Main File for part II of basal attributes article

1 Notes

2 create raw data files

# sets the directory of location of this script as the current directory
# setwd(dirname(rstudioapi::getSourceEditorContext()$path))

### load packages
require(pacman)
p_load('tidyverse', 'jsonlite', 'magrittr', 'xlsx',
       'stargazer', 'psych', 'jtools', 'DT', 'ggstatsplot', 
       'lavaan', 
       'regsem', 'MplusAutomation', 'igraph')

### list data files
setwd("data")
folders <- list.files(pattern = "^study_result.*")

### create data files
### get CAM data
writeLines("", "CAMdata.txt") # create file
text_connection <- file("CAMdata.txt", "a") # open connection to append

### get pre CAM data
writeLines("", "preCAM.txt") # create file
text_connection_pre <- file("preCAM.txt", "a") # open connection to append

### get post CAM data
writeLines("", "postCAM.txt") # create file
text_connection_post <- file("postCAM.txt", "a") # open connection to append

for(i in 1:length(folders)){
  setwd(folders[i])
  if(length(dir()) == 3){
    # print(i)
    ### CAM data
    setwd(dir()[2])
    tmp <- jsonlite::fromJSON(txt = "data.txt")
    writeLines(jsonlite::toJSON(x = tmp), text_connection)
    setwd("..")

    ### pre CAM data
    setwd(dir()[1])
    tmp <- jsonlite::fromJSON(txt = "data.txt")
    writeLines(jsonlite::toJSON(x = tmp), text_connection_pre)
    setwd("..")

    ### post CAM data
    setwd(dir()[3])
    tmp <- jsonlite::fromJSON(txt = "data.txt")
    writeLines(jsonlite::toJSON(x = tmp), text_connection_post)
    setwd("..")
  }
  setwd("..")
}

close(text_connection) # close connection CAM
close(text_connection_pre) # close connection
close(text_connection_post) # close connection

### move files to output folder
# copy files (not overwritten)
tmp_file_from <-  getwd()
setwd("../outputs/01_dataPreperation")
file.copy(from =  paste0(tmp_file_from, "/CAMdata.txt"), to = paste0(getwd(), "/CAMdata.txt"))

[1] FALSE

file.copy(from =  paste0(tmp_file_from, "/preCAM.txt"), to = paste0(getwd(), "/preCAM.txt"))

[1] FALSE

file.copy(from =  paste0(tmp_file_from, "/postCAM.txt"), to = paste0(getwd(), "/postCAM.txt"))

[1] FALSE

### remove files
file.remove(paste0(tmp_file_from, "/CAMdata.txt"))

[1] TRUE

file.remove(paste0(tmp_file_from, "/preCAM.txt"))

[1] TRUE

file.remove(paste0(tmp_file_from, "/postCAM.txt"))

[1] TRUE

### load functions
setwd("../../functions")
for(i in 1:length(dir())){
  # print(dir()[i])
  source(dir()[i], encoding = "utf-8")
}


setwd("../functions_CAMapp")
for(i in 1:length(dir())){
  # print(dir()[i])
  source(dir()[i], encoding = "utf-8")
}
rm(i)



### summary function
data_summary <- function(data, varname, groupnames){
  require(plyr)
  summary_func <- function(x, col){
    c(mean = mean(x[[col]], na.rm=TRUE),
      se = sd(x[[col]], na.rm=TRUE) / sqrt(length(x[[col]])))
  }
  data_sum<-ddply(data, groupnames, .fun=summary_func,
                  varname)
  data_sum <- plyr::rename(data_sum, c("mean" = varname))
  return(data_sum)
}

