+
+
+GRAPH REPLICATION
+This is the initial version of the graph replication about motive protest distribution across spanish provinces in 2022.
+Data Preparing
+
+
+
+
+library(readxl)
+library(dplyr)
+library(tidyverse)
+library(sf)
+library(mapSpain)
+library(rnaturalearth)
+library(rnaturalearthdata)
+library(ggplot2)
+library(grid)
+#library(extrafont)
+library(showtext)
+
+# Font adding
+font_add("Lexend", regular = "/Users/cguirao/Library/Fonts/LexendDeca-VariableFont_wght.ttf") # needed font for the graph
+showtext_auto()
+
+# Data import
+protests <- read_excel("~/Desktop/datavizu/_projects/2025/100453622/ReplicationDataBase.xlsx",
+ sheet = "TABLA 1-3-7",
+ skip = 1) # skipping the title
+
+prov <- esp_get_prov()
+
+library(giscoR)
+neighbors <- gisco_get_countries(
+ country = c("Portugal", "France", "Morocco", "Algeria", "Andorra"))
+
+# Adding those that are left bc they are named differently in INE
+protests <- protests |>
+ mutate(
+ `Comunidad autónoma y provincia` = recode(
+ `Comunidad autónoma y provincia`,
+ "Murcia, Región de" = "Murcia",
+ "Navarra, Comunidad Foral de" = "Navarra",
+ "Madrid, Comunidad de" = "Madrid",
+ "Asturias, Principado de" = "Asturias"
+ )
+ )
+
+# Filter only provinces
+data_prov <- protests %>%
+ filter(`Comunidad autónoma y provincia` %in% prov$ine.prov.name)
+
+# Jonning shapefile with filtered dataframe
+protest_prov <- prov %>%
+ left_join(data_prov,
+ by = c("ine.prov.name" = "Comunidad autónoma y provincia"))
+
+# Creating data for canary islands map
+canarias_data <- protest_prov %>%
+ filter(ine.prov.name %in% c("Palmas, Las", "Santa Cruz de Tenerife"))
+
+# Creating data for peninsula and baleares islands map
+peninsula_data <- protest_prov %>%
+ filter(!ine.prov.name %in% c("Palmas, Las", "Santa Cruz de Tenerife"))
+
+# Obtaining coordenates of each province
+peninsula_data <- peninsula_data |>
+ mutate(
+ centroide = st_centroid(geometry),
+ x = st_coordinates(centroide)[,1],
+ y = st_coordinates(centroide)[,2],
+
+ x = case_when(
+ ine.prov.name == "Segovia" ~ x + 0.2,
+ ine.prov.name == "Toledo" ~ x - 0.45,
+ ine.prov.name == "Pontevedra" ~ x - 0.2,
+ ine.prov.name == "Sevilla" ~ x - 0.05,
+ ine.prov.name == "Granada" ~ x - 0.4,
+ ine.prov.name == "Almería" ~ x + 0.1,
+ ine.prov.name == "Murcia" ~ x + 0.1,
+ ine.prov.name == "Alicante/Alacant" ~ x + 0.25,
+ ine.prov.name == "Castellón/Castelló" ~ x + 0.25,
+ ine.prov.name == "Valencia/València" ~ x + 0.4,
+ ine.prov.name == "Balears, Illes" ~ x - 0.3,
+ ine.prov.name == "Guadalajara" ~ x + 0.2,
+ ine.prov.name == "Ceuta" ~ x + 0.35,
+ ine.prov.name == "Melilla" ~ x + 0.25,
+ TRUE ~ x
+ ),
+ y = case_when(
+ ine.prov.name == "Segovia" ~ y + 0.35,
+ ine.prov.name == "Coruña, A" ~ y + 0.1,
+ ine.prov.name == "Pontevedra" ~ y - 0.3,
+ ine.prov.name == "Navarra" ~ y + 0.25,
+ ine.prov.name == "Sevilla" ~ y + 0.25,
+ ine.prov.name == "Granada" ~ y - 0.15,
+ ine.prov.name == "Almería" ~ y - 0.1,
+ ine.prov.name == "Murcia" ~ y - 0.25,
+ ine.prov.name == "Asturias" ~ y + 0.2,
+ ine.prov.name == "Cantabria" ~ y + 0.2,
