Most people who learned R before the tidyverse have likely started to feel a nibble of pressure to get aboard the tidyverse train. Sadly a fact of human nature is that once you’re comfortable doing something a certain way, it’s hard to find the motivation to learn a different way of doing something that you already know how to do. As someone first learnt R 10 years ago (long before the tidyverse) I’ve been there. Five years ago, I was pushed to stick my little toe into the shallow-end of the tidyverse pool by learning ggplot2, and I never went back.
While the tidyverse is primarily made up of a set of super useful R packages (ggplot2, dplyr, purrr, tidyr, readr, tibble), it is also a way of thinking about implementing “tidy” data analysis. If you combine tidy thinking with the tidy packages, you will inevitably become a master of tidy analysis. From where I float now substantially closer to the deep-end of the tidyverse pool, I would provide the following arguments you as well as to my past self for why it is a good idea to learn the tidyverse:
Regardless of whether you think the tidyverse or base R is “better”, it is always a good idea to keep up with what is current. It’s so easy to get left behind. What you learn today will most likely be out-of-date in a year, but next year’s iteration of what is current will almost certainly be built upon today’s iteration. If you make an effort keep up with things as they change, then you can take lots of little easy steps instead of finding yourself needing to take a big difficult jump in a few years.
Code written in the tidyverse style is much easier to read, and is more consistent than base R (e.g. the first argument of almost every tidyverse function is the data frame on which it acts, which allows us to make heavy use of something called “piping”). Base R, on the other hand, has a somewhat inconsistent mish-mash of function and argument styles.
The humans that make up the tidyverse community are amazing.
Much of the initial efforts of the tidyverse were the brainchild of Hadley Wickham, but these days there are a huge number of people who contribute to, maintain, and develop the tidyverse. The tidyverse is open-source and collaborative (which means that you - yes you - could contribute to it if you wanted to), and is hosted on the tidyverse github: https://github.com/tidyverse.
The goal of this post is to summarise the overall goals of the tidyverse, provide short tutorials on each of the packages that form the tidyverse (and how they play together), and to provide links to additional resources for learning them.
For new tidyverse practitioners, I’d recommend focusing on getting a handle on piping %>%, and learning the dplyr and ggplot2 packages (these form part one of this post). Once you feel comfortable with these core aspects of the tidyverse, you can move onto part two of this two-part series on the tidyverse to learn about the remaining packages.
It is important to remember that the tidyverse is constantly evolving. The best ways to keep up to date with the evolving tidyverse ecosystem is (1) to follow the RStudio blog (https://blog.rstudio.com/), and (2) start following R people on twitter. Mara Averick (@dataandme) and Hadley Wickham (@hadleywickham) are good people to follow. A great resource for learning about the tidyverse in more detail is R for Data Science by Garrett Grolemund and Hadley Wickham.
Entering the tidyverse
The fundamental object type of the tidyverse is the data frame (which, once you get a little deeper into the tidyverse ecosystem, becomes a “tibble” - more on that later in part 2). Thus the starting point for getting comfortable with the tidyverse is to always store your data as a data frame (rather than as a matrix or as vectors) with informative string-based column names where words are preferably separated by underscores (rather than periods).
The tidyverse is not simply a set of functions that replace base R functions. The tidyverse represents a way of thinking about how you conduct your data analysis:
Think of your data frame as the universe, and the columns of your data frame as the objects in your universe that you can explore, manipulate and model.
When coding in base R, it is very common to define many intermediate objects and modified versions of the same data frame (or data object). When coding in the tidyverse, the key is to minimize defining new data objects. Instead, focus on manipulating your current data frame and just printing the output of your manipulations (e.g. a summary or plot). Only create new data objects in your R environment if you will be using both the original data object and the new data object in your later analyses.
To load the tidyverse packages, you could install them all individually (once):
# only ever run once to install the individual packages on your system
install.packages("dplyr")
install.packages("ggplot2")
install.packages("purrr")
install.packages("tidyr")
install.packages("readr")
install.packages("tibble")and then load them all into your session individually every time:
library(dplyr)
library(ggplot2)
library(purrr)
library(tidyr)
library(readr)
library(tibble)or, you could just install and load the tidyverse package, which will do all of the above for you:
# only ever run once to install the tidyverse on your system
install.packages("tidyverse")library(tidyverse)which is much easier.
Throughout this tutorial, we will use the gapminder dataset that can be loaded directly if you’re connected to the internet.
My general workflow involves loading the original data and saving it as an object with a meaningful name and an _orig suffix. I then define a copy of the original dataset without the _orig suffix. Having an original copy of my data in my environment means that it is easy to check that my manipulations do what I expected. I will make direct data cleaning modifications to the gapminder data frame, but will never edit the gapminder_orig data frame.
# to download the data directly:
gapminder_orig <- read.csv("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder-FiveYearData.csv")
# define a copy of the original dataset that we will clean and play with
gapminder <- gapminder_origThe gapminder dataset has 1704 rows containing information on population, life expectancy and GDP per capita by year and country.
A “tidy” data frame is one where every row is a single observational unit (in this case, indexed by country and year), and every column corresponds to a variable that is measured for each observational unit (in this case, for each country and year, a measurement is made for population, continent, life expectancy and GDP). If you’d like to learn more about “tidy data”, I highly recommend reading Hadley Wickham’s tidy data article.
dim(gapminder)[1] 1704 6head(gapminder) country year pop continent lifeExp gdpPercap
1 Afghanistan 1952 8425333 Asia 28.801 779.4453
2 Afghanistan 1957 9240934 Asia 30.332 820.8530
3 Afghanistan 1962 10267083 Asia 31.997 853.1007
4 Afghanistan 1967 11537966 Asia 34.020 836.1971
5 Afghanistan 1972 13079460 Asia 36.088 739.9811
6 Afghanistan 1977 14880372 Asia 38.438 786.1134Now that you’ve loaded the tidyverse and the gapminder data, you’re are ready to learn about our first tidy analysis tool: the pipe.
Piping: %>%
Pipes are the workhorse of tidy analyses. Piping allows you to chain together many functions, eliminating the need to define multiple intermediate objects to use as the input to subsequent functions. In my eyes, pipes are also the primary reason that tidyverse code is fundamentally easier to read than base R code.
I always read the pipe symbol, %>%, in my head as “and then”. Consider the following code. Try to figure out what the following code will produce (even if you’ve never seen the filter and select dplyr functions before - I’ll formally introduce these later).
gapminder %>%
filter(continent == "Americas", year == "2007") %>%
select(country, lifeExp)I read this code in my head as: take the gapminder dataset and then filter to the “Americas” continents and the year 2007, and then select the country and life expectancy variables.
Running this code first filters the data frame only to the rows whose continent value is “Americas” and whose year value is “2007”, and then it shows you the country and lifeExp columns for those rows. Run it yourself to see.
# take the gapminder dataset
gapminder %>%
# and filter to the rows whose continent is Americas and year is 2007
filter(continent == "Americas", year == 2007) %>%
# show the country and lifeExp values for these rows
select(country, lifeExp) country lifeExp
1 Argentina 75.320
2 Bolivia 65.554
3 Brazil 72.390
4 Canada 80.653
5 Chile 78.553
6 Colombia 72.889
7 Costa Rica 78.782
8 Cuba 78.273
9 Dominican Republic 72.235
10 Ecuador 74.994
11 El Salvador 71.878
12 Guatemala 70.259
13 Haiti 60.916
14 Honduras 70.198
15 Jamaica 72.567
16 Mexico 76.195
17 Nicaragua 72.899
18 Panama 75.537
19 Paraguay 71.752
20 Peru 71.421
21 Puerto Rico 78.746
22 Trinidad and Tobago 69.819
23 United States 78.242
24 Uruguay 76.384
25 Venezuela 73.747To become a piping expert you’ll need to have a firm grasp on what it’s actually doing. The pipe uses the object on the left-hand-side of the %>% as the first argument of the function on the right-hand-side.
For instance, the un-piped version of
gapminder %>%
filter(continent == "Americas", year == 2007)is
filter(gapminder, continent == "Americas", year == 2007)If you were determined not to use piping, but you wanted to do many manipulations, your code would very quickly get messy and difficult to read. In the style of base R, the common way of making code more readable is to define intermediate objects.
gapminder_filtered <- filter(gapminder, continent == "Americas", year == 2007)
gapminder_filtered_selected <- select(gapminder_filtered, country, lifeExp)
gapminder_filtered_selected country lifeExp
1 Argentina 75.320
2 Bolivia 65.554
3 Brazil 72.390
4 Canada 80.653
5 Chile 78.553
6 Colombia 72.889
7 Costa Rica 78.782
8 Cuba 78.273
9 Dominican Republic 72.235
10 Ecuador 74.994
11 El Salvador 71.878
12 Guatemala 70.259
13 Haiti 60.916
14 Honduras 70.198
15 Jamaica 72.567
16 Mexico 76.195
17 Nicaragua 72.899
18 Panama 75.537
19 Paraguay 71.752
20 Peru 71.421
21 Puerto Rico 78.746
22 Trinidad and Tobago 69.819
23 United States 78.242
24 Uruguay 76.384
25 Venezuela 73.747To me, the piped version is infinitely more clear, and simultaneously got rid of the need to define any intermediate objects that I would have needed to keep track of while reading the code. Once I got more and more comfortable with piping, I started to find that pretty much all of my code uses pipes.
Data manipulation: dplyr
The filter() and select() functions that I just introduced are examples of data manipulation functions from the dplyr package.
In the tidyverse, you will almost never use the [,] indexing nor the $ data frame column indexing that are pervasive throughout base R code. Indexing in dplyr is done using filter() for rows and select() for columns.
You may have noticed that the variable names continent, year, country, lifeExp that were used inside the filter() and select() functions were unquoted. One of the key components of the tidyverse is thinking of your universe as the data frame, and the columns of the data frame as variables or objects that you can play with. Just like how you don’t need to quote variable names in your environment to play with them, you usually don’t need to quote data frame variables (columns) inside tidyverse functions.
Let’s contrast our piped dplyr code
# take the gapminder dataset
gapminder %>%
# and filter to the rows whose continent is Americas and year is 2007
filter(continent == "Americas", year == 2007) %>%
# show the country and lifeExp values for these rows
select(country, lifeExp)with one potential version of equivalent base R code:
# identify which rows correspond to the Americas and the year 2007
continent_year_index <- which(gapminder["continent"] == "Americas" & gapminder["year"] == 2007)
# pull only those rows and show the country and life expectency columns
gapminder[continent_year_index, c("country", "lifeExp")]There are a few key differences
The variable names are quoted in the base R version but not in the dplyr version
An intermediate row index variable was defined in the base R version but not in the dplyr version
The primary dplyr functions are
select: select columns
The arguments of the select function specify which data frame variables should be kept. select() is like indexing columns by name. You do not need to quote the column names (but you can if you want to).
gapminder %>%
select(country, gdpPercap) country gdpPercap
1 Afghanistan 779.4453
2 Afghanistan 820.8530
3 Afghanistan 853.1007
4 Afghanistan 836.1971
5 Afghanistan 739.9811
6 Afghanistan 786.1134
7 Afghanistan 978.0114
8 Afghanistan 852.3959
9 Afghanistan 649.3414
10 Afghanistan 635.3414
11 Afghanistan 726.7341
12 Afghanistan 974.5803
13 Albania 1601.0561
14 Albania 1942.2842
15 Albania 2312.8890
16 Albania 2760.1969
17 Albania 3313.4222
18 Albania 3533.0039
19 Albania 3630.8807
20 Albania 3738.9327
21 Albania 2497.4379
22 Albania 3193.0546
23 Albania 4604.2117
24 Albania 5937.0295
25 Algeria 2449.0082
26 Algeria 3013.9760
27 Algeria 2550.8169
28 Algeria 3246.9918
29 Algeria 4182.6638
30 Algeria 4910.4168
31 Algeria 5745.1602
32 Algeria 5681.3585
33 Algeria 5023.2166
34 Algeria 4797.2951
35 Algeria 5288.0404
36 Algeria 6223.3675
37 Angola 3520.6103
38 Angola 3827.9405
39 Angola 4269.2767
40 Angola 5522.7764
41 Angola 5473.2880
42 Angola 3008.6474
43 Angola 2756.9537
44 Angola 2430.2083
45 Angola 2627.8457
46 Angola 2277.1409
47 Angola 2773.2873
48 Angola 4797.2313
49 Argentina 5911.3151
50 Argentina 6856.8562
51 Argentina 7133.1660
52 Argentina 8052.9530
53 Argentina 9443.0385
54 Argentina 10079.0267
55 Argentina 8997.8974
56 Argentina 9139.6714
57 Argentina 9308.4187
58 Argentina 10967.2820
59 Argentina 8797.6407
60 Argentina 12779.3796
61 Australia 10039.5956
62 Australia 10949.6496
63 Australia 12217.2269
64 Australia 14526.1246
65 Australia 16788.6295
66 Australia 18334.1975
67 Australia 19477.0093
68 Australia 21888.8890
69 Australia 23424.7668
70 Australia 26997.9366
71 Australia 30687.7547
72 Australia 34435.3674
73 Austria 6137.0765
74 Austria 8842.5980
75 Austria 10750.7211
76 Austria 12834.6024
77 Austria 16661.6256
78 Austria 19749.4223
79 Austria 21597.0836
80 Austria 23687.8261
81 Austria 27042.0187
82 Austria 29095.9207
83 Austria 32417.6077
84 Austria 36126.4927
85 Bahrain 9867.0848
86 Bahrain 11635.7995
87 Bahrain 12753.2751
88 Bahrain 14804.6727
89 Bahrain 18268.6584
90 Bahrain 19340.1020
91 Bahrain 19211.1473
92 Bahrain 18524.0241
93 Bahrain 19035.5792
94 Bahrain 20292.0168
95 Bahrain 23403.5593
96 Bahrain 29796.0483
97 Bangladesh 684.2442
98 Bangladesh 661.6375
99 Bangladesh 686.3416
100 Bangladesh 721.1861
101 Bangladesh 630.2336
102 Bangladesh 659.8772
103 Bangladesh 676.9819
104 Bangladesh 751.9794
105 Bangladesh 837.8102
106 Bangladesh 972.7700
107 Bangladesh 1136.3904
108 Bangladesh 1391.2538
109 Belgium 8343.1051
110 Belgium 9714.9606
111 Belgium 10991.2068
112 Belgium 13149.0412
113 Belgium 16672.1436
114 Belgium 19117.9745
115 Belgium 20979.8459
116 Belgium 22525.5631
117 Belgium 25575.5707
118 Belgium 27561.1966
119 Belgium 30485.8838
120 Belgium 33692.6051
121 Benin 1062.7522
122 Benin 959.6011
123 Benin 949.4991
124 Benin 1035.8314
125 Benin 1085.7969
126 Benin 1029.1613
127 Benin 1277.8976
128 Benin 1225.8560
129 Benin 1191.2077
130 Benin 1232.9753
131 Benin 1372.8779
132 Benin 1441.2849
133 Bolivia 2677.3263
134 Bolivia 2127.6863
135 Bolivia 2180.9725
136 Bolivia 2586.8861
137 Bolivia 2980.3313
138 Bolivia 3548.0978
139 Bolivia 3156.5105
140 Bolivia 2753.6915
141 Bolivia 2961.6997
142 Bolivia 3326.1432
143 Bolivia 3413.2627
144 Bolivia 3822.1371
145 Bosnia and Herzegovina 973.5332
146 Bosnia and Herzegovina 1353.9892
147 Bosnia and Herzegovina 1709.6837
148 Bosnia and Herzegovina 2172.3524
149 Bosnia and Herzegovina 2860.1698
150 Bosnia and Herzegovina 3528.4813
151 Bosnia and Herzegovina 4126.6132
152 Bosnia and Herzegovina 4314.1148
153 Bosnia and Herzegovina 2546.7814
154 Bosnia and Herzegovina 4766.3559
155 Bosnia and Herzegovina 6018.9752
156 Bosnia and Herzegovina 7446.2988
157 Botswana 851.2411
158 Botswana 918.2325
159 Botswana 983.6540
160 Botswana 1214.7093
161 Botswana 2263.6111
162 Botswana 3214.8578
163 Botswana 4551.1421
164 Botswana 6205.8839
165 Botswana 7954.1116
166 Botswana 8647.1423
167 Botswana 11003.6051
168 Botswana 12569.8518
169 Brazil 2108.9444
170 Brazil 2487.3660
171 Brazil 3336.5858
172 Brazil 3429.8644
173 Brazil 4985.7115
174 Brazil 6660.1187
175 Brazil 7030.8359
176 Brazil 7807.0958
177 Brazil 6950.2830
178 Brazil 7957.9808
179 Brazil 8131.2128
180 Brazil 9065.8008
181 Bulgaria 2444.2866
182 Bulgaria 3008.6707
183 Bulgaria 4254.3378
184 Bulgaria 5577.0028
185 Bulgaria 6597.4944
186 Bulgaria 7612.2404
187 Bulgaria 8224.1916
188 Bulgaria 8239.8548
189 Bulgaria 6302.6234
190 Bulgaria 5970.3888
191 Bulgaria 7696.7777
192 Bulgaria 10680.7928
193 Burkina Faso 543.2552
194 Burkina Faso 617.1835
195 Burkina Faso 722.5120
196 Burkina Faso 794.8266
197 Burkina Faso 854.7360
198 Burkina Faso 743.3870
199 Burkina Faso 807.1986
200 Burkina Faso 912.0631
201 Burkina Faso 931.7528
202 Burkina Faso 946.2950
203 Burkina Faso 1037.6452
204 Burkina Faso 1217.0330
205 Burundi 339.2965
206 Burundi 379.5646
207 Burundi 355.2032
208 Burundi 412.9775
209 Burundi 464.0995
210 Burundi 556.1033
211 Burundi 559.6032
212 Burundi 621.8188
213 Burundi 631.6999
214 Burundi 463.1151
215 Burundi 446.4035
216 Burundi 430.0707
217 Cambodia 368.4693
218 Cambodia 434.0383
219 Cambodia 496.9136
220 Cambodia 523.4323
221 Cambodia 421.6240
222 Cambodia 524.9722
223 Cambodia 624.4755
224 Cambodia 683.8956
225 Cambodia 682.3032
226 Cambodia 734.2852
227 Cambodia 896.2260
228 Cambodia 1713.7787
229 Cameroon 1172.6677
230 Cameroon 1313.0481
231 Cameroon 1399.6074
232 Cameroon 1508.4531
233 Cameroon 1684.1465
234 Cameroon 1783.4329
235 Cameroon 2367.9833
236 Cameroon 2602.6642
237 Cameroon 1793.1633
238 Cameroon 1694.3375
239 Cameroon 1934.0114
240 Cameroon 2042.0952
241 Canada 11367.1611
242 Canada 12489.9501
243 Canada 13462.4855
244 Canada 16076.5880
245 Canada 18970.5709
246 Canada 22090.8831
247 Canada 22898.7921
248 Canada 26626.5150
249 Canada 26342.8843
250 Canada 28954.9259
251 Canada 33328.9651
252 Canada 36319.2350
253 Central African Republic 1071.3107
254 Central African Republic 1190.8443
255 Central African Republic 1193.0688
256 Central African Republic 1136.0566
257 Central African Republic 1070.0133
258 Central African Republic 1109.3743
259 Central African Republic 956.7530
260 Central African Republic 844.8764
261 Central African Republic 747.9055
262 Central African Republic 740.5063
