Wolfram Cloud Document

In[]:=

Clear["Global`*"];

(* a simple function to detect which environment we're in -- the local machine or the cloud *)

detectCloud[] := StringContainsQ[NotebookDirectory[], "wolframcloud"];

Data Collection: Decennial Population and Apportionment

Sources

◼

State Abbreviations: U .S . Postal Service

◼

State Decennial Census Populations: Census Department

◼

State Decennial Apportionment Data: Census Department

We’ll start with the state abbreviations from the Post Office

In[]:=

table = Import["http://about.usps.com/who-we-are/postal-history/state-abbreviations.htm", {"HTML", "Data",2}][[2]];

Print[Grid[table[[2;;6]], FrameAll]];

	1831	1874	1943	1963
Alabama	Al.	Ala.	Ala.	AL
Alaska	--	Alaska	Alaska	AK
Arizona	--	Ariz.	Ariz.	AZ
Arkansas	Ar. T.	Ark.	Ark.	AR

Let's toss in entities to the output, since we can be sure they’re uniform across different sources, spellings and punctuations

In[]:=

data = { StringTrim[First@#], StringTrim[Last@#] }& /@ Rest@table;

data = Select[data, First@# ≠ "" && First@# ≠ "Puerto Rico"&];

states = <| "name"  First@#, "abbr"  Last@#, "entity"  Interpreter["USState"][First@#] |>& /@ data;

And make an association of the associations

In[]:=

statesAssociation = AssociationThread[#["name"]& /@ states, states];

We’ll need to correct Nebraska:

In[]:=

statesAssociation["Nebraska"]["abbr"] = "NE";

Now let’s get decennial Census data.

While Mathematica has population conveniently built in to state entities, let’s be extra careful and use the same figures the U.S. used for apportionment.

We can get the table data from the URL at the top of the document directly as a CSV to save some time.

In[]:=

csv = Import["https://www2.census.gov/programs-surveys/decennial/tables/2010/2010-apportionment/pop_change.csv", "CSV"];

data = Most@Take[csv, -52];

header = csv[[3]]

Out[]=

{STATE_OR_REGION,1910_POPULATION,1920_POPULATION,1930_POPULATION,1940_POPULATION,1950_POPULATION,

1960_POPULATION,1970_POPULATION,1980_POPULATION,1990_POPULATION,2000_POPULATION,2010_POPULATION,

1910_CHANGE,1920_CHANGE,1930_CHANGE,1940_CHANGE,1950_CHANGE,1960_CHANGE,1970_CHANGE,1980_CHANGE,

1990_CHANGE,2000_CHANGE,2010_CHANGE}

Let’s clean up the header, reducing to just to the populations -- we can calculate the change ourselves if we ever need it.

In[]:=

shortHeader = { "name", "1910", "1920", "1930", "1940", "1950", "1960", "1970", "1980", "1990", "2000", "2010" };

And map the header to the data objects

In[]:=

population = Map[AssociationThread[shortHeader, #[[1;;12]]]&, data];

Grid[Prepend[Values /@ population[[1;;4]], shortHeader], FrameAll, Spacings{0.6,1}]

Out[]=

name	1910	1920	1930	1940	1950	1960	1970	1980	1990	2000	2010
Alabama	2138093	2348174	2646248	2832961	3061743	3266740	3444165	3893888	4040587	4447100	4779736
Alaska	64356	55036	59278	72524	128643	226167	300382	401851	550043	626932	710231
Arizona	204354	334162	435573	499261	749587	1302161	1770900	2718215	3665228	5130632	6392017
Arkansas	1574449	1752204	1854482	1949387	1909511	1786272	1923295	2286435	2350725	2673400	2915918

Looks good. Let’s connect to the object from the Post Office

In[]:=

statesAssociation = AssociationThread[

Keys@statesAssociation,

Append[statesAssociation[#["name"]], "population_decennial"  Rest@#]& /@ population

];

In[]:=

Print[statesAssociation["Alaska"]];

Print[statesAssociation["Arkansas"]];

nameAlaska,abbrAK,entity

Alaska, United States

,population_decennial191064356,192055036,193059

278,194072524,1950128643,1960226167,1970300382,1980401851,1990550043,2000626932,2010710

231

nameArkansas,abbrAR,entity

Arkansas, United States

,population_decennial19101574449,19201752204,

19301854482,19401949387,19501909511,19601786272,19701923295,19802286435,19902350725,

20002673400,20102915918

Data Collection: Decennial Population and Apportionment

Sources

We’ll start with the state abbreviations from the Post Office

Now let’s get decennial Census data.

And now the apportionment data