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[WSS19] Visualize World's Oldest People Data

Ahmed Elbanna

Ahmed Elbanna, Wolfram Research

Posted 5 years ago

This is a basic visualization of World's Oldest People: http://archive.is/4kwbk Importing the data either by the link above directly or by downloading the html data file directly results into two slightly different data structures. I downloaded the data file and imported it into Mathematica (attached below). The following code shows how to import the data and perform basic cleaning: SetDirectory[NotebookDirectory[]]; data = Import["agedata.html", "Data"]; data = data[[1]]; info = data[[1 ;; 7]]; data = data[[8 ;;]]; data = data[[1 ;; 65]]; The data is a list of lists, each list corresponds to a person. The heads of the data are: In[11]:= info[[6]] Out[11]= {"#", "Birthplace", "Name", "Born", "Died", "Age", "Race", \ "Sex", "Deathplace", "When Oldest", "Length of Reign", "Reign \ Length", "Age at Accession", "Case added to GRG Tables"} Here there is a typical list corresponding to a person: In[38]:= data[[2]] Out[38]= {2, "England (UK)", "Jennie Howell", "Feb. 11, 1845", "Dec. \ 16, 1956", 111, 309, "W", "F", "U.S. (CA)", "1955-1956", "110-111", \ 1, 53, " 1.14", 110, 255, "Aug. 29, 2017"} I decided to use only "Birthplace", "Age" and "Race" parameters. Since age is presented by years and days in two different elements of the list, I divided the days by 365 to convert them into a year fraction and added it to the year element. age = (data[[#, 6]] + data[[#, 7]]/365) & /@ Range[Length@data]; After reviewing the values of my age list, I found out that the last person in the data has extra notations and highlights which caused miscalculations: In[14]:= data[[65]] Out[14]= {65, "Japan", "Kane Tanaka", "Jan. 2, 1903", "115", "214", \ "EA", "F", "Japan (Fukuoka)", "2018-", "115-", 0, 9, " 0.02* ", 115, \ 201, "Mar. 18, 2014"} So I modified the last element of the age list: age = N@ReplacePart[age, 65 -> 115 + 214/365] Creating the race list and cleaning it: race = data[[#, 8]] & /@ Range[Length@data] race = ReplacePart[race, {58 -> "M", 65 -> "EA"}]; race = race /. "O" -> "EA"; Note that the data marked Japan as a stand alone race, so I changed it to join the East Asia race. Now, creating and cleaning the data for the birth place list: birthplace = data[[#, 2]] & /@ Range[Length@data] birthplace = StringTrim[birthplace]; birthplace = ReplacePart[ birthplace, {27 -> "Poland", 38 -> "Portugal", 62 -> "Jamaica"}]; birthplace = StringReplace[birthplace, ___ ~~ "(UK)" ~~ ___ -> "United Kingdom"]; birthplace = StringReplace[birthplace, ___ ~~ "U." ~~ ___ -> "United States"]; I intended to use the GeoGraghics capabilities of Mathematica which needs the countries' names to match the ones in Wolfram data repository, so I looked for the ones that didn't match and modified them: In[24]:= Position[ MemberQ[CountryData["Countries", "Name"], #] & /@ birthplace, False] Out[24]= {{13}, {32}} In[25]:= Extract[birthplace, Position[MemberQ[CountryData["Countries", "Name"], #] & /@ birthplace, False]] Out[25]= {"Czechoslovakia", "Canada (Que)"} In[26]:= birthplace = ReplacePart[birthplace, {13 -> "Czech Republic", 32 -> "Canada"}]; Now, lets compute the data for average age per country: joint = Transpose[{birthplace, age}]; geoData= Thread[Entity["Country", #] & /@ DeleteDuplicates[ StringDelete[#, " "] & /@ birthplace] -> (Mean@Cases[joint, {#, _}][[All, 2]] & /@ DeleteDuplicates[birthplace])] And compute the geo-range for our map: In[]:= geoRange=GeoBounds[geoData[[All,1]]] Out[]= {{-34.8341,83.1333},{-230.447,32.8917}} Now, here it is a map that shows the countries of the 65 oldest people in the world with colour scale showing the average age per country. You can see the result at the top of this post. GeoRegionValuePlot[geoData,GeoBackground->"CountryBorders", GeoProjection->"WinkelTripel",GeoRange->geoRange,ImageSize->800,PlotLabel-> Style["Average Years per Country of World's Oldest Person",25,Darker@Red,FontFamily->"Phosphate"]] To better comprehend the value per country we can plot the average age-above-105 using BarChart: BarChart[Sort[Association[geoData]]-105, BarOrigin->Left,ChartLabels->Automatic,AspectRatio->1, ChartElementFunction->"GradientScaleRectangle",BaseStyle->14, PlotTheme->"Detailed",PlotLabel->Style["Average Years Above 105",20,Darker@Red]] And here there is a bar chart showing the number of oldest people per each race: In[32]:= Counts@race Out[32]= <\|"W" -> 45, "EA" -> 11, "B" -> 7, "H" -> 1, "M" -> 1\|> In[34]:= BarChart[Counts@race, ChartLabels -> {"White", "East Asian", "Black", "Hispanic", "Multiracial"}, LabelingFunction -> Above] I stopped at this point, but there are many ways to visualize this dataset and even the two graphs here can be polished to be more informative. A way to handle the data effectively is to collect all the variables (age, race,...etc) in a matrix which will make data manipulation much easier. Tips on this dataset cleaning, preparation and visualization are much appreciated. Attachments: agedata.html

enter image description here

This is a basic visualization of World's Oldest People: http://archive.is/4kwbk

Importing the data either by the link above directly or by downloading the html data file directly results into two slightly different data structures. I downloaded the data file and imported it into Mathematica (attached below). The following code shows how to import the data and perform basic cleaning:

SetDirectory[NotebookDirectory[]];
data = Import["agedata.html", "Data"];
data = data[[1]];
info = data[[1 ;; 7]];
data = data[[8 ;;]];
data = data[[1 ;; 65]];

The data is a list of lists, each list corresponds to a person. The heads of the data are:

In[11]:= info[[6]]

Out[11]= {"#", "Birthplace", "Name", "Born", "Died", "Age", "Race", \
"Sex", "Deathplace", "When Oldest", "Length of Reign", "Reign \
Length", "Age at Accession", "Case added to GRG Tables"}

Here there is a typical list corresponding to a person:

In[38]:= data[[2]]

Out[38]= {2, "England (UK)", "Jennie Howell", "Feb. 11, 1845", "Dec. \
16, 1956", 111, 309, "W", "F", "U.S. (CA)", "1955-1956", "110-111", \
1, 53, " 1.14", 110, 255, "Aug. 29, 2017"}

I decided to use only "Birthplace", "Age" and "Race" parameters. Since age is presented by years and days in two different elements of the list, I divided the days by 365 to convert them into a year fraction and added it to the year element.

age = (data[[#, 6]] + data[[#, 7]]/365) & /@ Range[Length@data];

After reviewing the values of my age list, I found out that the last person in the data has extra notations and highlights which caused miscalculations:

In[14]:= data[[65]]

Out[14]= {65, "Japan", "Kane Tanaka", "Jan. 2, 1903", "115*", "214*", \
"EA", "F", "Japan (Fukuoka)", "2018-", "115-", 0, 9, " 0.02* ", 115, \
201, "Mar. 18, 2014"}

So I modified the last element of the age list:

age = N@ReplacePart[age, 65 -> 115 + 214/365]

Creating the race list and cleaning it:

race = data[[#, 8]] & /@ Range[Length@data]
race = ReplacePart[race, {58 -> "M", 65 -> "EA"}];
race = race /. "O" -> "EA";

Note that the data marked Japan as a stand alone race, so I changed it to join the East Asia race. Now, creating and cleaning the data for the birth place list:

birthplace = data[[#, 2]] & /@ Range[Length@data]
birthplace = StringTrim[birthplace];

birthplace = 
  ReplacePart[
   birthplace, {27 -> "Poland", 38 -> "Portugal", 62 -> "Jamaica"}];
birthplace = 
  StringReplace[birthplace, ___ ~~ "(UK)" ~~ ___ -> "United Kingdom"];

birthplace = 
  StringReplace[birthplace, ___ ~~ "U." ~~ ___ -> "United States"];

I intended to use the GeoGraghics capabilities of Mathematica which needs the countries' names to match the ones in Wolfram data repository, so I looked for the ones that didn't match and modified them:

In[24]:= Position[
 MemberQ[CountryData["Countries", "Name"], #] & /@ birthplace, False]

Out[24]= {{13}, {32}}

In[25]:= Extract[birthplace, 
 Position[MemberQ[CountryData["Countries", "Name"], #] & /@ 
   birthplace, False]]

Out[25]= {"Czechoslovakia", "Canada (Que)"}

In[26]:= birthplace = 
  ReplacePart[birthplace, {13 -> "Czech Republic", 32 -> "Canada"}];

Now, lets compute the data for average age per country:

joint = Transpose[{birthplace, age}];

geoData=
Thread[Entity["Country", #] & /@ 
DeleteDuplicates[
StringDelete[#, " "] & /@ 
birthplace] -> (Mean@Cases[joint, {#, _}][[All, 2]] & /@ 
DeleteDuplicates[birthplace])]

enter image description here

And compute the geo-range for our map:

In[]:= geoRange=GeoBounds[geoData[[All,1]]]
Out[]= {{-34.8341,83.1333},{-230.447,32.8917}}

Now, here it is a map that shows the countries of the 65 oldest people in the world with colour scale showing the average age per country. You can see the result at the top of this post.