263 Central African Republic 738.6906
264 Central African Republic 706.0165
265 Chad 1178.6659
266 Chad 1308.4956
267 Chad 1389.8176
268 Chad 1196.8106
269 Chad 1104.1040
270 Chad 1133.9850
271 Chad 797.9081
272 Chad 952.3861
273 Chad 1058.0643
274 Chad 1004.9614
275 Chad 1156.1819
276 Chad 1704.0637
277 Chile 3939.9788
278 Chile 4315.6227
279 Chile 4519.0943
280 Chile 5106.6543
281 Chile 5494.0244
282 Chile 4756.7638
283 Chile 5095.6657
284 Chile 5547.0638
285 Chile 7596.1260
286 Chile 10118.0532
287 Chile 10778.7838
288 Chile 13171.6388
289 China 400.4486
290 China 575.9870
291 China 487.6740
292 China 612.7057
293 China 676.9001
294 China 741.2375
295 China 962.4214
296 China 1378.9040
297 China 1655.7842
298 China 2289.2341
299 China 3119.2809
300 China 4959.1149
301 Colombia 2144.1151
302 Colombia 2323.8056
303 Colombia 2492.3511
304 Colombia 2678.7298
305 Colombia 3264.6600
306 Colombia 3815.8079
307 Colombia 4397.5757
308 Colombia 4903.2191
309 Colombia 5444.6486
310 Colombia 6117.3617
311 Colombia 5755.2600
312 Colombia 7006.5804
313 Comoros 1102.9909
314 Comoros 1211.1485
315 Comoros 1406.6483
316 Comoros 1876.0296
317 Comoros 1937.5777
318 Comoros 1172.6030
319 Comoros 1267.1001
320 Comoros 1315.9808
321 Comoros 1246.9074
322 Comoros 1173.6182
323 Comoros 1075.8116
324 Comoros 986.1479
325 Congo Dem. Rep. 780.5423
326 Congo Dem. Rep. 905.8602
327 Congo Dem. Rep. 896.3146
328 Congo Dem. Rep. 861.5932
329 Congo Dem. Rep. 904.8961
330 Congo Dem. Rep. 795.7573
331 Congo Dem. Rep. 673.7478
332 Congo Dem. Rep. 672.7748
333 Congo Dem. Rep. 457.7192
334 Congo Dem. Rep. 312.1884
335 Congo Dem. Rep. 241.1659
336 Congo Dem. Rep. 277.5519
337 Congo Rep. 2125.6214
338 Congo Rep. 2315.0566
339 Congo Rep. 2464.7832
340 Congo Rep. 2677.9396
341 Congo Rep. 3213.1527
342 Congo Rep. 3259.1790
343 Congo Rep. 4879.5075
344 Congo Rep. 4201.1949
345 Congo Rep. 4016.2395
346 Congo Rep. 3484.1644
347 Congo Rep. 3484.0620
348 Congo Rep. 3632.5578
349 Costa Rica 2627.0095
350 Costa Rica 2990.0108
351 Costa Rica 3460.9370
352 Costa Rica 4161.7278
353 Costa Rica 5118.1469
354 Costa Rica 5926.8770
355 Costa Rica 5262.7348
356 Costa Rica 5629.9153
357 Costa Rica 6160.4163
358 Costa Rica 6677.0453
359 Costa Rica 7723.4472
360 Costa Rica 9645.0614
361 Cote d'Ivoire 1388.5947
362 Cote d'Ivoire 1500.8959
363 Cote d'Ivoire 1728.8694
364 Cote d'Ivoire 2052.0505
365 Cote d'Ivoire 2378.2011
366 Cote d'Ivoire 2517.7365
367 Cote d'Ivoire 2602.7102
368 Cote d'Ivoire 2156.9561
369 Cote d'Ivoire 1648.0738
370 Cote d'Ivoire 1786.2654
371 Cote d'Ivoire 1648.8008
372 Cote d'Ivoire 1544.7501
373 Croatia 3119.2365
374 Croatia 4338.2316
375 Croatia 5477.8900
376 Croatia 6960.2979
377 Croatia 9164.0901
378 Croatia 11305.3852
379 Croatia 13221.8218
380 Croatia 13822.5839
381 Croatia 8447.7949
382 Croatia 9875.6045
383 Croatia 11628.3890
384 Croatia 14619.2227
385 Cuba 5586.5388
386 Cuba 6092.1744
387 Cuba 5180.7559
388 Cuba 5690.2680
389 Cuba 5305.4453
390 Cuba 6380.4950
391 Cuba 7316.9181
392 Cuba 7532.9248
393 Cuba 5592.8440
394 Cuba 5431.9904
395 Cuba 6340.6467
396 Cuba 8948.1029
397 Czech Republic 6876.1403
398 Czech Republic 8256.3439
399 Czech Republic 10136.8671
400 Czech Republic 11399.4449
401 Czech Republic 13108.4536
402 Czech Republic 14800.1606
403 Czech Republic 15377.2285
404 Czech Republic 16310.4434
405 Czech Republic 14297.0212
406 Czech Republic 16048.5142
407 Czech Republic 17596.2102
408 Czech Republic 22833.3085
409 Denmark 9692.3852
410 Denmark 11099.6593
411 Denmark 13583.3135
412 Denmark 15937.2112
413 Denmark 18866.2072
414 Denmark 20422.9015
415 Denmark 21688.0405
416 Denmark 25116.1758
417 Denmark 26406.7399
418 Denmark 29804.3457
419 Denmark 32166.5001
420 Denmark 35278.4187
421 Djibouti 2669.5295
422 Djibouti 2864.9691
423 Djibouti 3020.9893
424 Djibouti 3020.0505
425 Djibouti 3694.2124
426 Djibouti 3081.7610
427 Djibouti 2879.4681
428 Djibouti 2880.1026
429 Djibouti 2377.1562
430 Djibouti 1895.0170
431 Djibouti 1908.2609
432 Djibouti 2082.4816
433 Dominican Republic 1397.7171
434 Dominican Republic 1544.4030
435 Dominican Republic 1662.1374
436 Dominican Republic 1653.7230
437 Dominican Republic 2189.8745
438 Dominican Republic 2681.9889
439 Dominican Republic 2861.0924
440 Dominican Republic 2899.8422
441 Dominican Republic 3044.2142
442 Dominican Republic 3614.1013
443 Dominican Republic 4563.8082
444 Dominican Republic 6025.3748
445 Ecuador 3522.1107
446 Ecuador 3780.5467
447 Ecuador 4086.1141
448 Ecuador 4579.0742
449 Ecuador 5280.9947
450 Ecuador 6679.6233
451 Ecuador 7213.7913
452 Ecuador 6481.7770
453 Ecuador 7103.7026
454 Ecuador 7429.4559
455 Ecuador 5773.0445
456 Ecuador 6873.2623
457 Egypt 1418.8224
458 Egypt 1458.9153
459 Egypt 1693.3359
460 Egypt 1814.8807
461 Egypt 2024.0081
462 Egypt 2785.4936
463 Egypt 3503.7296
464 Egypt 3885.4607
465 Egypt 3794.7552
466 Egypt 4173.1818
467 Egypt 4754.6044
468 Egypt 5581.1810
469 El Salvador 3048.3029
470 El Salvador 3421.5232
471 El Salvador 3776.8036
472 El Salvador 4358.5954
473 El Salvador 4520.2460
474 El Salvador 5138.9224
475 El Salvador 4098.3442
476 El Salvador 4140.4421
477 El Salvador 4444.2317
478 El Salvador 5154.8255
479 El Salvador 5351.5687
480 El Salvador 5728.3535
481 Equatorial Guinea 375.6431
482 Equatorial Guinea 426.0964
483 Equatorial Guinea 582.8420
484 Equatorial Guinea 915.5960
485 Equatorial Guinea 672.4123
486 Equatorial Guinea 958.5668
487 Equatorial Guinea 927.8253
488 Equatorial Guinea 966.8968
489 Equatorial Guinea 1132.0550
490 Equatorial Guinea 2814.4808
491 Equatorial Guinea 7703.4959
492 Equatorial Guinea 12154.0897
493 Eritrea 328.9406
494 Eritrea 344.1619
495 Eritrea 380.9958
496 Eritrea 468.7950
497 Eritrea 514.3242
498 Eritrea 505.7538
499 Eritrea 524.8758
500 Eritrea 521.1341
501 Eritrea 582.8585
502 Eritrea 913.4708
503 Eritrea 765.3500
504 Eritrea 641.3695
505 Ethiopia 362.1463
506 Ethiopia 378.9042
507 Ethiopia 419.4564
508 Ethiopia 516.1186
509 Ethiopia 566.2439
510 Ethiopia 556.8084
511 Ethiopia 577.8607
512 Ethiopia 573.7413
513 Ethiopia 421.3535
514 Ethiopia 515.8894
515 Ethiopia 530.0535
516 Ethiopia 690.8056
517 Finland 6424.5191
518 Finland 7545.4154
519 Finland 9371.8426
520 Finland 10921.6363
521 Finland 14358.8759
522 Finland 15605.4228
523 Finland 18533.1576
524 Finland 21141.0122
525 Finland 20647.1650
526 Finland 23723.9502
527 Finland 28204.5906
528 Finland 33207.0844
529 France 7029.8093
530 France 8662.8349
531 France 10560.4855
532 France 12999.9177
533 France 16107.1917
534 France 18292.6351
535 France 20293.8975
536 France 22066.4421
537 France 24703.7961
538 France 25889.7849
539 France 28926.0323
540 France 30470.0167
541 Gabon 4293.4765
542 Gabon 4976.1981
543 Gabon 6631.4592
544 Gabon 8358.7620
545 Gabon 11401.9484
546 Gabon 21745.5733
547 Gabon 15113.3619
548 Gabon 11864.4084
549 Gabon 13522.1575
550 Gabon 14722.8419
551 Gabon 12521.7139
552 Gabon 13206.4845
553 Gambia 485.2307
554 Gambia 520.9267
555 Gambia 599.6503
556 Gambia 734.7829
557 Gambia 756.0868
558 Gambia 884.7553
559 Gambia 835.8096
560 Gambia 611.6589
561 Gambia 665.6244
562 Gambia 653.7302
563 Gambia 660.5856
564 Gambia 752.7497
565 Germany 7144.1144
566 Germany 10187.8267
567 Germany 12902.4629
568 Germany 14745.6256
569 Germany 18016.1803
570 Germany 20512.9212
571 Germany 22031.5327
572 Germany 24639.1857
573 Germany 26505.3032
574 Germany 27788.8842
575 Germany 30035.8020
576 Germany 32170.3744
577 Ghana 911.2989
578 Ghana 1043.5615
579 Ghana 1190.0411
580 Ghana 1125.6972
581 Ghana 1178.2237
582 Ghana 993.2240
583 Ghana 876.0326
584 Ghana 847.0061
585 Ghana 925.0602
586 Ghana 1005.2458
587 Ghana 1111.9846
588 Ghana 1327.6089
589 Greece 3530.6901
590 Greece 4916.2999
591 Greece 6017.1907
592 Greece 8513.0970
593 Greece 12724.8296
594 Greece 14195.5243
595 Greece 15268.4209
596 Greece 16120.5284
597 Greece 17541.4963
598 Greece 18747.6981
599 Greece 22514.2548
600 Greece 27538.4119
601 Guatemala 2428.2378
602 Guatemala 2617.1560
603 Guatemala 2750.3644
604 Guatemala 3242.5311
605 Guatemala 4031.4083
606 Guatemala 4879.9927
607 Guatemala 4820.4948
608 Guatemala 4246.4860
609 Guatemala 4439.4508
610 Guatemala 4684.3138
611 Guatemala 4858.3475
612 Guatemala 5186.0500
613 Guinea 510.1965
614 Guinea 576.2670
615 Guinea 686.3737
616 Guinea 708.7595
617 Guinea 741.6662
618 Guinea 874.6859
619 Guinea 857.2504
620 Guinea 805.5725
621 Guinea 794.3484
622 Guinea 869.4498
623 Guinea 945.5836
624 Guinea 942.6542
625 Guinea-Bissau 299.8503
626 Guinea-Bissau 431.7905
627 Guinea-Bissau 522.0344
628 Guinea-Bissau 715.5806
629 Guinea-Bissau 820.2246
630 Guinea-Bissau 764.7260
631 Guinea-Bissau 838.1240
632 Guinea-Bissau 736.4154
633 Guinea-Bissau 745.5399
634 Guinea-Bissau 796.6645
635 Guinea-Bissau 575.7047
636 Guinea-Bissau 579.2317
637 Haiti 1840.3669
638 Haiti 1726.8879
639 Haiti 1796.5890
640 Haiti 1452.0577
641 Haiti 1654.4569
642 Haiti 1874.2989
643 Haiti 2011.1595
644 Haiti 1823.0160
645 Haiti 1456.3095
646 Haiti 1341.7269
647 Haiti 1270.3649
648 Haiti 1201.6372
649 Honduras 2194.9262
650 Honduras 2220.4877
651 Honduras 2291.1568
652 Honduras 2538.2694
653 Honduras 2529.8423
654 Honduras 3203.2081
655 Honduras 3121.7608
656 Honduras 3023.0967
657 Honduras 3081.6946
658 Honduras 3160.4549
659 Honduras 3099.7287
660 Honduras 3548.3308
661 Hong Kong China 3054.4212
662 Hong Kong China 3629.0765
663 Hong Kong China 4692.6483
664 Hong Kong China 6197.9628
665 Hong Kong China 8315.9281
666 Hong Kong China 11186.1413
667 Hong Kong China 14560.5305
668 Hong Kong China 20038.4727
669 Hong Kong China 24757.6030
670 Hong Kong China 28377.6322
671 Hong Kong China 30209.0152
672 Hong Kong China 39724.9787
673 Hungary 5263.6738
674 Hungary 6040.1800
675 Hungary 7550.3599
676 Hungary 9326.6447
677 Hungary 10168.6561
678 Hungary 11674.8374
679 Hungary 12545.9907
680 Hungary 12986.4800
681 Hungary 10535.6285
682 Hungary 11712.7768
683 Hungary 14843.9356
684 Hungary 18008.9444
685 Iceland 7267.6884
686 Iceland 9244.0014
687 Iceland 10350.1591
688 Iceland 13319.8957
689 Iceland 15798.0636
690 Iceland 19654.9625
691 Iceland 23269.6075
692 Iceland 26923.2063
693 Iceland 25144.3920
694 Iceland 28061.0997
695 Iceland 31163.2020
696 Iceland 36180.7892
697 India 546.5657
698 India 590.0620
699 India 658.3472
700 India 700.7706
701 India 724.0325
702 India 813.3373
703 India 855.7235
704 India 976.5127
705 India 1164.4068
706 India 1458.8174
707 India 1746.7695
708 India 2452.2104
709 Indonesia 749.6817
710 Indonesia 858.9003
711 Indonesia 849.2898
712 Indonesia 762.4318
713 Indonesia 1111.1079
714 Indonesia 1382.7021
715 Indonesia 1516.8730
716 Indonesia 1748.3570
717 Indonesia 2383.1409
718 Indonesia 3119.3356
719 Indonesia 2873.9129
720 Indonesia 3540.6516
721 Iran 3035.3260
722 Iran 3290.2576
723 Iran 4187.3298
724 Iran 5906.7318
725 Iran 9613.8186
726 Iran 11888.5951
727 Iran 7608.3346
728 Iran 6642.8814
729 Iran 7235.6532
730 Iran 8263.5903
731 Iran 9240.7620
732 Iran 11605.7145
733 Iraq 4129.7661
734 Iraq 6229.3336
735 Iraq 8341.7378
736 Iraq 8931.4598
737 Iraq 9576.0376
738 Iraq 14688.2351
739 Iraq 14517.9071
740 Iraq 11643.5727
741 Iraq 3745.6407
742 Iraq 3076.2398
743 Iraq 4390.7173
744 Iraq 4471.0619
745 Ireland 5210.2803
746 Ireland 5599.0779
747 Ireland 6631.5973
748 Ireland 7655.5690
749 Ireland 9530.7729
750 Ireland 11150.9811
751 Ireland 12618.3214
752 Ireland 13872.8665
753 Ireland 17558.8155
754 Ireland 24521.9471
755 Ireland 34077.0494
756 Ireland 40675.9964
757 Israel 4086.5221
758 Israel 5385.2785
759 Israel 7105.6307
760 Israel 8393.7414
761 Israel 12786.9322
762 Israel 13306.6192
763 Israel 15367.0292
764 Israel 17122.4799
765 Israel 18051.5225
766 Israel 20896.6092
767 Israel 21905.5951
768 Israel 25523.2771
769 Italy 4931.4042
770 Italy 6248.6562
771 Italy 8243.5823
772 Italy 10022.4013
773 Italy 12269.2738
774 Italy 14255.9847
775 Italy 16537.4835
776 Italy 19207.2348
777 Italy 22013.6449
778 Italy 24675.0245
779 Italy 27968.0982
780 Italy 28569.7197
781 Jamaica 2898.5309
782 Jamaica 4756.5258
783 Jamaica 5246.1075
784 Jamaica 6124.7035
785 Jamaica 7433.8893
786 Jamaica 6650.1956
787 Jamaica 6068.0513
788 Jamaica 6351.2375
789 Jamaica 7404.9237
790 Jamaica 7121.9247
791 Jamaica 6994.7749
792 Jamaica 7320.8803
793 Japan 3216.9563
794 Japan 4317.6944
795 Japan 6576.6495
796 Japan 9847.7886
797 Japan 14778.7864
798 Japan 16610.3770
799 Japan 19384.1057
800 Japan 22375.9419
801 Japan 26824.8951
802 Japan 28816.5850
803 Japan 28604.5919
804 Japan 31656.0681
805 Jordan 1546.9078
806 Jordan 1886.0806
807 Jordan 2348.0092
808 Jordan 2741.7963
809 Jordan 2110.8563
810 Jordan 2852.3516
811 Jordan 4161.4160
812 Jordan 4448.6799
813 Jordan 3431.5936
814 Jordan 3645.3796
815 Jordan 3844.9172
816 Jordan 4519.4612
817 Kenya 853.5409
818 Kenya 944.4383
819 Kenya 896.9664
820 Kenya 1056.7365
821 Kenya 1222.3600
822 Kenya 1267.6132
823 Kenya 1348.2258
824 Kenya 1361.9369
825 Kenya 1341.9217
826 Kenya 1360.4850
827 Kenya 1287.5147
828 Kenya 1463.2493
829 Korea Dem. Rep. 1088.2778
830 Korea Dem. Rep. 1571.1347
831 Korea Dem. Rep. 1621.6936
832 Korea Dem. Rep. 2143.5406
833 Korea Dem. Rep. 3701.6215
834 Korea Dem. Rep. 4106.3012
835 Korea Dem. Rep. 4106.5253
836 Korea Dem. Rep. 4106.4923
837 Korea Dem. Rep. 3726.0635
838 Korea Dem. Rep. 1690.7568
839 Korea Dem. Rep. 1646.7582
840 Korea Dem. Rep. 1593.0655
841 Korea Rep. 1030.5922
842 Korea Rep. 1487.5935
843 Korea Rep. 1536.3444
844 Korea Rep. 2029.2281
845 Korea Rep. 3030.8767
846 Korea Rep. 4657.2210
847 Korea Rep. 5622.9425
848 Korea Rep. 8533.0888
849 Korea Rep. 12104.2787
850 Korea Rep. 15993.5280
851 Korea Rep. 19233.9882
852 Korea Rep. 23348.1397
853 Kuwait 108382.3529
854 Kuwait 113523.1329
855 Kuwait 95458.1118
856 Kuwait 80894.8833
857 Kuwait 109347.8670
858 Kuwait 59265.4771
859 Kuwait 31354.0357
860 Kuwait 28118.4300
861 Kuwait 34932.9196
862 Kuwait 40300.6200
863 Kuwait 35110.1057
864 Kuwait 47306.9898
865 Lebanon 4834.8041
866 Lebanon 6089.7869
867 Lebanon 5714.5606
868 Lebanon 6006.9830
869 Lebanon 7486.3843
870 Lebanon 8659.6968
871 Lebanon 7640.5195
872 Lebanon 5377.0913
873 Lebanon 6890.8069
874 Lebanon 8754.9639
875 Lebanon 9313.9388
876 Lebanon 10461.0587
877 Lesotho 298.8462
878 Lesotho 335.9971
879 Lesotho 411.8006
880 Lesotho 498.6390
881 Lesotho 496.5816
882 Lesotho 745.3695
883 Lesotho 797.2631
884 Lesotho 773.9932
885 Lesotho 977.4863
886 Lesotho 1186.1480
887 Lesotho 1275.1846
888 Lesotho 1569.3314
889 Liberia 575.5730
890 Liberia 620.9700
891 Liberia 634.1952
892 Liberia 713.6036
893 Liberia 803.0055
894 Liberia 640.3224
895 Liberia 572.1996
896 Liberia 506.1139
897 Liberia 636.6229
898 Liberia 609.1740
899 Liberia 531.4824
900 Liberia 414.5073
901 Libya 2387.5481
902 Libya 3448.2844
903 Libya 6757.0308
904 Libya 18772.7517
905 Libya 21011.4972
906 Libya 21951.2118
907 Libya 17364.2754
908 Libya 11770.5898
909 Libya 9640.1385
910 Libya 9467.4461
911 Libya 9534.6775
912 Libya 12057.4993
913 Madagascar 1443.0117
914 Madagascar 1589.2027
915 Madagascar 1643.3871
916 Madagascar 1634.0473
917 Madagascar 1748.5630
918 Madagascar 1544.2286
919 Madagascar 1302.8787
920 Madagascar 1155.4419
921 Madagascar 1040.6762
922 Madagascar 986.2959
923 Madagascar 894.6371
924 Madagascar 1044.7701
925 Malawi 369.1651
926 Malawi 416.3698
927 Malawi 427.9011
928 Malawi 495.5148
929 Malawi 584.6220
930 Malawi 663.2237
931 Malawi 632.8039
932 Malawi 635.5174
933 Malawi 563.2000
934 Malawi 692.2758
935 Malawi 665.4231
936 Malawi 759.3499
937 Malaysia 1831.1329
938 Malaysia 1810.0670
939 Malaysia 2036.8849
940 Malaysia 2277.7424
941 Malaysia 2849.0948
942 Malaysia 3827.9216
943 Malaysia 4920.3560
944 Malaysia 5249.8027
945 Malaysia 7277.9128
946 Malaysia 10132.9096
947 Malaysia 10206.9779
948 Malaysia 12451.6558
949 Mali 452.3370
950 Mali 490.3822
951 Mali 496.1743
952 Mali 545.0099
953 Mali 581.3689
954 Mali 686.3953
955 Mali 618.0141
956 Mali 684.1716
957 Mali 739.0144
958 Mali 790.2580
959 Mali 951.4098
960 Mali 1042.5816
961 Mauritania 743.1159
962 Mauritania 846.1203
963 Mauritania 1055.8960
964 Mauritania 1421.1452
965 Mauritania 1586.8518
966 Mauritania 1497.4922
967 Mauritania 1481.1502
968 Mauritania 1421.6036
969 Mauritania 1361.3698
970 Mauritania 1483.1361
971 Mauritania 1579.0195
972 Mauritania 1803.1515
973 Mauritius 1967.9557
974 Mauritius 2034.0380
975 Mauritius 2529.0675
976 Mauritius 2475.3876
977 Mauritius 2575.4842
978 Mauritius 3710.9830
979 Mauritius 3688.0377
980 Mauritius 4783.5869
981 Mauritius 6058.2538
982 Mauritius 7425.7053
983 Mauritius 9021.8159
984 Mauritius 10956.9911
985 Mexico 3478.1255
986 Mexico 4131.5466
987 Mexico 4581.6094
988 Mexico 5754.7339
989 Mexico 6809.4067
990 Mexico 7674.9291
991 Mexico 9611.1475
992 Mexico 8688.1560
993 Mexico 9472.3843
994 Mexico 9767.2975
995 Mexico 10742.4405
996 Mexico 11977.5750
997 Mongolia 786.5669
998 Mongolia 912.6626
999 Mongolia 1056.3540
1000 Mongolia 1226.0411
1001 Mongolia 1421.7420
1002 Mongolia 1647.5117
1003 Mongolia 2000.6031
1004 Mongolia 2338.0083
1005 Mongolia 1785.4020
1006 Mongolia 1902.2521
1007 Mongolia 2140.7393
1008 Mongolia 3095.7723
1009 Montenegro 2647.5856
1010 Montenegro 3682.2599
1011 Montenegro 4649.5938
1012 Montenegro 5907.8509
1013 Montenegro 7778.4140
1014 Montenegro 9595.9299
1015 Montenegro 11222.5876
1016 Montenegro 11732.5102
1017 Montenegro 7003.3390
1018 Montenegro 6465.6133
1019 Montenegro 6557.1943
1020 Montenegro 9253.8961
1021 Morocco 1688.2036
1022 Morocco 1642.0023
1023 Morocco 1566.3535
1024 Morocco 1711.0448
1025 Morocco 1930.1950
1026 Morocco 2370.6200
1027 Morocco 2702.6204
1028 Morocco 2755.0470
1029 Morocco 2948.0473
1030 Morocco 2982.1019
1031 Morocco 3258.4956
1032 Morocco 3820.1752
1033 Mozambique 468.5260
1034 Mozambique 495.5868
1035 Mozambique 556.6864
1036 Mozambique 566.6692
1037 Mozambique 724.9178
1038 Mozambique 502.3197
1039 Mozambique 462.2114
1040 Mozambique 389.8762
1041 Mozambique 410.8968
1042 Mozambique 472.3461
1043 Mozambique 633.6179
1044 Mozambique 823.6856
1045 Myanmar 331.0000
1046 Myanmar 350.0000
1047 Myanmar 388.0000
1048 Myanmar 349.0000
1049 Myanmar 357.0000
1050 Myanmar 371.0000
1051 Myanmar 424.0000
1052 Myanmar 385.0000
1053 Myanmar 347.0000
1054 Myanmar 415.0000
1055 Myanmar 611.0000
1056 Myanmar 944.0000
1057 Namibia 2423.7804
1058 Namibia 2621.4481
1059 Namibia 3173.2156
1060 Namibia 3793.6948
1061 Namibia 3746.0809
1062 Namibia 3876.4860
1063 Namibia 4191.1005
1064 Namibia 3693.7313
1065 Namibia 3804.5380
1066 Namibia 3899.5243
1067 Namibia 4072.3248
1068 Namibia 4811.0604
1069 Nepal 545.8657
1070 Nepal 597.9364
1071 Nepal 652.3969
1072 Nepal 676.4422
1073 Nepal 674.7881
1074 Nepal 694.1124
1075 Nepal 718.3731
1076 Nepal 775.6325
1077 Nepal 897.7404
1078 Nepal 1010.8921
1079 Nepal 1057.2063
1080 Nepal 1091.3598
1081 Netherlands 8941.5719
1082 Netherlands 11276.1934
1083 Netherlands 12790.8496
1084 Netherlands 15363.2514
1085 Netherlands 18794.7457
1086 Netherlands 21209.0592
1087 Netherlands 21399.4605