+ ine.prov.name == "Alicante/Alacant" ~ y - 0.2,
+ ine.prov.name == "Valencia/València" ~ y + 0.1,
+ ine.prov.name == "Ceuta" ~ y - 0.1,
+ ine.prov.name == "Melilla" ~ y + 0.1,
+ TRUE ~ y ))
+
+canarias_data <- canarias_data |>
+ mutate(
+ centroide = st_centroid(geometry),
+ x = st_coordinates(centroide)[,1],
+ y = st_coordinates(centroide)[,2],
+
+ x = case_when(ine.prov.name == "Palmas, Las" ~ x - 0.2,
+ ine.prov.name == "Santa Cruz de Tenerife" ~ x - 0.3,
+ TRUE ~ x ),
+ y = case_when(
+ TRUE ~ y ))
+
+# Renaming names legend from the dataset as in the original graph
+names(peninsula_data)[39] <- "Otras motivaciones*"
+names(canarias_data)[39] <- "Otras motivaciones*"
+
+# Creation of a vector of protest motives
+motivo_cols <- c(
+ "Temas laborales",
+ "Temas de inmigración",
+ "Asuntos vecinales",
+ "Contra la droga y la delincuencia",
+ "Apoyo a grupos terroristas",
+ "Libertad de presos de grupos terroristas",
+ "Contra el terrorismo",
+ "Enseñanza",
+ "Temas nacionalistas",
+ "Contra medidas políticas y legislativas",
+ "Sanidad",
+ "Agrarias",
+ "Ecologistas",
+ "Contra la violencia de género",
+ "1º de Mayo",
+ "Otras motivaciones*"
+)
+
+# Regrouping motives as in the graphic
+motivo_cols <- case_when(
+ motivo_cols %in% c(
+ "Temas de inmigración",
+ "Contra la droga y la delincuencia",
+ "Contra el terrorismo",
+ "Libertad de presos de grupos terroristas",
+ "Apoyo a grupos terroristas",
+ "Temas nacionalistas",
+ "1º de Mayo",
+ "Otras motivaciones*"
+ ) ~ "Otras motivaciones*",
+ TRUE ~ motivo_cols
+)
+
+# Turning characteres into numeric in order to calculate proportions
+peninsula_data <- peninsula_data %>%
+ mutate(across(
+ .cols = all_of(c(motivo_cols, "Total")),
+ ~ as.numeric(.)
+ ))
+
+canarias_data <- canarias_data %>%
+ mutate(across(
+ .cols = all_of(c(motivo_cols, "Total")),
+ ~ as.numeric(.)
+ ))
+
+# Ordering motives as in the legend from the graph
+orden_deseado <- c(
+ "Temas laborales",
+ "Asuntos vecinales",
+ "Enseñanza",
+ "Contra medidas políticas\ny legislativas",
+ "Sanidad",
+ "Agrarias",
+ "Ecologistas",
+ "Contra la violencia\nde género",
+ "Otras motivaciones*"
+)
+
+# Colors for each motive
+colores_motivos <- c(
+ "Temas laborales" = "#de5424",
+ "Asuntos vecinales" = "#e0945d",
+ "Enseñanza" = "#63a4bc",
+ "Contra medidas políticas\ny legislativas" = "#848ab5",
+ "Sanidad" = "#95bb9e",
+ "Agrarias" = "#ebd98c",
+ "Ecologistas" = "#b7c27e",
+ "Contra la violencia\nde género"= "#a47297",
+ "Otras motivaciones*" = "#8a898a"
+)
+
+# Creating data for pie charts
+pies_peninsula <- peninsula_data |>
+ select(ine.prov.name, x, y, all_of(motivo_cols), Total) |>
+ pivot_longer(
+ cols = all_of(motivo_cols),
+ names_to = "motivo",
+ values_to = "valor") |>
+ mutate(
+ # ← AQUÍ añades el recode para cambiar los nombres
+ motivo = recode(
+ motivo,
+ "Contra medidas políticas y legislativas" = "Contra medidas políticas\ny legislativas",
+ "Contra la violencia de género" = "Contra la violencia\nde género"
+ ),
+ # Y LUEGO lo conviertes a factor con los nombres ya modificados
+ motivo = factor(motivo, levels = orden_deseado)
+ ) |>
