Visualising Text Similarities (Figure Appendix 1)

Figure Appendix.1A

Figure 1A in the Appendix shows gamma values (x-axis, means ± sd) that are the strength of association between a set of subgroups of some stakeholder documents (y-axis) and the corresponding topic.

High gamma values are strong associations.

We plotted the values for four main topics (side labels): Open Research (green), Community and Support (red), Innovation and Solutions (yellow) and Publication Process (blue).

The data for this are produced in 2_Topic_modeling

R Setup and packages

# rm(list=ls())

Libraries

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.3     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.3     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Import data to create Figure 2A

# Importing data produced in "2_Topic_modeling.Rmd"

# df_doc_level_stm_gamma <- read_csv(file = "./output/created_datasets/df_doc_level_stm_gamma.csv")

# Importing data that we originally created and used in our analysis
df_doc_level_stm_gamma <- read.csv(file = "./data/original_dataset_reproducibility_check/original_df_doc_level_stm_gamma.csv")

# Adding a column with subgroups of some stakeholders (publishers not-for-profit and for profit, and journals OA and not OA)

# Using newly created dataset
# df_subgroups <- read_csv(file = "./output/created_datasets/cleaned_data.csv")
df_subgroups <- read.csv(file = "./data/original_dataset_reproducibility_check/original_cleaned_data.csv") 


colnames(df_subgroups)

 [1] "txt"           "filename"      "name"          "doc_type"     
 [5] "stakeholder"   "sentence_doc"  "orig_word"     "word_mix"     
 [9] "word"          "org_subgroups"

df_subgroups <- df_subgroups %>% 
  select(name, org_subgroups) %>% 
  distinct(.keep_all = FALSE)



df_doc_level_stm_gamma <- df_doc_level_stm_gamma %>% 
  left_join(df_subgroups, by = c("name" = "name")) %>% 
  select(-stakeholder) 

Data management

df_figure1_gamma <- df_doc_level_stm_gamma %>% 
  select(-total_topic, -total_sent) 

df_figure1_gamma$prop <- df_figure1_gamma$prop *100

df_figure1_gamma_wide <- df_figure1_gamma %>% 
  spread(topic, prop) %>% 
  rename(topic_1 = `1`, topic_2 = `2`, topic_3 = `3`, topic_4 = `4`)

# Topic/Category 1: Open Research 
# Topic/Category 2: Community & Support
# Topic/Category 3: Innovation & Solution
# Topic/Category 4: Publication process (control)

# Selecting only subgroups we are interested in
df_figure1_gamma_wide <- df_figure1_gamma_wide %>% 
  filter(org_subgroups %in% c("publishers_nonProfit", "publishers_Profit", "journals_nonOA", "journals_OA"))

dim(df_figure1_gamma_wide)

[1] 45  6

df_figure1_gamma_wide2 = data.frame(
  document = rep(df_figure1_gamma_wide$name,4),
  org_subgroups = rep(df_figure1_gamma_wide$org_subgroups,4),
    type = c(rep("1. Open Research",45), rep("2. Community and Support",45),  rep("3. Innovation and Solutions",45), rep("4. Publication Process", 45)), # the number is the number of rows in the data
    # type = c(rep("1. Open Research",129), rep("2. Community and Support",129),  rep("3. Innovation and Solutions",129), rep("4. Publication Process", 129)), # the number is the number of rows in the data
  perc = c(df_figure1_gamma_wide$topic_1, df_figure1_gamma_wide$topic_2, df_figure1_gamma_wide$topic_3, df_figure1_gamma_wide$topic_4),
  perc2 = c(df_figure1_gamma_wide$topic_1, df_figure1_gamma_wide$topic_2, df_figure1_gamma_wide$topic_3, df_figure1_gamma_wide$topic_4)
)

# adding quantiles values
sum_figure1_gamma_wide2 = 
  df_figure1_gamma_wide2 %>%
  group_by(org_subgroups, type) %>%
  dplyr::summarize(lower = quantile(perc, probs = .25), upper = quantile(perc, probs = .75), perc_fin = mean(perc)) %>% 
  rename(perc = perc_fin)

`summarise()` has grouped output by 'org_subgroups'. You can override using the
`.groups` argument.