1088 Netherlands 23651.3236
1089 Netherlands 26790.9496
1090 Netherlands 30246.1306
1091 Netherlands 33724.7578
1092 Netherlands 36797.9333
1093 New Zealand 10556.5757
1094 New Zealand 12247.3953
1095 New Zealand 13175.6780
1096 New Zealand 14463.9189
1097 New Zealand 16046.0373
1098 New Zealand 16233.7177
1099 New Zealand 17632.4104
1100 New Zealand 19007.1913
1101 New Zealand 18363.3249
1102 New Zealand 21050.4138
1103 New Zealand 23189.8014
1104 New Zealand 25185.0091
1105 Nicaragua 3112.3639
1106 Nicaragua 3457.4159
1107 Nicaragua 3634.3644
1108 Nicaragua 4643.3935
1109 Nicaragua 4688.5933
1110 Nicaragua 5486.3711
1111 Nicaragua 3470.3382
1112 Nicaragua 2955.9844
1113 Nicaragua 2170.1517
1114 Nicaragua 2253.0230
1115 Nicaragua 2474.5488
1116 Nicaragua 2749.3210
1117 Niger 761.8794
1118 Niger 835.5234
1119 Niger 997.7661
1120 Niger 1054.3849
1121 Niger 954.2092
1122 Niger 808.8971
1123 Niger 909.7221
1124 Niger 668.3000
1125 Niger 581.1827
1126 Niger 580.3052
1127 Niger 601.0745
1128 Niger 619.6769
1129 Nigeria 1077.2819
1130 Nigeria 1100.5926
1131 Nigeria 1150.9275
1132 Nigeria 1014.5141
1133 Nigeria 1698.3888
1134 Nigeria 1981.9518
1135 Nigeria 1576.9738
1136 Nigeria 1385.0296
1137 Nigeria 1619.8482
1138 Nigeria 1624.9413
1139 Nigeria 1615.2864
1140 Nigeria 2013.9773
1141 Norway 10095.4217
1142 Norway 11653.9730
1143 Norway 13450.4015
1144 Norway 16361.8765
1145 Norway 18965.0555
1146 Norway 23311.3494
1147 Norway 26298.6353
1148 Norway 31540.9748
1149 Norway 33965.6611
1150 Norway 41283.1643
1151 Norway 44683.9753
1152 Norway 49357.1902
1153 Oman 1828.2303
1154 Oman 2242.7466
1155 Oman 2924.6381
1156 Oman 4720.9427
1157 Oman 10618.0385
1158 Oman 11848.3439
1159 Oman 12954.7910
1160 Oman 18115.2231
1161 Oman 18616.7069
1162 Oman 19702.0558
1163 Oman 19774.8369
1164 Oman 22316.1929
1165 Pakistan 684.5971
1166 Pakistan 747.0835
1167 Pakistan 803.3427
1168 Pakistan 942.4083
1169 Pakistan 1049.9390
1170 Pakistan 1175.9212
1171 Pakistan 1443.4298
1172 Pakistan 1704.6866
1173 Pakistan 1971.8295
1174 Pakistan 2049.3505
1175 Pakistan 2092.7124
1176 Pakistan 2605.9476
1177 Panama 2480.3803
1178 Panama 2961.8009
1179 Panama 3536.5403
1180 Panama 4421.0091
1181 Panama 5364.2497
1182 Panama 5351.9121
1183 Panama 7009.6016
1184 Panama 7034.7792
1185 Panama 6618.7431
1186 Panama 7113.6923
1187 Panama 7356.0319
1188 Panama 9809.1856
1189 Paraguay 1952.3087
1190 Paraguay 2046.1547
1191 Paraguay 2148.0271
1192 Paraguay 2299.3763
1193 Paraguay 2523.3380
1194 Paraguay 3248.3733
1195 Paraguay 4258.5036
1196 Paraguay 3998.8757
1197 Paraguay 4196.4111
1198 Paraguay 4247.4003
1199 Paraguay 3783.6742
1200 Paraguay 4172.8385
1201 Peru 3758.5234
1202 Peru 4245.2567
1203 Peru 4957.0380
1204 Peru 5788.0933
1205 Peru 5937.8273
1206 Peru 6281.2909
1207 Peru 6434.5018
1208 Peru 6360.9434
1209 Peru 4446.3809
1210 Peru 5838.3477
1211 Peru 5909.0201
1212 Peru 7408.9056
1213 Philippines 1272.8810
1214 Philippines 1547.9448
1215 Philippines 1649.5522
1216 Philippines 1814.1274
1217 Philippines 1989.3741
1218 Philippines 2373.2043
1219 Philippines 2603.2738
1220 Philippines 2189.6350
1221 Philippines 2279.3240
1222 Philippines 2536.5349
1223 Philippines 2650.9211
1224 Philippines 3190.4810
1225 Poland 4029.3297
1226 Poland 4734.2530
1227 Poland 5338.7521
1228 Poland 6557.1528
1229 Poland 8006.5070
1230 Poland 9508.1415
1231 Poland 8451.5310
1232 Poland 9082.3512
1233 Poland 7738.8812
1234 Poland 10159.5837
1235 Poland 12002.2391
1236 Poland 15389.9247
1237 Portugal 3068.3199
1238 Portugal 3774.5717
1239 Portugal 4727.9549
1240 Portugal 6361.5180
1241 Portugal 9022.2474
1242 Portugal 10172.4857
1243 Portugal 11753.8429
1244 Portugal 13039.3088
1245 Portugal 16207.2666
1246 Portugal 17641.0316
1247 Portugal 19970.9079
1248 Portugal 20509.6478
1249 Puerto Rico 3081.9598
1250 Puerto Rico 3907.1562
1251 Puerto Rico 5108.3446
1252 Puerto Rico 6929.2777
1253 Puerto Rico 9123.0417
1254 Puerto Rico 9770.5249
1255 Puerto Rico 10330.9891
1256 Puerto Rico 12281.3419
1257 Puerto Rico 14641.5871
1258 Puerto Rico 16999.4333
1259 Puerto Rico 18855.6062
1260 Puerto Rico 19328.7090
1261 Reunion 2718.8853
1262 Reunion 2769.4518
1263 Reunion 3173.7233
1264 Reunion 4021.1757
1265 Reunion 5047.6586
1266 Reunion 4319.8041
1267 Reunion 5267.2194
1268 Reunion 5303.3775
1269 Reunion 6101.2558
1270 Reunion 6071.9414
1271 Reunion 6316.1652
1272 Reunion 7670.1226
1273 Romania 3144.6132
1274 Romania 3943.3702
1275 Romania 4734.9976
1276 Romania 6470.8665
1277 Romania 8011.4144
1278 Romania 9356.3972
1279 Romania 9605.3141
1280 Romania 9696.2733
1281 Romania 6598.4099
1282 Romania 7346.5476
1283 Romania 7885.3601
1284 Romania 10808.4756
1285 Rwanda 493.3239
1286 Rwanda 540.2894
1287 Rwanda 597.4731
1288 Rwanda 510.9637
1289 Rwanda 590.5807
1290 Rwanda 670.0806
1291 Rwanda 881.5706
1292 Rwanda 847.9912
1293 Rwanda 737.0686
1294 Rwanda 589.9445
1295 Rwanda 785.6538
1296 Rwanda 863.0885
1297 Sao Tome and Principe 879.5836
1298 Sao Tome and Principe 860.7369
1299 Sao Tome and Principe 1071.5511
1300 Sao Tome and Principe 1384.8406
1301 Sao Tome and Principe 1532.9853
1302 Sao Tome and Principe 1737.5617
1303 Sao Tome and Principe 1890.2181
1304 Sao Tome and Principe 1516.5255
1305 Sao Tome and Principe 1428.7778
1306 Sao Tome and Principe 1339.0760
1307 Sao Tome and Principe 1353.0924
1308 Sao Tome and Principe 1598.4351
1309 Saudi Arabia 6459.5548
1310 Saudi Arabia 8157.5912
1311 Saudi Arabia 11626.4197
1312 Saudi Arabia 16903.0489
1313 Saudi Arabia 24837.4287
1314 Saudi Arabia 34167.7626
1315 Saudi Arabia 33693.1753
1316 Saudi Arabia 21198.2614
1317 Saudi Arabia 24841.6178
1318 Saudi Arabia 20586.6902
1319 Saudi Arabia 19014.5412
1320 Saudi Arabia 21654.8319
1321 Senegal 1450.3570
1322 Senegal 1567.6530
1323 Senegal 1654.9887
1324 Senegal 1612.4046
1325 Senegal 1597.7121
1326 Senegal 1561.7691
1327 Senegal 1518.4800
1328 Senegal 1441.7207
1329 Senegal 1367.8994
1330 Senegal 1392.3683
1331 Senegal 1519.6353
1332 Senegal 1712.4721
1333 Serbia 3581.4594
1334 Serbia 4981.0909
1335 Serbia 6289.6292
1336 Serbia 7991.7071
1337 Serbia 10522.0675
1338 Serbia 12980.6696
1339 Serbia 15181.0927
1340 Serbia 15870.8785
1341 Serbia 9325.0682
1342 Serbia 7914.3203
1343 Serbia 7236.0753
1344 Serbia 9786.5347
1345 Sierra Leone 879.7877
1346 Sierra Leone 1004.4844
1347 Sierra Leone 1116.6399
1348 Sierra Leone 1206.0435
1349 Sierra Leone 1353.7598
1350 Sierra Leone 1348.2852
1351 Sierra Leone 1465.0108
1352 Sierra Leone 1294.4478
1353 Sierra Leone 1068.6963
1354 Sierra Leone 574.6482
1355 Sierra Leone 699.4897
1356 Sierra Leone 862.5408
1357 Singapore 2315.1382
1358 Singapore 2843.1044
1359 Singapore 3674.7356
1360 Singapore 4977.4185
1361 Singapore 8597.7562
1362 Singapore 11210.0895
1363 Singapore 15169.1611
1364 Singapore 18861.5308
1365 Singapore 24769.8912
1366 Singapore 33519.4766
1367 Singapore 36023.1054
1368 Singapore 47143.1796
1369 Slovak Republic 5074.6591
1370 Slovak Republic 6093.2630
1371 Slovak Republic 7481.1076
1372 Slovak Republic 8412.9024
1373 Slovak Republic 9674.1676
1374 Slovak Republic 10922.6640
1375 Slovak Republic 11348.5459
1376 Slovak Republic 12037.2676
1377 Slovak Republic 9498.4677
1378 Slovak Republic 12126.2306
1379 Slovak Republic 13638.7784
1380 Slovak Republic 18678.3144
1381 Slovenia 4215.0417
1382 Slovenia 5862.2766
1383 Slovenia 7402.3034
1384 Slovenia 9405.4894
1385 Slovenia 12383.4862
1386 Slovenia 15277.0302
1387 Slovenia 17866.7218
1388 Slovenia 18678.5349
1389 Slovenia 14214.7168
1390 Slovenia 17161.1073
1391 Slovenia 20660.0194
1392 Slovenia 25768.2576
1393 Somalia 1135.7498
1394 Somalia 1258.1474
1395 Somalia 1369.4883
1396 Somalia 1284.7332
1397 Somalia 1254.5761
1398 Somalia 1450.9925
1399 Somalia 1176.8070
1400 Somalia 1093.2450
1401 Somalia 926.9603
1402 Somalia 930.5964
1403 Somalia 882.0818
1404 Somalia 926.1411
1405 South Africa 4725.2955
1406 South Africa 5487.1042
1407 South Africa 5768.7297
1408 South Africa 7114.4780
1409 South Africa 7765.9626
1410 South Africa 8028.6514
1411 South Africa 8568.2662
1412 South Africa 7825.8234
1413 South Africa 7225.0693
1414 South Africa 7479.1882
1415 South Africa 7710.9464
1416 South Africa 9269.6578
1417 Spain 3834.0347
1418 Spain 4564.8024
1419 Spain 5693.8439
1420 Spain 7993.5123
1421 Spain 10638.7513
1422 Spain 13236.9212
1423 Spain 13926.1700
1424 Spain 15764.9831
1425 Spain 18603.0645
1426 Spain 20445.2990
1427 Spain 24835.4717
1428 Spain 28821.0637
1429 Sri Lanka 1083.5320
1430 Sri Lanka 1072.5466
1431 Sri Lanka 1074.4720
1432 Sri Lanka 1135.5143
1433 Sri Lanka 1213.3955
1434 Sri Lanka 1348.7757
1435 Sri Lanka 1648.0798
1436 Sri Lanka 1876.7668
1437 Sri Lanka 2153.7392
1438 Sri Lanka 2664.4773
1439 Sri Lanka 3015.3788
1440 Sri Lanka 3970.0954
1441 Sudan 1615.9911
1442 Sudan 1770.3371
1443 Sudan 1959.5938
1444 Sudan 1687.9976
1445 Sudan 1659.6528
1446 Sudan 2202.9884
1447 Sudan 1895.5441
1448 Sudan 1507.8192
1449 Sudan 1492.1970
1450 Sudan 1632.2108
1451 Sudan 1993.3983
1452 Sudan 2602.3950
1453 Swaziland 1148.3766
1454 Swaziland 1244.7084
1455 Swaziland 1856.1821
1456 Swaziland 2613.1017
1457 Swaziland 3364.8366
1458 Swaziland 3781.4106
1459 Swaziland 3895.3840
1460 Swaziland 3984.8398
1461 Swaziland 3553.0224
1462 Swaziland 3876.7685
1463 Swaziland 4128.1169
1464 Swaziland 4513.4806
1465 Sweden 8527.8447
1466 Sweden 9911.8782
1467 Sweden 12329.4419
1468 Sweden 15258.2970
1469 Sweden 17832.0246
1470 Sweden 18855.7252
1471 Sweden 20667.3812
1472 Sweden 23586.9293
1473 Sweden 23880.0168
1474 Sweden 25266.5950
1475 Sweden 29341.6309
1476 Sweden 33859.7484
1477 Switzerland 14734.2327
1478 Switzerland 17909.4897
1479 Switzerland 20431.0927
1480 Switzerland 22966.1443
1481 Switzerland 27195.1130
1482 Switzerland 26982.2905
1483 Switzerland 28397.7151
1484 Switzerland 30281.7046
1485 Switzerland 31871.5303
1486 Switzerland 32135.3230
1487 Switzerland 34480.9577
1488 Switzerland 37506.4191
1489 Syria 1643.4854
1490 Syria 2117.2349
1491 Syria 2193.0371
1492 Syria 1881.9236
1493 Syria 2571.4230
1494 Syria 3195.4846
1495 Syria 3761.8377
1496 Syria 3116.7743
1497 Syria 3340.5428
1498 Syria 4014.2390
1499 Syria 4090.9253
1500 Syria 4184.5481
1501 Taiwan 1206.9479
1502 Taiwan 1507.8613
1503 Taiwan 1822.8790
1504 Taiwan 2643.8587
1505 Taiwan 4062.5239
1506 Taiwan 5596.5198
1507 Taiwan 7426.3548
1508 Taiwan 11054.5618
1509 Taiwan 15215.6579
1510 Taiwan 20206.8210
1511 Taiwan 23235.4233
1512 Taiwan 28718.2768
1513 Tanzania 716.6501
1514 Tanzania 698.5356
1515 Tanzania 722.0038
1516 Tanzania 848.2187
1517 Tanzania 915.9851
1518 Tanzania 962.4923
1519 Tanzania 874.2426
1520 Tanzania 831.8221
1521 Tanzania 825.6825
1522 Tanzania 789.1862
1523 Tanzania 899.0742
1524 Tanzania 1107.4822
1525 Thailand 757.7974
1526 Thailand 793.5774
1527 Thailand 1002.1992
1528 Thailand 1295.4607
1529 Thailand 1524.3589
1530 Thailand 1961.2246
1531 Thailand 2393.2198
1532 Thailand 2982.6538
1533 Thailand 4616.8965
1534 Thailand 5852.6255
1535 Thailand 5913.1875
1536 Thailand 7458.3963
1537 Togo 859.8087
1538 Togo 925.9083
1539 Togo 1067.5348
1540 Togo 1477.5968
1541 Togo 1649.6602
1542 Togo 1532.7770
1543 Togo 1344.5780
1544 Togo 1202.2014
1545 Togo 1034.2989
1546 Togo 982.2869
1547 Togo 886.2206
1548 Togo 882.9699
1549 Trinidad and Tobago 3023.2719
1550 Trinidad and Tobago 4100.3934
1551 Trinidad and Tobago 4997.5240
1552 Trinidad and Tobago 5621.3685
1553 Trinidad and Tobago 6619.5514
1554 Trinidad and Tobago 7899.5542
1555 Trinidad and Tobago 9119.5286
1556 Trinidad and Tobago 7388.5978
1557 Trinidad and Tobago 7370.9909
1558 Trinidad and Tobago 8792.5731
1559 Trinidad and Tobago 11460.6002
1560 Trinidad and Tobago 18008.5092
1561 Tunisia 1468.4756
1562 Tunisia 1395.2325
1563 Tunisia 1660.3032
1564 Tunisia 1932.3602
1565 Tunisia 2753.2860
1566 Tunisia 3120.8768
1567 Tunisia 3560.2332
1568 Tunisia 3810.4193
1569 Tunisia 4332.7202
1570 Tunisia 4876.7986
1571 Tunisia 5722.8957
1572 Tunisia 7092.9230
1573 Turkey 1969.1010
1574 Turkey 2218.7543
1575 Turkey 2322.8699
1576 Turkey 2826.3564
1577 Turkey 3450.6964
1578 Turkey 4269.1223
1579 Turkey 4241.3563
1580 Turkey 5089.0437
1581 Turkey 5678.3483
1582 Turkey 6601.4299
1583 Turkey 6508.0857
1584 Turkey 8458.2764
1585 Uganda 734.7535
1586 Uganda 774.3711
1587 Uganda 767.2717
1588 Uganda 908.9185
1589 Uganda 950.7359
1590 Uganda 843.7331
1591 Uganda 682.2662
1592 Uganda 617.7244
1593 Uganda 644.1708
1594 Uganda 816.5591
1595 Uganda 927.7210
1596 Uganda 1056.3801
1597 United Kingdom 9979.5085
1598 United Kingdom 11283.1779
1599 United Kingdom 12477.1771
1600 United Kingdom 14142.8509
1601 United Kingdom 15895.1164
1602 United Kingdom 17428.7485
1603 United Kingdom 18232.4245
1604 United Kingdom 21664.7877
1605 United Kingdom 22705.0925
1606 United Kingdom 26074.5314
1607 United Kingdom 29478.9992
1608 United Kingdom 33203.2613
1609 United States 13990.4821
1610 United States 14847.1271
1611 United States 16173.1459
1612 United States 19530.3656
1613 United States 21806.0359
1614 United States 24072.6321
1615 United States 25009.5591
1616 United States 29884.3504
1617 United States 32003.9322
1618 United States 35767.4330
1619 United States 39097.0995
1620 United States 42951.6531
1621 Uruguay 5716.7667
1622 Uruguay 6150.7730
1623 Uruguay 5603.3577
1624 Uruguay 5444.6196
1625 Uruguay 5703.4089
1626 Uruguay 6504.3397
1627 Uruguay 6920.2231
1628 Uruguay 7452.3990
1629 Uruguay 8137.0048
1630 Uruguay 9230.2407
1631 Uruguay 7727.0020
1632 Uruguay 10611.4630
1633 Venezuela 7689.7998
1634 Venezuela 9802.4665
1635 Venezuela 8422.9742
1636 Venezuela 9541.4742
1637 Venezuela 10505.2597
1638 Venezuela 13143.9510
1639 Venezuela 11152.4101
1640 Venezuela 9883.5846
1641 Venezuela 10733.9263
1642 Venezuela 10165.4952
1643 Venezuela 8605.0478
1644 Venezuela 11415.8057
1645 Vietnam 605.0665
1646 Vietnam 676.2854
1647 Vietnam 772.0492
1648 Vietnam 637.1233
1649 Vietnam 699.5016
1650 Vietnam 713.5371
1651 Vietnam 707.2358
1652 Vietnam 820.7994
1653 Vietnam 989.0231
1654 Vietnam 1385.8968
1655 Vietnam 1764.4567
1656 Vietnam 2441.5764
1657 West Bank and Gaza 1515.5923
1658 West Bank and Gaza 1827.0677
1659 West Bank and Gaza 2198.9563
1660 West Bank and Gaza 2649.7150
1661 West Bank and Gaza 3133.4093
1662 West Bank and Gaza 3682.8315
1663 West Bank and Gaza 4336.0321
1664 West Bank and Gaza 5107.1974
1665 West Bank and Gaza 6017.6548
1666 West Bank and Gaza 7110.6676
1667 West Bank and Gaza 4515.4876
1668 West Bank and Gaza 3025.3498
1669 Yemen Rep. 781.7176
1670 Yemen Rep. 804.8305
1671 Yemen Rep. 825.6232
1672 Yemen Rep. 862.4421
1673 Yemen Rep. 1265.0470
1674 Yemen Rep. 1829.7652
1675 Yemen Rep. 1977.5570
1676 Yemen Rep. 1971.7415
1677 Yemen Rep. 1879.4967
1678 Yemen Rep. 2117.4845
1679 Yemen Rep. 2234.8208
1680 Yemen Rep. 2280.7699
1681 Zambia 1147.3888
1682 Zambia 1311.9568
1683 Zambia 1452.7258
1684 Zambia 1777.0773
1685 Zambia 1773.4983
1686 Zambia 1588.6883
1687 Zambia 1408.6786
1688 Zambia 1213.3151
1689 Zambia 1210.8846
1690 Zambia 1071.3538
1691 Zambia 1071.6139
1692 Zambia 1271.2116
1693 Zimbabwe 406.8841
1694 Zimbabwe 518.7643
1695 Zimbabwe 527.2722
1696 Zimbabwe 569.7951
1697 Zimbabwe 799.3622
1698 Zimbabwe 685.5877
1699 Zimbabwe 788.8550
1700 Zimbabwe 706.1573
1701 Zimbabwe 693.4208
1702 Zimbabwe 792.4500
1703 Zimbabwe 672.0386
1704 Zimbabwe 469.7093You can also specify columns to remove using negative selection, select(-varname)
gapminder %>%
select(-continent) country year pop lifeExp gdpPercap
1 Afghanistan 1952 8425333 28.80100 779.4453
2 Afghanistan 1957 9240934 30.33200 820.8530
3 Afghanistan 1962 10267083 31.99700 853.1007
4 Afghanistan 1967 11537966 34.02000 836.1971
5 Afghanistan 1972 13079460 36.08800 739.9811
6 Afghanistan 1977 14880372 38.43800 786.1134
7 Afghanistan 1982 12881816 39.85400 978.0114
8 Afghanistan 1987 13867957 40.82200 852.3959
9 Afghanistan 1992 16317921 41.67400 649.3414
10 Afghanistan 1997 22227415 41.76300 635.3414
11 Afghanistan 2002 25268405 42.12900 726.7341
12 Afghanistan 2007 31889923 43.82800 974.5803
13 Albania 1952 1282697 55.23000 1601.0561
14 Albania 1957 1476505 59.28000 1942.2842
15 Albania 1962 1728137 64.82000 2312.8890
16 Albania 1967 1984060 66.22000 2760.1969
17 Albania 1972 2263554 67.69000 3313.4222
18 Albania 1977 2509048 68.93000 3533.0039
19 Albania 1982 2780097 70.42000 3630.8807
20 Albania 1987 3075321 72.00000 3738.9327
21 Albania 1992 3326498 71.58100 2497.4379
22 Albania 1997 3428038 72.95000 3193.0546
23 Albania 2002 3508512 75.65100 4604.2117
24 Albania 2007 3600523 76.42300 5937.0295
25 Algeria 1952 9279525 43.07700 2449.0082
26 Algeria 1957 10270856 45.68500 3013.9760
27 Algeria 1962 11000948 48.30300 2550.8169
28 Algeria 1967 12760499 51.40700 3246.9918
29 Algeria 1972 14760787 54.51800 4182.6638
30 Algeria 1977 17152804 58.01400 4910.4168
31 Algeria 1982 20033753 61.36800 5745.1602
32 Algeria 1987 23254956 65.79900 5681.3585
33 Algeria 1992 26298373 67.74400 5023.2166
34 Algeria 1997 29072015 69.15200 4797.2951
35 Algeria 2002 31287142 70.99400 5288.0404
36 Algeria 2007 33333216 72.30100 6223.3675
37 Angola 1952 4232095 30.01500 3520.6103
38 Angola 1957 4561361 31.99900 3827.9405
39 Angola 1962 4826015 34.00000 4269.2767
40 Angola 1967 5247469 35.98500 5522.7764
41 Angola 1972 5894858 37.92800 5473.2880
42 Angola 1977 6162675 39.48300 3008.6474
43 Angola 1982 7016384 39.94200 2756.9537
44 Angola 1987 7874230 39.90600 2430.2083
45 Angola 1992 8735988 40.64700 2627.8457
46 Angola 1997 9875024 40.96300 2277.1409
47 Angola 2002 10866106 41.00300 2773.2873
48 Angola 2007 12420476 42.73100 4797.2313
49 Argentina 1952 17876956 62.48500 5911.3151
50 Argentina 1957 19610538 64.39900 6856.8562
51 Argentina 1962 21283783 65.14200 7133.1660
52 Argentina 1967 22934225 65.63400 8052.9530
53 Argentina 1972 24779799 67.06500 9443.0385
54 Argentina 1977 26983828 68.48100 10079.0267
55 Argentina 1982 29341374 69.94200 8997.8974
56 Argentina 1987 31620918 70.77400 9139.6714
57 Argentina 1992 33958947 71.86800 9308.4187
58 Argentina 1997 36203463 73.27500 10967.2820
59 Argentina 2002 38331121 74.34000 8797.6407
60 Argentina 2007 40301927 75.32000 12779.3796
61 Australia 1952 8691212 69.12000 10039.5956
62 Australia 1957 9712569 70.33000 10949.6496
63 Australia 1962 10794968 70.93000 12217.2269
64 Australia 1967 11872264 71.10000 14526.1246
65 Australia 1972 13177000 71.93000 16788.6295
66 Australia 1977 14074100 73.49000 18334.1975
67 Australia 1982 15184200 74.74000 19477.0093
68 Australia 1987 16257249 76.32000 21888.8890
69 Australia 1992 17481977 77.56000 23424.7668
70 Australia 1997 18565243 78.83000 26997.9366
71 Australia 2002 19546792 80.37000 30687.7547
72 Australia 2007 20434176 81.23500 34435.3674
73 Austria 1952 6927772 66.80000 6137.0765
74 Austria 1957 6965860 67.48000 8842.5980
75 Austria 1962 7129864 69.54000 10750.7211
76 Austria 1967 7376998 70.14000 12834.6024
77 Austria 1972 7544201 70.63000 16661.6256
78 Austria 1977 7568430 72.17000 19749.4223
79 Austria 1982 7574613 73.18000 21597.0836
