irm.R

library(deSolve)
library(reshape2)
library(ggplot2)
initial_state_values <- c(I = 1000000, R = 0, M = 0)
parameters <- c(gamma = 0.1, mu = 0.2)
times <- seq(from = 0, to = 28, by = 1)
cohort_model <- function(time, state, parameters) {
  with (as.list(c(state, parameters)), {
    dI <- -gamma * I - mu * I
    dR <- gamma * I
    dM <- mu * I
    return(list(c(dI, dR, dM)))
  })
}
output <- as.data.frame(ode(y = initial_state_values, times = times, func = cohort_model, parms = parameters))
output_long <- melt(as.data.frame(output), id = "time") 
ggplot(data = output_long, aes(x = time, y = value, colour = variable, group = variable)) + 
geom_line() +
xlab("Time (days)")+ 
ylab("Number of people") + 
labs(title = "Recoveries and Deaths over Time", colour= "Compartments") # add title