GeoRegionValuePlot[geoData,GeoBackground->"CountryBorders",
GeoProjection->"WinkelTripel",GeoRange->geoRange,ImageSize->800,PlotLabel->
Style["Average Years per Country of World's Oldest Person",25,Darker@Red,FontFamily->"Phosphate"]]

To better comprehend the value per country we can plot the average age-above-105 using BarChart:

BarChart[Sort[Association[geoData]]-105,
BarOrigin->Left,ChartLabels->Automatic,AspectRatio->1,
ChartElementFunction->"GradientScaleRectangle",BaseStyle->14,
PlotTheme->"Detailed",PlotLabel->Style["Average Years Above 105",20,Darker@Red]]

enter image description here

And here there is a bar chart showing the number of oldest people per each race:

In[32]:= Counts@race

Out[32]= <|"W" -> 45, "EA" -> 11, "B" -> 7, "H" -> 1, "M" -> 1|>

In[34]:= BarChart[Counts@race, 
 ChartLabels -> {"White", "East Asian", "Black", "Hispanic", 
   "Multiracial"}, LabelingFunction -> Above]

A bar chart of number of the oldest people in each race

I stopped at this point, but there are many ways to visualize this dataset and even the two graphs here can be polished to be more informative. A way to handle the data effectively is to collect all the variables (age, race,...etc) in a matrix which will make data manipulation much easier. Tips on this dataset cleaning, preparation and visualization are much appreciated.

POSTED BY: Ahmed Elbanna

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EDITORIAL BOARD

EDITORIAL BOARD, WOLFRAM

Posted 5 years ago

- Congratulations! This post is now featured in our Staff Pick column as distinguished by a badge on your profile of a Featured Contributor! Thank you, keep it coming, and consider contributing your work to the The Notebook Archive!

POSTED BY: EDITORIAL BOARD

John Shonder

John Shonder, Independent Researcher

Posted 5 years ago

Very interesting post, thank you.

POSTED BY: John Shonder

Ahmed Elbanna

Ahmed Elbanna, Wolfram Research

Posted 5 years ago

Thanks :)

