To answer this question we made three graphs, each graph can be used to analyze Covid-19 cases in 34 provinces in Indonesia. These three graphs display a 7-day rolling average of the number of positive Covid-19 cases in each province. The first and second graphs are replications of the dashboard created by **Financial Times** link. While the third graph is the heatmap replication of the account **twitter @VictimOfMaths** source. These three graphs complement each other, there are advantages and disadvantages in each graph. By displaying all three hopefully, conclusions can be drawn.

NOTE: In this first graph, we map the rolling-averages of one province in one time-series line so it is easier to do comparisons between provinces. In this graph, rolling averages begin when the number of cases has reached 4 or more cases.

```
heatmap_daily_indo %>% group_by(province) %>%
mutate(dott_y = if_else(seq_==max(seq_),casesroll_avg,NaN)) %>%
ungroup() %>%
ggplot(aes(x=seq_ ,group=province ))+
geom_line(aes( y = casesroll_avg))+
geom_point(aes(y = dott_y),size=1)+
gghighlight(use_direct_label = FALSE,unhighlighted_params = list(size=0.4))+
scale_y_continuous(trans='log2',
breaks=c(1,10,100,200,500,1000))+
facet_wrap(~province)+
labs(subtitle = "7 days rolling average positif cases, since # of positive cases above 4",
y = "Log Rolling Avg Positif Case",
x = "Number of days from since # of positive cases above 4",
caption = paste("Code by Dio Ariadi | www.datawizart.com. Graph inspired by twitter @jburnmurdoch\nData Source: kawalCOVID19 | covid19.go.id",date_variable))+
theme(panel.background = element_rect(fill = "#f5f5f5"),
text = element_text(family = "Proxima Nova"),
panel.grid.major = element_line(colour = "#f0f0f0"),
panel.grid.minor = element_blank(),
axis.ticks = element_blank(),
strip.background = element_rect(fill = "#f5f5f5"),
plot.background = element_rect(fill = "#f5f5f5"),
strip.text.x = element_text(hjust = 0),
plot.caption = element_text(hjust = 0))
```