3 set up data.frame questionnaires

setwd("outputs/01_dataPreperation")
# > pre study
suppressMessages(read_file('preCAM.txt') %>%
                   # ... split it into lines ...
                   str_split('\n') %>% first() %>%
                   # ... filter empty rows ...
                   discard(function(x) x == '') %>%
                   discard(function(x) x == '\r') %>%
                   # ... parse JSON into a data.frame
                   map_dfr(fromJSON, flatten=TRUE)) -> dat_preCAM
# > post CAM
read_file('postCAM.txt') %>%
                   # ... split it into lines ...
                   str_split('\n') %>% first() %>%
                   # ... filter empty rows ...
                   discard(function(x) x == '') %>%
                   discard(function(x) x == '\r') -> tmp_postCAM


tmp_out <- list()
for(i in 1:length(tmp_postCAM)){
tmp_out[[i]] <- fromJSON(tmp_postCAM[i])
tmp_out[[i]]$"21" <- NULL # causes error
}

dat_postCAM <- dplyr::bind_rows(tmp_out)
rm(tmp_postCAM); rm(tmp_out)



### create counter variable for both data sets
# > pre study
dat_preCAM$ID <- NA

tmp_IDcounter <- 0
for(i in 1:nrow(dat_preCAM)){
  if(!is.na(dat_preCAM$sender[i]) && dat_preCAM$sender[i] == "Greetings"){
    # tmp <- dat_preCAM$prolific_pid[i]
    tmp_IDcounter = tmp_IDcounter + 1
  }
  dat_preCAM$ID[i] <- tmp_IDcounter
}

# > post CAM
dat_postCAM$ID <- NA

tmp_IDcounter <- 0
for(i in 1:nrow(dat_postCAM)){
  if(!is.na(dat_postCAM$sender[i]) && dat_postCAM$sender[i] == "CAMfeedbackGeneral"){
    # tmp <- dat_postCAM$prolific_pid[i]
    tmp_IDcounter = tmp_IDcounter + 1
  }
  dat_postCAM$ID[i] <- tmp_IDcounter
}



### keep only complete data sets
# > pre study
# sort(table(dat_preCAM$ID))
sum(table(dat_preCAM$ID) != max(table(dat_preCAM$ID)))

[1] 0

sum(table(dat_preCAM$ID) == max(table(dat_preCAM$ID)))

[1] 193

dat_preCAM <- dat_preCAM[dat_preCAM$ID %in% names(table(dat_preCAM$ID))[table(dat_preCAM$ID) == max(table(dat_preCAM$ID))],]

# > post CAM
# sort(table(dat_postCAM$ID))
sum(table(dat_postCAM$ID) != max(table(dat_postCAM$ID)))

[1] 1

sum(table(dat_postCAM$ID) == max(table(dat_postCAM$ID)))

[1] 192

dat_postCAM <- dat_postCAM[dat_postCAM$ID %in% names(table(dat_postCAM$ID))[table(dat_postCAM$ID) == max(table(dat_postCAM$ID))],]



### json (from JATOS) to 2D data.frame
# > pre study
# add paradata
tmp_notNumeric <- str_subset(string = colnames(dat_preCAM), pattern = "^meta|^R")
tmp_notNumeric <- str_subset(string = tmp_notNumeric, pattern = "labjs|location", negate = TRUE)

### get survey
vec_ques <- c("PROLIFIC_PID",
              "dummy_informedconsent", 
              "commCheck",
              tmp_notNumeric)

vec_notNumeric = c("PROLIFIC_PID", tmp_notNumeric)

questionnaire_preCAM <- questionnairetype(dataset = dat_preCAM, 
                                        listvars = vec_ques, 
                                        notNumeric = vec_notNumeric, verbose = FALSE)

dim(questionnaire_preCAM)

[1] 193  17

# > post CAM
tmp_numeric <- str_subset(string = colnames(dat_postCAM), pattern = "^nfc")


vec_ques <- c("PROLIFIC_PID", "commCheck",
              "feedCAM_repres", "feedCAM_technicalprobs", "feedCAM_technicalprobsText", "feedCAM_already","feedCAM_alreadyText",
              "openQuestion_MaterialSystem",
              "openQuestion_Ethic",
              "openQuestion_missedPositive", "openQuestion_missedNegative",
              "outcome_research", "outcome_prohibition", "outcome_buy", "outcome_buy_text", 
              "education", "experienceMS",
              tmp_numeric,
              "feedback_critic")