+ arrange(ine.prov.name, motivo) |>
+ group_by(ine.prov.name) |>
+ mutate(
+ prop = valor / sum(valor, na.rm = TRUE),
+ angle_end = cumsum(prop) * 2 * pi,
+ angle_start = lag(angle_end, default = 0)
+ ) |>
+ ungroup()
+
+# Pie proportion for each province
+max_total <- max(pies_peninsula$Total, na.rm = TRUE)
+factor_escala <- 0.9
+
+pies_peninsula <- pies_peninsula %>%
+ mutate(
+ radius = sqrt(Total / max_total) * factor_escala
+ )
+
+# Same process for canary island pies
+canarias_pies <- canarias_data |>
+ select(ine.prov.name, x, y, all_of(motivo_cols), Total) |>
+ pivot_longer(
+ cols = all_of(motivo_cols),
+ names_to = "motivo",
+ values_to = "valor") |>
+ mutate(
+
+ motivo = recode(
+ motivo,
+ "Contra medidas políticas y legislativas" = "Contra medidas políticas\ny legislativas",
+ "Contra la violencia de género" = "Contra la violencia\nde género"
+ ),
+
+ motivo = factor(motivo, levels = orden_deseado)
+ ) |>
+ arrange(ine.prov.name, motivo) |>
+ group_by(ine.prov.name) |>
+ mutate(
+ prop = valor / sum(valor, na.rm = TRUE),
+ angle_end = cumsum(prop) * 2 * pi,
+ angle_start = lag(angle_end, default = 0)
+ ) |>
+ ungroup()
+
+max_total <- max(canarias_pies$Total, na.rm = TRUE)
+factor_escala <- 0.4 # Ajusta esto para círculos más grandes/pequeños
+
+canarias_pies <- canarias_pies %>%
+ mutate(
+ radius = sqrt(Total / max_total) * factor_escala
+ )Pie-charts size legend
+
+
+
+
+# STILL STRUGGLING WITH THIS
+
+valores_originales <- c(4251, 3498, 2464, 1579, 949, 252, 58)
+etiquetas_originales <- c("4.251", "3.500", "2.500", "1.500", "1.000", "250", "58")
+
+
+valores_reales <- rev(valores_originales)
+etiquetas_display <- rev(etiquetas_originales)
+
+max_total_peninsula <- 4251
+
+factor_escala_leyenda <- 0.00031 # Factor de escala para coordenadas geográficas
+
+radios <- sqrt(valores_reales / pi) * factor_escala_leyenda
+
+y_centers <- c(41.9, 42.05, 42.2, 42.35, 42.5, 42.65, 42.8) + 0.25
+
+y_tops <- y_centers + radios
+
+# Tibble with all the data
+legend_data <- tibble(
+ manifestaciones = valores_reales,
+ etiqueta = etiquetas_display,
+ radius = radios,
+ x = -11, # ← TODOS la misma X
+ y = y_centers # ← Y va subiendo
+) |>
+ mutate(
+ # Punto en el borde IZQUIERDO del semicírculo (para las líneas)
+ x_start = x - radius, # Borde izquierdo (en lugar de x + radius)
+ y_start = y
+ )Canary island Map
+
+
+
+
+mapa_canarias <- ggplot() +
+
+ geom_sf(
+ data = canarias_data,
+ fill = "#FEFAE0",
+ color = "#4D5662",
+ linewidth = 0.3
+ ) +
+
+ ggforce::geom_arc_bar(
+ data = canarias_pies,
+ aes(
+ x0 = x,
+ y0 = y,
+ r0 = 0,
+ r = radius,
+ start = angle_start,
+ end = angle_end,
+ fill = motivo
+ ),
+ color = "black",
+ linewidth = 0.2,
+ alpha = 0.8
+ ) +
+
+ scale_fill_manual(values = colores_motivos) +
+
+ coord_sf(
+ expand = FALSE,
+ clip = "off") +
+ theme_void() +
+ theme(
+ legend.position = "none",
+ plot.background = element_rect(
+ fill = "#e6ffff",
+ color = "#ffffff",
+ linewidth = 5
+ ),
+ panel.background = element_blank(),
+ plot.margin = margin(3,3,8,33, "pt")
+ )
+
