--- title: "CATAL Showcase JCN — Helpers Dataviz R & Python" subtitle: "Démonstration fonctions jcn-*.R / jcn-*.py + plugins avancés | Données ZE France" date: today date-format: "DD MMMM YYYY" execute: echo: true --- ```{r} #| label: setup-r #| include: false source("_setup-common.R") library(sf) library(tmap) library(reticulate) use_python("C:/ScoopApps/apps/python/current/python.exe", required = TRUE) source("C:/Users/vince/hh/pq/PDS/mutils/jrr/jcn-map.R") # Shapefile ZE shp_ze <- st_read( file.path(PROJECT_ROOT, "data/external/geo-shape/nodom_zones-emploi_2025.geojson"), quiet = TRUE ) %>% rename(code = ze2020) %>% mutate(code = sprintf("%04d", as.integer(code))) ze_map <- ze %>% mutate(code = sprintf("%04d", as.integer(code))) map_ze <- shp_ze %>% left_join(ze_map, by = "code", suffix = c("", ".data")) ``` ```{python} #| label: setup-python #| include: false import pandas as pd import numpy as np import plotly.express as px import plotly.graph_objects as go import plotly.io as pio pio.renderers.default = "notebook" import seaborn as sns import matplotlib.pyplot as plt import warnings warnings.filterwarnings('ignore') from pathlib import Path import sys MUTILS_JPY = Path(r"C:\Users\vince\hh\pq\PDS\mutils\jpy") if str(MUTILS_JPY) not in sys.path: sys.path.insert(0, str(MUTILS_JPY)) import importlib.util _spec = importlib.util.spec_from_file_location("jcn_graph", MUTILS_JPY / "jcn-graph.py") _mod = importlib.util.module_from_spec(_spec) _spec.loader.exec_module(_mod) _mod.init_style() COL_CYAN, COL_ORANGE = _mod.COL_CYAN, _mod.COL_ORANGE COL_GREEN, COL_RED = _mod.COL_GREEN, _mod.COL_RED plotly_layout = _mod.plotly_layout correlation_matrix = _mod.correlation_matrix PROJECT_ROOT = Path(r"C:\Users\vince\hh\pq\PDS\ptod-ttrajObserDev") df = pd.read_csv(PROJECT_ROOT / "data/output/dbcln-ze-vf.csv", encoding='utf-8-sig') df = df[~df['code'].isin(['FR', '00FR', 'ZZZZZ'])].copy() ``` ::: {.callout-note appearance="minimal"} **CATAL Showcase JCN** — Catalogue de visualisations sur données réelles (`r N_ZE` ZE, `r ncol(ze)` indicateurs). Structure : - **Parties 1-4** : Helpers JCN maison (`jcn-setup.R`, `jcn-graph.R`, `jcn-table.R`, `jcn-map.R`, `jcn-graph.py`) — showcase des fonctions existantes - **Parties 5-6** : Plugins avancés R (`{jrr}`) et Python (`{jpy}`) — fonctionnalités poussées au max - **Partie 7** : Layout Quarto — patterns de mise en page ::: # JCN Graphs R — jcn-graph.R + jcn-setup.R {jrr} {#sec-jcn-graph} ## jcn-setup.R — Palette col_* & pal_urbn_* {jrr} Constantes couleur Urban Institute importées globalement. 10 couleurs primaires + 8 pastel + 8 palettes (catégorielle, divergente, séquentielle). ```{r} #| label: fig-jcn-palette #| fig-cap: "jcn-setup.R — Palette Urban Institute complète" #| fig-height: 5 cols_main <- c(col_cyan, col_yellow, col_green, col_red, col_orange, col_magenta, col_darkblue, col_spacegray, col_gray, col_black) names_main <- c("col_cyan", "col_yellow", "col_green", "col_red", "col_orange", "col_magenta", "col_darkblue", "col_spacegray", "col_gray", "col_black") pal_data <- tibble( couleur = factor(names_main, levels = rev(names_main)), hex = cols_main ) p1 <- ggplot(pal_data, aes(x = 1, y = couleur, fill = hex)) + geom_tile(width = 0.8, height = 0.8) + geom_text(aes(label = hex), color = "white", fontface = "bold", size = 3.5) + scale_fill_identity() + labs(title = "Couleurs primaires jcn-setup.R", x = NULL, y = NULL) + theme(axis.text.x = element_blank(), axis.ticks = element_blank(), panel.grid = element_blank()) p2 <- ggplot(data.frame(x = seq_along(pal_urbn_div), fill = pal_urbn_div), aes(x = x, y = 1, fill = fill)) + geom_tile() + scale_fill_identity() + labs(title = "pal_urbn_div (8 couleurs)", x = NULL, y = NULL) + theme(axis.text = element_blank(), axis.ticks = element_blank(), panel.grid = element_blank()) gridExtra::grid.arrange(p1, p2, ncol = 1, heights = c(3, 1)) ``` ## jcn-graph.R — theme_urbn() {jrr} Thème ggplot2 appliqué automatiquement via `theme_set()` au chargement de `jcn-graph.R`. Titre aligné à gauche, légende en haut, grille minimale. ```{r} #| label: fig-jcn-theme #| fig-cap: "jcn-graph.R — theme_urbn() auto-appliqué" ggplot(ze, aes(x = dm_sma_vtcam_1622, y = logd_px2_global_24)) + geom_point(aes(color = typo_insee_lib, size = P22_POP), alpha = 0.6) + scale_size_continuous(range = c(1, 7), guide = "none") + scale_y_continuous(labels = label_comma(big.mark = "\u202f")) + labs(title = "theme_urbn() — titre aligné gauche, légende haut, grille légère", subtitle = "Automatiquement appliqué par jcn-graph.R via theme_set()", x = "SMA TCAM 16-22 (%/an)", y = "Prix m² global (EUR)", color = "Typologie", caption = SOURCE_INSEE) ``` ## jcn-graph.R — plot_bar_ranked() {jrr} Classement horizontal top N (cyan) + bottom N (orange) via Plotly. Paramètres : `n_show`, `max_bars`, `fill_top`, `fill_bot`. ```{r} #| label: fig-jcn-bar-ranked #| fig-cap: "jcn-graph.R — plot_bar_ranked()" plot_bar_ranked(ze, "logd_px2_global_24", "libelle", title = "Prix immobilier m² global", subtitle = "Top 15 (cyan) et bottom 15 (orange)", xlab = "Prix m² (EUR)") ``` ## jcn-graph.R — scale_fill_urbn_div() {jrr} Échelle divergente orange → blanc → bleu centrée sur 0. Idéal pour TCAM, évolutions ±. ```{r} #| label: fig-jcn-scale-div #| fig-cap: "jcn-graph.R — scale_fill_urbn_div() sur TCAM SMA" ggplot(ze %>% filter(!is.na(typo_insee_lib)), aes(x = reorder(libelle, dm_sma_vtcam_1622), y = dm_sma_vtcam_1622, fill = dm_sma_vtcam_1622)) + geom_col() + scale_fill_urbn_div(limits = c(-2, 2), name = "SMA (%/an)") + coord_flip() + labs(title = "scale_fill_urbn_div() — divergente centrée 0", x = NULL, y = "SMA TCAM 16-22 (%/an)") + theme(axis.text.y = element_text(size = 5)) ``` ## jcn-graph.R — fmt_fr(), format_tcam() {jrr} Formatage français : `fmt_fr(1234.5, 1)` → `"1 234,5"`, `format_tcam(0.023)` → `"+0.02%"`. ```{r} #| label: demo-fmt cat("fmt_fr(12345.67, 1) =", fmt_fr(12345.67, 1), "\n") cat("fmt_pct(23.456, 1) =", fmt_pct(23.456, 1), "\n") cat("fmt_k(1234567) =", fmt_k(1234567), "\n") cat("fmt_sign(-3.14, 2) =", fmt_sign(-3.14, 2), "\n") cat("format_tcam(0.0234) =", format_tcam(0.0234), "\n") cat("tcam(120, 100, 5) =", tcam(120, 100, 5), "\n") ``` ## _setup-common.R — plot_distribution() {jrr} Histogramme + rug + médiane. Helper projet pour distributions rapides. ```{r} #| label: fig-jcn-distribution #| fig-cap: "_setup-common.R — plot_distribution()" plot_distribution(ze, "logd_px2_global_24", bins = 30, title = "Distribution asymétrique droite — quelques ZE très chères", xlab = "Prix médian global m² (EUR)") ``` ## _setup-common.R — scatter_quadrants() {jrr} Scatter 2D avec quadrants, labels auto ggrepel, taille population. Helper projet pour hypothèses bivariées. ```{r} #| label: fig-jcn-scatter-quadrants #| fig-cap: "_setup-common.R — scatter_quadrants()" scatter_quadrants(ze, x = "logd_px2_global_24", y = "idxlogtens_ind_22", title = "Prix élevé + tension élevée = marché sous pression", xlab = "Prix m² global (EUR)", ylab = "Idx tension logement (0-100)", hline = 50, vline = median(ze$logd_px2_global_24, na.rm = TRUE), quad_labels = c("Cher + tendu", "Abordable + tendu", "Abordable + détendu", "Cher + détendu")) ``` # JCN Tables R — jcn-table.R {jrr} {#sec-jcn-table} ## jcn-table.R — rt_styled() + theme_rt_urban {jrr} Table reactable avec thème Urban (header gris, bordure cyan 2px), scroll auto, tri intégré. ```{r} #| label: tbl-jcn-rt-styled #| tbl-cap: "jcn-table.R — rt_styled() avec colonnes typées" ze %>% select(libelle, typo_insee_lib, P22_POP, logd_px2_global_24, dm_sma_vtcam_1622, idxlogtens_ind_22) %>% slice_max(P22_POP, n = 20) %>% rt_styled( columns = list( libelle = colDef(name = "Zone d'emploi", minWidth = 150), typo_insee_lib = colDef(name = "Typologie", minWidth = 140), P22_POP = col_pop(ze, "P22_POP"), logd_px2_global_24 = col_num("Prix m²", unit = "EUR"), dm_sma_vtcam_1622 = col_variation(ze, "dm_sma_vtcam_1622", label = "SMA", unit = "%/an"), idxlogtens_ind_22 = col_num("Idx tension", digits = 0) ) ) ``` ## jcn-table.R — bar_variation() + bar_pct() {jrr} Barres inline divergentes (vert/rouge pour ±) et barres proportionnelles (0-100). Renderers cellule. ```{r} #| label: tbl-jcn-bars #| tbl-cap: "jcn-table.R — bar_variation() et bar_pct() dans les cellules" tb_data <- ze %>% filter(!is.na(logd_px2_global_vevol_1624), !is.na(idxlogtens_ind_22)) %>% select(libelle, dm_sma_vtcam_1622, logd_px2_global_vevol_1624, idxlogtens_ind_22) %>% slice_max(abs(dm_sma_vtcam_1622), n = 25) stats_sma <- compute_bar_stats(tb_data$dm_sma_vtcam_1622) stats_px <- compute_bar_stats(tb_data$logd_px2_global_vevol_1624) reactable( tb_data, compact = TRUE, pagination = FALSE, theme = theme_rt_urban, columns = list( libelle = colDef(name = "ZE", minWidth = 150), dm_sma_vtcam_1622 = colDef(name = "SMA 16-22", cell = bar_variation(stats_sma)), logd_px2_global_vevol_1624 = colDef(name = "Evol prix %", cell = bar_variation(stats_px)), idxlogtens_ind_22 = colDef(name = "Idx tension", cell = bar_pct()) ) ) ``` ## jcn-table.R — style_divergent() + groupBy {jrr} Coloration conditionnelle vert/rouge + regroupement hiérarchique avec agrégats. ```{r} #| label: tbl-jcn-groupby #| tbl-cap: "jcn-table.R — style_divergent() + groupBy agrégats" ze %>% filter(!is.na(typo_insee_lib)) %>% select(typo_insee_lib, libelle, P22_POP, logd_px2_global_24, dm_sma_vtcam_1622, idxlogtens_ind_22) %>% reactable( groupBy = "typo_insee_lib", pagination = FALSE, theme = theme_rt_urban, columns = list( typo_insee_lib = colDef(name = "Typologie", minWidth = 180), libelle = colDef(name = "ZE", aggregate = "count", format = colFormat(suffix = " ZE")), P22_POP = colDef(name = "Pop. totale", aggregate = "sum", format = colFormat(separators = TRUE, digits = 0)), logd_px2_global_24 = colDef(name = "Prix m² méd.", aggregate = "median", format = colFormat(separators = TRUE, digits = 0)), dm_sma_vtcam_1622 = colDef(name = "SMA méd.", aggregate = "median", format = colFormat(digits = 2), style = style_divergent()), idxlogtens_ind_22 = colDef(name = "Idx tension méd.", aggregate = "median", format = colFormat(digits = 0)) ) ) ``` # JCN Cartes R — jcn-map.R {jrr} {#sec-jcn-map} ## jcn-map.R — make_carte_france() {jrr} Carte Leaflet interactive avec bins hybrides, légende, zoom, hover. Paramètres : `var`, `titre`, `bins`, `palette`, `label_col`, `suffix`, `source`. ```{r} #| label: fig-jcn-carte-france #| fig-cap: "jcn-map.R — make_carte_france() Leaflet interactif" make_carte_france( map_ze, var = "logd_px2_global_24", titre = "Prix m² (EUR)", titre_carte = "Prix immobilier — DVF 2024", bins = c(0, 1000, 1500, 2000, 2500, 3000, 4000, 6000, 12000), label_col = "libze2020", suffix = " EUR", source = "Source : DVF 2024" ) ``` ## jcn-map.R — make_pal() + pal_urbn_div {jrr} Générateur de rampe couleur + palette divergente. Utilisable dans tmap et ggplot2. ```{r} #| label: fig-jcn-tmap-grille #| fig-cap: "jcn-map.R — make_pal(pal_urbn_div) dans tmap grille comparative" #| fig-width: 14 #| fig-height: 7 tmap_mode("plot") breaks_vac <- c(0, 3, 5, 7, 10, 13, 16, 20, 35) m1 <- tm_shape(map_ze) + tm_polygons( fill = "log_vac_pct_22", fill.scale = tm_scale_intervals(breaks = breaks_vac, values = rev(make_pal(pal_urbn_div, 8)), midpoint = NA), fill.legend = tm_legend(title = "Vacance %"), col = "white", lwd = 0.3 ) + tm_title("Vacance RP 2022") m2 <- tm_shape(map_ze) + tm_polygons( fill = "logv_vac2ans_pct_24", fill.scale = tm_scale_intervals(breaks = c(0, 1, 2, 3, 4, 5, 7, 10, 20), values = rev(make_pal(pal_urbn_div, 8)), midpoint = NA), fill.legend = tm_legend(title = "Vacance 2+ans %"), col = "white", lwd = 0.3 ) + tm_title("Vacance LOVAC 2+ ans 2024") tmap_arrange(m1, m2, ncol = 2) ``` # JCN Python — jcn-graph.py {jpy} {#sec-jcn-python} ## jcn-graph.py — init_style() + plotly_layout() {jpy} `init_style()` initialise le template Plotly "urban" + configure matplotlib/seaborn. `plotly_layout()` applique titre gauche, caption bas, légende haut. ```{python} #| label: fig-jcn-plotly-layout #| fig-cap: "jcn-graph.py — plotly_layout() avec titre/subtitle/caption" dfp = df.dropna(subset=['logd_px2_global_24', 'dm_sma_vtcam_1622', 'typo_insee_lib']).copy() fig = px.scatter(dfp, x='dm_sma_vtcam_1622', y='logd_px2_global_24', size='P22_POP', color='typo_insee_lib', hover_name='libelle', size_max=25, opacity=0.6, labels={'dm_sma_vtcam_1622': 'SMA TCAM 16-22 (%/an)', 'logd_px2_global_24': 'Prix m² global (EUR)'}) fig = plotly_layout(fig, title="plotly_layout() — Style Urban appliqué", subtitle="Taille = population, couleur = typologie INSEE", caption="Source : DVF 2024, INSEE RP 2022") fig.update_layout(height=550) fig.show() ``` ## jcn-graph.py — correlation_matrix() {jpy} Matrice corrélation triangle inférieur, palette RdBu_r, annotations. Paramètres : `cols`, `labels`, `threshold`, `figsize`. ```{python} #| label: fig-jcn-corr-matrix #| fig-cap: "jcn-graph.py — correlation_matrix() heatmap" cols_corr = ['log_vac_pct_22', 'log_prop_pct_22', 'log_emmenrec_pct_22', 'logd_px2_global_24', 'logv_vac2ans_pct_24', 'rev_med_21', 'dm_sma_vtcam_1622', 'idxlogtens_ind_22', 'idxresid_dyn_ind_1623'] labels_corr = dict(zip(cols_corr, [ 'Vacance RP', 'Propriétaires', 'Emménagés', 'Prix m²', 'Vac 2+ans', 'Rev médian', 'SMA', 'Idx tension', 