56 lines
1.6 KiB
R
56 lines
1.6 KiB
R
library(data.table)
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library(mltools)
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library(dplyr)
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library(tidyr)
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library(here)
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library(ggplot2)
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library(ggdendro)
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library(factoextra)
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set.seed(1234)
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data <- data.frame(read.csv(file.path(here(), "data", "02_preprocessed_data.csv"),
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header = TRUE
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), stringsAsFactors = TRUE)
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colnames(data)[5:ncol(data)] <- c(
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"1AC-OUVERTURE", "1AC-MI",
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"2A-UEchoix-S1-UC1", "2A-UEchoix-S1-UC2", "2A-UEchoix-S2-UC3",
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"2A-UEchoix-S2-UC4",
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"2A-UEchoix-S2-UC5", "2A-UEchoix-S2-UC6",
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"2A-Projet-S2"
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)
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data <- data %>%
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mutate_if(sapply(data, is.character), as.factor)
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selected_cols <- c(
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"dominante3A", "parcours", "domaine2A", "1AC-MI",
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"2A-UEchoix-S1-UC1", "2A-UEchoix-S1-UC2", "2A-UEchoix-S2-UC4",
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"2A-UEchoix-S2-UC3", "2A-UEchoix-S2-UC5", "2A-UEchoix-S2-UC6",
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"2A-Projet-S2", "1AC-OUVERTURE"
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)
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onehot_data <- one_hot(as.data.table(data), cols = selected_cols, sparsifyNAs = TRUE)
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# Fonctionne bien avec binary
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dist_eucl <- dist(x = onehot_data[, - 1], method = "binary")
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hclust_avg <- hclust(dist_eucl, method = "average")
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dhc <- as.dendrogram(hclust_avg)
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plotdata <- dendro_data(dhc, type = "rectangle")
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p <- ggplot(segment(plotdata)) +
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geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
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coord_flip() +
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scale_y_reverse(expand = c(0.2, 0))
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p + theme_dendro()
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fviz_nbclust(onehot_data[, -c(1, 2, 3)], FUNcluster = hcut, k.max = 30, method = "wss")
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cut_avg <- cutree(hclust_avg, k = 6)
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names(cut_avg) <- data[["ine"]]
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table(cut_avg)
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data[["cluster"]] <- cut_avg
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write.csv(data, file.path(here(), "data", "03_cah_results.csv"), row.names = FALSE)
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