Q1: How did the rate of femicide change across the five continents (regions) from 2014 to 2023?
Q2-1: What are the rates of femicide in various countries in 2023?
Q2-2: Which 8 countries had the highest femicide rates in 2023?
Q3: What was the distribution of perpetrator-victim relationships in femicide cases across five continents (regions) in 2023?
Q4: What is the share of male and female homicide victims killed by an intimate partner or family member across five continents (regions)?
Load Packages
library(tidyverse)
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library(dplyr)library(ggplot2)library(scales)
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library(plotly)
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library(ggpubr)
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Load Dataset
load("data/femicide.RData")
Analysis
Q1: How did the rate of femicide change across the five continents (regions) from 2014 to 2023?
p1 <- df1 |>group_by(region, year) |>filter(year >=2014& year <=2023, sex =="Female", age =="Total", indicator =="Victims of intentional homicide", measurement =="Rate per 100,000 population", dimension =="Total") |>summarise(avg_region_rate =mean(value)) |>ggplot(aes(x = year, y = avg_region_rate, color = region)) +geom_line() +geom_point() +scale_color_brewer(palette ="Set1") +labs(title ="Femicide Rate across the Five Continents from 2014 to 2023",x ="Year",y ="Average Femicide Rate\n(per 100,000 Populaiton)",color ="Region") +theme_bw() +theme(plot.title =element_text(size =14, face ="bold"))
`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.
p1
Key Findings: The Americas persistently shows the highest femicide rate, followed by Africa. Americas and Oceania have seen a recent rise in their rates, while Asia and Europe maintain relatively lower and stable rates.
Q2-1: What are the rates of femicide in various countries in 2023?
world_map <-ne_countries(scale ="medium", returnclass ="sf") |>filter(name !="Antarctica") |>select(iso_a3, name, geometry)femicide_data <- df1 |>group_by(iso3_code) |>filter(year ==2023, sex =="Female", age =="Total", indicator =="Victims of intentional homicide", dimension =="Total", measurement =="Rate per 100,000 population") |>summarise(rate_all = value)map_data <- world_map |>left_join(femicide_data, by =c("iso_a3"="iso3_code"))p2 <-ggplot(map_data) +geom_sf(aes(fill = rate_all), color ="grey", size =0.1) +scale_fill_gradient(low ="white", high ="#3f007d",na.value ="grey90",name ="Femicide Rate\n(per 100,000)") +labs(title ="Global Femicide Rate in 2023",subtitle ="Rate per 100,000 Population",caption ="Source: UNODC\nGrey: No data available") +theme_void() +theme(legend.position ="bottom",legend.title =element_text(size =10, hjust =0.5),legend.text =element_text(size =9),plot.title =element_text(hjust =0.5, size =14, face ="bold"),plot.subtitle =element_text(hjust =0.5, size =11, color ="gray40"),legend.key.width =unit(1.5, "cm"),legend.key.height =unit(0.4, "cm"))p2
Key Findings: Severe data gaps in Asia and Africa in 2023. High femicide rates persist in Latin America, Southern Africa, and Eastern Europe, contrasting with lower rates in Western Europe and Oceania.
Q2-2: Which 8 countries had the highest femicide rates in 2023?
# Q2-2: Which 8 countries/territories had the highest femicide rates in 2023?top8_data <- df1 |>group_by(country) |>filter(year ==2023, sex =="Female", age =="Total", indicator =="Victims of intentional homicide", dimension =="Total", measurement =="Rate per 100,000 population") |>summarise(country_rate = value) |>arrange(-country_rate) |>slice_head(n =8) max_rate <-max(top8_data$country_rate)p3 <- top8_data |>ggplot(aes(x = country_rate, y =reorder(country, country_rate),fill = country_rate)) +geom_col(width =0.7) +geom_text(aes(label =round(country_rate, 2)), hjust =-0.2, size =3.5, color ="black") +scale_fill_gradientn(colors =c("#efedf5", "#dadaeb", "#bcbddc", "#9e9ac8","#807dba", "#6a51a3", "#54278f", "#3f007d")) +labs(title ="Top 8 Countries with Highest Femicide Rates (2023)",subtitle ="Rate per 100,000 population",x ="Femicide Rate (per 100,000 Population)",y ="Top 8 Countries",caption ="Source: UNODC") +theme_minimal() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14),plot.subtitle =element_text(color ="gray40", size =11),axis.text.y =element_text(size =9)) +expand_limits(x = max_rate *1.15)p3
Key Findings: All eight highest-rate countries located in the Caribbean and Central America.