'Idx résid.'])) fig = correlation_matrix(df, cols=cols_corr, labels=labels_corr, threshold=0.10, figsize=(650, 550)) fig.show() ``` # Plugins avancés R {jrr} {#sec-plugins-r} ## ggridges — Ridgeline densité multi-groupes {jrr} Superpose densités par catégorie avec gradient couleur + lignes de quantiles (Q25, Q50, Q75). Révèle bimodalités cachées par les boxplots. ```{r} #| label: fig-ridgeline-max #| fig-cap: "ggridges — Ridgeline gradient + quantiles + annotation" #| fig-height: 8 library(ggridges) ze_ridg <- ze %>% filter(!is.na(typo_insee_lib), !is.na(logd_px2_global_24)) %>% mutate(typo = reorder(typo_insee_lib, logd_px2_global_24, FUN = median)) # Médiane globale pour annotation med_global <- median(ze_ridg$logd_px2_global_24, na.rm = TRUE) ggplot(ze_ridg, aes(x = logd_px2_global_24, y = typo, fill = after_stat(x))) + geom_density_ridges_gradient(scale = 1.8, rel_min_height = 0.01, quantile_lines = TRUE, quantiles = c(0.25, 0.5, 0.75), jittered_points = TRUE, point_size = 0.8, point_alpha = 0.3, position = position_raincloud(adjust_vlines = TRUE)) + geom_vline(xintercept = med_global, linetype = "dashed", color = col_orange, linewidth = 0.6) + annotate("text", x = med_global + 100, y = 0.5, label = paste0("Médiane FR\n", fmt_fr(med_global, 0), " EUR"), color = col_orange, size = 2.8, hjust = 0, fontface = "italic") + scale_fill_viridis_c(name = "Prix m²", option = "C") + scale_x_continuous(labels = label_comma(big.mark = "\u202f")) + labs(title = "Les grandes métropoles forment un second mode au-dessus de 3 000 EUR", subtitle = "Lignes = Q25, médiane, Q75 | Points = observations individuelles", x = "Prix m² global (EUR)", y = NULL) ``` ## ggplot2 — Lollipop segmenté + couleur encodée {jrr} Lollipop avec couleur du point = variable secondaire. Encode 2 dimensions dans un ranking. ```{r} #| label: fig-lollipop-max #| fig-cap: "ggplot2 — Lollipop + couleur = SMA (2 dimensions)" top20 <- ze %>% filter(!is.na(logd_px2_global_vevol_1624), !is.na(dm_sma_vtcam_1622)) %>% slice_max(logd_px2_global_vevol_1624, n = 20) ggplot(top20, aes(x = logd_px2_global_vevol_1624, y = reorder(libelle, logd_px2_global_vevol_1624))) + geom_segment(aes(xend = 0, yend = libelle), color = col_gray, linewidth = 0.6) + geom_point(aes(color = dm_sma_vtcam_1622), size = 4) + geom_vline(xintercept = 0, color = "#333", linewidth = 0.4) + geom_text(aes(label = paste0("+", round(logd_px2_global_vevol_1624, 1), "%")), hjust = -0.3, size = 2.5, color = col_spacegray) + scale_color_gradient2(low = col_red, mid = "white", high = col_green, midpoint = 0, name = "SMA (%/an)") + labs(title = "Les ZE littorales et périurbaines mènent la hausse", subtitle = "Couleur = SMA — les hausses de prix ne corrèlent pas toujours avec l'attractivité", x = "Évolution prix m² 2016-2024 (%)", y = NULL) ``` ## ggplot2 — Dumbbell T1→T2 avec flèches directionnelles {jrr} Montre le changement entre deux dates. Segment + points + annotation direction. ```{r} #| label: fig-dumbbell-max #| fig-cap: "ggplot2 — Dumbbell + flèche direction + annotation ∆" top15 <- ze %>% filter(!is.na(logv_vac2ans_pct_20), !is.na(logv_vac2ans_pct_24)) %>% mutate(delta = logv_vac2ans_pct_24 - logv_vac2ans_pct_20, direction = ifelse(delta > 0, "Hausse", "Baisse")) %>% slice_max(abs(delta), n = 15) ggplot(top15, aes(y = reorder(libelle, delta))) + geom_segment(aes(x = logv_vac2ans_pct_20, xend = logv_vac2ans_pct_24, yend = libelle, color = direction), linewidth = 0.8, arrow = arrow(length = unit(0.15, "cm"), type = "closed")) + geom_point(aes(x = logv_vac2ans_pct_20), size = 3, color = col_orange) + geom_point(aes(x = logv_vac2ans_pct_24), size = 3, color = col_cyan) + geom_text(aes(x = logv_vac2ans_pct_24, label = sprintf("%+.1f pt", delta)), hjust = -0.2, size = 2.3, color = col_spacegray) + scale_color_manual(values = c("Hausse" = col_red, "Baisse" = col_green), guide = "none") + labs(title = "Vacance longue durée : 2020 (orange) → 2024 (bleu)", subtitle = "Flèche rouge = aggravation, verte = amélioration | 15 ZE plus forte variation", x = "Vacance LOVAC 2+ ans (%)", y = NULL) ``` ## ggplot2 — Slope chart T1→T2 + highlight trajectoire {jrr} Pente entre deux périodes. Highlight les trajectoires extrêmes, grisé les autres. ```{r} #| label: fig-slope-max #| fig-cap: "ggplot2 — Slope chart avec highlight montée/descente" #| fig-height: 8 top_ze <- ze %>% slice_max(P22_POP, n = 20) slope_data <- top_ze %>% mutate(delta = idxresid_dyn_ind_1623 - idxresid_dyn_ind_1116, highlight = case_when( delta > 5 ~ "Montée forte", delta < -5 ~ "Descente forte", TRUE ~ "Stable" )) %>% select(libelle, T1 = idxresid_dyn_ind_1116, T2 = idxresid_dyn_ind_1623, highlight) %>% pivot_longer(cols = c(T1, T2), names_to = "periode", values_to = "idx") ggplot(slope_data, aes(x = periode, y = idx, group = libelle)) + geom_line(data = slope_data %>% filter(highlight == "Stable"), color = col_gray, linewidth = 0.5, alpha = 0.5) + geom_line(data = slope_data %>% filter(highlight != "Stable"), aes(color = highlight), linewidth = 1.2) + geom_point(data = slope_data %>% filter(highlight != "Stable"), aes(color = highlight), size = 3) + geom_text_repel(data = slope_data %>% filter(periode == "T2", highlight != "Stable"), aes(label = libelle, color = highlight), hjust = -0.15, size = 3, direction = "y") + scale_x_discrete(expand = expansion(mult = c(0.05, 0.35))) + scale_color_manual(values = c("Montée forte" = col_green, "Descente forte" = col_red, "Stable" = col_gray)) + labs(title = "Qui monte, qui descend entre T1 (11-16) et T2 (16-22) ?", subtitle = "Vert = hausse >5 pts, rouge = baisse >5 pts, gris = stable", x = NULL, y = "Indice résidentiel (0-100)") + guides(color = "none") ``` ## ggplot2 — Small multiples facet_wrap + R² par groupe {jrr} Même graphique répliqué par catégorie. Ajout R² et droite régression par facette. ```{r} #| label: fig-small-mult-max #| fig-cap: "ggplot2 — Small multiples + R² par facette" #| fig-width: 12 #| fig-height: 8 ze_facet <- ze %>% filter(!is.na(typo_insee_lib)) # Calcul R² par groupe r2_by_typo <- ze_facet %>% group_by(typo_insee_lib) %>% summarise(r2 = cor(logd_px2_global_24, dm_sma_vtcam_1622, use = "complete.obs")^2, n = n(), x_pos = max(logd_px2_global_24, na.rm = TRUE) * 0.7, y_pos = max(dm_sma_vtcam_1622, na.rm = TRUE) * 0.9, .groups = "drop") ggplot(ze_facet, aes(x = logd_px2_global_24, y = dm_sma_vtcam_1622)) + geom_point(alpha = 0.5, color = col_cyan, size = 1.5) + geom_smooth(method = "lm", se = TRUE, color = col_orange, linewidth = 0.6, fill = col_yellow, alpha = 0.15) + geom_hline(yintercept = 0, linetype = "dashed", color = col_gray) + geom_text(data = r2_by_typo, aes(x = x_pos, y = y_pos, label = paste0("R²=", round(r2, 3), "\nn=", n)), color = col_spacegray, size = 2.8, hjust = 0.5) + facet_wrap(~ typo_insee_lib, scales = "free_x") + labs(title = "La relation prix × SMA n'est pas homogène entre typologies", subtitle = "Intervalle de confiance 95% + R² par groupe — attention aux n faibles", x = "Prix m² (EUR)", y = "SMA TCAM 16-22 (%/an)") ``` ## sf + ggplot2 — Choroplèthe statique publication {jrr} Carte statique haute qualité avec légende intégrée, labels grandes ZE, source. :::: {.column-body-outset} ```{r} #| label: fig-sf-choro-max #| fig-cap: "sf + ggplot2 — Choroplèthe statique publication-ready" #| fig-width: 10 #| fig-height: 8 top_ze_labels <- map_ze %>% filter(!is.na(logd_px2_global_24)) %>% slice_max(P22_POP, n = 12) %>% st_centroid() ggplot(map_ze) + geom_sf(aes(fill = logd_px2_global_24), color = "white", linewidth = 0.15) + geom_sf_text(data = top_ze_labels, aes(label = libze2020), size = 2, color = col_black, fontface = "bold", check_overlap = TRUE) + scale_fill_viridis_c(name = "Prix m² (EUR)", option = "C", labels = label_comma(big.mark = "\u202f"), breaks = c(1000, 2000, 3000, 5000, 8000), na.value = col_gray) + labs(title = "Prix immobilier global — Zones d'emploi", subtitle = "Labels = 12 ZE les plus peuplées", caption = "Source : DVF 2024 | Fond : IGN AdminExpress 2025") + theme(axis.text = element_blank(), axis.ticks = element_blank(), panel.grid = element_blank(), legend.position = c(0.15, 0.35)) ``` :::: ## tmap — Carte bivariée 3×3 croissance × chômage {jrr} Croise deux variables en 9 classes (ntile 3×3). Palette Joshua Stevens — chaque cellule encode 2 dimensions simultanément. Révèle des patterns invisibles en univarié. :::: {.column-body-outset} ```{r} #| label: fig-bivar-max #| fig-cap: "tmap — Carte bivariée 3×3 : Croissance pop × Chômage" #| fig-width: 12 #| fig-height: 10 map_ze_biv <- map_ze %>% mutate( q_pop = ntile(dm_pop_vtcam_1622, 3), q_chom = ntile(soc_txchom_1564_22, 3), bivar_class = paste0(q_pop, "-", q_chom) ) biv_colors <- c( "1-1" = "#e8e8e8", "1-2" = "#dfb0d6", "1-3" = "#be64ac", "2-1" = "#ace4e4", "2-2" = "#a5add3", "2-3" = "#8c62aa", "3-1" = "#5ac8c8", "3-2" = "#5698b9", "3-3" = "#3b4994" ) tmap_mode("plot") tm_shape(map_ze_biv) + tm_polygons( fill = "bivar_class", fill.scale = tm_scale_categorical(values = biv_colors), fill.legend = tm_legend(show = FALSE), col = "white", lwd = 0.3 ) + tm_title("Croissance population (\u2192) \u00d7 Ch\u00f4mage (\u2191)") + tm_credits( "Violet fonc\u00e9 = d\u00e9clin + ch\u00f4mage \u00e9lev\u00e9 | Cyan = croissance + faible ch\u00f4mage\nPalette Joshua Stevens (3\u00d73 ntile cross)", position = c("left", "bottom"), size = 0.8 ) ``` :::: ## tmap — Facettes par typologie INSEE {jrr} Même indicateur, décliné par profil territorial. `tm_facets(free.coords = FALSE)` maintient la même échelle géographique — les différences de pattern sautent aux yeux. :::: {.column-body-outset} ```{r} #| label: fig-facets-typo-max #| fig-cap: "tmap — Facettes TCAM population par profil INSEE (7 types)" #| fig-width: 14 #| fig-height: 10 map_ze_facet <- map_ze %>% filter(!is.na(typo_insee_7)) %>% mutate(typo_label = case_when( typo_insee_7 == 1 ~ "1-M\u00e9tropoles", typo_insee_7 == 2 ~ "2-Gdes agglo", typo_insee_7 == 3 ~ "3-R\u00e9sidentielles", typo_insee_7 == 4 ~ "4-Agriculture", typo_insee_7 == 5 ~ "5-Industrie", typo_insee_7 == 6 ~ "6-Tourisme", typo_insee_7 == 7 ~ "7-Diversifi\u00e9es" )) breaks_pop <- c(-2, -1, -0.5, 0, 0.3, 0.6, 1, 1.5, 3) tm_shape(map_ze_facet) + tm_polygons( fill = "dm_pop_vtcam_1622", fill.scale = tm_scale_intervals( breaks = breaks_pop, values = make_pal(pal_urbn_div, 8), midpoint = NA ), fill.legend = tm_legend(show = FALSE), col = "white", lwd = 0.2 ) + tm_facets(by = "typo_label", ncol = 4, free.coords = FALSE) + tm_title("TCAM Population 2016-2022 par profil INSEE") ``` :::: ## mapview — Exploration interactive hover {jrr} Tooltip au survol sans clic. Idéal pour EDA rapide. `mapview::mapview()` génère un Leaflet léger avec label au hover. ```{r} #| label: fig-mapview-max #| fig-cap: "mapview — Carte interactive SMA avec tooltip hover multi-champs" library(mapview) mapview(map_ze, zcol = "dm_sma_vtcam_1622", layer.name = "TCAM SMA 16-22", col.regions = make_pal(pal_urbn_div, 8), alpha.regions = 0.8, label = paste0( map_ze$libelle, " | SMA: ", round(map_ze$dm_sma_vtcam_1622, 2), "%/an", " | Pop: ", format(map_ze$P22_POP, big.mark = "\u202f") ), legend = TRUE, homebutton = FALSE) ``` # Plugins avancés Python {jpy} {#sec-plugins-py} ## seaborn — Violin + strip + annotations stats {jpy} Densité complète + points individuels + médiane annotée par groupe. ```{python} #| label: fig-violin-max #| fig-cap: "seaborn — Violin + strip + médiane annotée" #| fig-height: 8 dv = df.dropna(subset=['typo_insee_lib', 'log_vac_pct_22']).copy() order = dv.groupby('typo_insee_lib')['log_vac_pct_22'].median().sort_values(ascending=False).index medians = dv.groupby('typo_insee_lib')['log_vac_pct_22'].median() fig, ax = plt.subplots(figsize=(10, 8)) sns.violinplot(data=dv, y='typo_insee_lib', x='log_vac_pct_22', order=order, inner=None, palette='viridis', alpha=0.35, ax=ax) sns.stripplot(data=dv, y='typo_insee_lib', x='log_vac_pct_22', order=order, size=5, alpha=0.6, color=COL_CYAN, ax=ax, jitter=0.2) # Annotate medians for i, typo in enumerate(order): med = medians[typo] ax.plot(med, i, 'D', color=COL_ORANGE, markersize=8, zorder=5) ax.annotate(f'{med:.1f}%', xy=(med, i), xytext=(med + 0.5, i - 0.3), fontsize=8, color=COL_ORANGE, fontweight='bold') ax.axvline(dv['log_vac_pct_22'].median(), color=COL_ORANGE, linestyle='--', alpha=0.5, label='Médiane FR') ax.set_title('Les ZE rurales ont une vacance plus dispersée', fontweight='bold', fontsize=14) ax.set_xlabel('Taux de vacance RP 2022 (%)') ax.set_ylabel(None) ax.legend(fontsize=9) plt.tight_layout() plt.show() ``` ## plotly.express — Scatter 5D interactif hover enrichi {jpy} 5 dimensions visuelles : x, y, taille, couleur, hover multi-champs. Animation frame possible. ```{python} #| label: fig-scatter-5d-max #| fig-cap: "plotly.express — Scatter 5D : x, y, size, color, hover détaillé" dfp = df.dropna(subset=['logd_px2_global_24', 'dm_sma_vtcam_1622', 'typo_insee_lib', 'idxlogtens_ind_22']).copy() dfp['hover_text'] = dfp.apply( lambda r: f"{r['libelle']}
" f"Pop: {r['P22_POP']:,.0f}
" f"Prix: {r['logd_px2_global_24']:,.0f} EUR/m²
" f"SMA: {r['dm_sma_vtcam_1622']:+.2f}%/an
" f"Idx tension: {r['idxlogtens_ind_22']:.0f}/100
