# write this code directly into the console:
install.packages("tidyverse")6 Data Frames in the Tidyverse
Learn R live!
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R is an open source programming language, which means that anyone can extend it by creating their own R functions. When someone creates a collection of related R functions, they typically bundle them into what is called a “package” or a “library” (I use these terms interchangeably), which can then be downloaded and used by other people.
I don’t think it’s an exaggeration to say that you probably wouldn’t be learning R today were it not for one particular package called the “tidyverse” (so named because it helps you create and work with “tidy” data). The tidyverse is actually a collection of several important R packages, including one called “dplyr” and another one called “ggplot2” (this chapter will introduce dplyr and you’ll get to know ggplot2 in the next chapter).
Although the tidyverse was originally created by Hadley Wickham, it has since grown to include contributions from hundreds of brilliant R developers. Together, they have revolutionized the way we use R for the better. The tidyverse and its impacts are a true testament to the power of the open source community.
6.1 Installing and Loading R packages
R packages are collections of “add-on” R functions that you can “load” into your R session to provide additional functionality.
To use functions from a package, you need to do two things:
Install the package on your computer. You only need to do this once.
Load your package into your current R session. You need to do this every time you start a new R session (i.e., every time you open up RStudio).
I like to think of installing an R package like installing a new application onto your computer. You only ever need to install the application once (unless you’re updating it), but you need to open it every time you want to use it (in this analogy, loading a library is like “opening” your application).
6.1.1 Installing an R package
So to get started with dplyr, ggplot2, and the other tidyverse packages, we need to install them. But to make our lives easier, we can simultaneously install all the tidyverse packages (ggplot2, dplyr, reshape, purrr, readr, and many others) by just installing the “tidyverse” package itself.
To install the “tidyverse” package (or any other package), write the following code directly into your console (I do not recommend saving this code in a quarto document or R script, because once you’ve run this code, you don’t need to run it again):
Note that you need to be connected to the internet to install a package (since it’s like downloading an application from the internet.)
6.1.2 Loading an R package
Once you’ve installed it, every time you want to use an installed R package in a new R session, you need to “load” it using the library() function.
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsSince you need to run this every time you open RStudio, you should include this code in the first chunk of your quarto document or R script.
When you load libraries into R, you’ll often see a lot of message “output” (what I like to call “chatter”). This output (such as that printed below the library(tidyverse) chunk above) is completely normal. But if you’re loading a library in a quarto document, you might want to hide the message output in the resulting rendered document. To do that, you can use the chunk option #| message: false, as in:
```{r}
#| message: false
library(dplyr)
```Then the library loading “chatter” will be hidden from the rendered HTML document.
6.2 Tibbles and the read_csv() function
In the last chapter, we used a “base R” function (read.csv()) to load our gapminder dataset. The term “base R” refers to functions that are always available in R and do not require you to load any additional libraries.
While it’s perfectly fine to continue to use this read.csv() function, I recommend instead using a slightly different function that has an underscore instead of a period in its name: read_csv(). This function does pretty much the same thing as read.csv() but it is part of the tidyverse and is a little bit more efficient and user-friendly than read.csv().
Let’s use read_csv() (the tidyverse version of read.csv()) to load the gapminder dataset:
gapminder <- read_csv("data/gapminder.csv")Rows: 1704 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): country, continent
dbl (4): year, lifeExp, pop, gdpPercap
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.This function also tends to print out some “chatter” message text, which I can hide from my rendered quarto output by providing the #| message: false chunk option at the top of the relevant code chunk.
If you ran this in your own console and you got an error saying “Error in read_csv(”data/gapminder.csv”) : could not find function ”read_csv””, make sure you have installed the tidyverse and have run the code library(tidyverse) in your console! R can only find the read_csv() function if you have loaded the tidyverse!
Now let’s take a look at gapminder (without using head())
gapminder# A tibble: 1,704 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.
7 Afghanistan Asia 1982 39.9 12881816 978.
8 Afghanistan Asia 1987 40.8 13867957 852.
9 Afghanistan Asia 1992 41.7 16317921 649.
10 Afghanistan Asia 1997 41.8 22227415 635.
# ℹ 1,694 more rowsDo you notice any differences between this version of gapminder (that has been loaded using the tidyverse read_csv()) and the version from the previous chapter (that was loaded using the base R read.csv() function)?
To make your life easier, here is the version of gapminder that we loaded with the base R read.csv() function:
gapminder_base_r <- read.csv("data/gapminder.csv")
gapminder_base_r country continent year lifeExp pop gdpPercap
1 Afghanistan Asia 1952 28.80100 8425333 779.4453
2 Afghanistan Asia 1957 30.33200 9240934 820.8530
3 Afghanistan Asia 1962 31.99700 10267083 853.1007
4 Afghanistan Asia 1967 34.02000 11537966 836.1971
5 Afghanistan Asia 1972 36.08800 13079460 739.9811
6 Afghanistan Asia 1977 38.43800 14880372 786.1134
7 Afghanistan Asia 1982 39.85400 12881816 978.0114
8 Afghanistan Asia 1987 40.82200 13867957 852.3959
9 Afghanistan Asia 1992 41.67400 16317921 649.3414
10 Afghanistan Asia 1997 41.76300 22227415 635.3414
11 Afghanistan Asia 2002 42.12900 25268405 726.7341
12 Afghanistan Asia 2007 43.82800 31889923 974.5803
13 Albania Europe 1952 55.23000 1282697 1601.0561
14 Albania Europe 1957 59.28000 1476505 1942.2842
15 Albania Europe 1962 64.82000 1728137 2312.8890
16 Albania Europe 1967 66.22000 1984060 2760.1969
17 Albania Europe 1972 67.69000 2263554 3313.4222
18 Albania Europe 1977 68.93000 2509048 3533.0039
19 Albania Europe 1982 70.42000 2780097 3630.8807
20 Albania Europe 1987 72.00000 3075321 3738.9327
21 Albania Europe 1992 71.58100 3326498 2497.4379
22 Albania Europe 1997 72.95000 3428038 3193.0546
23 Albania Europe 2002 75.65100 3508512 4604.2117
24 Albania Europe 2007 76.42300 3600523 5937.0295
25 Algeria Africa 1952 43.07700 9279525 2449.0082
26 Algeria Africa 1957 45.68500 10270856 3013.9760
27 Algeria Africa 1962 48.30300 11000948 2550.8169
28 Algeria Africa 1967 51.40700 12760499 3246.9918
29 Algeria Africa 1972 54.51800 14760787 4182.6638
30 Algeria Africa 1977 58.01400 17152804 4910.4168
31 Algeria Africa 1982 61.36800 20033753 5745.1602
32 Algeria Africa 1987 65.79900 23254956 5681.3585
33 Algeria Africa 1992 67.74400 26298373 5023.2166
34 Algeria Africa 1997 69.15200 29072015 4797.2951
35 Algeria Africa 2002 70.99400 31287142 5288.0404
36 Algeria Africa 2007 72.30100 33333216 6223.3675
37 Angola Africa 1952 30.01500 4232095 3520.6103
38 Angola Africa 1957 31.99900 4561361 3827.9405
39 Angola Africa 1962 34.00000 4826015 4269.2767
40 Angola Africa 1967 35.98500 5247469 5522.7764
41 Angola Africa 1972 37.92800 5894858 5473.2880
42 Angola Africa 1977 39.48300 6162675 3008.6474
43 Angola Africa 1982 39.94200 7016384 2756.9537
44 Angola Africa 1987 39.90600 7874230 2430.2083
45 Angola Africa 1992 40.64700 8735988 2627.8457
46 Angola Africa 1997 40.96300 9875024 2277.1409
47 Angola Africa 2002 41.00300 10866106 2773.2873
48 Angola Africa 2007 42.73100 12420476 4797.2313
49 Argentina Americas 1952 62.48500 17876956 5911.3151
50 Argentina Americas 1957 64.39900 19610538 6856.8562
51 Argentina Americas 1962 65.14200 21283783 7133.1660
52 Argentina Americas 1967 65.63400 22934225 8052.9530
53 Argentina Americas 1972 67.06500 24779799 9443.0385
54 Argentina Americas 1977 68.48100 26983828 10079.0267
55 Argentina Americas 1982 69.94200 29341374 8997.8974
56 Argentina Americas 1987 70.77400 31620918 9139.6714
57 Argentina Americas 1992 71.86800 33958947 9308.4187
58 Argentina Americas 1997 73.27500 36203463 10967.2820
59 Argentina Americas 2002 74.34000 38331121 8797.6407
60 Argentina Americas 2007 75.32000 40301927 12779.3796
61 Australia Oceania 1952 69.12000 8691212 10039.5956
62 Australia Oceania 1957 70.33000 9712569 10949.6496
63 Australia Oceania 1962 70.93000 10794968 12217.2269
64 Australia Oceania 1967 71.10000 11872264 14526.1246
65 Australia Oceania 1972 71.93000 13177000 16788.6295
66 Australia Oceania 1977 73.49000 14074100 18334.1975
67 Australia Oceania 1982 74.74000 15184200 19477.0093
68 Australia Oceania 1987 76.32000 16257249 21888.8890
69 Australia Oceania 1992 77.56000 17481977 23424.7668
70 Australia Oceania 1997 78.83000 18565243 26997.9366
71 Australia Oceania 2002 80.37000 19546792 30687.7547
72 Australia Oceania 2007 81.23500 20434176 34435.3674
73 Austria Europe 1952 66.80000 6927772 6137.0765
74 Austria Europe 1957 67.48000 6965860 8842.5980
75 Austria Europe 1962 69.54000 7129864 10750.7211
76 Austria Europe 1967 70.14000 7376998 12834.6024
77 Austria Europe 1972 70.63000 7544201 16661.6256
78 Austria Europe 1977 72.17000 7568430 19749.4223
79 Austria Europe 1982 73.18000 7574613 21597.0836
80 Austria Europe 1987 74.94000 7578903 23687.8261
81 Austria Europe 1992 76.04000 7914969 27042.0187
82 Austria Europe 1997 77.51000 8069876 29095.9207
83 Austria Europe 2002 78.98000 8148312 32417.6077
84 Austria Europe 2007 79.82900 8199783 36126.4927
85 Bahrain Asia 1952 50.93900 120447 9867.0848
86 Bahrain Asia 1957 53.83200 138655 11635.7995
87 Bahrain Asia 1962 56.92300 171863 12753.2751
88 Bahrain Asia 1967 59.92300 202182 14804.6727
89 Bahrain Asia 1972 63.30000 230800 18268.6584
90 Bahrain Asia 1977 65.59300 297410 19340.1020
91 Bahrain Asia 1982 69.05200 377967 19211.1473
92 Bahrain Asia 1987 70.75000 454612 18524.0241
93 Bahrain Asia 1992 72.60100 529491 19035.5792
