R and Quarto

Modern Plain Text Social Science: Week 4

Kieran Healy

kieran.healy@duke.edu

October 29, 2024

Your Workbench

RStudio is an IDE for R

A kitchen is an IDE for Meals

R and RStudio

RStudio at startup

RStudio schematic overview

RStudio schematic overview

Think in terms of Data + Transformations, written out as code, rather than a series of point-and-click steps

Our starting data + our code is what’s “real” in our projects, not the final output or any intermediate objects

The IDE is the thing that helps us keep track of and control over the code we write and the outputs we produce.

RStudio at startup

RStudio at startup

RStudio at startup

RStudio at startup

RStudio at startup

Your code is what’s real in your project

Consider not showing output inline

Writing documents

Use Quarto to produce and reproduce your work

Where we want to end up

PDF out

Where we want to end up

HTML out

Where we want to end up

Word out

How to get there?

• We could write an R script with some notes inside, using it to create some figures and tables, paste them into our document.
• This will work well. The more complex our projects get the more likely it is we will write code like this. It will also look less and less like a single all-in-one-breath script and more like a structured collection R files that combine to do many things.
• But to begin we can also do things a little differently, by taking a more notebook-based approach. For many simpler and routine uses, this will be better.

We can make this …

… by writing this

The code gets replaced by its output

This way of doing things is called a Literate Programming or Notebook approach.

A Quarto file with markdown and code chunks

A Quarto file with markdown and code chunks, annotated

• This approach has its limitations, which we will return to, but it’s very useful and has many benefits.

Strengths and weaknesses

Notebooks work smoothly when

• Your document or report is small and self-contained.
• Your analysis is quick or lightweight.
• You are making slides.
• You are making a lot of similar reports from a template.
• You regularly refer to calculated items in the text of your analysis.

Notebooks can get awkward when

• Your analysis has many pieces.
• Your project has many authors.
• Your analysis needs a lot of scaffolding in code.
• You have a lot of different outputs.

In Practice

Notebook-style documents like Quarto files are great as part of larger projects. The more complex your project, the less likely it will straightforwardly fit into a single notebook. More likely you will find yourself, first, splitting parts of a complex project up into different notebooks; and then, second, writing R scripts that programatically clean and pre-process data, run analyses, and produce some outputs—such as key tables and figures—that you then incorporate into a Quarto document indirectly. Not by copying and pasting, but by pointing to those outputs.

Basic markdown summary

Desired style	Use the following Markdown annotation
Heading 1	# Heading 1
Heading 2	## Heading 2
Heading 3	### Heading 3 (Actual heading styles will vary.)
Paragraph	Just start typing
Bold	**Bold**
Italic	*Italic*
Images	[Alternate text for image](path/image.jpg)
Hyperlinks	[Link text](https://www.visualizingsociety.com/)
Unordered Lists
- First	- First
- Second.	- Second
- Third	- Third
Ordered Lists
1. First	1. First
2. Second.	2. Second
3. Third	3. Third
Footnote.¹	Footnote[^notelabel]
¹The note’s content.	[^notelabel] The note's content.

The right frame of mind

• This is like learning how to drive a car, or how to cook in a kitchen … or learning to speak a language.
• After some orientation to what’s where, you will learn best by doing.
• Software is a pain, but you won’t crash the car or burn your house down.

TYPE OUT
YOUR CODE
BY HAND

Samuel Beckett

Getting Oriented

Before we start

• Keep in mind the kinds of things we want to do.
• In particular, we are constantly working with rectangular tables of data consisting of columns with information about variables
• We want to do things like group, summarize, count, tally, calculate, model or otherwise work on these columns, singly or all at once.
• Once we learn how to do things like count up the number of rows within a group, or take the average of the values in a column, we will find that we are just a step or two away from doing much more complex operations in essentially the same fashion.