POSTED BY: Ahmed Elbanna

Julian Davis

Julian Davis, University of Southern Indiana

Posted 5 years ago

The goal here was to be able to automate some of the data processing to be able to read in new data if the we page changes. Some of the output is there to double check some of the data filters. It's not perfect, but it's what I've got now. In[1]:= data5 := Import["http://archive.is/4kwbk", "Data"] In[2]:= data5[[2, 1, 1, 6]] Out[2]= {"#", "Birthplace", "Name", "Born", "Died", "Age", "Race", "Sex", \ "Deathplace", "When Oldest", "Length of Reign", "Reign Length", "Age at \ Accession", "Case added to GRG Tables"} In[3]:= data5[[2, 1, 1, 8 ;; 10, 4 ;; 6]] // MatrixForm Out[3]//MatrixForm= \!\( TagBox[ RowBox[{"(", "", GridBox[{ {"\<\"Aug. 20, 1842\"\>", "\<\"Oct. 24, 1955\"\>", "113"}, {"\<\"Feb. 11, 1845\"\>", "\<\"Dec. 16, 1956\"\>", "111"}, {"\<\"Jan. 24, 1849\"\>", "\<\"Mar. 30, 1958\"\>", "109"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]\) In[4]:= data5[[2, 1, 1, 8 ;; 10]] Out[4]= {{1, "England (UK)", "Betsy Baker", "Aug. 20, 1842", "Oct. 24, 1955", 113, 65, "W", "F", "U.S. (NE)", "?-1955", "?-113", "N.A.", "N.A.", "N.A.", "N.A.", "N.A.", "N.A."}, {2, "England (UK)", "Jennie Howell", "Feb. 11, 1845", "Dec. 16, 1956", 111, 309, "W", "F", "U.S. (CA)", "1955-1956", "110-111", 1, 53, " 1.14", 110, 255, "Aug. 29, 2017"}, {3, "Denmark", "Anne Marie Carstenson", "Jan. 24, 1849", "Mar. 30, 1958", 109, 65, "W", "F", "U.S. (NE)", "1956-1958", "107-109", 1, 104, " 1.28", 107, 327, "N.A. (less than 110)"}} In[5]:= data5[[2, 1, 1, 9]] Out[5]= {2, "England (UK)", "Jennie Howell", "Feb. 11, 1845", "Dec. 16, 1956", 111, \ 309, "W", "F", "U.S. (CA)", "1955-1956", "110-111", 1, 53, " 1.14", 110, 255, \ "Aug. 29, 2017"} Remove the space to match up with how Mathematica refers to variables In[6]:= data6 = StringDelete[data5[[2, 1, 1, 8 ;; 71, 2]], " "] Out[6]= {"England(UK)", "England(UK)", "Denmark", "U.S.(IN)", "Netherlands", \ "Norway", "U.S.(MI)", "England(UK)", "U.S.(TX)", "England(UK)", \ "England(UK)", "SouthAfrica", "Czechoslovakia", "England(UK)", "Spain", \ "England(UK)", "N.Ireland(UK)", "Japan", "Japan", "U.S.(MO)", "France", \ "U.S.(IL)", "France", "U.S.(NH)", "U.S.(MO)", "U.S.(VA)", \ "Germany(nowPoland)[5]", "U.S.(CA)", "England(UK)", "U.S.(PA)", "France", \ "Canada(Que)", "U.S.(PA)", "England(UK)", "France", "U.S.(MI)", \ "England(UK)", "CapeVerde(Portugal)[8]", "U.S.(NY)", "Japan", "Japan", \ "PuertoRico", "Ecuador", "U.S.(TN)", "PuertoRico", "U.S.(NC)", "Japan", \ "U.S.(IN)", "Portugal", "U.S.(GA)", "Japan", "France", "Brazil", "U.S.(TN)", \ "Italy", "Japan", "Japan", "U.S.(AR)", "U.S.(GA)", "U.S.(AL)", "Italy", \ "BritishWestIndies(nowJamaica)", "Japan", "Japan"} Double check numbers by eye ... How do we handle U.S. (CA) In[7]:= StringCases[data5[[2, 1, 1, 8 ;; 71, 2]], "U.S. (" ~~ __ ~~ ")"] Out[7]= {{}, {}, {}, {"U.S. (IN)"}, {}, {}, {"U.S. (MI)"}, {}, {"U.S. (TX)"}, {}, {}, \ {}, {}, {}, {}, {}, {}, {}, {}, {"U.S. (MO)"}, {}, {"U.S. (IL)"}, {}, {"U.S. \ (NH)"}, {"U.S. (MO)"}, {"U.S. (VA)"}, {}, {"U.S. (CA)"}, {}, {"U.S. (PA)"}, \ {}, {}, {"U.S. (PA)"}, {}, {}, {"U.S. (MI)"}, {}, {}, {"U.S. (NY)"}, {}, {}, \ {}, {}, {"U.S. (TN)"}, {}, {"U.S. (NC)"}, {}, {"U.S. (IN)"}, {}, {"U.S. \ (GA)"}, {}, {}, {}, {"U.S. (TN)"}, {}, {}, {}, {"U.S. (AR)"}, {"U.S. (GA)"}, \ {"U.S. (AL)"}, {}, {}, {}, {}} In[8]:= Length[Cases[StringCases[data6, "U.S.(" ~~ __ ~~ ")"], Except[{}]]] Out[8]= 21 Test the case of Puerto Rico - without a space In[9]:= Length[Cases[data6, "PuertoRico"]] Out[9]= 2 OK ... Let's do it! In[10]:= data6 Out[10]= {"England(UK)", "England(UK)", "Denmark", "U.S.(IN)", "Netherlands", \ "Norway", "U.S.(MI)", "England(UK)", "U.S.(TX)", "England(UK)", \ "England(UK)", "SouthAfrica", "Czechoslovakia", "England(UK)", "Spain", \ "England(UK)", "N.Ireland(UK)", "Japan", "Japan", "U.S.(MO)", "France", \ "U.S.(IL)", "France", "U.S.(NH)", "U.S.(MO)", "U.S.(VA)", \ "Germany(nowPoland)[5]", "U.S.(CA)", "England(UK)", "U.S.(PA)", "France", \ "Canada(Que)", "U.S.(PA)", "England(UK)", "France", "U.S.(MI)", \ "England(UK)", "CapeVerde(Portugal)[8]", "U.S.(NY)", "Japan", "Japan", \ "PuertoRico", "Ecuador", "U.S.(TN)", "PuertoRico", "U.S.(NC)", "Japan", \ "U.S.(IN)", "Portugal", "U.S.(GA)", "Japan", "France", "Brazil", "U.S.(TN)", \ "Italy", "Japan", "Japan", "U.S.(AR)", "U.S.(GA)", "U.S.(AL)", "Italy", \ "BritishWestIndies(nowJamaica)", "Japan", "Japan"} In[11]:= Length[Cases[data5[[2, 1, 1, 8 ;; 71, 2]], "Japan"]] Length[Cases[data6, "England(UK)"]] Length[Cases[StringCases[data5[[2, 1, 1, 8 ;; 71, 2]], "U.S. (" ~~ __ ~~ ")"], Except[{}]]] + Length[Cases[data5[[2, 1, 1, 8 ;; 71, 2]], "Puerto Rico"]] Out[11]= 10 Out[12]= 10 Out[13]= 23 Let's figure our how many people are over 100 per capita for different regions of the world. In[14]:= countriesSA := CountryData["SouthAmerica"] countriesEU := CountryData["Europe"] countriesAF := CountryData["Africa"] countriesAS := CountryData["Asia"] countriesAU := {CountryData["Australia"], CountryData["NewZealand"], CountryData["PapuaNewGuinea"]} countriesNA := CountryData["NorthAmerica"] In[20]:= countriesAS[[ ;; ]] Out[20]= {Entity["Country", "Afghanistan"], Entity["Country", "Armenia"], Entity["Country", "Azerbaijan"], Entity["Country", "Bahrain"], Entity["Country", "Bangladesh"], Entity["Country", "Bhutan"], Entity["Country", "Brunei"], Entity["Country", "Cambodia"], Entity["Country", "China"], Entity["Country", "ChristmasIsland"], Entity["Country", "CocosKeelingIslands"], Entity["Country", "EastTimor"], Entity["Country", "Egypt"], Entity["Country", "GazaStrip"], Entity["Country", "Georgia"], Entity["Country", "HongKong"], Entity["Country", "India"], Entity["Country", "Indonesia"], Entity["Country", "Iran"], Entity["Country", "Iraq"], Entity["Country", "Israel"], Entity["Country", "Japan"], Entity["Country", "Jordan"], Entity["Country", "Kazakhstan"], Entity["Country", "Kuwait"], Entity["Country", "Kyrgyzstan"], Entity["Country", "Laos"], Entity["Country", "Lebanon"], Entity["Country", "Macau"], Entity["Country", "Malaysia"], Entity["Country", "Maldives"], Entity["Country", "Mongolia"], Entity["Country", "Myanmar"], Entity["Country", "Nepal"], Entity["Country", "NorthKorea"], Entity["Country", "Oman"], Entity["Country", "Pakistan"], Entity["Country", "Philippines"], Entity["Country", "Qatar"], Entity["Country", "Russia"], Entity["Country", "SaudiArabia"], Entity["Country", "Singapore"], Entity["Country", "SouthKorea"], Entity["Country", "SriLanka"], Entity["Country", "Syria"], Entity["Country", "Taiwan"], Entity["Country", "Tajikistan"], Entity["Country", "Thailand"], Entity["Country", "Turkey"], Entity["Country", "Turkmenistan"], Entity["Country", "UnitedArabEmirates"], Entity["Country", "Uzbekistan"], Entity["Country", "Vietnam"], Entity["Country", "WestBank"], Entity["Country", "Yemen"]} In[21]:= countriesAU Out[21]= {Entity["Country", "Australia"], Entity["Country", "NewZealand"], Entity["Country", "PapuaNewGuinea"]} Set up an array of zeros based on the number of countries in each region. In[22]:= eu = ConstantArray[0, Length[countriesEU[[;; , 2]]]]; sa = ConstantArray[0, Length[countriesSA[[;; , 2]]]]; af = ConstantArray[0, Length[countriesAF[[;; , 2]]]]; as = ConstantArray[0, Length[countriesAS[[;; , 2]]]]; au = ConstantArray[0, Length[countriesAU[[;; , 2]]]]; na = ConstantArray[0, Length[countriesNA[[;; , 2]]]]; In[28]:= eu Out[28]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0} Loop over all European countries to count number of people over 100 years old in Europe In[29]:= countriesEU Out[29]= {Entity["Country", "Albania"], Entity["Country", "Andorra"], Entity["Country", "Austria"], Entity["Country", "Belarus"], Entity["Country", "Belgium"], Entity["Country", "BosniaHerzegovina"], Entity["Country", "Bulgaria"], Entity["Country", "Croatia"], Entity["Country", "Cyprus"], Entity["Country", "CzechRepublic"], Entity["Country", "Denmark"], Entity["Country", "Estonia"], Entity["Country", "FaroeIslands"], Entity["Country", "Finland"], Entity["Country", "France"], Entity["Country", "Germany"], Entity["Country", "Gibraltar"], Entity["Country", "Greece"], Entity["Country", "Guernsey"], Entity["Country", "Hungary"], Entity["Country", "Iceland"], Entity["Country", "Ireland"], Entity["Country", "IsleOfMan"], Entity["Country", "Italy"], Entity["Country", "Jersey"], Entity["Country", "Kosovo"], Entity["Country", "Latvia"], Entity["Country", "Liechtenstein"], Entity["Country", "Lithuania"], Entity["Country", "Luxembourg"], Entity["Country", "Macedonia"], Entity["Country", "Malta"], Entity["Country", "Moldova"], Entity["Country", "Monaco"], Entity["Country", "Montenegro"], Entity["Country", "Netherlands"], Entity["Country", "Norway"], Entity["Country", "Poland"], Entity["Country", "Portugal"], Entity["Country", "Romania"], Entity["Country", "SanMarino"], Entity["Country", "Serbia"], Entity["Country", "Slovakia"], Entity["Country", "Slovenia"], Entity["Country", "Spain"], Entity["Country", "Svalbard"], Entity["Country", "Sweden"], Entity["Country", "Switzerland"], Entity["Country", "Ukraine"], Entity["Country", "UnitedKingdom"], Entity["Country", "VaticanCity"]} In[30]:= {Length[Cases[StringCases[data6, "England(UK)"]]]} (* without space ?? ) Out[30]= {1} In[31]:= Do[eu[[kk]] = Length[Cases[data6, countriesEU[[kk, 2]]]], {kk, 1, Length[countriesEU[[;; , 2]]], 1}] In[32]:= Cases[data6, "England(UK)"] Out[32]= {"England(UK)", "England(UK)", "England(UK)", "England(UK)", "England(UK)", \ "England(UK)", "England(UK)", "England(UK)", "England(UK)", "England(UK)"} In[33]:= eu Out[33]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, \ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0} In[34]:= Total[eu] Out[34]= 12 Loop over all South American countries to count number of people over 100 years old in South America In[35]:= countriesSA Out[35]= {Entity["Country", "Argentina"], Entity["Country", "Bolivia"], Entity["Country", "Brazil"], Entity["Country", "Chile"], Entity["Country", "Colombia"], Entity["Country", "Ecuador"], Entity["Country", "FalklandIslands"], Entity["Country", "FrenchGuiana"], Entity["Country", "Guyana"], Entity["Country", "Paraguay"], Entity["Country", "Peru"], Entity["Country", "Suriname"], Entity["Country", "Uruguay"], Entity["Country", "Venezuela"]} In[36]:= (Do[sa[[kk]]=Length[Cases[data5[[2,1,1,8;;71,2]],countriesSA[[kk,2]]]],{kk,1,\ Length[countriesSA[[;;,2]]],1}]) In[37]:= Do[sa[[kk]] = Length[Cases[data6, countriesSA[[kk, 2]]]], {kk, 1, Length[countriesSA[[;; , 2]]], 1}] In[38]:= sa Out[38]= {0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0} In[39]:= Total[sa] Out[39]= 2 Loop over all African countries to count number of people over 100 years old in African In[40]:= countriesAF Out[40]= {Entity["Country", "Algeria"], Entity["Country", "Angola"], Entity["Country", "Benin"], Entity["Country", "Botswana"], Entity["Country", "BurkinaFaso"], Entity["Country", "Burundi"], Entity["Country", "Cameroon"], Entity["Country", "CapeVerde"], Entity["Country", "CentralAfricanRepublic"], Entity["Country", "Chad"], Entity["Country", "Comoros"], Entity["Country", "DemocraticRepublicCongo"], Entity["Country", "Djibouti"], Entity["Country", "Egypt"], Entity["Country", "EquatorialGuinea"], Entity["Country", "Eritrea"], Entity["Country", "Ethiopia"], Entity["Country", "Gabon"], Entity["Country", "Gambia"], Entity["Country", "Ghana"], Entity["Country", "Guinea"], Entity["Country", "GuineaBissau"], Entity["Country", "IvoryCoast"], Entity["Country", "Kenya"], Entity["Country", "Lesotho"], Entity["Country", "Liberia"], Entity["Country", "Libya"], Entity["Country", "Madagascar"], Entity["Country", "Malawi"], Entity["Country", "Mali"], Entity["Country", "Mauritania"], Entity["Country", "Mauritius"], Entity["Country", "Mayotte"], Entity["Country", "Morocco"], Entity["Country", "Mozambique"], Entity["Country", "Namibia"], Entity["Country", "Niger"], Entity["Country", "Nigeria"], Entity["Country", "RepublicCongo"], Entity["Country", "Reunion"], Entity["Country", "Rwanda"], Entity["Country", "SaintHelena"], Entity["Country", "SaoTomePrincipe"], Entity["Country", "Senegal"], Entity["Country", "Seychelles"], Entity["Country", "SierraLeone"], Entity["Country", "Somalia"], Entity["Country", "SouthAfrica"], Entity["Country", "SouthSudan"], Entity["Country", "Sudan"], Entity["Country", "Swaziland"], Entity["Country", "Tanzania"], Entity["Country", "Togo"], Entity["Country", "Tunisia"], Entity["Country", "Uganda"], Entity["Country", "WesternSahara"], Entity["Country", "Zambia"], Entity["Country", "Zimbabwe"]} In[41]:= (Do[af[[kk]]=Length[Cases[data5[[2,1,1,8;;71,2]],countriesAF[[kk,2]]]],{kk,1,\ Length[countriesAF[[;;,2]]],1}]) In[42]:= Do[af[[kk]] = Length[Cases[data6, countriesAF[[kk, 2]]]], {kk, 1, Length[countriesAF[[;; , 2]]], 1}] In[43]:= af Out[43]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, \ 0, 0, 0, 0, 0, 0, 0} In[44]:= Total[af] Out[44]= 1 Loop over all Asian countries to count number of people over 100 years old in Asian In[45]:= countriesAS Out[45]= {Entity["Country", "Afghanistan"], Entity["Country", "Armenia"], Entity["Country", "Azerbaijan"], Entity["Country", "Bahrain"], Entity["Country", "Bangladesh"], Entity["Country", "Bhutan"], Entity["Country", "Brunei"], Entity["Country", "Cambodia"], Entity["Country", "China"], Entity["Country", "ChristmasIsland"], Entity["Country", "CocosKeelingIslands"], Entity["Country", "EastTimor"], Entity["Country", "Egypt"], Entity["Country", "GazaStrip"], Entity["Country", "Georgia"], Entity["Country", "HongKong"], Entity["Country", "India"], Entity["Country", "Indonesia"], Entity["Country", "Iran"], Entity["Country", "Iraq"], Entity["Country", "Israel"], Entity["Country", "Japan"], Entity["Country", "Jordan"], Entity["Country", "Kazakhstan"], Entity["Country", "Kuwait"], Entity["Country", "Kyrgyzstan"], Entity["Country", "Laos"], Entity["Country", "Lebanon"], Entity["Country", "Macau"], Entity["Country", "Malaysia"], Entity["Country", "Maldives"], Entity["Country", "Mongolia"], Entity["Country", "Myanmar"], Entity["Country", "Nepal"], Entity["Country", "NorthKorea"], Entity["Country", "Oman"], Entity["Country", "Pakistan"], Entity["Country", "Philippines"], Entity["Country", "Qatar"], Entity["Country", "Russia"], Entity["Country", "SaudiArabia"], Entity["Country", "Singapore"], Entity["Country", "SouthKorea"], Entity["Country", "SriLanka"], Entity["Country", "Syria"], Entity["Country", "Taiwan"], Entity["Country", "Tajikistan"], Entity["Country", "Thailand"], Entity["Country", "Turkey"], Entity["Country", "Turkmenistan"], Entity["Country", "UnitedArabEmirates"], Entity["Country", "Uzbekistan"], Entity["Country", "Vietnam"], Entity["Country", "WestBank"], Entity["Country", "Yemen"]} In[46]:= (Do[af[[kk]]=Length[Cases[data5[[2,1,1,8;;71,2]],countriesAF[[kk,2]]]],{kk,1,\ Length[countriesAF[[;;,2]]],1}]) In[47]:= Do[as[[kk]] = Length[Cases[data6, countriesAS[[kk, 2]]]], {kk, 1, Length[countriesAS[[;; , 2]]], 1}] In[48]:= as Out[48]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, \ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0} In[49]:= Total[as] Out[49]= 10 Loop over all "Australian" countries to count number of people over 100 years old in Australian In[50]:= countriesAU Out[50]= {Entity["Country", "Australia"], Entity["Country", "NewZealand"], Entity["Country", "PapuaNewGuinea"]} In[51]:= (Do[af[[kk]]=Length[Cases[data5[[2,1,1,8;;71,2]],countriesAF[[kk,2]]]],{kk,1,\ Length[countriesAF[[;;,2]]],1}]) In[52]:= Do[au[[kk]] = Length[Cases[data6, countriesAU[[kk, 2]]]], {kk, 1, Length[countriesAU[[;; , 2]]], 1}] In[53]:= au Out[53]= {0, 0, 0} In[54]:= Total[au] Out[54]= 0 Gotta do something special for the US In[55]:= countriesNA Out[55]= {Entity["Country", "Anguilla"], Entity["Country", "AntiguaBarbuda"], Entity["Country", "Aruba"], Entity["Country", "Bahamas"], Entity["Country", "Barbados"], Entity["Country", "Belize"], Entity["Country", "Bermuda"], Entity["Country", "BritishVirginIslands"], Entity["Country", "Canada"], Entity["Country", "CaymanIslands"], Entity["Country", "CostaRica"], Entity["Country", "Cuba"], Entity["Country", "Curacao"], Entity["Country", "Dominica"], Entity["Country", "DominicanRepublic"], Entity["Country", "ElSalvador"], Entity["Country", "Greenland"], Entity["Country", "Grenada"], Entity["Country", "Guadeloupe"], Entity["Country", "Guatemala"], Entity["Country", "Haiti"], Entity["Country", "Honduras"], Entity["Country", "Jamaica"], Entity["Country", "Martinique"], Entity["Country", "Mexico"], Entity["Country", "Montserrat"], Entity["Country", "Nicaragua"], Entity["Country", "Panama"], Entity["Country", "PuertoRico"], Entity["Country", "SaintKittsNevis"], Entity["Country", "SaintLucia"], Entity["Country", "SaintPierreMiquelon"], Entity["Country", "SaintVincentGrenadines"], Entity["Country", "SintMaarten"], Entity["Country", "TrinidadTobago"], Entity["Country", "TurksCaicosIslands"], Entity["Country", "UnitedStates"], Entity["Country", "UnitedStatesVirginIslands"]} Note ... this does not include United States - due to abbreviation... Had to use "remove space" to handle Puerto Rico ... due to space in country name In[56]:= Do[na[[jj]] = Length[Cases[data6, countriesNA[[jj, 2]]]], {jj, 1, Length[countriesNA[[;; , 2]]], 1}] In[57]:= na Total[na] naOver100 = Total[na] + Length[Cases[StringCases[data6, "U.S.(" ~~ __ ~~ ")"], Except[{}]]] Out[57]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0} Out[58]= 2 Out[59]= 23 Get Population Data In[60]:= SApop := CountryData["SouthAmerica", "Population"] EUpop := CountryData["Europe", "Population"] AFpop := CountryData["Africa", "Population"] ASpop := CountryData["Asia", "Population"] AUpop := {CountryData["Australia", "Population"], CountryData["NewZealand", "Population"], CountryData["PapuaNewGuinea", "Population"]} NApop := CountryData["NorthAmerica", "Population"] In[66]:= Total[SApop] Out[66]= Quantity[424393547, "People"] In[67]:= over100percapita = {{"South America", "Europe", "Africa", "Asia", "Australia", "North and Central America"}, {Total[sa]/Total[SApop], Total[eu]/Total[EUpop], Total[af]/Total[AFpop], Total[as]/Total[ASpop], Total[au]/Total[AUpop], naOver100/Total[NApop]}}; In[68]:= over100percapita // MatrixForm Out[68]//MatrixForm= \!\( TagBox[ RowBox[{"(", "", GridBox[{ {"\<\"South America\"\>", "\<\"Europe\"\>", "\<\"Africa\"\>", "\<\"Asia\"\>", \ "\<\"Australia\"\>", "\<\"North and Central America\"\>"}, { TemplateBox[{ FractionBox["2", "424393547"],RowBox[{"\"per \"", " ", "\"person\""}], "reciprocal people",FractionBox["1", "\"People\""]}, "Quantity"], TemplateBox[{ FractionBox["3", "150280378"],RowBox[{"\"per \"", " ", "\"person\""}], "reciprocal people",FractionBox["1", "\"People\""]}, "Quantity"], TemplateBox[{ FractionBox["1", "1256268024"], RowBox[{"\"per \"", " ", "\"person\""}],"reciprocal people", FractionBox["1", "\"People\""]}, "Quantity"], TemplateBox[{ FractionBox["2", "948873501"],RowBox[{"\"per \"", " ", "\"person\""}], "reciprocal people",FractionBox["1", "\"People\""]}, "Quantity"], TemplateBox[{ "0",RowBox[{"\"per \"", " ", "\"person\""}],"reciprocal people", FractionBox["1", "\"People\""]}, "Quantity"], TemplateBox[{ FractionBox["23", "582382049"], RowBox[{"\"per \"", " ", "\"person\""}],"reciprocal people", FractionBox["1", "\"People\""]}, "Quantity"]} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]\) In[69]:= Total[as] Out[69]= 10 In[70]:= euinfo = ConstantArray[0, Length[data6]]; sainfo = ConstantArray[0, Length[data6]]; afinfo = ConstantArray[0, Length[data6]]; asinfo = ConstantArray[0, Length[data6]]; auinfo = ConstantArray[0, Length[data6]]; nainfo = ConstantArray[0, Length[data6]]; In[76]:= data5[[2, 1, 1, 8 ;; 71, 6]] Out[76]= {113, 111, 109, 109, 110, 109, 113, 109, 110, 110, 111, 111, 111, 111, 112, \ 112, 110, 112, 113, 111, 111, 113, 112, 113, 113, 114, 115, 113, 114, 114, \ 122, 117, 119, 114, 115, 115, 114, 114, 113, 114, 114, 114, 116, 116, 115, \ 114, 114, 115, 115, 115, 114, 114, 114, 116, 115, 116, 117, 116, 116, 116, \ 117, 117, 117, 117} In[77]:= data6 Out[77]= {"England(UK)", "England(UK)", "Denmark", "U.S.(IN)", "Netherlands", \ "Norway", "U.S.(MI)", "England(UK)", "U.S.(TX)", "England(UK)", \ "England(UK)", "SouthAfrica", "Czechoslovakia", "England(UK)", "Spain", \ "England(UK)", "N.Ireland(UK)", "Japan", "Japan", "U.S.(MO)", "France", \ "U.S.(IL)", "France", "U.S.(NH)", "U.S.(MO)", "U.S.(VA)", \ "Germany(nowPoland)[5]", "U.S.(CA)", "England(UK)", "U.S.(PA)", "France", \ "Canada(Que)", "U.S.(PA)", "England(UK)", "France", "U.S.(MI)", \ "England(UK)", "CapeVerde(Portugal)[8]", "U.S.(NY)", "Japan", "Japan", \ "PuertoRico", "Ecuador", "U.S.(TN)", "PuertoRico", "U.S.(NC)", "Japan", \ "U.S.(IN)", "Portugal", "U.S.(GA)", "Japan", "France", "Brazil", "U.S.(TN)", \ "Italy", "Japan", "Japan", "U.S.(AR)", "U.S.(GA)", "U.S.(AL)", "Italy", \ "BritishWestIndies(nowJamaica)", "Japan", "Japan"} Average Age For Europe In[78]:= Do[ Do[ If[data6[[kk]] == countriesEU[[jj, 2]], euinfo[[kk]] = 1], {kk, 1, Length[data6], 1} ], {jj, 1, Length[countriesEU[[;; , 2]]], 1} ] In[79]:= euinfo data5[[2, 1, 1, 8 ;; 71, 6]] Out[79]= {0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, \ 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, \ 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0} Out[80]= {113, 111, 109, 109, 110, 109, 113, 109, 110, 110, 111, 111, 111, 111, 112, \ 112, 110, 112, 113, 111, 111, 113, 112, 113, 113, 114, 115, 113, 114, 114, \ 122, 117, 119, 114, 115, 115, 114, 114, 113, 114, 114, 114, 116, 116, 115, \ 114, 114, 115, 115, 115, 114, 114, 114, 116, 115, 116, 117, 116, 116, 116, \ 117, 117, 117, 117} In[81]:= DeleteCases[euinfodata5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity] Out[81]= {109, 110, 109, 112, 111, 112, 122, 115, 115, 114, 115, 117} In[82]:= euAvg = Mean[DeleteCases[euinfodata5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] // N Out[82]= 113.417 Average Age For South America In[83]:= Do[ Do[ If[data6[[kk]] == countriesSA[[jj, 2]], sainfo[[kk]] = 1], {kk, 1, Length[data6], 1} ], {jj, 1, Length[countriesSA[[;; , 2]]], 1} ] In[84]:= sainfo Out[84]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0} In[85]:= DeleteCases[sainfodata5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity] Out[85]= {116, 114} In[86]:= saAvg = Mean[DeleteCases[sainfodata5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] // N Out[86]= 115. Average Age For Africa In[87]:= Do[ Do[ If[data6[[kk]] == countriesAF[[jj, 2]], afinfo[[kk]] = 1], {kk, 1, Length[data6], 1} ], {jj, 1, Length[countriesAF[[;; , 2]]], 1} ] In[88]:= afAvg = Mean[DeleteCases[afinfodata5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] // N Out[88]= 111. Average Age For Asia In[89]:= Do[ Do[ If[data6[[kk]] == countriesAS[[jj, 2]], asinfo[[kk]] = 1], {kk, 1, Length[data6], 1} ], {jj, 1, Length[countriesAS[[;; , 2]]], 1} ] In[90]:= asAvg = Mean[DeleteCases[asinfodata5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] // N Out[90]= 114.8 Average Age For Australia In[91]:= Do[ Do[ If[data6[[kk]] == countriesAU[[jj, 2]], auinfo[[kk]] = 1], {kk, 1, Length[data6], 1} ], {jj, 1, Length[countriesAU[[;; , 2]]], 1} ] In[92]:= auAvg = Mean[DeleteCases[auinfodata5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] // N Out[92]= Mean[{}] Average Age For North America In[93]:= Do[ Do[ If[data6[[kk]] == countriesNA[[jj, 2]], nainfo[[kk]] = 1], {kk, 1, Length[data6], 1} ], {jj, 1, Length[countriesNA[[;; , 2]]], 1} ] In[94]:= naAvg = Mean[DeleteCases[nainfodata5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] // N Out[94]= 114.5 In[95]:= data5[[2, 1, 1, 8 ;; 71, 9]] Out[95]= {"F", "F", "F", "F", "F", "F", "F", "F", "M", "F", "M", "F", "F", "F", "F", \ "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "M", "F", "F", "F", "F", \ "F", "F", "F", "F", "F", "F", "F", "F", "F", "M", "F", "F", "F", "F", "M", \ "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "M", "F", "F", "F", "F", \ "F", "F", "F", "F"} In[96]:= Count[data5[[2, 1, 1, 8 ;; 71, 9]], "M"] Count[data5[[2, 1, 1, 8 ;; 71, 9]], "F"] Out[96]= 6 Out[97]= 58 Make the plot In[98]:= In[103]:= BarChart[over100percapita[[2, ;;]]100, PlotLabel -> Style["Percent of Total Population over 100 Years of Age (with Averge age)", 30], ChartLabels -> Placed[{over100percapita[[1, ;;]], {saAvg, euAvg, afAvg, asAvg, "", naAvg} // N}, {Below, Center}, {Rotate[#, Pi/2.4] &, Rotate[#, 0] &}], Epilog -> {Text[ Style[ {Count[data5[[2, 1, 1, 8 ;; 71, 9]], "F"] "Female", Count[data5[[2, 1, 1, 8 ;; 71, 9]], "M"] "Male"} , 25], Scaled[{0.55, 0.85}]], Red, Point@{.5, .5}}]

