  )
                                )
      familysummary$Percentage <- familysummary$N / 
        sapply(familysummary$MF, FUN = function(x)
          sum(familysummary[familysummary$MF == x, 'N'])
          )
    } else if (type == "ipbes") {
      familysummary <- data.frame(MF = c('MF1','MF1','MF2','MF2','MF3','MF3',
                                         'MF4','MF4','MF5','MF5'),
                                Answer = c('Yes','No','Yes','No','Yes','No',
                                           'Yes','No', 'Yes','No'),
                                N = NA,
                                Percentage = NA)
      familysummary$N <- sapply(1:nrow(familysummary), 
                                FUN = function(x) ifelse(
                                  familysummary$Answer[x] == 'No',
                                  sum(
                                    sbst[pmax(
                                      sbst[,str_detect(colnames(sbst),
                                                       'none|irrelevant')]) == 1,
                                      (9 + as.numeric(str_remove(
                                        familysummary[x,'MF'], "MF")))],
                                    na.rm = T),
                                  sum(
                                    sbst[pmax(
                                      sbst[,str_detect(colnames(sbst),
                                                       'none|irrelevant')]) == 0,
                                      (9 + as.numeric(str_remove(
                                        familysummary[x,'MF'], "MF")))], 
                                    na.rm = T)
                                  )
                                )
      familysummary$Percentage <- familysummary$N / 
        sapply(familysummary$MF, FUN = function(x)
          sum(familysummary[familysummary$MF == x, 'N'])
          )

    }else {
      warning('invalid type')
    }
    p <- ggplot(familysummary) + 
      geom_bar(color = 'black', 
               aes(x = MF, fill = Answer, y = Percentage * 100), 
               stat = 'identity',
               position = 'stack') + 
      scale_fill_manual(name = '',
                        breaks = c('Yes','No'), 
                        labels = c('It is assessed','Not assessed'),
                        values= c('grey', 'white')) + 
      ylab('Percentage in the method family') + 
      xlab('Method family') + 
      minimal_theme_bars 
    if(type != 'ipbes'){
      p <- p + scale_x_discrete(labels = MFLabels)
    } else {
      p <- p + scale_x_discrete(labels = MFIPBESLabels)
    }
    if (save) {
      ggsave(p, 
           filename = sprintf('output/Q%s%sMF%sbars.pdf', Q, short, 
                              ifelse(type == "mfkey", 
                                     "key", 
                                     ifelse(type == "mfsod",
                                            "SOD",
                                            "IPBES"))), 
           width = 10, 
           height = 4)
      ggsave(p, 
           filename = sprintf('output/Q%s%sMF%sbars.jpg', Q, short, 
                              ifelse(type == "mfkey", 
                                     "key", 
                                     ifelse(type == "mfsod",
                                            "SOD",
                                            "IPBES"))), 
           width = 10, 
           height = 4)
    }
    return(p)
}
```
## General overviews

After each theme, respondents were asked how certain the were about their answers. 

```{r certainty, echo=FALSE, message = FALSE, fig.width = 8, fig.height = 5, fig.cap= "Respondent ratings of how certain they are about their aswers for each of the topics and overall."}
A <- data.frame(colnm = c('1.7', '2.20', '3.6','4.6','5.7','6.6','7.4','8.15','8.17'),
              subject = c('topic 1: Methods and their use', 'topic 2: Context of application','topic 3: Application descriptors','topic 4: Reliability and Validity','topic 5: IPLMLC','topic 6: Human well-being','topic 7: Ecological Sustainability','topic 8: Justice','All topics'))

L <- as.data.frame(s3_single[,sapply(A$colnm,FUN = function(x)
  which(colnames(s3_single) == x))])