     

vec_notNumeric = c("PROLIFIC_PID", 
                   "feedCAM_technicalprobsText", "feedCAM_alreadyText", 
                                 "openQuestion_MaterialSystem",
                                 "openQuestion_Ethic",
              "openQuestion_missedPositive", "openQuestion_missedNegative",
   "outcome_research", "outcome_prohibition", "outcome_buy", "outcome_buy_text", 
                   "education", "experienceMS",
                   "feedback_critic")

questionnaire_postCAM <- questionnairetype(dataset = dat_postCAM,
                                        listvars = vec_ques,
                                        notNumeric = vec_notNumeric, verbose = FALSE)

questionnaire_postCAM$feedback_critic[questionnaire_postCAM$feedback_critic == ""] <- NA
dim(questionnaire_postCAM)

[1] 192  23

questionnaire_postCAM$multipleEthic <- NA
tmp_multipleEthic <- cbind(dat_postCAM$ID, dat_postCAM$multipleEthic)

questionnaire_postCAM$multiplePositive <- NA
tmp_multiplePositive <- cbind(dat_postCAM$ID, dat_postCAM$multiplePositive)

questionnaire_postCAM$multipleNegative <- NA
tmp_multipleNegative <- cbind(dat_postCAM$ID, dat_postCAM$multipleNegative)


for(i in questionnaire_postCAM$ID){
  # add multipleEthic
  tmp <- tmp_multipleEthic[tmp_multipleEthic[,1] == i, ]
  if(length(unlist(tmp[,2])) == 3){
    questionnaire_postCAM$multipleEthic[questionnaire_postCAM$ID == i] <- paste0(unlist(tmp[,2]), collapse = " \\ ")
  }
  
  # add multiplePositive
    tmp <- tmp_multiplePositive[tmp_multiplePositive[,1] == i, ]
  if(length(unlist(tmp[,2])) == 3){
    questionnaire_postCAM$multiplePositive[questionnaire_postCAM$ID == i] <- paste0(unlist(tmp[,2]), collapse = " \\ ")
  }
    
      # add multipleNegative
    tmp <- tmp_multipleNegative[tmp_multipleNegative[,1] == i, ]
  if(length(unlist(tmp[,2])) == 3){
    questionnaire_postCAM$multipleNegative[questionnaire_postCAM$ID == i] <- paste0(unlist(tmp[,2]), collapse = " \\ ")
  }
}


dim(questionnaire_postCAM)

[1] 192  26

### merge all data sets
# remove one missing
questionnaire_preCAM <- questionnaire_preCAM[questionnaire_preCAM$PROLIFIC_PID %in% questionnaire_postCAM$PROLIFIC_PID, ]


questionnaire_postCAM$ID <- NULL
questionnaire <- left_join(questionnaire_preCAM, questionnaire_postCAM, by="PROLIFIC_PID")


## all missing answers to NA
questionnaire[questionnaire == ""] <- NA
## all feedback smaller than 3 characters to NA
questionnaire$feedback_critic[nchar(questionnaire$feedback_critic) <= 2] <- NA

dim(questionnaire)

[1] 192  41

### save files
## save as .xlsx file
xlsx::write.xlsx2(x = questionnaire, file = "questionnaire.xlsx")
## save as R object
saveRDS(questionnaire, file = "questionnaire.rds")

4 get reaction times for single components

Plot time taken (in minutes) by participants for single components of study:

dat_duration <- data.frame(duration = NA, sender = NA, ID = NA, PROLIFIC_PID = NA)



for(i in 1:length(unique(dat_preCAM$ID))){

  tmp_PID <- dat_preCAM$PROLIFIC_PID[dat_preCAM$ID ==  unique(dat_preCAM$ID)[i] & !is.na(dat_preCAM$PROLIFIC_PID)]
  
  