80 Austria 1987 7578903 74.94000 23687.8261
81 Austria 1992 7914969 76.04000 27042.0187
82 Austria 1997 8069876 77.51000 29095.9207
83 Austria 2002 8148312 78.98000 32417.6077
84 Austria 2007 8199783 79.82900 36126.4927
85 Bahrain 1952 120447 50.93900 9867.0848
86 Bahrain 1957 138655 53.83200 11635.7995
87 Bahrain 1962 171863 56.92300 12753.2751
88 Bahrain 1967 202182 59.92300 14804.6727
89 Bahrain 1972 230800 63.30000 18268.6584
90 Bahrain 1977 297410 65.59300 19340.1020
91 Bahrain 1982 377967 69.05200 19211.1473
92 Bahrain 1987 454612 70.75000 18524.0241
93 Bahrain 1992 529491 72.60100 19035.5792
94 Bahrain 1997 598561 73.92500 20292.0168
95 Bahrain 2002 656397 74.79500 23403.5593
96 Bahrain 2007 708573 75.63500 29796.0483
97 Bangladesh 1952 46886859 37.48400 684.2442
98 Bangladesh 1957 51365468 39.34800 661.6375
99 Bangladesh 1962 56839289 41.21600 686.3416
100 Bangladesh 1967 62821884 43.45300 721.1861
101 Bangladesh 1972 70759295 45.25200 630.2336
102 Bangladesh 1977 80428306 46.92300 659.8772
103 Bangladesh 1982 93074406 50.00900 676.9819
104 Bangladesh 1987 103764241 52.81900 751.9794
105 Bangladesh 1992 113704579 56.01800 837.8102
106 Bangladesh 1997 123315288 59.41200 972.7700
107 Bangladesh 2002 135656790 62.01300 1136.3904
108 Bangladesh 2007 150448339 64.06200 1391.2538
109 Belgium 1952 8730405 68.00000 8343.1051
110 Belgium 1957 8989111 69.24000 9714.9606
111 Belgium 1962 9218400 70.25000 10991.2068
112 Belgium 1967 9556500 70.94000 13149.0412
113 Belgium 1972 9709100 71.44000 16672.1436
114 Belgium 1977 9821800 72.80000 19117.9745
115 Belgium 1982 9856303 73.93000 20979.8459
116 Belgium 1987 9870200 75.35000 22525.5631
117 Belgium 1992 10045622 76.46000 25575.5707
118 Belgium 1997 10199787 77.53000 27561.1966
119 Belgium 2002 10311970 78.32000 30485.8838
120 Belgium 2007 10392226 79.44100 33692.6051
121 Benin 1952 1738315 38.22300 1062.7522
122 Benin 1957 1925173 40.35800 959.6011
123 Benin 1962 2151895 42.61800 949.4991
124 Benin 1967 2427334 44.88500 1035.8314
125 Benin 1972 2761407 47.01400 1085.7969
126 Benin 1977 3168267 49.19000 1029.1613
127 Benin 1982 3641603 50.90400 1277.8976
128 Benin 1987 4243788 52.33700 1225.8560
129 Benin 1992 4981671 53.91900 1191.2077
130 Benin 1997 6066080 54.77700 1232.9753
131 Benin 2002 7026113 54.40600 1372.8779
132 Benin 2007 8078314 56.72800 1441.2849
133 Bolivia 1952 2883315 40.41400 2677.3263
134 Bolivia 1957 3211738 41.89000 2127.6863
135 Bolivia 1962 3593918 43.42800 2180.9725
136 Bolivia 1967 4040665 45.03200 2586.8861
137 Bolivia 1972 4565872 46.71400 2980.3313
138 Bolivia 1977 5079716 50.02300 3548.0978
139 Bolivia 1982 5642224 53.85900 3156.5105
140 Bolivia 1987 6156369 57.25100 2753.6915
141 Bolivia 1992 6893451 59.95700 2961.6997
142 Bolivia 1997 7693188 62.05000 3326.1432
143 Bolivia 2002 8445134 63.88300 3413.2627
144 Bolivia 2007 9119152 65.55400 3822.1371
145 Bosnia and Herzegovina 1952 2791000 53.82000 973.5332
146 Bosnia and Herzegovina 1957 3076000 58.45000 1353.9892
147 Bosnia and Herzegovina 1962 3349000 61.93000 1709.6837
148 Bosnia and Herzegovina 1967 3585000 64.79000 2172.3524
149 Bosnia and Herzegovina 1972 3819000 67.45000 2860.1698
150 Bosnia and Herzegovina 1977 4086000 69.86000 3528.4813
151 Bosnia and Herzegovina 1982 4172693 70.69000 4126.6132
152 Bosnia and Herzegovina 1987 4338977 71.14000 4314.1148
153 Bosnia and Herzegovina 1992 4256013 72.17800 2546.7814
154 Bosnia and Herzegovina 1997 3607000 73.24400 4766.3559
155 Bosnia and Herzegovina 2002 4165416 74.09000 6018.9752
156 Bosnia and Herzegovina 2007 4552198 74.85200 7446.2988
157 Botswana 1952 442308 47.62200 851.2411
158 Botswana 1957 474639 49.61800 918.2325
159 Botswana 1962 512764 51.52000 983.6540
160 Botswana 1967 553541 53.29800 1214.7093
161 Botswana 1972 619351 56.02400 2263.6111
162 Botswana 1977 781472 59.31900 3214.8578
163 Botswana 1982 970347 61.48400 4551.1421
164 Botswana 1987 1151184 63.62200 6205.8839
165 Botswana 1992 1342614 62.74500 7954.1116
166 Botswana 1997 1536536 52.55600 8647.1423
167 Botswana 2002 1630347 46.63400 11003.6051
168 Botswana 2007 1639131 50.72800 12569.8518
169 Brazil 1952 56602560 50.91700 2108.9444
170 Brazil 1957 65551171 53.28500 2487.3660
171 Brazil 1962 76039390 55.66500 3336.5858
172 Brazil 1967 88049823 57.63200 3429.8644
173 Brazil 1972 100840058 59.50400 4985.7115
174 Brazil 1977 114313951 61.48900 6660.1187
175 Brazil 1982 128962939 63.33600 7030.8359
176 Brazil 1987 142938076 65.20500 7807.0958
177 Brazil 1992 155975974 67.05700 6950.2830
178 Brazil 1997 168546719 69.38800 7957.9808
179 Brazil 2002 179914212 71.00600 8131.2128
180 Brazil 2007 190010647 72.39000 9065.8008
181 Bulgaria 1952 7274900 59.60000 2444.2866
182 Bulgaria 1957 7651254 66.61000 3008.6707
183 Bulgaria 1962 8012946 69.51000 4254.3378
184 Bulgaria 1967 8310226 70.42000 5577.0028
185 Bulgaria 1972 8576200 70.90000 6597.4944
186 Bulgaria 1977 8797022 70.81000 7612.2404
187 Bulgaria 1982 8892098 71.08000 8224.1916
188 Bulgaria 1987 8971958 71.34000 8239.8548
189 Bulgaria 1992 8658506 71.19000 6302.6234
190 Bulgaria 1997 8066057 70.32000 5970.3888
191 Bulgaria 2002 7661799 72.14000 7696.7777
192 Bulgaria 2007 7322858 73.00500 10680.7928
193 Burkina Faso 1952 4469979 31.97500 543.2552
194 Burkina Faso 1957 4713416 34.90600 617.1835
195 Burkina Faso 1962 4919632 37.81400 722.5120
196 Burkina Faso 1967 5127935 40.69700 794.8266
197 Burkina Faso 1972 5433886 43.59100 854.7360
198 Burkina Faso 1977 5889574 46.13700 743.3870
199 Burkina Faso 1982 6634596 48.12200 807.1986
200 Burkina Faso 1987 7586551 49.55700 912.0631
201 Burkina Faso 1992 8878303 50.26000 931.7528
202 Burkina Faso 1997 10352843 50.32400 946.2950
203 Burkina Faso 2002 12251209 50.65000 1037.6452
204 Burkina Faso 2007 14326203 52.29500 1217.0330
205 Burundi 1952 2445618 39.03100 339.2965
206 Burundi 1957 2667518 40.53300 379.5646
207 Burundi 1962 2961915 42.04500 355.2032
208 Burundi 1967 3330989 43.54800 412.9775
209 Burundi 1972 3529983 44.05700 464.0995
210 Burundi 1977 3834415 45.91000 556.1033
211 Burundi 1982 4580410 47.47100 559.6032
212 Burundi 1987 5126023 48.21100 621.8188
213 Burundi 1992 5809236 44.73600 631.6999
214 Burundi 1997 6121610 45.32600 463.1151
215 Burundi 2002 7021078 47.36000 446.4035
216 Burundi 2007 8390505 49.58000 430.0707
217 Cambodia 1952 4693836 39.41700 368.4693
218 Cambodia 1957 5322536 41.36600 434.0383
219 Cambodia 1962 6083619 43.41500 496.9136
220 Cambodia 1967 6960067 45.41500 523.4323
221 Cambodia 1972 7450606 40.31700 421.6240
222 Cambodia 1977 6978607 31.22000 524.9722
223 Cambodia 1982 7272485 50.95700 624.4755
224 Cambodia 1987 8371791 53.91400 683.8956
225 Cambodia 1992 10150094 55.80300 682.3032
226 Cambodia 1997 11782962 56.53400 734.2852
227 Cambodia 2002 12926707 56.75200 896.2260
228 Cambodia 2007 14131858 59.72300 1713.7787
229 Cameroon 1952 5009067 38.52300 1172.6677
230 Cameroon 1957 5359923 40.42800 1313.0481
231 Cameroon 1962 5793633 42.64300 1399.6074
232 Cameroon 1967 6335506 44.79900 1508.4531
233 Cameroon 1972 7021028 47.04900 1684.1465
234 Cameroon 1977 7959865 49.35500 1783.4329
235 Cameroon 1982 9250831 52.96100 2367.9833
236 Cameroon 1987 10780667 54.98500 2602.6642
237 Cameroon 1992 12467171 54.31400 1793.1633
238 Cameroon 1997 14195809 52.19900 1694.3375
239 Cameroon 2002 15929988 49.85600 1934.0114
240 Cameroon 2007 17696293 50.43000 2042.0952
241 Canada 1952 14785584 68.75000 11367.1611
242 Canada 1957 17010154 69.96000 12489.9501
243 Canada 1962 18985849 71.30000 13462.4855
244 Canada 1967 20819767 72.13000 16076.5880
245 Canada 1972 22284500 72.88000 18970.5709
246 Canada 1977 23796400 74.21000 22090.8831
247 Canada 1982 25201900 75.76000 22898.7921
248 Canada 1987 26549700 76.86000 26626.5150
249 Canada 1992 28523502 77.95000 26342.8843
250 Canada 1997 30305843 78.61000 28954.9259
251 Canada 2002 31902268 79.77000 33328.9651
252 Canada 2007 33390141 80.65300 36319.2350
253 Central African Republic 1952 1291695 35.46300 1071.3107
254 Central African Republic 1957 1392284 37.46400 1190.8443
255 Central African Republic 1962 1523478 39.47500 1193.0688
256 Central African Republic 1967 1733638 41.47800 1136.0566
257 Central African Republic 1972 1927260 43.45700 1070.0133
258 Central African Republic 1977 2167533 46.77500 1109.3743
259 Central African Republic 1982 2476971 48.29500 956.7530
260 Central African Republic 1987 2840009 50.48500 844.8764
261 Central African Republic 1992 3265124 49.39600 747.9055
262 Central African Republic 1997 3696513 46.06600 740.5063
263 Central African Republic 2002 4048013 43.30800 738.6906
264 Central African Republic 2007 4369038 44.74100 706.0165
265 Chad 1952 2682462 38.09200 1178.6659
266 Chad 1957 2894855 39.88100 1308.4956
267 Chad 1962 3150417 41.71600 1389.8176
268 Chad 1967 3495967 43.60100 1196.8106
269 Chad 1972 3899068 45.56900 1104.1040
270 Chad 1977 4388260 47.38300 1133.9850
271 Chad 1982 4875118 49.51700 797.9081
272 Chad 1987 5498955 51.05100 952.3861
273 Chad 1992 6429417 51.72400 1058.0643
274 Chad 1997 7562011 51.57300 1004.9614
275 Chad 2002 8835739 50.52500 1156.1819
276 Chad 2007 10238807 50.65100 1704.0637
277 Chile 1952 6377619 54.74500 3939.9788
278 Chile 1957 7048426 56.07400 4315.6227
279 Chile 1962 7961258 57.92400 4519.0943
280 Chile 1967 8858908 60.52300 5106.6543
281 Chile 1972 9717524 63.44100 5494.0244
282 Chile 1977 10599793 67.05200 4756.7638
283 Chile 1982 11487112 70.56500 5095.6657
284 Chile 1987 12463354 72.49200 5547.0638
285 Chile 1992 13572994 74.12600 7596.1260
286 Chile 1997 14599929 75.81600 10118.0532
287 Chile 2002 15497046 77.86000 10778.7838
288 Chile 2007 16284741 78.55300 13171.6388
289 China 1952 556263528 44.00000 400.4486
290 China 1957 637408000 50.54896 575.9870
291 China 1962 665770000 44.50136 487.6740
292 China 1967 754550000 58.38112 612.7057
293 China 1972 862030000 63.11888 676.9001
294 China 1977 943455000 63.96736 741.2375
295 China 1982 1000281000 65.52500 962.4214
296 China 1987 1084035000 67.27400 1378.9040
297 China 1992 1164970000 68.69000 1655.7842
298 China 1997 1230075000 70.42600 2289.2341
299 China 2002 1280400000 72.02800 3119.2809
300 China 2007 1318683096 72.96100 4959.1149
301 Colombia 1952 12350771 50.64300 2144.1151
302 Colombia 1957 14485993 55.11800 2323.8056
303 Colombia 1962 17009885 57.86300 2492.3511
304 Colombia 1967 19764027 59.96300 2678.7298
305 Colombia 1972 22542890 61.62300 3264.6600
306 Colombia 1977 25094412 63.83700 3815.8079
307 Colombia 1982 27764644 66.65300 4397.5757
308 Colombia 1987 30964245 67.76800 4903.2191
309 Colombia 1992 34202721 68.42100 5444.6486
310 Colombia 1997 37657830 70.31300 6117.3617
311 Colombia 2002 41008227 71.68200 5755.2600
312 Colombia 2007 44227550 72.88900 7006.5804
313 Comoros 1952 153936 40.71500 1102.9909
314 Comoros 1957 170928 42.46000 1211.1485
315 Comoros 1962 191689 44.46700 1406.6483
316 Comoros 1967 217378 46.47200 1876.0296
317 Comoros 1972 250027 48.94400 1937.5777
318 Comoros 1977 304739 50.93900 1172.6030
319 Comoros 1982 348643 52.93300 1267.1001
320 Comoros 1987 395114 54.92600 1315.9808
321 Comoros 1992 454429 57.93900 1246.9074
322 Comoros 1997 527982 60.66000 1173.6182
323 Comoros 2002 614382 62.97400 1075.8116
324 Comoros 2007 710960 65.15200 986.1479
325 Congo Dem. Rep. 1952 14100005 39.14300 780.5423
326 Congo Dem. Rep. 1957 15577932 40.65200 905.8602
327 Congo Dem. Rep. 1962 17486434 42.12200 896.3146
328 Congo Dem. Rep. 1967 19941073 44.05600 861.5932
329 Congo Dem. Rep. 1972 23007669 45.98900 904.8961
330 Congo Dem. Rep. 1977 26480870 47.80400 795.7573
331 Congo Dem. Rep. 1982 30646495 47.78400 673.7478
332 Congo Dem. Rep. 1987 35481645 47.41200 672.7748
333 Congo Dem. Rep. 1992 41672143 45.54800 457.7192
334 Congo Dem. Rep. 1997 47798986 42.58700 312.1884
335 Congo Dem. Rep. 2002 55379852 44.96600 241.1659
336 Congo Dem. Rep. 2007 64606759 46.46200 277.5519
337 Congo Rep. 1952 854885 42.11100 2125.6214
338 Congo Rep. 1957 940458 45.05300 2315.0566
339 Congo Rep. 1962 1047924 48.43500 2464.7832
340 Congo Rep. 1967 1179760 52.04000 2677.9396
341 Congo Rep. 1972 1340458 54.90700 3213.1527
342 Congo Rep. 1977 1536769 55.62500 3259.1790
343 Congo Rep. 1982 1774735 56.69500 4879.5075
344 Congo Rep. 1987 2064095 57.47000 4201.1949
345 Congo Rep. 1992 2409073 56.43300 4016.2395
346 Congo Rep. 1997 2800947 52.96200 3484.1644
347 Congo Rep. 2002 3328795 52.97000 3484.0620
348 Congo Rep. 2007 3800610 55.32200 3632.5578
349 Costa Rica 1952 926317 57.20600 2627.0095
350 Costa Rica 1957 1112300 60.02600 2990.0108
351 Costa Rica 1962 1345187 62.84200 3460.9370
352 Costa Rica 1967 1588717 65.42400 4161.7278
353 Costa Rica 1972 1834796 67.84900 5118.1469
354 Costa Rica 1977 2108457 70.75000 5926.8770
355 Costa Rica 1982 2424367 73.45000 5262.7348
356 Costa Rica 1987 2799811 74.75200 5629.9153
357 Costa Rica 1992 3173216 75.71300 6160.4163
358 Costa Rica 1997 3518107 77.26000 6677.0453
359 Costa Rica 2002 3834934 78.12300 7723.4472
360 Costa Rica 2007 4133884 78.78200 9645.0614
361 Cote d'Ivoire 1952 2977019 40.47700 1388.5947
362 Cote d'Ivoire 1957 3300000 42.46900 1500.8959
363 Cote d'Ivoire 1962 3832408 44.93000 1728.8694
364 Cote d'Ivoire 1967 4744870 47.35000 2052.0505
365 Cote d'Ivoire 1972 6071696 49.80100 2378.2011
366 Cote d'Ivoire 1977 7459574 52.37400 2517.7365
367 Cote d'Ivoire 1982 9025951 53.98300 2602.7102
368 Cote d'Ivoire 1987 10761098 54.65500 2156.9561
369 Cote d'Ivoire 1992 12772596 52.04400 1648.0738
370 Cote d'Ivoire 1997 14625967 47.99100 1786.2654
371 Cote d'Ivoire 2002 16252726 46.83200 1648.8008
372 Cote d'Ivoire 2007 18013409 48.32800 1544.7501
373 Croatia 1952 3882229 61.21000 3119.2365
374 Croatia 1957 3991242 64.77000 4338.2316
375 Croatia 1962 4076557 67.13000 5477.8900
376 Croatia 1967 4174366 68.50000 6960.2979
377 Croatia 1972 4225310 69.61000 9164.0901
378 Croatia 1977 4318673 70.64000 11305.3852
379 Croatia 1982 4413368 70.46000 13221.8218
380 Croatia 1987 4484310 71.52000 13822.5839
381 Croatia 1992 4494013 72.52700 8447.7949
382 Croatia 1997 4444595 73.68000 9875.6045
383 Croatia 2002 4481020 74.87600 11628.3890
384 Croatia 2007 4493312 75.74800 14619.2227
385 Cuba 1952 6007797 59.42100 5586.5388
386 Cuba 1957 6640752 62.32500 6092.1744
387 Cuba 1962 7254373 65.24600 5180.7559
388 Cuba 1967 8139332 68.29000 5690.2680
389 Cuba 1972 8831348 70.72300 5305.4453
390 Cuba 1977 9537988 72.64900 6380.4950
391 Cuba 1982 9789224 73.71700 7316.9181
392 Cuba 1987 10239839 74.17400 7532.9248
393 Cuba 1992 10723260 74.41400 5592.8440
394 Cuba 1997 10983007 76.15100 5431.9904
395 Cuba 2002 11226999 77.15800 6340.6467
396 Cuba 2007 11416987 78.27300 8948.1029
397 Czech Republic 1952 9125183 66.87000 6876.1403
398 Czech Republic 1957 9513758 69.03000 8256.3439
399 Czech Republic 1962 9620282 69.90000 10136.8671
400 Czech Republic 1967 9835109 70.38000 11399.4449
401 Czech Republic 1972 9862158 70.29000 13108.4536
402 Czech Republic 1977 10161915 70.71000 14800.1606
403 Czech Republic 1982 10303704 70.96000 15377.2285
404 Czech Republic 1987 10311597 71.58000 16310.4434
405 Czech Republic 1992 10315702 72.40000 14297.0212
406 Czech Republic 1997 10300707 74.01000 16048.5142
407 Czech Republic 2002 10256295 75.51000 17596.2102
408 Czech Republic 2007 10228744 76.48600 22833.3085
409 Denmark 1952 4334000 70.78000 9692.3852
410 Denmark 1957 4487831 71.81000 11099.6593
411 Denmark 1962 4646899 72.35000 13583.3135
412 Denmark 1967 4838800 72.96000 15937.2112
413 Denmark 1972 4991596 73.47000 18866.2072
414 Denmark 1977 5088419 74.69000 20422.9015
415 Denmark 1982 5117810 74.63000 21688.0405
416 Denmark 1987 5127024 74.80000 25116.1758
417 Denmark 1992 5171393 75.33000 26406.7399
418 Denmark 1997 5283663 76.11000 29804.3457
419 Denmark 2002 5374693 77.18000 32166.5001
420 Denmark 2007 5468120 78.33200 35278.4187
421 Djibouti 1952 63149 34.81200 2669.5295
422 Djibouti 1957 71851 37.32800 2864.9691
423 Djibouti 1962 89898 39.69300 3020.9893
424 Djibouti 1967 127617 42.07400 3020.0505
425 Djibouti 1972 178848 44.36600 3694.2124
426 Djibouti 1977 228694 46.51900 3081.7610
427 Djibouti 1982 305991 48.81200 2879.4681
428 Djibouti 1987 311025 50.04000 2880.1026
429 Djibouti 1992 384156 51.60400 2377.1562
430 Djibouti 1997 417908 53.15700 1895.0170
431 Djibouti 2002 447416 53.37300 1908.2609
432 Djibouti 2007 496374 54.79100 2082.4816
433 Dominican Republic 1952 2491346 45.92800 1397.7171
434 Dominican Republic 1957 2923186 49.82800 1544.4030
435 Dominican Republic 1962 3453434 53.45900 1662.1374
436 Dominican Republic 1967 4049146 56.75100 1653.7230
437 Dominican Republic 1972 4671329 59.63100 2189.8745
438 Dominican Republic 1977 5302800 61.78800 2681.9889
439 Dominican Republic 1982 5968349 63.72700 2861.0924
440 Dominican Republic 1987 6655297 66.04600 2899.8422
441 Dominican Republic 1992 7351181 68.45700 3044.2142
442 Dominican Republic 1997 7992357 69.95700 3614.1013
443 Dominican Republic 2002 8650322 70.84700 4563.8082
444 Dominican Republic 2007 9319622 72.23500 6025.3748
445 Ecuador 1952 3548753 48.35700 3522.1107
446 Ecuador 1957 4058385 51.35600 3780.5467
447 Ecuador 1962 4681707 54.64000 4086.1141
448 Ecuador 1967 5432424 56.67800 4579.0742
449 Ecuador 1972 6298651 58.79600 5280.9947
450 Ecuador 1977 7278866 61.31000 6679.6233
451 Ecuador 1982 8365850 64.34200 7213.7913
452 Ecuador 1987 9545158 67.23100 6481.7770
453 Ecuador 1992 10748394 69.61300 7103.7026
454 Ecuador 1997 11911819 72.31200 7429.4559
455 Ecuador 2002 12921234 74.17300 5773.0445
456 Ecuador 2007 13755680 74.99400 6873.2623
457 Egypt 1952 22223309 41.89300 1418.8224
458 Egypt 1957 25009741 44.44400 1458.9153
459 Egypt 1962 28173309 46.99200 1693.3359
460 Egypt 1967 31681188 49.29300 1814.8807
461 Egypt 1972 34807417 51.13700 2024.0081
462 Egypt 1977 38783863 53.31900 2785.4936
463 Egypt 1982 45681811 56.00600 3503.7296
464 Egypt 1987 52799062 59.79700 3885.4607
465 Egypt 1992 59402198 63.67400 3794.7552
466 Egypt 1997 66134291 67.21700 4173.1818
467 Egypt 2002 73312559 69.80600 4754.6044
468 Egypt 2007 80264543 71.33800 5581.1810
469 El Salvador 1952 2042865 45.26200 3048.3029
470 El Salvador 1957 2355805 48.57000 3421.5232
471 El Salvador 1962 2747687 52.30700 3776.8036
472 El Salvador 1967 3232927 55.85500 4358.5954
473 El Salvador 1972 3790903 58.20700 4520.2460
474 El Salvador 1977 4282586 56.69600 5138.9224
475 El Salvador 1982 4474873 56.60400 4098.3442
476 El Salvador 1987 4842194 63.15400 4140.4421
477 El Salvador 1992 5274649 66.79800 4444.2317
478 El Salvador 1997 5783439 69.53500 5154.8255
479 El Salvador 2002 6353681 70.73400 5351.5687
480 El Salvador 2007 6939688 71.87800 5728.3535
481 Equatorial Guinea 1952 216964 34.48200 375.6431
482 Equatorial Guinea 1957 232922 35.98300 426.0964
483 Equatorial Guinea 1962 249220 37.48500 582.8420
484 Equatorial Guinea 1967 259864 38.98700 915.5960
485 Equatorial Guinea 1972 277603 40.51600 672.4123
486 Equatorial Guinea 1977 192675 42.02400 958.5668