+# Transforming the canary island into a graphical object (grob) to insert in the final map
+grob_canarias <- ggplotGrob(mapa_canarias)
+
+mapa_canarias
Plotting final map
+
+
+
+
+final_map <- ggplot() +
+
+ # Neighbor Countries
+ geom_sf(
+ data = neighbors,
+ fill = "#D6D6D6",
+ color = "#4D5662",
+ linewidth = 0.3
+ ) +
+
+ # Spanish provinces
+ geom_sf(
+ data = peninsula_data,
+ fill = "#FEFAE0",
+ color = "#4D5662",
+ linewidth = 0.3
+ ) +
+
+ # Pie-charts
+ ggforce::geom_arc_bar(
+ data = pies_peninsula,
+ aes(
+ x0 = x,
+ y0 = y,
+ r0 = 0,
+ r = radius,
+ start = angle_start,
+ end = angle_end,
+ fill = motivo,
+ color = "black",
+ ),
+ color = "black",
+ linewidth = 0.2,
+ alpha = 0.8
+ )
+
+# Colors for protest motives
+final_map <- final_map + scale_fill_manual(
+ values = colores_motivos)
+
+# Protest motives guides legend
+final_map <- final_map + guides(
+ fill = guide_legend(
+ override.aes = list(
+ shape = 24,
+ size = 1.3, #2
+ color = "black",
+ stroke = 0.4
+ ),
+ byrow = TRUE, # ← Organiza en una sola columna
+ spacing.y = unit(0.8, "cm")
+ ))
+
+# Coordenates
+final_map <- final_map + coord_sf(xlim = c(-11.7, 3.8), ylim = c(35.2, 44))
+
+# Pie-charts size legend
+final_map <- final_map +
+ ggforce::geom_arc_bar(
+ data = legend_data,
+ aes(
+ x0 = x,
+ y0 = y,
+ r0 = 0,
+ r = radius,
+ start = pi/2, # Empieza arriba (90°)
+ end = 3*pi/2 # Termina abajo (270°) → semicírculo IZQUIERDO
+ ),
+ color = "black",
+ fill = NA,
+ linewidth = 0.4
+ ) +
+
+ geom_segment(
+ data = legend_data,
+ aes(
+ x = x, # Empieza en el borde izquierdo del semicírculo
+ xend = x + max(radius) + 0.3, # Todas terminan a la IZQUIERDA
+ y = y_tops, # En la parte superior
+ yend = y_tops # Línea horizontal
+ ),
+ linetype = "dotted",
+ linewidth = 0.3,
+ color = "black"
+ ) +
+
+ geom_text(
+ data = legend_data,
+ aes(
+ x = x + max(radius) + 0.4,
+ y = y_tops, # Alineadas con la parte superior
+ label = etiqueta
+ ),
+ hjust = 1, # Alineado a la derecha del texto
+ size = 2.5, # 4
+ family = "Lexend"
+ ) +
+
+ annotate(
+ "text",
+ x = -11,
+ y = 43.8,
+ label = "MANIFESTACIONES\n2022",
+ size = 3.7, # 6
+ hjust = 0.5,
+ vjust = 0,
+ lineheight = 0.8,
+ fontface = "plain",
+ )
+
+# Adding texts
+final_map <- final_map + geom_text(
+ data = data.frame(
+ x = 4,
+ y = 44,
+ label = "MANIFESTACIONES SEGÚN MOTIVACIÓN"
+ ),
+ aes(x = x, y = y, label = label),
+ size = 5, # 8
+ fontface = "bold",
+ hjust = 1, # alineado a la derecha
+ vjust = 1, # alineado arriba
+ color = "black"
+ ) +
+
+ geom_text(
+ data = data.frame(
+ x = 4.15,
+ y = 35.38,
+ label = "Fuente: Anuario estadístico del Ministerio del Interior 2022"
+ ),
+ aes(x = x, y = y, label = label),
+ size = 2.5, # 4
+ family = "Lexend",
+ hjust = 1,
+ vjust = 0,
+ color = "black"
+ ) +
+
+ geom_text(
+ data = data.frame(
+ x = 4.15,
+ y = 34.99,
+ label = "Atlas Nacional de España (ANE) CC BY 4.0 ign.es\n\nParticipantes:www.ign.es/resources/ane/participantes.pdf"
+ ),
+ aes(x = x, y = y, label = label),
+ size = 2.5, # 4
+ family = "Lexend",
+ hjust = 1,
+ vjust = 0,
+ color = "black",
+ lineheight = 0.4
+ ) +
+ geom_text(
