6_Subgroups_comparison_text_similarities

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Last updated: 2023-12-20

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Visualising Text Similarities (Figure 3)

Figure 3A

Figure 3A 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

library(arrow)

Attaching package: 'arrow'

The following object is masked from 'package:lubridate':

    duration

The following object is masked from 'package:utils':

    timestamp

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_feather(file = "./data/original_dataset_reproducibility_check/original_cleaned_data.arrow") 


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

Version	Author	Date
c494066	zuzannazagrodzka	2023-12-02
8a4c1d4	zuzannazagrodzka	2023-11-10

# # UNCOMMENT TO SAVE FIGURES

figure_name <- paste0("./output/Figure_3A/Figure_3A.png")
png(file=figure_name,
  width= 3000, height= 2000, res=400)
  figure_topics
  dev.off()

quartz_off_screen 
                2 

Figure 3B

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_feather(file = "./data/original_dataset_reproducibility_check/original_dataset_words_stm_5topics.arrow")

Creating a variable with the subgroups information

head(df_subgroups, 5)

# A tibble: 5 × 2
  name                             org_subgroups
  <chr>                            <chr>        
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)

# A tibble: 3 × 28
  txt        filename name  doc_type stakeholder sentence_doc orig_word word_mix
  <chr>      <chr>    <chr> <chr>    <chr>       <chr>        <chr>     <chr>   
1 "   Since… America… Amer… Aims     journals    American Na… inception incepti…
2 "   Since… America… Amer… Aims     journals    American Na… maintain… maintain
3 "   Since… America… Amer… Aims     journals    American Na… position  position
# ℹ 20 more variables: word <chr>, org_subgroups <chr>, mean_beta_t1 <dbl>,
#   mean_beta_t2 <dbl>, mean_beta_t3 <dbl>, mean_beta_t4 <dbl>,
#   mean_beta_t5 <dbl>, mean_score_t1 <dbl>, mean_score_t2 <dbl>,
#   mean_score_t3 <dbl>, mean_score_t4 <dbl>, mean_score_t5 <dbl>,
#   sum_beta_t1 <dbl>, sum_beta_t2 <dbl>, sum_beta_t3 <dbl>, sum_beta_t4 <dbl>,
#   sum_beta_t5 <dbl>, topic <int>, highest_mean_score <dbl>,
#   highest_mean_beta <dbl>

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], "_figure3B"), figure_to_save)
  # name_temp <- assign(paste0(stake_names[n], "_figure2b"), figure_to_save)
  figure_list <- append(figure_list, paste0(stake_names[n], "_figure3B"))
  
  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

Version	Author	Date
8a4c1d4	zuzannazagrodzka	2023-11-10

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8a4c1d4	zuzannazagrodzka	2023-11-10

[1] 29 29
[1] "journals_OA"

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8a4c1d4	zuzannazagrodzka	2023-11-10

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[1] 100 100
[1] "publishers_nonProfit"

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

Version	Author	Date
c494066	zuzannazagrodzka	2023-12-02
8a4c1d4	zuzannazagrodzka	2023-11-10

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

Version	Author	Date
c494066	zuzannazagrodzka	2023-12-02
8a4c1d4	zuzannazagrodzka	2023-11-10

[1] 55 55
[1] "publishers_Profit"

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

Version	Author	Date
c494066	zuzannazagrodzka	2023-12-02
8a4c1d4	zuzannazagrodzka	2023-11-10

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

Version	Author	Date
c494066	zuzannazagrodzka	2023-12-02
8a4c1d4	zuzannazagrodzka	2023-11-10

Figure production

# Saving figures individually 

figure_list

figure_name <- paste0("./output/Figure_3B/journals_nonOA_figure3B.png")
png(file=figure_name,
  width= 3000, height= 3000, res=400)
  journals_nonOA_figure3B
  dev.off()


figure_name <- paste0("./output/Figure_3B/journals_OA_figure3B.png")
png(file=figure_name,
  width= 3000, height= 3000, res=400)
  journals_OA_figure3B
  dev.off()
  
figure_name <- paste0("./output/Figure_3B/publishers_nonProfit_figure3B.png")
png(file=figure_name,
  width= 3000, height= 3000, res=400)
  publishers_nonProfit_figure3B
  dev.off()
  
  
figure_name <- paste0("./output/Figure_3B/publishers_Profit_figure3B.png")
png(file=figure_name,
  width= 3000, height= 3000, res=400)
  publishers_Profit_figure3B
  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 arrow_13.0.0.1    lubridate_1.9.3   forcats_1.0.0    
 [9] stringr_1.5.0     dplyr_1.1.3       purrr_1.0.2       readr_2.1.4      
[13] tidyr_1.3.0       tibble_3.2.1      ggplot2_3.4.3     tidyverse_2.0.0  
[17] workflowr_1.7.1  