Graph production

figure_topics <- ggplot() +
  geom_point(data = df_figure1_gamma_wide2, aes(x = perc, y = org_subgroups, colour = type), alpha = 0.2, position = position_jitter(), show.legend = FALSE) +
  geom_pointrange(data = sum_figure1_gamma_wide2, aes(x = perc, xmin = lower, xmax = upper, y = org_subgroups, colour = type), show.legend = FALSE) +
  facet_grid(.~type) + 
  labs(x = "Topics occuring in documents (%) gamma", y = "") +
  scale_color_manual(values = c("dark green", "dark red", "darkgoldenrod", "dark blue")) +
  theme_classic()

figure_topics

Past versions of unnamed-chunk-5-1.png

# # UNCOMMENT TO SAVE FIGURES

figure_name <- paste0("./output/Figure_Appendix_1A/Figure_1A.png")
png(file=figure_name,
  width= 3000, height= 2000, res=400)
  figure_topics
  dev.off()

quartz_off_screen 
                2 

Figure Appendix.1B

The figures will be reproduced but for subgroups: journals nonOa and OA, publishers for- and not-for-profit.

Source of the functions: https://www.markhw.com/blog/word-similarity-graphs We combined two methods - word similarity graphs and stm modelling) to be able visually explore topics and words’ connections in the aims and missions documents. We used the word similarity method (https://www.markhw.com/blog/word-similarity-graphs) to calculate similarities between words and later to be able to plot them with topic modeling score values (package stm)

The three primary steps are: 1. Calculating the similarities between words (Cosine matrix). 2. Formatting these similarity scores into a symmetric matrix, where the diagonal contains 0s and the off-diagonal cells are similarities between words.

3. Clustering nodes using a community detection algorithm (here: Walktrap algorithm) and plotting the data into circular hierarchical dendrograms for each of the stakeholder group separately (Figure 2B).

Libraries

Loading packages

# remotes::install_github('talgalili/dendextend')
library(dendextend)
library(igraph)
library(ggraph)
library(reshape2)
library(dplyr)
library(tidyverse)
library(tidytext)

Word similarity

Functions to create words similarities matrix.

• cosine_matrix
• Walktrap_topics

Source of the code: https://www.markhw.com/blog/word-similarity-graphs

# Cosine matrix
cosine_matrix <- function(tokenized_data, lower = 0, upper = 1, filt = 0) {
  
  if (!all(c("word", "id") %in% names(tokenized_data))) {
    stop("tokenized_data must contain variables named word and id")
  }
  
  if (lower < 0 | lower > 1 | upper < 0 | upper > 1 | filt < 0 | filt > 1) {
    stop("lower, upper, and filt must be 0 <= x <= 1")
  }
  docs <- length(unique(tokenized_data$id))
  
  out <- tokenized_data %>%
    count(id, word) %>%
    group_by(word) %>%
    mutate(n_docs = n()) %>%
    ungroup() %>%
    filter(n_docs <= (docs * upper) & n_docs > (docs * lower)) %>%
    select(-n_docs) %>%
    mutate(n = 1) %>%
    spread(word, n, fill = 0) %>%
    select(-id) %>%
    as.matrix() %>%
    lsa::cosine()
  
  filt <- quantile(out[lower.tri(out)], filt)
  out[out < filt] <- diag(out) <- 0
  out <- out[rowSums(out) != 0, colSums(out) != 0]
  
  return(out)
}


# Walktrap_topics

walktrap_topics <- function(g, ...) {
  wt <- igraph::cluster_walktrap(g, ...)
  