" f"Vacance 2+ans: {r.get('logv_vac2ans_pct_24', 0):.1f}%", axis=1) fig = px.scatter(dfp, x='dm_sma_vtcam_1622', y='logd_px2_global_24', size='P22_POP', color='idxlogtens_ind_22', hover_name='libelle', size_max=30, opacity=0.7, color_continuous_scale='RdYlBu_r', range_color=[10, 90], custom_data=['hover_text'], labels={'dm_sma_vtcam_1622': 'SMA TCAM 16-22 (%/an)', 'logd_px2_global_24': 'Prix m² global (EUR)', 'idxlogtens_ind_22': 'Tension'}) fig.update_traces(hovertemplate='%{customdata[0]}') fig.add_hline(y=dfp['logd_px2_global_24'].median(), line_dash="dash", line_color=COL_ORANGE, annotation_text="Médiane prix") fig.add_vline(x=0, line_dash="dash", line_color="gray", annotation_text="SMA = 0") fig = plotly_layout(fig, title="Prix vs attractivité — 5 dimensions", subtitle="Taille=pop, couleur=tension, hover=multi-indicateurs", caption="Source : DVF 2024, INSEE RP, LOVAC") fig.update_layout(height=600) fig.show() ``` ## plotly.express — Parallel coordinates sélection interactive {jpy} Profil multi-dimensionnel. Sélection par plage sur chaque axe pour filtrer les ZE. ```{python} #| label: fig-parallel-max #| fig-cap: "plotly.express — Parallel coordinates avec sélection interactive" cols_tmi = ['dmf_tmi_cscadre_22', 'dmf_tmi_csouvrier_22', 'dmf_tmi_a65p_22', 'dmf_tmi_a2529_22'] dfpc = df.dropna(subset=cols_tmi + ['dm_sma_vtcam_1622']).copy() fig = px.parallel_coordinates( dfpc, dimensions=cols_tmi + ['dm_sma_vtcam_1622'], color='dm_sma_vtcam_1622', color_continuous_scale='RdBu', color_continuous_midpoint=0, labels={ 'dmf_tmi_cscadre_22': 'TMI Cadres', 'dmf_tmi_csouvrier_22': 'TMI Ouvriers', 'dmf_tmi_a65p_22': 'TMI 65+', 'dmf_tmi_a2529_22': 'TMI 25-29', 'dm_sma_vtcam_1622': 'SMA' } ) fig.update_layout( title=dict(text="Profil migratoire par catégorie — glisser pour filtrer
" "Cliquer-glisser sur un axe pour sélectionner une plage"), height=550, font_size=12 ) fig.show() ``` ## plotly.express — Treemap hiérarchique drill-down {jpy} Hiérarchie imbriquée avec surface ∝ population, couleur = indicateur. Click pour zoomer. ```{python} #| label: fig-treemap-max #| fig-cap: "plotly.express — Treemap hiérarchique population × prix" dft = df.dropna(subset=['typo_insee_lib', 'logd_px2_global_24', 'libelle']).copy() dft = dft[dft['libelle'].str.strip() != ''].copy() dft['prix_cat'] = pd.cut(dft['logd_px2_global_24'], bins=[0, 1500, 2500, 4000, 15000], labels=['< 1 500', '1 500-2 500', '2 500-4 000', '> 4 000']) fig = px.treemap(dft, path=['typo_insee_lib', 'prix_cat', 'libelle'], values='P22_POP', color='logd_px2_global_24', color_continuous_scale='Viridis', color_continuous_midpoint=2500, hover_data={'P22_POP': ':,.0f', 'logd_px2_global_24': ':,.0f'}) fig.update_layout(title="Répartition population : Typologie → Gamme prix → ZE
" "Cliquer pour zoomer dans la hiérarchie", height=650) fig.update_traces(textinfo='label+percent parent', hovertemplate='%{label}
Pop: %{value:,.0f}
Prix: %{color:,.0f} EUR/m²') fig.show() ``` ## plotly.go — Sankey transitions quadrants T1→T2 {jpy} Flux entre états : épaisseur ∝ volume. Couleur des liens = quadrant source. ```{python} #| label: fig-sankey-max #| fig-cap: "plotly.go — Sankey transitions SN×SMA avec flux colorés" def quad(sn, sma): if sn >= 0 and sma >= 0: return "Double moteur" if sn < 0 and sma >= 0: return "Compensation" if sn < 0 and sma < 0: return "Double déficit" return "Fuite" cols_q = ['dm_sn_vtcam_1116','dm_sma_vtcam_1116','dm_sn_vtcam_1622','dm_sma_vtcam_1622'] dfs = df.dropna(subset=cols_q).copy() dfs['q_t1'] = dfs.apply(lambda r: quad(r[cols_q[0]], r[cols_q[1]]), axis=1) dfs['q_t2'] = dfs.apply(lambda r: quad(r[cols_q[2]], r[cols_q[3]]), axis=1) flows = dfs.groupby(['q_t1','q_t2']).size().reset_index(name='n') # Add average population per flow flows_pop = dfs.groupby(['q_t1','q_t2'])['P22_POP'].sum().reset_index(name='pop_total') flows = flows.merge(flows_pop, on=['q_t1','q_t2']) cats = ["Double moteur", "Compensation", "Double déficit", "Fuite"] colors_node = [COL_CYAN, COL_GREEN, COL_RED, "#fdbf11"] colors_link_map = {c: f"rgba({int(h[1:3],16)},{int(h[3:5],16)},{int(h[5:7],16)},0.4)" for c, h in zip(cats, colors_node)} labels = [f"{c} (T1)" for c in cats] + [f"{c} (T2)" for c in cats] node_colors = colors_node * 2 idx_t1 = {c: i for i, c in enumerate(cats)} idx_t2 = {c: i + 4 for i, c in enumerate(cats)} link_colors = [colors_link_map[r['q_t1']] for _, r in flows.iterrows()] hover_texts = [f"{r['q_t1']} → {r['q_t2']}
{r['n']} ZE