94 Bahrain Asia 1997 73.92500 598561 20292.0168
95 Bahrain Asia 2002 74.79500 656397 23403.5593
96 Bahrain Asia 2007 75.63500 708573 29796.0483
97 Bangladesh Asia 1952 37.48400 46886859 684.2442
98 Bangladesh Asia 1957 39.34800 51365468 661.6375
99 Bangladesh Asia 1962 41.21600 56839289 686.3416
100 Bangladesh Asia 1967 43.45300 62821884 721.1861
101 Bangladesh Asia 1972 45.25200 70759295 630.2336
102 Bangladesh Asia 1977 46.92300 80428306 659.8772
103 Bangladesh Asia 1982 50.00900 93074406 676.9819
104 Bangladesh Asia 1987 52.81900 103764241 751.9794
105 Bangladesh Asia 1992 56.01800 113704579 837.8102
106 Bangladesh Asia 1997 59.41200 123315288 972.7700
107 Bangladesh Asia 2002 62.01300 135656790 1136.3904
108 Bangladesh Asia 2007 64.06200 150448339 1391.2538
109 Belgium Europe 1952 68.00000 8730405 8343.1051
110 Belgium Europe 1957 69.24000 8989111 9714.9606
111 Belgium Europe 1962 70.25000 9218400 10991.2068
112 Belgium Europe 1967 70.94000 9556500 13149.0412
113 Belgium Europe 1972 71.44000 9709100 16672.1436
114 Belgium Europe 1977 72.80000 9821800 19117.9745
115 Belgium Europe 1982 73.93000 9856303 20979.8459
116 Belgium Europe 1987 75.35000 9870200 22525.5631
117 Belgium Europe 1992 76.46000 10045622 25575.5707
118 Belgium Europe 1997 77.53000 10199787 27561.1966
119 Belgium Europe 2002 78.32000 10311970 30485.8838
120 Belgium Europe 2007 79.44100 10392226 33692.6051
121 Benin Africa 1952 38.22300 1738315 1062.7522
122 Benin Africa 1957 40.35800 1925173 959.6011
123 Benin Africa 1962 42.61800 2151895 949.4991
124 Benin Africa 1967 44.88500 2427334 1035.8314
125 Benin Africa 1972 47.01400 2761407 1085.7969
126 Benin Africa 1977 49.19000 3168267 1029.1613
127 Benin Africa 1982 50.90400 3641603 1277.8976
128 Benin Africa 1987 52.33700 4243788 1225.8560
129 Benin Africa 1992 53.91900 4981671 1191.2077
130 Benin Africa 1997 54.77700 6066080 1232.9753
131 Benin Africa 2002 54.40600 7026113 1372.8779
132 Benin Africa 2007 56.72800 8078314 1441.2849
133 Bolivia Americas 1952 40.41400 2883315 2677.3263
134 Bolivia Americas 1957 41.89000 3211738 2127.6863
135 Bolivia Americas 1962 43.42800 3593918 2180.9725
136 Bolivia Americas 1967 45.03200 4040665 2586.8861
137 Bolivia Americas 1972 46.71400 4565872 2980.3313
138 Bolivia Americas 1977 50.02300 5079716 3548.0978
139 Bolivia Americas 1982 53.85900 5642224 3156.5105
140 Bolivia Americas 1987 57.25100 6156369 2753.6915
141 Bolivia Americas 1992 59.95700 6893451 2961.6997
142 Bolivia Americas 1997 62.05000 7693188 3326.1432
143 Bolivia Americas 2002 63.88300 8445134 3413.2627
144 Bolivia Americas 2007 65.55400 9119152 3822.1371
145 Bosnia and Herzegovina Europe 1952 53.82000 2791000 973.5332
146 Bosnia and Herzegovina Europe 1957 58.45000 3076000 1353.9892
147 Bosnia and Herzegovina Europe 1962 61.93000 3349000 1709.6837
148 Bosnia and Herzegovina Europe 1967 64.79000 3585000 2172.3524
149 Bosnia and Herzegovina Europe 1972 67.45000 3819000 2860.1698
150 Bosnia and Herzegovina Europe 1977 69.86000 4086000 3528.4813
151 Bosnia and Herzegovina Europe 1982 70.69000 4172693 4126.6132
152 Bosnia and Herzegovina Europe 1987 71.14000 4338977 4314.1148
153 Bosnia and Herzegovina Europe 1992 72.17800 4256013 2546.7814
154 Bosnia and Herzegovina Europe 1997 73.24400 3607000 4766.3559
155 Bosnia and Herzegovina Europe 2002 74.09000 4165416 6018.9752
156 Bosnia and Herzegovina Europe 2007 74.85200 4552198 7446.2988
157 Botswana Africa 1952 47.62200 442308 851.2411
158 Botswana Africa 1957 49.61800 474639 918.2325
159 Botswana Africa 1962 51.52000 512764 983.6540
160 Botswana Africa 1967 53.29800 553541 1214.7093
161 Botswana Africa 1972 56.02400 619351 2263.6111
162 Botswana Africa 1977 59.31900 781472 3214.8578
163 Botswana Africa 1982 61.48400 970347 4551.1421
164 Botswana Africa 1987 63.62200 1151184 6205.8839
165 Botswana Africa 1992 62.74500 1342614 7954.1116
166 Botswana Africa 1997 52.55600 1536536 8647.1423
167 Botswana Africa 2002 46.63400 1630347 11003.6051
168 Botswana Africa 2007 50.72800 1639131 12569.8518
169 Brazil Americas 1952 50.91700 56602560 2108.9444
170 Brazil Americas 1957 53.28500 65551171 2487.3660
171 Brazil Americas 1962 55.66500 76039390 3336.5858
172 Brazil Americas 1967 57.63200 88049823 3429.8644
173 Brazil Americas 1972 59.50400 100840058 4985.7115
174 Brazil Americas 1977 61.48900 114313951 6660.1187
175 Brazil Americas 1982 63.33600 128962939 7030.8359
176 Brazil Americas 1987 65.20500 142938076 7807.0958
177 Brazil Americas 1992 67.05700 155975974 6950.2830
178 Brazil Americas 1997 69.38800 168546719 7957.9808
179 Brazil Americas 2002 71.00600 179914212 8131.2128
180 Brazil Americas 2007 72.39000 190010647 9065.8008
181 Bulgaria Europe 1952 59.60000 7274900 2444.2866
182 Bulgaria Europe 1957 66.61000 7651254 3008.6707
183 Bulgaria Europe 1962 69.51000 8012946 4254.3378
184 Bulgaria Europe 1967 70.42000 8310226 5577.0028
185 Bulgaria Europe 1972 70.90000 8576200 6597.4944
186 Bulgaria Europe 1977 70.81000 8797022 7612.2404
187 Bulgaria Europe 1982 71.08000 8892098 8224.1916
188 Bulgaria Europe 1987 71.34000 8971958 8239.8548
189 Bulgaria Europe 1992 71.19000 8658506 6302.6234
190 Bulgaria Europe 1997 70.32000 8066057 5970.3888
191 Bulgaria Europe 2002 72.14000 7661799 7696.7777
192 Bulgaria Europe 2007 73.00500 7322858 10680.7928
193 Burkina Faso Africa 1952 31.97500 4469979 543.2552
194 Burkina Faso Africa 1957 34.90600 4713416 617.1835
195 Burkina Faso Africa 1962 37.81400 4919632 722.5120
196 Burkina Faso Africa 1967 40.69700 5127935 794.8266
197 Burkina Faso Africa 1972 43.59100 5433886 854.7360
198 Burkina Faso Africa 1977 46.13700 5889574 743.3870
199 Burkina Faso Africa 1982 48.12200 6634596 807.1986
200 Burkina Faso Africa 1987 49.55700 7586551 912.0631
201 Burkina Faso Africa 1992 50.26000 8878303 931.7528
202 Burkina Faso Africa 1997 50.32400 10352843 946.2950
203 Burkina Faso Africa 2002 50.65000 12251209 1037.6452
204 Burkina Faso Africa 2007 52.29500 14326203 1217.0330
205 Burundi Africa 1952 39.03100 2445618 339.2965
206 Burundi Africa 1957 40.53300 2667518 379.5646
207 Burundi Africa 1962 42.04500 2961915 355.2032
208 Burundi Africa 1967 43.54800 3330989 412.9775
209 Burundi Africa 1972 44.05700 3529983 464.0995
210 Burundi Africa 1977 45.91000 3834415 556.1033
211 Burundi Africa 1982 47.47100 4580410 559.6032
212 Burundi Africa 1987 48.21100 5126023 621.8188
213 Burundi Africa 1992 44.73600 5809236 631.6999
214 Burundi Africa 1997 45.32600 6121610 463.1151
215 Burundi Africa 2002 47.36000 7021078 446.4035
216 Burundi Africa 2007 49.58000 8390505 430.0707
217 Cambodia Asia 1952 39.41700 4693836 368.4693
218 Cambodia Asia 1957 41.36600 5322536 434.0383
219 Cambodia Asia 1962 43.41500 6083619 496.9136
220 Cambodia Asia 1967 45.41500 6960067 523.4323
221 Cambodia Asia 1972 40.31700 7450606 421.6240
222 Cambodia Asia 1977 31.22000 6978607 524.9722
223 Cambodia Asia 1982 50.95700 7272485 624.4755
224 Cambodia Asia 1987 53.91400 8371791 683.8956
225 Cambodia Asia 1992 55.80300 10150094 682.3032
226 Cambodia Asia 1997 56.53400 11782962 734.2852
227 Cambodia Asia 2002 56.75200 12926707 896.2260
228 Cambodia Asia 2007 59.72300 14131858 1713.7787
229 Cameroon Africa 1952 38.52300 5009067 1172.6677
230 Cameroon Africa 1957 40.42800 5359923 1313.0481
231 Cameroon Africa 1962 42.64300 5793633 1399.6074
232 Cameroon Africa 1967 44.79900 6335506 1508.4531
233 Cameroon Africa 1972 47.04900 7021028 1684.1465
234 Cameroon Africa 1977 49.35500 7959865 1783.4329
235 Cameroon Africa 1982 52.96100 9250831 2367.9833
236 Cameroon Africa 1987 54.98500 10780667 2602.6642
237 Cameroon Africa 1992 54.31400 12467171 1793.1633
238 Cameroon Africa 1997 52.19900 14195809 1694.3375
239 Cameroon Africa 2002 49.85600 15929988 1934.0114
240 Cameroon Africa 2007 50.43000 17696293 2042.0952
241 Canada Americas 1952 68.75000 14785584 11367.1611
242 Canada Americas 1957 69.96000 17010154 12489.9501
243 Canada Americas 1962 71.30000 18985849 13462.4855
244 Canada Americas 1967 72.13000 20819767 16076.5880
245 Canada Americas 1972 72.88000 22284500 18970.5709
246 Canada Americas 1977 74.21000 23796400 22090.8831
247 Canada Americas 1982 75.76000 25201900 22898.7921
248 Canada Americas 1987 76.86000 26549700 26626.5150
249 Canada Americas 1992 77.95000 28523502 26342.8843
250 Canada Americas 1997 78.61000 30305843 28954.9259
251 Canada Americas 2002 79.77000 31902268 33328.9651
252 Canada Americas 2007 80.65300 33390141 36319.2350
253 Central African Republic Africa 1952 35.46300 1291695 1071.3107
254 Central African Republic Africa 1957 37.46400 1392284 1190.8443
255 Central African Republic Africa 1962 39.47500 1523478 1193.0688
256 Central African Republic Africa 1967 41.47800 1733638 1136.0566
257 Central African Republic Africa 1972 43.45700 1927260 1070.0133
258 Central African Republic Africa 1977 46.77500 2167533 1109.3743
259 Central African Republic Africa 1982 48.29500 2476971 956.7530
260 Central African Republic Africa 1987 50.48500 2840009 844.8764
261 Central African Republic Africa 1992 49.39600 3265124 747.9055
262 Central African Republic Africa 1997 46.06600 3696513 740.5063
263 Central African Republic Africa 2002 43.30800 4048013 738.6906
264 Central African Republic Africa 2007 44.74100 4369038 706.0165
265 Chad Africa 1952 38.09200 2682462 1178.6659
266 Chad Africa 1957 39.88100 2894855 1308.4956
267 Chad Africa 1962 41.71600 3150417 1389.8176
268 Chad Africa 1967 43.60100 3495967 1196.8106
269 Chad Africa 1972 45.56900 3899068 1104.1040
270 Chad Africa 1977 47.38300 4388260 1133.9850
271 Chad Africa 1982 49.51700 4875118 797.9081
272 Chad Africa 1987 51.05100 5498955 952.3861
273 Chad Africa 1992 51.72400 6429417 1058.0643
274 Chad Africa 1997 51.57300 7562011 1004.9614
275 Chad Africa 2002 50.52500 8835739 1156.1819
276 Chad Africa 2007 50.65100 10238807 1704.0637
277 Chile Americas 1952 54.74500 6377619 3939.9788
278 Chile Americas 1957 56.07400 7048426 4315.6227
279 Chile Americas 1962 57.92400 7961258 4519.0943
280 Chile Americas 1967 60.52300 8858908 5106.6543
281 Chile Americas 1972 63.44100 9717524 5494.0244
282 Chile Americas 1977 67.05200 10599793 4756.7638
283 Chile Americas 1982 70.56500 11487112 5095.6657
284 Chile Americas 1987 72.49200 12463354 5547.0638
285 Chile Americas 1992 74.12600 13572994 7596.1260
286 Chile Americas 1997 75.81600 14599929 10118.0532
287 Chile Americas 2002 77.86000 15497046 10778.7838
288 Chile Americas 2007 78.55300 16284741 13171.6388
289 China Asia 1952 44.00000 556263527 400.4486
290 China Asia 1957 50.54896 637408000 575.9870
291 China Asia 1962 44.50136 665770000 487.6740
