Cardiovascular Death Rate & Diabetes Prevalence: 2020-2024

# Load necessary libraries
library(dplyr)

## 
## 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

library(ggplot2)
library(tidyr)

# Load the data
data <- read.csv("covid.csv")

# Convert date column to Date format
data$date <- as.Date(data$date)

# Reshape the data for easier plotting
data_long <- data %>%
  filter(aged_65_older > 0) %>%
  select(date, cardiovasc_death_rate, diabetes_prevalence) %>%
  pivot_longer(cols = c(cardiovasc_death_rate, diabetes_prevalence),
               names_to = "variable",
               values_to = "value")

# Plotting
combined_plot <- ggplot(data_long, aes(x = date, y = value, color = variable)) +
  geom_line() +
  labs(title = "Cardiovascular Death Rate and Diabetes Prevalence Over Time",
       x = "Date",
       y = "Rate / Prevalence") +
  scale_color_manual(values = c("#0033FF", "#CC3300")) +
  facet_wrap(~variable, scales = "free_y") +
  coord_cartesian(ylim = c(0, 750))  # Adjust y-axis limits for cardiovascular death rate

# Display plots
print(combined_plot)