     # pre CAM
    tmp_preCAM <- data.frame(duration = dat_preCAM$duration[dat_preCAM$ID == unique(dat_preCAM$ID)[i]] / 1000,
                    sender = dat_preCAM$sender[dat_preCAM$ID == unique(dat_preCAM$ID)[i]])
    tmp_preCAM <- tmp_preCAM[!is.na(tmp_preCAM$sender),]
    
     # post CAM
    tmp_postCAM <- data.frame(duration = dat_postCAM$duration[dat_postCAM$ID == unique(dat_postCAM$ID)[i]] / 1000,
                    sender = dat_postCAM$sender[dat_postCAM$ID == unique(dat_postCAM$ID)[i]])
    tmp_postCAM <- tmp_postCAM[!is.na(tmp_postCAM$sender),]
    
  
    tmp <- rbind(tmp_preCAM, tmp_postCAM)
    

  if(all(is.na(dat_duration))){
    dat_duration <- data.frame(duration = tmp$duration,
                              sender = tmp$sender,
                              ID = rep(i, times=nrow(tmp)),
                              PROLIFIC_PID = rep(tmp_PID, times=nrow(tmp)))


  }else{
    dat_duration <- rbind(dat_duration,  data.frame(duration = tmp$duration,
                                                    sender = tmp$sender,
                                                    ID = rep(i, times=nrow(tmp)),
                                                    PROLIFIC_PID = rep(tmp_PID, times=nrow(tmp))))
  }
}

## remove empty sender 
dat_duration <- dat_duration[!is.na(dat_duration$sender), ]
dat_duration <- dat_duration[!is.na(dat_duration$duration), ]

dat_duration$sender[dat_duration$sender == "done"] <- "CAM instructions"

## save as .xlsx
# write.xlsx2(x = dat_duration, file = "outputs/para_duration_singleComponents.xlsx")

#### plot
dat_duration$ID <- factor(dat_duration$ID)
p <- dat_duration %>%
  ggplot(aes(x=sender, y=duration, color=PROLIFIC_PID)) +
  geom_point() +
  geom_jitter(width=0.15)+
  theme(axis.text.x = element_text(angle = 90)) + theme(legend.position="none")
p

## save ggplot as PDF
ggsave(filename = "outputs/01_dataPreperation/durations_components.pdf", p)


# Calculate the mean duration in seconds for each sender and sort by mean duration
tmp <- dat_duration %>%
  group_by(sender) %>%
  summarise(N = n(), mean_duration = mean(duration, na.rm = TRUE)) %>%
  arrange(desc(mean_duration))
DT::datatable(tmp, options = list(pageLength = 5)) 

4.1 set up CAM data

Load CAM data

setwd("outputs/01_dataPreperation")
suppressMessages(read_file("CAMdata.txt") %>%
  # ... split it into lines ...
  str_split('\n') %>% first() %>%
    discard(function(x) x == '') %>%
    discard(function(x) x == '\r') %>%
  # ... filter empty rows ...
  discard(function(x) x == '')) -> dat_CAM_pre

raw_CAM <- list()
for(i in 1:length(dat_CAM_pre)){
  raw_CAM[[i]] <- jsonlite::fromJSON(txt = dat_CAM_pre[[i]])
}

Create CAM files, draw CAMs and compute network indicators

### create CAM single files (nodes, connectors, merged)
CAMfiles <- create_CAMfiles(datCAM = raw_CAM, reDeleted = TRUE)

Nodes and connectors, which were deleted by participants were removed. 
 # deleted nodes:  0 
 # deleted connectors:  434

# remove testing data sets
CAMfiles[[1]] <- CAMfiles[[1]][CAMfiles[[1]]$participantCAM %in% questionnaire$PROLIFIC_PID,]
CAMfiles[[2]] <- CAMfiles[[2]][CAMfiles[[2]]$participantCAM %in% questionnaire$PROLIFIC_PID,]
CAMfiles[[3]] <- CAMfiles[[3]][CAMfiles[[3]]$participantCAM.x %in% questionnaire$PROLIFIC_PID,]