  membership <- igraph::cluster_walktrap(g, ...) %>% 
      igraph::membership() %>% 
      as.matrix() %>% 
      as.data.frame() %>% 
      rownames_to_column("word") %>% 
      arrange(V1) %>% 
      rename(group = V1)
  
  dendrogram <- stats::as.dendrogram(wt)
  
  return(list(membership = membership, dendrogram = dendrogram))
}

Data

# Importing data (created in `2_topic_modeling`)
# topics_df <- read.csv("./output/created_datasets/dataset_words_stm_5topics.csv")

# Importing dataset that we originally created and used in our analysis
topics_df <- read.csv(file = "./data/original_dataset_reproducibility_check/original_dataset_words_stm_5topics.csv")

Creating a variable with the subgroups information

head(df_subgroups, 5)

                              name org_subgroups
1 Africa Open Science and Hardware     advocates
2                          Amelica     advocates
3            Bioline International     advocates
4          Center for Open Science     advocates
5                       coalitionS     advocates

df_subgroups <- df_subgroups %>% 
  select("org_subgroups","name") %>% 
  distinct(.keep_all= FALSE)

# Keeping only subgroups I am interested in
topics_df <- topics_df %>% 
  filter(org_subgroups %in% c("publishers_nonProfit", "publishers_Profit", "journals_nonOA", "journals_OA"))

head(topics_df, 3)

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          txt
1    Since its inception in , The  has maintained its position as one of the world s premier peer-reviewed publications in ecology, evolution, and behavior research. Its goals are to publish articles that are of broad interest to the readership, pose new and significant problems, introduce novel subjects, develop conceptual unification, and change the way people think. The  emphasizes sophisticated methodologies and innovative theoretical syntheses   all in an effort to advance the knowledge of organic evolution and other broad biological principles. 
2    Since its inception in , The  has maintained its position as one of the world s premier peer-reviewed publications in ecology, evolution, and behavior research. Its goals are to publish articles that are of broad interest to the readership, pose new and significant problems, introduce novel subjects, develop conceptual unification, and change the way people think. The  emphasizes sophisticated methodologies and innovative theoretical syntheses   all in an effort to advance the knowledge of organic evolution and other broad biological principles. 
3    Since its inception in , The  has maintained its position as one of the world s premier peer-reviewed publications in ecology, evolution, and behavior research. Its goals are to publish articles that are of broad interest to the readership, pose new and significant problems, introduce novel subjects, develop conceptual unification, and change the way people think. The  emphasizes sophisticated methodologies and innovative theoretical syntheses   all in an effort to advance the knowledge of organic evolution and other broad biological principles. 
                      filename                name doc_type stakeholder
1 American Naturalist_Aims.txt American Naturalist     Aims    journals
2 American Naturalist_Aims.txt American Naturalist     Aims    journals
3 American Naturalist_Aims.txt American Naturalist     Aims    journals
           sentence_doc  orig_word  word_mix      word  org_subgroups
1 American Naturalist_1  inception inception inception journals_nonOA
2 American Naturalist_1 maintained  maintain  maintain journals_nonOA
3 American Naturalist_1   position  position  position journals_nonOA
  mean_beta_t1  mean_beta_t2 mean_beta_t3  mean_beta_t4 mean_beta_t5
1 2.405878e-91 4.352779e-180  0.000424290 4.788456e-126 0.0002722336
2 9.211223e-04  1.647354e-03  0.001511078  1.051811e-95 0.0007933616
3 1.978509e-04  3.145697e-04  0.000516377  8.752072e-04 0.0008261841
  mean_score_t1 mean_score_t2 mean_score_t3 mean_score_t4 mean_score_t5
1  1.648413e-89 2.982353e-178    0.02907068 3.280862e-124    0.01865237
2  1.200462e-02  2.146931e-02    0.01969328  1.370784e-94    0.01033957
3  4.666891e-03  7.420044e-03    0.01218026  2.064432e-02    0.01948796
   sum_beta_t1   sum_beta_t2 sum_beta_t3   sum_beta_t4 sum_beta_t5 topic
1 2.405878e-90 5.223335e-179 0.008061510 3.830764e-125 0.006533606     3
2 9.211223e-03  1.976825e-02 0.028710484  8.414491e-95 0.019040677     2
3 1.978509e-03  3.774836e-03 0.009811162  7.001657e-03 0.019828419     4
  highest_mean_score highest_mean_beta
1         0.02907068      0.0004242900
2         0.02146931      0.0016473540
3         0.02064432      0.0008752072

dim(topics_df)

