81 lines
3.1 KiB
R
81 lines
3.1 KiB
R
library(tidyverse)
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neurobiber_csv <-"~/p2/quest/071525_neurobiber_labels.csv"
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neurobiber_df <- read.csv(neurobiber_csv , header = TRUE)
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neurobiber_df$features_vec <- lapply(neurobiber_df$neurobiber_preds, function(x) {
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x <- gsub("\\[|\\]", "", x)
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x <- trimws(x)
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as.numeric(unlist(strsplit(x, "\\s+")))
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})
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X <- do.call(rbind, neurobiber_df$features_vec )
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set.seed(808)
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library(dplyr)
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library(purrr)
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table(neurobiber_df$source)
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#neurobiber_df <- neurobiber_df %>%
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# group_by(source) %>%
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# mutate(cluster = {
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# X_sub <- do.call(rbind, features_vec)
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# as.factor(kmeans(X_sub, centers = 50)$cluster)
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# }) %>%
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# ungroup()
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kmeans_result <- kmeans(X, centers = 10)
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neurobiber_df$cluster <- as.factor(kmeans_result$cluster)
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table(neurobiber_df$cluster)
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pca <- prcomp(X, center = TRUE, scale. = TRUE)
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neurobiber_df$PC1 <- pca$x[,1]
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neurobiber_df$PC2 <- pca$x[,2]
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ggplot(neurobiber_df, aes(x = PC1, y = PC2, color = cluster)) +
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geom_point(size = 2, alpha = 0.7) +
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theme_minimal() +
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labs(title = "Within case comment clusters (kmeans) by cross-case PCA",
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x = "Principal Component 1",
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y = "Principal Component 2") +
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facet_wrap(~ source)
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ggplot(neurobiber_df, aes(x = phase, y=cluster, fill=AuthorWMFAffil)) +
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geom_violin(trim = FALSE, position = position_dodge(width = 0.8), alpha = 0.6) +
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theme_minimal() +
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labs(title = "Across-case comment clusters by feature deployment phase",
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x = "Feature deployment phase",
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y = "Neurobiber feature vector cluster (kmeans)") +
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facet_wrap(~ source)
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cluster_means <- aggregate(X, by = list(Cluster = neurobiber_df$cluster), FUN = mean)
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rownames(cluster_means) <- paste0("Cluster_", cluster_means$Cluster)
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cluster_means <- cluster_means[,-1] # Remove cluster label column
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BIBER_FEATURES <- c(
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"BIN_QUAN","BIN_QUPR","BIN_AMP","BIN_PASS","BIN_XX0","BIN_JJ",
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"BIN_BEMA","BIN_CAUS","BIN_CONC","BIN_COND","BIN_CONJ","BIN_CONT",
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"BIN_DPAR","BIN_DWNT","BIN_EX","BIN_FPP1","BIN_GER","BIN_RB",
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"BIN_PIN","BIN_INPR","BIN_TO","BIN_NEMD","BIN_OSUB","BIN_PASTP",
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"BIN_VBD","BIN_PHC","BIN_PIRE","BIN_PLACE","BIN_POMD","BIN_PRMD",
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"BIN_WZPRES","BIN_VPRT","BIN_PRIV","BIN_PIT","BIN_PUBV","BIN_SPP2",
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"BIN_SMP","BIN_SERE","BIN_STPR","BIN_SUAV","BIN_SYNE","BIN_TPP3",
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"BIN_TIME","BIN_NOMZ","BIN_BYPA","BIN_PRED","BIN_TOBJ","BIN_TSUB",
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"BIN_THVC","BIN_NN","BIN_DEMP","BIN_DEMO","BIN_WHQU","BIN_EMPH",
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"BIN_HDG","BIN_WZPAST","BIN_THAC","BIN_PEAS","BIN_ANDC","BIN_PRESP",
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"BIN_PROD","BIN_SPAU","BIN_SPIN","BIN_THATD","BIN_WHOBJ","BIN_WHSUB",
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"BIN_WHCL","BIN_ART","BIN_AUXB","BIN_CAP","BIN_SCONJ","BIN_CCONJ",
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"BIN_DET","BIN_EMOJ","BIN_EMOT","BIN_EXCL","BIN_HASH","BIN_INF",
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"BIN_UH","BIN_NUM","BIN_LAUGH","BIN_PRP","BIN_PREP","BIN_NNP",
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"BIN_QUES","BIN_QUOT","BIN_AT","BIN_SBJP","BIN_URL","BIN_WH",
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"BIN_INDA","BIN_ACCU","BIN_PGAS","BIN_CMADJ","BIN_SPADJ","BIN_X"
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)
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colnames(cluster_means) <- BIBER_FEATURES
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library(pheatmap)
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pheatmap(cluster_means,
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cluster_rows = FALSE, # Now features are clustered (rows)
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cluster_cols = TRUE, # Clusters (columns) are not clustered
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scale = "row") # Standardize features
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