### draw CAMs
CAMdrawn <- draw_CAM(dat_merged = CAMfiles[[3]],
                     dat_nodes = CAMfiles[[1]],ids_CAMs = "all",
                     plot_CAM = FALSE,
                     useCoordinates = TRUE,
                     relvertexsize = 3,
                     reledgesize = 1)

processing 192 CAMs... 
[1] "== participantCAM in drawnCAM"

### network indicators
tmp_microIndicator <- V(CAMdrawn[[1]])$label
networkIndicators <- compute_indicatorsCAM(drawn_CAM = CAMdrawn, 
                                           micro_degree = tmp_microIndicator, 
                                           micro_valence = tmp_microIndicator, 
                                           micro_centr_clo = tmp_microIndicator, 
                                           micro_transitivity = tmp_microIndicator, 
                                           largestClique = FALSE)


### wordlist
CAMwordlist <- create_wordlist(
  dat_nodes =  CAMfiles[[1]],
  dat_merged =  CAMfiles[[3]],
  useSummarized = TRUE,
  order = "frequency",
  splitByValence = FALSE,
  comments = TRUE,
  raterSubsetWords = NULL,
  rater = FALSE
)

[1] "create_wordlist - use raw words"
[1] 0
[1] 6336
[1] "temporarily suffixes are added, because not all words have been summarized"
processing 192 CAMs... 
[1] "== participantCAM in drawnCAM"

DT::datatable(CAMwordlist, options = list(pageLength = 5))

4.1.1 plot CAMs using igraph package

Just plot first 10 CAMs for an impression

for(i in 1:10){ # length(CAMdrawn)
  plot(CAMdrawn[[i]], edge.arrow.size = .7,
       layout=layout_nicely, vertex.frame.color="black", asp = .5, margin = -0.1,
       vertex.size = 10, vertex.label.cex = .9)
}

4.1.2 save CAMs as .json files, and as .png (igraph)

save_CAMs_as_pictures = FALSE

if(save_CAMs_as_pictures){
setwd("outputs/01_dataPreperation")

setwd("savedCAMs")
setwd("png")
### remove all files if there are any
if(length(list.files()) >= 1){
  file.remove(list.files())
  cat('\n!
      all former .png files have been deleted')
}

### if no participant ID was provided replace by randomly generated CAM ID
if(all(CAMfiles[[3]]$participantCAM.x == "noID")){
  CAMfiles[[3]]$participantCAM.x <- CAMfiles[[3]]$CAM.x
}

### save as .json files, and as .png (igraph)
ids_CAMs <- unique(CAMfiles[[3]]$participantCAM.x); length(ids_CAMs)


for(i in 1:length(ids_CAMs)){
  save_graphic(filename = paste0("CAM", ids_CAMs[i])) #  paste0(ids_CAMs[i]))
  CAM_igraph <- CAMdrawn[[c(1:length(CAMdrawn))[
    names(CAMdrawn) == paste0(unique(CAMfiles[[3]]$participantCAM.x)[i])]]]
  plot(CAM_igraph, edge.arrow.size = .7,
       layout=layout_nicely, vertex.frame.color="black", asp = .5, margin = -0.1,
       vertex.size = 10, vertex.label.cex = .9)
  dev.off()
}

setwd("../json")
### remove all files if there are any
if(length(list.files()) >= 1){
  file.remove(list.files())
  cat('\n!
      all former .json files have been deleted')
}
for(i in 1:length(raw_CAM)){
  if(!is_empty(raw_CAM[[i]]$nodes)){
    if(nrow(raw_CAM[[i]]$nodes) > 5){
      if(raw_CAM[[i]]$creator %in% questionnaire$PROLIFIC_PID){ # only if not deleted previously
              write(toJSON(raw_CAM[[i]], encoding = "UTF-8"),
            paste0(raw_CAM[[i]]$creator, ".json"))
      }
    }
  }
}
}