Pop totale: {r['pop_total']:,.0f}" for _, r in flows.iterrows()] fig = go.Figure(go.Sankey( node=dict(label=labels, color=node_colors, pad=20, thickness=25), link=dict( source=[idx_t1[r['q_t1']] for _, r in flows.iterrows()], target=[idx_t2[r['q_t2']] for _, r in flows.iterrows()], value=flows['n'].tolist(), color=link_colors, customdata=hover_texts, hovertemplate='%{customdata}' ) )) fig.update_layout( title=dict(text="Transitions démographiques T1 (11-16) → T2 (16-22)
" "Épaisseur = nombre de ZE | Couleur = quadrant source"), height=500, font_size=13 ) fig.show() ``` # Layout Quarto {#sec-layout} ## body-outset — graphique élargi {.column-body-outset} Déborde du corps de texte. `:::: {.column-body-outset}` ou attribut H2. ```{r} #| label: fig-layout-outset #| fig-cap: "body-outset — graphique plus large que le texte" #| fig-width: 12 ggplot(ze, aes(x = logd_px2_global_24, y = idxlogtens_ind_22, color = typo_insee_lib)) + geom_point(alpha = 0.6, size = 2) + labs(title = "Démonstration body-outset — déborde du corps de texte", x = "Prix m²", y = "Idx tension", color = "Typo") ``` ## layout-ncol: 2 — côte à côte Deux graphiques juxtaposés. Syntaxe : `::: {layout-ncol=2}`. ::: {layout-ncol=2} ```{r} #| label: fig-layout-left #| fig-cap: "Gauche — distribution prix" #| fig-height: 4 plot_distribution(ze, "logd_px2_global_24", bins = 25, title = "Prix m² global", xlab = "EUR") ``` ```{r} #| label: fig-layout-right #| fig-cap: "Droite — distribution vacance" #| fig-height: 4 plot_distribution(ze, "logv_vac2ans_pct_24", bins = 25, title = "Vacance 2+ ans", xlab = "%", fill = col_orange) ``` ::: ## panel-tabset — graphique + tableau Pattern INSEE : synthèse graphique vs données complètes. ::: {.panel-tabset} ### Graphique ```{r} #| label: fig-tabset-graph plot_bar_ranked(ze, "idxlogtens_ind_22", "libelle", title = "Indice tension logement", subtitle = "Top/bottom 15 ZE", xlab = "Indice (0-100)") ``` ### Tableau complet ```{r} #| label: tbl-tabset-table ze %>% select(libelle, idxlogtens_ind_22, logd_px2_global_24, logv_vac2ans_pct_24) %>% arrange(desc(idxlogtens_ind_22)) %>% rt_styled( searchable = TRUE, height = 400, columns = list( libelle = colDef(name = "ZE", minWidth = 150), idxlogtens_ind_22 = colDef(name = "Idx tension", format = colFormat(digits = 0)), logd_px2_global_24 = col_num("Prix m²", unit = "EUR"), logv_vac2ans_pct_24 = col_pct("Vac 2+ans", unit = "%") ) ) ``` ::: ## column-margin — note latérale ::: {.column-margin} **Note technique** : Les indices composites sont calculés par z-score (cap ±3σ, rescale 0-100, centre 50). Voir `rpt-11-idx-sensibilite.qmd` pour l'analyse de sensibilité. ::: Le texte principal coule normalement. La note en marge s'affiche à droite sur écrans larges (> 1200px), en dessous sur mobile. --- ::: {.callout-tip appearance="minimal"} **Récapitulatif — Helpers JCN vs Plugins** | Fonction | Source | Type | Usage | |----------|--------|------|-------| | `theme_urbn()` | jcn-graph.R | Auto | Thème ggplot2 global | | `plot_bar_ranked()` | jcn-graph.R | {jrr} | Ranking top/bottom N | | `scale_fill_urbn_div()` | jcn-graph.R | {jrr} | Palette divergente ± | | `fmt_fr()`, `fmt_pct()` | jcn-graph.R | {jrr} | Formatage français | | `plot_distribution()` | _setup-common.R | {jrr} | Histogramme + rug | | `scatter_quadrants()` | _setup-common.R | {jrr} | Scatter 2D quadrants | | `rt_styled()` | jcn-table.R | {jrr} | Table reactable stylée | | `col_pop()`, `col_variation()` | jcn-table.R | {jrr} | Colonnes typées | | `bar_variation()`, `bar_pct()` | jcn-table.R | {jrr} | Barres inline ±/% | | `style_divergent()` | jcn-table.R | {jrr} | Coloration vert/rouge | | `make_carte_france()` | jcn-map.R | {jrr} | Leaflet interactif | | `make_pal()` | jcn-map.R | {jrr} | Rampe couleur N | | `plotly_layout()` | jcn-graph.py | {jpy} | Layout Plotly Urban | | `correlation_matrix()` | jcn-graph.py | {jpy} | Heatmap corrélation | | `init_style()` | jcn-graph.py | {jpy} | Init template global | | Plugin externe | Type | Usage max | |----------------|------|-----------| | ggridges | {jrr} | Ridgeline gradient + quantiles + rain cloud | | seaborn | {jpy} | Violin + strip + médiane annotée | | plotly.express | {jpy} | Scatter 5D, parallel coords, treemap | | plotly.go | {jpy} | Sankey flux colorés + hover enrichi | | sf + ggplot2 | {jrr} | Choroplèthe + labels centroids | | tmap | {jrr} | Grille, bivariée 3×3, facettes typo | | mapview | {jrr} | Interactive hover, EDA rapide | :::