The goal here was to be able to automate some of the data processing to be able to read in new data if the we page changes. Some of the output is there to double check some of the data filters. It's not perfect, but it's what I've got now.

In[1]:= data5 := Import["http://archive.is/4kwbk", "Data"]

In[2]:= data5[[2, 1, 1, 6]]

Out[2]= {"#", "Birthplace", "Name", "Born", "Died", "Age", "Race", "Sex", \
"Deathplace", "When Oldest", "Length of Reign", "Reign Length", "Age at \
Accession", "Case added to GRG Tables"}

In[3]:= data5[[2, 1, 1, 8 ;; 10, 4 ;; 6]] // MatrixForm

Out[3]//MatrixForm= \!\(
TagBox[
RowBox[{"(", "", GridBox[{
{"\<\"Aug. 20, 1842\"\>", "\<\"Oct. 24, 1955\"\>", "113"},
{"\<\"Feb. 11, 1845\"\>", "\<\"Dec. 16, 1956\"\>", "111"},
{"\<\"Jan. 24, 1849\"\>", "\<\"Mar. 30, 1958\"\>", "109"}
},
GridBoxAlignment->{
      "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}},
        "RowsIndexed" -> {}},
GridBoxSpacings->{"Columns" -> {
Offset[0.27999999999999997`], {
Offset[0.7]}, 
Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> {
Offset[0.2], {
Offset[0.4]}, 
Offset[0.2]}, "RowsIndexed" -> {}}], "", ")"}],
Function[BoxForm`e$, 
MatrixForm[BoxForm`e$]]]\)

In[4]:= data5[[2, 1, 1, 8 ;; 10]]

Out[4]= {{1, "England (UK)", "Betsy Baker", "Aug. 20, 1842", "Oct. 24, 1955", 113, 65,
   "W", "F", "U.S. (NE)", "?-1955", "?-113", "N.A.", "N.A.", "N.A.", "N.A.", 
  "N.A.", "N.A."}, {2, "England (UK)", "Jennie Howell", "Feb. 11, 1845", 
  "Dec. 16, 1956", 111, 309, "W", "F", "U.S. (CA)", "1955-1956", "110-111", 1,
   53, " 1.14", 110, 255, "Aug. 29, 2017"}, {3, "Denmark", 
  "Anne Marie Carstenson", "Jan. 24, 1849", "Mar. 30, 1958", 109, 65, "W", 
  "F", "U.S. (NE)", "1956-1958", "107-109", 1, 104, " 1.28", 107, 327, 
  "N.A. (less than 110)"}}

In[5]:= data5[[2, 1, 1, 9]]

Out[5]= {2, "England (UK)", "Jennie Howell", "Feb. 11, 1845", "Dec. 16, 1956", 111, \
309, "W", "F", "U.S. (CA)", "1955-1956", "110-111", 1, 53, " 1.14", 110, 255, \
"Aug. 29, 2017"}

Remove the space to match up with how Mathematica refers to variables

In[6]:= data6 = StringDelete[data5[[2, 1, 1, 8 ;; 71, 2]], " "]

Out[6]= {"England(UK)", "England(UK)", "Denmark", "U.S.(IN)", "Netherlands", \
"Norway", "U.S.(MI)", "England(UK)", "U.S.(TX)", "England(UK)", \
"England(UK)", "SouthAfrica", "Czechoslovakia", "England(UK)", "Spain", \
"England(UK)", "N.Ireland(UK)", "Japan", "Japan", "U.S.(MO)", "France", \
"U.S.(IL)", "France", "U.S.(NH)", "U.S.(MO)", "U.S.(VA)", \
"Germany(nowPoland)[5]", "U.S.(CA)", "England(UK)", "U.S.(PA)", "France", \
"Canada(Que)", "U.S.(PA)", "England(UK)", "France", "U.S.(MI)", \
"England(UK)", "CapeVerde(Portugal)[8]", "U.S.(NY)", "Japan", "Japan", \
"PuertoRico", "Ecuador", "U.S.(TN)", "PuertoRico", "U.S.(NC)", "Japan", \
"U.S.(IN)", "Portugal", "U.S.(GA)", "Japan", "France", "Brazil", "U.S.(TN)", \
"Italy", "Japan", "Japan", "U.S.(AR)", "U.S.(GA)", "U.S.(AL)", "Italy", \
"BritishWestIndies(nowJamaica)", "Japan", "Japan"}

Double check numbers by eye ... How do we handle U.S. (CA)

In[7]:= StringCases[data5[[2, 1, 1, 8 ;; 71, 2]], "U.S. (" ~~ __ ~~ ")"]

Out[7]= {{}, {}, {}, {"U.S. (IN)"}, {}, {}, {"U.S. (MI)"}, {}, {"U.S. (TX)"}, {}, {}, \
{}, {}, {}, {}, {}, {}, {}, {}, {"U.S. (MO)"}, {}, {"U.S. (IL)"}, {}, {"U.S. \
(NH)"}, {"U.S. (MO)"}, {"U.S. (VA)"}, {}, {"U.S. (CA)"}, {}, {"U.S. (PA)"}, \
{}, {}, {"U.S. (PA)"}, {}, {}, {"U.S. (MI)"}, {}, {}, {"U.S. (NY)"}, {}, {}, \
{}, {}, {"U.S. (TN)"}, {}, {"U.S. (NC)"}, {}, {"U.S. (IN)"}, {}, {"U.S. \
(GA)"}, {}, {}, {}, {"U.S. (TN)"}, {}, {}, {}, {"U.S. (AR)"}, {"U.S. (GA)"}, \
{"U.S. (AL)"}, {}, {}, {}, {}}

In[8]:= Length[Cases[StringCases[data6, "U.S.(" ~~ __ ~~ ")"], Except[{}]]]

Out[8]= 21

Test the case of Puerto Rico - without a space

In[9]:= Length[Cases[data6, "PuertoRico"]]

Out[9]= 2

OK ... Let's do it!