Q3: What was the distribution of perpetrator-victim relationships in femicide cases across five continents (regions) in 2023?
df_relation <- df1 |>group_by(region, category) |>filter(year ==2023, sex =="Female", age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>summarise(relation_country =sum(value)) |>filter(!str_detect(category, ":")) |>group_by(region) |>mutate(region_total =sum(relation_country),percentage = (relation_country / region_total) *100)
`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.
df_relation
# A tibble: 18 × 5
# Groups: region [5]
region category relation_country region_total percentage
<chr> <chr> <dbl> <dbl> <dbl>
1 Africa Intimate partner or family… 87 197 44.2
2 Africa Other Perpetrator known to… 52 197 26.4
3 Africa Perpetrator to victim rela… 51 197 25.9
4 Africa Perpetrator unknown to the… 7 197 3.55
5 Americas Intimate partner or family… 3674 13455 27.3
6 Americas Other Perpetrator known to… 1318 13455 9.80
7 Americas Perpetrator to victim rela… 7654 13455 56.9
8 Americas Perpetrator unknown to the… 809 13455 6.01
9 Asia Intimate partner or family… 187 247 75.7
10 Asia Other Perpetrator known to… 50 247 20.2
11 Asia Perpetrator to victim rela… 9 247 3.64
12 Asia Perpetrator unknown to the… 1 247 0.405
13 Europe Intimate partner or family… 820 1270 64.6
14 Europe Other Perpetrator known to… 128 1270 10.1
15 Europe Perpetrator to victim rela… 212 1270 16.7
16 Europe Perpetrator unknown to the… 110 1270 8.66
17 Oceania Intimate partner or family… 60 69 87.0
18 Oceania Perpetrator to victim rela… 9 69 13.0
p4 <-ggplot(df_relation, aes(x = region, y = percentage, fill = category)) +geom_col(position ="fill", width =0.7) +scale_fill_brewer(palette ="Set3",name ="Perpetrator-Victim Relationship",guide =guide_legend(nrow =2)) +labs(title ="Distribution of Femicide by Perpetrator-Victim Relationship by Region",x ="Region",y ="Percentage",caption ="Note: 'Intimate/Family' includes both intimate partners and other family members") +theme_minimal() +theme(legend.position ="bottom",legend.title =element_text(face ="bold"),legend.text =element_text(size =10),axis.text.x =element_text(face ="bold", size =11),plot.title =element_text(face ="bold", size =14, hjust =0),plot.background =element_rect(fill ="white"))p4
p4_interactive <-ggplotly(p4)
Key Findings: Killed by family members and intimate partners is the most common manifestation of femicide.
Q4: What is the share of male and female homicide victims killed by an intimate partner or family member across five continents (regions)?
# America maleamericas_male_two_categories <- df1 |>filter(region =="Americas", sex =="Male", year ==2023, age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>mutate(category_simple =ifelse(str_detect(category, "Intimate partner or family member") &!str_detect(category, ":"),"Intimate/Family", "Other")) |>group_by(category_simple) |>summarise(case_count =sum(value)) |>mutate(percentage = case_count /sum(case_count) *100)p_america_male <-ggplot(americas_male_two_categories,aes(x ="", y = percentage, fill = category_simple)) +geom_col(width =1, color ="white", linewidth =3) +coord_polar("y", start =0) +# Central text annotationannotate("text", x =0, y =0, label =paste0(round(americas_male_two_categories$percentage[1], 1), "%"),size =8, fontface ="bold", color ="#3182bd") +scale_fill_manual(values =c("Intimate/Family"="#3182bd", "Other"="#D5DBDB")) +theme_void() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14, hjust =0.5))p_america_male
# America Femaleamericas_female_two_categories <- df1 |>filter(region =="Americas", sex =="Female", year ==2023, age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>mutate(category_simple =ifelse(str_detect(category, "Intimate partner or family member") &!str_detect(category, ":"),"Intimate/Family", "Other")) |>group_by(category_simple) |>summarise(case_count =sum(value)) |>mutate(percentage = case_count /sum(case_count) *100)p_america_female <-ggplot(americas_female_two_categories,aes(x ="", y = percentage, fill = category_simple)) +geom_col(width =1, color ="white", linewidth =3) +coord_polar("y", start =0) +# Central text annotationannotate("text", x =0, y =0, label =paste0(round(americas_female_two_categories$percentage[1], 1), "%"),size =8, fontface ="bold", color ="#cb181d") +scale_fill_manual(values =c("Intimate/Family"="#cb181d", "Other"="#D5DBDB")) +theme_void() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14, hjust =0.5))p_america_female
arranged_plot_america <-ggarrange(p_america_male, p_america_female, ncol =2, nrow =1)final_plot_america <-annotate_figure( arranged_plot_america,top =text_grob("Share of Male and Female Killed by Intimate Partners or Family Members in the Americas (2023)", face ="bold", size =11),bottom =text_grob("Data source: UNODC", size =8, color ="gray40"))final_plot_america