487 Equatorial Guinea 1982 285483 43.66200 927.8253
488 Equatorial Guinea 1987 341244 45.66400 966.8968
489 Equatorial Guinea 1992 387838 47.54500 1132.0550
490 Equatorial Guinea 1997 439971 48.24500 2814.4808
491 Equatorial Guinea 2002 495627 49.34800 7703.4959
492 Equatorial Guinea 2007 551201 51.57900 12154.0897
493 Eritrea 1952 1438760 35.92800 328.9406
494 Eritrea 1957 1542611 38.04700 344.1619
495 Eritrea 1962 1666618 40.15800 380.9958
496 Eritrea 1967 1820319 42.18900 468.7950
497 Eritrea 1972 2260187 44.14200 514.3242
498 Eritrea 1977 2512642 44.53500 505.7538
499 Eritrea 1982 2637297 43.89000 524.8758
500 Eritrea 1987 2915959 46.45300 521.1341
501 Eritrea 1992 3668440 49.99100 582.8585
502 Eritrea 1997 4058319 53.37800 913.4708
503 Eritrea 2002 4414865 55.24000 765.3500
504 Eritrea 2007 4906585 58.04000 641.3695
505 Ethiopia 1952 20860941 34.07800 362.1463
506 Ethiopia 1957 22815614 36.66700 378.9042
507 Ethiopia 1962 25145372 40.05900 419.4564
508 Ethiopia 1967 27860297 42.11500 516.1186
509 Ethiopia 1972 30770372 43.51500 566.2439
510 Ethiopia 1977 34617799 44.51000 556.8084
511 Ethiopia 1982 38111756 44.91600 577.8607
512 Ethiopia 1987 42999530 46.68400 573.7413
513 Ethiopia 1992 52088559 48.09100 421.3535
514 Ethiopia 1997 59861301 49.40200 515.8894
515 Ethiopia 2002 67946797 50.72500 530.0535
516 Ethiopia 2007 76511887 52.94700 690.8056
517 Finland 1952 4090500 66.55000 6424.5191
518 Finland 1957 4324000 67.49000 7545.4154
519 Finland 1962 4491443 68.75000 9371.8426
520 Finland 1967 4605744 69.83000 10921.6363
521 Finland 1972 4639657 70.87000 14358.8759
522 Finland 1977 4738902 72.52000 15605.4228
523 Finland 1982 4826933 74.55000 18533.1576
524 Finland 1987 4931729 74.83000 21141.0122
525 Finland 1992 5041039 75.70000 20647.1650
526 Finland 1997 5134406 77.13000 23723.9502
527 Finland 2002 5193039 78.37000 28204.5906
528 Finland 2007 5238460 79.31300 33207.0844
529 France 1952 42459667 67.41000 7029.8093
530 France 1957 44310863 68.93000 8662.8349
531 France 1962 47124000 70.51000 10560.4855
532 France 1967 49569000 71.55000 12999.9177
533 France 1972 51732000 72.38000 16107.1917
534 France 1977 53165019 73.83000 18292.6351
535 France 1982 54433565 74.89000 20293.8975
536 France 1987 55630100 76.34000 22066.4421
537 France 1992 57374179 77.46000 24703.7961
538 France 1997 58623428 78.64000 25889.7849
539 France 2002 59925035 79.59000 28926.0323
540 France 2007 61083916 80.65700 30470.0167
541 Gabon 1952 420702 37.00300 4293.4765
542 Gabon 1957 434904 38.99900 4976.1981
543 Gabon 1962 455661 40.48900 6631.4592
544 Gabon 1967 489004 44.59800 8358.7620
545 Gabon 1972 537977 48.69000 11401.9484
546 Gabon 1977 706367 52.79000 21745.5733
547 Gabon 1982 753874 56.56400 15113.3619
548 Gabon 1987 880397 60.19000 11864.4084
549 Gabon 1992 985739 61.36600 13522.1575
550 Gabon 1997 1126189 60.46100 14722.8419
551 Gabon 2002 1299304 56.76100 12521.7139
552 Gabon 2007 1454867 56.73500 13206.4845
553 Gambia 1952 284320 30.00000 485.2307
554 Gambia 1957 323150 32.06500 520.9267
555 Gambia 1962 374020 33.89600 599.6503
556 Gambia 1967 439593 35.85700 734.7829
557 Gambia 1972 517101 38.30800 756.0868
558 Gambia 1977 608274 41.84200 884.7553
559 Gambia 1982 715523 45.58000 835.8096
560 Gambia 1987 848406 49.26500 611.6589
561 Gambia 1992 1025384 52.64400 665.6244
562 Gambia 1997 1235767 55.86100 653.7302
563 Gambia 2002 1457766 58.04100 660.5856
564 Gambia 2007 1688359 59.44800 752.7497
565 Germany 1952 69145952 67.50000 7144.1144
566 Germany 1957 71019069 69.10000 10187.8267
567 Germany 1962 73739117 70.30000 12902.4629
568 Germany 1967 76368453 70.80000 14745.6256
569 Germany 1972 78717088 71.00000 18016.1803
570 Germany 1977 78160773 72.50000 20512.9212
571 Germany 1982 78335266 73.80000 22031.5327
572 Germany 1987 77718298 74.84700 24639.1857
573 Germany 1992 80597764 76.07000 26505.3032
574 Germany 1997 82011073 77.34000 27788.8842
575 Germany 2002 82350671 78.67000 30035.8020
576 Germany 2007 82400996 79.40600 32170.3744
577 Ghana 1952 5581001 43.14900 911.2989
578 Ghana 1957 6391288 44.77900 1043.5615
579 Ghana 1962 7355248 46.45200 1190.0411
580 Ghana 1967 8490213 48.07200 1125.6972
581 Ghana 1972 9354120 49.87500 1178.2237
582 Ghana 1977 10538093 51.75600 993.2240
583 Ghana 1982 11400338 53.74400 876.0326
584 Ghana 1987 14168101 55.72900 847.0061
585 Ghana 1992 16278738 57.50100 925.0602
586 Ghana 1997 18418288 58.55600 1005.2458
587 Ghana 2002 20550751 58.45300 1111.9846
588 Ghana 2007 22873338 60.02200 1327.6089
589 Greece 1952 7733250 65.86000 3530.6901
590 Greece 1957 8096218 67.86000 4916.2999
591 Greece 1962 8448233 69.51000 6017.1907
592 Greece 1967 8716441 71.00000 8513.0970
593 Greece 1972 8888628 72.34000 12724.8296
594 Greece 1977 9308479 73.68000 14195.5243
595 Greece 1982 9786480 75.24000 15268.4209
596 Greece 1987 9974490 76.67000 16120.5284
597 Greece 1992 10325429 77.03000 17541.4963
598 Greece 1997 10502372 77.86900 18747.6981
599 Greece 2002 10603863 78.25600 22514.2548
600 Greece 2007 10706290 79.48300 27538.4119
601 Guatemala 1952 3146381 42.02300 2428.2378
602 Guatemala 1957 3640876 44.14200 2617.1560
603 Guatemala 1962 4208858 46.95400 2750.3644
604 Guatemala 1967 4690773 50.01600 3242.5311
605 Guatemala 1972 5149581 53.73800 4031.4083
606 Guatemala 1977 5703430 56.02900 4879.9927
607 Guatemala 1982 6395630 58.13700 4820.4948
608 Guatemala 1987 7326406 60.78200 4246.4860
609 Guatemala 1992 8486949 63.37300 4439.4508
610 Guatemala 1997 9803875 66.32200 4684.3138
611 Guatemala 2002 11178650 68.97800 4858.3475
612 Guatemala 2007 12572928 70.25900 5186.0500
613 Guinea 1952 2664249 33.60900 510.1965
614 Guinea 1957 2876726 34.55800 576.2670
615 Guinea 1962 3140003 35.75300 686.3737
616 Guinea 1967 3451418 37.19700 708.7595
617 Guinea 1972 3811387 38.84200 741.6662
618 Guinea 1977 4227026 40.76200 874.6859
619 Guinea 1982 4710497 42.89100 857.2504
620 Guinea 1987 5650262 45.55200 805.5725
621 Guinea 1992 6990574 48.57600 794.3484
622 Guinea 1997 8048834 51.45500 869.4498
623 Guinea 2002 8807818 53.67600 945.5836
624 Guinea 2007 9947814 56.00700 942.6542
625 Guinea-Bissau 1952 580653 32.50000 299.8503
626 Guinea-Bissau 1957 601095 33.48900 431.7905
627 Guinea-Bissau 1962 627820 34.48800 522.0344
628 Guinea-Bissau 1967 601287 35.49200 715.5806
629 Guinea-Bissau 1972 625361 36.48600 820.2246
630 Guinea-Bissau 1977 745228 37.46500 764.7260
631 Guinea-Bissau 1982 825987 39.32700 838.1240
632 Guinea-Bissau 1987 927524 41.24500 736.4154
633 Guinea-Bissau 1992 1050938 43.26600 745.5399
634 Guinea-Bissau 1997 1193708 44.87300 796.6645
635 Guinea-Bissau 2002 1332459 45.50400 575.7047
636 Guinea-Bissau 2007 1472041 46.38800 579.2317
637 Haiti 1952 3201488 37.57900 1840.3669
638 Haiti 1957 3507701 40.69600 1726.8879
639 Haiti 1962 3880130 43.59000 1796.5890
640 Haiti 1967 4318137 46.24300 1452.0577
641 Haiti 1972 4698301 48.04200 1654.4569
642 Haiti 1977 4908554 49.92300 1874.2989
643 Haiti 1982 5198399 51.46100 2011.1595
644 Haiti 1987 5756203 53.63600 1823.0160
645 Haiti 1992 6326682 55.08900 1456.3095
646 Haiti 1997 6913545 56.67100 1341.7269
647 Haiti 2002 7607651 58.13700 1270.3649
648 Haiti 2007 8502814 60.91600 1201.6372
649 Honduras 1952 1517453 41.91200 2194.9262
650 Honduras 1957 1770390 44.66500 2220.4877
651 Honduras 1962 2090162 48.04100 2291.1568
652 Honduras 1967 2500689 50.92400 2538.2694
653 Honduras 1972 2965146 53.88400 2529.8423
654 Honduras 1977 3055235 57.40200 3203.2081
655 Honduras 1982 3669448 60.90900 3121.7608
656 Honduras 1987 4372203 64.49200 3023.0967
657 Honduras 1992 5077347 66.39900 3081.6946
658 Honduras 1997 5867957 67.65900 3160.4549
659 Honduras 2002 6677328 68.56500 3099.7287
660 Honduras 2007 7483763 70.19800 3548.3308
661 Hong Kong China 1952 2125900 60.96000 3054.4212
662 Hong Kong China 1957 2736300 64.75000 3629.0765
663 Hong Kong China 1962 3305200 67.65000 4692.6483
664 Hong Kong China 1967 3722800 70.00000 6197.9628
665 Hong Kong China 1972 4115700 72.00000 8315.9281
666 Hong Kong China 1977 4583700 73.60000 11186.1413
667 Hong Kong China 1982 5264500 75.45000 14560.5305
668 Hong Kong China 1987 5584510 76.20000 20038.4727
669 Hong Kong China 1992 5829696 77.60100 24757.6030
670 Hong Kong China 1997 6495918 80.00000 28377.6322
671 Hong Kong China 2002 6762476 81.49500 30209.0152
672 Hong Kong China 2007 6980412 82.20800 39724.9787
673 Hungary 1952 9504000 64.03000 5263.6738
674 Hungary 1957 9839000 66.41000 6040.1800
675 Hungary 1962 10063000 67.96000 7550.3599
676 Hungary 1967 10223422 69.50000 9326.6447
677 Hungary 1972 10394091 69.76000 10168.6561
678 Hungary 1977 10637171 69.95000 11674.8374
679 Hungary 1982 10705535 69.39000 12545.9907
680 Hungary 1987 10612740 69.58000 12986.4800
681 Hungary 1992 10348684 69.17000 10535.6285
682 Hungary 1997 10244684 71.04000 11712.7768
683 Hungary 2002 10083313 72.59000 14843.9356
684 Hungary 2007 9956108 73.33800 18008.9444
685 Iceland 1952 147962 72.49000 7267.6884
686 Iceland 1957 165110 73.47000 9244.0014
687 Iceland 1962 182053 73.68000 10350.1591
688 Iceland 1967 198676 73.73000 13319.8957
689 Iceland 1972 209275 74.46000 15798.0636
690 Iceland 1977 221823 76.11000 19654.9625
691 Iceland 1982 233997 76.99000 23269.6075
692 Iceland 1987 244676 77.23000 26923.2063
693 Iceland 1992 259012 78.77000 25144.3920
694 Iceland 1997 271192 78.95000 28061.0997
695 Iceland 2002 288030 80.50000 31163.2020
696 Iceland 2007 301931 81.75700 36180.7892
697 India 1952 372000000 37.37300 546.5657
698 India 1957 409000000 40.24900 590.0620
699 India 1962 454000000 43.60500 658.3472
700 India 1967 506000000 47.19300 700.7706
701 India 1972 567000000 50.65100 724.0325
702 India 1977 634000000 54.20800 813.3373
703 India 1982 708000000 56.59600 855.7235
704 India 1987 788000000 58.55300 976.5127
705 India 1992 872000000 60.22300 1164.4068
706 India 1997 959000000 61.76500 1458.8174
707 India 2002 1034172547 62.87900 1746.7695
708 India 2007 1110396331 64.69800 2452.2104
709 Indonesia 1952 82052000 37.46800 749.6817
710 Indonesia 1957 90124000 39.91800 858.9003
711 Indonesia 1962 99028000 42.51800 849.2898
712 Indonesia 1967 109343000 45.96400 762.4318
713 Indonesia 1972 121282000 49.20300 1111.1079
714 Indonesia 1977 136725000 52.70200 1382.7021
715 Indonesia 1982 153343000 56.15900 1516.8730
716 Indonesia 1987 169276000 60.13700 1748.3570
717 Indonesia 1992 184816000 62.68100 2383.1409
718 Indonesia 1997 199278000 66.04100 3119.3356
719 Indonesia 2002 211060000 68.58800 2873.9129
720 Indonesia 2007 223547000 70.65000 3540.6516
721 Iran 1952 17272000 44.86900 3035.3260
722 Iran 1957 19792000 47.18100 3290.2576
723 Iran 1962 22874000 49.32500 4187.3298
724 Iran 1967 26538000 52.46900 5906.7318
725 Iran 1972 30614000 55.23400 9613.8186
726 Iran 1977 35480679 57.70200 11888.5951
727 Iran 1982 43072751 59.62000 7608.3346
728 Iran 1987 51889696 63.04000 6642.8814
729 Iran 1992 60397973 65.74200 7235.6532
730 Iran 1997 63327987 68.04200 8263.5903
731 Iran 2002 66907826 69.45100 9240.7620
732 Iran 2007 69453570 70.96400 11605.7145
733 Iraq 1952 5441766 45.32000 4129.7661
734 Iraq 1957 6248643 48.43700 6229.3336
735 Iraq 1962 7240260 51.45700 8341.7378
736 Iraq 1967 8519282 54.45900 8931.4598
737 Iraq 1972 10061506 56.95000 9576.0376
738 Iraq 1977 11882916 60.41300 14688.2351
739 Iraq 1982 14173318 62.03800 14517.9071
740 Iraq 1987 16543189 65.04400 11643.5727
741 Iraq 1992 17861905 59.46100 3745.6407
742 Iraq 1997 20775703 58.81100 3076.2398
743 Iraq 2002 24001816 57.04600 4390.7173
744 Iraq 2007 27499638 59.54500 4471.0619
745 Ireland 1952 2952156 66.91000 5210.2803
746 Ireland 1957 2878220 68.90000 5599.0779
747 Ireland 1962 2830000 70.29000 6631.5973
748 Ireland 1967 2900100 71.08000 7655.5690
749 Ireland 1972 3024400 71.28000 9530.7729
750 Ireland 1977 3271900 72.03000 11150.9811
751 Ireland 1982 3480000 73.10000 12618.3214
752 Ireland 1987 3539900 74.36000 13872.8665
753 Ireland 1992 3557761 75.46700 17558.8155
754 Ireland 1997 3667233 76.12200 24521.9471
755 Ireland 2002 3879155 77.78300 34077.0494
756 Ireland 2007 4109086 78.88500 40675.9964
757 Israel 1952 1620914 65.39000 4086.5221
758 Israel 1957 1944401 67.84000 5385.2785
759 Israel 1962 2310904 69.39000 7105.6307
760 Israel 1967 2693585 70.75000 8393.7414
761 Israel 1972 3095893 71.63000 12786.9322
762 Israel 1977 3495918 73.06000 13306.6192
763 Israel 1982 3858421 74.45000 15367.0292
764 Israel 1987 4203148 75.60000 17122.4799
765 Israel 1992 4936550 76.93000 18051.5225
766 Israel 1997 5531387 78.26900 20896.6092
767 Israel 2002 6029529 79.69600 21905.5951
768 Israel 2007 6426679 80.74500 25523.2771
769 Italy 1952 47666000 65.94000 4931.4042
770 Italy 1957 49182000 67.81000 6248.6562
771 Italy 1962 50843200 69.24000 8243.5823
772 Italy 1967 52667100 71.06000 10022.4013
773 Italy 1972 54365564 72.19000 12269.2738
774 Italy 1977 56059245 73.48000 14255.9847
775 Italy 1982 56535636 74.98000 16537.4835
776 Italy 1987 56729703 76.42000 19207.2348
777 Italy 1992 56840847 77.44000 22013.6449
778 Italy 1997 57479469 78.82000 24675.0245
779 Italy 2002 57926999 80.24000 27968.0982
780 Italy 2007 58147733 80.54600 28569.7197
781 Jamaica 1952 1426095 58.53000 2898.5309
782 Jamaica 1957 1535090 62.61000 4756.5258
783 Jamaica 1962 1665128 65.61000 5246.1075
784 Jamaica 1967 1861096 67.51000 6124.7035
785 Jamaica 1972 1997616 69.00000 7433.8893
786 Jamaica 1977 2156814 70.11000 6650.1956
787 Jamaica 1982 2298309 71.21000 6068.0513
788 Jamaica 1987 2326606 71.77000 6351.2375
789 Jamaica 1992 2378618 71.76600 7404.9237
790 Jamaica 1997 2531311 72.26200 7121.9247
791 Jamaica 2002 2664659 72.04700 6994.7749
792 Jamaica 2007 2780132 72.56700 7320.8803
793 Japan 1952 86459025 63.03000 3216.9563
794 Japan 1957 91563009 65.50000 4317.6944
795 Japan 1962 95831757 68.73000 6576.6495
796 Japan 1967 100825279 71.43000 9847.7886
797 Japan 1972 107188273 73.42000 14778.7864
798 Japan 1977 113872473 75.38000 16610.3770
799 Japan 1982 118454974 77.11000 19384.1057
800 Japan 1987 122091325 78.67000 22375.9419
801 Japan 1992 124329269 79.36000 26824.8951
802 Japan 1997 125956499 80.69000 28816.5850
803 Japan 2002 127065841 82.00000 28604.5919
804 Japan 2007 127467972 82.60300 31656.0681
805 Jordan 1952 607914 43.15800 1546.9078
806 Jordan 1957 746559 45.66900 1886.0806
807 Jordan 1962 933559 48.12600 2348.0092
808 Jordan 1967 1255058 51.62900 2741.7963
809 Jordan 1972 1613551 56.52800 2110.8563
810 Jordan 1977 1937652 61.13400 2852.3516
811 Jordan 1982 2347031 63.73900 4161.4160
812 Jordan 1987 2820042 65.86900 4448.6799
813 Jordan 1992 3867409 68.01500 3431.5936
814 Jordan 1997 4526235 69.77200 3645.3796
815 Jordan 2002 5307470 71.26300 3844.9172
816 Jordan 2007 6053193 72.53500 4519.4612
817 Kenya 1952 6464046 42.27000 853.5409
818 Kenya 1957 7454779 44.68600 944.4383
819 Kenya 1962 8678557 47.94900 896.9664
820 Kenya 1967 10191512 50.65400 1056.7365
821 Kenya 1972 12044785 53.55900 1222.3600
822 Kenya 1977 14500404 56.15500 1267.6132
823 Kenya 1982 17661452 58.76600 1348.2258
824 Kenya 1987 21198082 59.33900 1361.9369
825 Kenya 1992 25020539 59.28500 1341.9217
826 Kenya 1997 28263827 54.40700 1360.4850
827 Kenya 2002 31386842 50.99200 1287.5147
828 Kenya 2007 35610177 54.11000 1463.2493
829 Korea Dem. Rep. 1952 8865488 50.05600 1088.2778
830 Korea Dem. Rep. 1957 9411381 54.08100 1571.1347
831 Korea Dem. Rep. 1962 10917494 56.65600 1621.6936
832 Korea Dem. Rep. 1967 12617009 59.94200 2143.5406
833 Korea Dem. Rep. 1972 14781241 63.98300 3701.6215
834 Korea Dem. Rep. 1977 16325320 67.15900 4106.3012
835 Korea Dem. Rep. 1982 17647518 69.10000 4106.5253
836 Korea Dem. Rep. 1987 19067554 70.64700 4106.4923
837 Korea Dem. Rep. 1992 20711375 69.97800 3726.0635
838 Korea Dem. Rep. 1997 21585105 67.72700 1690.7568
839 Korea Dem. Rep. 2002 22215365 66.66200 1646.7582
840 Korea Dem. Rep. 2007 23301725 67.29700 1593.0655
841 Korea Rep. 1952 20947571 47.45300 1030.5922
842 Korea Rep. 1957 22611552 52.68100 1487.5935
843 Korea Rep. 1962 26420307 55.29200 1536.3444
844 Korea Rep. 1967 30131000 57.71600 2029.2281
845 Korea Rep. 1972 33505000 62.61200 3030.8767
846 Korea Rep. 1977 36436000 64.76600 4657.2210
847 Korea Rep. 1982 39326000 67.12300 5622.9425
848 Korea Rep. 1987 41622000 69.81000 8533.0888
849 Korea Rep. 1992 43805450 72.24400 12104.2787
850 Korea Rep. 1997 46173816 74.64700 15993.5280
851 Korea Rep. 2002 47969150 77.04500 19233.9882
852 Korea Rep. 2007 49044790 78.62300 23348.1397
853 Kuwait 1952 160000 55.56500 108382.3529
854 Kuwait 1957 212846 58.03300 113523.1329
855 Kuwait 1962 358266 60.47000 95458.1118
856 Kuwait 1967 575003 64.62400 80894.8833
857 Kuwait 1972 841934 67.71200 109347.8670
858 Kuwait 1977 1140357 69.34300 59265.4771
859 Kuwait 1982 1497494 71.30900 31354.0357
860 Kuwait 1987 1891487 74.17400 28118.4300
861 Kuwait 1992 1418095 75.19000 34932.9196
862 Kuwait 1997 1765345 76.15600 40300.6200
863 Kuwait 2002 2111561 76.90400 35110.1057
864 Kuwait 2007 2505559 77.58800 47306.9898
865 Lebanon 1952 1439529 55.92800 4834.8041
866 Lebanon 1957 1647412 59.48900 6089.7869
867 Lebanon 1962 1886848 62.09400 5714.5606
868 Lebanon 1967 2186894 63.87000 6006.9830
869 Lebanon 1972 2680018 65.42100 7486.3843
870 Lebanon 1977 3115787 66.09900 8659.6968
871 Lebanon 1982 3086876 66.98300 7640.5195
872 Lebanon 1987 3089353 67.92600 5377.0913
873 Lebanon 1992 3219994 69.29200 6890.8069
874 Lebanon 1997 3430388 70.26500 8754.9639
875 Lebanon 2002 3677780 71.02800 9313.9388
876 Lebanon 2007 3921278 71.99300 10461.0587
877 Lesotho 1952 748747 42.13800 298.8462
878 Lesotho 1957 813338 45.04700 335.9971
879 Lesotho 1962 893143 47.74700 411.8006
880 Lesotho 1967 996380 48.49200 498.6390
881 Lesotho 1972 1116779 49.76700 496.5816
882 Lesotho 1977 1251524 52.20800 745.3695
883 Lesotho 1982 1411807 55.07800 797.2631
884 Lesotho 1987 1599200 57.18000 773.9932
885 Lesotho 1992 1803195 59.68500 977.4863
886 Lesotho 1997 1982823 55.55800 1186.1480
887 Lesotho 2002 2046772 44.59300 1275.1846
888 Lesotho 2007 2012649 42.59200 1569.3314
889 Liberia 1952 863308 38.48000 575.5730
890 Liberia 1957 975950 39.48600 620.9700
891 Liberia 1962 1112796 40.50200 634.1952
892 Liberia 1967 1279406 41.53600 713.6036
893 Liberia 1972 1482628 42.61400 803.0055
894 Liberia 1977 1703617 43.76400 640.3224
895 Liberia 1982 1956875 44.85200 572.1996
896 Liberia 1987 2269414 46.02700 506.1139
897 Liberia 1992 1912974 40.80200 636.6229
898 Liberia 1997 2200725 42.22100 609.1740
899 Liberia 2002 2814651 43.75300 531.4824
900 Liberia 2007 3193942 45.67800 414.5073
901 Libya 1952 1019729 42.72300 2387.5481
902 Libya 1957 1201578 45.28900 3448.2844
903 Libya 1962 1441863 47.80800 6757.0308
904 Libya 1967 1759224 50.22700 18772.7517
905 Libya 1972 2183877 52.77300 21011.4972
906 Libya 1977 2721783 57.44200 21951.2118
907 Libya 1982 3344074 62.15500 17364.2754
908 Libya 1987 3799845 66.23400 11770.5898
909 Libya 1992 4364501 68.75500 9640.1385
910 Libya 1997 4759670 71.55500 9467.4461