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0   viridisLite_0.4.2  farver_2.1.1       viridis_0.6.4     
 [5] fastmap_1.1.1      tweenr_2.0.2       janeaustenr_1.0.0  promises_1.2.1    
 [9] digest_0.6.33      timechange_0.2.0   lifecycle_1.0.3    tokenizers_0.3.0  
[13] processx_3.8.2     magrittr_2.0.3     compiler_4.3.1     rlang_1.1.1       
[17] sass_0.4.7         tools_4.3.1        utf8_1.2.3         yaml_2.3.7        
[21] knitr_1.44         labeling_0.4.3     graphlayouts_1.0.2 bit_4.0.5         
[25] plyr_1.8.9         withr_2.5.1        grid_4.3.1         polyclip_1.10-6   
[29] fansi_1.0.4        git2r_0.32.0       colorspace_2.1-0   scales_1.2.1      
[33] MASS_7.3-60        cli_3.6.1          rmarkdown_2.25     crayon_1.5.2      
[37] generics_0.1.3     rstudioapi_0.15.0  httr_1.4.7         tzdb_0.4.0        
[41] cachem_1.0.8       ggforce_0.4.1      assertthat_0.2.1   vctrs_0.6.3       
[45] Matrix_1.5-4.1     jsonlite_1.8.7     lsa_0.73.3         callr_3.7.3       
[49] hms_1.1.3          bit64_4.0.5        ggrepel_0.9.4      jquerylib_0.1.4   
[53] glue_1.6.2         ps_1.7.5           stringi_1.7.12     gtable_0.3.4      
[57] later_1.3.1        munsell_0.5.0      pillar_1.9.0       htmltools_0.5.6   
[61] R6_2.5.1           tidygraph_1.2.3    rprojroot_2.0.3    evaluate_0.21     
[65] lattice_0.21-8     SnowballC_0.7.1    httpuv_1.6.11      bslib_0.5.1       
[69] Rcpp_1.0.11        gridExtra_2.3      whisker_0.4.1      xfun_0.40         
[73] fs_1.6.3           getPass_0.2-2      pkgconfig_2.0.3   

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 arrow_13.0.0.1    lubridate_1.9.3   forcats_1.0.0    
 [9] stringr_1.5.0     dplyr_1.1.3       purrr_1.0.2       readr_2.1.4      
[13] tidyr_1.3.0       tibble_3.2.1      ggplot2_3.4.3     tidyverse_2.0.0  
[17] workflowr_1.7.1  

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0   viridisLite_0.4.2  farver_2.1.1       viridis_0.6.4     
 [5] fastmap_1.1.1      tweenr_2.0.2       janeaustenr_1.0.0  promises_1.2.1    
 [9] digest_0.6.33      timechange_0.2.0   lifecycle_1.0.3    tokenizers_0.3.0  
[13] processx_3.8.2     magrittr_2.0.3     compiler_4.3.1     rlang_1.1.1       
[17] sass_0.4.7         tools_4.3.1        utf8_1.2.3         yaml_2.3.7        
[21] knitr_1.44         labeling_0.4.3     graphlayouts_1.0.2 bit_4.0.5         
[25] plyr_1.8.9         withr_2.5.1        grid_4.3.1         polyclip_1.10-6   
[29] fansi_1.0.4        git2r_0.32.0       colorspace_2.1-0   scales_1.2.1      
[33] MASS_7.3-60        cli_3.6.1          rmarkdown_2.25     crayon_1.5.2      
[37] generics_0.1.3     rstudioapi_0.15.0  httr_1.4.7         tzdb_0.4.0        
[41] cachem_1.0.8       ggforce_0.4.1      assertthat_0.2.1   vctrs_0.6.3       
[45] Matrix_1.5-4.1     jsonlite_1.8.7     lsa_0.73.3         callr_3.7.3       
[49] hms_1.1.3          bit64_4.0.5        ggrepel_0.9.4      jquerylib_0.1.4   
[53] glue_1.6.2         ps_1.7.5           stringi_1.7.12     gtable_0.3.4      
[57] later_1.3.1        munsell_0.5.0      pillar_1.9.0       htmltools_0.5.6   
[61] R6_2.5.1           tidygraph_1.2.3    rprojroot_2.0.3    evaluate_0.21     
[65] lattice_0.21-8     SnowballC_0.7.1    httpuv_1.6.11      bslib_0.5.1       
[69] Rcpp_1.0.11        gridExtra_2.3      whisker_0.4.1      xfun_0.40         
[73] fs_1.6.3           getPass_0.2-2      pkgconfig_2.0.3