for (i in 1:ncol(L)) {
  L[,i] <- recode_factor(factor(as.factor(L[,i]), 
                                levels = c('5','4','3','2','1')),
                         '1' = 'very sure',
                         '2' = 'sure',
                         '3' = 'in between',
                         '4' = 'unsure',
                         '5' = 'very unsure')
  L[,i] <- factor(L[,i],levels = 
                    c('very unsure','unsure','in between','sure','very sure'),
                  ordered = TRUE)
}
  l <- likert(L)
  a <- order(rowSums(l$results[,5:6]))
  plotCertainty <- likert.bar.plot(l, 
                                   low.color = 'red', 
                                   high.color = 'forestgreen', 
                                   plot.percent.low = TRUE, 
                                   plot.percent.neutral = TRUE,
                                   plot.percent.high = TRUE,
                                   legend.position = "bottom", legend = '') +
    theme_classic() + ylab('Percentage of the respondents') +
    theme(legend.position = 'bottom') + 
    scale_x_discrete(limits = summary(l, center = 3)[a,'Item'],
                     labels = sapply(summary(l,center = 3)[a,'Item'], 
                                     FUN = function(x)  
                                       str_wrap(A[A$colnm == x,'subject'], 
                                                width = 22))
                     )
  plotCertainty
  ggsave(plotCertainty, filename = 'output/CertaintyLikertPlot.pdf', width = 8, height = 5)
  ggsave(plotCertainty, filename = 'output/CertaintyLikertPlot.jpg', width = 8, height = 5)
```

Figure \@ref(fig:certainty) shows how certain the respondents were for their answers in each of the topics. At the end of the questionnaire, they were also asked to rate their confidence foor all topics (denoted by "All topics" in Figure \@ref(fig:certainty)). It can be seen that the highest certainty is observed for "`r A[A$colnm == l$results[a[length(a)], 1],"subject"]`" while the highest uncertainty is observed for "`r A[A$colnm == l$results[a[1], 1],"subject"]`".

The alluvial plot in Figure \@ref(fig:alluvial) shows how the method families are related to the IPBES categories. The behaviour based valuation and statement-based method family are the largest (they envelop the most methods). The alluvial plot shows how many methods in each of the families belong to each of the IPBES categories. As expected, many statement-based methods belong to the socio-cultural valuation category. Many of the nature-based methods belong to biophysical valuation category.
```{r alluvial, echo=FALSE, message = FALSE, fig.width = 8, fig.height = 12, fig.cap= "Alluvial plot to show the relation between the method families and the IPBES categories."}
library(ggalluvial)
lMF <- read.xlsx('data/methods x MF x IPBESclasses SOD.xlsx')
#Only keep the unique SOD methods
a <- sapply(X = unique(lMF[!is.na(lMF$method.name.SOD),'method.name.SOD']), 
            FUN = function(x) min(which(lMF$method.name == x)))
A <- lMF[a,c("method.name.SOD", 
             "MF1.-.Nature.based.valuation",
             "MF2.-.Statement-based",
             "MF3.-.Behaviour.based.valuation",
             "MF4.-.Integrated.valuation" ,
             "Economic.valuation",
             "socio-cultural.valuation",
             "biophysical.valuation",
             "health.related",
             "ILK.related")]
limit <- 1 #we only say the method belong to the metod family/ipbes category if it scores higher than 'limit'
Along <- data.frame(MF = rep(c("Nature based","Statement-based",
                             "Behaviour based valuation","Integrated"), 
                           each = 5),
                  IPBES = rep(c('Economic valuation','socio-cultural valuation',
                                'biophysical valuation','health related',
                                'ILK related'),4),
                  n = NA)
for (i in 1:nrow(Along)) {
Along[i,'n'] <- sum((lMF[,which(str_detect(colnames(lMF),str_replace_all(
  as.character(Along[i,'IPBES'])," ",".")))] > limit) & 
    (lMF[,which(str_detect(colnames(lMF),
                           str_replace_all(as.character(
                             Along[i,'MF'])," ",".")))] > limit) 
  )
}