In[10]:= data6

Out[10]= {"England(UK)", "England(UK)", "Denmark", "U.S.(IN)", "Netherlands", \
"Norway", "U.S.(MI)", "England(UK)", "U.S.(TX)", "England(UK)", \
"England(UK)", "SouthAfrica", "Czechoslovakia", "England(UK)", "Spain", \
"England(UK)", "N.Ireland(UK)", "Japan", "Japan", "U.S.(MO)", "France", \
"U.S.(IL)", "France", "U.S.(NH)", "U.S.(MO)", "U.S.(VA)", \
"Germany(nowPoland)[5]", "U.S.(CA)", "England(UK)", "U.S.(PA)", "France", \
"Canada(Que)", "U.S.(PA)", "England(UK)", "France", "U.S.(MI)", \
"England(UK)", "CapeVerde(Portugal)[8]", "U.S.(NY)", "Japan", "Japan", \
"PuertoRico", "Ecuador", "U.S.(TN)", "PuertoRico", "U.S.(NC)", "Japan", \
"U.S.(IN)", "Portugal", "U.S.(GA)", "Japan", "France", "Brazil", "U.S.(TN)", \
"Italy", "Japan", "Japan", "U.S.(AR)", "U.S.(GA)", "U.S.(AL)", "Italy", \
"BritishWestIndies(nowJamaica)", "Japan", "Japan"}

In[11]:= Length[Cases[data5[[2, 1, 1, 8 ;; 71, 2]], "Japan"]]
Length[Cases[data6, "England(UK)"]]
Length[Cases[StringCases[data5[[2, 1, 1, 8 ;; 71, 2]], "U.S. (" ~~ __ ~~ ")"],
    Except[{}]]] + Length[Cases[data5[[2, 1, 1, 8 ;; 71, 2]], "Puerto Rico"]]

Out[11]= 10

Out[12]= 10

Out[13]= 23

Let's figure our how many people are over 100 per capita for different regions of the world.

In[14]:= countriesSA := CountryData["SouthAmerica"]
countriesEU := CountryData["Europe"]
countriesAF := CountryData["Africa"]
countriesAS := CountryData["Asia"]
countriesAU := {CountryData["Australia"], CountryData["NewZealand"], 
  CountryData["PapuaNewGuinea"]}
countriesNA := CountryData["NorthAmerica"]

In[20]:= countriesAS[[ ;; ]]

Out[20]= {Entity["Country", "Afghanistan"], Entity["Country", "Armenia"], 
 Entity["Country", "Azerbaijan"], Entity["Country", "Bahrain"], 
 Entity["Country", "Bangladesh"], Entity["Country", "Bhutan"], 
 Entity["Country", "Brunei"], Entity["Country", "Cambodia"], 
 Entity["Country", "China"], Entity["Country", "ChristmasIsland"], 
 Entity["Country", "CocosKeelingIslands"], Entity["Country", "EastTimor"], 
 Entity["Country", "Egypt"], Entity["Country", "GazaStrip"], 
 Entity["Country", "Georgia"], Entity["Country", "HongKong"], 
 Entity["Country", "India"], Entity["Country", "Indonesia"], 
 Entity["Country", "Iran"], Entity["Country", "Iraq"], 
 Entity["Country", "Israel"], Entity["Country", "Japan"], 
 Entity["Country", "Jordan"], Entity["Country", "Kazakhstan"], 
 Entity["Country", "Kuwait"], Entity["Country", "Kyrgyzstan"], 
 Entity["Country", "Laos"], Entity["Country", "Lebanon"], 
 Entity["Country", "Macau"], Entity["Country", "Malaysia"], 
 Entity["Country", "Maldives"], Entity["Country", "Mongolia"], 
 Entity["Country", "Myanmar"], Entity["Country", "Nepal"], 
 Entity["Country", "NorthKorea"], Entity["Country", "Oman"], 
 Entity["Country", "Pakistan"], Entity["Country", "Philippines"], 
 Entity["Country", "Qatar"], Entity["Country", "Russia"], 
 Entity["Country", "SaudiArabia"], Entity["Country", "Singapore"], 
 Entity["Country", "SouthKorea"], Entity["Country", "SriLanka"], 
 Entity["Country", "Syria"], Entity["Country", "Taiwan"], 
 Entity["Country", "Tajikistan"], Entity["Country", "Thailand"], 
 Entity["Country", "Turkey"], Entity["Country", "Turkmenistan"], 
 Entity["Country", "UnitedArabEmirates"], Entity["Country", "Uzbekistan"], 
 Entity["Country", "Vietnam"], Entity["Country", "WestBank"], 
 Entity["Country", "Yemen"]}

In[21]:= countriesAU

Out[21]= {Entity["Country", "Australia"], Entity["Country", "NewZealand"], 
 Entity["Country", "PapuaNewGuinea"]}

Set up an array of zeros based on the number of countries in each region.

In[22]:= eu = ConstantArray[0, Length[countriesEU[[;; , 2]]]]; 
sa = ConstantArray[0, Length[countriesSA[[;; , 2]]]]; 
af = ConstantArray[0, Length[countriesAF[[;; , 2]]]]; 
as = ConstantArray[0, Length[countriesAS[[;; , 2]]]]; 
au = ConstantArray[0, Length[countriesAU[[;; , 2]]]]; 
na = ConstantArray[0, Length[countriesNA[[;; , 2]]]];

In[28]:= eu

Out[28]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}

Loop over all European countries to count number of people over 100 years old in Europe

In[29]:= countriesEU

Out[29]= {Entity["Country", "Albania"], Entity["Country", "Andorra"], 
 Entity["Country", "Austria"], Entity["Country", "Belarus"], 
 Entity["Country", "Belgium"], Entity["Country", "BosniaHerzegovina"], 
 Entity["Country", "Bulgaria"], Entity["Country", "Croatia"], 
 Entity["Country", "Cyprus"], Entity["Country", "CzechRepublic"], 
 Entity["Country", "Denmark"], Entity["Country", "Estonia"], 
 Entity["Country", "FaroeIslands"], Entity["Country", "Finland"], 
 Entity["Country", "France"], Entity["Country", "Germany"], 
 Entity["Country", "Gibraltar"], Entity["Country", "Greece"], 
 Entity["Country", "Guernsey"], Entity["Country", "Hungary"], 
 Entity["Country", "Iceland"], Entity["Country", "Ireland"], 
 Entity["Country", "IsleOfMan"], Entity["Country", "Italy"], 
 Entity["Country", "Jersey"], Entity["Country", "Kosovo"], 
 Entity["Country", "Latvia"], Entity["Country", "Liechtenstein"], 
 Entity["Country", "Lithuania"], Entity["Country", "Luxembourg"], 
 Entity["Country", "Macedonia"], Entity["Country", "Malta"], 
 Entity["Country", "Moldova"], Entity["Country", "Monaco"], 
 Entity["Country", "Montenegro"], Entity["Country", "Netherlands"], 
 Entity["Country", "Norway"], Entity["Country", "Poland"], 
 Entity["Country", "Portugal"], Entity["Country", "Romania"], 
 Entity["Country", "SanMarino"], Entity["Country", "Serbia"], 
 Entity["Country", "Slovakia"], Entity["Country", "Slovenia"], 
 Entity["Country", "Spain"], Entity["Country", "Svalbard"], 
 Entity["Country", "Sweden"], Entity["Country", "Switzerland"], 
 Entity["Country", "Ukraine"], Entity["Country", "UnitedKingdom"], 
 Entity["Country", "VaticanCity"]}

In[30]:= {Length[Cases[StringCases[data6, "England(UK)"]]]} (* without space ?? *)

Out[30]= {1}

In[31]:= Do[eu[[kk]] = Length[Cases[data6, countriesEU[[kk, 2]]]], {kk, 1, 
  Length[countriesEU[[;; , 2]]], 1}]

In[32]:= Cases[data6, "England(UK)"]

Out[32]= {"England(UK)", "England(UK)", "England(UK)", "England(UK)", "England(UK)", \
"England(UK)", "England(UK)", "England(UK)", "England(UK)", "England(UK)"}

In[33]:= eu

Out[33]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, \
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0}

In[34]:= Total[eu]

Out[34]= 12

Loop over all South American countries to count number of people over 100 years old in South America

In[35]:= countriesSA

Out[35]= {Entity["Country", "Argentina"], Entity["Country", "Bolivia"], 
 Entity["Country", "Brazil"], Entity["Country", "Chile"], 
 Entity["Country", "Colombia"], Entity["Country", "Ecuador"], 
 Entity["Country", "FalklandIslands"], Entity["Country", "FrenchGuiana"], 
 Entity["Country", "Guyana"], Entity["Country", "Paraguay"], 
 Entity["Country", "Peru"], Entity["Country", "Suriname"], 
 Entity["Country", "Uruguay"], Entity["Country", "Venezuela"]}

In[36]:= (*Do[sa[[kk]]=Length[Cases[data5[[2,1,1,8;;71,2]],countriesSA[[kk,2]]]],{kk,1,\
Length[countriesSA[[;;,2]]],1}]*)

In[37]:= Do[sa[[kk]] = Length[Cases[data6, countriesSA[[kk, 2]]]], {kk, 1, 
  Length[countriesSA[[;; , 2]]], 1}]

In[38]:= sa 

Out[38]= {0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0}

In[39]:= Total[sa]

Out[39]= 2

Loop over all African countries to count number of people over 100 years old in African