# Africa maleafrica_male_two_categories <- df1 |>filter(region =="Africa", sex =="Male", year ==2023, age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>mutate(category_simple =ifelse(str_detect(category, "Intimate partner or family member") &!str_detect(category, ":"),"Intimate/Family", "Other")) |>group_by(category_simple) |>summarise(case_count =sum(value)) |>mutate(percentage = case_count /sum(case_count) *100)p_africa_male <-ggplot(africa_male_two_categories,aes(x ="", y = percentage, fill = category_simple)) +geom_col(width =1, color ="white", linewidth =3) +coord_polar("y", start =0) +# Central text annotationannotate("text", x =0, y =0, label =paste0(round(africa_male_two_categories$percentage[1], 1), "%"),size =8, fontface ="bold", color ="#3182bd") +scale_fill_manual(values =c("Intimate/Family"="#3182bd", "Other"="#D5DBDB")) +theme_void() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14, hjust =0.5))p_africa_male
# Africa Femaleafrica_female_two_categories <- df1 |>filter(region =="Africa", sex =="Female", year ==2023, age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>mutate(category_simple =ifelse(str_detect(category, "Intimate partner or family member") &!str_detect(category, ":"),"Intimate/Family", "Other")) |>group_by(category_simple) |>summarise(case_count =sum(value)) |>mutate(percentage = case_count /sum(case_count) *100)p_africa_female <-ggplot(africa_female_two_categories,aes(x ="", y = percentage, fill = category_simple)) +geom_col(width =1, color ="white", linewidth =3) +coord_polar("y", start =0) +# Central text annotationannotate("text", x =0, y =0, label =paste0(round(africa_female_two_categories$percentage[1], 1), "%"),size =8, fontface ="bold", color ="#cb181d") +scale_fill_manual(values =c("Intimate/Family"="#cb181d", "Other"="#D5DBDB")) +theme_void() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14, hjust =0.5))p_africa_female
arranged_plot_africa <-ggarrange(p_africa_male, p_africa_female, ncol =2, nrow =1)final_plot_africa <-annotate_figure( arranged_plot_africa,top =text_grob("Share of Male and Female Killed by Intimate Partners or Family Members in Africa (2023)", face ="bold", size =11),bottom =text_grob("Data source: UNODC", size =8, color ="gray40"))final_plot_africa
# Asia maleasia_male_two_categories <- df1 |>filter(region =="Asia", sex =="Male", year ==2023, age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>mutate(category_simple =ifelse(str_detect(category, "Intimate partner or family member") &!str_detect(category, ":"),"Intimate/Family", "Other")) |>group_by(category_simple) |>summarise(case_count =sum(value)) |>mutate(percentage = case_count /sum(case_count) *100)p_asia_male <-ggplot(asia_male_two_categories,aes(x ="", y = percentage, fill = category_simple)) +geom_col(width =1, color ="white", linewidth =3) +coord_polar("y", start =0) +# Central text annotationannotate("text", x =0, y =0, label =paste0(round(asia_male_two_categories$percentage[1], 1), "%"),size =8, fontface ="bold", color ="#3182bd") +scale_fill_manual(values =c("Intimate/Family"="#3182bd", "Other"="#D5DBDB")) +theme_void() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14, hjust =0.5))p_asia_male
# Asia femaleasia_female_two_categories <- df1 |>filter(region =="Asia", sex =="Female", year ==2023, age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>mutate(category_simple =ifelse(str_detect(category, "Intimate partner or family member") &!str_detect(category, ":"),"Intimate/Family", "Other")) |>group_by(category_simple) |>summarise(case_count =sum(value)) |>mutate(percentage = case_count /sum(case_count) *100)p_asia_female <-ggplot(asia_female_two_categories,aes(x ="", y = percentage, fill = category_simple)) +geom_col(width =1, color ="white", linewidth =3) +coord_polar("y", start =0) +# Central text annotationannotate("text", x =0, y =0, label =paste0(round(asia_female_two_categories$percentage[1], 1), "%"),size =8, fontface ="bold", color ="#cb181d") +scale_fill_manual(values =c("Intimate/Family"="#cb181d", "Other"="#D5DBDB")) +theme_void() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14, hjust =0.5))p_asia_female
arranged_plot_asia <-ggarrange(p_asia_male, p_asia_female, ncol =2, nrow =1)final_plot_asia <-annotate_figure( arranged_plot_asia,top =text_grob("Share of Male and Female Killed by Intimate Partners or Family Members in Asia (2023)", face ="bold", size =11),bottom =text_grob("Data source: UNODC", size =8, color ="gray40"))final_plot_asia