292 China Asia 1967 58.38112 754550000 612.7057
293 China Asia 1972 63.11888 862030000 676.9001
294 China Asia 1977 63.96736 943455000 741.2375
295 China Asia 1982 65.52500 1000281000 962.4214
296 China Asia 1987 67.27400 1084035000 1378.9040
297 China Asia 1992 68.69000 1164970000 1655.7842
298 China Asia 1997 70.42600 1230075000 2289.2341
299 China Asia 2002 72.02800 1280400000 3119.2809
300 China Asia 2007 72.96100 1318683096 4959.1149
301 Colombia Americas 1952 50.64300 12350771 2144.1151
302 Colombia Americas 1957 55.11800 14485993 2323.8056
303 Colombia Americas 1962 57.86300 17009885 2492.3511
304 Colombia Americas 1967 59.96300 19764027 2678.7298
305 Colombia Americas 1972 61.62300 22542890 3264.6600
306 Colombia Americas 1977 63.83700 25094412 3815.8079
307 Colombia Americas 1982 66.65300 27764644 4397.5757
308 Colombia Americas 1987 67.76800 30964245 4903.2191
309 Colombia Americas 1992 68.42100 34202721 5444.6486
310 Colombia Americas 1997 70.31300 37657830 6117.3617
311 Colombia Americas 2002 71.68200 41008227 5755.2600
312 Colombia Americas 2007 72.88900 44227550 7006.5804
313 Comoros Africa 1952 40.71500 153936 1102.9909
314 Comoros Africa 1957 42.46000 170928 1211.1485
315 Comoros Africa 1962 44.46700 191689 1406.6483
316 Comoros Africa 1967 46.47200 217378 1876.0296
317 Comoros Africa 1972 48.94400 250027 1937.5777
318 Comoros Africa 1977 50.93900 304739 1172.6030
319 Comoros Africa 1982 52.93300 348643 1267.1001
320 Comoros Africa 1987 54.92600 395114 1315.9808
321 Comoros Africa 1992 57.93900 454429 1246.9074
322 Comoros Africa 1997 60.66000 527982 1173.6182
323 Comoros Africa 2002 62.97400 614382 1075.8116
324 Comoros Africa 2007 65.15200 710960 986.1479
325 Congo, Dem. Rep. Africa 1952 39.14300 14100005 780.5423
326 Congo, Dem. Rep. Africa 1957 40.65200 15577932 905.8602
327 Congo, Dem. Rep. Africa 1962 42.12200 17486434 896.3146
328 Congo, Dem. Rep. Africa 1967 44.05600 19941073 861.5932
329 Congo, Dem. Rep. Africa 1972 45.98900 23007669 904.8961
330 Congo, Dem. Rep. Africa 1977 47.80400 26480870 795.7573
331 Congo, Dem. Rep. Africa 1982 47.78400 30646495 673.7478
332 Congo, Dem. Rep. Africa 1987 47.41200 35481645 672.7748
333 Congo, Dem. Rep. Africa 1992 45.54800 41672143 457.7192
334 Congo, Dem. Rep. Africa 1997 42.58700 47798986 312.1884
335 Congo, Dem. Rep. Africa 2002 44.96600 55379852 241.1659
336 Congo, Dem. Rep. Africa 2007 46.46200 64606759 277.5519
337 Congo, Rep. Africa 1952 42.11100 854885 2125.6214
338 Congo, Rep. Africa 1957 45.05300 940458 2315.0566
339 Congo, Rep. Africa 1962 48.43500 1047924 2464.7832
340 Congo, Rep. Africa 1967 52.04000 1179760 2677.9396
341 Congo, Rep. Africa 1972 54.90700 1340458 3213.1527
342 Congo, Rep. Africa 1977 55.62500 1536769 3259.1790
343 Congo, Rep. Africa 1982 56.69500 1774735 4879.5075
344 Congo, Rep. Africa 1987 57.47000 2064095 4201.1949
345 Congo, Rep. Africa 1992 56.43300 2409073 4016.2395
346 Congo, Rep. Africa 1997 52.96200 2800947 3484.1644
347 Congo, Rep. Africa 2002 52.97000 3328795 3484.0620
348 Congo, Rep. Africa 2007 55.32200 3800610 3632.5578
349 Costa Rica Americas 1952 57.20600 926317 2627.0095
350 Costa Rica Americas 1957 60.02600 1112300 2990.0108
351 Costa Rica Americas 1962 62.84200 1345187 3460.9370
352 Costa Rica Americas 1967 65.42400 1588717 4161.7278
353 Costa Rica Americas 1972 67.84900 1834796 5118.1469
354 Costa Rica Americas 1977 70.75000 2108457 5926.8770
355 Costa Rica Americas 1982 73.45000 2424367 5262.7348
356 Costa Rica Americas 1987 74.75200 2799811 5629.9153
357 Costa Rica Americas 1992 75.71300 3173216 6160.4163
358 Costa Rica Americas 1997 77.26000 3518107 6677.0453
359 Costa Rica Americas 2002 78.12300 3834934 7723.4472
360 Costa Rica Americas 2007 78.78200 4133884 9645.0614
361 Cote d'Ivoire Africa 1952 40.47700 2977019 1388.5947
362 Cote d'Ivoire Africa 1957 42.46900 3300000 1500.8959
363 Cote d'Ivoire Africa 1962 44.93000 3832408 1728.8694
364 Cote d'Ivoire Africa 1967 47.35000 4744870 2052.0505
365 Cote d'Ivoire Africa 1972 49.80100 6071696 2378.2011
366 Cote d'Ivoire Africa 1977 52.37400 7459574 2517.7365
367 Cote d'Ivoire Africa 1982 53.98300 9025951 2602.7102
368 Cote d'Ivoire Africa 1987 54.65500 10761098 2156.9561
369 Cote d'Ivoire Africa 1992 52.04400 12772596 1648.0738
370 Cote d'Ivoire Africa 1997 47.99100 14625967 1786.2654
371 Cote d'Ivoire Africa 2002 46.83200 16252726 1648.8008
372 Cote d'Ivoire Africa 2007 48.32800 18013409 1544.7501
373 Croatia Europe 1952 61.21000 3882229 3119.2365
374 Croatia Europe 1957 64.77000 3991242 4338.2316
375 Croatia Europe 1962 67.13000 4076557 5477.8900
376 Croatia Europe 1967 68.50000 4174366 6960.2979
377 Croatia Europe 1972 69.61000 4225310 9164.0901
378 Croatia Europe 1977 70.64000 4318673 11305.3852
379 Croatia Europe 1982 70.46000 4413368 13221.8218
380 Croatia Europe 1987 71.52000 4484310 13822.5839
381 Croatia Europe 1992 72.52700 4494013 8447.7949
382 Croatia Europe 1997 73.68000 4444595 9875.6045
383 Croatia Europe 2002 74.87600 4481020 11628.3890
384 Croatia Europe 2007 75.74800 4493312 14619.2227
385 Cuba Americas 1952 59.42100 6007797 5586.5388
386 Cuba Americas 1957 62.32500 6640752 6092.1744
387 Cuba Americas 1962 65.24600 7254373 5180.7559
388 Cuba Americas 1967 68.29000 8139332 5690.2680
389 Cuba Americas 1972 70.72300 8831348 5305.4453
390 Cuba Americas 1977 72.64900 9537988 6380.4950
391 Cuba Americas 1982 73.71700 9789224 7316.9181
392 Cuba Americas 1987 74.17400 10239839 7532.9248
393 Cuba Americas 1992 74.41400 10723260 5592.8440
394 Cuba Americas 1997 76.15100 10983007 5431.9904
395 Cuba Americas 2002 77.15800 11226999 6340.6467
396 Cuba Americas 2007 78.27300 11416987 8948.1029
397 Czech Republic Europe 1952 66.87000 9125183 6876.1403
398 Czech Republic Europe 1957 69.03000 9513758 8256.3439
399 Czech Republic Europe 1962 69.90000 9620282 10136.8671
400 Czech Republic Europe 1967 70.38000 9835109 11399.4449
401 Czech Republic Europe 1972 70.29000 9862158 13108.4536
402 Czech Republic Europe 1977 70.71000 10161915 14800.1606
403 Czech Republic Europe 1982 70.96000 10303704 15377.2285
404 Czech Republic Europe 1987 71.58000 10311597 16310.4434
405 Czech Republic Europe 1992 72.40000 10315702 14297.0212
406 Czech Republic Europe 1997 74.01000 10300707 16048.5142
407 Czech Republic Europe 2002 75.51000 10256295 17596.2102
408 Czech Republic Europe 2007 76.48600 10228744 22833.3085
409 Denmark Europe 1952 70.78000 4334000 9692.3852
410 Denmark Europe 1957 71.81000 4487831 11099.6593
411 Denmark Europe 1962 72.35000 4646899 13583.3135
412 Denmark Europe 1967 72.96000 4838800 15937.2112
413 Denmark Europe 1972 73.47000 4991596 18866.2072
414 Denmark Europe 1977 74.69000 5088419 20422.9015
415 Denmark Europe 1982 74.63000 5117810 21688.0405
416 Denmark Europe 1987 74.80000 5127024 25116.1758
417 Denmark Europe 1992 75.33000 5171393 26406.7399
418 Denmark Europe 1997 76.11000 5283663 29804.3457
419 Denmark Europe 2002 77.18000 5374693 32166.5001
420 Denmark Europe 2007 78.33200 5468120 35278.4187
421 Djibouti Africa 1952 34.81200 63149 2669.5295
422 Djibouti Africa 1957 37.32800 71851 2864.9691
423 Djibouti Africa 1962 39.69300 89898 3020.9893
424 Djibouti Africa 1967 42.07400 127617 3020.0505
425 Djibouti Africa 1972 44.36600 178848 3694.2124
426 Djibouti Africa 1977 46.51900 228694 3081.7610
427 Djibouti Africa 1982 48.81200 305991 2879.4681
428 Djibouti Africa 1987 50.04000 311025 2880.1026
429 Djibouti Africa 1992 51.60400 384156 2377.1562
430 Djibouti Africa 1997 53.15700 417908 1895.0170
431 Djibouti Africa 2002 53.37300 447416 1908.2609
432 Djibouti Africa 2007 54.79100 496374 2082.4816
433 Dominican Republic Americas 1952 45.92800 2491346 1397.7171
434 Dominican Republic Americas 1957 49.82800 2923186 1544.4030
435 Dominican Republic Americas 1962 53.45900 3453434 1662.1374
436 Dominican Republic Americas 1967 56.75100 4049146 1653.7230
437 Dominican Republic Americas 1972 59.63100 4671329 2189.8745
438 Dominican Republic Americas 1977 61.78800 5302800 2681.9889
439 Dominican Republic Americas 1982 63.72700 5968349 2861.0924
440 Dominican Republic Americas 1987 66.04600 6655297 2899.8422
441 Dominican Republic Americas 1992 68.45700 7351181 3044.2142
442 Dominican Republic Americas 1997 69.95700 7992357 3614.1013
443 Dominican Republic Americas 2002 70.84700 8650322 4563.8082
444 Dominican Republic Americas 2007 72.23500 9319622 6025.3748
445 Ecuador Americas 1952 48.35700 3548753 3522.1107
446 Ecuador Americas 1957 51.35600 4058385 3780.5467
447 Ecuador Americas 1962 54.64000 4681707 4086.1141
448 Ecuador Americas 1967 56.67800 5432424 4579.0742
449 Ecuador Americas 1972 58.79600 6298651 5280.9947
450 Ecuador Americas 1977 61.31000 7278866 6679.6233
451 Ecuador Americas 1982 64.34200 8365850 7213.7913
452 Ecuador Americas 1987 67.23100 9545158 6481.7770
453 Ecuador Americas 1992 69.61300 10748394 7103.7026
454 Ecuador Americas 1997 72.31200 11911819 7429.4559
455 Ecuador Americas 2002 74.17300 12921234 5773.0445
456 Ecuador Americas 2007 74.99400 13755680 6873.2623
457 Egypt Africa 1952 41.89300 22223309 1418.8224
458 Egypt Africa 1957 44.44400 25009741 1458.9153
459 Egypt Africa 1962 46.99200 28173309 1693.3359
460 Egypt Africa 1967 49.29300 31681188 1814.8807
461 Egypt Africa 1972 51.13700 34807417 2024.0081
462 Egypt Africa 1977 53.31900 38783863 2785.4936
463 Egypt Africa 1982 56.00600 45681811 3503.7296
464 Egypt Africa 1987 59.79700 52799062 3885.4607
465 Egypt Africa 1992 63.67400 59402198 3794.7552
466 Egypt Africa 1997 67.21700 66134291 4173.1818
467 Egypt Africa 2002 69.80600 73312559 4754.6044
468 Egypt Africa 2007 71.33800 80264543 5581.1810
469 El Salvador Americas 1952 45.26200 2042865 3048.3029
470 El Salvador Americas 1957 48.57000 2355805 3421.5232
471 El Salvador Americas 1962 52.30700 2747687 3776.8036
472 El Salvador Americas 1967 55.85500 3232927 4358.5954
473 El Salvador Americas 1972 58.20700 3790903 4520.2460
474 El Salvador Americas 1977 56.69600 4282586 5138.9224
475 El Salvador Americas 1982 56.60400 4474873 4098.3442
476 El Salvador Americas 1987 63.15400 4842194 4140.4421
477 El Salvador Americas 1992 66.79800 5274649 4444.2317
478 El Salvador Americas 1997 69.53500 5783439 5154.8255
479 El Salvador Americas 2002 70.73400 6353681 5351.5687
480 El Salvador Americas 2007 71.87800 6939688 5728.3535
481 Equatorial Guinea Africa 1952 34.48200 216964 375.6431
482 Equatorial Guinea Africa 1957 35.98300 232922 426.0964
483 Equatorial Guinea Africa 1962 37.48500 249220 582.8420
484 Equatorial Guinea Africa 1967 38.98700 259864 915.5960
485 Equatorial Guinea Africa 1972 40.51600 277603 672.4123
486 Equatorial Guinea Africa 1977 42.02400 192675 958.5668