4.1.3 delete CAMs with strong star topology

barplot(table(networkIndicators$degreetot_micro_AkzeptanzeinesneuenMaterialsystems) / nrow(networkIndicators))

for(i in 1:nrow(networkIndicators)){
  if(networkIndicators$degreetot_micro_AkzeptanzeinesneuenMaterialsystems[i] == 32){
      plot(CAMdrawn[[i]], edge.arrow.size = .7,
       layout=layout_nicely, vertex.frame.color="black", asp = .5, margin = -0.1,
       vertex.size = 10, vertex.label.cex = .9)
  }
}

4.1.4 plot aggregated CAM

sel_ids <- unique(CAMfiles[[1]]$participantCAM)
CAMaggregated <- aggregate_CAMs(dat_merged = CAMfiles[[3]], dat_nodes = CAMfiles[[1]],
                                ids_CAMs = sel_ids)

[1] "aggregate_CAMs: using participant CAM ids"
processing 192 CAMs... 
[1] "== participantCAM in drawnCAM"

# plot(CAMaggregated[[2]], vertex.size=diag(CAMaggregated[[1]]) / max(diag(CAMaggregated[[1]]))*20, edge.arrow.size=0.01)
# plot(CAMaggregated[[2]], vertex.size=(abs(V(CAMaggregated[[2]])$value)+1)*5, edge.arrow.size=0.01)


g = CAMaggregated[[2]]
g2 = simplify(CAMaggregated[[2]])
# plot(g2, edge.arrow.size=0.01,
#      vertex.size=diag(CAMaggregated[[1]]) / max(diag(CAMaggregated[[1]]))*20)

E(g2)$weight = sapply(E(g2), function(e) {
  length(all_shortest_paths(g, from=ends(g2, e)[1], to=ends(g2, e)[2])$res) } )
E(g2)$weight = E(g2)$weight * 2
# E(g2)$weight[E(g2)$weight == 1] <- NA

V(g2)$color[V(g2)$value <= .5 & V(g2)$value >= -.5] <- "yellow"

V(g2)$shape <- NA
V(g2)$shape <- ifelse(test = V(g2)$color == "yellow", yes = "square", no = "circle")



### > plot multiple times because of random layout
for(i in 1:3){
  plot(g2, edge.arrow.size = 0,
     layout=layout_nicely, vertex.frame.color="black", asp = .5, margin = -0.1,
     vertex.size=diag(CAMaggregated[[1]]) / max(diag(CAMaggregated[[1]]))*5,
     vertex.label.cex = .9, 
     edge.weight=2, edge.width=(E(g2)$weight/35))
}

CAMaggregated[[1]][1:5, 1:5]

                                            Akzeptanz eines neuen Materialsystems
Akzeptanz eines neuen Materialsystems                                         192
wartungsintensiv                                                               53
bioinspiriert                                                                  56
intelligent                                                                    51
passive Formänderung durch Umwelteinwirkung                                    43
                                            wartungsintensiv bioinspiriert
Akzeptanz eines neuen Materialsystems                     33            25
wartungsintensiv                                         192             0
bioinspiriert                                              0           192
intelligent                                                2             6
passive Formänderung durch Umwelteinwirkung                3             3
                                            intelligent
Akzeptanz eines neuen Materialsystems                41
wartungsintensiv                                      6
bioinspiriert                                         4
intelligent                                         192
passive Formänderung durch Umwelteinwirkung          10
                                            passive Formänderung durch Umwelteinwirkung
Akzeptanz eines neuen Materialsystems                                                23
wartungsintensiv                                                                      1
bioinspiriert                                                                         4
intelligent                                                                           3
passive Formänderung durch Umwelteinwirkung                                         192

5 analyze data

5.1 describe data set

5.1.1 feedback to the study

Question:

DT::datatable(questionnaire[!is.na(questionnaire$feedback_critic),c("PROLIFIC_PID", "feedback_critic")], options = list(pageLength = 5)) 

5.1.2 Material system that has been thought of

Question: Bitte beschreiben Sie an an welches Materialsystem oder welche Materialsysteme Sie dabei gedacht haben.

DT::datatable(questionnaire[,c("PROLIFIC_PID", "openQuestion_MaterialSystem")], options = list(pageLength = 5)) 

5.1.3 basal attributes: most pos. / neg.

