## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
evaluate = FALSE

## ----eval = evaluate, warning=FALSE, message=FALSE, dpi=300-------------------
#  # For text-version => 0.9.99
#  # Install text from CRAN
#  install.packages("text")
#  library(text)
#  
#  # Set-up en environment with text-required python packages
#  textrpp_install()
#  
#  # Initialize the environment – and save the settings for next time
#  textrpp_initialize(save_profile = TRUE)
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  # Example text
#  texts <- c("I am feeling relatedness with others", "That's great!")
#  
#  # Defaults
#  embeddings <- textEmbed(texts)
#  
#  # Output
#  embeddings$tokens
#  
#  # Output
#  embeddings$texts
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  # Look at example data included in the text- package comprising both text and numerical variables (note that there are only 40 participants in this example).
#  Language_based_assessment_data_8
#  
#  # Transform the text/word data to word embeddings (see help(textEmbed) to see the default settings).
#  word_embeddings <- textEmbed(
#    Language_based_assessment_data_8,
#    model = "bert-base-uncased",
#    aggregation_from_layers_to_tokens = "concatenate",
#    aggregation_from_tokens_to_texts = "mean",
#    keep_token_embeddings = FALSE
#  )
#  
#  # See how the word embeddings are structured
#  word_embeddings
#  
#  # Save the word embeddings to avoid having to embed the text again. It is good practice to save output from analyses that take a lot of time to compute, which is often the case when analyzing text data.
#  saveRDS(word_embeddings, "word_embeddings.rds")
#  
#  # Get the saved word embeddings (again)
#  word_embeddings <- readRDS("word_embeddings.rds")
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  # Get hidden states for "I am fine"
#  imf_embeddings_11_12 <- textEmbedRawLayers(
#    "I am fine",
#    layers = 11:12
#  )
#  imf_embeddings_11_12
#  
#  #OUTPUT
#  
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  
#  # 1. Concatenate layers(results in 1,536 dimensions).
#  textEmbedLayerAggregation(
#    imf_embeddings_11_12$context_tokens,
#    layers = 11:12,
#    aggregation_from_layers_to_tokens = "concatenate",
#    aggregation_from_tokens_to_texts = "mean"
#  )
#  # OUTPUT
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  # 2. Aggregate layers using mean (results in 768).
#  textEmbedLayerAggregation(
#    imf_embeddings_11_12$context_tokens,
#    layers = 11,
#    aggregation_from_tokens_to_texts = "mean"
#  )
#  
#  # OUTPUT
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  
#  # Examine the relationship between satisfactiontext and the corresponding rating scale
#  model_satisfactiontext_swls <- textTrain(
#    x = word_embeddings$texts$satisfactiontexts, # the predictor variables (i.e., the word embeddings)
#    y = Language_based_assessment_data_8$swlstotal, # the criterion variable (i.e.,the rating scale score.
#    model_description = "author(s): Kjell, Giorgi, & Schwartz; data: N=40, population =  Online, Mechanical Turk; publication: title = Example for demo; description: swls = the satisfaction with life scale"
#  )
#  
#  # Examine the correlation between predicted and observed Harmony in life scale scores
#  model_satisfactiontext_swls$results
#  
#  # OUTPUT:
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  # Save the mode
#  saveRDS(
#    model_satisfactiontext_swls,
#    "model_satisfactiontext_swls.rds"
#  )
#  # Read the model
#  model_satisfactiontext_swls <- readRDS(
#    "model_satisfactiontext_swls.rds"
#  )
#  
#  # Examine the names in the object returned from training
#  names(model_satisfactiontext_swls)
#  
#  #OUTPUT:
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  
#  # Predicting several outcomes from several word embeddings
#  models_words_ratings <- textTrainLists(
#    word_embeddings$texts[1:2],
#    Language_based_assessment_data_8[5:6]
#  )
#  
#  # See results
#  models_words_ratings$results
#  
#  # OUTPUT
#  
#  
#  # Save model
#  saveRDS(models_words_ratings, "models_words_ratings.rds")
#  # Read model
#  models_words_ratings <- readRDS(
#    "models_words_ratings.rds"
#  )
#  
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  # Read a valence trained prediction model (download it from https://osf.io/dgczt/)
#  valence_Warriner_L11 <- readRDS(
#    "valence_Warriner_L11.rds"
#  )
#  
#  # Examine the model
#  valence_Warriner_L11
#  
#  # PART OF THE OUTPUT
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  # Apply the model to the satisfaction text
#  satisfaction_text_valence <- textPredict(
#    valence_Warriner_L11,
#    word_embeddings$texts$satisfactiontexts,
#    dim_names = FALSE
#  )
#  
#  # Examine the correlation between the predicted valence and the Satisfaction with life scale score
#  psych::corr.test(
#    satisfaction_text_valence$word_embeddings__ypred,
#    Language_based_assessment_data_8$swlstotal
#  )
#  
#  
#  # OUTPUT
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  
#  
#  # Compute semantic similarity scores between two text columns, using the previously created word_embeddings.
#  semantic_similarity_scores <- textSimilarity(
#    word_embeddings$texts$harmonytexts,
#    word_embeddings$texts$satisfactiontexts
#  )
#  # Look at the first scores
#  head(semantic_similarity_scores)
#  
#  # OUTPUT
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  
#  # Read word norms text (later we will use these for the semantic centrality plot)
#  word_norms <- read.csv(
#    "Word_Norms_Mental_Health_Kjell2018_text.csv"
#  )
#  
#  # Read the word embeddings for the word norms
#  word_norms_embeddings <- readRDS(
#    "Word_Norms_Mental_Health_Kjell2018_text_embedding_L11.rds"
#  )
#  
#  # Examine which word norms there are.
#  names(word_norms_embeddings$texts)
#  
#  # OUTPUT
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  
#  # Compute semantic similarity score between the harmony answers and the harmony norm
#  # Note that the descriptive word answers are used instead of text answers to correspond with how the word norm was created.