# Europe maleeurope_male_two_categories <- df1 |>filter(region =="Europe", sex =="Male", year ==2023, age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>mutate(category_simple =ifelse(str_detect(category, "Intimate partner or family member") &!str_detect(category, ":"),"Intimate/Family", "Other")) |>group_by(category_simple) |>summarise(case_count =sum(value)) |>mutate(percentage = case_count /sum(case_count) *100)p_europe_male <-ggplot(europe_male_two_categories,aes(x ="", y = percentage, fill = category_simple)) +geom_col(width =1, color ="white", linewidth =3) +coord_polar("y", start =0) +# Central text annotationannotate("text", x =0, y =0, label =paste0(round(europe_male_two_categories$percentage[1], 1), "%"),size =8, fontface ="bold", color ="#3182bd") +scale_fill_manual(values =c("Intimate/Family"="#3182bd", "Other"="#D5DBDB")) +theme_void() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14, hjust =0.5))p_europe_male
# Europe femaleeurope_female_two_categories <- df1 |>filter(region =="Europe", sex =="Female", year ==2023, age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>mutate(category_simple =ifelse(str_detect(category, "Intimate partner or family member") &!str_detect(category, ":"),"Intimate/Family", "Other")) |>group_by(category_simple) |>summarise(case_count =sum(value)) |>mutate(percentage = case_count /sum(case_count) *100)p_europe_female <-ggplot(europe_female_two_categories,aes(x ="", y = percentage, fill = category_simple)) +geom_col(width =1, color ="white", linewidth =3) +coord_polar("y", start =0) +# Central text annotationannotate("text", x =0, y =0, label =paste0(round(europe_female_two_categories$percentage[1], 1), "%"),size =8, fontface ="bold", color ="#cb181d") +scale_fill_manual(values =c("Intimate/Family"="#cb181d", "Other"="#D5DBDB")) +theme_void() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14, hjust =0.5))p_europe_female
arranged_plot_europe <-ggarrange(p_europe_male, p_europe_female, ncol =2, nrow =1)final_plot_europe <-annotate_figure( arranged_plot_europe,top =text_grob("Share of Male and Female Killed by Intimate Partners or Family Members in Europe (2023)", face ="bold", size =11),bottom =text_grob("Data source: UNODC", size =8, color ="gray40"))final_plot_europe
# Oceania maleoceania_male_two_categories <- df1 |>filter(region =="Oceania", sex =="Male", year ==2023, age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>mutate(category_simple =ifelse(str_detect(category, "Intimate partner or family member") &!str_detect(category, ":"),"Intimate/Family", "Other")) |>group_by(category_simple) |>summarise(case_count =sum(value)) |>mutate(percentage = case_count /sum(case_count) *100)p_oceania_male <-ggplot(oceania_male_two_categories,aes(x ="", y = percentage, fill = category_simple)) +geom_col(width =1, color ="white", linewidth =3) +coord_polar("y", start =0) +# Central text annotationannotate("text", x =0, y =0, label =paste0(round(oceania_male_two_categories$percentage[1], 1), "%"),size =8, fontface ="bold", color ="#3182bd") +scale_fill_manual(values =c("Intimate/Family"="#3182bd", "Other"="#D5DBDB")) +theme_void() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14, hjust =0.5))p_oceania_male
# Oceania femaleoceania_female_two_categories <- df1 |>filter(region =="Oceania", sex =="Female", year ==2023, age =="Total", indicator =="Victims of intentional homicide", dimension =="by relationship to perpetrator", measurement =="Counts") |>mutate(category_simple =ifelse(str_detect(category, "Intimate partner or family member") &!str_detect(category, ":"),"Intimate/Family", "Other")) |>group_by(category_simple) |>summarise(case_count =sum(value), .groups ='drop') |>mutate(percentage = case_count /sum(case_count) *100)p_oceania_female <-ggplot(oceania_female_two_categories,aes(x ="", y = percentage, fill = category_simple)) +geom_col(width =1, color ="white", linewidth =3) +coord_polar("y", start =0) +# Central text annotationannotate("text", x =0, y =0, label =paste0(round(oceania_female_two_categories$percentage[1], 1), "%"),size =8, fontface ="bold", color ="#cb181d") +scale_fill_manual(values =c("Intimate/Family"="#cb181d", "Other"="#D5DBDB")) +theme_void() +theme(legend.position ="none",plot.title =element_text(face ="bold", size =14, hjust =0.5))p_oceania_female
arranged_plot_oceania <-ggarrange(p_oceania_male, p_oceania_female, ncol =2, nrow =1)final_plot_oceania <-annotate_figure( arranged_plot_oceania,top =text_grob("Share of Male and Female Killed by Intimate Partners or Family Members in Oceania (2023)", face ="bold", size =11),bottom =text_grob("Data source: UNODC", size =8, color ="gray40"))final_plot_oceania
Key Findings: Homicide within the family takes a much higher toll on women than men.