487 Equatorial Guinea Africa 1982 43.66200 285483 927.8253
488 Equatorial Guinea Africa 1987 45.66400 341244 966.8968
489 Equatorial Guinea Africa 1992 47.54500 387838 1132.0550
490 Equatorial Guinea Africa 1997 48.24500 439971 2814.4808
491 Equatorial Guinea Africa 2002 49.34800 495627 7703.4959
492 Equatorial Guinea Africa 2007 51.57900 551201 12154.0897
493 Eritrea Africa 1952 35.92800 1438760 328.9406
494 Eritrea Africa 1957 38.04700 1542611 344.1619
495 Eritrea Africa 1962 40.15800 1666618 380.9958
496 Eritrea Africa 1967 42.18900 1820319 468.7950
497 Eritrea Africa 1972 44.14200 2260187 514.3242
498 Eritrea Africa 1977 44.53500 2512642 505.7538
499 Eritrea Africa 1982 43.89000 2637297 524.8758
500 Eritrea Africa 1987 46.45300 2915959 521.1341
501 Eritrea Africa 1992 49.99100 3668440 582.8585
502 Eritrea Africa 1997 53.37800 4058319 913.4708
503 Eritrea Africa 2002 55.24000 4414865 765.3500
504 Eritrea Africa 2007 58.04000 4906585 641.3695
505 Ethiopia Africa 1952 34.07800 20860941 362.1463
506 Ethiopia Africa 1957 36.66700 22815614 378.9042
507 Ethiopia Africa 1962 40.05900 25145372 419.4564
508 Ethiopia Africa 1967 42.11500 27860297 516.1186
509 Ethiopia Africa 1972 43.51500 30770372 566.2439
510 Ethiopia Africa 1977 44.51000 34617799 556.8084
511 Ethiopia Africa 1982 44.91600 38111756 577.8607
512 Ethiopia Africa 1987 46.68400 42999530 573.7413
513 Ethiopia Africa 1992 48.09100 52088559 421.3535
514 Ethiopia Africa 1997 49.40200 59861301 515.8894
515 Ethiopia Africa 2002 50.72500 67946797 530.0535
516 Ethiopia Africa 2007 52.94700 76511887 690.8056
517 Finland Europe 1952 66.55000 4090500 6424.5191
518 Finland Europe 1957 67.49000 4324000 7545.4154
519 Finland Europe 1962 68.75000 4491443 9371.8426
520 Finland Europe 1967 69.83000 4605744 10921.6363
521 Finland Europe 1972 70.87000 4639657 14358.8759
522 Finland Europe 1977 72.52000 4738902 15605.4228
523 Finland Europe 1982 74.55000 4826933 18533.1576
524 Finland Europe 1987 74.83000 4931729 21141.0122
525 Finland Europe 1992 75.70000 5041039 20647.1650
526 Finland Europe 1997 77.13000 5134406 23723.9502
527 Finland Europe 2002 78.37000 5193039 28204.5906
528 Finland Europe 2007 79.31300 5238460 33207.0844
529 France Europe 1952 67.41000 42459667 7029.8093
530 France Europe 1957 68.93000 44310863 8662.8349
531 France Europe 1962 70.51000 47124000 10560.4855
532 France Europe 1967 71.55000 49569000 12999.9177
533 France Europe 1972 72.38000 51732000 16107.1917
534 France Europe 1977 73.83000 53165019 18292.6351
535 France Europe 1982 74.89000 54433565 20293.8975
536 France Europe 1987 76.34000 55630100 22066.4421
537 France Europe 1992 77.46000 57374179 24703.7961
538 France Europe 1997 78.64000 58623428 25889.7849
539 France Europe 2002 79.59000 59925035 28926.0323
540 France Europe 2007 80.65700 61083916 30470.0167
541 Gabon Africa 1952 37.00300 420702 4293.4765
542 Gabon Africa 1957 38.99900 434904 4976.1981
543 Gabon Africa 1962 40.48900 455661 6631.4592
544 Gabon Africa 1967 44.59800 489004 8358.7620
545 Gabon Africa 1972 48.69000 537977 11401.9484
546 Gabon Africa 1977 52.79000 706367 21745.5733
547 Gabon Africa 1982 56.56400 753874 15113.3619
548 Gabon Africa 1987 60.19000 880397 11864.4084
549 Gabon Africa 1992 61.36600 985739 13522.1575
550 Gabon Africa 1997 60.46100 1126189 14722.8419
551 Gabon Africa 2002 56.76100 1299304 12521.7139
552 Gabon Africa 2007 56.73500 1454867 13206.4845
553 Gambia Africa 1952 30.00000 284320 485.2307
554 Gambia Africa 1957 32.06500 323150 520.9267
555 Gambia Africa 1962 33.89600 374020 599.6503
556 Gambia Africa 1967 35.85700 439593 734.7829
557 Gambia Africa 1972 38.30800 517101 756.0868
558 Gambia Africa 1977 41.84200 608274 884.7553
559 Gambia Africa 1982 45.58000 715523 835.8096
560 Gambia Africa 1987 49.26500 848406 611.6589
561 Gambia Africa 1992 52.64400 1025384 665.6244
562 Gambia Africa 1997 55.86100 1235767 653.7302
563 Gambia Africa 2002 58.04100 1457766 660.5856
564 Gambia Africa 2007 59.44800 1688359 752.7497
565 Germany Europe 1952 67.50000 69145952 7144.1144
566 Germany Europe 1957 69.10000 71019069 10187.8267
567 Germany Europe 1962 70.30000 73739117 12902.4629
568 Germany Europe 1967 70.80000 76368453 14745.6256
569 Germany Europe 1972 71.00000 78717088 18016.1803
570 Germany Europe 1977 72.50000 78160773 20512.9212
571 Germany Europe 1982 73.80000 78335266 22031.5327
572 Germany Europe 1987 74.84700 77718298 24639.1857
573 Germany Europe 1992 76.07000 80597764 26505.3032
574 Germany Europe 1997 77.34000 82011073 27788.8842
575 Germany Europe 2002 78.67000 82350671 30035.8020
576 Germany Europe 2007 79.40600 82400996 32170.3744
577 Ghana Africa 1952 43.14900 5581001 911.2989
578 Ghana Africa 1957 44.77900 6391288 1043.5615
579 Ghana Africa 1962 46.45200 7355248 1190.0411
580 Ghana Africa 1967 48.07200 8490213 1125.6972
581 Ghana Africa 1972 49.87500 9354120 1178.2237
582 Ghana Africa 1977 51.75600 10538093 993.2240
583 Ghana Africa 1982 53.74400 11400338 876.0326
584 Ghana Africa 1987 55.72900 14168101 847.0061
585 Ghana Africa 1992 57.50100 16278738 925.0602
586 Ghana Africa 1997 58.55600 18418288 1005.2458
587 Ghana Africa 2002 58.45300 20550751 1111.9846
588 Ghana Africa 2007 60.02200 22873338 1327.6089
589 Greece Europe 1952 65.86000 7733250 3530.6901
590 Greece Europe 1957 67.86000 8096218 4916.2999
591 Greece Europe 1962 69.51000 8448233 6017.1907
592 Greece Europe 1967 71.00000 8716441 8513.0970
593 Greece Europe 1972 72.34000 8888628 12724.8296
594 Greece Europe 1977 73.68000 9308479 14195.5243
595 Greece Europe 1982 75.24000 9786480 15268.4209
596 Greece Europe 1987 76.67000 9974490 16120.5284
597 Greece Europe 1992 77.03000 10325429 17541.4963
598 Greece Europe 1997 77.86900 10502372 18747.6981
599 Greece Europe 2002 78.25600 10603863 22514.2548
600 Greece Europe 2007 79.48300 10706290 27538.4119
601 Guatemala Americas 1952 42.02300 3146381 2428.2378
602 Guatemala Americas 1957 44.14200 3640876 2617.1560
603 Guatemala Americas 1962 46.95400 4208858 2750.3644
604 Guatemala Americas 1967 50.01600 4690773 3242.5311
605 Guatemala Americas 1972 53.73800 5149581 4031.4083
606 Guatemala Americas 1977 56.02900 5703430 4879.9927
607 Guatemala Americas 1982 58.13700 6395630 4820.4948
608 Guatemala Americas 1987 60.78200 7326406 4246.4860
609 Guatemala Americas 1992 63.37300 8486949 4439.4508
610 Guatemala Americas 1997 66.32200 9803875 4684.3138
611 Guatemala Americas 2002 68.97800 11178650 4858.3475
612 Guatemala Americas 2007 70.25900 12572928 5186.0500
613 Guinea Africa 1952 33.60900 2664249 510.1965
614 Guinea Africa 1957 34.55800 2876726 576.2670
615 Guinea Africa 1962 35.75300 3140003 686.3737
616 Guinea Africa 1967 37.19700 3451418 708.7595
617 Guinea Africa 1972 38.84200 3811387 741.6662
618 Guinea Africa 1977 40.76200 4227026 874.6859
619 Guinea Africa 1982 42.89100 4710497 857.2504
620 Guinea Africa 1987 45.55200 5650262 805.5725
621 Guinea Africa 1992 48.57600 6990574 794.3484
622 Guinea Africa 1997 51.45500 8048834 869.4498
623 Guinea Africa 2002 53.67600 8807818 945.5836
624 Guinea Africa 2007 56.00700 9947814 942.6542
625 Guinea-Bissau Africa 1952 32.50000 580653 299.8503
626 Guinea-Bissau Africa 1957 33.48900 601095 431.7905
627 Guinea-Bissau Africa 1962 34.48800 627820 522.0344
628 Guinea-Bissau Africa 1967 35.49200 601287 715.5806
629 Guinea-Bissau Africa 1972 36.48600 625361 820.2246
630 Guinea-Bissau Africa 1977 37.46500 745228 764.7260
631 Guinea-Bissau Africa 1982 39.32700 825987 838.1240
632 Guinea-Bissau Africa 1987 41.24500 927524 736.4154
633 Guinea-Bissau Africa 1992 43.26600 1050938 745.5399
634 Guinea-Bissau Africa 1997 44.87300 1193708 796.6645
635 Guinea-Bissau Africa 2002 45.50400 1332459 575.7047
636 Guinea-Bissau Africa 2007 46.38800 1472041 579.2317
637 Haiti Americas 1952 37.57900 3201488 1840.3669
638 Haiti Americas 1957 40.69600 3507701 1726.8879
639 Haiti Americas 1962 43.59000 3880130 1796.5890
640 Haiti Americas 1967 46.24300 4318137 1452.0577
641 Haiti Americas 1972 48.04200 4698301 1654.4569
642 Haiti Americas 1977 49.92300 4908554 1874.2989
643 Haiti Americas 1982 51.46100 5198399 2011.1595
644 Haiti Americas 1987 53.63600 5756203 1823.0160
645 Haiti Americas 1992 55.08900 6326682 1456.3095
646 Haiti Americas 1997 56.67100 6913545 1341.7269
647 Haiti Americas 2002 58.13700 7607651 1270.3649
648 Haiti Americas 2007 60.91600 8502814 1201.6372
649 Honduras Americas 1952 41.91200 1517453 2194.9262
650 Honduras Americas 1957 44.66500 1770390 2220.4877
651 Honduras Americas 1962 48.04100 2090162 2291.1568
652 Honduras Americas 1967 50.92400 2500689 2538.2694
653 Honduras Americas 1972 53.88400 2965146 2529.8423
654 Honduras Americas 1977 57.40200 3055235 3203.2081
655 Honduras Americas 1982 60.90900 3669448 3121.7608
656 Honduras Americas 1987 64.49200 4372203 3023.0967
657 Honduras Americas 1992 66.39900 5077347 3081.6946
658 Honduras Americas 1997 67.65900 5867957 3160.4549
659 Honduras Americas 2002 68.56500 6677328 3099.7287
660 Honduras Americas 2007 70.19800 7483763 3548.3308
661 Hong Kong, China Asia 1952 60.96000 2125900 3054.4212
662 Hong Kong, China Asia 1957 64.75000 2736300 3629.0765
663 Hong Kong, China Asia 1962 67.65000 3305200 4692.6483
664 Hong Kong, China Asia 1967 70.00000 3722800 6197.9628
665 Hong Kong, China Asia 1972 72.00000 4115700 8315.9281
666 Hong Kong, China Asia 1977 73.60000 4583700 11186.1413
667 Hong Kong, China Asia 1982 75.45000 5264500 14560.5305
668 Hong Kong, China Asia 1987 76.20000 5584510 20038.4727
669 Hong Kong, China Asia 1992 77.60100 5829696 24757.6030
670 Hong Kong, China Asia 1997 80.00000 6495918 28377.6322
671 Hong Kong, China Asia 2002 81.49500 6762476 30209.0152
672 Hong Kong, China Asia 2007 82.20800 6980412 39724.9787
673 Hungary Europe 1952 64.03000 9504000 5263.6738
674 Hungary Europe 1957 66.41000 9839000 6040.1800
675 Hungary Europe 1962 67.96000 10063000 7550.3599
676 Hungary Europe 1967 69.50000 10223422 9326.6447
677 Hungary Europe 1972 69.76000 10394091 10168.6561
678 Hungary Europe 1977 69.95000 10637171 11674.8374
679 Hungary Europe 1982 69.39000 10705535 12545.9907
680 Hungary Europe 1987 69.58000 10612740 12986.4800
681 Hungary Europe 1992 69.17000 10348684 10535.6285
682 Hungary Europe 1997 71.04000 10244684 11712.7768
683 Hungary Europe 2002 72.59000 10083313 14843.9356
684 Hungary Europe 2007 73.33800 9956108 18008.9444
685 Iceland Europe 1952 72.49000 147962 7267.6884
686 Iceland Europe 1957 73.47000 165110 9244.0014
687 Iceland Europe 1962 73.68000 182053 10350.1591
688 Iceland Europe 1967 73.73000 198676 13319.8957
689 Iceland Europe 1972 74.46000 209275 15798.0636