In[40]:= countriesAF

Out[40]= {Entity["Country", "Algeria"], Entity["Country", "Angola"], 
 Entity["Country", "Benin"], Entity["Country", "Botswana"], 
 Entity["Country", "BurkinaFaso"], Entity["Country", "Burundi"], 
 Entity["Country", "Cameroon"], Entity["Country", "CapeVerde"], 
 Entity["Country", "CentralAfricanRepublic"], Entity["Country", "Chad"], 
 Entity["Country", "Comoros"], Entity["Country", "DemocraticRepublicCongo"], 
 Entity["Country", "Djibouti"], Entity["Country", "Egypt"], 
 Entity["Country", "EquatorialGuinea"], Entity["Country", "Eritrea"], 
 Entity["Country", "Ethiopia"], Entity["Country", "Gabon"], 
 Entity["Country", "Gambia"], Entity["Country", "Ghana"], 
 Entity["Country", "Guinea"], Entity["Country", "GuineaBissau"], 
 Entity["Country", "IvoryCoast"], Entity["Country", "Kenya"], 
 Entity["Country", "Lesotho"], Entity["Country", "Liberia"], 
 Entity["Country", "Libya"], Entity["Country", "Madagascar"], 
 Entity["Country", "Malawi"], Entity["Country", "Mali"], 
 Entity["Country", "Mauritania"], Entity["Country", "Mauritius"], 
 Entity["Country", "Mayotte"], Entity["Country", "Morocco"], 
 Entity["Country", "Mozambique"], Entity["Country", "Namibia"], 
 Entity["Country", "Niger"], Entity["Country", "Nigeria"], 
 Entity["Country", "RepublicCongo"], Entity["Country", "Reunion"], 
 Entity["Country", "Rwanda"], Entity["Country", "SaintHelena"], 
 Entity["Country", "SaoTomePrincipe"], Entity["Country", "Senegal"], 
 Entity["Country", "Seychelles"], Entity["Country", "SierraLeone"], 
 Entity["Country", "Somalia"], Entity["Country", "SouthAfrica"], 
 Entity["Country", "SouthSudan"], Entity["Country", "Sudan"], 
 Entity["Country", "Swaziland"], Entity["Country", "Tanzania"], 
 Entity["Country", "Togo"], Entity["Country", "Tunisia"], 
 Entity["Country", "Uganda"], Entity["Country", "WesternSahara"], 
 Entity["Country", "Zambia"], Entity["Country", "Zimbabwe"]}

In[41]:= (*Do[af[[kk]]=Length[Cases[data5[[2,1,1,8;;71,2]],countriesAF[[kk,2]]]],{kk,1,\
Length[countriesAF[[;;,2]]],1}]*)

In[42]:= Do[af[[kk]] = Length[Cases[data6, countriesAF[[kk, 2]]]], {kk, 1, 
  Length[countriesAF[[;; , 2]]], 1}]

In[43]:= af

Out[43]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, \
0, 0, 0, 0, 0, 0, 0}

In[44]:= Total[af]

Out[44]= 1

Loop over all Asian countries to count number of people over 100 years old in Asian

In[45]:= countriesAS

Out[45]= {Entity["Country", "Afghanistan"], Entity["Country", "Armenia"], 
 Entity["Country", "Azerbaijan"], Entity["Country", "Bahrain"], 
 Entity["Country", "Bangladesh"], Entity["Country", "Bhutan"], 
 Entity["Country", "Brunei"], Entity["Country", "Cambodia"], 
 Entity["Country", "China"], Entity["Country", "ChristmasIsland"], 
 Entity["Country", "CocosKeelingIslands"], Entity["Country", "EastTimor"], 
 Entity["Country", "Egypt"], Entity["Country", "GazaStrip"], 
 Entity["Country", "Georgia"], Entity["Country", "HongKong"], 
 Entity["Country", "India"], Entity["Country", "Indonesia"], 
 Entity["Country", "Iran"], Entity["Country", "Iraq"], 
 Entity["Country", "Israel"], Entity["Country", "Japan"], 
 Entity["Country", "Jordan"], Entity["Country", "Kazakhstan"], 
 Entity["Country", "Kuwait"], Entity["Country", "Kyrgyzstan"], 
 Entity["Country", "Laos"], Entity["Country", "Lebanon"], 
 Entity["Country", "Macau"], Entity["Country", "Malaysia"], 
 Entity["Country", "Maldives"], Entity["Country", "Mongolia"], 
 Entity["Country", "Myanmar"], Entity["Country", "Nepal"], 
 Entity["Country", "NorthKorea"], Entity["Country", "Oman"], 
 Entity["Country", "Pakistan"], Entity["Country", "Philippines"], 
 Entity["Country", "Qatar"], Entity["Country", "Russia"], 
 Entity["Country", "SaudiArabia"], Entity["Country", "Singapore"], 
 Entity["Country", "SouthKorea"], Entity["Country", "SriLanka"], 
 Entity["Country", "Syria"], Entity["Country", "Taiwan"], 
 Entity["Country", "Tajikistan"], Entity["Country", "Thailand"], 
 Entity["Country", "Turkey"], Entity["Country", "Turkmenistan"], 
 Entity["Country", "UnitedArabEmirates"], Entity["Country", "Uzbekistan"], 
 Entity["Country", "Vietnam"], Entity["Country", "WestBank"], 
 Entity["Country", "Yemen"]}

In[46]:= (*Do[af[[kk]]=Length[Cases[data5[[2,1,1,8;;71,2]],countriesAF[[kk,2]]]],{kk,1,\
Length[countriesAF[[;;,2]]],1}]*)

In[47]:= Do[as[[kk]] = Length[Cases[data6, countriesAS[[kk, 2]]]], {kk, 1, 
  Length[countriesAS[[;; , 2]]], 1}]

In[48]:= as

Out[48]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, \
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \
0, 0, 0, 0}

In[49]:= Total[as]

Out[49]= 10

Loop over all "Australian" countries to count number of people over 100 years old in Australian

In[50]:= countriesAU

Out[50]= {Entity["Country", "Australia"], Entity["Country", "NewZealand"], 
 Entity["Country", "PapuaNewGuinea"]}

In[51]:= (*Do[af[[kk]]=Length[Cases[data5[[2,1,1,8;;71,2]],countriesAF[[kk,2]]]],{kk,1,\
Length[countriesAF[[;;,2]]],1}]*)

In[52]:= Do[au[[kk]] = Length[Cases[data6, countriesAU[[kk, 2]]]], {kk, 1, 
  Length[countriesAU[[;; , 2]]], 1}]

In[53]:= au

Out[53]= {0, 0, 0}

In[54]:= Total[au]

Out[54]= 0

Gotta do something special for the US

In[55]:= countriesNA

Out[55]= {Entity["Country", "Anguilla"], Entity["Country", "AntiguaBarbuda"], 
 Entity["Country", "Aruba"], Entity["Country", "Bahamas"], 
 Entity["Country", "Barbados"], Entity["Country", "Belize"], 
 Entity["Country", "Bermuda"], Entity["Country", "BritishVirginIslands"], 
 Entity["Country", "Canada"], Entity["Country", "CaymanIslands"], 
 Entity["Country", "CostaRica"], Entity["Country", "Cuba"], 
 Entity["Country", "Curacao"], Entity["Country", "Dominica"], 
 Entity["Country", "DominicanRepublic"], Entity["Country", "ElSalvador"], 
 Entity["Country", "Greenland"], Entity["Country", "Grenada"], 
 Entity["Country", "Guadeloupe"], Entity["Country", "Guatemala"], 
 Entity["Country", "Haiti"], Entity["Country", "Honduras"], 
 Entity["Country", "Jamaica"], Entity["Country", "Martinique"], 
 Entity["Country", "Mexico"], Entity["Country", "Montserrat"], 
 Entity["Country", "Nicaragua"], Entity["Country", "Panama"], 
 Entity["Country", "PuertoRico"], Entity["Country", "SaintKittsNevis"], 
 Entity["Country", "SaintLucia"], Entity["Country", "SaintPierreMiquelon"], 
 Entity["Country", "SaintVincentGrenadines"], 
 Entity["Country", "SintMaarten"], Entity["Country", "TrinidadTobago"], 
 Entity["Country", "TurksCaicosIslands"], Entity["Country", "UnitedStates"], 
 Entity["Country", "UnitedStatesVirginIslands"]}

Note ... this does not include United States - due to abbreviation... Had to use "remove space" to handle Puerto Rico ... due to space in country name

In[56]:= Do[na[[jj]] = Length[Cases[data6, countriesNA[[jj, 2]]]], {jj, 1, 
  Length[countriesNA[[;; , 2]]], 1}]

In[57]:= na
Total[na]
naOver100 = 
 Total[na] + 
  Length[Cases[StringCases[data6, "U.S.(" ~~ __ ~~ ")"], Except[{}]]]


Out[57]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \
0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0}

Out[58]= 2

Out[59]= 23

Get Population Data

In[60]:= SApop := CountryData["SouthAmerica", "Population"]
EUpop := CountryData["Europe", "Population"]
AFpop := CountryData["Africa", "Population"]
ASpop := CountryData["Asia", "Population"]
AUpop := {CountryData["Australia", "Population"], 
  CountryData["NewZealand", "Population"], 
  CountryData["PapuaNewGuinea", "Population"]}
NApop := CountryData["NorthAmerica", "Population"]

In[66]:= Total[SApop]

Out[66]= Quantity[424393547, "People"]

In[67]:= over100percapita = {{"South America", "Europe", "Africa", "Asia", "Australia",
     "North and Central America"}, {Total[sa]/Total[SApop], 
    Total[eu]/Total[EUpop], Total[af]/Total[AFpop], Total[as]/Total[ASpop], 
    Total[au]/Total[AUpop], naOver100/Total[NApop]}};