[1] 6659   28

Running above functions for each substakeholder

stake_names <- unique(topics_df$org_subgroups)
count <- 1
figure_list <- vector()

for (stake in stake_names) {
  n <- count # number of the subsubgroups in the list
  stakeholder_name <- stake_names[n] # substakeholder's name
  
  # selecting data with the right name of the substakeholder
  dat <- topics_df %>%
  select(org_subgroups, sentence_doc, word) %>%
  rename(id = sentence_doc) %>% 
  filter(org_subgroups %in% stakeholder_name) %>% 
  select(-org_subgroups)
  ##################### 
  
  # Calculating similarity matrix
  cos_mat <- cosine_matrix(dat, lower = 0.050, upper = 1, filt = 0.9)
  
  # Getting words used in the network
  word_list_net <- rownames(cos_mat)
  grep("workflow", word_list_net)

  print(dim(cos_mat))
  
  # Creating a graph
  g <- graph_from_adjacency_matrix(cos_mat, mode = "undirected", weighted = TRUE)
  topics = walktrap_topics(g)
  print(stakeholder_name)
  plot(topics$dendrogram)

  subtrees <- partition_leaves(topics$dendrogram)
  leaves <- subtrees[[1]]  


  pathRoutes <- function(leaf) {
    which(sapply(subtrees, function(x) leaf %in% x))
  }

  paths <- lapply(leaves, pathRoutes)

  edges = NULL
  for(a in 1:length(paths)){
    # print(a)
    for(b in c(1:(length(paths[[a]])-1))){
     if(b == (length(paths[[a]])-1)){
        tmp_df = data.frame(
         from = paths[[a]][b],
         to = leaves[a]
       )
     } else {
        tmp_df = data.frame(
          from = paths[[a]][b],
          to = paths[[a]][b+1]
       )
     }
     edges = rbind(edges, tmp_df)
   }
  }


  connect = melt(cos_mat) # library reshape required
  colnames(connect) = c("from", "to", "value")
  connect = subset(connect, value != 0)

  # create a vertices data.frame. One line per object of our hierarchy
  vertices  <-  data.frame(
    name = unique(c(as.character(edges$from), as.character(edges$to))) 
  ) 
  # Let's add a column with the group of each name. It will be useful later to color points
  vertices$group  <-  edges$from[ match( vertices$name, edges$to ) ]


  #Let's add information concerning the label we are going to add: angle, horizontal adjustement and potential flip
  #calculate the ANGLE of the labels
  #vertices$id <- NA
  #myleaves <- which(is.na( match(vertices$name, edges$from) ))
  #nleaves <- length(myleaves)
  #vertices$id[ myleaves ] <- seq(1:nleaves)
  #vertices$angle <- 90 - 360 * vertices$id / nleaves

  # calculate the alignment of labels: right or left
  # If I am on the left part of the plot, my labels have currently an angle < -90
  #vertices$hjust <- ifelse( vertices$angle < -90, 1, 0)

  # flip angle BY to make them readable
  #vertices$angle <- ifelse(vertices$angle < -90, vertices$angle+180, vertices$angle)
  
  # replacing "group" value with the topic numer from the STM
  
  # Colour words by topic modelling topics (STM)
  # I have to change the group numbers in topics

  
  stm_top <- topics_df %>%
    select(org_subgroups, word, topic = topic) %>% 
    filter(org_subgroups %in% stakeholder_name) %>%
    select(-org_subgroups)
  

  stm_top$topic <- as.factor(stm_top$topic)
  stm_top <- unique(stm_top)


  # Removing group column and creating a new one based on the stm info
  vertices <- vertices

  vertices <- vertices %>% 
    left_join(stm_top, by = c("name" = "word")) %>% 
    select(-group) %>% 
    rename(group = topic)