p <- ggplot(data = Along, aes(axis1 = MF, axis2 = IPBES, y = n)) + 
  geom_alluvium(aes(fill = MF, colour = MF), alpha = .75, width = 0, 
                reverse = FALSE) +
  guides(fill = "none") +
  geom_stratum(width = 1/8, reverse = FALSE) +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)),
            reverse = FALSE, angle = 90) +
  scale_x_continuous(breaks = c(1,2), 
                     labels = c("Method family", "IPBES category")) + 
  ylab('Nuber of methods') + theme_bw() + 
  theme(legend.position = "none")
p
ggsave(p,file = "output/MethodsAlluvialPlot.pdf", height = 12, width=8)
ggsave(p,file = "output/MethodsAlluvialPlot.jpg", height = 12, width=8)
```



## Topic 1: Methods and their use

## Topic 2: Context of application
```{r plot215, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 6, fig.cap= "Assessment of multiple values."}
p <- PieBar(Q = '2.15', 
       title = 'The application assesses multiple values and brings them together into an overall value or importance by:',
       short = 'MultipleValues', 
       ordered = TRUE, 
       save = params$printfigures)
p
```
```{r plot215MFkey, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 6, fig.cap= "Assessment of multiple values."}
p <- MFBar(Q = "2.15",
           short = 'MultipleValues',
           save = params$printfigures, 
           type = "mfkey")
p
```
```{r plot215MFsod, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 6, fig.cap= "Assessment of multiple values."}
p <- MFBar(Q = "2.15",
           short = 'MultipleValues',
           save = params$printfigures, 
           type = "mfsod")
p
```
```{r plot215ipbes, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 6, fig.cap= "Assessment of multiple values."}
p <- MFBar(Q = "2.15",
           short = 'MultipleValues',
           save = params$printfigures, 
           type = "ipbes")
p
```


```{r plot216, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Interests and conflicts."}
p <- PieBar(Q = '2.16', 
       title = 'The application mentions interests and conflicts:',
       short = 'nterestsConflicts', 
       ordered = FALSE, 
       save = params$printfigures)
p
```



## Topic 3: Application descriptors

## Topic 4: Reliability and Validity
```{r plot41, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of the replicability of the results."}
p <- PieBar(Q = '4.1', 
       title = 'Replicability of the results',
       short = 'Replicability', 
       ordered = TRUE, 
       save = params$printfigures,
       type = "pie")
p
```

```{r plot42, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of the consistency of the results."}
p <- PieBar(Q = '4.2', 
       title = 'Consistency of the results',
       short = 'Consistency', 
       ordered = TRUE, 
       save = params$printfigures,
       type = "pie")
p
```

```{r plot43, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of the precision of the results."}
p <- PieBar(Q = '4.3', 
       title = 'Precision of the results',
       short = 'Precision', 
       ordered = TRUE, 
       save = params$printfigures,
       type = "pie")
p
```

```{r plot44, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of the internal validity of the results."}
p <- PieBar(Q = '4.4', 
       title = 'Internal validity of the results',
       short = 'IntValidity', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot45, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of the external validity of the results."}
p <- PieBar(Q = '4.5', 
       title = 'External validity of the results',
       short = 'ExtValidity', 
       ordered = TRUE, 
       save = params$printfigures)
p
```


## Topic 5: IPLMLC
```{r plot52, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of respect towards nature."}
p <- PieBar(Q = '5.2', 
       title = 'The application identifies/assesses respect towards nature by...',
       short = 'NatureRespect', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot53, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of responsibility or care for the land."}
p <- PieBar(Q = '5.3', 
       title = 'The application assesses responsibility or care for the land by...',
       short = 'ResponsibilityLand', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot54, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of kinship/communality with other people."}
p <- PieBar(Q = '5.4', 
       title = 'The application assesses kinship/communality with other people by...',
       short = 'KinshipPeople', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot55, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of kinship/communality with non-human entities."}
p <- PieBar(Q = '5.5', 
       title = 'The application assesses kinship/communality with non-human entities by...',
       short = 'KinshipNonHuman', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot56, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of values of self-determination and ancestral law."}
p <- PieBar(Q = '5.6', 
       title = 'The application assesses values of self-determination and ancestral law, by...',
       short = 'SelfDeterminationAncestralLaw', 
       ordered = TRUE, 
       save = params$printfigures)
p
```


## Topic 6: Human well-being

```{r plot61, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 6, fig.cap= "Assessment of ecological human well-being."}
p <- PieBar(Q = '6.1', 
       title = 'The application assesses human well-being using one of the following indicators (multiple possible):',
       short = 'HumanWellBeing', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot62, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of preferences (or importance) that humans assign to nature and biodiversity."}
p <- PieBar(Q = '6.2', 
       title = 'The application assesses the preferences (or importance) that humans assign to nature and biodiversity in terms of (multiple possible):',
       short = 'AssessBiodiversity', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot63, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 6, fig.cap= "Assessment of the costs to protect nature & biodiversity."}
p <- PieBar(Q = '6.3', 
       title = 'The application assesses the costs to protect nature & biodiversity for its own sake or to maintain nature’s contributions to people in the form of (multiple possible):',
       short = 'CostsProtect', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot65, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of well-being indicator for different socio-demographic groups."}
p <- PieBar(Q = '6.5', 
       title = 'If the application assesses human well-being (specified above), is the well-being indicator assessed for different socio-demographic groups?',
       short = 'WellBeingIndicator', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