690 Iceland Europe 1977 76.11000 221823 19654.9625
691 Iceland Europe 1982 76.99000 233997 23269.6075
692 Iceland Europe 1987 77.23000 244676 26923.2063
693 Iceland Europe 1992 78.77000 259012 25144.3920
694 Iceland Europe 1997 78.95000 271192 28061.0997
695 Iceland Europe 2002 80.50000 288030 31163.2020
696 Iceland Europe 2007 81.75700 301931 36180.7892
697 India Asia 1952 37.37300 372000000 546.5657
698 India Asia 1957 40.24900 409000000 590.0620
699 India Asia 1962 43.60500 454000000 658.3472
700 India Asia 1967 47.19300 506000000 700.7706
701 India Asia 1972 50.65100 567000000 724.0325
702 India Asia 1977 54.20800 634000000 813.3373
703 India Asia 1982 56.59600 708000000 855.7235
704 India Asia 1987 58.55300 788000000 976.5127
705 India Asia 1992 60.22300 872000000 1164.4068
706 India Asia 1997 61.76500 959000000 1458.8174
707 India Asia 2002 62.87900 1034172547 1746.7695
708 India Asia 2007 64.69800 1110396331 2452.2104
709 Indonesia Asia 1952 37.46800 82052000 749.6817
710 Indonesia Asia 1957 39.91800 90124000 858.9003
711 Indonesia Asia 1962 42.51800 99028000 849.2898
712 Indonesia Asia 1967 45.96400 109343000 762.4318
713 Indonesia Asia 1972 49.20300 121282000 1111.1079
714 Indonesia Asia 1977 52.70200 136725000 1382.7021
715 Indonesia Asia 1982 56.15900 153343000 1516.8730
716 Indonesia Asia 1987 60.13700 169276000 1748.3570
717 Indonesia Asia 1992 62.68100 184816000 2383.1409
718 Indonesia Asia 1997 66.04100 199278000 3119.3356
719 Indonesia Asia 2002 68.58800 211060000 2873.9129
720 Indonesia Asia 2007 70.65000 223547000 3540.6516
721 Iran Asia 1952 44.86900 17272000 3035.3260
722 Iran Asia 1957 47.18100 19792000 3290.2576
723 Iran Asia 1962 49.32500 22874000 4187.3298
724 Iran Asia 1967 52.46900 26538000 5906.7318
725 Iran Asia 1972 55.23400 30614000 9613.8186
726 Iran Asia 1977 57.70200 35480679 11888.5951
727 Iran Asia 1982 59.62000 43072751 7608.3346
728 Iran Asia 1987 63.04000 51889696 6642.8814
729 Iran Asia 1992 65.74200 60397973 7235.6532
730 Iran Asia 1997 68.04200 63327987 8263.5903
731 Iran Asia 2002 69.45100 66907826 9240.7620
732 Iran Asia 2007 70.96400 69453570 11605.7145
733 Iraq Asia 1952 45.32000 5441766 4129.7661
734 Iraq Asia 1957 48.43700 6248643 6229.3336
735 Iraq Asia 1962 51.45700 7240260 8341.7378
736 Iraq Asia 1967 54.45900 8519282 8931.4598
737 Iraq Asia 1972 56.95000 10061506 9576.0376
738 Iraq Asia 1977 60.41300 11882916 14688.2351
739 Iraq Asia 1982 62.03800 14173318 14517.9071
740 Iraq Asia 1987 65.04400 16543189 11643.5727
741 Iraq Asia 1992 59.46100 17861905 3745.6407
742 Iraq Asia 1997 58.81100 20775703 3076.2398
743 Iraq Asia 2002 57.04600 24001816 4390.7173
744 Iraq Asia 2007 59.54500 27499638 4471.0619
745 Ireland Europe 1952 66.91000 2952156 5210.2803
746 Ireland Europe 1957 68.90000 2878220 5599.0779
747 Ireland Europe 1962 70.29000 2830000 6631.5973
748 Ireland Europe 1967 71.08000 2900100 7655.5690
749 Ireland Europe 1972 71.28000 3024400 9530.7729
750 Ireland Europe 1977 72.03000 3271900 11150.9811
751 Ireland Europe 1982 73.10000 3480000 12618.3214
752 Ireland Europe 1987 74.36000 3539900 13872.8665
753 Ireland Europe 1992 75.46700 3557761 17558.8155
754 Ireland Europe 1997 76.12200 3667233 24521.9471
755 Ireland Europe 2002 77.78300 3879155 34077.0494
756 Ireland Europe 2007 78.88500 4109086 40675.9964
757 Israel Asia 1952 65.39000 1620914 4086.5221
758 Israel Asia 1957 67.84000 1944401 5385.2785
759 Israel Asia 1962 69.39000 2310904 7105.6307
760 Israel Asia 1967 70.75000 2693585 8393.7414
761 Israel Asia 1972 71.63000 3095893 12786.9322
762 Israel Asia 1977 73.06000 3495918 13306.6192
763 Israel Asia 1982 74.45000 3858421 15367.0292
764 Israel Asia 1987 75.60000 4203148 17122.4799
765 Israel Asia 1992 76.93000 4936550 18051.5225
766 Israel Asia 1997 78.26900 5531387 20896.6092
767 Israel Asia 2002 79.69600 6029529 21905.5951
768 Israel Asia 2007 80.74500 6426679 25523.2771
769 Italy Europe 1952 65.94000 47666000 4931.4042
770 Italy Europe 1957 67.81000 49182000 6248.6562
771 Italy Europe 1962 69.24000 50843200 8243.5823
772 Italy Europe 1967 71.06000 52667100 10022.4013
773 Italy Europe 1972 72.19000 54365564 12269.2738
774 Italy Europe 1977 73.48000 56059245 14255.9847
775 Italy Europe 1982 74.98000 56535636 16537.4835
776 Italy Europe 1987 76.42000 56729703 19207.2348
777 Italy Europe 1992 77.44000 56840847 22013.6449
778 Italy Europe 1997 78.82000 57479469 24675.0245
779 Italy Europe 2002 80.24000 57926999 27968.0982
780 Italy Europe 2007 80.54600 58147733 28569.7197
781 Jamaica Americas 1952 58.53000 1426095 2898.5309
782 Jamaica Americas 1957 62.61000 1535090 4756.5258
783 Jamaica Americas 1962 65.61000 1665128 5246.1075
784 Jamaica Americas 1967 67.51000 1861096 6124.7035
785 Jamaica Americas 1972 69.00000 1997616 7433.8893
786 Jamaica Americas 1977 70.11000 2156814 6650.1956
787 Jamaica Americas 1982 71.21000 2298309 6068.0513
788 Jamaica Americas 1987 71.77000 2326606 6351.2375
789 Jamaica Americas 1992 71.76600 2378618 7404.9237
790 Jamaica Americas 1997 72.26200 2531311 7121.9247
791 Jamaica Americas 2002 72.04700 2664659 6994.7749
792 Jamaica Americas 2007 72.56700 2780132 7320.8803
793 Japan Asia 1952 63.03000 86459025 3216.9563
794 Japan Asia 1957 65.50000 91563009 4317.6944
795 Japan Asia 1962 68.73000 95831757 6576.6495
796 Japan Asia 1967 71.43000 100825279 9847.7886
797 Japan Asia 1972 73.42000 107188273 14778.7864
798 Japan Asia 1977 75.38000 113872473 16610.3770
799 Japan Asia 1982 77.11000 118454974 19384.1057
800 Japan Asia 1987 78.67000 122091325 22375.9419
801 Japan Asia 1992 79.36000 124329269 26824.8951
802 Japan Asia 1997 80.69000 125956499 28816.5850
803 Japan Asia 2002 82.00000 127065841 28604.5919
804 Japan Asia 2007 82.60300 127467972 31656.0681
805 Jordan Asia 1952 43.15800 607914 1546.9078
806 Jordan Asia 1957 45.66900 746559 1886.0806
807 Jordan Asia 1962 48.12600 933559 2348.0092
808 Jordan Asia 1967 51.62900 1255058 2741.7963
809 Jordan Asia 1972 56.52800 1613551 2110.8563
810 Jordan Asia 1977 61.13400 1937652 2852.3516
811 Jordan Asia 1982 63.73900 2347031 4161.4160
812 Jordan Asia 1987 65.86900 2820042 4448.6799
813 Jordan Asia 1992 68.01500 3867409 3431.5936
814 Jordan Asia 1997 69.77200 4526235 3645.3796
815 Jordan Asia 2002 71.26300 5307470 3844.9172
816 Jordan Asia 2007 72.53500 6053193 4519.4612
817 Kenya Africa 1952 42.27000 6464046 853.5409
818 Kenya Africa 1957 44.68600 7454779 944.4383
819 Kenya Africa 1962 47.94900 8678557 896.9664
820 Kenya Africa 1967 50.65400 10191512 1056.7365
821 Kenya Africa 1972 53.55900 12044785 1222.3600
822 Kenya Africa 1977 56.15500 14500404 1267.6132
823 Kenya Africa 1982 58.76600 17661452 1348.2258
824 Kenya Africa 1987 59.33900 21198082 1361.9369
825 Kenya Africa 1992 59.28500 25020539 1341.9217
826 Kenya Africa 1997 54.40700 28263827 1360.4850
827 Kenya Africa 2002 50.99200 31386842 1287.5147
828 Kenya Africa 2007 54.11000 35610177 1463.2493
829 Korea, Dem. Rep. Asia 1952 50.05600 8865488 1088.2778
830 Korea, Dem. Rep. Asia 1957 54.08100 9411381 1571.1347
831 Korea, Dem. Rep. Asia 1962 56.65600 10917494 1621.6936
832 Korea, Dem. Rep. Asia 1967 59.94200 12617009 2143.5406
833 Korea, Dem. Rep. Asia 1972 63.98300 14781241 3701.6215
834 Korea, Dem. Rep. Asia 1977 67.15900 16325320 4106.3012
835 Korea, Dem. Rep. Asia 1982 69.10000 17647518 4106.5253
836 Korea, Dem. Rep. Asia 1987 70.64700 19067554 4106.4923
837 Korea, Dem. Rep. Asia 1992 69.97800 20711375 3726.0635
838 Korea, Dem. Rep. Asia 1997 67.72700 21585105 1690.7568
839 Korea, Dem. Rep. Asia 2002 66.66200 22215365 1646.7582
840 Korea, Dem. Rep. Asia 2007 67.29700 23301725 1593.0655
841 Korea, Rep. Asia 1952 47.45300 20947571 1030.5922
842 Korea, Rep. Asia 1957 52.68100 22611552 1487.5935
843 Korea, Rep. Asia 1962 55.29200 26420307 1536.3444
844 Korea, Rep. Asia 1967 57.71600 30131000 2029.2281
845 Korea, Rep. Asia 1972 62.61200 33505000 3030.8767
846 Korea, Rep. Asia 1977 64.76600 36436000 4657.2210
847 Korea, Rep. Asia 1982 67.12300 39326000 5622.9425
848 Korea, Rep. Asia 1987 69.81000 41622000 8533.0888
849 Korea, Rep. Asia 1992 72.24400 43805450 12104.2787
850 Korea, Rep. Asia 1997 74.64700 46173816 15993.5280
851 Korea, Rep. Asia 2002 77.04500 47969150 19233.9882
852 Korea, Rep. Asia 2007 78.62300 49044790 23348.1397
853 Kuwait Asia 1952 55.56500 160000 108382.3529
854 Kuwait Asia 1957 58.03300 212846 113523.1329
855 Kuwait Asia 1962 60.47000 358266 95458.1118
856 Kuwait Asia 1967 64.62400 575003 80894.8833
857 Kuwait Asia 1972 67.71200 841934 109347.8670
858 Kuwait Asia 1977 69.34300 1140357 59265.4771
859 Kuwait Asia 1982 71.30900 1497494 31354.0357
860 Kuwait Asia 1987 74.17400 1891487 28118.4300
861 Kuwait Asia 1992 75.19000 1418095 34932.9196
862 Kuwait Asia 1997 76.15600 1765345 40300.6200
863 Kuwait Asia 2002 76.90400 2111561 35110.1057
864 Kuwait Asia 2007 77.58800 2505559 47306.9898
865 Lebanon Asia 1952 55.92800 1439529 4834.8041
866 Lebanon Asia 1957 59.48900 1647412 6089.7869
867 Lebanon Asia 1962 62.09400 1886848 5714.5606
868 Lebanon Asia 1967 63.87000 2186894 6006.9830
869 Lebanon Asia 1972 65.42100 2680018 7486.3843
870 Lebanon Asia 1977 66.09900 3115787 8659.6968
871 Lebanon Asia 1982 66.98300 3086876 7640.5195
872 Lebanon Asia 1987 67.92600 3089353 5377.0913
873 Lebanon Asia 1992 69.29200 3219994 6890.8069
874 Lebanon Asia 1997 70.26500 3430388 8754.9639
875 Lebanon Asia 2002 71.02800 3677780 9313.9388
876 Lebanon Asia 2007 71.99300 3921278 10461.0587
877 Lesotho Africa 1952 42.13800 748747 298.8462
878 Lesotho Africa 1957 45.04700 813338 335.9971
879 Lesotho Africa 1962 47.74700 893143 411.8006
880 Lesotho Africa 1967 48.49200 996380 498.6390
881 Lesotho Africa 1972 49.76700 1116779 496.5816
882 Lesotho Africa 1977 52.20800 1251524 745.3695
883 Lesotho Africa 1982 55.07800 1411807 797.2631
884 Lesotho Africa 1987 57.18000 1599200 773.9932
885 Lesotho Africa 1992 59.68500 1803195 977.4863
886 Lesotho Africa 1997 55.55800 1982823 1186.1480
887 Lesotho Africa 2002 44.59300 2046772 1275.1846
888 Lesotho Africa 2007 42.59200 2012649 1569.3314
889 Liberia Africa 1952 38.48000 863308 575.5730
890 Liberia Africa 1957 39.48600 975950 620.9700
891 Liberia Africa 1962 40.50200 1112796 634.1952
892 Liberia Africa 1967 41.53600 1279406 713.6036
893 Liberia Africa 1972 42.61400 1482628 803.0055
894 Liberia Africa 1977 43.76400 1703617 640.3224
895 Liberia Africa 1982 44.85200 1956875 572.1996
896 Liberia Africa 1987 46.02700 2269414 506.1139
897 Liberia Africa 1992 40.80200 1912974 636.6229
898 Liberia Africa 1997 42.22100 2200725 609.1740
899 Liberia Africa 2002 43.75300 2814651 531.4824
