nightingale.md

Nightingale’s Rose Charts

Neil Saunders compiled 2021-03-19 08:11:52

• Summary
• The data
• Reshaping
• Charts
  • The “rose chart”
  • Column chart
  • Line chart
• Conclusions

Summary

A brief exploration of Florence Nightingale’s Crimean War dataset, inspired by “Florence Nightingale: Data Viz Pioneer”, an episode of Cautionary Tales premiered by 99% Invisible.

The data

We can get the dataset Nightingale from the R package histData.

It’s quite small (24 rows) and looks like this:

Date	Month	Year	Army	Disease	Wounds	Other	Disease.rate	Wounds.rate	Other.rate
1854-04-01	Apr	1854	8571	1	0	5	1.4	0.0	7.0
1854-05-01	May	1854	23333	12	0	9	6.2	0.0	4.6
1854-06-01	Jun	1854	28333	11	0	6	4.7	0.0	2.5
1854-07-01	Jul	1854	28722	359	0	23	150.0	0.0	9.6
1854-08-01	Aug	1854	30246	828	1	30	328.5	0.4	11.9
1854-09-01	Sep	1854	30290	788	81	70	312.2	32.1	27.7
1854-10-01	Oct	1854	30643	503	132	128	197.0	51.7	50.1
1854-11-01	Nov	1854	29736	844	287	106	340.6	115.8	42.8
1854-12-01	Dec	1854	32779	1725	114	131	631.5	41.7	48.0
1855-01-01	Jan	1855	32393	2761	83	324	1022.8	30.7	120.0
1855-02-01	Feb	1855	30919	2120	42	361	822.8	16.3	140.1
1855-03-01	Mar	1855	30107	1205	32	172	480.3	12.8	68.6
1855-04-01	Apr	1855	32252	477	48	57	177.5	17.9	21.2
1855-05-01	May	1855	35473	508	49	37	171.8	16.6	12.5
1855-06-01	Jun	1855	38863	802	209	31	247.6	64.5	9.6
1855-07-01	Jul	1855	42647	382	134	33	107.5	37.7	9.3
1855-08-01	Aug	1855	44614	483	164	25	129.9	44.1	6.7
1855-09-01	Sep	1855	47751	189	276	20	47.5	69.4	5.0
1855-10-01	Oct	1855	46852	128	53	18	32.8	13.6	4.6
1855-11-01	Nov	1855	37853	178	33	32	56.4	10.5	10.1
1855-12-01	Dec	1855	43217	91	18	28	25.3	5.0	7.8
1856-01-01	Jan	1856	44212	42	2	48	11.4	0.5	13.0
1856-02-01	Feb	1856	43485	24	0	19	6.6	0.0	5.2
1856-03-01	Mar	1856	46140	15	0	35	3.9	0.0	9.1

Reshaping

The dataset is not tidy.

• each cause has its own column, rather than columns for cause + value
• columns are a mixture of rates and absolute values

We can select the rate columns and use pivot_longer to convert to long format.

Date	Month	Year	Cause	Rate
1854-04-01	Apr	1854	Disease	1.4
1854-04-01	Apr	1854	Wounds	0.0
1854-04-01	Apr	1854	Other	7.0
1854-05-01	May	1854	Disease	6.2
1854-05-01	May	1854	Wounds	0.0
1854-05-01	May	1854	Other	4.6

Charts

The help page, ?Nightingale provides some R code to generate polar area and line charts but it’s somewhat dated and cumbersome. Let’s give it the tidyverse treatment.

The “rose chart”

The “rose chart”, also called (incorrectly) a Coxcomb chart, or polar area chart, is a bar chart projected onto polar coordinates.

We can generate something very similar to Nightingale’s original chart like this:

Column chart

We can’t simply remove the polar coordinates, as this will place some months in the wrong position on the basic column chart. So now we use Date on the x-axis.

We can also indicate the period before the arrival of the Sanitary Commission using grey shading.

Line chart

We can also show the data as a line chart.

Conclusions

The Cautionary Tales podcast episode concludes that deaths from disease were falling before the arrival of the Sanitary Commission, and that this is obscured - perhaps deliberately - by the choice of the polar area chart.

It’s a fair point. However, what we can’t know is what would have happened through 1855 in the absence of the Sanitary Commission. Is there a hint of the same “double peak”, with a seasonal cycle, but smaller? Is that evidence for the effect of sanitation improvement?