11.45 Rose Chart Discussion

20210318

According to Neil Saunders Florence Nightingale developed the concept of a rose chart to visualise the cause of mortality in the army. Neil implemented the chart in R.

The basic concept of a rose chart is that it is a bar chart projected onto polar coordinates.

The plot here replicates the rose chart for the weather dataset. It nicely illustrates the changed weather pattern from 2019 to 2020. The weather patterns used are a count of the number of days each month that are hot, wet, and windy.

The charts tell quite an interesting story. Comparing 2019 to 2020 we can see some stark weather pattern differences. The year 2019, in Canberra, was generally hotter than 2020, but 2020 was a lot wetter. We had major bush fires at the end of a drought in Canberra from the end of 2019 to the beginning of 2020. Then 2020 was particularly wet.

The data processing begins with dplyr::filter()’ing the daily weather observations from Canberra airport for the 2019 and 2020 years. The date is dplyr::mutate()’d into month and year which is how the data will be dplyr::group_by() for display. It is then dplyr::summarise()’d to include measures of hot, wet, and windy as the base::sum() of the number of days in the month above a threshold. With a tidyr::pivot_longer() the table is in the format required for plotting.

The ggplot2::ggplot() itself is a ggplot2::geom_col() using the supplied data (day counts) as the column values. It is plot on to ggplot2::coord_polar() and ggplot2::facet_wrap()’d on the year. The remainder deals with aspects of the presentation.



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