Split

Apply

Combine

Before we start

gapminder

# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows

Before we start

Country	Continent	Year	Lifeexp	Pop	Gdppercap
Afghanistan	Asia	1952	28.8	8425333	779.4
Afghanistan	Asia	1957	30.3	9240934	820.9
Afghanistan	Asia	1962	32.0	10267083	853.1
Afghanistan	Asia	1967	34.0	11537966	836.2
Afghanistan	Asia	1972	36.1	13079460	740.0
Afghanistan	Asia	1977	38.4	14880372	786.1
Afghanistan	Asia	1982	39.9	12881816	978.0
Afghanistan	Asia	1987	40.8	13867957	852.4
Afghanistan	Asia	1992	41.7	16317921	649.3
Afghanistan	Asia	1997	41.8	22227415	635.3
Afghanistan	Asia	2002	42.1	25268405	726.7
Afghanistan	Asia	2007	43.8	31889923	974.6
Albania	Europe	1952	55.2	1282697	1601.1
Albania	Europe	1957	59.3	1476505	1942.3
Albania	Europe	1962	64.8	1728137	2312.9
Albania	Europe	1967	66.2	1984060	2760.2
Albania	Europe	1972	67.7	2263554	3313.4
Albania	Europe	1977	68.9	2509048	3533.0
Albania	Europe	1982	70.4	2780097	3630.9
Albania	Europe	1987	72.0	3075321	3738.9
Albania	Europe	1992	71.6	3326498	2497.4
Albania	Europe	1997	73.0	3428038	3193.1
Albania	Europe	2002	75.7	3508512	4604.2
Albania	Europe	2007	76.4	3600523	5937.0
Algeria	Africa	1952	43.1	9279525	2449.0
Algeria	Africa	1957	45.7	10270856	3014.0
Algeria	Africa	1962	48.3	11000948	2550.8
Algeria	Africa	1967	51.4	12760499	3247.0
Algeria	Africa	1972	54.5	14760787	4182.7
Algeria	Africa	1977	58.0	17152804	4910.4
Algeria	Africa	1982	61.4	20033753	5745.2
Algeria	Africa	1987	65.8	23254956	5681.4
Algeria	Africa	1992	67.7	26298373	5023.2
Algeria	Africa	1997	69.2	29072015	4797.3
Algeria	Africa	2002	71.0	31287142	5288.0
Algeria	Africa	2007	72.3	33333216	6223.4
Angola	Africa	1952	30.0	4232095	3520.6
Angola	Africa	1957	32.0	4561361	3827.9
Angola	Africa	1962	34.0	4826015	4269.3
Angola	Africa	1967	36.0	5247469	5522.8
Angola	Africa	1972	37.9	5894858	5473.3
Angola	Africa	1977	39.5	6162675	3008.6
Angola	Africa	1982	39.9	7016384	2757.0
Angola	Africa	1987	39.9	7874230	2430.2
Angola	Africa	1992	40.6	8735988	2627.8
Angola	Africa	1997	41.0	9875024	2277.1
Angola	Africa	2002	41.0	10866106	2773.3
Angola	Africa	2007	42.7	12420476	4797.2
Argentina	Americas	1952	62.5	17876956	5911.3
Argentina	Americas	1957	64.4	19610538	6856.9
Argentina	Americas	1962	65.1	21283783	7133.2
Argentina	Americas	1967	65.6	22934225	8053.0
Argentina	Americas	1972	67.1	24779799	9443.0
Argentina	Americas	1977	68.5	26983828	10079.0
Argentina	Americas	1982	69.9	29341374	8997.9
Argentina	Americas	1987	70.8	31620918	9139.7
Argentina	Americas	1992	71.9	33958947	9308.4
Argentina	Americas	1997	73.3	36203463	10967.3
Argentina	Americas	2002	74.3	38331121	8797.6
Argentina	Americas	2007	75.3	40301927	12779.4
Australia	Oceania	1952	69.1	8691212	10039.6
Australia	Oceania	1957	70.3	9712569	10949.6
Australia	Oceania	1962	70.9	10794968	12217.2
Australia	Oceania	1967	71.1	11872264	14526.1
Australia	Oceania	1972	71.9	13177000	16788.6
Australia	Oceania	1977	73.5	14074100	18334.2
Australia	Oceania	1982	74.7	15184200	19477.0
Australia	Oceania	1987	76.3	16257249	21888.9
Australia	Oceania	1992	77.6	17481977	23424.8
Australia	Oceania	1997	78.8	18565243	26997.9
Australia	Oceania	2002	80.4	19546792	30687.8
Australia	Oceania	2007	81.2	20434176	34435.4
Austria	Europe	1952	66.8	6927772	6137.1
Austria	Europe	1957	67.5	6965860	8842.6
Austria	Europe	1962	69.5	7129864	10750.7
Austria	Europe	1967	70.1	7376998	12834.6
Austria	Europe	1972	70.6	7544201	16661.6
Austria	Europe	1977	72.2	7568430	19749.4
Austria	Europe	1982	73.2	7574613	21597.1
Austria	Europe	1987	74.9	7578903	23687.8
Austria	Europe	1992	76.0	7914969	27042.0
Austria	Europe	1997	77.5	8069876	29095.9
Austria	Europe	2002	79.0	8148312	32417.6
Austria	Europe	2007	79.8	8199783	36126.5
Bahrain	Asia	1952	50.9	120447	9867.1
Bahrain	Asia	1957	53.8	138655	11635.8
Bahrain	Asia	1962	56.9	171863	12753.3
Bahrain	Asia	1967	59.9	202182	14804.7
Bahrain	Asia	1972	63.3	230800	18268.7
Bahrain	Asia	1977	65.6	297410	19340.1
Bahrain	Asia	1982	69.1	377967	19211.1
Bahrain	Asia	1987	70.8	454612	18524.0
Bahrain	Asia	1992	72.6	529491	19035.6
Bahrain	Asia	1997	73.9	598561	20292.0
Bahrain	Asia	2002	74.8	656397	23403.6
Bahrain	Asia	2007	75.6	708573	29796.0
Bangladesh	Asia	1952	37.5	46886859	684.2
Bangladesh	Asia	1957	39.3	51365468	661.6
Bangladesh	Asia	1962	41.2	56839289	686.3
Bangladesh	Asia	1967	43.5	62821884	721.2
Bangladesh	Asia	1972	45.3	70759295	630.2
Bangladesh	Asia	1977	46.9	80428306	659.9
Bangladesh	Asia	1982	50.0	93074406	677.0
Bangladesh	Asia	1987	52.8	103764241	752.0
Bangladesh	Asia	1992	56.0	113704579	837.8
Bangladesh	Asia	1997	59.4	123315288	972.8
Bangladesh	Asia	2002	62.0	135656790	1136.4
Bangladesh	Asia	2007	64.1	150448339	1391.3
Belgium	Europe	1952	68.0	8730405	8343.1
Belgium	Europe	1957	69.2	8989111	9715.0
Belgium	Europe	1962	70.2	9218400	10991.2
Belgium	Europe	1967	70.9	9556500	13149.0
Belgium	Europe	1972	71.4	9709100	16672.1
Belgium	Europe	1977	72.8	9821800	19118.0
Belgium	Europe	1982	73.9	9856303	20979.8
Belgium	Europe	1987	75.3	9870200	22525.6
Belgium	Europe	1992	76.5	10045622	25575.6
Belgium	Europe	1997	77.5	10199787	27561.2
Belgium	Europe	2002	78.3	10311970	30485.9
Belgium	Europe	2007	79.4	10392226	33692.6
Benin	Africa	1952	38.2	1738315	1062.8
Benin	Africa	1957	40.4	1925173	959.6
Benin	Africa	1962	42.6	2151895	949.5
Benin	Africa	1967	44.9	2427334	1035.8
Benin	Africa	1972	47.0	2761407	1085.8
Benin	Africa	1977	49.2	3168267	1029.2
Benin	Africa	1982	50.9	3641603	1277.9
Benin	Africa	1987	52.3	4243788	1225.9
Benin	Africa	1992	53.9	4981671	1191.2
Benin	Africa	1997	54.8	6066080	1233.0
Benin	Africa	2002	54.4	7026113	1372.9
Benin	Africa	2007	56.7	8078314	1441.3
Bolivia	Americas	1952	40.4	2883315	2677.3
Bolivia	Americas	1957	41.9	3211738	2127.7
Bolivia	Americas	1962	43.4	3593918	2181.0
Bolivia	Americas	1967	45.0	4040665	2586.9
Bolivia	Americas	1972	46.7	4565872	2980.3
Bolivia	Americas	1977	50.0	5079716	3548.1
Bolivia	Americas	1982	53.9	5642224	3156.5
Bolivia	Americas	1987	57.3	6156369	2753.7
Bolivia	Americas	1992	60.0	6893451	2961.7
Bolivia	Americas	1997	62.0	7693188	3326.1
Bolivia	Americas	2002	63.9	8445134	3413.3
Bolivia	Americas	2007	65.6	9119152	3822.1
Bosnia and Herzegovina	Europe	1952	53.8	2791000	973.5
Bosnia and Herzegovina	Europe	1957	58.5	3076000	1354.0
Bosnia and Herzegovina	Europe	1962	61.9	3349000	1709.7
Bosnia and Herzegovina	Europe	1967	64.8	3585000	2172.4
Bosnia and Herzegovina	Europe	1972	67.4	3819000	2860.2
Bosnia and Herzegovina	Europe	1977	69.9	4086000	3528.5
Bosnia and Herzegovina	Europe	1982	70.7	4172693	4126.6
Bosnia and Herzegovina	Europe	1987	71.1	4338977	4314.1
Bosnia and Herzegovina	Europe	1992	72.2	4256013	2546.8
Bosnia and Herzegovina	Europe	1997	73.2	3607000	4766.4
Bosnia and Herzegovina	Europe	2002	74.1	4165416	6019.0
Bosnia and Herzegovina	Europe	2007	74.9	4552198	7446.3
Botswana	Africa	1952	47.6	442308	851.2
Botswana	Africa	1957	49.6	474639	918.2
Botswana	Africa	1962	51.5	512764	983.7
Botswana	Africa	1967	53.3	553541	1214.7
Botswana	Africa	1972	56.0	619351	2263.6
Botswana	Africa	1977	59.3	781472	3214.9
Botswana	Africa	1982	61.5	970347	4551.1
Botswana	Africa	1987	63.6	1151184	6205.9
Botswana	Africa	1992	62.7	1342614	7954.1
Botswana	Africa	1997	52.6	1536536	8647.1
Botswana	Africa	2002	46.6	1630347	11003.6
Botswana	Africa	2007	50.7	1639131	12569.9
Brazil	Americas	1952	50.9	56602560	2108.9
Brazil	Americas	1957	53.3	65551171	2487.4
Brazil	Americas	1962	55.7	76039390	3336.6
Brazil	Americas	1967	57.6	88049823	3429.9
Brazil	Americas	1972	59.5	100840058	4985.7
Brazil	Americas	1977	61.5	114313951	6660.1
Brazil	Americas	1982	63.3	128962939	7030.8
Brazil	Americas	1987	65.2	142938076	7807.1
Brazil	Americas	1992	67.1	155975974	6950.3
Brazil	Americas	1997	69.4	168546719	7958.0
Brazil	Americas	2002	71.0	179914212	8131.2
Brazil	Americas	2007	72.4	190010647	9065.8
Bulgaria	Europe	1952	59.6	7274900	2444.3
Bulgaria	Europe	1957	66.6	7651254	3008.7
Bulgaria	Europe	1962	69.5	8012946	4254.3
Bulgaria	Europe	1967	70.4	8310226	5577.0
Bulgaria	Europe	1972	70.9	8576200	6597.5
Bulgaria	Europe	1977	70.8	8797022	7612.2
Bulgaria	Europe	1982	71.1	8892098	8224.2
Bulgaria	Europe	1987	71.3	8971958	8239.9
Bulgaria	Europe	1992	71.2	8658506	6302.6
Bulgaria	Europe	1997	70.3	8066057	5970.4
Bulgaria	Europe	2002	72.1	7661799	7696.8
Bulgaria	Europe	2007	73.0	7322858	10680.8
Burkina Faso	Africa	1952	32.0	4469979	543.3
Burkina Faso	Africa	1957	34.9	4713416	617.2
Burkina Faso	Africa	1962	37.8	4919632	722.5
Burkina Faso	Africa	1967	40.7	5127935	794.8
Burkina Faso	Africa	1972	43.6	5433886	854.7
Burkina Faso	Africa	1977	46.1	5889574	743.4
Burkina Faso	Africa	1982	48.1	6634596	807.2
Burkina Faso	Africa	1987	49.6	7586551	912.1
Burkina Faso	Africa	1992	50.3	8878303	931.8
Burkina Faso	Africa	1997	50.3	10352843	946.3
Burkina Faso	Africa	2002	50.6	12251209	1037.6
Burkina Faso	Africa	2007	52.3	14326203	1217.0
Burundi	Africa	1952	39.0	2445618	339.3
Burundi	Africa	1957	40.5	2667518	379.6
Burundi	Africa	1962	42.0	2961915	355.2
Burundi	Africa	1967	43.5	3330989	413.0
Burundi	Africa	1972	44.1	3529983	464.1
Burundi	Africa	1977	45.9	3834415	556.1
Burundi	Africa	1982	47.5	4580410	559.6
Burundi	Africa	1987	48.2	5126023	621.8
Burundi	Africa	1992	44.7	5809236	631.7
Burundi	Africa	1997	45.3	6121610	463.1
Burundi	Africa	2002	47.4	7021078	446.4
Burundi	Africa	2007	49.6	8390505	430.1
Cambodia	Asia	1952	39.4	4693836	368.5
Cambodia	Asia	1957	41.4	5322536	434.0
Cambodia	Asia	1962	43.4	6083619	496.9
Cambodia	Asia	1967	45.4	6960067	523.4
Cambodia	Asia	1972	40.3	7450606	421.6
Cambodia	Asia	1977	31.2	6978607	525.0
Cambodia	Asia	1982	51.0	7272485	624.5
Cambodia	Asia	1987	53.9	8371791	683.9
Cambodia	Asia	1992	55.8	10150094	682.3
Cambodia	Asia	1997	56.5	11782962	734.3
Cambodia	Asia	2002	56.8	12926707	896.2
Cambodia	Asia	2007	59.7	14131858	1713.8
Cameroon	Africa	1952	38.5	5009067	1172.7
Cameroon	Africa	1957	40.4	5359923	1313.0
Cameroon	Africa	1962	42.6	5793633	1399.6
Cameroon	Africa	1967	44.8	6335506	1508.5
Cameroon	Africa	1972	47.0	7021028	1684.1
Cameroon	Africa	1977	49.4	7959865	1783.4
Cameroon	Africa	1982	53.0	9250831	2368.0
Cameroon	Africa	1987	55.0	10780667	2602.7
Cameroon	Africa	1992	54.3	12467171	1793.2
Cameroon	Africa	1997	52.2	14195809	1694.3
Cameroon	Africa	2002	49.9	15929988	1934.0
Cameroon	Africa	2007	50.4	17696293	2042.1
Canada	Americas	1952	68.8	14785584	11367.2
Canada	Americas	1957	70.0	17010154	12490.0
Canada	Americas	1962	71.3	18985849	13462.5
Canada	Americas	1967	72.1	20819767	16076.6
Canada	Americas	1972	72.9	22284500	18970.6
Canada	Americas	1977	74.2	23796400	22090.9
Canada	Americas	1982	75.8	25201900	22898.8
Canada	Americas	1987	76.9	26549700	26626.5
Canada	Americas	1992	78.0	28523502	26342.9
Canada	Americas	1997	78.6	30305843	28954.9
Canada	Americas	2002	79.8	31902268	33329.0
Canada	Americas	2007	80.7	33390141	36319.2
Central African Republic	Africa	1952	35.5	1291695	1071.3
Central African Republic	Africa	1957	37.5	1392284	1190.8
Central African Republic	Africa	1962	39.5	1523478	1193.1
Central African Republic	Africa	1967	41.5	1733638	1136.1
Central African Republic	Africa	1972	43.5	1927260	1070.0
Central African Republic	Africa	1977	46.8	2167533	1109.4
Central African Republic	Africa	1982	48.3	2476971	956.8
Central African Republic	Africa	1987	50.5	2840009	844.9
Central African Republic	Africa	1992	49.4	3265124	747.9
Central African Republic	Africa	1997	46.1	3696513	740.5
Central African Republic	Africa	2002	43.3	4048013	738.7
Central African Republic	Africa	2007	44.7	4369038	706.0
Chad	Africa	1952	38.1	2682462	1178.7
Chad	Africa	1957	39.9	2894855	1308.5
Chad	Africa	1962	41.7	3150417	1389.8
Chad	Africa	1967	43.6	3495967	1196.8
Chad	Africa	1972	45.6	3899068	1104.1
Chad	Africa	1977	47.4	4388260	1134.0
Chad	Africa	1982	49.5	4875118	797.9
Chad	Africa	1987	51.1	5498955	952.4
Chad	Africa	1992	51.7	6429417	1058.1
Chad	Africa	1997	51.6	7562011	1005.0
Chad	Africa	2002	50.5	8835739	1156.2
Chad	Africa	2007	50.7	10238807	1704.1
Chile	Americas	1952	54.7	6377619	3940.0
Chile	Americas	1957	56.1	7048426	4315.6
Chile	Americas	1962	57.9	7961258	4519.1
Chile	Americas	1967	60.5	8858908	5106.7
Chile	Americas	1972	63.4	9717524	5494.0
Chile	Americas	1977	67.1	10599793	4756.8
Chile	Americas	1982	70.6	11487112	5095.7
Chile	Americas	1987	72.5	12463354	5547.1
Chile	Americas	1992	74.1	13572994	7596.1
Chile	Americas	1997	75.8	14599929	10118.1
Chile	Americas	2002	77.9	15497046	10778.8
Chile	Americas	2007	78.6	16284741	13171.6
China	Asia	1952	44.0	556263527	400.4
China	Asia	1957	50.5	637408000	576.0
China	Asia	1962	44.5	665770000	487.7
China	Asia	1967	58.4	754550000	612.7
China	Asia	1972	63.1	862030000	676.9
China	Asia	1977	64.0	943455000	741.2
China	Asia	1982	65.5	1000281000	962.4
China	Asia	1987	67.3	1084035000	1378.9
China	Asia	1992	68.7	1164970000	1655.8
China	Asia	1997	70.4	1230075000	2289.2
China	Asia	2002	72.0	1280400000	3119.3
China	Asia	2007	73.0	1318683096	4959.1
Colombia	Americas	1952	50.6	12350771	2144.1
Colombia	Americas	1957	55.1	14485993	2323.8
Colombia	Americas	1962	57.9	17009885	2492.4
Colombia	Americas	1967	60.0	19764027	2678.7
Colombia	Americas	1972	61.6	22542890	3264.7
Colombia	Americas	1977	63.8	25094412	3815.8
Colombia	Americas	1982	66.7	27764644	4397.6
Colombia	Americas	1987	67.8	30964245	4903.2
Colombia	Americas	1992	68.4	34202721	5444.6
Colombia	Americas	1997	70.3	37657830	6117.4
Colombia	Americas	2002	71.7	41008227	5755.3
Colombia	Americas	2007	72.9	44227550	7006.6
Comoros	Africa	1952	40.7	153936	1103.0
Comoros	Africa	1957	42.5	170928	1211.1
Comoros	Africa	1962	44.5	191689	1406.6
Comoros	Africa	1967	46.5	217378	1876.0
Comoros	Africa	1972	48.9	250027	1937.6
Comoros	Africa	1977	50.9	304739	1172.6
Comoros	Africa	1982	52.9	348643	1267.1
Comoros	Africa	1987	54.9	395114	1316.0
Comoros	Africa	1992	57.9	454429	1246.9
Comoros	Africa	1997	60.7	527982	1173.6
Comoros	Africa	2002	63.0	614382	1075.8
Comoros	Africa	2007	65.2	710960	986.1
Congo, Dem. Rep.	Africa	1952	39.1	14100005	780.5
Congo, Dem. Rep.	Africa	1957	40.7	15577932	905.9
Congo, Dem. Rep.	Africa	1962	42.1	17486434	896.3
Congo, Dem. Rep.	Africa	1967	44.1	19941073	861.6
Congo, Dem. Rep.	Africa	1972	46.0	23007669	904.9
Congo, Dem. Rep.	Africa	1977	47.8	26480870	795.8
Congo, Dem. Rep.	Africa	1982	47.8	30646495	673.7
Congo, Dem. Rep.	Africa	1987	47.4	35481645	672.8
Congo, Dem. Rep.	Africa	1992	45.5	41672143	457.7
