```{r}

total_change <- murder_tiny %>%
  group_by(state) %>%
  summarise(murders2015= sum(murders_2015),
  murders2016= sum(murders_2016),
  change= sum(change),
  per_change= change*100/murders2015) %>%
  arrange(desc(change))%>%
  head(10)

total_change
```
```{r}
p1 <- ggplot(total_change,
              aes(x= reorder(state, change), y=change))+
        geom_col(aes(fill=state), alpha=0.7) +
        coord_flip() +
        labs(x="State", y="Increase Number", title = "Top 10 Murder Increase in each State") +
        theme_minimal()
p1

```