tutorial8.qmd

---
title: "Tutorial8"
format: html
editor: visual
---

```{r}
library(readr)
library(ggplot2)

library(tidyverse)

```

```{r}
Data_Tutorial <- read_csv("Data_Tables_Criminal_Incidents_Visualisation_Year_Ending_December_2019.csv")
```



```{r}
# Group by 'Year' and 'Offence Division', and summarize the total Incidents Recorded
data_summary <- Data_Tutorial %>%
  group_by(Year, `Offence Division`) %>%
  summarise(Total_Incidents = sum(`Incidents Recorded`))

# Create ggplot
ggplot(data_summary, aes(x = Year, y = Total_Incidents, color = `Offence Division`)) +
  geom_line() +
  geom_point() +
  labs(title = "Total Incidents Recorded by Year and Offence Division",
       x = "Year",
       y = "Total Incidents Recorded",
       color = "Offence Division") +
  theme_minimal()


```

```{r}
ggplot(data = Data_Tutorial, aes(x = `Incidents Recorded`, y = `Rate per 100,000 population`, color = `Offence Division`)) +
  geom_point() +  # Use points for a scatter plot
  labs(title = "Relationship between Incidents and Rate per 100,000 Population",
       x = "Incidents Recorded",
       y = "Rate per 100,000 Population") +
  theme_minimal()  # Use a minimal theme for simplicity
```


```{r}
# Create a boxplot with ggplot
ggplot(data = Data_Tutorial, aes(x = `Offence Division`, y = `Incidents Recorded`, fill = `Offence Division`)) +
  geom_boxplot() +  # Use boxplots to visualize the distribution
  labs(title = "Distribution of Incidents Recorded by Offence Division",
       x = "Offence Division",
       y = "Incidents Recorded") +
  theme_minimal()  # Use a minimal theme
```