## Topic 7: Ecological Sustainability

```{r plot71, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of ecological condition."}
p <- PieBar(Q = '7.1', 
       title = 'Was ECOLOGICAL CONDITION assessed & how?',
       short = 'EcologicalCondition', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot72, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of ecosystem capacity."}
p <- PieBar(Q = '7.2', 
       title = 'Was ECOSYSTEM CAPACITY assessed and how?',
       short = 'EcosystemCapacity', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot73, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of sustainable use/management."}
p <- PieBar(Q = '7.3', 
       title = 'Was SUSTAINABLE USE/MANAGEMENT  of ecosystems assessed and how?',
       short = 'SustainableUseManagement', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

## Topic 8: Justice

```{r plot89, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of recognition."}
p <- PieBar(Q = '8.9', 
       title = 'Recognition: knowledge types',
       short = 'RecogKnowledgeTypes', 
       ordered = TRUE, 
       save = params$printfigures)
p
```
```{r plot810, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of recognition."}
p <- PieBar(Q = '8.10', 
       title = 'Recognition: broad values',
       short = 'RecogBroadValues', 
       ordered = TRUE, 
       save = params$printfigures)
p
```
```{r plot811, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of recognition."}
p <- PieBar(Q = '8.12', 
       title = 'Recognition: value types',
       short = 'RecogValueTypes', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot812, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of recognition."}
p <- PieBar(Q = '8.13', 
       title = 'Recognition: value dimensions',
       short = 'RecogValueDimensions', 
       ordered = TRUE, 
       save = params$printfigures)
p
```


```{r plot83, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 6, fig.cap= "Transparency of the valuation process."}
p <- PieBar(Q = '8.3', 
       title = 'Transparency level',
       short = 'Transparency', 
       ordered = FALSE, 
       save = params$printfigures)
p
```

```{r plot84, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "- Representation of different stakeholders in the valuation process."}
p <- PieBar(Q = '8.4', 
       title = 'Stakeholder representation',
       short = 'StkhldrRepr', 
       ordered = FALSE, 
       save = params$printfigures)
p
```

```{r plot85, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 6, fig.cap= "Based on what are stakeholder groups identified and targeted (for sample or for participation)."}
p <- PieBar(Q = '8.5', 
       title = 'Stakeholder identification',
       short = 'StkhldrIdent', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot86, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "The application addresses inclusiveness of multiple stakeholders and minorities in the design of the valuation process by adjusting/providing:."}
p <- PieBar(Q = '8.6', 
       title = 'Stakeholder inclusion actions',
       short = 'StkhldrIncl', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot87, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Does the application present results and data on internal power dynamics?(by e.g. showing speaking time, interruptions, use of physical space, testing difference in outputs in different power context..., )"}
p <- PieBar(Q = '8.7', 
       title = 'Power',
       short = 'Power', 
       ordered = TRUE, 
       save = params$printfigures,
       type = "pie")
p
```

```{r plot88, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Regarding participation level, the application:"}
p <- PieBar(Q = '8.8', 
       title = 'Participation',
       short = 'Participation', 
       ordered = FALSE, 
       save = params$printfigures)
p
```

```{r plot81, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of intra-generational justice"}
p <- PieBar(Q = '8.1', 
       title = 'Was INTRA-GENERATIONAL JUSTICE assessed and how?',
       short = 'IntraJustice', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot82, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of inter-generational justice"}
p <- PieBar(Q = '8.2', 
       title = 'Was INTER-GENERATIONAL JUSTICE assessed and how?',
       short = 'InterGenJustice', 
       ordered = TRUE, 
       save = params$printfigures)
p
```

```{r plot814, echo=FALSE, message = FALSE, fig.width = 10, fig.height = 4, fig.cap= "Assessment of community of justice"}
p <- PieBar(Q = '8.14', 
       title = 'Who is part of the community of justice?',
       short = 'CommunityJustice', 
       ordered = TRUE, 
       save = params$printfigures)
p
```