In[68]:= over100percapita // MatrixForm

Out[68]//MatrixForm= \!\(
TagBox[
RowBox[{"(", "", GridBox[{
{"\<\"South America\"\>", "\<\"Europe\"\>", "\<\"Africa\"\>", "\<\"Asia\"\>", \
"\<\"Australia\"\>", "\<\"North and Central America\"\>"},
{
TemplateBox[{
        FractionBox["2", "424393547"],RowBox[{"\"per \"", " ", "\"person\""}],
         "reciprocal people",FractionBox["1", "\"People\""]},
"Quantity"], 
TemplateBox[{
        FractionBox["3", "150280378"],RowBox[{"\"per \"", " ", "\"person\""}],
         "reciprocal people",FractionBox["1", "\"People\""]},
"Quantity"], 
TemplateBox[{
        FractionBox["1", "1256268024"],
         RowBox[{"\"per \"", " ", "\"person\""}],"reciprocal people",
         FractionBox["1", "\"People\""]},
"Quantity"], 
TemplateBox[{
        FractionBox["2", "948873501"],RowBox[{"\"per \"", " ", "\"person\""}],
         "reciprocal people",FractionBox["1", "\"People\""]},
"Quantity"], 
TemplateBox[{
        "0",RowBox[{"\"per \"", " ", "\"person\""}],"reciprocal people",
         FractionBox["1", "\"People\""]},
"Quantity"], 
TemplateBox[{
        FractionBox["23", "582382049"],
         RowBox[{"\"per \"", " ", "\"person\""}],"reciprocal people",
         FractionBox["1", "\"People\""]},
"Quantity"]}
},
GridBoxAlignment->{
      "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}},
        "RowsIndexed" -> {}},
GridBoxSpacings->{"Columns" -> {
Offset[0.27999999999999997`], {
Offset[0.7]}, 
Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> {
Offset[0.2], {
Offset[0.4]}, 
Offset[0.2]}, "RowsIndexed" -> {}}], "", ")"}],
Function[BoxForm`e$, 
MatrixForm[BoxForm`e$]]]\)

In[69]:= Total[as]

Out[69]= 10

In[70]:= euinfo = ConstantArray[0, Length[data6]];
sainfo = ConstantArray[0, Length[data6]];
afinfo = ConstantArray[0, Length[data6]];
asinfo = ConstantArray[0, Length[data6]];
auinfo = ConstantArray[0, Length[data6]];
nainfo = ConstantArray[0, Length[data6]];

In[76]:= data5[[2, 1, 1, 8 ;; 71, 6]]

Out[76]= {113, 111, 109, 109, 110, 109, 113, 109, 110, 110, 111, 111, 111, 111, 112, \
112, 110, 112, 113, 111, 111, 113, 112, 113, 113, 114, 115, 113, 114, 114, \
122, 117, 119, 114, 115, 115, 114, 114, 113, 114, 114, 114, 116, 116, 115, \
114, 114, 115, 115, 115, 114, 114, 114, 116, 115, 116, 117, 116, 116, 116, \
117, 117, 117, 117}

In[77]:= data6

Out[77]= {"England(UK)", "England(UK)", "Denmark", "U.S.(IN)", "Netherlands", \
"Norway", "U.S.(MI)", "England(UK)", "U.S.(TX)", "England(UK)", \
"England(UK)", "SouthAfrica", "Czechoslovakia", "England(UK)", "Spain", \
"England(UK)", "N.Ireland(UK)", "Japan", "Japan", "U.S.(MO)", "France", \
"U.S.(IL)", "France", "U.S.(NH)", "U.S.(MO)", "U.S.(VA)", \
"Germany(nowPoland)[5]", "U.S.(CA)", "England(UK)", "U.S.(PA)", "France", \
"Canada(Que)", "U.S.(PA)", "England(UK)", "France", "U.S.(MI)", \
"England(UK)", "CapeVerde(Portugal)[8]", "U.S.(NY)", "Japan", "Japan", \
"PuertoRico", "Ecuador", "U.S.(TN)", "PuertoRico", "U.S.(NC)", "Japan", \
"U.S.(IN)", "Portugal", "U.S.(GA)", "Japan", "France", "Brazil", "U.S.(TN)", \
"Italy", "Japan", "Japan", "U.S.(AR)", "U.S.(GA)", "U.S.(AL)", "Italy", \
"BritishWestIndies(nowJamaica)", "Japan", "Japan"}

Average Age For Europe

In[78]:= Do[
 Do[
  If[data6[[kk]] == countriesEU[[jj, 2]], euinfo[[kk]] = 1], {kk, 1, 
   Length[data6], 1}
  ], {jj, 1, Length[countriesEU[[;; , 2]]], 1}
 ]

In[79]:= euinfo
data5[[2, 1, 1, 8 ;; 71, 6]]

Out[79]= {0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, \
0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, \
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0}

Out[80]= {113, 111, 109, 109, 110, 109, 113, 109, 110, 110, 111, 111, 111, 111, 112, \
112, 110, 112, 113, 111, 111, 113, 112, 113, 113, 114, 115, 113, 114, 114, \
122, 117, 119, 114, 115, 115, 114, 114, 113, 114, 114, 114, 116, 116, 115, \
114, 114, 115, 115, 115, 114, 114, 114, 116, 115, 116, 117, 116, 116, 116, \
117, 117, 117, 117}

In[81]:= DeleteCases[euinfo*data5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]

Out[81]= {109, 110, 109, 112, 111, 112, 122, 115, 115, 114, 115, 117}

In[82]:= euAvg = Mean[DeleteCases[euinfo*data5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] //
   N

Out[82]= 113.417

Average Age For South America

In[83]:= Do[
 Do[
  If[data6[[kk]] == countriesSA[[jj, 2]], sainfo[[kk]] = 1], {kk, 1, 
   Length[data6], 1}
  ], {jj, 1, Length[countriesSA[[;; , 2]]], 1}
 ]

In[84]:= sainfo

Out[84]= {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, \
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}

In[85]:= DeleteCases[sainfo*data5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]

Out[85]= {116, 114}

In[86]:= saAvg = Mean[DeleteCases[sainfo*data5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] //
   N

Out[86]= 115.

Average Age For Africa

In[87]:= Do[
 Do[
  If[data6[[kk]] == countriesAF[[jj, 2]], afinfo[[kk]] = 1], {kk, 1, 
   Length[data6], 1}
  ], {jj, 1, Length[countriesAF[[;; , 2]]], 1}
 ]

In[88]:= afAvg = Mean[DeleteCases[afinfo*data5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] //
   N

Out[88]= 111.

Average Age For Asia

In[89]:= Do[
 Do[
  If[data6[[kk]] == countriesAS[[jj, 2]], asinfo[[kk]] = 1], {kk, 1, 
   Length[data6], 1}
  ], {jj, 1, Length[countriesAS[[;; , 2]]], 1}
 ]

In[90]:= asAvg = Mean[DeleteCases[asinfo*data5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] //
   N

Out[90]= 114.8

Average Age For Australia

In[91]:= Do[
 Do[
  If[data6[[kk]] == countriesAU[[jj, 2]], auinfo[[kk]] = 1], {kk, 1, 
   Length[data6], 1}
  ], {jj, 1, Length[countriesAU[[;; , 2]]], 1}
 ]

In[92]:= auAvg = Mean[DeleteCases[auinfo*data5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] //
   N

Out[92]= Mean[{}]

Average Age For North America

In[93]:= Do[
 Do[
  If[data6[[kk]] == countriesNA[[jj, 2]], nainfo[[kk]] = 1], {kk, 1, 
   Length[data6], 1}
  ], {jj, 1, Length[countriesNA[[;; , 2]]], 1}
 ]

In[94]:= naAvg = Mean[DeleteCases[nainfo*data5[[2, 1, 1, 8 ;; 71, 6]], 0, Infinity]] //
   N

Out[94]= 114.5

In[95]:= data5[[2, 1, 1, 8 ;; 71, 9]]

Out[95]= {"F", "F", "F", "F", "F", "F", "F", "F", "M", "F", "M", "F", "F", "F", "F", \
"F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "M", "F", "F", "F", "F", \
"F", "F", "F", "F", "F", "F", "F", "F", "F", "M", "F", "F", "F", "F", "M", \
"F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "M", "F", "F", "F", "F", \
"F", "F", "F", "F"}

In[96]:= Count[data5[[2, 1, 1, 8 ;; 71, 9]], "M"]
Count[data5[[2, 1, 1, 8 ;; 71, 9]], "F"]

Out[96]= 6

Out[97]= 58

 Make the plot

In[98]:= 

In[103]:= BarChart[over100percapita[[2, ;;]]*100, 
 PlotLabel -> 
  Style["Percent of Total Population over 100 Years of Age (with Averge age)",
    30], ChartLabels -> Placed[{over100percapita[[1, ;;]],
    {saAvg, euAvg, afAvg, asAvg, "", naAvg} // N},
   {Below, Center}, {Rotate[#, Pi/2.4] &, Rotate[#, 0] &}], 
 Epilog -> {Text[
    Style[ {Count[data5[[2, 1, 1, 8 ;; 71, 9]], "F"] "Female", 
      Count[data5[[2, 1, 1, 8 ;; 71, 9]], "M"] "Male"} , 25], 
    Scaled[{0.55, 0.85}]], Red, Point@{.5, .5}}]

enter image description here

POSTED BY: Julian Davis

Ahmed Elbanna

Ahmed Elbanna, Wolfram Research

Posted 5 years ago

Amazing. With some tuning, you can even make it shorter, as you started the code arranging the data as a matrix which I wished I had time to do it. This can handle several variables in at same time like the way I did it in the GeoRegionValuePlot line. Thank you for sharing.

POSTED BY: Ahmed Elbanna

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