---
title: "The Family Violence Statistics in Victoria, Australia"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(png)
library(htmltools)
library(ggplot2)
library(plotly)
library(gridExtra)
library(ggpie)
```

```{r}
# Load your first data from a local CSV file
d1 <- read.csv("C:/Users/janel/Desktop/tech/RMIT/courses/Data Visualisation and Communication/Ax3/AmbVicGender.csv")
d2 <- read.csv("C:/Users/janel/Desktop/tech/RMIT/courses/Data Visualisation and Communication/Ax3/AmbVIcLGA.csv")
d3 <- read.csv("C:/Users/janel/Desktop/tech/RMIT/courses/Data Visualisation and Communication/Ax3/AmbVicPerp.csv")
d4 <- read.csv("C:/Users/janel/Desktop/tech/RMIT/courses/Data Visualisation and Communication/Ax3/AmbVicAbuse.csv")
```

### Introduction: What are the numbers regarding Family Violence in Victoria, Australia?

```{=html}
<div style="background-color: black; padding: 20px;">
    <center>
        <img src="C:/Users/janel/Desktop/tech/RMIT/courses/Data Visualisation and Communication/Ax3/Family Violence Intro.png" width="90%">
    </center>
</div>

```

***

The latest report on family violence from the Victorian State Government's Crime Statistics website, which has gathered data from the year 2018 to 2023, highlights various aspects. Collected from ambulance dispatch and paramedic interventions across Victoria for all family violence-related cases, this data serves as a resource for policymakers, community leaders, healthcare professionals, social workers, advocacy groups, and the general public. Our aim is to provide insights and answers to pertinent questions surrounding family violence. We hope to equip our audience with the knowledge concerning family violence, fostering collaborative efforts toward addressing this pressing societal concern. We hope to answer these questions:

- Which gender is more affected by family violence?
- What local government area (LGA) has the highest and lowest incidence of family violence?
- What is the usual perpetrator relationship regarding these incidents?
- What kinds of abuse types were inflicted on the victims?

Data Source: Victoria State Government, 'Family Violence Data Tables-Ambulance Victoria Data Tables(2023)'. Accessed [13 April 2024],Victoria State Government website:
https://www.crimestatistics.vic.gov.au/family-violence-data/family-violence-data-tables/


### What is the potential gender bias regarding family violence?

```{r}

p1 <- ggplot(d1, aes(x = Age.Group, y= Incidents, fill = Gender)) + 
  geom_bar(position = "dodge", stat="identity")+
  labs(x = "Age Group", y = "Number of Incidents", title= "Family Violence by Gender") +
  theme(panel.grid.major = element_blank(),  # Remove major grid lines
        panel.grid.minor = element_blank())  # Remove minor grid lines 

ggplotly(p1)
```


***

From the years 2018 to 2023, the grouped bar chart illustrates the incidence of family violence between males and females across different age groups, the following observations have been made:

- Females are significantly more vulnerable to family violence compared to males, with a notable disparity in incidence rates.
- Particularly, females aged 25-34 exhibit the highest risk of experiencing family violence, while those aged 65 and over are comparatively less affected.
- A trend emerges suggesting that as individuals age, the incidence of family violence decreases for both genders.

In conclusion, the analysis underscores the alarming prevalence of family violence among females, especially within certain age brackets.

Data Source: Victoria State Government, 'Family Violence Data Tables-Ambulance Victoria Data Tables-Table 1(2023)'. Accessed [13 April 2024],Victoria State Government website:
https://www.crimestatistics.vic.gov.au/family-violence-data/family-violence-data-tables/


### What are the local government areas with the highest and lowest family violence incidence?

```{r}
# Identify top 10 and bottom 10 LGAs based on incidence
top10 <- head(d2[order(d2$Incidents, decreasing = TRUE), ], 10)
bottom10 <- head(d2[order(d2$Incidents, decreasing = FALSE), ], 10)

max_incidents <- max(max(top10$Incidents), max(bottom10$Incidents))

# Create separate bar plots for top 10 and bottom 10 LGAs both in descending order
p_top10 <- ggplot(top10, aes(x = reorder(Local.Government.Area, -Incidents) , y = Incidents)) +
  geom_bar(stat = "identity", fill = "red") +
  labs(title = "Top 10 LGAs by Family Violence Incidence") +
  ylim(0, max_incidents) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
   theme(panel.grid.major = element_blank(),  # Remove major grid lines
        panel.grid.minor = element_blank())  # Remove minor grid lines

p_bottom10 <- ggplot(bottom10, aes(x = reorder(Local.Government.Area, -Incidents), y = Incidents)) +
  geom_bar(stat = "identity", fill = "blue") +
  labs(title = "The Top 10 and Bottom 10 LGAs by Family Violence Incidence") +
  ylim(0, max_incidents) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
   theme(panel.grid.major = element_blank(),  # Remove major grid lines
        panel.grid.minor = element_blank())  # Remove minor grid lines

# Display side-by-side bar graphs
p_top10_plotly <- ggplotly(p_top10, tooltip = "y")  # Show y value in tooltip
p_bottom10_plotly <- ggplotly(p_bottom10, tooltip = "y")  # Show y value in tooltip