911 Libya 2002 5368585 72.73700 9534.6775
912 Libya 2007 6036914 73.95200 12057.4993
913 Madagascar 1952 4762912 36.68100 1443.0117
914 Madagascar 1957 5181679 38.86500 1589.2027
915 Madagascar 1962 5703324 40.84800 1643.3871
916 Madagascar 1967 6334556 42.88100 1634.0473
917 Madagascar 1972 7082430 44.85100 1748.5630
918 Madagascar 1977 8007166 46.88100 1544.2286
919 Madagascar 1982 9171477 48.96900 1302.8787
920 Madagascar 1987 10568642 49.35000 1155.4419
921 Madagascar 1992 12210395 52.21400 1040.6762
922 Madagascar 1997 14165114 54.97800 986.2959
923 Madagascar 2002 16473477 57.28600 894.6371
924 Madagascar 2007 19167654 59.44300 1044.7701
925 Malawi 1952 2917802 36.25600 369.1651
926 Malawi 1957 3221238 37.20700 416.3698
927 Malawi 1962 3628608 38.41000 427.9011
928 Malawi 1967 4147252 39.48700 495.5148
929 Malawi 1972 4730997 41.76600 584.6220
930 Malawi 1977 5637246 43.76700 663.2237
931 Malawi 1982 6502825 45.64200 632.8039
932 Malawi 1987 7824747 47.45700 635.5174
933 Malawi 1992 10014249 49.42000 563.2000
934 Malawi 1997 10419991 47.49500 692.2758
935 Malawi 2002 11824495 45.00900 665.4231
936 Malawi 2007 13327079 48.30300 759.3499
937 Malaysia 1952 6748378 48.46300 1831.1329
938 Malaysia 1957 7739235 52.10200 1810.0670
939 Malaysia 1962 8906385 55.73700 2036.8849
940 Malaysia 1967 10154878 59.37100 2277.7424
941 Malaysia 1972 11441462 63.01000 2849.0948
942 Malaysia 1977 12845381 65.25600 3827.9216
943 Malaysia 1982 14441916 68.00000 4920.3560
944 Malaysia 1987 16331785 69.50000 5249.8027
945 Malaysia 1992 18319502 70.69300 7277.9128
946 Malaysia 1997 20476091 71.93800 10132.9096
947 Malaysia 2002 22662365 73.04400 10206.9779
948 Malaysia 2007 24821286 74.24100 12451.6558
949 Mali 1952 3838168 33.68500 452.3370
950 Mali 1957 4241884 35.30700 490.3822
951 Mali 1962 4690372 36.93600 496.1743
952 Mali 1967 5212416 38.48700 545.0099
953 Mali 1972 5828158 39.97700 581.3689
954 Mali 1977 6491649 41.71400 686.3953
955 Mali 1982 6998256 43.91600 618.0141
956 Mali 1987 7634008 46.36400 684.1716
957 Mali 1992 8416215 48.38800 739.0144
958 Mali 1997 9384984 49.90300 790.2580
959 Mali 2002 10580176 51.81800 951.4098
960 Mali 2007 12031795 54.46700 1042.5816
961 Mauritania 1952 1022556 40.54300 743.1159
962 Mauritania 1957 1076852 42.33800 846.1203
963 Mauritania 1962 1146757 44.24800 1055.8960
964 Mauritania 1967 1230542 46.28900 1421.1452
965 Mauritania 1972 1332786 48.43700 1586.8518
966 Mauritania 1977 1456688 50.85200 1497.4922
967 Mauritania 1982 1622136 53.59900 1481.1502
968 Mauritania 1987 1841240 56.14500 1421.6036
969 Mauritania 1992 2119465 58.33300 1361.3698
970 Mauritania 1997 2444741 60.43000 1483.1361
971 Mauritania 2002 2828858 62.24700 1579.0195
972 Mauritania 2007 3270065 64.16400 1803.1515
973 Mauritius 1952 516556 50.98600 1967.9557
974 Mauritius 1957 609816 58.08900 2034.0380
975 Mauritius 1962 701016 60.24600 2529.0675
976 Mauritius 1967 789309 61.55700 2475.3876
977 Mauritius 1972 851334 62.94400 2575.4842
978 Mauritius 1977 913025 64.93000 3710.9830
979 Mauritius 1982 992040 66.71100 3688.0377
980 Mauritius 1987 1042663 68.74000 4783.5869
981 Mauritius 1992 1096202 69.74500 6058.2538
982 Mauritius 1997 1149818 70.73600 7425.7053
983 Mauritius 2002 1200206 71.95400 9021.8159
984 Mauritius 2007 1250882 72.80100 10956.9911
985 Mexico 1952 30144317 50.78900 3478.1255
986 Mexico 1957 35015548 55.19000 4131.5466
987 Mexico 1962 41121485 58.29900 4581.6094
988 Mexico 1967 47995559 60.11000 5754.7339
989 Mexico 1972 55984294 62.36100 6809.4067
990 Mexico 1977 63759976 65.03200 7674.9291
991 Mexico 1982 71640904 67.40500 9611.1475
992 Mexico 1987 80122492 69.49800 8688.1560
993 Mexico 1992 88111030 71.45500 9472.3843
994 Mexico 1997 95895146 73.67000 9767.2975
995 Mexico 2002 102479927 74.90200 10742.4405
996 Mexico 2007 108700891 76.19500 11977.5750
997 Mongolia 1952 800663 42.24400 786.5669
998 Mongolia 1957 882134 45.24800 912.6626
999 Mongolia 1962 1010280 48.25100 1056.3540
1000 Mongolia 1967 1149500 51.25300 1226.0411
1001 Mongolia 1972 1320500 53.75400 1421.7420
1002 Mongolia 1977 1528000 55.49100 1647.5117
1003 Mongolia 1982 1756032 57.48900 2000.6031
1004 Mongolia 1987 2015133 60.22200 2338.0083
1005 Mongolia 1992 2312802 61.27100 1785.4020
1006 Mongolia 1997 2494803 63.62500 1902.2521
1007 Mongolia 2002 2674234 65.03300 2140.7393
1008 Mongolia 2007 2874127 66.80300 3095.7723
1009 Montenegro 1952 413834 59.16400 2647.5856
1010 Montenegro 1957 442829 61.44800 3682.2599
1011 Montenegro 1962 474528 63.72800 4649.5938
1012 Montenegro 1967 501035 67.17800 5907.8509
1013 Montenegro 1972 527678 70.63600 7778.4140
1014 Montenegro 1977 560073 73.06600 9595.9299
1015 Montenegro 1982 562548 74.10100 11222.5876
1016 Montenegro 1987 569473 74.86500 11732.5102
1017 Montenegro 1992 621621 75.43500 7003.3390
1018 Montenegro 1997 692651 75.44500 6465.6133
1019 Montenegro 2002 720230 73.98100 6557.1943
1020 Montenegro 2007 684736 74.54300 9253.8961
1021 Morocco 1952 9939217 42.87300 1688.2036
1022 Morocco 1957 11406350 45.42300 1642.0023
1023 Morocco 1962 13056604 47.92400 1566.3535
1024 Morocco 1967 14770296 50.33500 1711.0448
1025 Morocco 1972 16660670 52.86200 1930.1950
1026 Morocco 1977 18396941 55.73000 2370.6200
1027 Morocco 1982 20198730 59.65000 2702.6204
1028 Morocco 1987 22987397 62.67700 2755.0470
1029 Morocco 1992 25798239 65.39300 2948.0473
1030 Morocco 1997 28529501 67.66000 2982.1019
1031 Morocco 2002 31167783 69.61500 3258.4956
1032 Morocco 2007 33757175 71.16400 3820.1752
1033 Mozambique 1952 6446316 31.28600 468.5260
1034 Mozambique 1957 7038035 33.77900 495.5868
1035 Mozambique 1962 7788944 36.16100 556.6864
1036 Mozambique 1967 8680909 38.11300 566.6692
1037 Mozambique 1972 9809596 40.32800 724.9178
1038 Mozambique 1977 11127868 42.49500 502.3197
1039 Mozambique 1982 12587223 42.79500 462.2114
1040 Mozambique 1987 12891952 42.86100 389.8762
1041 Mozambique 1992 13160731 44.28400 410.8968
1042 Mozambique 1997 16603334 46.34400 472.3461
1043 Mozambique 2002 18473780 44.02600 633.6179
1044 Mozambique 2007 19951656 42.08200 823.6856
1045 Myanmar 1952 20092996 36.31900 331.0000
1046 Myanmar 1957 21731844 41.90500 350.0000
1047 Myanmar 1962 23634436 45.10800 388.0000
1048 Myanmar 1967 25870271 49.37900 349.0000
1049 Myanmar 1972 28466390 53.07000 357.0000
1050 Myanmar 1977 31528087 56.05900 371.0000
1051 Myanmar 1982 34680442 58.05600 424.0000
1052 Myanmar 1987 38028578 58.33900 385.0000
1053 Myanmar 1992 40546538 59.32000 347.0000
1054 Myanmar 1997 43247867 60.32800 415.0000
1055 Myanmar 2002 45598081 59.90800 611.0000
1056 Myanmar 2007 47761980 62.06900 944.0000
1057 Namibia 1952 485831 41.72500 2423.7804
1058 Namibia 1957 548080 45.22600 2621.4481
1059 Namibia 1962 621392 48.38600 3173.2156
1060 Namibia 1967 706640 51.15900 3793.6948
1061 Namibia 1972 821782 53.86700 3746.0809
1062 Namibia 1977 977026 56.43700 3876.4860
1063 Namibia 1982 1099010 58.96800 4191.1005
1064 Namibia 1987 1278184 60.83500 3693.7313
1065 Namibia 1992 1554253 61.99900 3804.5380
1066 Namibia 1997 1774766 58.90900 3899.5243
1067 Namibia 2002 1972153 51.47900 4072.3248
1068 Namibia 2007 2055080 52.90600 4811.0604
1069 Nepal 1952 9182536 36.15700 545.8657
1070 Nepal 1957 9682338 37.68600 597.9364
1071 Nepal 1962 10332057 39.39300 652.3969
1072 Nepal 1967 11261690 41.47200 676.4422
1073 Nepal 1972 12412593 43.97100 674.7881
1074 Nepal 1977 13933198 46.74800 694.1124
1075 Nepal 1982 15796314 49.59400 718.3731
1076 Nepal 1987 17917180 52.53700 775.6325
1077 Nepal 1992 20326209 55.72700 897.7404
1078 Nepal 1997 23001113 59.42600 1010.8921
1079 Nepal 2002 25873917 61.34000 1057.2063
1080 Nepal 2007 28901790 63.78500 1091.3598
1081 Netherlands 1952 10381988 72.13000 8941.5719
1082 Netherlands 1957 11026383 72.99000 11276.1934
1083 Netherlands 1962 11805689 73.23000 12790.8496
1084 Netherlands 1967 12596822 73.82000 15363.2514
1085 Netherlands 1972 13329874 73.75000 18794.7457
1086 Netherlands 1977 13852989 75.24000 21209.0592
1087 Netherlands 1982 14310401 76.05000 21399.4605
1088 Netherlands 1987 14665278 76.83000 23651.3236
1089 Netherlands 1992 15174244 77.42000 26790.9496
1090 Netherlands 1997 15604464 78.03000 30246.1306
1091 Netherlands 2002 16122830 78.53000 33724.7578
1092 Netherlands 2007 16570613 79.76200 36797.9333
1093 New Zealand 1952 1994794 69.39000 10556.5757
1094 New Zealand 1957 2229407 70.26000 12247.3953
1095 New Zealand 1962 2488550 71.24000 13175.6780
1096 New Zealand 1967 2728150 71.52000 14463.9189
1097 New Zealand 1972 2929100 71.89000 16046.0373
1098 New Zealand 1977 3164900 72.22000 16233.7177
1099 New Zealand 1982 3210650 73.84000 17632.4104
1100 New Zealand 1987 3317166 74.32000 19007.1913
1101 New Zealand 1992 3437674 76.33000 18363.3249
1102 New Zealand 1997 3676187 77.55000 21050.4138
1103 New Zealand 2002 3908037 79.11000 23189.8014
1104 New Zealand 2007 4115771 80.20400 25185.0091
1105 Nicaragua 1952 1165790 42.31400 3112.3639
1106 Nicaragua 1957 1358828 45.43200 3457.4159
1107 Nicaragua 1962 1590597 48.63200 3634.3644
1108 Nicaragua 1967 1865490 51.88400 4643.3935
1109 Nicaragua 1972 2182908 55.15100 4688.5933
1110 Nicaragua 1977 2554598 57.47000 5486.3711
1111 Nicaragua 1982 2979423 59.29800 3470.3382
1112 Nicaragua 1987 3344353 62.00800 2955.9844
1113 Nicaragua 1992 4017939 65.84300 2170.1517
1114 Nicaragua 1997 4609572 68.42600 2253.0230
1115 Nicaragua 2002 5146848 70.83600 2474.5488
1116 Nicaragua 2007 5675356 72.89900 2749.3210
1117 Niger 1952 3379468 37.44400 761.8794
1118 Niger 1957 3692184 38.59800 835.5234
1119 Niger 1962 4076008 39.48700 997.7661
1120 Niger 1967 4534062 40.11800 1054.3849
1121 Niger 1972 5060262 40.54600 954.2092
1122 Niger 1977 5682086 41.29100 808.8971
1123 Niger 1982 6437188 42.59800 909.7221
1124 Niger 1987 7332638 44.55500 668.3000
1125 Niger 1992 8392818 47.39100 581.1827
1126 Niger 1997 9666252 51.31300 580.3052
1127 Niger 2002 11140655 54.49600 601.0745
1128 Niger 2007 12894865 56.86700 619.6769
1129 Nigeria 1952 33119096 36.32400 1077.2819
1130 Nigeria 1957 37173340 37.80200 1100.5926
1131 Nigeria 1962 41871351 39.36000 1150.9275
1132 Nigeria 1967 47287752 41.04000 1014.5141
1133 Nigeria 1972 53740085 42.82100 1698.3888
1134 Nigeria 1977 62209173 44.51400 1981.9518
1135 Nigeria 1982 73039376 45.82600 1576.9738
1136 Nigeria 1987 81551520 46.88600 1385.0296
1137 Nigeria 1992 93364244 47.47200 1619.8482
1138 Nigeria 1997 106207839 47.46400 1624.9413
1139 Nigeria 2002 119901274 46.60800 1615.2864
1140 Nigeria 2007 135031164 46.85900 2013.9773
1141 Norway 1952 3327728 72.67000 10095.4217
1142 Norway 1957 3491938 73.44000 11653.9730
1143 Norway 1962 3638919 73.47000 13450.4015
1144 Norway 1967 3786019 74.08000 16361.8765
1145 Norway 1972 3933004 74.34000 18965.0555
1146 Norway 1977 4043205 75.37000 23311.3494
1147 Norway 1982 4114787 75.97000 26298.6353
1148 Norway 1987 4186147 75.89000 31540.9748
1149 Norway 1992 4286357 77.32000 33965.6611
1150 Norway 1997 4405672 78.32000 41283.1643
1151 Norway 2002 4535591 79.05000 44683.9753
1152 Norway 2007 4627926 80.19600 49357.1902
1153 Oman 1952 507833 37.57800 1828.2303
1154 Oman 1957 561977 40.08000 2242.7466
1155 Oman 1962 628164 43.16500 2924.6381
1156 Oman 1967 714775 46.98800 4720.9427
1157 Oman 1972 829050 52.14300 10618.0385
1158 Oman 1977 1004533 57.36700 11848.3439
1159 Oman 1982 1301048 62.72800 12954.7910
1160 Oman 1987 1593882 67.73400 18115.2231
1161 Oman 1992 1915208 71.19700 18616.7069
1162 Oman 1997 2283635 72.49900 19702.0558
1163 Oman 2002 2713462 74.19300 19774.8369
1164 Oman 2007 3204897 75.64000 22316.1929
1165 Pakistan 1952 41346560 43.43600 684.5971
1166 Pakistan 1957 46679944 45.55700 747.0835
1167 Pakistan 1962 53100671 47.67000 803.3427
1168 Pakistan 1967 60641899 49.80000 942.4083
1169 Pakistan 1972 69325921 51.92900 1049.9390
1170 Pakistan 1977 78152686 54.04300 1175.9212
1171 Pakistan 1982 91462088 56.15800 1443.4298
1172 Pakistan 1987 105186881 58.24500 1704.6866
1173 Pakistan 1992 120065004 60.83800 1971.8295
1174 Pakistan 1997 135564834 61.81800 2049.3505
1175 Pakistan 2002 153403524 63.61000 2092.7124
1176 Pakistan 2007 169270617 65.48300 2605.9476
1177 Panama 1952 940080 55.19100 2480.3803
1178 Panama 1957 1063506 59.20100 2961.8009
1179 Panama 1962 1215725 61.81700 3536.5403
1180 Panama 1967 1405486 64.07100 4421.0091
1181 Panama 1972 1616384 66.21600 5364.2497
1182 Panama 1977 1839782 68.68100 5351.9121
1183 Panama 1982 2036305 70.47200 7009.6016
1184 Panama 1987 2253639 71.52300 7034.7792
1185 Panama 1992 2484997 72.46200 6618.7431
1186 Panama 1997 2734531 73.73800 7113.6923
1187 Panama 2002 2990875 74.71200 7356.0319
1188 Panama 2007 3242173 75.53700 9809.1856
1189 Paraguay 1952 1555876 62.64900 1952.3087
1190 Paraguay 1957 1770902 63.19600 2046.1547
1191 Paraguay 1962 2009813 64.36100 2148.0271
1192 Paraguay 1967 2287985 64.95100 2299.3763
1193 Paraguay 1972 2614104 65.81500 2523.3380
1194 Paraguay 1977 2984494 66.35300 3248.3733
1195 Paraguay 1982 3366439 66.87400 4258.5036
1196 Paraguay 1987 3886512 67.37800 3998.8757
1197 Paraguay 1992 4483945 68.22500 4196.4111
1198 Paraguay 1997 5154123 69.40000 4247.4003
1199 Paraguay 2002 5884491 70.75500 3783.6742
1200 Paraguay 2007 6667147 71.75200 4172.8385
1201 Peru 1952 8025700 43.90200 3758.5234
1202 Peru 1957 9146100 46.26300 4245.2567
1203 Peru 1962 10516500 49.09600 4957.0380
1204 Peru 1967 12132200 51.44500 5788.0933
1205 Peru 1972 13954700 55.44800 5937.8273
1206 Peru 1977 15990099 58.44700 6281.2909
1207 Peru 1982 18125129 61.40600 6434.5018
1208 Peru 1987 20195924 64.13400 6360.9434
1209 Peru 1992 22430449 66.45800 4446.3809
1210 Peru 1997 24748122 68.38600 5838.3477
1211 Peru 2002 26769436 69.90600 5909.0201
1212 Peru 2007 28674757 71.42100 7408.9056
1213 Philippines 1952 22438691 47.75200 1272.8810
1214 Philippines 1957 26072194 51.33400 1547.9448
1215 Philippines 1962 30325264 54.75700 1649.5522
1216 Philippines 1967 35356600 56.39300 1814.1274
1217 Philippines 1972 40850141 58.06500 1989.3741
1218 Philippines 1977 46850962 60.06000 2373.2043
1219 Philippines 1982 53456774 62.08200 2603.2738
1220 Philippines 1987 60017788 64.15100 2189.6350
1221 Philippines 1992 67185766 66.45800 2279.3240
1222 Philippines 1997 75012988 68.56400 2536.5349
1223 Philippines 2002 82995088 70.30300 2650.9211
1224 Philippines 2007 91077287 71.68800 3190.4810
1225 Poland 1952 25730551 61.31000 4029.3297
1226 Poland 1957 28235346 65.77000 4734.2530
1227 Poland 1962 30329617 67.64000 5338.7521
1228 Poland 1967 31785378 69.61000 6557.1528
1229 Poland 1972 33039545 70.85000 8006.5070
1230 Poland 1977 34621254 70.67000 9508.1415
1231 Poland 1982 36227381 71.32000 8451.5310
1232 Poland 1987 37740710 70.98000 9082.3512
1233 Poland 1992 38370697 70.99000 7738.8812
1234 Poland 1997 38654957 72.75000 10159.5837
1235 Poland 2002 38625976 74.67000 12002.2391
1236 Poland 2007 38518241 75.56300 15389.9247
1237 Portugal 1952 8526050 59.82000 3068.3199
1238 Portugal 1957 8817650 61.51000 3774.5717
1239 Portugal 1962 9019800 64.39000 4727.9549
1240 Portugal 1967 9103000 66.60000 6361.5180
1241 Portugal 1972 8970450 69.26000 9022.2474
1242 Portugal 1977 9662600 70.41000 10172.4857
1243 Portugal 1982 9859650 72.77000 11753.8429
1244 Portugal 1987 9915289 74.06000 13039.3088
1245 Portugal 1992 9927680 74.86000 16207.2666
1246 Portugal 1997 10156415 75.97000 17641.0316
1247 Portugal 2002 10433867 77.29000 19970.9079
1248 Portugal 2007 10642836 78.09800 20509.6478
1249 Puerto Rico 1952 2227000 64.28000 3081.9598
1250 Puerto Rico 1957 2260000 68.54000 3907.1562
1251 Puerto Rico 1962 2448046 69.62000 5108.3446
1252 Puerto Rico 1967 2648961 71.10000 6929.2777
1253 Puerto Rico 1972 2847132 72.16000 9123.0417
1254 Puerto Rico 1977 3080828 73.44000 9770.5249
1255 Puerto Rico 1982 3279001 73.75000 10330.9891
1256 Puerto Rico 1987 3444468 74.63000 12281.3419
1257 Puerto Rico 1992 3585176 73.91100 14641.5871
1258 Puerto Rico 1997 3759430 74.91700 16999.4333
1259 Puerto Rico 2002 3859606 77.77800 18855.6062
1260 Puerto Rico 2007 3942491 78.74600 19328.7090
1261 Reunion 1952 257700 52.72400 2718.8853
1262 Reunion 1957 308700 55.09000 2769.4518
1263 Reunion 1962 358900 57.66600 3173.7233
1264 Reunion 1967 414024 60.54200 4021.1757
1265 Reunion 1972 461633 64.27400 5047.6586
1266 Reunion 1977 492095 67.06400 4319.8041
1267 Reunion 1982 517810 69.88500 5267.2194
1268 Reunion 1987 562035 71.91300 5303.3775
1269 Reunion 1992 622191 73.61500 6101.2558
1270 Reunion 1997 684810 74.77200 6071.9414
1271 Reunion 2002 743981 75.74400 6316.1652
1272 Reunion 2007 798094 76.44200 7670.1226
1273 Romania 1952 16630000 61.05000 3144.6132
1274 Romania 1957 17829327 64.10000 3943.3702
1275 Romania 1962 18680721 66.80000 4734.9976
1276 Romania 1967 19284814 66.80000 6470.8665
1277 Romania 1972 20662648 69.21000 8011.4144
1278 Romania 1977 21658597 69.46000 9356.3972
1279 Romania 1982 22356726 69.66000 9605.3141
1280 Romania 1987 22686371 69.53000 9696.2733
1281 Romania 1992 22797027 69.36000 6598.4099
1282 Romania 1997 22562458 69.72000 7346.5476
1283 Romania 2002 22404337 71.32200 7885.3601
1284 Romania 2007 22276056 72.47600 10808.4756
1285 Rwanda 1952 2534927 40.00000 493.3239
1286 Rwanda 1957 2822082 41.50000 540.2894
1287 Rwanda 1962 3051242 43.00000 597.4731
1288 Rwanda 1967 3451079 44.10000 510.9637
1289 Rwanda 1972 3992121 44.60000 590.5807
1290 Rwanda 1977 4657072 45.00000 670.0806
1291 Rwanda 1982 5507565 46.21800 881.5706
1292 Rwanda 1987 6349365 44.02000 847.9912
1293 Rwanda 1992 7290203 23.59900 737.0686
1294 Rwanda 1997 7212583 36.08700 589.9445
1295 Rwanda 2002 7852401 43.41300 785.6538
1296 Rwanda 2007 8860588 46.24200 863.0885
1297 Sao Tome and Principe 1952 60011 46.47100 879.5836
1298 Sao Tome and Principe 1957 61325 48.94500 860.7369
1299 Sao Tome and Principe 1962 65345 51.89300 1071.5511
1300 Sao Tome and Principe 1967 70787 54.42500 1384.8406
1301 Sao Tome and Principe 1972 76595 56.48000 1532.9853
1302 Sao Tome and Principe 1977 86796 58.55000 1737.5617
1303 Sao Tome and Principe 1982 98593 60.35100 1890.2181
1304 Sao Tome and Principe 1987 110812 61.72800 1516.5255
1305 Sao Tome and Principe 1992 125911 62.74200 1428.7778
1306 Sao Tome and Principe 1997 145608 63.30600 1339.0760
1307 Sao Tome and Principe 2002 170372 64.33700 1353.0924
1308 Sao Tome and Principe 2007 199579 65.52800 1598.4351
1309 Saudi Arabia 1952 4005677 39.87500 6459.5548
1310 Saudi Arabia 1957 4419650 42.86800 8157.5912
1311 Saudi Arabia 1962 4943029 45.91400 11626.4197
1312 Saudi Arabia 1967 5618198 49.90100 16903.0489
1313 Saudi Arabia 1972 6472756 53.88600 24837.4287
1314 Saudi Arabia 1977 8128505 58.69000 34167.7626
1315 Saudi Arabia 1982 11254672 63.01200 33693.1753
1316 Saudi Arabia 1987 14619745 66.29500 21198.2614
1317 Saudi Arabia 1992 16945857 68.76800 24841.6178
1318 Saudi Arabia 1997 21229759 70.53300 20586.6902
1319 Saudi Arabia 2002 24501530 71.62600 19014.5412
1320 Saudi Arabia 2007 27601038 72.77700 21654.8319
