gistfile1.txt

grouped_crime = summarise(group_by(statemerge, Year), Violent.Crime.Total = sum(Violent.crime.total), Property.Crime.Total = sum(Property.crime.total)

grouped_crime = mutate(grouped_crime, Total_rate = (Violent.Crime.Total+Property.Crime.Total)*1000/Population, Violent_rate = Violent.Crime.Total*1000/Population, Property_rate = Property.Crime.Total*1000/Population

graph1 = ggplot(grouped_crime, aes(Year, colour = 'red')) + 
  geom_line(aes(Year, Total_rate), size = 1.5) +
  ylab('Crimes per 1000 Residents') +
  ggtitle('Total Crime from 1977-2012') +
  theme_bw() +
  theme(plot.title = element_text(size = rel(2)), legend.position="none")

graph2 = ggplot(grouped_crime, aes(Year)) + 
  geom_line(aes(Year, Property_rate, color = 'Property_rate'), size = 1.5) +
  ylab('Crimes per 1000 Residents') +
  geom_line(aes(Year, Violent_rate, color = 'Violent_rate'), size = 1.5) +
  ggtitle('Violent and Nonviolent Crime from 1977-2012') +
  theme_bw() +
 theme(plot.title = element_text(size = rel(2))) +
  scale_color_manual(values = c('blue', 'indianred1'),name= "Crime Type", labels=c("Property", "Violent"))

graph3 = ggplot(grouped_crime, aes(Year, colour = 'red')) + 
  ylab('Crimes per 1000 Residents') +
  geom_line(aes(Year, Violent_rate), size = 1.5) +
  ggtitle('Violent Crime from 1977-2012') +
  theme_bw() +
  theme(plot.title = element_text(size = rel(2)), legend.position = 'none')