Question: Welche drei Begriffen nehmen Sie am positivsten bzw. negativsten für die Beschreibung neuer Materialsysteme wahr?

## most positive
tmp <- str_trim(unlist(str_split(string = questionnaire$multiplePositive, pattern = "\\\\")))
sort(table(tmp))

tmp
 aktive Formänderung durch Umwelteinwirkung 
                                          1 
                         enthält Kunststoff 
                                          1 
                             reaktionsfähig 
                                          1 
                              lebensähnlich 
                                          2 
passive Formänderung durch Umwelteinwirkung 
                                          2 
                              technologisch 
                                          3 
                                    autonom 
                                          8 
                              bioinspiriert 
                                         11 
                         Energie speichernd 
                                         11 
                           widerstandsfähig 
                                         13 
                        Energie generierend 
                                         14 
                              selbstheilend 
                                         15 
                             energieautonom 
                                         16 
                          selbstreparierend 
                                         16 
                            multifunktional 
                                         18 
                                     robust 
                                         20 
                                intelligent 
                                         26 
                               wartungsfrei 
                                         33 
                                zuverlässig 
                                         34 
                           energieeffizient 
                                         37 
                                  langlebig 
                                         39 
                                    haltbar 
                                         43 
                                 ökologisch 
                                         53 
                                 nachhaltig 
                                         54 
                           umweltfreundlich 
                                        105 

## most negative
tmp <- str_trim(unlist(str_split(string = questionnaire$multipleNegative, pattern = "\\\\")))
sort(table(tmp))

tmp
                                    bioinspiriert 
                                                1 
                              Energie generierend 
                                                1 
                                          haltbar 
                                                1 
                                        langlebig 
                                                1 
                                       nachhaltig 
                                                1 
                                   reaktionsfähig 
                                                1 
                                           robust 
                                                1 
                                 widerstandsfähig 
                                                1 
                                      zuverlässig 
                                                1 
                                     wartungsfrei 
                                                2 
                                          autonom 
                                                3 
                                 umweltfreundlich 
                                                3 
       aktive Formänderung durch Umwelteinwirkung 
                                                4 
                                    lebensähnlich 
                                                4 
 aktive Verhaltensänderung durch Umwelteinwirkung 
                                                5 
                                   elektronikfrei 
                                                5 
      passive Formänderung durch Umwelteinwirkung 
                                               14 
passive Verhaltensänderung durch Umwelteinwirkung 
                                               16 
                                 Insekten ähnlich 
                                               19 
                               enthält Kunststoff 
                                               79 
                                leicht zerstörbar 
                                              123 
                                 wartungsintensiv 
                                              130 
                                  umweltschädlich 
                                              160 

## answers of participants
DT::datatable(questionnaire[,c("PROLIFIC_PID", "multiplePositive", "multipleNegative")], options = list(pageLength = 5)) 

any basal attributes missing?

Question: Fallen Ihnen weitere Eigenschaften ein, die Sie als relevant oder negativ für die Beschreibung neuer Materialsysteme erachten, die in der Liste nicht aufgeführt sind, so können Sie diese gerne in folgenden Textfeldern ergänzen:

## answers of participants
DT::datatable(questionnaire[,c("PROLIFIC_PID", "openQuestion_missedPositive", "openQuestion_missedNegative")], options = list(pageLength = 5)) 

5.1.4 basal attributes: ethical most relevant

Question: Welche drei Begriffe sind aus Ihrer Sicht in moralischer Hinsicht am “relevantesten”?