+ data = data.frame(
+ x = -12.2,
+ y = 37,
+ label = "*Incluye inmigración, droga y\n\ndelincuencia, apoyo a grupos\n\nterroristas, contra el terrorismo,\n\ntemas nacionalistas o 1ª de mayo\n\n\nCataluña y País Vasco sin datos"
+ ),
+ aes(x = x, y = y, label = label),
+ size = 2.5, # 4
+ family = "Lexend",
+ hjust = 0,
+ vjust = 0,
+ color = "black",
+ lineheight = 0.5,
+ fontface = "bold" # ← Esto junta las dos últimas líneas
+ )
+
+# Themes
+ final_map <- final_map + theme_void() +
+ theme(
+ plot.background = element_rect(fill = "#ffffff",
+ color = "#ffffff",
+ linewidth = 5),
+ panel.background = element_rect(fill = "#e6ffff" ),
+ plot.margin = margin(5),
+
+ legend.spacing.y = unit(0.8, "cm"),
+
+ legend.position = c(0.012, 0.52),
+ legend.justification = c(0, 0.5),
+
+ legend.text = element_text(
+ size = 7.5, # 11.5 # hay que ajustar los valores a la escala de showtext()
+ family = "Lexend",
+ lineheight = 0.9
+ ),
+
+ legend.title = element_blank(),
+ legend.background = element_blank(),
+
+ legend.key = element_rect(
+ fill = "#e6ffff",
+ color = NA )
+ )
+
+# Adding Grob (Canary island map)
+final_map <- final_map +
+annotation_custom(
+ grob = grob_canarias,
+ xmin = -12.87,
+ xmax = -6.63,
+ ymin = 34.47,
+ ymax = 36.8)
+
+# ggsave("mi_grafico.png", plot = final_map, width = 10, height = 8, dpi = 150)
+# system("open mi_grafico.png")
+
+final_map
+
GRAPH IMPROVEMENT
+This is my initial improved version of the original graph about motive protest distribution in Spain (across autonomous communities in 2024)
+Data Cleaning and Variable Transformation
+
+
+
+
+library(readxl)
+library(dplyr)
+library(tidyverse)
+library(ggplot2)
+library(grid)
+library(showtext)
+library(tidytext)
+library(showtext)
+
+# FONTS IMPORT
+font_add("Gravitas", regular = "/Users/cguirao/Library/Fonts/GravitasOne-Regular.ttf")
+font_add("Sans Code", regular = " /Users/cguirao/Library/Fonts/GoogleSansCode-Regular.ttf")
+ # bold = "/Users/cguirao/Library/Fonts/GoogleSansCode-ExtraBold.ttf")
+font_add("Sans Code Extrabold", regular = "/Users/cguirao/Library/Fonts/GoogleSansCode-ExtraBold.ttf")
+font_add("Mulish", regular = "/Users/cguirao/Library/Fonts/Mulish-VariableFont_wght.ttf")
+# font_add("Mulish Semibold", regular = "/Users/cguirao/Library/Fonts/Mulish-VariableFont_wght.ttf")
+showtext_auto()
+
+#DATA IMPORT
+improvdata <- read_excel("~/Desktop/datavizu/_projects/2025/100453622/ImprovementDataBase.xlsx",
+ sheet = "TABLA 1-3-7",
+ skip = 1) # skipping the title
+
+
+improvdata <- improvdata |> rename(`Autonomous Communities` = `Comunidad autónoma y provincia`)
+
+improvdata <- improvdata |> filter(`Autonomous Communities` %in% c(
+ "Andalucía",
+ "Aragón",
+ "Asturias, Principado de",
+ "Balears, Illes",
+ "Canarias",
+ "Cantabria",
+ "Castilla y León",
+ "Castilla-La Mancha",
+ "Comunitat Valenciana",
+ "Extremadura",
+ "Galicia",
+ "Madrid, Comunidad de",
+ "Murcia, Región de",
+ "Navarra, Comunidad Foral de",
+ "Rioja, La"
+ ))
+
+
+# Creating new group categories of protest motives:
+improvdata <- improvdata |> rename(`Labour-Economic` = `Motivos laborales / económicos`,