900 Liberia Africa 2007 45.67800 3193942 414.5073
901 Libya Africa 1952 42.72300 1019729 2387.5481
902 Libya Africa 1957 45.28900 1201578 3448.2844
903 Libya Africa 1962 47.80800 1441863 6757.0308
904 Libya Africa 1967 50.22700 1759224 18772.7517
905 Libya Africa 1972 52.77300 2183877 21011.4972
906 Libya Africa 1977 57.44200 2721783 21951.2118
907 Libya Africa 1982 62.15500 3344074 17364.2754
908 Libya Africa 1987 66.23400 3799845 11770.5898
909 Libya Africa 1992 68.75500 4364501 9640.1385
910 Libya Africa 1997 71.55500 4759670 9467.4461
911 Libya Africa 2002 72.73700 5368585 9534.6775
912 Libya Africa 2007 73.95200 6036914 12057.4993
913 Madagascar Africa 1952 36.68100 4762912 1443.0117
914 Madagascar Africa 1957 38.86500 5181679 1589.2027
915 Madagascar Africa 1962 40.84800 5703324 1643.3871
916 Madagascar Africa 1967 42.88100 6334556 1634.0473
917 Madagascar Africa 1972 44.85100 7082430 1748.5630
918 Madagascar Africa 1977 46.88100 8007166 1544.2286
919 Madagascar Africa 1982 48.96900 9171477 1302.8787
920 Madagascar Africa 1987 49.35000 10568642 1155.4419
921 Madagascar Africa 1992 52.21400 12210395 1040.6762
922 Madagascar Africa 1997 54.97800 14165114 986.2959
923 Madagascar Africa 2002 57.28600 16473477 894.6371
924 Madagascar Africa 2007 59.44300 19167654 1044.7701
925 Malawi Africa 1952 36.25600 2917802 369.1651
926 Malawi Africa 1957 37.20700 3221238 416.3698
927 Malawi Africa 1962 38.41000 3628608 427.9011
928 Malawi Africa 1967 39.48700 4147252 495.5148
929 Malawi Africa 1972 41.76600 4730997 584.6220
930 Malawi Africa 1977 43.76700 5637246 663.2237
931 Malawi Africa 1982 45.64200 6502825 632.8039
932 Malawi Africa 1987 47.45700 7824747 635.5174
933 Malawi Africa 1992 49.42000 10014249 563.2000
934 Malawi Africa 1997 47.49500 10419991 692.2758
935 Malawi Africa 2002 45.00900 11824495 665.4231
936 Malawi Africa 2007 48.30300 13327079 759.3499
937 Malaysia Asia 1952 48.46300 6748378 1831.1329
938 Malaysia Asia 1957 52.10200 7739235 1810.0670
939 Malaysia Asia 1962 55.73700 8906385 2036.8849
940 Malaysia Asia 1967 59.37100 10154878 2277.7424
941 Malaysia Asia 1972 63.01000 11441462 2849.0948
942 Malaysia Asia 1977 65.25600 12845381 3827.9216
943 Malaysia Asia 1982 68.00000 14441916 4920.3560
944 Malaysia Asia 1987 69.50000 16331785 5249.8027
945 Malaysia Asia 1992 70.69300 18319502 7277.9128
946 Malaysia Asia 1997 71.93800 20476091 10132.9096
947 Malaysia Asia 2002 73.04400 22662365 10206.9779
948 Malaysia Asia 2007 74.24100 24821286 12451.6558
949 Mali Africa 1952 33.68500 3838168 452.3370
950 Mali Africa 1957 35.30700 4241884 490.3822
951 Mali Africa 1962 36.93600 4690372 496.1743
952 Mali Africa 1967 38.48700 5212416 545.0099
953 Mali Africa 1972 39.97700 5828158 581.3689
954 Mali Africa 1977 41.71400 6491649 686.3953
955 Mali Africa 1982 43.91600 6998256 618.0141
956 Mali Africa 1987 46.36400 7634008 684.1716
957 Mali Africa 1992 48.38800 8416215 739.0144
958 Mali Africa 1997 49.90300 9384984 790.2580
959 Mali Africa 2002 51.81800 10580176 951.4098
960 Mali Africa 2007 54.46700 12031795 1042.5816
961 Mauritania Africa 1952 40.54300 1022556 743.1159
962 Mauritania Africa 1957 42.33800 1076852 846.1203
963 Mauritania Africa 1962 44.24800 1146757 1055.8960
964 Mauritania Africa 1967 46.28900 1230542 1421.1452
965 Mauritania Africa 1972 48.43700 1332786 1586.8518
966 Mauritania Africa 1977 50.85200 1456688 1497.4922
967 Mauritania Africa 1982 53.59900 1622136 1481.1502
968 Mauritania Africa 1987 56.14500 1841240 1421.6036
969 Mauritania Africa 1992 58.33300 2119465 1361.3698
970 Mauritania Africa 1997 60.43000 2444741 1483.1361
971 Mauritania Africa 2002 62.24700 2828858 1579.0195
972 Mauritania Africa 2007 64.16400 3270065 1803.1515
973 Mauritius Africa 1952 50.98600 516556 1967.9557
974 Mauritius Africa 1957 58.08900 609816 2034.0380
975 Mauritius Africa 1962 60.24600 701016 2529.0675
976 Mauritius Africa 1967 61.55700 789309 2475.3876
977 Mauritius Africa 1972 62.94400 851334 2575.4842
978 Mauritius Africa 1977 64.93000 913025 3710.9830
979 Mauritius Africa 1982 66.71100 992040 3688.0377
980 Mauritius Africa 1987 68.74000 1042663 4783.5869
981 Mauritius Africa 1992 69.74500 1096202 6058.2538
982 Mauritius Africa 1997 70.73600 1149818 7425.7053
983 Mauritius Africa 2002 71.95400 1200206 9021.8159
984 Mauritius Africa 2007 72.80100 1250882 10956.9911
985 Mexico Americas 1952 50.78900 30144317 3478.1255
986 Mexico Americas 1957 55.19000 35015548 4131.5466
987 Mexico Americas 1962 58.29900 41121485 4581.6094
988 Mexico Americas 1967 60.11000 47995559 5754.7339
989 Mexico Americas 1972 62.36100 55984294 6809.4067
990 Mexico Americas 1977 65.03200 63759976 7674.9291
991 Mexico Americas 1982 67.40500 71640904 9611.1475
992 Mexico Americas 1987 69.49800 80122492 8688.1560
993 Mexico Americas 1992 71.45500 88111030 9472.3843
994 Mexico Americas 1997 73.67000 95895146 9767.2975
995 Mexico Americas 2002 74.90200 102479927 10742.4405
996 Mexico Americas 2007 76.19500 108700891 11977.5750
997 Mongolia Asia 1952 42.24400 800663 786.5669
998 Mongolia Asia 1957 45.24800 882134 912.6626
999 Mongolia Asia 1962 48.25100 1010280 1056.3540
1000 Mongolia Asia 1967 51.25300 1149500 1226.0411
1001 Mongolia Asia 1972 53.75400 1320500 1421.7420
1002 Mongolia Asia 1977 55.49100 1528000 1647.5117
1003 Mongolia Asia 1982 57.48900 1756032 2000.6031
1004 Mongolia Asia 1987 60.22200 2015133 2338.0083
1005 Mongolia Asia 1992 61.27100 2312802 1785.4020
1006 Mongolia Asia 1997 63.62500 2494803 1902.2521
1007 Mongolia Asia 2002 65.03300 2674234 2140.7393
1008 Mongolia Asia 2007 66.80300 2874127 3095.7723
1009 Montenegro Europe 1952 59.16400 413834 2647.5856
1010 Montenegro Europe 1957 61.44800 442829 3682.2599
1011 Montenegro Europe 1962 63.72800 474528 4649.5938
1012 Montenegro Europe 1967 67.17800 501035 5907.8509
1013 Montenegro Europe 1972 70.63600 527678 7778.4140
1014 Montenegro Europe 1977 73.06600 560073 9595.9299
1015 Montenegro Europe 1982 74.10100 562548 11222.5876
1016 Montenegro Europe 1987 74.86500 569473 11732.5102
1017 Montenegro Europe 1992 75.43500 621621 7003.3390
1018 Montenegro Europe 1997 75.44500 692651 6465.6133
1019 Montenegro Europe 2002 73.98100 720230 6557.1943
1020 Montenegro Europe 2007 74.54300 684736 9253.8961
1021 Morocco Africa 1952 42.87300 9939217 1688.2036
1022 Morocco Africa 1957 45.42300 11406350 1642.0023
1023 Morocco Africa 1962 47.92400 13056604 1566.3535
1024 Morocco Africa 1967 50.33500 14770296 1711.0448
1025 Morocco Africa 1972 52.86200 16660670 1930.1950
1026 Morocco Africa 1977 55.73000 18396941 2370.6200
1027 Morocco Africa 1982 59.65000 20198730 2702.6204
1028 Morocco Africa 1987 62.67700 22987397 2755.0470
1029 Morocco Africa 1992 65.39300 25798239 2948.0473
1030 Morocco Africa 1997 67.66000 28529501 2982.1019
1031 Morocco Africa 2002 69.61500 31167783 3258.4956
1032 Morocco Africa 2007 71.16400 33757175 3820.1752
1033 Mozambique Africa 1952 31.28600 6446316 468.5260
1034 Mozambique Africa 1957 33.77900 7038035 495.5868
1035 Mozambique Africa 1962 36.16100 7788944 556.6864
1036 Mozambique Africa 1967 38.11300 8680909 566.6692
1037 Mozambique Africa 1972 40.32800 9809596 724.9178
1038 Mozambique Africa 1977 42.49500 11127868 502.3197
1039 Mozambique Africa 1982 42.79500 12587223 462.2114
1040 Mozambique Africa 1987 42.86100 12891952 389.8762
1041 Mozambique Africa 1992 44.28400 13160731 410.8968
1042 Mozambique Africa 1997 46.34400 16603334 472.3461
1043 Mozambique Africa 2002 44.02600 18473780 633.6179
1044 Mozambique Africa 2007 42.08200 19951656 823.6856
1045 Myanmar Asia 1952 36.31900 20092996 331.0000
1046 Myanmar Asia 1957 41.90500 21731844 350.0000
1047 Myanmar Asia 1962 45.10800 23634436 388.0000
1048 Myanmar Asia 1967 49.37900 25870271 349.0000
1049 Myanmar Asia 1972 53.07000 28466390 357.0000
1050 Myanmar Asia 1977 56.05900 31528087 371.0000
1051 Myanmar Asia 1982 58.05600 34680442 424.0000
1052 Myanmar Asia 1987 58.33900 38028578 385.0000
1053 Myanmar Asia 1992 59.32000 40546538 347.0000
1054 Myanmar Asia 1997 60.32800 43247867 415.0000
1055 Myanmar Asia 2002 59.90800 45598081 611.0000
1056 Myanmar Asia 2007 62.06900 47761980 944.0000
1057 Namibia Africa 1952 41.72500 485831 2423.7804
1058 Namibia Africa 1957 45.22600 548080 2621.4481
1059 Namibia Africa 1962 48.38600 621392 3173.2156
1060 Namibia Africa 1967 51.15900 706640 3793.6948
1061 Namibia Africa 1972 53.86700 821782 3746.0809
1062 Namibia Africa 1977 56.43700 977026 3876.4860
1063 Namibia Africa 1982 58.96800 1099010 4191.1005
1064 Namibia Africa 1987 60.83500 1278184 3693.7313
1065 Namibia Africa 1992 61.99900 1554253 3804.5380
1066 Namibia Africa 1997 58.90900 1774766 3899.5243
1067 Namibia Africa 2002 51.47900 1972153 4072.3248
1068 Namibia Africa 2007 52.90600 2055080 4811.0604
1069 Nepal Asia 1952 36.15700 9182536 545.8657
1070 Nepal Asia 1957 37.68600 9682338 597.9364
1071 Nepal Asia 1962 39.39300 10332057 652.3969
1072 Nepal Asia 1967 41.47200 11261690 676.4422
1073 Nepal Asia 1972 43.97100 12412593 674.7881
1074 Nepal Asia 1977 46.74800 13933198 694.1124
1075 Nepal Asia 1982 49.59400 15796314 718.3731
1076 Nepal Asia 1987 52.53700 17917180 775.6325
1077 Nepal Asia 1992 55.72700 20326209 897.7404
1078 Nepal Asia 1997 59.42600 23001113 1010.8921
1079 Nepal Asia 2002 61.34000 25873917 1057.2063
1080 Nepal Asia 2007 63.78500 28901790 1091.3598
1081 Netherlands Europe 1952 72.13000 10381988 8941.5719
1082 Netherlands Europe 1957 72.99000 11026383 11276.1934
1083 Netherlands Europe 1962 73.23000 11805689 12790.8496
1084 Netherlands Europe 1967 73.82000 12596822 15363.2514
1085 Netherlands Europe 1972 73.75000 13329874 18794.7457
1086 Netherlands Europe 1977 75.24000 13852989 21209.0592
1087 Netherlands Europe 1982 76.05000 14310401 21399.4605
1088 Netherlands Europe 1987 76.83000 14665278 23651.3236
1089 Netherlands Europe 1992 77.42000 15174244 26790.9496
1090 Netherlands Europe 1997 78.03000 15604464 30246.1306
1091 Netherlands Europe 2002 78.53000 16122830 33724.7578
1092 Netherlands Europe 2007 79.76200 16570613 36797.9333
1093 New Zealand Oceania 1952 69.39000 1994794 10556.5757
1094 New Zealand Oceania 1957 70.26000 2229407 12247.3953
1095 New Zealand Oceania 1962 71.24000 2488550 13175.6780
1096 New Zealand Oceania 1967 71.52000 2728150 14463.9189
1097 New Zealand Oceania 1972 71.89000 2929100 16046.0373
1098 New Zealand Oceania 1977 72.22000 3164900 16233.7177
1099 New Zealand Oceania 1982 73.84000 3210650 17632.4104
1100 New Zealand Oceania 1987 74.32000 3317166 19007.1913
1101 New Zealand Oceania 1992 76.33000 3437674 18363.3249
1102 New Zealand Oceania 1997 77.55000 3676187 21050.4138