Congo, Dem. Rep.	Africa	1997	42.6	47798986	312.2
Congo, Dem. Rep.	Africa	2002	45.0	55379852	241.2
Congo, Dem. Rep.	Africa	2007	46.5	64606759	277.6
Congo, Rep.	Africa	1952	42.1	854885	2125.6
Congo, Rep.	Africa	1957	45.1	940458	2315.1
Congo, Rep.	Africa	1962	48.4	1047924	2464.8
Congo, Rep.	Africa	1967	52.0	1179760	2677.9
Congo, Rep.	Africa	1972	54.9	1340458	3213.2
Congo, Rep.	Africa	1977	55.6	1536769	3259.2
Congo, Rep.	Africa	1982	56.7	1774735	4879.5
Congo, Rep.	Africa	1987	57.5	2064095	4201.2
Congo, Rep.	Africa	1992	56.4	2409073	4016.2
Congo, Rep.	Africa	1997	53.0	2800947	3484.2
Congo, Rep.	Africa	2002	53.0	3328795	3484.1
Congo, Rep.	Africa	2007	55.3	3800610	3632.6
Costa Rica	Americas	1952	57.2	926317	2627.0
Costa Rica	Americas	1957	60.0	1112300	2990.0
Costa Rica	Americas	1962	62.8	1345187	3460.9
Costa Rica	Americas	1967	65.4	1588717	4161.7
Costa Rica	Americas	1972	67.8	1834796	5118.1
Costa Rica	Americas	1977	70.8	2108457	5926.9
Costa Rica	Americas	1982	73.4	2424367	5262.7
Costa Rica	Americas	1987	74.8	2799811	5629.9
Costa Rica	Americas	1992	75.7	3173216	6160.4
Costa Rica	Americas	1997	77.3	3518107	6677.0
Costa Rica	Americas	2002	78.1	3834934	7723.4
Costa Rica	Americas	2007	78.8	4133884	9645.1
Cote d'Ivoire	Africa	1952	40.5	2977019	1388.6
Cote d'Ivoire	Africa	1957	42.5	3300000	1500.9
Cote d'Ivoire	Africa	1962	44.9	3832408	1728.9
Cote d'Ivoire	Africa	1967	47.4	4744870	2052.1
Cote d'Ivoire	Africa	1972	49.8	6071696	2378.2
Cote d'Ivoire	Africa	1977	52.4	7459574	2517.7
Cote d'Ivoire	Africa	1982	54.0	9025951	2602.7
Cote d'Ivoire	Africa	1987	54.7	10761098	2157.0
Cote d'Ivoire	Africa	1992	52.0	12772596	1648.1
Cote d'Ivoire	Africa	1997	48.0	14625967	1786.3
Cote d'Ivoire	Africa	2002	46.8	16252726	1648.8
Cote d'Ivoire	Africa	2007	48.3	18013409	1544.8
Croatia	Europe	1952	61.2	3882229	3119.2
Croatia	Europe	1957	64.8	3991242	4338.2
Croatia	Europe	1962	67.1	4076557	5477.9
Croatia	Europe	1967	68.5	4174366	6960.3
Croatia	Europe	1972	69.6	4225310	9164.1
Croatia	Europe	1977	70.6	4318673	11305.4
Croatia	Europe	1982	70.5	4413368	13221.8
Croatia	Europe	1987	71.5	4484310	13822.6
Croatia	Europe	1992	72.5	4494013	8447.8
Croatia	Europe	1997	73.7	4444595	9875.6
Croatia	Europe	2002	74.9	4481020	11628.4
Croatia	Europe	2007	75.7	4493312	14619.2
Cuba	Americas	1952	59.4	6007797	5586.5
Cuba	Americas	1957	62.3	6640752	6092.2
Cuba	Americas	1962	65.2	7254373	5180.8
Cuba	Americas	1967	68.3	8139332	5690.3
Cuba	Americas	1972	70.7	8831348	5305.4
Cuba	Americas	1977	72.6	9537988	6380.5
Cuba	Americas	1982	73.7	9789224	7316.9
Cuba	Americas	1987	74.2	10239839	7532.9
Cuba	Americas	1992	74.4	10723260	5592.8
Cuba	Americas	1997	76.2	10983007	5432.0
Cuba	Americas	2002	77.2	11226999	6340.6
Cuba	Americas	2007	78.3	11416987	8948.1
Czech Republic	Europe	1952	66.9	9125183	6876.1
Czech Republic	Europe	1957	69.0	9513758	8256.3
Czech Republic	Europe	1962	69.9	9620282	10136.9
Czech Republic	Europe	1967	70.4	9835109	11399.4
Czech Republic	Europe	1972	70.3	9862158	13108.5
Czech Republic	Europe	1977	70.7	10161915	14800.2
Czech Republic	Europe	1982	71.0	10303704	15377.2
Czech Republic	Europe	1987	71.6	10311597	16310.4
Czech Republic	Europe	1992	72.4	10315702	14297.0
Czech Republic	Europe	1997	74.0	10300707	16048.5
Czech Republic	Europe	2002	75.5	10256295	17596.2
Czech Republic	Europe	2007	76.5	10228744	22833.3
Denmark	Europe	1952	70.8	4334000	9692.4
Denmark	Europe	1957	71.8	4487831	11099.7
Denmark	Europe	1962	72.3	4646899	13583.3
Denmark	Europe	1967	73.0	4838800	15937.2
Denmark	Europe	1972	73.5	4991596	18866.2
Denmark	Europe	1977	74.7	5088419	20422.9
Denmark	Europe	1982	74.6	5117810	21688.0
Denmark	Europe	1987	74.8	5127024	25116.2
Denmark	Europe	1992	75.3	5171393	26406.7
Denmark	Europe	1997	76.1	5283663	29804.3
Denmark	Europe	2002	77.2	5374693	32166.5
Denmark	Europe	2007	78.3	5468120	35278.4
Djibouti	Africa	1952	34.8	63149	2669.5
Djibouti	Africa	1957	37.3	71851	2865.0
Djibouti	Africa	1962	39.7	89898	3021.0
Djibouti	Africa	1967	42.1	127617	3020.1
Djibouti	Africa	1972	44.4	178848	3694.2
Djibouti	Africa	1977	46.5	228694	3081.8
Djibouti	Africa	1982	48.8	305991	2879.5
Djibouti	Africa	1987	50.0	311025	2880.1
Djibouti	Africa	1992	51.6	384156	2377.2
Djibouti	Africa	1997	53.2	417908	1895.0
Djibouti	Africa	2002	53.4	447416	1908.3
Djibouti	Africa	2007	54.8	496374	2082.5
Dominican Republic	Americas	1952	45.9	2491346	1397.7
Dominican Republic	Americas	1957	49.8	2923186	1544.4
Dominican Republic	Americas	1962	53.5	3453434	1662.1
Dominican Republic	Americas	1967	56.8	4049146	1653.7
Dominican Republic	Americas	1972	59.6	4671329	2189.9
Dominican Republic	Americas	1977	61.8	5302800	2682.0
Dominican Republic	Americas	1982	63.7	5968349	2861.1
Dominican Republic	Americas	1987	66.0	6655297	2899.8
Dominican Republic	Americas	1992	68.5	7351181	3044.2
Dominican Republic	Americas	1997	70.0	7992357	3614.1
Dominican Republic	Americas	2002	70.8	8650322	4563.8
Dominican Republic	Americas	2007	72.2	9319622	6025.4
Ecuador	Americas	1952	48.4	3548753	3522.1
Ecuador	Americas	1957	51.4	4058385	3780.5
Ecuador	Americas	1962	54.6	4681707	4086.1
Ecuador	Americas	1967	56.7	5432424	4579.1
Ecuador	Americas	1972	58.8	6298651	5281.0
Ecuador	Americas	1977	61.3	7278866	6679.6
Ecuador	Americas	1982	64.3	8365850	7213.8
Ecuador	Americas	1987	67.2	9545158	6481.8
Ecuador	Americas	1992	69.6	10748394	7103.7
Ecuador	Americas	1997	72.3	11911819	7429.5
Ecuador	Americas	2002	74.2	12921234	5773.0
Ecuador	Americas	2007	75.0	13755680	6873.3
Egypt	Africa	1952	41.9	22223309	1418.8
Egypt	Africa	1957	44.4	25009741	1458.9
Egypt	Africa	1962	47.0	28173309	1693.3
Egypt	Africa	1967	49.3	31681188	1814.9
Egypt	Africa	1972	51.1	34807417	2024.0
Egypt	Africa	1977	53.3	38783863	2785.5
Egypt	Africa	1982	56.0	45681811	3503.7
Egypt	Africa	1987	59.8	52799062	3885.5
Egypt	Africa	1992	63.7	59402198	3794.8
Egypt	Africa	1997	67.2	66134291	4173.2
Egypt	Africa	2002	69.8	73312559	4754.6
Egypt	Africa	2007	71.3	80264543	5581.2
El Salvador	Americas	1952	45.3	2042865	3048.3
El Salvador	Americas	1957	48.6	2355805	3421.5
El Salvador	Americas	1962	52.3	2747687	3776.8
El Salvador	Americas	1967	55.9	3232927	4358.6
El Salvador	Americas	1972	58.2	3790903	4520.2
El Salvador	Americas	1977	56.7	4282586	5138.9
El Salvador	Americas	1982	56.6	4474873	4098.3
El Salvador	Americas	1987	63.2	4842194	4140.4
El Salvador	Americas	1992	66.8	5274649	4444.2
El Salvador	Americas	1997	69.5	5783439	5154.8
El Salvador	Americas	2002	70.7	6353681	5351.6
El Salvador	Americas	2007	71.9	6939688	5728.4
Equatorial Guinea	Africa	1952	34.5	216964	375.6
Equatorial Guinea	Africa	1957	36.0	232922	426.1
Equatorial Guinea	Africa	1962	37.5	249220	582.8
Equatorial Guinea	Africa	1967	39.0	259864	915.6
Equatorial Guinea	Africa	1972	40.5	277603	672.4
Equatorial Guinea	Africa	1977	42.0	192675	958.6
Equatorial Guinea	Africa	1982	43.7	285483	927.8
Equatorial Guinea	Africa	1987	45.7	341244	966.9
Equatorial Guinea	Africa	1992	47.5	387838	1132.1
Equatorial Guinea	Africa	1997	48.2	439971	2814.5
Equatorial Guinea	Africa	2002	49.3	495627	7703.5
Equatorial Guinea	Africa	2007	51.6	551201	12154.1
Eritrea	Africa	1952	35.9	1438760	328.9
Eritrea	Africa	1957	38.0	1542611	344.2
Eritrea	Africa	1962	40.2	1666618	381.0
Eritrea	Africa	1967	42.2	1820319	468.8
Eritrea	Africa	1972	44.1	2260187	514.3
Eritrea	Africa	1977	44.5	2512642	505.8
Eritrea	Africa	1982	43.9	2637297	524.9
Eritrea	Africa	1987	46.5	2915959	521.1
Eritrea	Africa	1992	50.0	3668440	582.9
Eritrea	Africa	1997	53.4	4058319	913.5
Eritrea	Africa	2002	55.2	4414865	765.4
Eritrea	Africa	2007	58.0	4906585	641.4
Ethiopia	Africa	1952	34.1	20860941	362.1
Ethiopia	Africa	1957	36.7	22815614	378.9
Ethiopia	Africa	1962	40.1	25145372	419.5
Ethiopia	Africa	1967	42.1	27860297	516.1
Ethiopia	Africa	1972	43.5	30770372	566.2
Ethiopia	Africa	1977	44.5	34617799	556.8
Ethiopia	Africa	1982	44.9	38111756	577.9
Ethiopia	Africa	1987	46.7	42999530	573.7
Ethiopia	Africa	1992	48.1	52088559	421.4
Ethiopia	Africa	1997	49.4	59861301	515.9
Ethiopia	Africa	2002	50.7	67946797	530.1
Ethiopia	Africa	2007	52.9	76511887	690.8
Finland	Europe	1952	66.6	4090500	6424.5
Finland	Europe	1957	67.5	4324000	7545.4
Finland	Europe	1962	68.8	4491443	9371.8
Finland	Europe	1967	69.8	4605744	10921.6
Finland	Europe	1972	70.9	4639657	14358.9
Finland	Europe	1977	72.5	4738902	15605.4
Finland	Europe	1982	74.6	4826933	18533.2
Finland	Europe	1987	74.8	4931729	21141.0
Finland	Europe	1992	75.7	5041039	20647.2
Finland	Europe	1997	77.1	5134406	23724.0
Finland	Europe	2002	78.4	5193039	28204.6
Finland	Europe	2007	79.3	5238460	33207.1
France	Europe	1952	67.4	42459667	7029.8
France	Europe	1957	68.9	44310863	8662.8
France	Europe	1962	70.5	47124000	10560.5
France	Europe	1967	71.6	49569000	12999.9
France	Europe	1972	72.4	51732000	16107.2
France	Europe	1977	73.8	53165019	18292.6
France	Europe	1982	74.9	54433565	20293.9
France	Europe	1987	76.3	55630100	22066.4
France	Europe	1992	77.5	57374179	24703.8
France	Europe	1997	78.6	58623428	25889.8
France	Europe	2002	79.6	59925035	28926.0
France	Europe	2007	80.7	61083916	30470.0
Gabon	Africa	1952	37.0	420702	4293.5
Gabon	Africa	1957	39.0	434904	4976.2
Gabon	Africa	1962	40.5	455661	6631.5
Gabon	Africa	1967	44.6	489004	8358.8
Gabon	Africa	1972	48.7	537977	11401.9
Gabon	Africa	1977	52.8	706367	21745.6
Gabon	Africa	1982	56.6	753874	15113.4
Gabon	Africa	1987	60.2	880397	11864.4
Gabon	Africa	1992	61.4	985739	13522.2
Gabon	Africa	1997	60.5	1126189	14722.8
Gabon	Africa	2002	56.8	1299304	12521.7
Gabon	Africa	2007	56.7	1454867	13206.5
Gambia	Africa	1952	30.0	284320	485.2
Gambia	Africa	1957	32.1	323150	520.9
Gambia	Africa	1962	33.9	374020	599.7
Gambia	Africa	1967	35.9	439593	734.8
Gambia	Africa	1972	38.3	517101	756.1
Gambia	Africa	1977	41.8	608274	884.8
Gambia	Africa	1982	45.6	715523	835.8
Gambia	Africa	1987	49.3	848406	611.7
Gambia	Africa	1992	52.6	1025384	665.6
Gambia	Africa	1997	55.9	1235767	653.7
Gambia	Africa	2002	58.0	1457766	660.6
Gambia	Africa	2007	59.4	1688359	752.7
Germany	Europe	1952	67.5	69145952	7144.1
Germany	Europe	1957	69.1	71019069	10187.8
Germany	Europe	1962	70.3	73739117	12902.5
Germany	Europe	1967	70.8	76368453	14745.6
Germany	Europe	1972	71.0	78717088	18016.2
Germany	Europe	1977	72.5	78160773	20512.9
Germany	Europe	1982	73.8	78335266	22031.5
Germany	Europe	1987	74.8	77718298	24639.2
Germany	Europe	1992	76.1	80597764	26505.3
Germany	Europe	1997	77.3	82011073	27788.9
Germany	Europe	2002	78.7	82350671	30035.8
Germany	Europe	2007	79.4	82400996	32170.4
Ghana	Africa	1952	43.1	5581001	911.3
Ghana	Africa	1957	44.8	6391288	1043.6
Ghana	Africa	1962	46.5	7355248	1190.0
Ghana	Africa	1967	48.1	8490213	1125.7
Ghana	Africa	1972	49.9	9354120	1178.2
Ghana	Africa	1977	51.8	10538093	993.2
Ghana	Africa	1982	53.7	11400338	876.0
Ghana	Africa	1987	55.7	14168101	847.0
Ghana	Africa	1992	57.5	16278738	925.1
Ghana	Africa	1997	58.6	18418288	1005.2
Ghana	Africa	2002	58.5	20550751	1112.0
Ghana	Africa	2007	60.0	22873338	1327.6
Greece	Europe	1952	65.9	7733250	3530.7
Greece	Europe	1957	67.9	8096218	4916.3
Greece	Europe	1962	69.5	8448233	6017.2
Greece	Europe	1967	71.0	8716441	8513.1
Greece	Europe	1972	72.3	8888628	12724.8
Greece	Europe	1977	73.7	9308479	14195.5
Greece	Europe	1982	75.2	9786480	15268.4
Greece	Europe	1987	76.7	9974490	16120.5
Greece	Europe	1992	77.0	10325429	17541.5
Greece	Europe	1997	77.9	10502372	18747.7
Greece	Europe	2002	78.3	10603863	22514.3
Greece	Europe	2007	79.5	10706290	27538.4
Guatemala	Americas	1952	42.0	3146381	2428.2
Guatemala	Americas	1957	44.1	3640876	2617.2
Guatemala	Americas	1962	47.0	4208858	2750.4
Guatemala	Americas	1967	50.0	4690773	3242.5
Guatemala	Americas	1972	53.7	5149581	4031.4
Guatemala	Americas	1977	56.0	5703430	4880.0
Guatemala	Americas	1982	58.1	6395630	4820.5
Guatemala	Americas	1987	60.8	7326406	4246.5
Guatemala	Americas	1992	63.4	8486949	4439.5
Guatemala	Americas	1997	66.3	9803875	4684.3
Guatemala	Americas	2002	69.0	11178650	4858.3
Guatemala	Americas	2007	70.3	12572928	5186.1
Guinea	Africa	1952	33.6	2664249	510.2
Guinea	Africa	1957	34.6	2876726	576.3
Guinea	Africa	1962	35.8	3140003	686.4
Guinea	Africa	1967	37.2	3451418	708.8
Guinea	Africa	1972	38.8	3811387	741.7
Guinea	Africa	1977	40.8	4227026	874.7
Guinea	Africa	1982	42.9	4710497	857.3
Guinea	Africa	1987	45.6	5650262	805.6
Guinea	Africa	1992	48.6	6990574	794.3
Guinea	Africa	1997	51.5	8048834	869.4
Guinea	Africa	2002	53.7	8807818	945.6
Guinea	Africa	2007	56.0	9947814	942.7
Guinea-Bissau	Africa	1952	32.5	580653	299.9
Guinea-Bissau	Africa	1957	33.5	601095	431.8
Guinea-Bissau	Africa	1962	34.5	627820	522.0
Guinea-Bissau	Africa	1967	35.5	601287	715.6
Guinea-Bissau	Africa	1972	36.5	625361	820.2
Guinea-Bissau	Africa	1977	37.5	745228	764.7
Guinea-Bissau	Africa	1982	39.3	825987	838.1
Guinea-Bissau	Africa	1987	41.2	927524	736.4
Guinea-Bissau	Africa	1992	43.3	1050938	745.5
Guinea-Bissau	Africa	1997	44.9	1193708	796.7
Guinea-Bissau	Africa	2002	45.5	1332459	575.7
Guinea-Bissau	Africa	2007	46.4	1472041	579.2
Haiti	Americas	1952	37.6	3201488	1840.4
Haiti	Americas	1957	40.7	3507701	1726.9
Haiti	Americas	1962	43.6	3880130	1796.6
Haiti	Americas	1967	46.2	4318137	1452.1
Haiti	Americas	1972	48.0	4698301	1654.5
Haiti	Americas	1977	49.9	4908554	1874.3
Haiti	Americas	1982	51.5	5198399	2011.2
Haiti	Americas	1987	53.6	5756203	1823.0
Haiti	Americas	1992	55.1	6326682	1456.3
Haiti	Americas	1997	56.7	6913545	1341.7
Haiti	Americas	2002	58.1	7607651	1270.4
Haiti	Americas	2007	60.9	8502814	1201.6
Honduras	Americas	1952	41.9	1517453	2194.9
Honduras	Americas	1957	44.7	1770390	2220.5
Honduras	Americas	1962	48.0	2090162	2291.2
Honduras	Americas	1967	50.9	2500689	2538.3
Honduras	Americas	1972	53.9	2965146	2529.8
Honduras	Americas	1977	57.4	3055235	3203.2
Honduras	Americas	1982	60.9	3669448	3121.8
Honduras	Americas	1987	64.5	4372203	3023.1
Honduras	Americas	1992	66.4	5077347	3081.7
Honduras	Americas	1997	67.7	5867957	3160.5
Honduras	Americas	2002	68.6	6677328	3099.7
Honduras	Americas	2007	70.2	7483763	3548.3
Hong Kong, China	Asia	1952	61.0	2125900	3054.4
Hong Kong, China	Asia	1957	64.8	2736300	3629.1
Hong Kong, China	Asia	1962	67.7	3305200	4692.6
Hong Kong, China	Asia	1967	70.0	3722800	6198.0
Hong Kong, China	Asia	1972	72.0	4115700	8315.9
Hong Kong, China	Asia	1977	73.6	4583700	11186.1
Hong Kong, China	Asia	1982	75.4	5264500	14560.5
Hong Kong, China	Asia	1987	76.2	5584510	20038.5