  # Adding a beta value so I can use it in the graph
  stm_beta <- topics_df %>% 
    select(org_subgroups, word, beta = highest_mean_beta) %>% 
    filter(org_subgroups %in% stakeholder_name) %>%
    select(-org_subgroups)
  
  
  stm_beta <- unique(stm_beta)
  dim(stm_beta)

  vertices <- vertices %>% 
   left_join(stm_beta, by = c("name" = "word")) 


  vertices$beta_size = vertices$beta
  
  ##############################################################

  # Create a graph object
  mygraph <- igraph::graph_from_data_frame(edges, vertices=vertices)

  # The connection object must refer to the ids of the leaves:
  from  <-  match( connect$from, vertices$name)
  to  <-  match( connect$to, vertices$name)

  # Basic usual argument
  tmp_plt = ggraph(mygraph, layout = 'dendrogram', circular = T) + 
    geom_node_point(aes(filter = leaf, x = x*1.05, y=y*1.05, alpha=0.2), size =0, colour = "white") +
    #colour=group, size=value
          scale_colour_manual(values= c("dark green", "dark red", "darkgoldenrod", "dark blue", "black"), na.value = "black") +   
    geom_conn_bundle(data = get_con(from = from, to = to, col = connect$value), tension = 0.9, aes(colour = col, alpha = col+0.5), width = 1.1) +
    scale_edge_color_continuous(low="white", high="black") +
    # geom_node_text(aes(x = x*1.1, y=y*1.1, filter = leaf, label=name, angle = angle, hjust=hjust), size=5, alpha=1) +
    geom_node_text(aes(x = x*1.1, y=y*1.1, filter = leaf, label=name, colour = group, angle = 0, hjust = 0.5, size = beta_size),  alpha=1) +
    
     theme_void() +
    theme(
      legend.position="none",
      plot.margin=unit(c(0,0,0,0),"cm"),
    ) +
    expand_limits(x = c(-1.5, 1.5), y = c(-1.5, 1.5)) 

  g <- ggplot_build(tmp_plt)
  tmp_dat = g[[3]]$data
  tmp_dat$position = NA
  tmp_dat_a = subset(tmp_dat, x >= 0)
  tmp_dat_a = tmp_dat_a[order(-tmp_dat_a$y),]
  tmp_dat_a = subset(tmp_dat_a, !is.na(beta))
  tmp_dat_a$order = 1:nrow(tmp_dat_a)
  
  tmp_dat_b = subset(tmp_dat, x < 0)
  tmp_dat_b = tmp_dat_b[order(-tmp_dat_b$y),]
  tmp_dat_b = subset(tmp_dat_b, !is.na(beta))
  tmp_dat_b$order = (nrow(tmp_dat_a) +nrow(tmp_dat_b)) : (nrow(tmp_dat_a) +1)
  
  tmp_dat = rbind(tmp_dat_a, tmp_dat_b)
  
  vertices = left_join(vertices, tmp_dat[,c("name", "order")], by = c("name"))
  vert_save = vertices
  vertices = vert_save
  
  vertices$id <- NA
  vertices = vertices[order(vertices$order),]
  myleaves <- which(is.na( match(vertices$name, edges$from) ))
  nleaves <- length(myleaves)
  vertices$id[ myleaves ] <- seq(1:nleaves)
  vertices$angle <- 90 - 360 * vertices$id / nleaves
  
  # calculate the alignment of labels: right or left
  # If I am on the left part of the plot, my labels have currently an angle < -90
  vertices$hjust <- ifelse( vertices$angle < -90, 1, 0)
  