1103 New Zealand Oceania 2002 79.11000 3908037 23189.8014
1104 New Zealand Oceania 2007 80.20400 4115771 25185.0091
1105 Nicaragua Americas 1952 42.31400 1165790 3112.3639
1106 Nicaragua Americas 1957 45.43200 1358828 3457.4159
1107 Nicaragua Americas 1962 48.63200 1590597 3634.3644
1108 Nicaragua Americas 1967 51.88400 1865490 4643.3935
1109 Nicaragua Americas 1972 55.15100 2182908 4688.5933
1110 Nicaragua Americas 1977 57.47000 2554598 5486.3711
1111 Nicaragua Americas 1982 59.29800 2979423 3470.3382
1112 Nicaragua Americas 1987 62.00800 3344353 2955.9844
1113 Nicaragua Americas 1992 65.84300 4017939 2170.1517
1114 Nicaragua Americas 1997 68.42600 4609572 2253.0230
1115 Nicaragua Americas 2002 70.83600 5146848 2474.5488
1116 Nicaragua Americas 2007 72.89900 5675356 2749.3210
1117 Niger Africa 1952 37.44400 3379468 761.8794
1118 Niger Africa 1957 38.59800 3692184 835.5234
1119 Niger Africa 1962 39.48700 4076008 997.7661
1120 Niger Africa 1967 40.11800 4534062 1054.3849
1121 Niger Africa 1972 40.54600 5060262 954.2092
1122 Niger Africa 1977 41.29100 5682086 808.8971
1123 Niger Africa 1982 42.59800 6437188 909.7221
1124 Niger Africa 1987 44.55500 7332638 668.3000
1125 Niger Africa 1992 47.39100 8392818 581.1827
1126 Niger Africa 1997 51.31300 9666252 580.3052
1127 Niger Africa 2002 54.49600 11140655 601.0745
1128 Niger Africa 2007 56.86700 12894865 619.6769
1129 Nigeria Africa 1952 36.32400 33119096 1077.2819
1130 Nigeria Africa 1957 37.80200 37173340 1100.5926
1131 Nigeria Africa 1962 39.36000 41871351 1150.9275
1132 Nigeria Africa 1967 41.04000 47287752 1014.5141
1133 Nigeria Africa 1972 42.82100 53740085 1698.3888
1134 Nigeria Africa 1977 44.51400 62209173 1981.9518
1135 Nigeria Africa 1982 45.82600 73039376 1576.9738
1136 Nigeria Africa 1987 46.88600 81551520 1385.0296
1137 Nigeria Africa 1992 47.47200 93364244 1619.8482
1138 Nigeria Africa 1997 47.46400 106207839 1624.9413
1139 Nigeria Africa 2002 46.60800 119901274 1615.2864
1140 Nigeria Africa 2007 46.85900 135031164 2013.9773
1141 Norway Europe 1952 72.67000 3327728 10095.4217
1142 Norway Europe 1957 73.44000 3491938 11653.9730
1143 Norway Europe 1962 73.47000 3638919 13450.4015
1144 Norway Europe 1967 74.08000 3786019 16361.8765
1145 Norway Europe 1972 74.34000 3933004 18965.0555
1146 Norway Europe 1977 75.37000 4043205 23311.3494
1147 Norway Europe 1982 75.97000 4114787 26298.6353
1148 Norway Europe 1987 75.89000 4186147 31540.9748
1149 Norway Europe 1992 77.32000 4286357 33965.6611
1150 Norway Europe 1997 78.32000 4405672 41283.1643
1151 Norway Europe 2002 79.05000 4535591 44683.9753
1152 Norway Europe 2007 80.19600 4627926 49357.1902
1153 Oman Asia 1952 37.57800 507833 1828.2303
1154 Oman Asia 1957 40.08000 561977 2242.7466
1155 Oman Asia 1962 43.16500 628164 2924.6381
1156 Oman Asia 1967 46.98800 714775 4720.9427
1157 Oman Asia 1972 52.14300 829050 10618.0385
1158 Oman Asia 1977 57.36700 1004533 11848.3439
1159 Oman Asia 1982 62.72800 1301048 12954.7910
1160 Oman Asia 1987 67.73400 1593882 18115.2231
1161 Oman Asia 1992 71.19700 1915208 18616.7069
1162 Oman Asia 1997 72.49900 2283635 19702.0558
1163 Oman Asia 2002 74.19300 2713462 19774.8369
1164 Oman Asia 2007 75.64000 3204897 22316.1929
1165 Pakistan Asia 1952 43.43600 41346560 684.5971
1166 Pakistan Asia 1957 45.55700 46679944 747.0835
1167 Pakistan Asia 1962 47.67000 53100671 803.3427
1168 Pakistan Asia 1967 49.80000 60641899 942.4083
1169 Pakistan Asia 1972 51.92900 69325921 1049.9390
1170 Pakistan Asia 1977 54.04300 78152686 1175.9212
1171 Pakistan Asia 1982 56.15800 91462088 1443.4298
1172 Pakistan Asia 1987 58.24500 105186881 1704.6866
1173 Pakistan Asia 1992 60.83800 120065004 1971.8295
1174 Pakistan Asia 1997 61.81800 135564834 2049.3505
1175 Pakistan Asia 2002 63.61000 153403524 2092.7124
1176 Pakistan Asia 2007 65.48300 169270617 2605.9476
1177 Panama Americas 1952 55.19100 940080 2480.3803
1178 Panama Americas 1957 59.20100 1063506 2961.8009
1179 Panama Americas 1962 61.81700 1215725 3536.5403
1180 Panama Americas 1967 64.07100 1405486 4421.0091
1181 Panama Americas 1972 66.21600 1616384 5364.2497
1182 Panama Americas 1977 68.68100 1839782 5351.9121
1183 Panama Americas 1982 70.47200 2036305 7009.6016
1184 Panama Americas 1987 71.52300 2253639 7034.7792
1185 Panama Americas 1992 72.46200 2484997 6618.7431
1186 Panama Americas 1997 73.73800 2734531 7113.6923
1187 Panama Americas 2002 74.71200 2990875 7356.0319
1188 Panama Americas 2007 75.53700 3242173 9809.1856
1189 Paraguay Americas 1952 62.64900 1555876 1952.3087
1190 Paraguay Americas 1957 63.19600 1770902 2046.1547
1191 Paraguay Americas 1962 64.36100 2009813 2148.0271
1192 Paraguay Americas 1967 64.95100 2287985 2299.3763
1193 Paraguay Americas 1972 65.81500 2614104 2523.3380
1194 Paraguay Americas 1977 66.35300 2984494 3248.3733
1195 Paraguay Americas 1982 66.87400 3366439 4258.5036
1196 Paraguay Americas 1987 67.37800 3886512 3998.8757
1197 Paraguay Americas 1992 68.22500 4483945 4196.4111
1198 Paraguay Americas 1997 69.40000 5154123 4247.4003
1199 Paraguay Americas 2002 70.75500 5884491 3783.6742
1200 Paraguay Americas 2007 71.75200 6667147 4172.8385
1201 Peru Americas 1952 43.90200 8025700 3758.5234
1202 Peru Americas 1957 46.26300 9146100 4245.2567
1203 Peru Americas 1962 49.09600 10516500 4957.0380
1204 Peru Americas 1967 51.44500 12132200 5788.0933
1205 Peru Americas 1972 55.44800 13954700 5937.8273
1206 Peru Americas 1977 58.44700 15990099 6281.2909
1207 Peru Americas 1982 61.40600 18125129 6434.5018
1208 Peru Americas 1987 64.13400 20195924 6360.9434
1209 Peru Americas 1992 66.45800 22430449 4446.3809
1210 Peru Americas 1997 68.38600 24748122 5838.3477
1211 Peru Americas 2002 69.90600 26769436 5909.0201
1212 Peru Americas 2007 71.42100 28674757 7408.9056
1213 Philippines Asia 1952 47.75200 22438691 1272.8810
1214 Philippines Asia 1957 51.33400 26072194 1547.9448
1215 Philippines Asia 1962 54.75700 30325264 1649.5522
1216 Philippines Asia 1967 56.39300 35356600 1814.1274
1217 Philippines Asia 1972 58.06500 40850141 1989.3741
1218 Philippines Asia 1977 60.06000 46850962 2373.2043
1219 Philippines Asia 1982 62.08200 53456774 2603.2738
1220 Philippines Asia 1987 64.15100 60017788 2189.6350
1221 Philippines Asia 1992 66.45800 67185766 2279.3240
1222 Philippines Asia 1997 68.56400 75012988 2536.5349
1223 Philippines Asia 2002 70.30300 82995088 2650.9211
1224 Philippines Asia 2007 71.68800 91077287 3190.4810
1225 Poland Europe 1952 61.31000 25730551 4029.3297
1226 Poland Europe 1957 65.77000 28235346 4734.2530
1227 Poland Europe 1962 67.64000 30329617 5338.7521
1228 Poland Europe 1967 69.61000 31785378 6557.1528
1229 Poland Europe 1972 70.85000 33039545 8006.5070
1230 Poland Europe 1977 70.67000 34621254 9508.1415
1231 Poland Europe 1982 71.32000 36227381 8451.5310
1232 Poland Europe 1987 70.98000 37740710 9082.3512
1233 Poland Europe 1992 70.99000 38370697 7738.8812
1234 Poland Europe 1997 72.75000 38654957 10159.5837
1235 Poland Europe 2002 74.67000 38625976 12002.2391
1236 Poland Europe 2007 75.56300 38518241 15389.9247
1237 Portugal Europe 1952 59.82000 8526050 3068.3199
1238 Portugal Europe 1957 61.51000 8817650 3774.5717
1239 Portugal Europe 1962 64.39000 9019800 4727.9549
1240 Portugal Europe 1967 66.60000 9103000 6361.5180
1241 Portugal Europe 1972 69.26000 8970450 9022.2474
1242 Portugal Europe 1977 70.41000 9662600 10172.4857
1243 Portugal Europe 1982 72.77000 9859650 11753.8429
1244 Portugal Europe 1987 74.06000 9915289 13039.3088
1245 Portugal Europe 1992 74.86000 9927680 16207.2666
1246 Portugal Europe 1997 75.97000 10156415 17641.0316
1247 Portugal Europe 2002 77.29000 10433867 19970.9079
1248 Portugal Europe 2007 78.09800 10642836 20509.6478
1249 Puerto Rico Americas 1952 64.28000 2227000 3081.9598
1250 Puerto Rico Americas 1957 68.54000 2260000 3907.1562
1251 Puerto Rico Americas 1962 69.62000 2448046 5108.3446
1252 Puerto Rico Americas 1967 71.10000 2648961 6929.2777
1253 Puerto Rico Americas 1972 72.16000 2847132 9123.0417
1254 Puerto Rico Americas 1977 73.44000 3080828 9770.5249
1255 Puerto Rico Americas 1982 73.75000 3279001 10330.9891
1256 Puerto Rico Americas 1987 74.63000 3444468 12281.3419
1257 Puerto Rico Americas 1992 73.91100 3585176 14641.5871
1258 Puerto Rico Americas 1997 74.91700 3759430 16999.4333
1259 Puerto Rico Americas 2002 77.77800 3859606 18855.6062
1260 Puerto Rico Americas 2007 78.74600 3942491 19328.7090
1261 Reunion Africa 1952 52.72400 257700 2718.8853
1262 Reunion Africa 1957 55.09000 308700 2769.4518
1263 Reunion Africa 1962 57.66600 358900 3173.7233
1264 Reunion Africa 1967 60.54200 414024 4021.1757
1265 Reunion Africa 1972 64.27400 461633 5047.6586
1266 Reunion Africa 1977 67.06400 492095 4319.8041
1267 Reunion Africa 1982 69.88500 517810 5267.2194
1268 Reunion Africa 1987 71.91300 562035 5303.3775
1269 Reunion Africa 1992 73.61500 622191 6101.2558
1270 Reunion Africa 1997 74.77200 684810 6071.9414
1271 Reunion Africa 2002 75.74400 743981 6316.1652
1272 Reunion Africa 2007 76.44200 798094 7670.1226
1273 Romania Europe 1952 61.05000 16630000 3144.6132
1274 Romania Europe 1957 64.10000 17829327 3943.3702
1275 Romania Europe 1962 66.80000 18680721 4734.9976
1276 Romania Europe 1967 66.80000 19284814 6470.8665
1277 Romania Europe 1972 69.21000 20662648 8011.4144
1278 Romania Europe 1977 69.46000 21658597 9356.3972
1279 Romania Europe 1982 69.66000 22356726 9605.3141
1280 Romania Europe 1987 69.53000 22686371 9696.2733
1281 Romania Europe 1992 69.36000 22797027 6598.4099
1282 Romania Europe 1997 69.72000 22562458 7346.5476
1283 Romania Europe 2002 71.32200 22404337 7885.3601
1284 Romania Europe 2007 72.47600 22276056 10808.4756
1285 Rwanda Africa 1952 40.00000 2534927 493.3239
1286 Rwanda Africa 1957 41.50000 2822082 540.2894
1287 Rwanda Africa 1962 43.00000 3051242 597.4731
1288 Rwanda Africa 1967 44.10000 3451079 510.9637
1289 Rwanda Africa 1972 44.60000 3992121 590.5807
1290 Rwanda Africa 1977 45.00000 4657072 670.0806
1291 Rwanda Africa 1982 46.21800 5507565 881.5706
1292 Rwanda Africa 1987 44.02000 6349365 847.9912
1293 Rwanda Africa 1992 23.59900 7290203 737.0686
1294 Rwanda Africa 1997 36.08700 7212583 589.9445
1295 Rwanda Africa 2002 43.41300 7852401 785.6538
1296 Rwanda Africa 2007 46.24200 8860588 863.0885
1297 Sao Tome and Principe Africa 1952 46.47100 60011 879.5836
1298 Sao Tome and Principe Africa 1957 48.94500 61325 860.7369
1299 Sao Tome and Principe Africa 1962 51.89300 65345 1071.5511
1300 Sao Tome and Principe Africa 1967 54.42500 70787 1384.8406
1301 Sao Tome and Principe Africa 1972 56.48000 76595 1532.9853
1302 Sao Tome and Principe Africa 1977 58.55000 86796 1737.5617