#  norm_similarity_scores_harmony <- textSimilarityNorm(
#    word_embeddings$texts$harmonywords,
#    word_norms_embeddings$texts$harmonynorm
#  )
#  
#  # Correlating the semantic measure with the corresponding rating scale
#  psych::corr.test(
#    norm_similarity_scores_harmony,
#    Language_based_assessment_data_8$hilstotal
#  )
#  
#  # OUTPUT
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  
#  # Extract word type embeddings and text embeddings for harmony words
#  harmony_words_embeddings <- textEmbed(
#    texts = Language_based_assessment_data_8["harmonywords"],
#    aggregation_from_layers_to_tokens = "concatenate",
#    aggregation_from_tokens_to_texts = "mean",
#    aggregation_from_tokens_to_word_types = "mean",
#    keep_token_embeddings = FALSE
#  )
#  
#  # Pre-processing data for plotting
#  projection_results <- textProjection(
#    words = Language_based_assessment_data_8$harmonywords,
#    word_embeddings = harmony_words_embeddings$texts,
#    word_types_embeddings = harmony_words_embeddings$word_types,
#    x = Language_based_assessment_data_8$hilstotal,
#    y = Language_based_assessment_data_8$age
#  )
#  
#  projection_results$word_data
#  
#  # To avoid warnings -- and that words do not get plotted, first increase the max.overlaps for the entire session:
#  options(ggrepel.max.overlaps = 1000)
#  
#  # Plot
#  plot_projection <- textPlot(
#    projection_results,
#    min_freq_words_plot = 1,
#    plot_n_word_extreme = 10,
#    plot_n_word_frequency = 5,
#    plot_n_words_middle = 5,
#    y_axes = FALSE,
#    p_alpha = 0.05,
#    p_adjust_method = "fdr",
#    title_top = "Harmony Words Responses (Supervised Dimension Projection)",
#    x_axes_label = "Low to High Harmony in Life Scale Score",
#    y_axes_label = "",
#    bivariate_color_codes = c("#FFFFFF", "#FFFFFF", "#FFFFFF",
#                              "#E07f6a", "#EAEAEA", "#85DB8E",
#                              "#FFFFFF", "#FFFFFF", "#FFFFFF"
#    )
#  )
#  # View plot
#  
#  plot_projection$final_plot
#  
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  # Plot
#  plot_projection_2D <- textPlot(
#    projection_results,
#    min_freq_words_plot = 1,
#    plot_n_word_extreme = 10,
#    plot_n_word_frequency = 5,
#    plot_n_words_middle = 5,
#    y_axes = TRUE, # Change to TRUE/FALSE
#    p_alpha = 0.05,
#    p_adjust_method = "fdr",
#    title_top = "Harmony Words Responses (Supervised Dimension Projection)",
#    x_axes_label = "Low vs. High Harmony in Life Scale Score",
#    y_axes_label = "Low vs.High Age",
#    bivariate_color_codes = c("#E07f6b", "#60A1F7", "#85DB8D",
#                              "#FF0000", "#EAEAEA", "#5dc688",
#                              "#E07f6a", "#60A1F7", "#85DB8E"
#    )
#  )
#  # View plot
#  plot_projection_2D$final_plot
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  
#  # Computing words' centrality (semantic similarity) score to the aggregated embedding of all words
#  centrality_results <- textCentrality(
#    words = word_norms$satisfactionnorm,
#    word_embeddings = word_norms_embeddings$texts$satisfactionnorm,
#    word_types_embeddings = word_norms_embeddings$word_types
#  )
#  
#  options(ggrepel.max.overlaps = 1000)
#  centrality_plot <- textCentralityPlot(
#    word_data = centrality_results,
#    min_freq_words_test = 2,
#    plot_n_word_extreme = 10,
#    plot_n_word_frequency = 5,
#    plot_n_words_middle = 5,
#    title_top = "Satisfaction with life word norm: Semantic Centrality Plot",
#    x_axes_label = "Satisfaction with Life Semantic Centrality"
#  )
#  
#  centrality_plot$final_plot
#  
#  # OUTPUT
#  
#  # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#  
#  
#  # Supplementary
#  
#  # PCA results to be plotted help(textPCA)
#  textPCA_results <- textPCA(
#    words = Language_based_assessment_data_8$satisfactionwords,
#    word_types_embeddings = harmony_words_embeddings$word_types
#  )
#  
#  
#  # Plotting the PCA results
#  plot_PCA <- textPCAPlot(
#    word_data = textPCA_results,
#    min_freq_words_test = 2,
#    plot_n_word_extreme = 5,
#    plot_n_word_frequency = 5,
#    plot_n_words_middle = 5
#  )
#  plot_PCA$final_plot
#  