Hong Kong, China	Asia	1992	77.6	5829696	24757.6
Hong Kong, China	Asia	1997	80.0	6495918	28377.6
Hong Kong, China	Asia	2002	81.5	6762476	30209.0
Hong Kong, China	Asia	2007	82.2	6980412	39725.0
Hungary	Europe	1952	64.0	9504000	5263.7
Hungary	Europe	1957	66.4	9839000	6040.2
Hungary	Europe	1962	68.0	10063000	7550.4
Hungary	Europe	1967	69.5	10223422	9326.6
Hungary	Europe	1972	69.8	10394091	10168.7
Hungary	Europe	1977	70.0	10637171	11674.8
Hungary	Europe	1982	69.4	10705535	12546.0
Hungary	Europe	1987	69.6	10612740	12986.5
Hungary	Europe	1992	69.2	10348684	10535.6
Hungary	Europe	1997	71.0	10244684	11712.8
Hungary	Europe	2002	72.6	10083313	14843.9
Hungary	Europe	2007	73.3	9956108	18008.9
Iceland	Europe	1952	72.5	147962	7267.7
Iceland	Europe	1957	73.5	165110	9244.0
Iceland	Europe	1962	73.7	182053	10350.2
Iceland	Europe	1967	73.7	198676	13319.9
Iceland	Europe	1972	74.5	209275	15798.1
Iceland	Europe	1977	76.1	221823	19655.0
Iceland	Europe	1982	77.0	233997	23269.6
Iceland	Europe	1987	77.2	244676	26923.2
Iceland	Europe	1992	78.8	259012	25144.4
Iceland	Europe	1997	79.0	271192	28061.1
Iceland	Europe	2002	80.5	288030	31163.2
Iceland	Europe	2007	81.8	301931	36180.8
India	Asia	1952	37.4	372000000	546.6
India	Asia	1957	40.2	409000000	590.1
India	Asia	1962	43.6	454000000	658.3
India	Asia	1967	47.2	506000000	700.8
India	Asia	1972	50.7	567000000	724.0
India	Asia	1977	54.2	634000000	813.3
India	Asia	1982	56.6	708000000	855.7
India	Asia	1987	58.6	788000000	976.5
India	Asia	1992	60.2	872000000	1164.4
India	Asia	1997	61.8	959000000	1458.8
India	Asia	2002	62.9	1034172547	1746.8
India	Asia	2007	64.7	1110396331	2452.2
Indonesia	Asia	1952	37.5	82052000	749.7
Indonesia	Asia	1957	39.9	90124000	858.9
Indonesia	Asia	1962	42.5	99028000	849.3
Indonesia	Asia	1967	46.0	109343000	762.4
Indonesia	Asia	1972	49.2	121282000	1111.1
Indonesia	Asia	1977	52.7	136725000	1382.7
Indonesia	Asia	1982	56.2	153343000	1516.9
Indonesia	Asia	1987	60.1	169276000	1748.4
Indonesia	Asia	1992	62.7	184816000	2383.1
Indonesia	Asia	1997	66.0	199278000	3119.3
Indonesia	Asia	2002	68.6	211060000	2873.9
Indonesia	Asia	2007	70.7	223547000	3540.7
Iran	Asia	1952	44.9	17272000	3035.3
Iran	Asia	1957	47.2	19792000	3290.3
Iran	Asia	1962	49.3	22874000	4187.3
Iran	Asia	1967	52.5	26538000	5906.7
Iran	Asia	1972	55.2	30614000	9613.8
Iran	Asia	1977	57.7	35480679	11888.6
Iran	Asia	1982	59.6	43072751	7608.3
Iran	Asia	1987	63.0	51889696	6642.9
Iran	Asia	1992	65.7	60397973	7235.7
Iran	Asia	1997	68.0	63327987	8263.6
Iran	Asia	2002	69.5	66907826	9240.8
Iran	Asia	2007	71.0	69453570	11605.7
Iraq	Asia	1952	45.3	5441766	4129.8
Iraq	Asia	1957	48.4	6248643	6229.3
Iraq	Asia	1962	51.5	7240260	8341.7
Iraq	Asia	1967	54.5	8519282	8931.5
Iraq	Asia	1972	57.0	10061506	9576.0
Iraq	Asia	1977	60.4	11882916	14688.2
Iraq	Asia	1982	62.0	14173318	14517.9
Iraq	Asia	1987	65.0	16543189	11643.6
Iraq	Asia	1992	59.5	17861905	3745.6
Iraq	Asia	1997	58.8	20775703	3076.2
Iraq	Asia	2002	57.0	24001816	4390.7
Iraq	Asia	2007	59.5	27499638	4471.1
Ireland	Europe	1952	66.9	2952156	5210.3
Ireland	Europe	1957	68.9	2878220	5599.1
Ireland	Europe	1962	70.3	2830000	6631.6
Ireland	Europe	1967	71.1	2900100	7655.6
Ireland	Europe	1972	71.3	3024400	9530.8
Ireland	Europe	1977	72.0	3271900	11151.0
Ireland	Europe	1982	73.1	3480000	12618.3
Ireland	Europe	1987	74.4	3539900	13872.9
Ireland	Europe	1992	75.5	3557761	17558.8
Ireland	Europe	1997	76.1	3667233	24521.9
Ireland	Europe	2002	77.8	3879155	34077.0
Ireland	Europe	2007	78.9	4109086	40676.0
Israel	Asia	1952	65.4	1620914	4086.5
Israel	Asia	1957	67.8	1944401	5385.3
Israel	Asia	1962	69.4	2310904	7105.6
Israel	Asia	1967	70.8	2693585	8393.7
Israel	Asia	1972	71.6	3095893	12786.9
Israel	Asia	1977	73.1	3495918	13306.6
Israel	Asia	1982	74.4	3858421	15367.0
Israel	Asia	1987	75.6	4203148	17122.5
Israel	Asia	1992	76.9	4936550	18051.5
Israel	Asia	1997	78.3	5531387	20896.6
Israel	Asia	2002	79.7	6029529	21905.6
Israel	Asia	2007	80.7	6426679	25523.3
Italy	Europe	1952	65.9	47666000	4931.4
Italy	Europe	1957	67.8	49182000	6248.7
Italy	Europe	1962	69.2	50843200	8243.6
Italy	Europe	1967	71.1	52667100	10022.4
Italy	Europe	1972	72.2	54365564	12269.3
Italy	Europe	1977	73.5	56059245	14256.0
Italy	Europe	1982	75.0	56535636	16537.5
Italy	Europe	1987	76.4	56729703	19207.2
Italy	Europe	1992	77.4	56840847	22013.6
Italy	Europe	1997	78.8	57479469	24675.0
Italy	Europe	2002	80.2	57926999	27968.1
Italy	Europe	2007	80.5	58147733	28569.7
Jamaica	Americas	1952	58.5	1426095	2898.5
Jamaica	Americas	1957	62.6	1535090	4756.5
Jamaica	Americas	1962	65.6	1665128	5246.1
Jamaica	Americas	1967	67.5	1861096	6124.7
Jamaica	Americas	1972	69.0	1997616	7433.9
Jamaica	Americas	1977	70.1	2156814	6650.2
Jamaica	Americas	1982	71.2	2298309	6068.1
Jamaica	Americas	1987	71.8	2326606	6351.2
Jamaica	Americas	1992	71.8	2378618	7404.9
Jamaica	Americas	1997	72.3	2531311	7121.9
Jamaica	Americas	2002	72.0	2664659	6994.8
Jamaica	Americas	2007	72.6	2780132	7320.9
Japan	Asia	1952	63.0	86459025	3217.0
Japan	Asia	1957	65.5	91563009	4317.7
Japan	Asia	1962	68.7	95831757	6576.6
Japan	Asia	1967	71.4	100825279	9847.8
Japan	Asia	1972	73.4	107188273	14778.8
Japan	Asia	1977	75.4	113872473	16610.4
Japan	Asia	1982	77.1	118454974	19384.1
Japan	Asia	1987	78.7	122091325	22375.9
Japan	Asia	1992	79.4	124329269	26824.9
Japan	Asia	1997	80.7	125956499	28816.6
Japan	Asia	2002	82.0	127065841	28604.6
Japan	Asia	2007	82.6	127467972	31656.1
Jordan	Asia	1952	43.2	607914	1546.9
Jordan	Asia	1957	45.7	746559	1886.1
Jordan	Asia	1962	48.1	933559	2348.0
Jordan	Asia	1967	51.6	1255058	2741.8
Jordan	Asia	1972	56.5	1613551	2110.9
Jordan	Asia	1977	61.1	1937652	2852.4
Jordan	Asia	1982	63.7	2347031	4161.4
Jordan	Asia	1987	65.9	2820042	4448.7
Jordan	Asia	1992	68.0	3867409	3431.6
Jordan	Asia	1997	69.8	4526235	3645.4
Jordan	Asia	2002	71.3	5307470	3844.9
Jordan	Asia	2007	72.5	6053193	4519.5
Kenya	Africa	1952	42.3	6464046	853.5
Kenya	Africa	1957	44.7	7454779	944.4
Kenya	Africa	1962	47.9	8678557	897.0
Kenya	Africa	1967	50.7	10191512	1056.7
Kenya	Africa	1972	53.6	12044785	1222.4
Kenya	Africa	1977	56.2	14500404	1267.6
Kenya	Africa	1982	58.8	17661452	1348.2
Kenya	Africa	1987	59.3	21198082	1361.9
Kenya	Africa	1992	59.3	25020539	1341.9
Kenya	Africa	1997	54.4	28263827	1360.5
Kenya	Africa	2002	51.0	31386842	1287.5
Kenya	Africa	2007	54.1	35610177	1463.2
Korea, Dem. Rep.	Asia	1952	50.1	8865488	1088.3
Korea, Dem. Rep.	Asia	1957	54.1	9411381	1571.1
Korea, Dem. Rep.	Asia	1962	56.7	10917494	1621.7
Korea, Dem. Rep.	Asia	1967	59.9	12617009	2143.5
Korea, Dem. Rep.	Asia	1972	64.0	14781241	3701.6
Korea, Dem. Rep.	Asia	1977	67.2	16325320	4106.3
Korea, Dem. Rep.	Asia	1982	69.1	17647518	4106.5
Korea, Dem. Rep.	Asia	1987	70.6	19067554	4106.5
Korea, Dem. Rep.	Asia	1992	70.0	20711375	3726.1
Korea, Dem. Rep.	Asia	1997	67.7	21585105	1690.8
Korea, Dem. Rep.	Asia	2002	66.7	22215365	1646.8
Korea, Dem. Rep.	Asia	2007	67.3	23301725	1593.1
Korea, Rep.	Asia	1952	47.5	20947571	1030.6
Korea, Rep.	Asia	1957	52.7	22611552	1487.6
Korea, Rep.	Asia	1962	55.3	26420307	1536.3
Korea, Rep.	Asia	1967	57.7	30131000	2029.2
Korea, Rep.	Asia	1972	62.6	33505000	3030.9
Korea, Rep.	Asia	1977	64.8	36436000	4657.2
Korea, Rep.	Asia	1982	67.1	39326000	5622.9
Korea, Rep.	Asia	1987	69.8	41622000	8533.1
Korea, Rep.	Asia	1992	72.2	43805450	12104.3
Korea, Rep.	Asia	1997	74.6	46173816	15993.5
Korea, Rep.	Asia	2002	77.0	47969150	19234.0
Korea, Rep.	Asia	2007	78.6	49044790	23348.1
Kuwait	Asia	1952	55.6	160000	108382.4
Kuwait	Asia	1957	58.0	212846	113523.1
Kuwait	Asia	1962	60.5	358266	95458.1
Kuwait	Asia	1967	64.6	575003	80894.9
Kuwait	Asia	1972	67.7	841934	109347.9
Kuwait	Asia	1977	69.3	1140357	59265.5
Kuwait	Asia	1982	71.3	1497494	31354.0
Kuwait	Asia	1987	74.2	1891487	28118.4
Kuwait	Asia	1992	75.2	1418095	34932.9
Kuwait	Asia	1997	76.2	1765345	40300.6
Kuwait	Asia	2002	76.9	2111561	35110.1
Kuwait	Asia	2007	77.6	2505559	47307.0
Lebanon	Asia	1952	55.9	1439529	4834.8
Lebanon	Asia	1957	59.5	1647412	6089.8
Lebanon	Asia	1962	62.1	1886848	5714.6
Lebanon	Asia	1967	63.9	2186894	6007.0
Lebanon	Asia	1972	65.4	2680018	7486.4
Lebanon	Asia	1977	66.1	3115787	8659.7
Lebanon	Asia	1982	67.0	3086876	7640.5
Lebanon	Asia	1987	67.9	3089353	5377.1
Lebanon	Asia	1992	69.3	3219994	6890.8
Lebanon	Asia	1997	70.3	3430388	8755.0
Lebanon	Asia	2002	71.0	3677780	9313.9
Lebanon	Asia	2007	72.0	3921278	10461.1
Lesotho	Africa	1952	42.1	748747	298.8
Lesotho	Africa	1957	45.0	813338	336.0
Lesotho	Africa	1962	47.7	893143	411.8
Lesotho	Africa	1967	48.5	996380	498.6
Lesotho	Africa	1972	49.8	1116779	496.6
Lesotho	Africa	1977	52.2	1251524	745.4
Lesotho	Africa	1982	55.1	1411807	797.3
Lesotho	Africa	1987	57.2	1599200	774.0
Lesotho	Africa	1992	59.7	1803195	977.5
Lesotho	Africa	1997	55.6	1982823	1186.1
Lesotho	Africa	2002	44.6	2046772	1275.2
Lesotho	Africa	2007	42.6	2012649	1569.3
Liberia	Africa	1952	38.5	863308	575.6
Liberia	Africa	1957	39.5	975950	621.0
Liberia	Africa	1962	40.5	1112796	634.2
Liberia	Africa	1967	41.5	1279406	713.6
Liberia	Africa	1972	42.6	1482628	803.0
Liberia	Africa	1977	43.8	1703617	640.3
Liberia	Africa	1982	44.9	1956875	572.2
Liberia	Africa	1987	46.0	2269414	506.1
Liberia	Africa	1992	40.8	1912974	636.6
Liberia	Africa	1997	42.2	2200725	609.2
Liberia	Africa	2002	43.8	2814651	531.5
Liberia	Africa	2007	45.7	3193942	414.5
Libya	Africa	1952	42.7	1019729	2387.5
Libya	Africa	1957	45.3	1201578	3448.3
Libya	Africa	1962	47.8	1441863	6757.0
Libya	Africa	1967	50.2	1759224	18772.8
Libya	Africa	1972	52.8	2183877	21011.5
Libya	Africa	1977	57.4	2721783	21951.2
Libya	Africa	1982	62.2	3344074	17364.3
Libya	Africa	1987	66.2	3799845	11770.6
Libya	Africa	1992	68.8	4364501	9640.1
Libya	Africa	1997	71.6	4759670	9467.4
Libya	Africa	2002	72.7	5368585	9534.7
Libya	Africa	2007	74.0	6036914	12057.5
Madagascar	Africa	1952	36.7	4762912	1443.0
Madagascar	Africa	1957	38.9	5181679	1589.2
Madagascar	Africa	1962	40.8	5703324	1643.4
Madagascar	Africa	1967	42.9	6334556	1634.0
Madagascar	Africa	1972	44.9	7082430	1748.6
Madagascar	Africa	1977	46.9	8007166	1544.2
Madagascar	Africa	1982	49.0	9171477	1302.9
Madagascar	Africa	1987	49.4	10568642	1155.4
Madagascar	Africa	1992	52.2	12210395	1040.7
Madagascar	Africa	1997	55.0	14165114	986.3
Madagascar	Africa	2002	57.3	16473477	894.6
Madagascar	Africa	2007	59.4	19167654	1044.8
Malawi	Africa	1952	36.3	2917802	369.2
Malawi	Africa	1957	37.2	3221238	416.4
Malawi	Africa	1962	38.4	3628608	427.9
Malawi	Africa	1967	39.5	4147252	495.5
Malawi	Africa	1972	41.8	4730997	584.6
Malawi	Africa	1977	43.8	5637246	663.2
Malawi	Africa	1982	45.6	6502825	632.8
Malawi	Africa	1987	47.5	7824747	635.5
Malawi	Africa	1992	49.4	10014249	563.2
Malawi	Africa	1997	47.5	10419991	692.3
Malawi	Africa	2002	45.0	11824495	665.4
Malawi	Africa	2007	48.3	13327079	759.3
Malaysia	Asia	1952	48.5	6748378	1831.1
Malaysia	Asia	1957	52.1	7739235	1810.1
Malaysia	Asia	1962	55.7	8906385	2036.9
Malaysia	Asia	1967	59.4	10154878	2277.7
Malaysia	Asia	1972	63.0	11441462	2849.1
Malaysia	Asia	1977	65.3	12845381	3827.9
Malaysia	Asia	1982	68.0	14441916	4920.4
Malaysia	Asia	1987	69.5	16331785	5249.8
Malaysia	Asia	1992	70.7	18319502	7277.9
Malaysia	Asia	1997	71.9	20476091	10132.9
Malaysia	Asia	2002	73.0	22662365	10207.0
Malaysia	Asia	2007	74.2	24821286	12451.7
Mali	Africa	1952	33.7	3838168	452.3
Mali	Africa	1957	35.3	4241884	490.4
Mali	Africa	1962	36.9	4690372	496.2
Mali	Africa	1967	38.5	5212416	545.0
Mali	Africa	1972	40.0	5828158	581.4
Mali	Africa	1977	41.7	6491649	686.4
Mali	Africa	1982	43.9	6998256	618.0
Mali	Africa	1987	46.4	7634008	684.2
Mali	Africa	1992	48.4	8416215	739.0
Mali	Africa	1997	49.9	9384984	790.3
Mali	Africa	2002	51.8	10580176	951.4
Mali	Africa	2007	54.5	12031795	1042.6
Mauritania	Africa	1952	40.5	1022556	743.1
Mauritania	Africa	1957	42.3	1076852	846.1
Mauritania	Africa	1962	44.2	1146757	1055.9
Mauritania	Africa	1967	46.3	1230542	1421.1
Mauritania	Africa	1972	48.4	1332786	1586.9
Mauritania	Africa	1977	50.9	1456688	1497.5
Mauritania	Africa	1982	53.6	1622136	1481.2
Mauritania	Africa	1987	56.1	1841240	1421.6
Mauritania	Africa	1992	58.3	2119465	1361.4
Mauritania	Africa	1997	60.4	2444741	1483.1
Mauritania	Africa	2002	62.2	2828858	1579.0
Mauritania	Africa	2007	64.2	3270065	1803.2
Mauritius	Africa	1952	51.0	516556	1968.0
Mauritius	Africa	1957	58.1	609816	2034.0
Mauritius	Africa	1962	60.2	701016	2529.1
Mauritius	Africa	1967	61.6	789309	2475.4
Mauritius	Africa	1972	62.9	851334	2575.5
Mauritius	Africa	1977	64.9	913025	3711.0
Mauritius	Africa	1982	66.7	992040	3688.0
Mauritius	Africa	1987	68.7	1042663	4783.6
Mauritius	Africa	1992	69.7	1096202	6058.3
Mauritius	Africa	1997	70.7	1149818	7425.7
Mauritius	Africa	2002	72.0	1200206	9021.8
Mauritius	Africa	2007	72.8	1250882	10957.0
Mexico	Americas	1952	50.8	30144317	3478.1
Mexico	Americas	1957	55.2	35015548	4131.5
Mexico	Americas	1962	58.3	41121485	4581.6
Mexico	Americas	1967	60.1	47995559	5754.7
Mexico	Americas	1972	62.4	55984294	6809.4
Mexico	Americas	1977	65.0	63759976	7674.9
Mexico	Americas	1982	67.4	71640904	9611.1
Mexico	Americas	1987	69.5	80122492	8688.2
Mexico	Americas	1992	71.5	88111030	9472.4
Mexico	Americas	1997	73.7	95895146	9767.3
Mexico	Americas	2002	74.9	102479927	10742.4
Mexico	Americas	2007	76.2	108700891	11977.6
Mongolia	Asia	1952	42.2	800663	786.6
Mongolia	Asia	1957	45.2	882134	912.7
Mongolia	Asia	1962	48.3	1010280	1056.4
Mongolia	Asia	1967	51.3	1149500	1226.0
Mongolia	Asia	1972	53.8	1320500	1421.7
Mongolia	Asia	1977	55.5	1528000	1647.5
Mongolia	Asia	1982	57.5	1756032	2000.6
Mongolia	Asia	1987	60.2	2015133	2338.0
Mongolia	Asia	1992	61.3	2312802	1785.4
Mongolia	Asia	1997	63.6	2494803	1902.3
Mongolia	Asia	2002	65.0	2674234	2140.7
Mongolia	Asia	2007	66.8	2874127	3095.8
Montenegro	Europe	1952	59.2	413834	2647.6
Montenegro	Europe	1957	61.4	442829	3682.3
Montenegro	Europe	1962	63.7	474528	4649.6
Montenegro	Europe	1967	67.2	501035	5907.9
Montenegro	Europe	1972	70.6	527678	7778.4
Montenegro	Europe	1977	73.1	560073	9595.9
Montenegro	Europe	1982	74.1	562548	11222.6
Montenegro	Europe	1987	74.9	569473	11732.5
Montenegro	Europe	1992	75.4	621621	7003.3
Montenegro	Europe	1997	75.4	692651	6465.6
Montenegro	Europe	2002	74.0	720230	6557.2
Montenegro	Europe	2007	74.5	684736	9253.9
Morocco	Africa	1952	42.9	9939217	1688.2
Morocco	Africa	1957	45.4	11406350	1642.0
Morocco	Africa	1962	47.9	13056604	1566.4
Morocco	Africa	1967	50.3	14770296	1711.0