## ethical relevant
tmp <- str_trim(unlist(str_split(string = questionnaire$multipleEthic, pattern = "\\\\")))
sort(table(tmp))

tmp
       aktive Formänderung durch Umwelteinwirkung 
                                                1 
      passive Formänderung durch Umwelteinwirkung 
                                                1 
passive Verhaltensänderung durch Umwelteinwirkung 
                                                1 
                                 widerstandsfähig 
                                                1 
                                   elektronikfrei 
                                                2 
                                selbstreparierend 
                                                2 
                                    technologisch 
                                                2 
 aktive Verhaltensänderung durch Umwelteinwirkung 
                                                3 
                                leicht zerstörbar 
                                                3 
                                   reaktionsfähig 
                                                3 
                                           robust 
                                                3 
                                  multifunktional 
                                                4 
                               enthält Kunststoff 
                                                5 
                               Energie speichernd 
                                                7 
                                   energieautonom 
                                                8 
                                 Insekten ähnlich 
                                                8 
                                    selbstheilend 
                                                9 
                                     wartungsfrei 
                                                9 
                                          autonom 
                                               11 
                                    bioinspiriert 
                                               14 
                              Energie generierend 
                                               14 
                                      intelligent 
                                               17 
                                    lebensähnlich 
                                               18 
                                          haltbar 
                                               19 
                                      zuverlässig 
                                               21 
                                  umweltschädlich 
                                               23 
                                        langlebig 
                                               32 
                                 energieeffizient 
                                               41 
                                       ökologisch 
                                               77 
                                       nachhaltig 
                                               93 
                                 umweltfreundlich 
                                              124 

## answers of participants
DT::datatable(questionnaire[,c("PROLIFIC_PID", "multipleEthic")], options = list(pageLength = 5)) 

argument for choosen basal attributes

Question: Bitte begründen Sie kurz die Auswahl der ethisch relevanten Begriffe:

## answers of participants
DT::datatable(questionnaire[,c("PROLIFIC_PID", "openQuestion_Ethic")], options = list(pageLength = 5)) 

5.1.5 outcome questions

Question: Sollte die Entwicklung innovativer Materialsysteme mit öffentlichen Mitteln gefördert werden?

table(questionnaire$outcome_research)

    no unsure    yes 
     4     32    156 

Question: Sollten die Erforschung und Entwicklung solcher innovativer Materialsysteme verboten werden?

table(questionnaire$outcome_prohibition)

    no unsure    yes 
   182      7      3 

Question: Wären Sie bereit, Produkte zu kaufen, die innovative Materialsysteme enthalten?

table(questionnaire$outcome_buy)

    no unsure    yes 
     2     85    105 

if yes to previous question

Question: An welche möglichen Produkte haben Sie gedacht?

## answers of participants
DT::datatable(questionnaire[,c("PROLIFIC_PID", "outcome_buy_text")], options = list(pageLength = 5)) 

5.2 co-variation of basal attributes

Compare the probability that two concepts are connected in randomly generated networks to drawn CAMs:

## get average number of drawn concepts (here fixed)
numConcepts <- mean(networkIndicators$num_nodes_macro)
## get average density
numDensity <- mean(networkIndicators$density_macro)

## simply get the average probability that two concepts are connected:
g <- igraph::random.graph.game(n = numConcepts, p.or.m = numDensity)
plot(g)

are.connected(g, 1, 2)

[1] FALSE

#> whereby each edge to be drawn has the identical probability in the Erdős–Rényi model
vec_booleanConnected <- c()
# vec_booleanConnected2 <- c()

for(i in 1:10000){
  g <- igraph::random.graph.game(n = numConcepts, p.or.m = numDensity)
  vec_booleanConnected[i] <- are.connected(g, 1, 2)
    # vec_booleanConnected2[i] <- are.connected(g, 22, 26)
}
baselineProbability <- mean(vec_booleanConnected)
baselineProbability

[1] 0.0387

# mean(vec_booleanConnected2)

This baseline probability can be compared to all possible combinations of drawn concepts:

vec_boolean <- c()
for(i in 1:length(CAMdrawn)){
  vec_boolean[i] <- are.connected(graph = CAMdrawn[[i]], 
              v1 = V(CAMdrawn[[i]])[V(CAMdrawn[[i]])$label == "robust"], 
              v2 = V(CAMdrawn[[i]])[V(CAMdrawn[[i]])$label == "widerstandsfähig"])
}

mean(vec_boolean)

[1] 0.1666667