This is an R Markdown presentation. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.

• Affected family members of violence incidents in Victoria
• Bullet 2
• Bullet 3

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

## [1] "/Users/gezhang/Desktop/Semester2/visulization/A3/Data_Tables_Family_Incidents_Visualisation_Year_Ending_June_2024 2"

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## 888

install.packages("reshape2")

## 
## The downloaded binary packages are in
##  /var/folders/9k/ggkns9sx5v7fl6bm32fwrb0r0000gn/T//RtmpVIM3hj/downloaded_packages

library(ggplot2)
library(reshape2)
# read the data
AFM_data <- read.csv("AFM-data.csv")

# ensure the correct data type
AFM_data$Rate.per.100.000.population <- as.numeric(gsub(",", "", AFM_data$Rate.per.100.000.population))
AFM_data$Year <- as.factor(AFM_data$Year)
AFM_data$AFM.Age.Group <- as.factor(AFM_data$AFM.Age.Group)

# Convert the data into a format suitable for creating a heatmap, using the mean as the aggregation function
heatmap_data <- dcast(AFM_data, AFM.Age.Group ~ Year, value.var = "Rate.per.100.000.population", fun.aggregate = mean)


# convert to long type
heatmap_long <- melt(heatmap_data, id.vars = "AFM.Age.Group", variable.name = "Year", value.name = "Rate")
print(heatmap_long)

##        AFM.Age.Group Year      Rate
## 1        00-04 years 2020  118.5333
## 2        05-09 years 2020  283.8333
## 3        10-14 years 2020  638.4333
## 4        15-17 years 2020 1347.1333
## 5        18-19 years 2020 1812.1667
## 6        20-24 years 2020 1899.2333
## 7        25-29 years 2020 2075.8000
## 8        30-34 years 2020 2064.7000
## 9        35-39 years 2020 2241.0000
## 10       40-44 years 2020 2223.4000
## 11       45-49 years 2020 2029.6333
## 12       50-54 years 2020 1582.8333
## 13       55-59 years 2020 1018.6333
## 14       60-64 years 2020  754.8667
## 15       65-69 years 2020  629.3667
## 16       70-74 years 2020  483.8333
## 17 75 years and over 2020  333.2000
## 18             Total 2020 1328.1667
## 19       00-04 years 2021  129.3000
## 20       05-09 years 2021  280.1667
## 21       10-14 years 2021  727.0667
## 22       15-17 years 2021 1520.1667
## 23       18-19 years 2021 1981.9333
## 24       20-24 years 2021 2151.5667
## 25       25-29 years 2021 2260.4667
## 26       30-34 years 2021 2284.5000
## 27       35-39 years 2021 2350.1000
## 28       40-44 years 2021 2325.9000
## 29       45-49 years 2021 2172.4667
## 30       50-54 years 2021 1639.3667
## 31       55-59 years 2021 1127.0333
## 32       60-64 years 2021  770.8333
## 33       65-69 years 2021  632.2000
## 34       70-74 years 2021  490.6667
## 35 75 years and over 2021  373.5333
## 36             Total 2021 1420.9000
## 37       00-04 years 2022  100.2667
## 38       05-09 years 2022  255.9333
## 39       10-14 years 2022  718.4000
## 40       15-17 years 2022 1405.4667
## 41       18-19 years 2022 1853.1667
## 42       20-24 years 2022 1929.5333
## 43       25-29 years 2022 2150.3333
## 44       30-34 years 2022 2209.2333
## 45       35-39 years 2022 2238.0000
## 46       40-44 years 2022 2225.0000
## 47       45-49 years 2022 2045.7667
## 48       50-54 years 2022 1652.4667
## 49       55-59 years 2022 1165.5000
## 50       60-64 years 2022  762.2000
## 51       65-69 years 2022  616.0333
## 52       70-74 years 2022  509.2667
## 53 75 years and over 2022  360.2667
## 54             Total 2022 1359.4667
## 55       00-04 years 2023  115.6000
## 56       05-09 years 2023  319.4000
## 57       10-14 years 2023  789.2667
## 58       15-17 years 2023 1483.2000
## 59       18-19 years 2023 1602.4000
## 60       20-24 years 2023 1795.7333
## 61       25-29 years 2023 1991.0667
## 62       30-34 years 2023 2164.2667
## 63       35-39 years 2023 2272.7000
## 64       40-44 years 2023 2265.6000
## 65       45-49 years 2023 2032.5000
## 66       50-54 years 2023 1652.6667
## 67       55-59 years 2023 1178.5333
## 68       60-64 years 2023  785.0333
## 69       65-69 years 2023  599.3333
## 70       70-74 years 2023  569.5333
## 71 75 years and over 2023  406.8333
## 72             Total 2023 1360.5000
## 73       00-04 years 2024  102.2000
## 74       05-09 years 2024  366.0000
## 75       10-14 years 2024  786.0000
## 76       15-17 years 2024 1550.2000
## 77       18-19 years 2024 1687.9333
## 78       20-24 years 2024 1749.4000
## 79       25-29 years 2024 2005.2333
## 80       30-34 years 2024 2209.7000
## 81       35-39 years 2024 2440.2333
## 82       40-44 years 2024 2324.9000
## 83       45-49 years 2024 2127.1667
## 84       50-54 years 2024 1687.1333
## 85       55-59 years 2024 1234.3667
## 86       60-64 years 2024  903.8667
## 87       65-69 years 2024  675.3000
## 88       70-74 years 2024  605.7000
## 89 75 years and over 2024  420.6667
## 90             Total 2024 1412.1667

# plot heatmap
ggplot(heatmap_long, aes(x = Year, y = AFM.Age.Group, fill = Rate)) +
  geom_tile(color = "white") +
  scale_fill_gradient(low = "#FEE0D2", high = "#DE2D26", na.value = "#F7F7F7") +
  labs(title = "Heatmap of family violence victimization rates across different age groups",
       x = "year",
       y = "age group",
       fill = "Rate.per.100.000.population") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))