+ `Political-Legislative` = `Contra medidas políticas / legislativas`,
+ `Healthcare` = `Motivos sanitarios`,
+ `Neighborhood Affairs` = `Movilizaciones vecinales`,
+ `Against Crime` = `Contra la droga / delincuencia`,
+ `Education` = `Movilizaciones enseñanza / educación`,
+ `Nationalism` = `Temas nacionalistas`,
+ `International Phenomena` = `Asuntos internacionales`,
+ `Commemorative Days` = `Conmemoración/ homenajes`,
+ `Religion` = `Temas religiosos`,
+ `Feminism` = `Contra violencia de género`,
+ `Other motives*` = `Otras`)
+
+improvdata <- improvdata |>
+ mutate(across(
+ -`Autonomous Communities`,
+ as.numeric
+ ))
+
+improvdata <- improvdata |>
+ mutate(`Enviromental Matters` = `Ecologismo` + `Cambio climático`,
+ `Human Rights` = `Derechos humanos` + `Contra el odio, racismo, xenofobia, etc.` + `Insumisión`,
+ `Terrorism`= Terrorismo + `Contra la radicalización violenta` ) |>
+ select(-Ecologismo, -`Cambio climático`, -`Derechos humanos`, -`Contra el odio, racismo, xenofobia, etc.`, -`Insumisión`, -Terrorismo, -`Contra la radicalización violenta`)
+
+improvdata <- improvdata |> relocate(Total, .after = everything()) |> relocate(`Other motives*`, .before = Total)Relative weights for each CCAA and Protest motive
+
+
+
+
+datos_graphs <- improvdata |>
+
+ pivot_longer(
+ cols = -c(`Autonomous Communities`, Total),
+ names_to = "Motivo",
+ values_to = "Frecuencia"
+ ) %>%
+
+ mutate(
+ Porcentaje = (Frecuencia / Total) * 100
+ ) %>%
+ select(`Autonomous Communities`, Motivo, Porcentaje)
+
+# Filtering top 10 most important motives and ordering them from the most to the least
+datos_graphs <- datos_graphs |> filter(Motivo %in% c(
+ "Labour-Economic",
+ "Other motives*",
+ "Political-Legislative",
+ "Neighborhood Affairs",
+ "International Phenomena",
+ "Healthcare",
+ "Feminism",
+ "Human Rights",
+ "Education",
+ "Enviromental Matters"
+))
+
+datos_graphs <- datos_graphs |> select(Motivo, `Autonomous Communities`, Porcentaje) |> arrange(Motivo, `Autonomous Communities`)
+
+# Calculating national average for each motive
+national_average <- improvdata |>
+
+ pivot_longer(
+ cols = -c(`Autonomous Communities`, Total),
+ names_to = "Motivo",
+ values_to = "Frecuencia"
+ ) |>
+
+ group_by(Motivo) |>
+
+ summarise(
+ Frecuencia_Total = sum(Frecuencia),
+ Total_General = sum(Total)
+ ) |>
+
+ mutate(
+ Media_Nacional_pct = (Frecuencia_Total / Total_General) * 100
+ ) |>
+
+ select(Motivo, Media_Nacional_pct)
+
+# Joinning everithing in the same dataframe
+datos_graphs <- datos_graphs |> left_join(national_average, by = "Motivo")
+
+# Creating divergence column and ordering its values
+datos_graphs <- datos_graphs |>
+ mutate(Divergencia = Porcentaje - Media_Nacional_pct,
+ CCAA_ordenada = reorder_within(`Autonomous Communities`, Divergencia, Motivo))
+ # ¿Qué hace reorder_within()?\n")
+ # 1. Toma cada CCAA (ej: 'Aragón')\n")
+ # 2. Le añade el motivo: 'Aragón___Education'\n")
+ # 3. Ordena por Divergencia DENTRO de cada motivo\n")
+ # 4. Resultado: orden correcto en cada faceta\n\n")
+
+# Changing names (ABREVIATURAS?)