Morocco	Africa	1972	52.9	16660670	1930.2
Morocco	Africa	1977	55.7	18396941	2370.6
Morocco	Africa	1982	59.6	20198730	2702.6
Morocco	Africa	1987	62.7	22987397	2755.0
Morocco	Africa	1992	65.4	25798239	2948.0
Morocco	Africa	1997	67.7	28529501	2982.1
Morocco	Africa	2002	69.6	31167783	3258.5
Morocco	Africa	2007	71.2	33757175	3820.2
Mozambique	Africa	1952	31.3	6446316	468.5
Mozambique	Africa	1957	33.8	7038035	495.6
Mozambique	Africa	1962	36.2	7788944	556.7
Mozambique	Africa	1967	38.1	8680909	566.7
Mozambique	Africa	1972	40.3	9809596	724.9
Mozambique	Africa	1977	42.5	11127868	502.3
Mozambique	Africa	1982	42.8	12587223	462.2
Mozambique	Africa	1987	42.9	12891952	389.9
Mozambique	Africa	1992	44.3	13160731	410.9
Mozambique	Africa	1997	46.3	16603334	472.3
Mozambique	Africa	2002	44.0	18473780	633.6
Mozambique	Africa	2007	42.1	19951656	823.7
Myanmar	Asia	1952	36.3	20092996	331.0
Myanmar	Asia	1957	41.9	21731844	350.0
Myanmar	Asia	1962	45.1	23634436	388.0
Myanmar	Asia	1967	49.4	25870271	349.0
Myanmar	Asia	1972	53.1	28466390	357.0
Myanmar	Asia	1977	56.1	31528087	371.0
Myanmar	Asia	1982	58.1	34680442	424.0
Myanmar	Asia	1987	58.3	38028578	385.0
Myanmar	Asia	1992	59.3	40546538	347.0
Myanmar	Asia	1997	60.3	43247867	415.0
Myanmar	Asia	2002	59.9	45598081	611.0
Myanmar	Asia	2007	62.1	47761980	944.0
Namibia	Africa	1952	41.7	485831	2423.8
Namibia	Africa	1957	45.2	548080	2621.4
Namibia	Africa	1962	48.4	621392	3173.2
Namibia	Africa	1967	51.2	706640	3793.7
Namibia	Africa	1972	53.9	821782	3746.1
Namibia	Africa	1977	56.4	977026	3876.5
Namibia	Africa	1982	59.0	1099010	4191.1
Namibia	Africa	1987	60.8	1278184	3693.7
Namibia	Africa	1992	62.0	1554253	3804.5
Namibia	Africa	1997	58.9	1774766	3899.5
Namibia	Africa	2002	51.5	1972153	4072.3
Namibia	Africa	2007	52.9	2055080	4811.1
Nepal	Asia	1952	36.2	9182536	545.9
Nepal	Asia	1957	37.7	9682338	597.9
Nepal	Asia	1962	39.4	10332057	652.4
Nepal	Asia	1967	41.5	11261690	676.4
Nepal	Asia	1972	44.0	12412593	674.8
Nepal	Asia	1977	46.7	13933198	694.1
Nepal	Asia	1982	49.6	15796314	718.4
Nepal	Asia	1987	52.5	17917180	775.6
Nepal	Asia	1992	55.7	20326209	897.7
Nepal	Asia	1997	59.4	23001113	1010.9
Nepal	Asia	2002	61.3	25873917	1057.2
Nepal	Asia	2007	63.8	28901790	1091.4
Netherlands	Europe	1952	72.1	10381988	8941.6
Netherlands	Europe	1957	73.0	11026383	11276.2
Netherlands	Europe	1962	73.2	11805689	12790.8
Netherlands	Europe	1967	73.8	12596822	15363.3
Netherlands	Europe	1972	73.8	13329874	18794.7
Netherlands	Europe	1977	75.2	13852989	21209.1
Netherlands	Europe	1982	76.0	14310401	21399.5
Netherlands	Europe	1987	76.8	14665278	23651.3
Netherlands	Europe	1992	77.4	15174244	26790.9
Netherlands	Europe	1997	78.0	15604464	30246.1
Netherlands	Europe	2002	78.5	16122830	33724.8
Netherlands	Europe	2007	79.8	16570613	36797.9
New Zealand	Oceania	1952	69.4	1994794	10556.6
New Zealand	Oceania	1957	70.3	2229407	12247.4
New Zealand	Oceania	1962	71.2	2488550	13175.7
New Zealand	Oceania	1967	71.5	2728150	14463.9
New Zealand	Oceania	1972	71.9	2929100	16046.0
New Zealand	Oceania	1977	72.2	3164900	16233.7
New Zealand	Oceania	1982	73.8	3210650	17632.4
New Zealand	Oceania	1987	74.3	3317166	19007.2
New Zealand	Oceania	1992	76.3	3437674	18363.3
New Zealand	Oceania	1997	77.6	3676187	21050.4
New Zealand	Oceania	2002	79.1	3908037	23189.8
New Zealand	Oceania	2007	80.2	4115771	25185.0
Nicaragua	Americas	1952	42.3	1165790	3112.4
Nicaragua	Americas	1957	45.4	1358828	3457.4
Nicaragua	Americas	1962	48.6	1590597	3634.4
Nicaragua	Americas	1967	51.9	1865490	4643.4
Nicaragua	Americas	1972	55.2	2182908	4688.6
Nicaragua	Americas	1977	57.5	2554598	5486.4
Nicaragua	Americas	1982	59.3	2979423	3470.3
Nicaragua	Americas	1987	62.0	3344353	2956.0
Nicaragua	Americas	1992	65.8	4017939	2170.2
Nicaragua	Americas	1997	68.4	4609572	2253.0
Nicaragua	Americas	2002	70.8	5146848	2474.5
Nicaragua	Americas	2007	72.9	5675356	2749.3
Niger	Africa	1952	37.4	3379468	761.9
Niger	Africa	1957	38.6	3692184	835.5
Niger	Africa	1962	39.5	4076008	997.8
Niger	Africa	1967	40.1	4534062	1054.4
Niger	Africa	1972	40.5	5060262	954.2
Niger	Africa	1977	41.3	5682086	808.9
Niger	Africa	1982	42.6	6437188	909.7
Niger	Africa	1987	44.6	7332638	668.3
Niger	Africa	1992	47.4	8392818	581.2
Niger	Africa	1997	51.3	9666252	580.3
Niger	Africa	2002	54.5	11140655	601.1
Niger	Africa	2007	56.9	12894865	619.7
Nigeria	Africa	1952	36.3	33119096	1077.3
Nigeria	Africa	1957	37.8	37173340	1100.6
Nigeria	Africa	1962	39.4	41871351	1150.9
Nigeria	Africa	1967	41.0	47287752	1014.5
Nigeria	Africa	1972	42.8	53740085	1698.4
Nigeria	Africa	1977	44.5	62209173	1982.0
Nigeria	Africa	1982	45.8	73039376	1577.0
Nigeria	Africa	1987	46.9	81551520	1385.0
Nigeria	Africa	1992	47.5	93364244	1619.8
Nigeria	Africa	1997	47.5	106207839	1624.9
Nigeria	Africa	2002	46.6	119901274	1615.3
Nigeria	Africa	2007	46.9	135031164	2014.0
Norway	Europe	1952	72.7	3327728	10095.4
Norway	Europe	1957	73.4	3491938	11654.0
Norway	Europe	1962	73.5	3638919	13450.4
Norway	Europe	1967	74.1	3786019	16361.9
Norway	Europe	1972	74.3	3933004	18965.1
Norway	Europe	1977	75.4	4043205	23311.3
Norway	Europe	1982	76.0	4114787	26298.6
Norway	Europe	1987	75.9	4186147	31541.0
Norway	Europe	1992	77.3	4286357	33965.7
Norway	Europe	1997	78.3	4405672	41283.2
Norway	Europe	2002	79.0	4535591	44684.0
Norway	Europe	2007	80.2	4627926	49357.2
Oman	Asia	1952	37.6	507833	1828.2
Oman	Asia	1957	40.1	561977	2242.7
Oman	Asia	1962	43.2	628164	2924.6
Oman	Asia	1967	47.0	714775	4720.9
Oman	Asia	1972	52.1	829050	10618.0
Oman	Asia	1977	57.4	1004533	11848.3
Oman	Asia	1982	62.7	1301048	12954.8
Oman	Asia	1987	67.7	1593882	18115.2
Oman	Asia	1992	71.2	1915208	18616.7
Oman	Asia	1997	72.5	2283635	19702.1
Oman	Asia	2002	74.2	2713462	19774.8
Oman	Asia	2007	75.6	3204897	22316.2
Pakistan	Asia	1952	43.4	41346560	684.6
Pakistan	Asia	1957	45.6	46679944	747.1
Pakistan	Asia	1962	47.7	53100671	803.3
Pakistan	Asia	1967	49.8	60641899	942.4
Pakistan	Asia	1972	51.9	69325921	1049.9
Pakistan	Asia	1977	54.0	78152686	1175.9
Pakistan	Asia	1982	56.2	91462088	1443.4
Pakistan	Asia	1987	58.2	105186881	1704.7
Pakistan	Asia	1992	60.8	120065004	1971.8
Pakistan	Asia	1997	61.8	135564834	2049.4
Pakistan	Asia	2002	63.6	153403524	2092.7
Pakistan	Asia	2007	65.5	169270617	2605.9
Panama	Americas	1952	55.2	940080	2480.4
Panama	Americas	1957	59.2	1063506	2961.8
Panama	Americas	1962	61.8	1215725	3536.5
Panama	Americas	1967	64.1	1405486	4421.0
Panama	Americas	1972	66.2	1616384	5364.2
Panama	Americas	1977	68.7	1839782	5351.9
Panama	Americas	1982	70.5	2036305	7009.6
Panama	Americas	1987	71.5	2253639	7034.8
Panama	Americas	1992	72.5	2484997	6618.7
Panama	Americas	1997	73.7	2734531	7113.7
Panama	Americas	2002	74.7	2990875	7356.0
Panama	Americas	2007	75.5	3242173	9809.2
Paraguay	Americas	1952	62.6	1555876	1952.3
Paraguay	Americas	1957	63.2	1770902	2046.2
Paraguay	Americas	1962	64.4	2009813	2148.0
Paraguay	Americas	1967	65.0	2287985	2299.4
Paraguay	Americas	1972	65.8	2614104	2523.3
Paraguay	Americas	1977	66.4	2984494	3248.4
Paraguay	Americas	1982	66.9	3366439	4258.5
Paraguay	Americas	1987	67.4	3886512	3998.9
Paraguay	Americas	1992	68.2	4483945	4196.4
Paraguay	Americas	1997	69.4	5154123	4247.4
Paraguay	Americas	2002	70.8	5884491	3783.7
Paraguay	Americas	2007	71.8	6667147	4172.8
Peru	Americas	1952	43.9	8025700	3758.5
Peru	Americas	1957	46.3	9146100	4245.3
Peru	Americas	1962	49.1	10516500	4957.0
Peru	Americas	1967	51.4	12132200	5788.1
Peru	Americas	1972	55.4	13954700	5937.8
Peru	Americas	1977	58.4	15990099	6281.3
Peru	Americas	1982	61.4	18125129	6434.5
Peru	Americas	1987	64.1	20195924	6360.9
Peru	Americas	1992	66.5	22430449	4446.4
Peru	Americas	1997	68.4	24748122	5838.3
Peru	Americas	2002	69.9	26769436	5909.0
Peru	Americas	2007	71.4	28674757	7408.9
Philippines	Asia	1952	47.8	22438691	1272.9
Philippines	Asia	1957	51.3	26072194	1547.9
Philippines	Asia	1962	54.8	30325264	1649.6
Philippines	Asia	1967	56.4	35356600	1814.1
Philippines	Asia	1972	58.1	40850141	1989.4
Philippines	Asia	1977	60.1	46850962	2373.2
Philippines	Asia	1982	62.1	53456774	2603.3
Philippines	Asia	1987	64.2	60017788	2189.6
Philippines	Asia	1992	66.5	67185766	2279.3
Philippines	Asia	1997	68.6	75012988	2536.5
Philippines	Asia	2002	70.3	82995088	2650.9
Philippines	Asia	2007	71.7	91077287	3190.5
Poland	Europe	1952	61.3	25730551	4029.3
Poland	Europe	1957	65.8	28235346	4734.3
Poland	Europe	1962	67.6	30329617	5338.8
Poland	Europe	1967	69.6	31785378	6557.2
Poland	Europe	1972	70.8	33039545	8006.5
Poland	Europe	1977	70.7	34621254	9508.1
Poland	Europe	1982	71.3	36227381	8451.5
Poland	Europe	1987	71.0	37740710	9082.4
Poland	Europe	1992	71.0	38370697	7738.9
Poland	Europe	1997	72.8	38654957	10159.6
Poland	Europe	2002	74.7	38625976	12002.2
Poland	Europe	2007	75.6	38518241	15389.9
Portugal	Europe	1952	59.8	8526050	3068.3
Portugal	Europe	1957	61.5	8817650	3774.6
Portugal	Europe	1962	64.4	9019800	4728.0
Portugal	Europe	1967	66.6	9103000	6361.5
Portugal	Europe	1972	69.3	8970450	9022.2
Portugal	Europe	1977	70.4	9662600	10172.5
Portugal	Europe	1982	72.8	9859650	11753.8
Portugal	Europe	1987	74.1	9915289	13039.3
Portugal	Europe	1992	74.9	9927680	16207.3
Portugal	Europe	1997	76.0	10156415	17641.0
Portugal	Europe	2002	77.3	10433867	19970.9
Portugal	Europe	2007	78.1	10642836	20509.6
Puerto Rico	Americas	1952	64.3	2227000	3082.0
Puerto Rico	Americas	1957	68.5	2260000	3907.2
Puerto Rico	Americas	1962	69.6	2448046	5108.3
Puerto Rico	Americas	1967	71.1	2648961	6929.3
Puerto Rico	Americas	1972	72.2	2847132	9123.0
Puerto Rico	Americas	1977	73.4	3080828	9770.5
Puerto Rico	Americas	1982	73.8	3279001	10331.0
Puerto Rico	Americas	1987	74.6	3444468	12281.3
Puerto Rico	Americas	1992	73.9	3585176	14641.6
Puerto Rico	Americas	1997	74.9	3759430	16999.4
Puerto Rico	Americas	2002	77.8	3859606	18855.6
Puerto Rico	Americas	2007	78.7	3942491	19328.7
Reunion	Africa	1952	52.7	257700	2718.9
Reunion	Africa	1957	55.1	308700	2769.5
Reunion	Africa	1962	57.7	358900	3173.7
Reunion	Africa	1967	60.5	414024	4021.2
Reunion	Africa	1972	64.3	461633	5047.7
Reunion	Africa	1977	67.1	492095	4319.8
Reunion	Africa	1982	69.9	517810	5267.2
Reunion	Africa	1987	71.9	562035	5303.4
Reunion	Africa	1992	73.6	622191	6101.3
Reunion	Africa	1997	74.8	684810	6071.9
Reunion	Africa	2002	75.7	743981	6316.2
Reunion	Africa	2007	76.4	798094	7670.1
Romania	Europe	1952	61.0	16630000	3144.6
Romania	Europe	1957	64.1	17829327	3943.4
Romania	Europe	1962	66.8	18680721	4735.0
Romania	Europe	1967	66.8	19284814	6470.9
Romania	Europe	1972	69.2	20662648	8011.4
Romania	Europe	1977	69.5	21658597	9356.4
Romania	Europe	1982	69.7	22356726	9605.3
Romania	Europe	1987	69.5	22686371	9696.3
Romania	Europe	1992	69.4	22797027	6598.4
Romania	Europe	1997	69.7	22562458	7346.5
Romania	Europe	2002	71.3	22404337	7885.4
Romania	Europe	2007	72.5	22276056	10808.5
Rwanda	Africa	1952	40.0	2534927	493.3
Rwanda	Africa	1957	41.5	2822082	540.3
Rwanda	Africa	1962	43.0	3051242	597.5
Rwanda	Africa	1967	44.1	3451079	511.0
Rwanda	Africa	1972	44.6	3992121	590.6
Rwanda	Africa	1977	45.0	4657072	670.1
Rwanda	Africa	1982	46.2	5507565	881.6
Rwanda	Africa	1987	44.0	6349365	848.0
Rwanda	Africa	1992	23.6	7290203	737.1
Rwanda	Africa	1997	36.1	7212583	589.9
Rwanda	Africa	2002	43.4	7852401	785.7
Rwanda	Africa	2007	46.2	8860588	863.1
Sao Tome and Principe	Africa	1952	46.5	60011	879.6
Sao Tome and Principe	Africa	1957	48.9	61325	860.7
Sao Tome and Principe	Africa	1962	51.9	65345	1071.6
Sao Tome and Principe	Africa	1967	54.4	70787	1384.8
Sao Tome and Principe	Africa	1972	56.5	76595	1533.0
Sao Tome and Principe	Africa	1977	58.5	86796	1737.6
Sao Tome and Principe	Africa	1982	60.4	98593	1890.2
Sao Tome and Principe	Africa	1987	61.7	110812	1516.5
Sao Tome and Principe	Africa	1992	62.7	125911	1428.8
Sao Tome and Principe	Africa	1997	63.3	145608	1339.1
Sao Tome and Principe	Africa	2002	64.3	170372	1353.1
Sao Tome and Principe	Africa	2007	65.5	199579	1598.4
Saudi Arabia	Asia	1952	39.9	4005677	6459.6
Saudi Arabia	Asia	1957	42.9	4419650	8157.6
Saudi Arabia	Asia	1962	45.9	4943029	11626.4
Saudi Arabia	Asia	1967	49.9	5618198	16903.0
Saudi Arabia	Asia	1972	53.9	6472756	24837.4
Saudi Arabia	Asia	1977	58.7	8128505	34167.8
Saudi Arabia	Asia	1982	63.0	11254672	33693.2
Saudi Arabia	Asia	1987	66.3	14619745	21198.3
Saudi Arabia	Asia	1992	68.8	16945857	24841.6
Saudi Arabia	Asia	1997	70.5	21229759	20586.7
Saudi Arabia	Asia	2002	71.6	24501530	19014.5
Saudi Arabia	Asia	2007	72.8	27601038	21654.8
Senegal	Africa	1952	37.3	2755589	1450.4
Senegal	Africa	1957	39.3	3054547	1567.7
Senegal	Africa	1962	41.5	3430243	1655.0
Senegal	Africa	1967	43.6	3965841	1612.4
Senegal	Africa	1972	45.8	4588696	1597.7
Senegal	Africa	1977	48.9	5260855	1561.8
Senegal	Africa	1982	52.4	6147783	1518.5
Senegal	Africa	1987	55.8	7171347	1441.7
Senegal	Africa	1992	58.2	8307920	1367.9
Senegal	Africa	1997	60.2	9535314	1392.4
Senegal	Africa	2002	61.6	10870037	1519.6
Senegal	Africa	2007	63.1	12267493	1712.5
Serbia	Europe	1952	58.0	6860147	3581.5
Serbia	Europe	1957	61.7	7271135	4981.1
Serbia	Europe	1962	64.5	7616060	6289.6
Serbia	Europe	1967	66.9	7971222	7991.7
Serbia	Europe	1972	68.7	8313288	10522.1
Serbia	Europe	1977	70.3	8686367	12980.7
Serbia	Europe	1982	70.2	9032824	15181.1
Serbia	Europe	1987	71.2	9230783	15870.9
Serbia	Europe	1992	71.7	9826397	9325.1
Serbia	Europe	1997	72.2	10336594	7914.3
Serbia	Europe	2002	73.2	10111559	7236.1
Serbia	Europe	2007	74.0	10150265	9786.5
Sierra Leone	Africa	1952	30.3	2143249	879.8
Sierra Leone	Africa	1957	31.6	2295678	1004.5
Sierra Leone	Africa	1962	32.8	2467895	1116.6
Sierra Leone	Africa	1967	34.1	2662190	1206.0
Sierra Leone	Africa	1972	35.4	2879013	1353.8
Sierra Leone	Africa	1977	36.8	3140897	1348.3
Sierra Leone	Africa	1982	38.4	3464522	1465.0
Sierra Leone	Africa	1987	40.0	3868905	1294.4
Sierra Leone	Africa	1992	38.3	4260884	1068.7
Sierra Leone	Africa	1997	39.9	4578212	574.6
Sierra Leone	Africa	2002	41.0	5359092	699.5
Sierra Leone	Africa	2007	42.6	6144562	862.5
Singapore	Asia	1952	60.4	1127000	2315.1
Singapore	Asia	1957	63.2	1445929	2843.1
Singapore	Asia	1962	65.8	1750200	3674.7
Singapore	Asia	1967	67.9	1977600	4977.4
Singapore	Asia	1972	69.5	2152400	8597.8
Singapore	Asia	1977	70.8	2325300	11210.1
Singapore	Asia	1982	71.8	2651869	15169.2
Singapore	Asia	1987	73.6	2794552	18861.5
Singapore	Asia	1992	75.8	3235865	24769.9
Singapore	Asia	1997	77.2	3802309	33519.5
Singapore	Asia	2002	78.8	4197776	36023.1
