My Children are Seven in Number

WYTfX Not sure why but for some reason over lunch I got interested in old labor songs. This one was particularly bleak. Apparently, it is intended to be sung over “My Bonnie Lies Over The Ocean.” As our administration erodes labor and environmental protections for the inexplicable sake of bringing back coal mining, it pays to have a look back at how bad it really was.

Song: My Children are Seven in Number
Lyrics: Eleanor Kellogg⁽¹⁾

Music: to the tune of “My Bonnie Lies Over the Ocean”
Year: c.1933
Genre:
Country: USA

My children are seven in number,
We have to sleep four in a bed;
I’m striking with my fellow workers.
To get them more clothes and more bread.

CHORUS:
Shoes, shoes, we’re striking for pairs of shoes,
Shoes, shoes, we’re striking for pairs of shoes.

Pellagra⁽³⁾ is cramping my stomach,
My wife is sick with TB⁽⁴⁾;
My babies are starving for sweet milk,
Oh, there as so much sickness for me.

Milk, milk, we’re striking for gallons of milk,
Milk, milk, we’re striking for gallons of milk.

I’m needing a shave and a haircut,
But barbers I cannot afford;
My wife cannot wash without soapsuds,
And she had to borrow a board.
This song was originally posted on protestsonglyrics.net
Soap, soap, we’re striking for bars of soap,
Soap, soap, we’re striking for bars of soap.

My house is a shack on the hillside,
Its doors are unpainted and bare;
I haven’t a screen to my windows,
And carbide cans do for a chair.

Homes, homes, we’re striking for better homes,
Homes, homes, we’re striking for better homes.

They shot Barney Graham⁽⁵⁾ our leader,
His spirit abides with us still;
The spirit of strength for justice,
No bullets have power to kill.
This song was originally posted on protestsonglyrics.net
Barney, Barney, we’re thinking of you today,
Barney, Barney, we’re thinking of you today.

Oh, miners, go on with the union,
Oh, miners, go on with the fight;
For we’re in the struggle for justice,
And we’re in the struggle for right.

Justice, justice, we’re striking for justice for all,
Justice, justice, we’re striking for justice for all.

January 25, 2018

in Academics, Africa, GIS, Maps, Pointless Data Exercises, R

Mapmaking with ggmap

I am always looking for free alternatives to ArcGIS for making pretty maps. R is great for graphics and the new-to-me ggmap package is no exception.

I’m working with some data from Botswana for a contract and needed to plot maps for several years of count based data, where the GPS coordinates for facilities were known. ArcGIS is unwieldy for creating multiple maps of the same type of data based on time points, so R is an ideal choice…. the trouble is the maps I can easily make don’t look all that good (though with tweaking can be made to look better.)

ggmap offered me an easy solution. It downloads a topographic base map from Google and I can easily overlay proportionally sized points represent counts at various geo-located points. This is just a map of Botswanan health facilities (downloaded from Humanitarian Data Exchange) with the square of counts chosen from a normal distribution. The results are rather nice.

BotswanaHF

library(rgdal)
library(ggmap)
library(scales)

#read in grographic extent and boundary for bots
btw <- admin<-readOGR(“GIS Layers/Admin”,”BWA_adm2″) #from DIVA-GIS

# fortify bots boundary for ggplot
btw_df <- fortify(btw)

# get a basemap
btw_basemap <- get_map(location = “botswana”, zoom = 6)

# get the hf data
HFs.open.street.map<-read.csv(“BotswanaHealthFacilitiesOpenStreetMap.csv”)
# create random counts
HFs.open.street.map$Counts<-round((rnorm(112,mean=10,sd=5))^2,0)

# Plot this dog
plot
ggmap(btw_basemap) +
geom_polygon(data=btw_df, aes(x=long, y=lat, group=group), fill=”red”, alpha=0.1) +
geom_point(data=HFs.open.street.map, aes(x=X, y=Y, size=Counts, fill=Counts), shape=21, alpha=0.8) +
scale_size_continuous(range = c(2, 12), breaks=pretty_breaks(5)) +
scale_fill_distiller(breaks = pretty_breaks(5))

January 8, 2018

in Academics

Health Care Expenditures and Life Expectancy – What is this picture really telling us?

HealthCareSpendingLifeExpectancy I keep staring at this picture, which appeared on “Economist’s View” last March and wondering exactly what I’m supposed to learn from this, aside from the obvious fact that health care in the US is too expensive.

We have known that health care in the US is too expensive for a long while now. We are also pretty sure of the reasons why, none of which are easily solved.

But we shouldn’t assume that there is a causal relationship between health care expenditures and life expectancy. The message here seems to be that other countries increase their health budgets and their citizens live progressively longer, but for some reason it doesn’t work in the US. Well, I don’t think it works anywhere.

There’s no evidence to suggest that extra spending this year will increase life expectancy this year. If anything, it is long past expenditures and improvements to health care that will increase life expectancy today. I think that if we looked at overall economic growth and life expectancy, we would see the same trend. Most of us will live longer, because we were born under better conditions than our grandparents, not because of government spending for health care, the vast majority of which goes to the elderly.

What this tells us, though, is two things: one, that health care in the US costs too much and seems to be increasing without bound (math talk). Second, that life expectancy in the US is shorter than these other countries. This is true, but the US is a fundamentally different place than any of the countries on that list, some of which has to do with social problems (racism) and some of which likely has to do with the fact that we take in larger numbers of immigrants from countries which have low life expectancies than any country on that list. These places aren’t comparable. While solving the problem of racism is noble, I don’t think that many people (except our President and his bigoted minions) want to suggest that we increase US life expectancy by deporting immigrants or closing the door to people from, say, Africa.

But we should be careful not to take home the message that there is an intrinsic relationship between spending and lifespan because that would be just misleading in my opinion.