+datos_graphs <- datos_graphs |>
+ mutate(
+ `Autonomous Communities` = recode(
+ `Autonomous Communities`,
+ "Murcia, Región de" = "Murcia",
+ "Navarra, Comunidad Foral de" = "Navarra",
+ "Madrid, Comunidad de" = "Madrid",
+ "Asturias, Principado de" = "Asturias",
+ "Balears, Illes" = "Baleares",
+ "Rioja, La" = "La Rioja",
+ "Comunitat Valenciana" = "Comunidad Valenciana"
+ )
+ )
+
+# Choosing colors for each CCAA
+colores_CCAA <- c(
+ "Andalucía" = "#EFCE7B",
+ "Aragón" = "#2B2B23",
+ "Asturias" = "#238BB0",
+ "Balears" = "#D8560E",
+ "Canarias" = "#B28622",
+ "Cantabria" = "#92A2A6",
+ "Castilla y León" = "#849E15",
+ "Castilla-La Mancha" = "#6D1F42",
+ "Comunidad Valenciana" = "#876929",
+ "Extremadura" = "#25533F",
+ "Galicia" = "#F4BEAE",
+ "Madrid" = "#105666",
+ "Navarra" = "#976D90",
+ "La Rioja" = "#D9CBC2",
+ "Murcia" = "#112250")
+
+# Creating text labels with national average value for each motive
+datos_graphs <- datos_graphs %>%
+ mutate(
+ Motivo_label = paste0(Motivo, "\n(avg: ",
+ round(Media_Nacional_pct, 1),
+ "%)"))Plotting the graph
+
+
+
+
+g <- ggplot(datos_graphs, aes(x = Divergencia, y = CCAA_ordenada))
+
+# Creating color-filled bars with CCAA categories
+g <- g + geom_col(aes(fill = `Autonomous Communities`), width = 0.7, alpha = 0.85)
+
+# Creating middle-line representing average values
+g <- g + geom_vline(xintercept = 0, linewidth = 0.3, color = "#6B6B6B")
+
+# Doing faceting
+g <- g + facet_wrap(~ Motivo_label, ncol = 5, scales = "free_y")
+ #labeller = labeller(group = label_wrap_gen(width = 25)))
+
+# Colors
+g <- g + scale_fill_manual(values = colores_CCAA) +
+ scale_x_continuous(labels = function(x) sprintf("%+.1f", x)) +
+ scale_y_reordered()
+
+# Labs
+g <- g + labs(title = "Divergences in Protest Activity and Motives Across Spanish Autonomous Communities, 2024", x = NULL, y = NULL)
+
+# Text
+g <- g + geom_text(
+ aes(
+ label = paste0(`Autonomous Communities`, " (", round(Porcentaje, 1), "%)"),
+ x = Divergencia, # Posición X = final de la barra
+ hjust = if_else(Divergencia >= 0, -0.05, 1.05) # Ajustar según lado
+ ),
+ size = 2.6, # 1.5 html # Tamaño nombres CCAA
+ family = "Mulish",
+ fontface = "plain"
+)
+
+# Themes
+g <- g + theme_void() +
+ theme(
+ panel.spacing = unit(0.25, "cm"),
+ plot.background = element_rect(fill = "#EFE7DA"),
+ panel.background = element_rect(fill = "#EFE7DA" ),
+ plot.margin = margin(10, 10, 30, 10),
+ aspect.ratio = 1.5,
+ plot.title = element_text(
+ size = 15, # 7.5 para html, # Tamaño título principal
+ fontface = "plain",
+ family = "Gravitas",
+ hjust = 0.5, # Centrado (0 = izq, 0.5 = centro, 1 = der)
+ margin = margin(b = 17) # Espacio abajo
+ ),
+ strip.text = element_text(
+ size = 9.3, # 5 para html --> 5.7
+ fontface = "regular",
+ family = "Sans Code Extrabold",
+ hjust = 0,
+ color = "#3D211A",
+ ),
+ legend.position = "none"
+ )
+
+g