Singapore	Asia	2007	80.0	4553009	47143.2
Slovak Republic	Europe	1952	64.4	3558137	5074.7
Slovak Republic	Europe	1957	67.4	3844277	6093.3
Slovak Republic	Europe	1962	70.3	4237384	7481.1
Slovak Republic	Europe	1967	71.0	4442238	8412.9
Slovak Republic	Europe	1972	70.3	4593433	9674.2
Slovak Republic	Europe	1977	70.4	4827803	10922.7
Slovak Republic	Europe	1982	70.8	5048043	11348.5
Slovak Republic	Europe	1987	71.1	5199318	12037.3
Slovak Republic	Europe	1992	71.4	5302888	9498.5
Slovak Republic	Europe	1997	72.7	5383010	12126.2
Slovak Republic	Europe	2002	73.8	5410052	13638.8
Slovak Republic	Europe	2007	74.7	5447502	18678.3
Slovenia	Europe	1952	65.6	1489518	4215.0
Slovenia	Europe	1957	67.8	1533070	5862.3
Slovenia	Europe	1962	69.2	1582962	7402.3
Slovenia	Europe	1967	69.2	1646912	9405.5
Slovenia	Europe	1972	69.8	1694510	12383.5
Slovenia	Europe	1977	71.0	1746919	15277.0
Slovenia	Europe	1982	71.1	1861252	17866.7
Slovenia	Europe	1987	72.2	1945870	18678.5
Slovenia	Europe	1992	73.6	1999210	14214.7
Slovenia	Europe	1997	75.1	2011612	17161.1
Slovenia	Europe	2002	76.7	2011497	20660.0
Slovenia	Europe	2007	77.9	2009245	25768.3
Somalia	Africa	1952	33.0	2526994	1135.7
Somalia	Africa	1957	35.0	2780415	1258.1
Somalia	Africa	1962	37.0	3080153	1369.5
Somalia	Africa	1967	39.0	3428839	1284.7
Somalia	Africa	1972	41.0	3840161	1254.6
Somalia	Africa	1977	42.0	4353666	1451.0
Somalia	Africa	1982	43.0	5828892	1176.8
Somalia	Africa	1987	44.5	6921858	1093.2
Somalia	Africa	1992	39.7	6099799	927.0
Somalia	Africa	1997	43.8	6633514	930.6
Somalia	Africa	2002	45.9	7753310	882.1
Somalia	Africa	2007	48.2	9118773	926.1
South Africa	Africa	1952	45.0	14264935	4725.3
South Africa	Africa	1957	48.0	16151549	5487.1
South Africa	Africa	1962	50.0	18356657	5768.7
South Africa	Africa	1967	51.9	20997321	7114.5
South Africa	Africa	1972	53.7	23935810	7766.0
South Africa	Africa	1977	55.5	27129932	8028.7
South Africa	Africa	1982	58.2	31140029	8568.3
South Africa	Africa	1987	60.8	35933379	7825.8
South Africa	Africa	1992	61.9	39964159	7225.1
South Africa	Africa	1997	60.2	42835005	7479.2
South Africa	Africa	2002	53.4	44433622	7710.9
South Africa	Africa	2007	49.3	43997828	9269.7
Spain	Europe	1952	64.9	28549870	3834.0
Spain	Europe	1957	66.7	29841614	4564.8
Spain	Europe	1962	69.7	31158061	5693.8
Spain	Europe	1967	71.4	32850275	7993.5
Spain	Europe	1972	73.1	34513161	10638.8
Spain	Europe	1977	74.4	36439000	13236.9
Spain	Europe	1982	76.3	37983310	13926.2
Spain	Europe	1987	76.9	38880702	15765.0
Spain	Europe	1992	77.6	39549438	18603.1
Spain	Europe	1997	78.8	39855442	20445.3
Spain	Europe	2002	79.8	40152517	24835.5
Spain	Europe	2007	80.9	40448191	28821.1
Sri Lanka	Asia	1952	57.6	7982342	1083.5
Sri Lanka	Asia	1957	61.5	9128546	1072.5
Sri Lanka	Asia	1962	62.2	10421936	1074.5
Sri Lanka	Asia	1967	64.3	11737396	1135.5
Sri Lanka	Asia	1972	65.0	13016733	1213.4
Sri Lanka	Asia	1977	65.9	14116836	1348.8
Sri Lanka	Asia	1982	68.8	15410151	1648.1
Sri Lanka	Asia	1987	69.0	16495304	1876.8
Sri Lanka	Asia	1992	70.4	17587060	2153.7
Sri Lanka	Asia	1997	70.5	18698655	2664.5
Sri Lanka	Asia	2002	70.8	19576783	3015.4
Sri Lanka	Asia	2007	72.4	20378239	3970.1
Sudan	Africa	1952	38.6	8504667	1616.0
Sudan	Africa	1957	39.6	9753392	1770.3
Sudan	Africa	1962	40.9	11183227	1959.6
Sudan	Africa	1967	42.9	12716129	1688.0
Sudan	Africa	1972	45.1	14597019	1659.7
Sudan	Africa	1977	47.8	17104986	2203.0
Sudan	Africa	1982	50.3	20367053	1895.5
Sudan	Africa	1987	51.7	24725960	1507.8
Sudan	Africa	1992	53.6	28227588	1492.2
Sudan	Africa	1997	55.4	32160729	1632.2
Sudan	Africa	2002	56.4	37090298	1993.4
Sudan	Africa	2007	58.6	42292929	2602.4
Swaziland	Africa	1952	41.4	290243	1148.4
Swaziland	Africa	1957	43.4	326741	1244.7
Swaziland	Africa	1962	45.0	370006	1856.2
Swaziland	Africa	1967	46.6	420690	2613.1
Swaziland	Africa	1972	49.6	480105	3364.8
Swaziland	Africa	1977	52.5	551425	3781.4
Swaziland	Africa	1982	55.6	649901	3895.4
Swaziland	Africa	1987	57.7	779348	3984.8
Swaziland	Africa	1992	58.5	962344	3553.0
Swaziland	Africa	1997	54.3	1054486	3876.8
Swaziland	Africa	2002	43.9	1130269	4128.1
Swaziland	Africa	2007	39.6	1133066	4513.5
Sweden	Europe	1952	71.9	7124673	8527.8
Sweden	Europe	1957	72.5	7363802	9911.9
Sweden	Europe	1962	73.4	7561588	12329.4
Sweden	Europe	1967	74.2	7867931	15258.3
Sweden	Europe	1972	74.7	8122293	17832.0
Sweden	Europe	1977	75.4	8251648	18855.7
Sweden	Europe	1982	76.4	8325260	20667.4
Sweden	Europe	1987	77.2	8421403	23586.9
Sweden	Europe	1992	78.2	8718867	23880.0
Sweden	Europe	1997	79.4	8897619	25266.6
Sweden	Europe	2002	80.0	8954175	29341.6
Sweden	Europe	2007	80.9	9031088	33859.7
Switzerland	Europe	1952	69.6	4815000	14734.2
Switzerland	Europe	1957	70.6	5126000	17909.5
Switzerland	Europe	1962	71.3	5666000	20431.1
Switzerland	Europe	1967	72.8	6063000	22966.1
Switzerland	Europe	1972	73.8	6401400	27195.1
Switzerland	Europe	1977	75.4	6316424	26982.3
Switzerland	Europe	1982	76.2	6468126	28397.7
Switzerland	Europe	1987	77.4	6649942	30281.7
Switzerland	Europe	1992	78.0	6995447	31871.5
Switzerland	Europe	1997	79.4	7193761	32135.3
Switzerland	Europe	2002	80.6	7361757	34481.0
Switzerland	Europe	2007	81.7	7554661	37506.4
Syria	Asia	1952	45.9	3661549	1643.5
Syria	Asia	1957	48.3	4149908	2117.2
Syria	Asia	1962	50.3	4834621	2193.0
Syria	Asia	1967	53.7	5680812	1881.9
Syria	Asia	1972	57.3	6701172	2571.4
Syria	Asia	1977	61.2	7932503	3195.5
Syria	Asia	1982	64.6	9410494	3761.8
Syria	Asia	1987	67.0	11242847	3116.8
Syria	Asia	1992	69.2	13219062	3340.5
Syria	Asia	1997	71.5	15081016	4014.2
Syria	Asia	2002	73.1	17155814	4090.9
Syria	Asia	2007	74.1	19314747	4184.5
Taiwan	Asia	1952	58.5	8550362	1206.9
Taiwan	Asia	1957	62.4	10164215	1507.9
Taiwan	Asia	1962	65.2	11918938	1822.9
Taiwan	Asia	1967	67.5	13648692	2643.9
Taiwan	Asia	1972	69.4	15226039	4062.5
Taiwan	Asia	1977	70.6	16785196	5596.5
Taiwan	Asia	1982	72.2	18501390	7426.4
Taiwan	Asia	1987	73.4	19757799	11054.6
Taiwan	Asia	1992	74.3	20686918	15215.7
Taiwan	Asia	1997	75.2	21628605	20206.8
Taiwan	Asia	2002	77.0	22454239	23235.4
Taiwan	Asia	2007	78.4	23174294	28718.3
Tanzania	Africa	1952	41.2	8322925	716.7
Tanzania	Africa	1957	43.0	9452826	698.5
Tanzania	Africa	1962	44.2	10863958	722.0
Tanzania	Africa	1967	45.8	12607312	848.2
Tanzania	Africa	1972	47.6	14706593	916.0
Tanzania	Africa	1977	49.9	17129565	962.5
Tanzania	Africa	1982	50.6	19844382	874.2
Tanzania	Africa	1987	51.5	23040630	831.8
Tanzania	Africa	1992	50.4	26605473	825.7
Tanzania	Africa	1997	48.5	30686889	789.2
Tanzania	Africa	2002	49.7	34593779	899.1
Tanzania	Africa	2007	52.5	38139640	1107.5
Thailand	Asia	1952	50.8	21289402	757.8
Thailand	Asia	1957	53.6	25041917	793.6
Thailand	Asia	1962	56.1	29263397	1002.2
Thailand	Asia	1967	58.3	34024249	1295.5
Thailand	Asia	1972	60.4	39276153	1524.4
Thailand	Asia	1977	62.5	44148285	1961.2
Thailand	Asia	1982	64.6	48827160	2393.2
Thailand	Asia	1987	66.1	52910342	2982.7
Thailand	Asia	1992	67.3	56667095	4616.9
Thailand	Asia	1997	67.5	60216677	5852.6
Thailand	Asia	2002	68.6	62806748	5913.2
Thailand	Asia	2007	70.6	65068149	7458.4
Togo	Africa	1952	38.6	1219113	859.8
Togo	Africa	1957	41.2	1357445	925.9
Togo	Africa	1962	43.9	1528098	1067.5
Togo	Africa	1967	46.8	1735550	1477.6
Togo	Africa	1972	49.8	2056351	1649.7
Togo	Africa	1977	52.9	2308582	1532.8
Togo	Africa	1982	55.5	2644765	1344.6
Togo	Africa	1987	56.9	3154264	1202.2
Togo	Africa	1992	58.1	3747553	1034.3
Togo	Africa	1997	58.4	4320890	982.3
Togo	Africa	2002	57.6	4977378	886.2
Togo	Africa	2007	58.4	5701579	883.0
Trinidad and Tobago	Americas	1952	59.1	662850	3023.3
Trinidad and Tobago	Americas	1957	61.8	764900	4100.4
Trinidad and Tobago	Americas	1962	64.9	887498	4997.5
Trinidad and Tobago	Americas	1967	65.4	960155	5621.4
Trinidad and Tobago	Americas	1972	65.9	975199	6619.6
Trinidad and Tobago	Americas	1977	68.3	1039009	7899.6
Trinidad and Tobago	Americas	1982	68.8	1116479	9119.5
Trinidad and Tobago	Americas	1987	69.6	1191336	7388.6
Trinidad and Tobago	Americas	1992	69.9	1183669	7371.0
Trinidad and Tobago	Americas	1997	69.5	1138101	8792.6
Trinidad and Tobago	Americas	2002	69.0	1101832	11460.6
Trinidad and Tobago	Americas	2007	69.8	1056608	18008.5
Tunisia	Africa	1952	44.6	3647735	1468.5
Tunisia	Africa	1957	47.1	3950849	1395.2
Tunisia	Africa	1962	49.6	4286552	1660.3
Tunisia	Africa	1967	52.1	4786986	1932.4
Tunisia	Africa	1972	55.6	5303507	2753.3
Tunisia	Africa	1977	59.8	6005061	3120.9
Tunisia	Africa	1982	64.0	6734098	3560.2
Tunisia	Africa	1987	66.9	7724976	3810.4
Tunisia	Africa	1992	70.0	8523077	4332.7
Tunisia	Africa	1997	72.0	9231669	4876.8
Tunisia	Africa	2002	73.0	9770575	5722.9
Tunisia	Africa	2007	73.9	10276158	7092.9
Turkey	Europe	1952	43.6	22235677	1969.1
Turkey	Europe	1957	48.1	25670939	2218.8
Turkey	Europe	1962	52.1	29788695	2322.9
Turkey	Europe	1967	54.3	33411317	2826.4
Turkey	Europe	1972	57.0	37492953	3450.7
Turkey	Europe	1977	59.5	42404033	4269.1
Turkey	Europe	1982	61.0	47328791	4241.4
Turkey	Europe	1987	63.1	52881328	5089.0
Turkey	Europe	1992	66.1	58179144	5678.3
Turkey	Europe	1997	68.8	63047647	6601.4
Turkey	Europe	2002	70.8	67308928	6508.1
Turkey	Europe	2007	71.8	71158647	8458.3
Uganda	Africa	1952	40.0	5824797	734.8
Uganda	Africa	1957	42.6	6675501	774.4
Uganda	Africa	1962	45.3	7688797	767.3
Uganda	Africa	1967	48.1	8900294	908.9
Uganda	Africa	1972	51.0	10190285	950.7
Uganda	Africa	1977	50.4	11457758	843.7
Uganda	Africa	1982	49.8	12939400	682.3
Uganda	Africa	1987	51.5	15283050	617.7
Uganda	Africa	1992	48.8	18252190	644.2
Uganda	Africa	1997	44.6	21210254	816.6
Uganda	Africa	2002	47.8	24739869	927.7
Uganda	Africa	2007	51.5	29170398	1056.4
United Kingdom	Europe	1952	69.2	50430000	9979.5
United Kingdom	Europe	1957	70.4	51430000	11283.2
United Kingdom	Europe	1962	70.8	53292000	12477.2
United Kingdom	Europe	1967	71.4	54959000	14142.9
United Kingdom	Europe	1972	72.0	56079000	15895.1
United Kingdom	Europe	1977	72.8	56179000	17428.7
United Kingdom	Europe	1982	74.0	56339704	18232.4
United Kingdom	Europe	1987	75.0	56981620	21664.8
United Kingdom	Europe	1992	76.4	57866349	22705.1
United Kingdom	Europe	1997	77.2	58808266	26074.5
United Kingdom	Europe	2002	78.5	59912431	29479.0
United Kingdom	Europe	2007	79.4	60776238	33203.3
United States	Americas	1952	68.4	157553000	13990.5
United States	Americas	1957	69.5	171984000	14847.1
United States	Americas	1962	70.2	186538000	16173.1
United States	Americas	1967	70.8	198712000	19530.4
United States	Americas	1972	71.3	209896000	21806.0
United States	Americas	1977	73.4	220239000	24072.6
United States	Americas	1982	74.7	232187835	25009.6
United States	Americas	1987	75.0	242803533	29884.4
United States	Americas	1992	76.1	256894189	32003.9
United States	Americas	1997	76.8	272911760	35767.4
United States	Americas	2002	77.3	287675526	39097.1
United States	Americas	2007	78.2	301139947	42951.7
Uruguay	Americas	1952	66.1	2252965	5716.8
Uruguay	Americas	1957	67.0	2424959	6150.8
Uruguay	Americas	1962	68.3	2598466	5603.4
Uruguay	Americas	1967	68.5	2748579	5444.6
Uruguay	Americas	1972	68.7	2829526	5703.4
Uruguay	Americas	1977	69.5	2873520	6504.3
Uruguay	Americas	1982	70.8	2953997	6920.2
Uruguay	Americas	1987	71.9	3045153	7452.4
Uruguay	Americas	1992	72.8	3149262	8137.0
Uruguay	Americas	1997	74.2	3262838	9230.2
Uruguay	Americas	2002	75.3	3363085	7727.0
Uruguay	Americas	2007	76.4	3447496	10611.5
Venezuela	Americas	1952	55.1	5439568	7689.8
Venezuela	Americas	1957	57.9	6702668	9802.5
Venezuela	Americas	1962	60.8	8143375	8423.0
Venezuela	Americas	1967	63.5	9709552	9541.5
Venezuela	Americas	1972	65.7	11515649	10505.3
Venezuela	Americas	1977	67.5	13503563	13144.0
Venezuela	Americas	1982	68.6	15620766	11152.4
Venezuela	Americas	1987	70.2	17910182	9883.6
Venezuela	Americas	1992	71.2	20265563	10733.9
Venezuela	Americas	1997	72.1	22374398	10165.5
Venezuela	Americas	2002	72.8	24287670	8605.0
Venezuela	Americas	2007	73.7	26084662	11415.8
Vietnam	Asia	1952	40.4	26246839	605.1
Vietnam	Asia	1957	42.9	28998543	676.3
Vietnam	Asia	1962	45.4	33796140	772.0
Vietnam	Asia	1967	47.8	39463910	637.1
Vietnam	Asia	1972	50.3	44655014	699.5
Vietnam	Asia	1977	55.8	50533506	713.5
Vietnam	Asia	1982	58.8	56142181	707.2
Vietnam	Asia	1987	62.8	62826491	820.8
Vietnam	Asia	1992	67.7	69940728	989.0
Vietnam	Asia	1997	70.7	76048996	1385.9
Vietnam	Asia	2002	73.0	80908147	1764.5
Vietnam	Asia	2007	74.2	85262356	2441.6
West Bank and Gaza	Asia	1952	43.2	1030585	1515.6
West Bank and Gaza	Asia	1957	45.7	1070439	1827.1
West Bank and Gaza	Asia	1962	48.1	1133134	2199.0
West Bank and Gaza	Asia	1967	51.6	1142636	2649.7
West Bank and Gaza	Asia	1972	56.5	1089572	3133.4
West Bank and Gaza	Asia	1977	60.8	1261091	3682.8
West Bank and Gaza	Asia	1982	64.4	1425876	4336.0
West Bank and Gaza	Asia	1987	67.0	1691210	5107.2
West Bank and Gaza	Asia	1992	69.7	2104779	6017.7
West Bank and Gaza	Asia	1997	71.1	2826046	7110.7
West Bank and Gaza	Asia	2002	72.4	3389578	4515.5
West Bank and Gaza	Asia	2007	73.4	4018332	3025.3
Yemen, Rep.	Asia	1952	32.5	4963829	781.7
Yemen, Rep.	Asia	1957	34.0	5498090	804.8
Yemen, Rep.	Asia	1962	35.2	6120081	825.6
Yemen, Rep.	Asia	1967	37.0	6740785	862.4
Yemen, Rep.	Asia	1972	39.8	7407075	1265.0
Yemen, Rep.	Asia	1977	44.2	8403990	1829.8
Yemen, Rep.	Asia	1982	49.1	9657618	1977.6
Yemen, Rep.	Asia	1987	52.9	11219340	1971.7
Yemen, Rep.	Asia	1992	55.6	13367997	1879.5
Yemen, Rep.	Asia	1997	58.0	15826497	2117.5
Yemen, Rep.	Asia	2002	60.3	18701257	2234.8
Yemen, Rep.	Asia	2007	62.7	22211743	2280.8
Zambia	Africa	1952	42.0	2672000	1147.4
Zambia	Africa	1957	44.1	3016000	1312.0
Zambia	Africa	1962	46.0	3421000	1452.7
Zambia	Africa	1967	47.8	3900000	1777.1
Zambia	Africa	1972	50.1	4506497	1773.5
Zambia	Africa	1977	51.4	5216550	1588.7
Zambia	Africa	1982	51.8	6100407	1408.7
Zambia	Africa	1987	50.8	7272406	1213.3
Zambia	Africa	1992	46.1	8381163	1210.9
Zambia	Africa	1997	40.2	9417789	1071.4
Zambia	Africa	2002	39.2	10595811	1071.6
Zambia	Africa	2007	42.4	11746035	1271.2
Zimbabwe	Africa	1952	48.5	3080907	406.9
Zimbabwe	Africa	1957	50.5	3646340	518.8
Zimbabwe	Africa	1962	52.4	4277736	527.3
Zimbabwe	Africa	1967	54.0	4995432	569.8
Zimbabwe	Africa	1972	55.6	5861135	799.4
Zimbabwe	Africa	1977	57.7	6642107	685.6
Zimbabwe	Africa	1982	60.4	7636524	788.9
Zimbabwe	Africa	1987	62.4	9216418	706.2
Zimbabwe	Africa	1992	60.4	10704340	693.4
Zimbabwe	Africa	1997	46.8	11404948	792.4
Zimbabwe	Africa	2002	40.0	11926563	672.0
Zimbabwe	Africa	2007	43.5	12311143	469.7