  # flip angle BY to make them readable
  vertices$angle <- ifelse(vertices$angle < -90, vertices$angle+180, vertices$angle)
  vertices = vertices[order(as.numeric(rownames(vertices))),]
  
  mygraph <- igraph::graph_from_data_frame(edges, vertices=vertices)
  
  figure_to_save <- ggraph(mygraph, layout = 'dendrogram', circular = T) + 
    geom_node_point(aes(filter = leaf, x = x*1.05, y=y*1.05, alpha=0.2, colour = group, size = beta_size)) +
    #colour=group, size=value
          scale_colour_manual(values= c("dark green", "dark red", "darkgoldenrod", "dark blue", "black"), na.value = "black") +   
    geom_conn_bundle(data = get_con(from = from, to = to, col = connect$value), tension = 0.99, aes(colour = col, alpha = col+0.5), width = 1.1) +
    scale_edge_color_continuous(low="grey90", high="black") +
    geom_node_text(aes(x = x*1.1, y=y*1.1, filter = leaf, label=name, colour = group, angle = angle, hjust = hjust, size = beta_size)) +
    
    theme_void() +
    theme(
      legend.position="none",
      plot.margin=unit(c(0,0,0,0),"cm"),
    ) +
    expand_limits(x = c(-1.5, 1.5), y = c(-1.5, 1.5))

  assign(paste0(stake_names[n], "_figure1B"), figure_to_save)
  # name_temp <- assign(paste0(stake_names[n], "_figure2b"), figure_to_save)
  figure_list <- append(figure_list, paste0(stake_names[n], "_figure1B"))
  
  print(figure_to_save)

  #####################
  count <-  count + 1 

}

[1] 71 71
[1] "journals_nonOA"

Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

Past versions of unnamed-chunk-11-1.png

Past versions of unnamed-chunk-11-2.png

[1] 29 29
[1] "journals_OA"

Past versions of unnamed-chunk-11-3.png

Past versions of unnamed-chunk-11-4.png

[1] 80 80
[1] "publishers_nonProfit"

Past versions of unnamed-chunk-11-5.png

Past versions of unnamed-chunk-11-6.png

[1] 43 43
[1] "publishers_Profit"

Past versions of unnamed-chunk-11-7.png

Past versions of unnamed-chunk-11-8.png

# Saving figures individually 

figure_list

figure_name <- paste0("./output/Figure_Appendix_1B/journals_nonOA_figure1B.png")
png(file=figure_name,
  width= 3000, height= 3000, res=400)
  journals_nonOA_figure1B
  dev.off()


figure_name <- paste0("./output/Figure_Appendix_1B/journals_OA_figure1B.png")
png(file=figure_name,
  width= 3000, height= 3000, res=400)
  journals_OA_figure1B
  dev.off()
  
figure_name <- paste0("./output/Figure_Appendix_1B/publishers_nonProfit_figure1B.png")
png(file=figure_name,
  width= 3000, height= 3000, res=400)
  publishers_nonProfit_figure1B
  dev.off()
  
  
figure_name <- paste0("./output/Figure_Appendix_1B/publishers_Profit_figure1B.png")
png(file=figure_name,
  width= 3000, height= 3000, res=400)
  publishers_Profit_figure1B
  dev.off()

Session information

sessionInfo()

R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/London
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tidytext_0.4.1    reshape2_1.4.4    ggraph_2.1.0      igraph_1.5.1     
 [5] dendextend_1.17.1 lubridate_1.9.3   forcats_1.0.0     stringr_1.5.0    
 [9] dplyr_1.1.3       purrr_1.0.2       readr_2.1.4       tidyr_1.3.0      
[13] tibble_3.2.1      ggplot2_3.4.3     tidyverse_2.0.0   workflowr_1.7.1  

