This question is about visualising temporal data.
The example data is on pedestrian counts in the city of Melbourne. The below plot looks at distribution of the pedestrian counts over weekdays in March across 24hrs, comparing 2019 to 2020.
ped %%
ggplot(aes(x=Time, y=Count, group=Date, colour=as.factor(year))) +
geom_boxplot() +
facet_wrap(~ year, ncol= 1, scales = "free") +
scale_colour_brewer("", palette="Dark2") +
theme(legend.position="bottom", legend.title = element_blank())

By looking at the above plots, select all statements that are TRUE.

Question

This question is about visualising temporal data. 
 The example data is on pedestrian counts in the city of Melbourne. The below plot looks at distribution of the pedestrian counts over weekdays in March across 24hrs, comparing 2019 to 2020. 
 ped %>% 
  ggplot(aes(x=Time, y=Count, group=Date, colour=as.factor(year))) +
    geom_boxplot() +
    facet_wrap(~ year, ncol= 1, scales = "free") + 
    scale_colour_brewer("", palette="Dark2") + 
    theme(legend.position="bottom", legend.title = element_blank())  
 
 By looking at the above plots, select all statements that are TRUE.

Accepted Answer

It would be easier to compare the plots if the y-scales for both years would be the same.

Accepted Answer

The median values of pedestrian counts over weekdays at 3am are similar between 2019 and 2020

Accepted Answer

The breaks in the x-axis are adequate

Answer

The variability in the pedestrian counts for different hours in 2019 and 2020 is similar

Crowdly

This question is about visualising temporal data. The example data is on ped...

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