1321 Senegal 1952 2755589 37.27800 1450.3570
1322 Senegal 1957 3054547 39.32900 1567.6530
1323 Senegal 1962 3430243 41.45400 1654.9887
1324 Senegal 1967 3965841 43.56300 1612.4046
1325 Senegal 1972 4588696 45.81500 1597.7121
1326 Senegal 1977 5260855 48.87900 1561.7691
1327 Senegal 1982 6147783 52.37900 1518.4800
1328 Senegal 1987 7171347 55.76900 1441.7207
1329 Senegal 1992 8307920 58.19600 1367.8994
1330 Senegal 1997 9535314 60.18700 1392.3683
1331 Senegal 2002 10870037 61.60000 1519.6353
1332 Senegal 2007 12267493 63.06200 1712.4721
1333 Serbia 1952 6860147 57.99600 3581.4594
1334 Serbia 1957 7271135 61.68500 4981.0909
1335 Serbia 1962 7616060 64.53100 6289.6292
1336 Serbia 1967 7971222 66.91400 7991.7071
1337 Serbia 1972 8313288 68.70000 10522.0675
1338 Serbia 1977 8686367 70.30000 12980.6696
1339 Serbia 1982 9032824 70.16200 15181.0927
1340 Serbia 1987 9230783 71.21800 15870.8785
1341 Serbia 1992 9826397 71.65900 9325.0682
1342 Serbia 1997 10336594 72.23200 7914.3203
1343 Serbia 2002 10111559 73.21300 7236.0753
1344 Serbia 2007 10150265 74.00200 9786.5347
1345 Sierra Leone 1952 2143249 30.33100 879.7877
1346 Sierra Leone 1957 2295678 31.57000 1004.4844
1347 Sierra Leone 1962 2467895 32.76700 1116.6399
1348 Sierra Leone 1967 2662190 34.11300 1206.0435
1349 Sierra Leone 1972 2879013 35.40000 1353.7598
1350 Sierra Leone 1977 3140897 36.78800 1348.2852
1351 Sierra Leone 1982 3464522 38.44500 1465.0108
1352 Sierra Leone 1987 3868905 40.00600 1294.4478
1353 Sierra Leone 1992 4260884 38.33300 1068.6963
1354 Sierra Leone 1997 4578212 39.89700 574.6482
1355 Sierra Leone 2002 5359092 41.01200 699.4897
1356 Sierra Leone 2007 6144562 42.56800 862.5408
1357 Singapore 1952 1127000 60.39600 2315.1382
1358 Singapore 1957 1445929 63.17900 2843.1044
1359 Singapore 1962 1750200 65.79800 3674.7356
1360 Singapore 1967 1977600 67.94600 4977.4185
1361 Singapore 1972 2152400 69.52100 8597.7562
1362 Singapore 1977 2325300 70.79500 11210.0895
1363 Singapore 1982 2651869 71.76000 15169.1611
1364 Singapore 1987 2794552 73.56000 18861.5308
1365 Singapore 1992 3235865 75.78800 24769.8912
1366 Singapore 1997 3802309 77.15800 33519.4766
1367 Singapore 2002 4197776 78.77000 36023.1054
1368 Singapore 2007 4553009 79.97200 47143.1796
1369 Slovak Republic 1952 3558137 64.36000 5074.6591
1370 Slovak Republic 1957 3844277 67.45000 6093.2630
1371 Slovak Republic 1962 4237384 70.33000 7481.1076
1372 Slovak Republic 1967 4442238 70.98000 8412.9024
1373 Slovak Republic 1972 4593433 70.35000 9674.1676
1374 Slovak Republic 1977 4827803 70.45000 10922.6640
1375 Slovak Republic 1982 5048043 70.80000 11348.5459
1376 Slovak Republic 1987 5199318 71.08000 12037.2676
1377 Slovak Republic 1992 5302888 71.38000 9498.4677
1378 Slovak Republic 1997 5383010 72.71000 12126.2306
1379 Slovak Republic 2002 5410052 73.80000 13638.7784
1380 Slovak Republic 2007 5447502 74.66300 18678.3144
1381 Slovenia 1952 1489518 65.57000 4215.0417
1382 Slovenia 1957 1533070 67.85000 5862.2766
1383 Slovenia 1962 1582962 69.15000 7402.3034
1384 Slovenia 1967 1646912 69.18000 9405.4894
1385 Slovenia 1972 1694510 69.82000 12383.4862
1386 Slovenia 1977 1746919 70.97000 15277.0302
1387 Slovenia 1982 1861252 71.06300 17866.7218
1388 Slovenia 1987 1945870 72.25000 18678.5349
1389 Slovenia 1992 1999210 73.64000 14214.7168
1390 Slovenia 1997 2011612 75.13000 17161.1073
1391 Slovenia 2002 2011497 76.66000 20660.0194
1392 Slovenia 2007 2009245 77.92600 25768.2576
1393 Somalia 1952 2526994 32.97800 1135.7498
1394 Somalia 1957 2780415 34.97700 1258.1474
1395 Somalia 1962 3080153 36.98100 1369.4883
1396 Somalia 1967 3428839 38.97700 1284.7332
1397 Somalia 1972 3840161 40.97300 1254.5761
1398 Somalia 1977 4353666 41.97400 1450.9925
1399 Somalia 1982 5828892 42.95500 1176.8070
1400 Somalia 1987 6921858 44.50100 1093.2450
1401 Somalia 1992 6099799 39.65800 926.9603
1402 Somalia 1997 6633514 43.79500 930.5964
1403 Somalia 2002 7753310 45.93600 882.0818
1404 Somalia 2007 9118773 48.15900 926.1411
1405 South Africa 1952 14264935 45.00900 4725.2955
1406 South Africa 1957 16151549 47.98500 5487.1042
1407 South Africa 1962 18356657 49.95100 5768.7297
1408 South Africa 1967 20997321 51.92700 7114.4780
1409 South Africa 1972 23935810 53.69600 7765.9626
1410 South Africa 1977 27129932 55.52700 8028.6514
1411 South Africa 1982 31140029 58.16100 8568.2662
1412 South Africa 1987 35933379 60.83400 7825.8234
1413 South Africa 1992 39964159 61.88800 7225.0693
1414 South Africa 1997 42835005 60.23600 7479.1882
1415 South Africa 2002 44433622 53.36500 7710.9464
1416 South Africa 2007 43997828 49.33900 9269.6578
1417 Spain 1952 28549870 64.94000 3834.0347
1418 Spain 1957 29841614 66.66000 4564.8024
1419 Spain 1962 31158061 69.69000 5693.8439
1420 Spain 1967 32850275 71.44000 7993.5123
1421 Spain 1972 34513161 73.06000 10638.7513
1422 Spain 1977 36439000 74.39000 13236.9212
1423 Spain 1982 37983310 76.30000 13926.1700
1424 Spain 1987 38880702 76.90000 15764.9831
1425 Spain 1992 39549438 77.57000 18603.0645
1426 Spain 1997 39855442 78.77000 20445.2990
1427 Spain 2002 40152517 79.78000 24835.4717
1428 Spain 2007 40448191 80.94100 28821.0637
1429 Sri Lanka 1952 7982342 57.59300 1083.5320
1430 Sri Lanka 1957 9128546 61.45600 1072.5466
1431 Sri Lanka 1962 10421936 62.19200 1074.4720
1432 Sri Lanka 1967 11737396 64.26600 1135.5143
1433 Sri Lanka 1972 13016733 65.04200 1213.3955
1434 Sri Lanka 1977 14116836 65.94900 1348.7757
1435 Sri Lanka 1982 15410151 68.75700 1648.0798
1436 Sri Lanka 1987 16495304 69.01100 1876.7668
1437 Sri Lanka 1992 17587060 70.37900 2153.7392
1438 Sri Lanka 1997 18698655 70.45700 2664.4773
1439 Sri Lanka 2002 19576783 70.81500 3015.3788
1440 Sri Lanka 2007 20378239 72.39600 3970.0954
1441 Sudan 1952 8504667 38.63500 1615.9911
1442 Sudan 1957 9753392 39.62400 1770.3371
1443 Sudan 1962 11183227 40.87000 1959.5938
1444 Sudan 1967 12716129 42.85800 1687.9976
1445 Sudan 1972 14597019 45.08300 1659.6528
1446 Sudan 1977 17104986 47.80000 2202.9884
1447 Sudan 1982 20367053 50.33800 1895.5441
1448 Sudan 1987 24725960 51.74400 1507.8192
1449 Sudan 1992 28227588 53.55600 1492.1970
1450 Sudan 1997 32160729 55.37300 1632.2108
1451 Sudan 2002 37090298 56.36900 1993.3983
1452 Sudan 2007 42292929 58.55600 2602.3950
1453 Swaziland 1952 290243 41.40700 1148.3766
1454 Swaziland 1957 326741 43.42400 1244.7084
1455 Swaziland 1962 370006 44.99200 1856.1821
1456 Swaziland 1967 420690 46.63300 2613.1017
1457 Swaziland 1972 480105 49.55200 3364.8366
1458 Swaziland 1977 551425 52.53700 3781.4106
1459 Swaziland 1982 649901 55.56100 3895.3840
1460 Swaziland 1987 779348 57.67800 3984.8398
1461 Swaziland 1992 962344 58.47400 3553.0224
1462 Swaziland 1997 1054486 54.28900 3876.7685
1463 Swaziland 2002 1130269 43.86900 4128.1169
1464 Swaziland 2007 1133066 39.61300 4513.4806
1465 Sweden 1952 7124673 71.86000 8527.8447
1466 Sweden 1957 7363802 72.49000 9911.8782
1467 Sweden 1962 7561588 73.37000 12329.4419
1468 Sweden 1967 7867931 74.16000 15258.2970
1469 Sweden 1972 8122293 74.72000 17832.0246
1470 Sweden 1977 8251648 75.44000 18855.7252
1471 Sweden 1982 8325260 76.42000 20667.3812
1472 Sweden 1987 8421403 77.19000 23586.9293
1473 Sweden 1992 8718867 78.16000 23880.0168
1474 Sweden 1997 8897619 79.39000 25266.5950
1475 Sweden 2002 8954175 80.04000 29341.6309
1476 Sweden 2007 9031088 80.88400 33859.7484
1477 Switzerland 1952 4815000 69.62000 14734.2327
1478 Switzerland 1957 5126000 70.56000 17909.4897
1479 Switzerland 1962 5666000 71.32000 20431.0927
1480 Switzerland 1967 6063000 72.77000 22966.1443
1481 Switzerland 1972 6401400 73.78000 27195.1130
1482 Switzerland 1977 6316424 75.39000 26982.2905
1483 Switzerland 1982 6468126 76.21000 28397.7151
1484 Switzerland 1987 6649942 77.41000 30281.7046
1485 Switzerland 1992 6995447 78.03000 31871.5303
1486 Switzerland 1997 7193761 79.37000 32135.3230
1487 Switzerland 2002 7361757 80.62000 34480.9577
1488 Switzerland 2007 7554661 81.70100 37506.4191
1489 Syria 1952 3661549 45.88300 1643.4854
1490 Syria 1957 4149908 48.28400 2117.2349
1491 Syria 1962 4834621 50.30500 2193.0371
1492 Syria 1967 5680812 53.65500 1881.9236
1493 Syria 1972 6701172 57.29600 2571.4230
1494 Syria 1977 7932503 61.19500 3195.4846
1495 Syria 1982 9410494 64.59000 3761.8377
1496 Syria 1987 11242847 66.97400 3116.7743
1497 Syria 1992 13219062 69.24900 3340.5428
1498 Syria 1997 15081016 71.52700 4014.2390
1499 Syria 2002 17155814 73.05300 4090.9253
1500 Syria 2007 19314747 74.14300 4184.5481
1501 Taiwan 1952 8550362 58.50000 1206.9479
1502 Taiwan 1957 10164215 62.40000 1507.8613
1503 Taiwan 1962 11918938 65.20000 1822.8790
1504 Taiwan 1967 13648692 67.50000 2643.8587
1505 Taiwan 1972 15226039 69.39000 4062.5239
1506 Taiwan 1977 16785196 70.59000 5596.5198
1507 Taiwan 1982 18501390 72.16000 7426.3548
1508 Taiwan 1987 19757799 73.40000 11054.5618
1509 Taiwan 1992 20686918 74.26000 15215.6579
1510 Taiwan 1997 21628605 75.25000 20206.8210
1511 Taiwan 2002 22454239 76.99000 23235.4233
1512 Taiwan 2007 23174294 78.40000 28718.2768
1513 Tanzania 1952 8322925 41.21500 716.6501
1514 Tanzania 1957 9452826 42.97400 698.5356
1515 Tanzania 1962 10863958 44.24600 722.0038
1516 Tanzania 1967 12607312 45.75700 848.2187
1517 Tanzania 1972 14706593 47.62000 915.9851
1518 Tanzania 1977 17129565 49.91900 962.4923
1519 Tanzania 1982 19844382 50.60800 874.2426
1520 Tanzania 1987 23040630 51.53500 831.8221
1521 Tanzania 1992 26605473 50.44000 825.6825
1522 Tanzania 1997 30686889 48.46600 789.1862
1523 Tanzania 2002 34593779 49.65100 899.0742
1524 Tanzania 2007 38139640 52.51700 1107.4822
1525 Thailand 1952 21289402 50.84800 757.7974
1526 Thailand 1957 25041917 53.63000 793.5774
1527 Thailand 1962 29263397 56.06100 1002.1992
1528 Thailand 1967 34024249 58.28500 1295.4607
1529 Thailand 1972 39276153 60.40500 1524.3589
1530 Thailand 1977 44148285 62.49400 1961.2246
1531 Thailand 1982 48827160 64.59700 2393.2198
1532 Thailand 1987 52910342 66.08400 2982.6538
1533 Thailand 1992 56667095 67.29800 4616.8965
1534 Thailand 1997 60216677 67.52100 5852.6255
1535 Thailand 2002 62806748 68.56400 5913.1875
1536 Thailand 2007 65068149 70.61600 7458.3963
1537 Togo 1952 1219113 38.59600 859.8087
1538 Togo 1957 1357445 41.20800 925.9083
1539 Togo 1962 1528098 43.92200 1067.5348
1540 Togo 1967 1735550 46.76900 1477.5968
1541 Togo 1972 2056351 49.75900 1649.6602
1542 Togo 1977 2308582 52.88700 1532.7770
1543 Togo 1982 2644765 55.47100 1344.5780
1544 Togo 1987 3154264 56.94100 1202.2014
1545 Togo 1992 3747553 58.06100 1034.2989
1546 Togo 1997 4320890 58.39000 982.2869
1547 Togo 2002 4977378 57.56100 886.2206
1548 Togo 2007 5701579 58.42000 882.9699
1549 Trinidad and Tobago 1952 662850 59.10000 3023.2719
1550 Trinidad and Tobago 1957 764900 61.80000 4100.3934
1551 Trinidad and Tobago 1962 887498 64.90000 4997.5240
1552 Trinidad and Tobago 1967 960155 65.40000 5621.3685
1553 Trinidad and Tobago 1972 975199 65.90000 6619.5514
1554 Trinidad and Tobago 1977 1039009 68.30000 7899.5542
1555 Trinidad and Tobago 1982 1116479 68.83200 9119.5286
1556 Trinidad and Tobago 1987 1191336 69.58200 7388.5978
1557 Trinidad and Tobago 1992 1183669 69.86200 7370.9909
1558 Trinidad and Tobago 1997 1138101 69.46500 8792.5731
1559 Trinidad and Tobago 2002 1101832 68.97600 11460.6002
1560 Trinidad and Tobago 2007 1056608 69.81900 18008.5092
1561 Tunisia 1952 3647735 44.60000 1468.4756
1562 Tunisia 1957 3950849 47.10000 1395.2325
1563 Tunisia 1962 4286552 49.57900 1660.3032
1564 Tunisia 1967 4786986 52.05300 1932.3602
1565 Tunisia 1972 5303507 55.60200 2753.2860
1566 Tunisia 1977 6005061 59.83700 3120.8768
1567 Tunisia 1982 6734098 64.04800 3560.2332
1568 Tunisia 1987 7724976 66.89400 3810.4193
1569 Tunisia 1992 8523077 70.00100 4332.7202
1570 Tunisia 1997 9231669 71.97300 4876.7986
1571 Tunisia 2002 9770575 73.04200 5722.8957
1572 Tunisia 2007 10276158 73.92300 7092.9230
1573 Turkey 1952 22235677 43.58500 1969.1010
1574 Turkey 1957 25670939 48.07900 2218.7543
1575 Turkey 1962 29788695 52.09800 2322.8699
1576 Turkey 1967 33411317 54.33600 2826.3564
1577 Turkey 1972 37492953 57.00500 3450.6964
1578 Turkey 1977 42404033 59.50700 4269.1223
1579 Turkey 1982 47328791 61.03600 4241.3563
1580 Turkey 1987 52881328 63.10800 5089.0437
1581 Turkey 1992 58179144 66.14600 5678.3483
1582 Turkey 1997 63047647 68.83500 6601.4299
1583 Turkey 2002 67308928 70.84500 6508.0857
1584 Turkey 2007 71158647 71.77700 8458.2764
1585 Uganda 1952 5824797 39.97800 734.7535
1586 Uganda 1957 6675501 42.57100 774.3711
1587 Uganda 1962 7688797 45.34400 767.2717
1588 Uganda 1967 8900294 48.05100 908.9185
1589 Uganda 1972 10190285 51.01600 950.7359
1590 Uganda 1977 11457758 50.35000 843.7331
1591 Uganda 1982 12939400 49.84900 682.2662
1592 Uganda 1987 15283050 51.50900 617.7244
1593 Uganda 1992 18252190 48.82500 644.1708
1594 Uganda 1997 21210254 44.57800 816.5591
1595 Uganda 2002 24739869 47.81300 927.7210
1596 Uganda 2007 29170398 51.54200 1056.3801
1597 United Kingdom 1952 50430000 69.18000 9979.5085
1598 United Kingdom 1957 51430000 70.42000 11283.1779
1599 United Kingdom 1962 53292000 70.76000 12477.1771
1600 United Kingdom 1967 54959000 71.36000 14142.8509
1601 United Kingdom 1972 56079000 72.01000 15895.1164
1602 United Kingdom 1977 56179000 72.76000 17428.7485
1603 United Kingdom 1982 56339704 74.04000 18232.4245
1604 United Kingdom 1987 56981620 75.00700 21664.7877
1605 United Kingdom 1992 57866349 76.42000 22705.0925
1606 United Kingdom 1997 58808266 77.21800 26074.5314
1607 United Kingdom 2002 59912431 78.47100 29478.9992
1608 United Kingdom 2007 60776238 79.42500 33203.2613
1609 United States 1952 157553000 68.44000 13990.4821
1610 United States 1957 171984000 69.49000 14847.1271
1611 United States 1962 186538000 70.21000 16173.1459
1612 United States 1967 198712000 70.76000 19530.3656
1613 United States 1972 209896000 71.34000 21806.0359
1614 United States 1977 220239000 73.38000 24072.6321
1615 United States 1982 232187835 74.65000 25009.5591
1616 United States 1987 242803533 75.02000 29884.3504
1617 United States 1992 256894189 76.09000 32003.9322
1618 United States 1997 272911760 76.81000 35767.4330
1619 United States 2002 287675526 77.31000 39097.0995
1620 United States 2007 301139947 78.24200 42951.6531
1621 Uruguay 1952 2252965 66.07100 5716.7667
1622 Uruguay 1957 2424959 67.04400 6150.7730
1623 Uruguay 1962 2598466 68.25300 5603.3577
1624 Uruguay 1967 2748579 68.46800 5444.6196
1625 Uruguay 1972 2829526 68.67300 5703.4089
1626 Uruguay 1977 2873520 69.48100 6504.3397
1627 Uruguay 1982 2953997 70.80500 6920.2231
1628 Uruguay 1987 3045153 71.91800 7452.3990
1629 Uruguay 1992 3149262 72.75200 8137.0048
1630 Uruguay 1997 3262838 74.22300 9230.2407
1631 Uruguay 2002 3363085 75.30700 7727.0020
1632 Uruguay 2007 3447496 76.38400 10611.4630
1633 Venezuela 1952 5439568 55.08800 7689.7998
1634 Venezuela 1957 6702668 57.90700 9802.4665
1635 Venezuela 1962 8143375 60.77000 8422.9742
1636 Venezuela 1967 9709552 63.47900 9541.4742
1637 Venezuela 1972 11515649 65.71200 10505.2597
1638 Venezuela 1977 13503563 67.45600 13143.9510
1639 Venezuela 1982 15620766 68.55700 11152.4101
1640 Venezuela 1987 17910182 70.19000 9883.5846
1641 Venezuela 1992 20265563 71.15000 10733.9263
1642 Venezuela 1997 22374398 72.14600 10165.4952
1643 Venezuela 2002 24287670 72.76600 8605.0478
1644 Venezuela 2007 26084662 73.74700 11415.8057
1645 Vietnam 1952 26246839 40.41200 605.0665
1646 Vietnam 1957 28998543 42.88700 676.2854
1647 Vietnam 1962 33796140 45.36300 772.0492
1648 Vietnam 1967 39463910 47.83800 637.1233
1649 Vietnam 1972 44655014 50.25400 699.5016
1650 Vietnam 1977 50533506 55.76400 713.5371
1651 Vietnam 1982 56142181 58.81600 707.2358
1652 Vietnam 1987 62826491 62.82000 820.7994
1653 Vietnam 1992 69940728 67.66200 989.0231
1654 Vietnam 1997 76048996 70.67200 1385.8968
1655 Vietnam 2002 80908147 73.01700 1764.4567
1656 Vietnam 2007 85262356 74.24900 2441.5764
1657 West Bank and Gaza 1952 1030585 43.16000 1515.5923
1658 West Bank and Gaza 1957 1070439 45.67100 1827.0677
1659 West Bank and Gaza 1962 1133134 48.12700 2198.9563
1660 West Bank and Gaza 1967 1142636 51.63100 2649.7150
1661 West Bank and Gaza 1972 1089572 56.53200 3133.4093
1662 West Bank and Gaza 1977 1261091 60.76500 3682.8315
1663 West Bank and Gaza 1982 1425876 64.40600 4336.0321
1664 West Bank and Gaza 1987 1691210 67.04600 5107.1974
1665 West Bank and Gaza 1992 2104779 69.71800 6017.6548
1666 West Bank and Gaza 1997 2826046 71.09600 7110.6676
1667 West Bank and Gaza 2002 3389578 72.37000 4515.4876
1668 West Bank and Gaza 2007 4018332 73.42200 3025.3498
1669 Yemen Rep. 1952 4963829 32.54800 781.7176
1670 Yemen Rep. 1957 5498090 33.97000 804.8305
1671 Yemen Rep. 1962 6120081 35.18000 825.6232
1672 Yemen Rep. 1967 6740785 36.98400 862.4421
1673 Yemen Rep. 1972 7407075 39.84800 1265.0470
1674 Yemen Rep. 1977 8403990 44.17500 1829.7652
1675 Yemen Rep. 1982 9657618 49.11300 1977.5570
1676 Yemen Rep. 1987 11219340 52.92200 1971.7415
1677 Yemen Rep. 1992 13367997 55.59900 1879.4967
1678 Yemen Rep. 1997 15826497 58.02000 2117.4845
1679 Yemen Rep. 2002 18701257 60.30800 2234.8208
1680 Yemen Rep. 2007 22211743 62.69800 2280.7699
1681 Zambia 1952 2672000 42.03800 1147.3888
1682 Zambia 1957 3016000 44.07700 1311.9568
1683 Zambia 1962 3421000 46.02300 1452.7258
1684 Zambia 1967 3900000 47.76800 1777.0773
1685 Zambia 1972 4506497 50.10700 1773.4983
1686 Zambia 1977 5216550 51.38600 1588.6883
1687 Zambia 1982 6100407 51.82100 1408.6786
1688 Zambia 1987 7272406 50.82100 1213.3151
1689 Zambia 1992 8381163 46.10000 1210.8846
1690 Zambia 1997 9417789 40.23800 1071.3538
1691 Zambia 2002 10595811 39.19300 1071.6139
1692 Zambia 2007 11746035 42.38400 1271.2116
1693 Zimbabwe 1952 3080907 48.45100 406.8841
1694 Zimbabwe 1957 3646340 50.46900 518.7643
1695 Zimbabwe 1962 4277736 52.35800 527.2722
1696 Zimbabwe 1967 4995432 53.99500 569.7951
1697 Zimbabwe 1972 5861135 55.63500 799.3622
1698 Zimbabwe 1977 6642107 57.67400 685.5877
1699 Zimbabwe 1982 7636524 60.36300 788.8550
1700 Zimbabwe 1987 9216418 62.35100 706.1573
1701 Zimbabwe 1992 10704340 60.37700 693.4208
1702 Zimbabwe 1997 11404948 46.80900 792.4500
1703 Zimbabwe 2002 11926563 39.98900 672.0386
1704 Zimbabwe 2007 12311143 43.48700 469.7093Note that there are some other packages (e.g. the MASS package) that also have a function called select(). If you happen to load one of those packages after loading the tidyverse package in your session, you may end up with an error that says Error in select(., x) : unused argument (x). To fix this, you will either need to directly call the select() function from the dplyr package using dplyr::select() or ensure that you load such packages before the tidyverse package (which automatically loads the dplyr package).
filter: filter to rows that satisfy certain conditions
Filtering is a very simple way of only keeping rows that satisfy certain conditions. These conditions are always based on logical statements involving variables/columns of the data frame.