loaded via a namespace (and not attached):
 [1] gtable_0.3.4       xfun_0.40          bslib_0.5.1        ggrepel_0.9.4     
 [5] processx_3.8.2     lattice_0.21-8     callr_3.7.3        tzdb_0.4.0        
 [9] vctrs_0.6.3        tools_4.3.1        ps_1.7.5           generics_0.1.3    
[13] fansi_1.0.4        janeaustenr_1.0.0  tokenizers_0.3.0   pkgconfig_2.0.3   
[17] Matrix_1.5-4.1     lifecycle_1.0.3    compiler_4.3.1     farver_2.1.1      
[21] git2r_0.32.0       munsell_0.5.0      lsa_0.73.3         ggforce_0.4.1     
[25] graphlayouts_1.0.2 getPass_0.2-2      httpuv_1.6.11      SnowballC_0.7.1   
[29] htmltools_0.5.6    sass_0.4.7         yaml_2.3.7         crayon_1.5.2      
[33] later_1.3.1        pillar_1.9.0       jquerylib_0.1.4    whisker_0.4.1     
[37] MASS_7.3-60        cachem_1.0.8       viridis_0.6.4      tidyselect_1.2.0  
[41] digest_0.6.33      stringi_1.7.12     labeling_0.4.3     polyclip_1.10-6   
[45] rprojroot_2.0.3    fastmap_1.1.1      grid_4.3.1         colorspace_2.1-0  
[49] cli_3.6.1          magrittr_2.0.3     tidygraph_1.2.3    utf8_1.2.3        
[53] withr_2.5.1        scales_1.2.1       promises_1.2.1     timechange_0.2.0  
[57] rmarkdown_2.25     httr_1.4.7         gridExtra_2.3      hms_1.1.3         
[61] evaluate_0.21      knitr_1.44         viridisLite_0.4.2  rlang_1.1.1       
[65] Rcpp_1.0.11        glue_1.6.2         tweenr_2.0.2       rstudioapi_0.15.0 
[69] jsonlite_1.8.7     plyr_1.8.9         R6_2.5.1           fs_1.6.3          

Session information

sessionInfo()

R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/London
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tidytext_0.4.1    reshape2_1.4.4    ggraph_2.1.0      igraph_1.5.1     
 [5] dendextend_1.17.1 lubridate_1.9.3   forcats_1.0.0     stringr_1.5.0    
 [9] dplyr_1.1.3       purrr_1.0.2       readr_2.1.4       tidyr_1.3.0      
[13] tibble_3.2.1      ggplot2_3.4.3     tidyverse_2.0.0   workflowr_1.7.1  

loaded via a namespace (and not attached):
 [1] gtable_0.3.4       xfun_0.40          bslib_0.5.1        ggrepel_0.9.4     
 [5] processx_3.8.2     lattice_0.21-8     callr_3.7.3        tzdb_0.4.0        
 [9] vctrs_0.6.3        tools_4.3.1        ps_1.7.5           generics_0.1.3    
[13] fansi_1.0.4        janeaustenr_1.0.0  tokenizers_0.3.0   pkgconfig_2.0.3   
[17] Matrix_1.5-4.1     lifecycle_1.0.3    compiler_4.3.1     farver_2.1.1      
[21] git2r_0.32.0       munsell_0.5.0      lsa_0.73.3         ggforce_0.4.1     
[25] graphlayouts_1.0.2 getPass_0.2-2      httpuv_1.6.11      SnowballC_0.7.1   
[29] htmltools_0.5.6    sass_0.4.7         yaml_2.3.7         crayon_1.5.2      
[33] later_1.3.1        pillar_1.9.0       jquerylib_0.1.4    whisker_0.4.1     
[37] MASS_7.3-60        cachem_1.0.8       viridis_0.6.4      tidyselect_1.2.0  
[41] digest_0.6.33      stringi_1.7.12     labeling_0.4.3     polyclip_1.10-6   
[45] rprojroot_2.0.3    fastmap_1.1.1      grid_4.3.1         colorspace_2.1-0  
[49] cli_3.6.1          magrittr_2.0.3     tidygraph_1.2.3    utf8_1.2.3        
[53] withr_2.5.1        scales_1.2.1       promises_1.2.1     timechange_0.2.0  
[57] rmarkdown_2.25     httr_1.4.7         gridExtra_2.3      hms_1.1.3         
[61] evaluate_0.21      knitr_1.44         viridisLite_0.4.2  rlang_1.1.1       
[65] Rcpp_1.0.11        glue_1.6.2         tweenr_2.0.2       rstudioapi_0.15.0 
[69] jsonlite_1.8.7     plyr_1.8.9         R6_2.5.1           fs_1.6.3