Before we start

Table 1: Average life expectancy by continent, 1952-2007

Year	Africa	Americas	Asia	Europe	Oceania
1952	39.1	53.3	46.3	64.4	69.3
1957	41.3	56.0	49.3	66.7	70.3
1962	43.3	58.4	51.6	68.5	71.1
1967	45.3	60.4	54.7	69.7	71.3
1972	47.5	62.4	57.3	70.8	71.9
1977	49.6	64.4	59.6	71.9	72.9
1982	51.6	66.2	62.6	72.8	74.3
1987	53.3	68.1	64.9	73.6	75.3
1992	53.6	69.6	66.5	74.4	76.9
1997	53.6	71.2	68.0	75.5	78.2
2002	53.3	72.4	69.2	76.7	79.7
2007	54.8	73.6	70.7	77.6	80.7

Loading the tidyverse libraries

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.3     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package to force all conflicts to become errors

The tidyverse has several components.

We’ll return to this message about Conflicts later.

Tidyverse components

• library(tidyverse)
• Loading tidyverse: ggplot2
• Loading tidyverse: tibble
• Loading tidyverse: tidyr
• Loading tidyverse: readr
• Loading tidyverse: purrr
• Loading tidyverse: dplyr

• Call the package and …
• <| Draw graphs
• <| Nicer data tables
• <| Tidy your data
• <| Get data into R
• <| Fancy Iteration
• <| Action verbs for tables

What R looks like

Code you can type and run:

## Inside code chunks, lines beginning with a # character are comments
## Comments are ignored by R

my_numbers <- c(1, 1, 2, 4, 1, 3, 1, 5) # Anything after a # character is ignored as well

Output:

my_numbers 

[1] 1 1 2 4 1 3 1 5

This is equivalent to running the code above, typing my_numbers at the console, and hitting enter.

What R looks like

By convention, code output in documents is prefixed by ##

Also by convention, outputting vectors, etc, gets a counter keeping track of the number of elements. For example,

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
[20] "t" "u" "v" "w" "x" "y" "z"

Some things to know about R

0. It’s a calculator

• Arithmetic

(31 * 12) / 2^4

[1] 23.25

sqrt(25)

[1] 5

log(100)

[1] 4.60517

log10(100)

[1] 2

0. It’s a calculator

• Arithmetic

(31 * 12) / 2^4

[1] 23.25

sqrt(25)

[1] 5

log(100)

[1] 4.60517

log10(100)

[1] 2

• Logic

4 < 10

[1] TRUE

4 > 2 & 1 > 0.5 # The "&" means "and"

[1] TRUE

4 < 2 | 1 > 0.5 # The "|" means "or"

[1] TRUE

4 < 2 | 1 < 0.5

[1] FALSE

Boolean and Logical operators

Logical equality and inequality (yielding a TRUE or FALSE result) is done with == and !=. Other logical operators include <, >, <=, >=, and ! for negation.

## A logical test
2 == 2 # Write `=` twice

[1] TRUE

## This will cause an error, because R will think you are trying to assign a value
2 = 2

## Error in 2 = 2 : invalid (do_set) left-hand side to assignment

3 != 7 # Write `!` and then `=` to make `!=`

[1] TRUE

1. Everything in R has a name

my_numbers # We created this a few minutes ago

[1] 1 1 2 4 1 3 1 5

letters  # This one is built-in

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
[20] "t" "u" "v" "w" "x" "y" "z"

pi  # Also built-in

[1] 3.141593

Some names are forbidden

Or it’s a really bad idea to try to use them

TRUE
FALSE
Inf
NaN 
NA 
NULL

for
if
while
break
function

2. Everything is an object

There are a few built-in objects:

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
[20] "t" "u" "v" "w" "x" "y" "z"

pi

[1] 3.141593

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
[20] "T" "U" "V" "W" "X" "Y" "Z"

3. You can create objects

In fact, this is mostly what we will be doing.

Objects are created by assigning a thing to a name:

## name... gets ... this stuff
my_numbers <- c(1, 2, 3, 1, 3, 5, 25, 10)

## name ... gets ... the output of the function `c()`
your_numbers <- c(5, 31, 71, 1, 3, 21, 6, 52)

The c() function combines or concatenates things

The core thing we do in R is create objects by assigning a thing to a name. That thing is usually the output of some function. There are a lot of built-in functions.

We can create an object with the c() function and the assignment operator, <-. The c() function concatenates things together.

The assignment operator

• The assignment operator performs the action of creating objects
• Use a keyboard shortcut to write it:
• Press option and - on a Mac
• Press alt and - on Windows

4. Do things to objects with functions

## this object... gets ... the output of this function
my_numbers <- c(1, 2, 3, 1, 3, 5, 25, 10)

your_numbers <- c(5, 31, 71, 1, 3, 21, 6, 52)

my_numbers

[1]  1  2  3  1  3  5 25 10

4. Do things to objects with functions

• Functions can be identified by the parentheses after their names.

my_numbers 

[1]  1  2  3  1  3  5 25 10

## If you run this you'll get an error
mean()

What functions usually do

• They take inputs to arguments
• They perform actions
• They produce, or return, outputs

mean(x = my_numbers)

What functions usually do

• They take inputs to arguments
• They perform actions
• They produce, or return, outputs

mean(x = my_numbers)

[1] 6.25

What functions usually do

## Get the mean of what? Of x.
## You need to tell the function what x is
mean(x = my_numbers)

[1] 6.25

mean(x = your_numbers)

[1] 23.75

What functions usually do

If you don’t name the arguments, R assumes you are providing them in the order the function expects.

mean(your_numbers)

[1] 23.75

What functions usually do

What arguments? Which order? Read the function’s help page

help(mean)

## quicker
?mean

• How to read an R help page?

What functions usually do

• Arguments often tell the function what to do in specific circumstances

missing_numbers <- c(1:10, NA, 20, 32, 50, 104, 32, 147, 99, NA, 45)

mean(missing_numbers)

[1] NA

mean(missing_numbers, na.rm = TRUE)

[1] 32.44444

Or select from one of several options

## Look at ?mean to see what `trim` does
mean(missing_numbers, na.rm = TRUE, trim = 0.1)

[1] 27.25

What functions usually do

There are all kinds of functions. They return different things.

summary(my_numbers)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    1.75    3.00    6.25    6.25   25.00 

What functions usually do

You can assign the output of a function to a name, which turns it into an object. (Otherwise it’ll send its output to the console.)

my_summary <- summary(my_numbers)

my_summary

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    1.75    3.00    6.25    6.25   25.00 

What functions usually do

Objects hang around in your work environment until they are overwritten by you, or are deleted.