# Display side-by-side bar graphs
subplot(p_top10_plotly %>% layout(title = "Top 10 LGAs by Family Violence Incidence"),
        p_bottom10_plotly %>% layout(title = "The Top 10 and Bottom 10 LGAs by Family Violence Incidence rate"),
        nrows = 1)
```


***

The side-by-side bar chart illustrates the incidence rates of family violence per 100,000 individuals across different local government areas (LGAs) in Victoria, Australia. The following insights that were found were;

- Hindmarsh exhibits the highest incidence rate, recording 123.7 incidents per 100,000 individuals, while Bayside reports the lowest rate at 3.9.

- The data shows that there is a high incidence of family violence in regional areas. 

The analysis emphasizes the importance of tailored strategies and resources to combat family violence, particularly in regional areas where incidence rates are notably higher.

Data Source: Victoria State Government, 'Family Violence Data Tables-Ambulance Victoria Data Tables-Table 3(2023)'. Accessed [13 April 2024],Victoria State Government website:
https://www.crimestatistics.vic.gov.au/family-violence-data/family-violence-data-tables/


### What is the usual perpetrator relationship regarding family violence?

```{r}
p3 <- ggplot(d3, aes(x = "", y = Incidents, fill = Perpetrator)) +
  geom_bar(width = 1, stat = "identity") + 
  coord_polar(theta = "y") + 
  labs(fill = "Relationship of Perpetrator") + 
  ggtitle("Pie Chart of Incidents by Perpetrator") + 
  theme_void()

p3_plotly <- plot_ly(d3, labels = ~Perpetrator, values = ~Incidents, type = "pie") %>%
  layout(title = "Perpetrator Relationship by Incident")

# Display the interactive plot
p3_plotly
```


***

The pie chart illustrates the perpetrator relationships of incidents from the year 2018 to 2023. These are the findings;

- The perpetrator of relationships is 56.3% likely for it to be a Partner/Spouse.

- Perpetrators are least likely to be of familial relation to the victim.

The analysis underscores the pronounced prevalence of Partner/Spouse involvement in incidents over the studied period. While familial relations appear less common in perpetrating such incidents, further exploration of the dynamics behind these relationships could be beneficial for preventive measures and support mechanisms.

Data Source: Victoria State Government, 'Family Violence Data Tables-Ambulance Victoria Data Tables-Table 6(2023)'. Accessed [13 April 2024],Victoria State Government website:
https://www.crimestatistics.vic.gov.au/family-violence-data/family-violence-data-tables/


### What are the types of abuse that were inflicted onto the victims?

```{r}
p4 <- ggplot(d4, aes(x = "", y = Incidents, fill = Abuse.Type)) +
  geom_bar(width = 1, stat = "identity") + 
  coord_polar(theta = "y") + 
  labs(fill = "Abuse Type") + 
  ggtitle("Abuse Type by Incident") + 
  theme_void()

p4_plotly <- plot_ly(d4, labels = ~Abuse.Type, values = ~Incidents, type = "pie") %>%
  layout(title = "Abuse Type by Incident")

# Display the interactive plot
p4_plotly
```


***

The pie chart illustrates the Abuse types by incidents from the year 2018 to 2023. These are the findings;

- There is a high dominance of over 72.6% being physical types of abuse.

- For the rest of the abuse types, they only amount to around <10% each.

The findings encourage the urgent need for targeted interventions addressing the prevalence of physical abuse, while also emphasizing the importance of strategies for the lesser reported abuse types, ensuring comprehensive support and prevention measures across all forms of abuse.

Data Source: Victoria State Government, 'Family Violence Data Tables-Ambulance Victoria Data Tables-Table 7(2023)'. Accessed [13 April 2024],Victoria State Government website:
https://www.crimestatistics.vic.gov.au/family-violence-data/family-violence-data-tables/


### Conclusion: How do we overcome Family violence?

```{=html}
<div style="background-color: black; padding: 20px;">
    <center>
        <img src="C:/Users/janel/Desktop/tech/RMIT/courses/Data Visualisation and Communication/Ax3/Support Conclusion.jpg" width="90%" height="90%">
    </center>
</div>

```

***

The analysis of family violence data from 2018 to 2023 in Victoria, Australia, reveals significant gender disparities, with females, particularly those aged 25-34, experiencing disproportionately high rates of violence. The prevalence of family violence, especially in regional areas, show the need for targeted interventions tailored to specific local government areas. The prevalence of partner/spouse perpetrators emphasizes the urgency of addressing intimate partner violence through comprehensive prevention and support initiatives. While physical abuse is predominant, efforts must extend to addressing all forms of abuse to ensure inclusive support for victims. 

In conclusion, collaborative action involving government agencies, social organizations, and everyone in the community is crucial to effectively combatting family violence and creating safer environments for all individuals affected.

Data Source: Victoria State Government, 'Family Violence Data Tables-Ambulance Victoria Data Tables(2023)'. Accessed [13 April 2024],Victoria State Government website:
https://www.crimestatistics.vic.gov.au/family-violence-data/family-violence-data-tables/