For instance, to keep only the rows that have a recorded population of at least 1 billion, you can use a filtering with a logical statement involving the pop variable (again unquoted).
gapminder %>%
filter(pop > 1000000000) country year pop continent lifeExp gdpPercap
1 China 1982 1000281000 Asia 65.525 962.4214
2 China 1987 1084035000 Asia 67.274 1378.9040
3 China 1992 1164970000 Asia 68.690 1655.7842
4 China 1997 1230075000 Asia 70.426 2289.2341
5 China 2002 1280400000 Asia 72.028 3119.2809
6 China 2007 1318683096 Asia 72.961 4959.1149
7 India 2002 1034172547 Asia 62.879 1746.7695
8 India 2007 1110396331 Asia 64.698 2452.2104You can specify multiple filter conditions using a comma (and in this case the filter function will return rows that satisfy all of the conditions specified). Below I filter to rows from 1992 that have a population of at least 100 million in that year.
gapminder %>%
filter(pop > 100000000, year == 1992) country year pop continent lifeExp gdpPercap
1 Bangladesh 1992 113704579 Asia 56.018 837.8102
2 Brazil 1992 155975974 Americas 67.057 6950.2830
3 China 1992 1164970000 Asia 68.690 1655.7842
4 India 1992 872000000 Asia 60.223 1164.4068
5 Indonesia 1992 184816000 Asia 62.681 2383.1409
6 Japan 1992 124329269 Asia 79.360 26824.8951
7 Pakistan 1992 120065004 Asia 60.838 1971.8295
8 United States 1992 256894189 Americas 76.090 32003.9322mutate: add a new variable
Mutating the data frame involves adding a new variable. This new variable is usually a function of existing variables, but it can also be defined based on external objects.
For instance below I add a new variable, gdp, to the gapminder data frame. gdp is equal to gdpPercap multiplied by pop, and then look at the first 6 rows of the data frame using the classic head() function.
gapminder %>%
mutate(gdp = gdpPercap * pop) %>%
head country year pop continent lifeExp gdpPercap gdp
1 Afghanistan 1952 8425333 Asia 28.801 779.4453 6567086330
2 Afghanistan 1957 9240934 Asia 30.332 820.8530 7585448670
3 Afghanistan 1962 10267083 Asia 31.997 853.1007 8758855797
4 Afghanistan 1967 11537966 Asia 34.020 836.1971 9648014150
5 Afghanistan 1972 13079460 Asia 36.088 739.9811 9678553274
6 Afghanistan 1977 14880372 Asia 38.438 786.1134 11697659231Note that I haven’t re-defined the gapminder data frame, so all I have done here is print out the data frame with the additional gdp variable.
If you wanted to be able to use this gdp variable down the line, you would need to re-define the gapminder data frame so that gapminder now corresponds to the version with the gdp variable.
gapminder <- gapminder %>%
mutate(gdp = gdpPercap * pop)arrange: arrange the rows of the data frame in order a variable
The arrange function allows you to easily reorder the rows of the data frame in increasing or decreasing order of one (or more) of the variables of the data frame.
For instance, you could arrange all rows in the data frame in order of increasing life expectancy.
gapminder %>%
arrange(lifeExp) %>%
head country year pop continent lifeExp gdpPercap gdp
1 Rwanda 1992 7290203 Africa 23.599 737.0686 5373379682
2 Afghanistan 1952 8425333 Asia 28.801 779.4453 6567086330
3 Gambia 1952 284320 Africa 30.000 485.2307 137960781
4 Angola 1952 4232095 Africa 30.015 3520.6103 14899557133
5 Sierra Leone 1952 2143249 Africa 30.331 879.7877 1885604185
6 Afghanistan 1957 9240934 Asia 30.332 820.8530 7585448670To arrange in descending order, you need to wrap the variable name in the desc() function.
gapminder %>%
arrange(desc(gdpPercap)) %>%
head country year pop continent lifeExp gdpPercap gdp
1 Kuwait 1957 212846 Asia 58.033 113523.13 24162944745
2 Kuwait 1972 841934 Asia 67.712 109347.87 92063687055
3 Kuwait 1952 160000 Asia 55.565 108382.35 17341176464
4 Kuwait 1962 358266 Asia 60.470 95458.11 34199395868
5 Kuwait 1967 575003 Asia 64.624 80894.88 46514800559
6 Kuwait 1977 1140357 Asia 69.343 59265.48 67583801715Again, if you wanted your data frame to actually be arranged as specified, you would need to re-define the gapminder data frame. But if you only need it for one quick analysis (e.g. creating a summary table), then you don’t need to redefine the data frame.
Below I re-define the gapminder dataset so that the rows are in order of increasing year, and the countries are in alphabetical order within each year (the secondary arrange variable).
gapminder %>%
arrange(year, country) %>%
head country year pop continent lifeExp gdpPercap gdp
1 Afghanistan 1952 8425333 Asia 28.801 779.4453 6567086330
2 Albania 1952 1282697 Europe 55.230 1601.0561 2053669902
3 Algeria 1952 9279525 Africa 43.077 2449.0082 22725632678
4 Angola 1952 4232095 Africa 30.015 3520.6103 14899557133
5 Argentina 1952 17876956 Americas 62.485 5911.3151 105676319105
6 Australia 1952 8691212 Oceania 69.120 10039.5956 87256254102group_by: apply other dplyr functions separately within within a group defined by one or more variables
The group_by() function can be really useful if you want to apply a function independently within groups of observations (where the groups are specified by a categorical variable in your data frame). Think of group_by() as splitting your data frame into several separate data frames based on the categorical variable you specify. All functions that you apply to the grouped data frame are applied separately to each group until you specify an ungroup() function.
The code below filters the data frame to only the country-years that have life expectancy above the average life expectancy for their continent.
gapminder %>%
group_by(continent) %>%
filter(lifeExp > mean(lifeExp)) %>%
ungroup() # A tibble: 873 × 7
country year pop continent lifeExp gdpPercap gdp
<chr> <int> <dbl> <chr> <dbl> <dbl> <dbl>
1 Albania 1987 3075321 Europe 72 3739. 11498418358.
2 Albania 1997 3428038 Europe 73.0 3193. 10945912519.
3 Albania 2002 3508512 Europe 75.7 4604. 16153932130.
4 Albania 2007 3600523 Europe 76.4 5937. 21376411360.
5 Algeria 1967 12760499 Africa 51.4 3247. 41433235247.
6 Algeria 1972 14760787 Africa 54.5 4183. 61739408943.
7 Algeria 1977 17152804 Africa 58.0 4910. 84227416174.
8 Algeria 1982 20033753 Africa 61.4 5745. 115097120653.
9 Algeria 1987 23254956 Africa 65.8 5681. 132119742845.
10 Algeria 1992 26298373 Africa 67.7 5023. 132102425043.
# … with 863 more rowsTo check that this does something different than it would without the group_by() (i.e. filtering to the country-years that have life expectancy above the average global life expectancy), compare the distribution of continents from each filter() command using the count() function (another handly dplyr function):
The number of countries from each continent included post-filtering when grouping by continent is:
gapminder %>%
group_by(continent) %>%
filter(lifeExp > mean(lifeExp)) %>%
ungroup() %>%
count(continent)# A tibble: 5 × 2
continent n
<chr> <int>
1 Africa 282
2 Americas 176
3 Asia 216
4 Europe 189
5 Oceania 10The number of countries from each continent included post-filtering when not grouping by continent is:
gapminder %>%
filter(lifeExp > mean(lifeExp)) %>%
count(continent) continent n
1 Africa 80
2 Americas 218
3 Asia 224
4 Europe 349
5 Oceania 24Notice that when you don’t group by continent, substantially fewer African countries are included since they tend to have lower life expectencies than the global average.
To combine some of the things you’ve just learnt, the code below first filters to the year 2007, and then splits the data frame into groups by continent and adds a row to each group corresponding to the average life expectancy of all of the countries in that group/continent.
gapminder %>%
# first filter to 2007
filter(year == 2007) %>%
# group by continent
group_by(continent) %>%
# add a column within each continent corresponding to the average life expectancy
mutate(continent_lifeExp = mean(lifeExp)) %>%
# ungroup the data frame
ungroup() %>%
# only show a few variables
select(country, continent, lifeExp, continent_lifeExp) %>%
head(10)# A tibble: 10 × 4
country continent lifeExp continent_lifeExp
<chr> <chr> <dbl> <dbl>
1 Afghanistan Asia 43.8 70.7
2 Albania Europe 76.4 77.6
3 Algeria Africa 72.3 54.8
4 Angola Africa 42.7 54.8
5 Argentina Americas 75.3 73.6
6 Australia Oceania 81.2 80.7
7 Austria Europe 79.8 77.6
8 Bahrain Asia 75.6 70.7
9 Bangladesh Asia 64.1 70.7
10 Belgium Europe 79.4 77.6Notice that all rows from the same continent have the same value for continent_lifeExp. Note that even though this example defines a single value for each continent, this value is repeated for all rows within the continent.
summarise/summarize: define a variable that is a summary of other variables
The summarise() (or summarize()) function aggregates across the rows of the data frame. For instance, you can calculate the average life expectancy, as well as the total GDP.
gapminder %>%
summarise(mean_lifeExp = mean(lifeExp),
total_gdp = sum(gdp)) mean_lifeExp total_gdp
1 59.47444 3.183235e+14The summarise function plays very nicely with the group_by() function. For instance, by first grouping by year and then calculating the average life expectancy and total GDP for each year.
gapminder %>%
group_by(year) %>%
summarise(mean_lifeExp = mean(lifeExp),
total_gdp = sum(gdp)) # A tibble: 12 × 3
year mean_lifeExp total_gdp
<int> <dbl> <dbl>
1 1952 49.1 7.04e12
2 1957 51.5 8.90e12
3 1962 53.6 1.10e13
4 1967 55.7 1.42e13
5 1972 57.6 1.84e13
6 1977 59.6 2.23e13
7 1982 61.5 2.54e13
8 1987 63.2 3.01e13
9 1992 64.2 3.45e13
10 1997 65.0 4.10e13
11 2002 65.7 4.73e13
12 2007 67.0 5.81e13Note that since these are summaries of the data frame itself, I just want to print them out, rather than re-defining the gapminder data frame to be equal to these summaries. And since I won’t be using them for anything other than to look at, I don’t need to define them as a variable.
More dplyr functions
Other dplyr functions that are incredibly useful include:
rename() for renaming variables of the data frame
gapminder %>%
rename(gdp_per_capita = gdpPercap,
life_exp = lifeExp) %>%
head country year pop continent life_exp gdp_per_capita gdp
1 Afghanistan 1952 8425333 Asia 28.801 779.4453 6567086330
2 Afghanistan 1957 9240934 Asia 30.332 820.8530 7585448670
3 Afghanistan 1962 10267083 Asia 31.997 853.1007 8758855797
4 Afghanistan 1967 11537966 Asia 34.020 836.1971 9648014150
5 Afghanistan 1972 13079460 Asia 36.088 739.9811 9678553274
6 Afghanistan 1977 14880372 Asia 38.438 786.1134 11697659231distinct() for extracting the distinct values of a variable
gapminder %>%
distinct(continent) continent
1 Asia
2 Europe
3 Africa
4 Americas
5 Oceaniasample_n() and sample_frac() for taking random samples of rows
gapminder %>%
sample_n(2) country year pop continent lifeExp gdpPercap gdp
1 Eritrea 1977 2512642 Africa 44.535 505.7538 1270778259
2 Lesotho 1957 813338 Africa 45.047 335.9971 273279222count() for counting the number of rows with each value of a categorical variable
gapminder %>%
count(continent) continent n
1 Africa 624
2 Americas 300
3 Asia 396
4 Europe 360
5 Oceania 24transmute() for doing a mutate and select at the same time: only the variables defined in the mutation are retained.
gapminder %>%
transmute(gdp = gdpPercap * pop) %>%
head gdp
1 6567086330
2 7585448670
3 8758855797
4 9648014150
5 9678553274
6 11697659231Advanced dplyr practitioners will eventually want to learn about scoped verbs.
Visualization: ggplot2
The first tidyverse package I ever learnt was ggplot2 (the more popular older sibling of the non-existent ggplot1). Ggplot2 is the data visualization package made by Hadley Wickham, and it is based a set of principles called the layered grammar of graphics. The basic idea is that a ggplot graphic layers geometric objects (circles, lines, etc), themes, and scales ontop of data. The form of the geometric object is defined by a geom_xxx() function and the properties (position, size, colour) of the geometric objects that based on the data variables are specified by the aesthetic (aes()) function (within the geom_xxx() function).
The base layer of any ggplot graph is the empty ggplot layer defined by the ggplot() function, which describes the data frame that the plot will be based on. I haven’t told ggplot what type of geometric object(s) I want yet, nor how the variables should be mapped to the geometric objects, so I just have a blank plot.
ggplot(gapminder)
Since you now know about pipes, you could pipe in the data that you want to plot. Piping makes it easy to do intermediate manipulations that you don’t necessarily want to save in the data frame itself, such as only plotting one year’s worth of data
gapminder %>%
filter(year == 2007) %>%
ggplot()
Adding geom layers
Next, I will add a “geom” layer to our ggplot object.
Layers are added to ggplot objects using +, instead of %>%, since you are not explicitly piping the LHS into each subsequent layer (we are actually adding a layer on top). The error messages have recently been improved to warn you if you are accidentally using a pipe %>% to add layers to ggplot objects (which, once you start piping everything into everything, becomes an easy mistake to make).
Probably the most common geom layer is geom_point. Inside geom_point(), you will specify the aesthetic mappings from the variables to the geometric objects that you want. For instance, if you want to plot a scatterplot with gdpPercap on the x-axis and lifeExp on the y-axis, then you would add a geom_point() geometric layer with relevant aesthetic function: geom_point(aes(x = gdpPercap, y = lifeExp)).
# describe the base ggplot object and tell it what data we are interested in along with the aesthetic mapping
gapminder %>%
filter(year == 2007) %>%
ggplot() +
# add a points layer on top
geom_point(aes(x = gdpPercap, y = lifeExp))
We could also add a smoothed trend line layer on top of the points using geom_smooth().
# describe the base ggplot object and tell it what data we are interested in along with the aesthetic mapping
gapminder %>%
filter(year == 2007) %>%
ggplot() +
# add a points layer on top
geom_point(aes(x = gdpPercap, y = lifeExp)) +
# add a smoothed LOESS layer
geom_smooth(aes(x = gdpPercap, y = lifeExp), method = "loess")`geom_smooth()` using formula = 'y ~ x'
Note that since the aesthetics for geom_point() and geom_smooth() are the same, you might want to just specify global aesthetics in the ggplot() function, rather than layer-specific aesthetics.
# describe the base ggplot object and tell it what data we are interested in along with the aesthetic mapping
gapminder %>%
filter(year == 2007) %>%
# specify global aesthetic mappings
ggplot(aes(x = gdpPercap, y = lifeExp)) +
# add a points layer on top
geom_point() +
# add a smoothed LOESS layer
geom_smooth(method = "loess")`geom_smooth()` using formula = 'y ~ x'
We could also combine a points geom layer with a line geom layer, or any other type of geom layer. Line plots work well for plotting time series, so below we plot the average life expectancy over time using both point and line layers.
Here you can do some clever combinations of dplyr manipulations with ggplot2 by summarising life expectancy by year and piping the results into a ggplot without having to define any intermediate variables.
gapminder %>%
# calcualte the average life expectency for each year
group_by(year) %>%
summarise(avg_lifeExp = mean(lifeExp)) %>%
ungroup() %>%
# specify global aesthetic mappings
ggplot(aes(x = year, y = avg_lifeExp)) +
# add a points layer on top
geom_point() +
# add a line layer on top
geom_line()
If you wanted to have a separate line on our plot for each continent (rather than an aggregated line across all continents), you don’t need to add an individual layer for each continent to get the following plot:
`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.
Instead, start by also grouping by continent when you calculate the average life expectency by year.
gapminder %>%
group_by(continent, year) %>%
summarise(avg_lifeExp = mean(lifeExp))`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.# A tibble: 60 × 3
# Groups: continent [5]
continent year avg_lifeExp
<chr> <int> <dbl>
1 Africa 1952 39.1
2 Africa 1957 41.3
3 Africa 1962 43.3
4 Africa 1967 45.3
5 Africa 1972 47.5
6 Africa 1977 49.6
7 Africa 1982 51.6
8 Africa 1987 53.3
9 Africa 1992 53.6
10 Africa 1997 53.6
# … with 50 more rowsHowever if you try to use the same code as above to plot a line on the country-year grouped data frame, you get a weird zig-zag pattern.
gapminder %>%
group_by(continent, year) %>%
summarise(avg_lifeExp = mean(lifeExp)) %>%
ungroup() %>%
ggplot() +
# add a points layer on top
geom_point(aes(x = year, y = avg_lifeExp)) +
# add a lines layer ontop
geom_line(aes(x = year, y = avg_lifeExp))`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.
This happens because you now have multiple average life expectancy values for each year, but you haven’t specified which ones go together. To fix this plot, you need to specify how the rows are grouped together (i.e. which variable defines the individual lines) by specifying the group = continent argument in the aes() function of the geom_line() layer.
gapminder %>%
group_by(continent, year) %>%
summarise(avg_lifeExp = mean(lifeExp)) %>%
ggplot() +
# add a points layer on top
geom_point(aes(x = year, y = avg_lifeExp)) +
# add a lines layer on top that is grouped by continent
geom_line(aes(x = year, y = avg_lifeExp, group = continent))`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.
More aesthetic mappings based on variables
So far we have only specified the x- and y-position aesthetic mappings from the data to the geom objects. But you can also specify other types of aesthetic mappings, such as using a variable to specify the colour of the points.
If you want all of the points to be the same colour, you can specify a global point colour argument (that lies outside the aes() function).
gapminder %>%
ggplot() +
geom_point(aes(x = gdpPercap, y = lifeExp),
col = "cornflowerblue")
However, if you wanted to use a variable from the data frame to define the colour (or any other aesthetic feature) of the geoms, you will need to include it inside the aes() function.
gapminder %>%
ggplot() +
geom_point(aes(x = gdpPercap,
y = lifeExp,
col = continent))
Note that the continent variable does not specify the colours themselves: this is done automatically. You can specify the colours you want yourself by adding a scale layer for colour.
gapminder %>%
ggplot() +
geom_point(aes(x = gdpPercap,
y = lifeExp,
col = continent)) +
scale_colour_manual(values = c("orange", "red4", "purple", "darkgreen", "blue"))
There are lots of types of scales that you can use for every type of aesthetic mapping (including x- and y-positions), and typically scales are specific to whether your variable using in the aesthetic mapping is discrete or continuous.
We could also add aesthetic mappings for other features such as shape, size, transparency (alpha) and more! For example, changing the size based on population:
gapminder %>%
ggplot() +
geom_point(aes(x = gdpPercap, y = lifeExp,
col = continent, size = pop),
alpha = 0.5)
For the line plot example above where we plotted an average life expectancy time line for each continent, instead of specifying a group argument, you could instead specify a colour argument to be continent. This will will automatically group and colour by continent.
gapminder %>%
group_by(continent, year) %>%
summarise(avg_lifeExp = mean(lifeExp)) %>%
# specify global aesthetic mappings
ggplot() +
# add a points layer on top
geom_line(aes(x = year, y = avg_lifeExp, colour = continent))`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.
Other types of layers
So far, we have only seen scatterplots (points) and line plots, however, there are many other geoms you could add, including:
Histograms
Histograms only require an x-aesthetic (the y-aesthetic is a count by default, but you can force it to be a density by specifying y = ..density..).
gapminder %>%
ggplot() +
geom_histogram(aes(x = lifeExp), binwidth = 3)
Boxplots
Boxplots are automatically grouped by the x-aesthetic provided (e.g. continent in the plot below). To colour boxplots, use the fill argument instead of the col (or color/colour) argument.
gapminder %>%
ggplot() +
geom_boxplot(aes(x = continent, y = lifeExp, fill = continent))
Faceting
You can create a grid (or “facet”) of plots separated by a categorical variable of your choosing (e.g. continent) by adding a facet layer.
gapminder %>%
ggplot() +
geom_point(aes(x = gdpPercap, y = lifeExp)) +
facet_wrap(~continent, ncol = 2)
Customizing ggplot2
While we have stayed within the default ggplot2 functionalities here, there is a lot you can do with ggplot2. For instance, with practice, you will learn how to produce highly-customized plots by combining many layers together. As motivation, here is a much more beautiful plot that can be made with ggplot2:
gapminder %>%
filter(year == 2007) %>%
ggplot() +
# add scatter points
geom_point(aes(x = gdpPercap, y = lifeExp, col = continent, size = pop),
alpha = 0.5) +
# add some text annotations for the very large countries
geom_text(aes(x = gdpPercap, y = lifeExp + 3, label = country),
col = "grey50",
data = filter(gapminder, year == 2007, pop > 1000000000 | country %in% c("Nigeria", "United States"))) +
# clean the axes names and breaks
scale_x_log10(limits = c(200, 60000)) +
# change labels
labs(title = "GDP versus life expectancy in 2007",
x = "GDP per capita (log scale)",
y = "Life expectancy",
size = "Population",
col = "Continent") +
# change the size scale
scale_size(range = c(0.1, 10),
# remove size legend
guide = "none") +
# add a nicer theme
theme_classic() +
# place legend at top and grey axis lines
theme(legend.position = "top")
In part two of this post on the tidyverse, you will see some ggplot2 code (under the guise of learning about factors) that makes this plot:
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
If you’d like to learn more about ggplot2, such as themes, scales and advanced geoms, check out my more detailed ggplot2 blog post.
If the tidyverse is new to you, I suggest that you stop here for now. Focus on incorporating piping, dplyr, and ggplot2 into every analysis that you do for the next few months (even if it would initially be quicker to use base R versions). When you feel comfortable with your new skills, move onto part two of this blog post and start to incorporate the remaining tidyverse packages (below) into your analytic workflow. Trying to learn everything at once is a sure-fire way to become discouraged. First get comfortable with the main ideas, then learn some more.