## rm() function removes objects
rm(my_summary)

my_summary

## Error: object 'my_summary' not found

Functions can be nested

c(1:20)

 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

mean(c(1:20))

[1] 10.5

summary(mean(c(1:20)))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   10.5    10.5    10.5    10.5    10.5    10.5 

names(summary(mean(c(1:20))))

[1] "Min."    "1st Qu." "Median"  "Mean"    "3rd Qu." "Max."   

length(names(summary(mean(c(1:20)))))

[1] 6

Nested functions are evaluated from the inside out.

Use the pipe operator: |>

Instead of deeply nesting functions in parentheses, we can use the pipe operator:

c(1:20) |> mean() |> summary() |> names() |>  length()

[1] 6

Read this operator as “and then”

Use the pipe operator: |>

Better, vertical space is free in R:

c(1:20) |> 
  mean() |> 
  summary() |> 
  names() |> 
  length()

[1] 6

Pipelines make code more readable

• Not great, Bob:

  serve(stir(pour_in_pan(whisk(crack_eggs(get_from_fridge(eggs), into = "bowl"), len = 40), temp = "med-high")))

• Notice how the first thing you read is the last operation performed.

Pipelines make code more readable

• We can use vertical space and indents, but it’s really not much better:

serve(
  stir(
    pour_in_pan(
      whisk(
        crack_eggs(
          get_from_fridge(eggs), 
        into = "bowl"), 
      len = 40), 
    temp = "med-high")
  )
)

Pipelines make code more readable

• Much nicer:

eggs |> 
  get_from_fridge() |> 
  crack_eggs(into = "bowl") |> 
  whisk(len = 40) |> 
  pour_in_pan(temp = "med-high") |> 
  stir() |> 
  serve()

• We’ll still use nested parentheses quite a bit, often in the context of a function working inside a pipeline. But it’s good not to have too many levels of nesting.

Functions are bundled into packages

Packages are loaded into your working environment using the library() function:

## A package containing a dataset rather than functions
library(gapminder)

gapminder

# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows

Functions are bundled into packages

You need only install a package once (and occasionally update it):

## Do at least once for each package. Once done, not needed each time.
install.packages("palmerpenguins", repos = "http://cran.rstudio.com")

## Needed sometimes, especially after an R major version upgrade.
update.packages(repos = "http://cran.rstudio.com")

Functions are bundled into packages

But you must load the package in each R session before you can access its contents:

## To load a package, usually at the start of your RMarkdown document or script file
library(palmerpenguins)
penguins

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>

Grabbing a single function with ::

# A little glimpse of what we'll do soon
penguins |> 
  count(species, sex, year) |> 
  pivot_wider(names_from = year, values_from = n) |> 
  tinytable::tt()

species	sex	2007	2008	2009
Adelie	female	22	25	26
Adelie	male	22	25	26
Adelie	NA	6	NA	NA
Chinstrap	female	13	9	12
Chinstrap	male	13	9	12
Gentoo	female	16	22	20
Gentoo	male	17	23	21
Gentoo	NA	1	1	3

Remember those conflicts?

• Notice how some functions in different packages have the same names.
• Related concepts of namespaces and environments.

The scope of names

x <- c(1:10)
y <- c(90:100)

x

 [1]  1  2  3  4  5  6  7  8  9 10

y

 [1]  90  91  92  93  94  95  96  97  98  99 100

mean()

## Error in mean.default() : argument "x" is missing, with no default

The scope of names

mean(x) # argument names are internal to functions

[1] 5.5

mean(x = x)

[1] 5.5

mean(x = y)

[1] 95

x

 [1]  1  2  3  4  5  6  7  8  9 10

y

 [1]  90  91  92  93  94  95  96  97  98  99 100

5. Vector types; Object classes

I’m going to speak somewhat loosely here for now, and gloss over some distinctions between object classes and data structures, as well as kinds of objects and their attributes.

5. Vector types; Object classes

Objects are made of one or more vectors. A vector can, in effect, have a single type: integer, double, logical, character, factor, date, etc. That is, vectors are “atomic”. Complex objects are mostly lists of vectors of different sorts, or nested lists of other simpler objects that are themselves ultimately made up of vectors of

5. Vector types; Object classes

The object inspector in RStudio is your friend.

You can ask an object what it is at the console, too:

class(my_numbers)

[1] "numeric"

typeof(my_numbers)

[1] "double"

Types of vector

Hadley Wickham, Advanced R

Types of vector

my_int <- c(1, 3, 5, 6, 10)
is.integer(my_int)

[1] FALSE

is.double(my_int)

[1] TRUE

my_int <- as.integer(my_int)
is.integer(my_int)

[1] TRUE

my_chr <- c("Mary", "had", "a", "little", "lamb")
is.character(my_chr)

[1] TRUE

my_lgl <- c(TRUE, FALSE, TRUE)
is.logical(my_lgl)

[1] TRUE

Hadley Wickham, Advanced R

Types of vector

## Factors are for storing undordered or ordered categorical variables
x <- factor(c("Yes", "No", "No", "Maybe", "Yes", "Yes", "Yes", "No"))
x

[1] Yes   No    No    Maybe Yes   Yes   Yes   No   
Levels: Maybe No Yes

summary(x) # Alphabetical order by default

Maybe    No   Yes 
    1     3     4 

typeof(x)       # Underneath, a factor is a type of integer ...

[1] "integer"

attributes(x)   # ... with labels for its numbers, or "levels" 

$levels
[1] "Maybe" "No"    "Yes"  

$class
[1] "factor"

levels(x)

[1] "Maybe" "No"    "Yes"  

is.ordered(x)

[1] FALSE

HW:

Categorical data, where values come from a fixed set of levels recorded in factor vectors.

Dates (with day resolution), which are recorded in Date vectors.

Date-times (with second or sub-second resolution), which are stored in POSIXct vectors.

Durations, which are stored in difftime vectors.

Vectors can’t be heterogenous

• Objects can be manually or automatically coerced from one class to another. Take care.

class(my_numbers)

[1] "numeric"

my_new_vector <- c(my_numbers, "Apple")

my_new_vector # vectors are homogeneous/atomic

[1] "1"     "2"     "3"     "1"     "3"     "5"     "25"    "10"    "Apple"

class(my_new_vector)

[1] "character"

Vectors can’t be heterogenous

• Objects can be manually or automatically coerced from one class to another. Take care.

my_dbl <- c(2.1, 4.77, 30.111, 3.14519)
is.double(my_dbl)

[1] TRUE

my_dbl <- as.integer(my_dbl)

my_dbl

[1]  2  4 30  3

A table of data is a kind of list

gapminder # tibbles and data frames can contain vectors of different types

# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows

class(gapminder)

[1] "tbl_df"     "tbl"        "data.frame"

typeof(gapminder) # hmm

[1] "list"

A table of data is a kind of list

• Lists are collections of vectors of possibly different types and lengths, or collections of more complex objects that are themselves ultimately made out of vectors. Underneath, most complex R objects are some kind of list with different components that can be accessed by some function that knows the names of the things inside the list.

• A data frame is a list of vectors of the same length, where the vectors can be of different types (e.g. numeric, character, logical, etc).

• A data frame is a natural representation of what most real tables of data look like. Having it be a basic sort of entity in the programming language IS ONE OF R’s BEST IDEAS AND EASILY UNDERRATED!

• A tibble is an enhanced data frame

Tibbles have an enhanced print method, never coerce strings to factors, and provide stricter subsetting methods. (HW)

Again the object inspector is helpful here

Some classes are versions of others

• Base R’s trusty data.frame

library(socviz)
titanic

      fate    sex    n percent
1 perished   male 1364    62.0
2 perished female  126     5.7
3 survived   male  367    16.7
4 survived female  344    15.6

class(titanic)

[1] "data.frame"

## The `$` idiom picks out a named column here; 
## more generally, the named element of a list
titanic$percent  

[1] 62.0  5.7 16.7 15.6

Some classes are versions of others

• Base R’s trusty data.frame

library(socviz)
titanic

      fate    sex    n percent
1 perished   male 1364    62.0
2 perished female  126     5.7
3 survived   male  367    16.7
4 survived female  344    15.6

class(titanic)

[1] "data.frame"

## The `$` idiom picks out a named column here; 
## more generally, the named element of a list
titanic$percent  

[1] 62.0  5.7 16.7 15.6

• The Tidyverse’s enhanced tibble

## tibbles are build on data frames 
titanic_tb <- as_tibble(titanic) 
titanic_tb

# A tibble: 4 × 4
  fate     sex        n percent
  <fct>    <fct>  <dbl>   <dbl>
1 perished male    1364    62  
2 perished female   126     5.7
3 survived male     367    16.7
4 survived female   344    15.6

class(titanic_tb)

[1] "tbl_df"     "tbl"        "data.frame"

• A data frame and a tibble are both fundamentally a list of vectors of the same length, where the vectors can be of different types (e.g. numeric, character, logical, etc)

All of this will be clearer in use

gss_sm

# A tibble: 2,867 × 32
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

• Tidyverse tools are generally type safe, meaning their functions return the same type of thing every time, or fail if they cannot do this. So it’s good to know about the various data types.

6. Arithmetic on vectors

• In R, all numbers are vectors of different sorts. Even single numbers (“scalars”) are conceptually vectors of length 1.

• Arithmetic on vectors (and arrays generally) follows a series of recycling rules that favor ease of expression of vectorized, “elementwise” operations.

• See if you can predict what the following operations do:

6. Arithmetic on vectors

my_numbers

[1]  1  2  3  1  3  5 25 10

result1 <- my_numbers + 1

6. Arithmetic on vectors

my_numbers

[1]  1  2  3  1  3  5 25 10

result1 <- my_numbers + 1

result1

[1]  2  3  4  2  4  6 26 11

6. Arithmetic on vectors

result2 <- my_numbers + my_numbers

6. Arithmetic on vectors

result2 <- my_numbers + my_numbers

result2

[1]  2  4  6  2  6 10 50 20

6. Arithmetic on vectors

two_nums <- c(5, 10)

result3 <- my_numbers + two_nums

6. Arithmetic on vectors

two_nums <- c(5, 10)

result3 <- my_numbers + two_nums

result3

[1]  6 12  8 11  8 15 30 20

6. Arithmetic on vectors

three_nums <- c(1, 5, 10)

result4 <- my_numbers + three_nums

Warning in my_numbers + three_nums: longer object length is not a multiple of
shorter object length

6. Arithmetic on vectors

three_nums <- c(1, 5, 10)

result4 <- my_numbers + three_nums

Warning in my_numbers + three_nums: longer object length is not a multiple of
shorter object length

result4

[1]  2  7 13  2  8 15 26 15

Note that you get a warning here. It’ll still do it, though! Don’t ignore warnings until you understand what they mean.

7. R will be frustrating

• The IDE tries its best to help you. Learn to attend to what it is trying to say.

Let’s Go!

Time to make a plot

Like before:

gapminder

# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows

Like before

library(tidyverse)
library(gapminder)

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp)) 

p + geom_point()

What we did

library(gapminder)

• Load the packages we need: tidyverse and gapminder

What we did

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp)) 

]

• New object named p gets the output of the ggplot() function, given these arguments
• Notice how one of the arguments, mapping, is itself taking the output of a function named aes()

What we did

p + geom_point()

• Show me the output of the p object and the geom_point() function.
• The + here acts just like the |> pipe, but for ggplot functions only. (This is an accident of history.)

And what is R doing?

R objects are just lists of stuff to use or things to do

Objects are like Bento Boxes

The p object

Peek in with the object inspector

Peek in with the object inspector

Appendix: A Few More R Details

Logic: Watch out!

Here’s a gotcha. You might think you could write 3 < 5 & 7 and have it be interpreted as “Three is less than five and also less than seven [True or False?]”:

3 < 5 & 7

[1] TRUE

It seems to work!

Logic: Watch out!

But now try 3 < 5 & 1, where your intention is “Three is less than five and also less than one [True or False?]”

3 < 5 & 1

[1] TRUE

• What’s happening is that 3 < 5 is evaluated first, and resolves to TRUE, leaving us with the expression TRUE & 1.
• R interprets this as TRUE & as.logical(1).
• In Boolean algebra, 1 resolves to TRUE. Any other number is FALSE. So,

Logic: Watch out!

TRUE & as.logical(1)

[1] TRUE

3 < 5 & 3 < 1

[1] FALSE

• You have to make your comparisons explicit.

Logic and floating point arithmetic

Let’s evaluate 0.6 + 0.2 == 0.8

Logic and floating point arithmetic

Let’s evaluate 0.6 + 0.2 == 0.8

0.6 + 0.2 == 0.8

[1] TRUE

Logic and floating point arithmetic

Let’s evaluate 0.6 + 0.2 == 0.8

0.6 + 0.2 == 0.8

[1] TRUE

Now let’s try 0.6 + 0.3 == 0.9

Logic and floating point arithmetic

Let’s evaluate 0.6 + 0.2 == 0.8

0.6 + 0.2 == 0.8

[1] TRUE

Now let’s try 0.6 + 0.3 == 0.9

0.6 + 0.3 == 0.9

[1] FALSE

Er. That’s not right.

Welcome to floating point math!

In Base 10, you can’t precisely express fractions like \(\frac{1}{3}\) and \(\frac{1}{9}\). They come out as repeating decimals: 0.3333… or 0.1111… You can cleanly represent fractions that use a prime factor of the base, which in the case of Base 10 are 2 and 5.

Welcome to floating point math!

In Base 10, you can’t precisely express fractions like \(\frac{1}{3}\) and \(\frac{1}{9}\). They come out as repeating decimals: 0.3333… or 0.1111… You can cleanly represent fractions that use a prime factor of the base, which in the case of Base 10 are 2 and 5.

Computers represent numbers as binary (i.e. Base 2) floating-points. In Base 2, the only prime factor is 2. So \(\frac{1}{5}\) or \(\frac{1}{10}\) in binary would be repeating.

Logic and floating point arithmetic

When you do binary math on repeating numbers and convert back to decimals you get tiny leftovers, and this can mess up logical comparisons of equality. The all.equal() function exists for this purpose.

print(.1 + .2)

[1] 0.3

print(.1 + .2, digits=18)

[1] 0.300000000000000044

all.equal(.1 + .2, 0.3)

[1] TRUE

See e.g. https://0.30000000000000004.com

More later on why this might bite you, and how to deal with it

For now, “Be very careful about doing logical comparisons on floating-point numbers” is not a bad rule.

Assignment with =

• You can use = as well as <- for assignment.

my_numbers = c(1, 2, 3, 1, 3, 5, 25)

my_numbers

[1]  1  2  3  1  3  5 25

• On the other hand, = has a different meaning when used in functions.
• I’m going to use <- for assignment throughout.
• Be consistent either way.

Assignment with =

The other pipe: %>%

• The Base R pipe operator, |> is a relatively recent addition to R.
• Piping operations were originally introduced in a package called called magrittr, where it took the form %>%

The other pipe: %>%

• The Base R pipe operator, |> is a relatively recent addition to R.
• Piping operations were originally introduced in a package called called magrittr, where it took the form %>%
• It’s been so successful, a version of it has been incorporated into Base R. It mostly but does not quite work the same way as %>% in every case.

The other pipe: %>%

• The Base R pipe operator, |> is a relatively recent addition to R.
• Piping operations were originally introduced in a package called called magrittr, where it took the form %>%
• It’s been so successful, a version of it has been incorporated into Base R. It mostly but does not quite work the same way as %>% in every case. We’ll use the Base R pipe in this course, but you’ll see the Magrittr pipe a lot out in the world.

With the Base R pipe, you can only pass an object to the first argument in a function. This is fine for most tidyverse pipelines, where the first argument is usually (implicitly) the data. But it does mean that most Base R functions will continue not to be easily piped, as most of them do not follow the convention of passing the current data as the first argument

Object classes

Objects can have more than one (nested) class:

summary(my_numbers)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.500   3.000   5.714   4.000  25.000 

my_smry <- summary(my_numbers) # remember, outputs can be assigned to a name, creating an object

class(summary(my_numbers)) # functions can be nested, and are evaluated from the inside out

[1] "summaryDefault" "table"         

class(my_smry) # equivalent to the previous line

[1] "summaryDefault" "table"         

Object classes

typeof(my_smry)

[1] "double"

attributes(my_smry)

$names
[1] "Min."    "1st Qu." "Median"  "Mean"    "3rd Qu." "Max."   

$class
[1] "summaryDefault" "table"         

## In this case, the functions extract the corresponding attribute
class(my_smry)

[1] "summaryDefault" "table"         

names(my_smry)

[1] "Min."    "1st Qu." "Median"  "Mean"    "3rd Qu." "Max."