for report : improving code

code/for_report/inference_analyze.R

@@ -297,7 +297,11 @@ ARI_plots <- ggplot(averaged_data) +
     aes(x = epsilon_alpha, y = ARI_mean, color = model) +
     geom_point(aes(fill = model)) +
     geom_line() +
-    geom_ribbon(aes(ymin = ARI_mean - ARI_sd, ymax = ARI_mean + ARI_sd),
+    geom_ribbon(
+        aes(
+            ymin = ARI_mean - ARI_sd,
+            ymax = ARI_mean + ARI_sd, fill = model
+        ),
         alpha = 0.05
     ) +
     scale_color_okabe_ito() +
@@ -310,7 +314,7 @@ ARI_plots <- ggplot(averaged_data) +
         fill = guide_legend(title = "Model"),
         color = guide_legend(title = "Model")
     ) +
-    labs(y = "", x = "$\\epsilon_{\\alpha}$", fill = model.labs) +
+    labs(y = "", x = "$\\epsilon_{\\alpha}$") +
     theme_minimal() +
     theme(aspect.ratio = 1L, axis.text.x = element_text(angle = -45, vjust = .5, hjust = 0))
 

code/for_report/network_clustering_analyze.R

@@ -0,0 +1,44 @@
+## ----libraries, echo = FALSE, include = FALSE----------------------------------------------------------------------------------------------------------------------------------------
+library("ggplot2")
+library("ggokabeito")
+library("tidyr")
+library("dplyr")
+library("stringr")
+library("knitr")
+library("kableExtra")
+library("stringr")
+library("here")
+library("tikzDevice")
+
+
+
+## ----impoting-data, echo = FALSE----------------------------------------------------------------------------------------------------------------------------
+filenames <- list.files(
+    path = here("code", "results", "simulations", "clustering", "synthetic"),
+    pattern = "synthetic",
+    full.names = TRUE
+)
+
+filenames <- filenames[order(file.info(filenames)$ctime, decreasing = TRUE)]
+# data_list <- lapply(filenames, function(file) lapply(readRDS(file), function(model) model$list_clustering))
+df_netclust <- do.call("rbind", lapply(filenames, readRDS))
+df_netclust$model <- factor(df_netclust$model, levels = c(
+    "iid", "pi",
+    "rho", "pirho"
+))
+
+
+
+## ----netclustering-ARI-boxplot, echo = FALSE----------------------------------------------------------------------------------------------------------------
+#| dpi = 300,
+#| fig.asp = 0.5,
+#| fig.cap = "\\label{}ARI of the partition obtained by clustering in function of $\\eps$"
+df_netclust %>%
+    ggplot() +
+    aes(x = as.factor(epsilon), y = ARI) +
+    scale_color_okabe_ito() +
+    scale_fill_okabe_ito() +
+    xlab(TeX("$\\epsilon$")) +
+    guides(fill = guide_legend(title = "Model")) +
+    ylab("ARI of obtained netclustering") +
+    geom_boxplot(aes(fill = model))