1303 Sao Tome and Principe Africa 1982 60.35100 98593 1890.2181
1304 Sao Tome and Principe Africa 1987 61.72800 110812 1516.5255
1305 Sao Tome and Principe Africa 1992 62.74200 125911 1428.7778
1306 Sao Tome and Principe Africa 1997 63.30600 145608 1339.0760
1307 Sao Tome and Principe Africa 2002 64.33700 170372 1353.0924
1308 Sao Tome and Principe Africa 2007 65.52800 199579 1598.4351
1309 Saudi Arabia Asia 1952 39.87500 4005677 6459.5548
1310 Saudi Arabia Asia 1957 42.86800 4419650 8157.5912
1311 Saudi Arabia Asia 1962 45.91400 4943029 11626.4197
1312 Saudi Arabia Asia 1967 49.90100 5618198 16903.0489
1313 Saudi Arabia Asia 1972 53.88600 6472756 24837.4287
1314 Saudi Arabia Asia 1977 58.69000 8128505 34167.7626
1315 Saudi Arabia Asia 1982 63.01200 11254672 33693.1753
1316 Saudi Arabia Asia 1987 66.29500 14619745 21198.2614
1317 Saudi Arabia Asia 1992 68.76800 16945857 24841.6178
1318 Saudi Arabia Asia 1997 70.53300 21229759 20586.6902
1319 Saudi Arabia Asia 2002 71.62600 24501530 19014.5412
1320 Saudi Arabia Asia 2007 72.77700 27601038 21654.8319
1321 Senegal Africa 1952 37.27800 2755589 1450.3570
1322 Senegal Africa 1957 39.32900 3054547 1567.6530
1323 Senegal Africa 1962 41.45400 3430243 1654.9887
1324 Senegal Africa 1967 43.56300 3965841 1612.4046
1325 Senegal Africa 1972 45.81500 4588696 1597.7121
1326 Senegal Africa 1977 48.87900 5260855 1561.7691
1327 Senegal Africa 1982 52.37900 6147783 1518.4800
1328 Senegal Africa 1987 55.76900 7171347 1441.7207
1329 Senegal Africa 1992 58.19600 8307920 1367.8994
1330 Senegal Africa 1997 60.18700 9535314 1392.3683
1331 Senegal Africa 2002 61.60000 10870037 1519.6353
1332 Senegal Africa 2007 63.06200 12267493 1712.4721
1333 Serbia Europe 1952 57.99600 6860147 3581.4594
1334 Serbia Europe 1957 61.68500 7271135 4981.0909
1335 Serbia Europe 1962 64.53100 7616060 6289.6292
1336 Serbia Europe 1967 66.91400 7971222 7991.7071
1337 Serbia Europe 1972 68.70000 8313288 10522.0675
1338 Serbia Europe 1977 70.30000 8686367 12980.6696
1339 Serbia Europe 1982 70.16200 9032824 15181.0927
1340 Serbia Europe 1987 71.21800 9230783 15870.8785
1341 Serbia Europe 1992 71.65900 9826397 9325.0682
1342 Serbia Europe 1997 72.23200 10336594 7914.3203
1343 Serbia Europe 2002 73.21300 10111559 7236.0753
1344 Serbia Europe 2007 74.00200 10150265 9786.5347
1345 Sierra Leone Africa 1952 30.33100 2143249 879.7877
1346 Sierra Leone Africa 1957 31.57000 2295678 1004.4844
1347 Sierra Leone Africa 1962 32.76700 2467895 1116.6399
1348 Sierra Leone Africa 1967 34.11300 2662190 1206.0435
1349 Sierra Leone Africa 1972 35.40000 2879013 1353.7598
1350 Sierra Leone Africa 1977 36.78800 3140897 1348.2852
1351 Sierra Leone Africa 1982 38.44500 3464522 1465.0108
1352 Sierra Leone Africa 1987 40.00600 3868905 1294.4478
1353 Sierra Leone Africa 1992 38.33300 4260884 1068.6963
1354 Sierra Leone Africa 1997 39.89700 4578212 574.6482
1355 Sierra Leone Africa 2002 41.01200 5359092 699.4897
1356 Sierra Leone Africa 2007 42.56800 6144562 862.5408
1357 Singapore Asia 1952 60.39600 1127000 2315.1382
1358 Singapore Asia 1957 63.17900 1445929 2843.1044
1359 Singapore Asia 1962 65.79800 1750200 3674.7356
1360 Singapore Asia 1967 67.94600 1977600 4977.4185
1361 Singapore Asia 1972 69.52100 2152400 8597.7562
1362 Singapore Asia 1977 70.79500 2325300 11210.0895
1363 Singapore Asia 1982 71.76000 2651869 15169.1611
1364 Singapore Asia 1987 73.56000 2794552 18861.5308
1365 Singapore Asia 1992 75.78800 3235865 24769.8912
1366 Singapore Asia 1997 77.15800 3802309 33519.4766
1367 Singapore Asia 2002 78.77000 4197776 36023.1054
1368 Singapore Asia 2007 79.97200 4553009 47143.1796
1369 Slovak Republic Europe 1952 64.36000 3558137 5074.6591
1370 Slovak Republic Europe 1957 67.45000 3844277 6093.2630
1371 Slovak Republic Europe 1962 70.33000 4237384 7481.1076
1372 Slovak Republic Europe 1967 70.98000 4442238 8412.9024
1373 Slovak Republic Europe 1972 70.35000 4593433 9674.1676
1374 Slovak Republic Europe 1977 70.45000 4827803 10922.6640
1375 Slovak Republic Europe 1982 70.80000 5048043 11348.5459
1376 Slovak Republic Europe 1987 71.08000 5199318 12037.2676
1377 Slovak Republic Europe 1992 71.38000 5302888 9498.4677
1378 Slovak Republic Europe 1997 72.71000 5383010 12126.2306
1379 Slovak Republic Europe 2002 73.80000 5410052 13638.7784
1380 Slovak Republic Europe 2007 74.66300 5447502 18678.3144
1381 Slovenia Europe 1952 65.57000 1489518 4215.0417
1382 Slovenia Europe 1957 67.85000 1533070 5862.2766
1383 Slovenia Europe 1962 69.15000 1582962 7402.3034
1384 Slovenia Europe 1967 69.18000 1646912 9405.4894
1385 Slovenia Europe 1972 69.82000 1694510 12383.4862
1386 Slovenia Europe 1977 70.97000 1746919 15277.0302
1387 Slovenia Europe 1982 71.06300 1861252 17866.7218
1388 Slovenia Europe 1987 72.25000 1945870 18678.5349
1389 Slovenia Europe 1992 73.64000 1999210 14214.7168
1390 Slovenia Europe 1997 75.13000 2011612 17161.1073
1391 Slovenia Europe 2002 76.66000 2011497 20660.0194
1392 Slovenia Europe 2007 77.92600 2009245 25768.2576
1393 Somalia Africa 1952 32.97800 2526994 1135.7498
1394 Somalia Africa 1957 34.97700 2780415 1258.1474
1395 Somalia Africa 1962 36.98100 3080153 1369.4883
1396 Somalia Africa 1967 38.97700 3428839 1284.7332
1397 Somalia Africa 1972 40.97300 3840161 1254.5761
1398 Somalia Africa 1977 41.97400 4353666 1450.9925
1399 Somalia Africa 1982 42.95500 5828892 1176.8070
1400 Somalia Africa 1987 44.50100 6921858 1093.2450
1401 Somalia Africa 1992 39.65800 6099799 926.9603
1402 Somalia Africa 1997 43.79500 6633514 930.5964
1403 Somalia Africa 2002 45.93600 7753310 882.0818
1404 Somalia Africa 2007 48.15900 9118773 926.1411
1405 South Africa Africa 1952 45.00900 14264935 4725.2955
1406 South Africa Africa 1957 47.98500 16151549 5487.1042
1407 South Africa Africa 1962 49.95100 18356657 5768.7297
1408 South Africa Africa 1967 51.92700 20997321 7114.4780
1409 South Africa Africa 1972 53.69600 23935810 7765.9626
1410 South Africa Africa 1977 55.52700 27129932 8028.6514
1411 South Africa Africa 1982 58.16100 31140029 8568.2662
1412 South Africa Africa 1987 60.83400 35933379 7825.8234
1413 South Africa Africa 1992 61.88800 39964159 7225.0693
1414 South Africa Africa 1997 60.23600 42835005 7479.1882
1415 South Africa Africa 2002 53.36500 44433622 7710.9464
1416 South Africa Africa 2007 49.33900 43997828 9269.6578
1417 Spain Europe 1952 64.94000 28549870 3834.0347
1418 Spain Europe 1957 66.66000 29841614 4564.8024
1419 Spain Europe 1962 69.69000 31158061 5693.8439
1420 Spain Europe 1967 71.44000 32850275 7993.5123
1421 Spain Europe 1972 73.06000 34513161 10638.7513
1422 Spain Europe 1977 74.39000 36439000 13236.9212
1423 Spain Europe 1982 76.30000 37983310 13926.1700
1424 Spain Europe 1987 76.90000 38880702 15764.9831
1425 Spain Europe 1992 77.57000 39549438 18603.0645
1426 Spain Europe 1997 78.77000 39855442 20445.2990
1427 Spain Europe 2002 79.78000 40152517 24835.4717
1428 Spain Europe 2007 80.94100 40448191 28821.0637
1429 Sri Lanka Asia 1952 57.59300 7982342 1083.5320
1430 Sri Lanka Asia 1957 61.45600 9128546 1072.5466
1431 Sri Lanka Asia 1962 62.19200 10421936 1074.4720
1432 Sri Lanka Asia 1967 64.26600 11737396 1135.5143
1433 Sri Lanka Asia 1972 65.04200 13016733 1213.3955
1434 Sri Lanka Asia 1977 65.94900 14116836 1348.7757
1435 Sri Lanka Asia 1982 68.75700 15410151 1648.0798
1436 Sri Lanka Asia 1987 69.01100 16495304 1876.7668
1437 Sri Lanka Asia 1992 70.37900 17587060 2153.7392
1438 Sri Lanka Asia 1997 70.45700 18698655 2664.4773
1439 Sri Lanka Asia 2002 70.81500 19576783 3015.3788
1440 Sri Lanka Asia 2007 72.39600 20378239 3970.0954
1441 Sudan Africa 1952 38.63500 8504667 1615.9911
1442 Sudan Africa 1957 39.62400 9753392 1770.3371
1443 Sudan Africa 1962 40.87000 11183227 1959.5938
1444 Sudan Africa 1967 42.85800 12716129 1687.9976
1445 Sudan Africa 1972 45.08300 14597019 1659.6528
1446 Sudan Africa 1977 47.80000 17104986 2202.9884
1447 Sudan Africa 1982 50.33800 20367053 1895.5441
1448 Sudan Africa 1987 51.74400 24725960 1507.8192
1449 Sudan Africa 1992 53.55600 28227588 1492.1970
1450 Sudan Africa 1997 55.37300 32160729 1632.2108
1451 Sudan Africa 2002 56.36900 37090298 1993.3983
1452 Sudan Africa 2007 58.55600 42292929 2602.3950
1453 Swaziland Africa 1952 41.40700 290243 1148.3766
1454 Swaziland Africa 1957 43.42400 326741 1244.7084
1455 Swaziland Africa 1962 44.99200 370006 1856.1821
1456 Swaziland Africa 1967 46.63300 420690 2613.1017
1457 Swaziland Africa 1972 49.55200 480105 3364.8366
1458 Swaziland Africa 1977 52.53700 551425 3781.4106
1459 Swaziland Africa 1982 55.56100 649901 3895.3840
1460 Swaziland Africa 1987 57.67800 779348 3984.8398
1461 Swaziland Africa 1992 58.47400 962344 3553.0224
1462 Swaziland Africa 1997 54.28900 1054486 3876.7685
1463 Swaziland Africa 2002 43.86900 1130269 4128.1169
1464 Swaziland Africa 2007 39.61300 1133066 4513.4806
1465 Sweden Europe 1952 71.86000 7124673 8527.8447
1466 Sweden Europe 1957 72.49000 7363802 9911.8782
1467 Sweden Europe 1962 73.37000 7561588 12329.4419
1468 Sweden Europe 1967 74.16000 7867931 15258.2970
1469 Sweden Europe 1972 74.72000 8122293 17832.0246
1470 Sweden Europe 1977 75.44000 8251648 18855.7252
1471 Sweden Europe 1982 76.42000 8325260 20667.3812
1472 Sweden Europe 1987 77.19000 8421403 23586.9293
1473 Sweden Europe 1992 78.16000 8718867 23880.0168
1474 Sweden Europe 1997 79.39000 8897619 25266.5950
1475 Sweden Europe 2002 80.04000 8954175 29341.6309
1476 Sweden Europe 2007 80.88400 9031088 33859.7484
1477 Switzerland Europe 1952 69.62000 4815000 14734.2327
1478 Switzerland Europe 1957 70.56000 5126000 17909.4897
1479 Switzerland Europe 1962 71.32000 5666000 20431.0927
1480 Switzerland Europe 1967 72.77000 6063000 22966.1443
1481 Switzerland Europe 1972 73.78000 6401400 27195.1130
1482 Switzerland Europe 1977 75.39000 6316424 26982.2905
1483 Switzerland Europe 1982 76.21000 6468126 28397.7151
1484 Switzerland Europe 1987 77.41000 6649942 30281.7046
1485 Switzerland Europe 1992 78.03000 6995447 31871.5303
1486 Switzerland Europe 1997 79.37000 7193761 32135.3230
1487 Switzerland Europe 2002 80.62000 7361757 34480.9577
1488 Switzerland Europe 2007 81.70100 7554661 37506.4191
1489 Syria Asia 1952 45.88300 3661549 1643.4854
1490 Syria Asia 1957 48.28400 4149908 2117.2349
1491 Syria Asia 1962 50.30500 4834621 2193.0371
1492 Syria Asia 1967 53.65500 5680812 1881.9236
1493 Syria Asia 1972 57.29600 6701172 2571.4230
1494 Syria Asia 1977 61.19500 7932503 3195.4846
1495 Syria Asia 1982 64.59000 9410494 3761.8377
1496 Syria Asia 1987 66.97400 11242847 3116.7743
1497 Syria Asia 1992 69.24900 13219062 3340.5428
1498 Syria Asia 1997 71.52700 15081016 4014.2390
1499 Syria Asia 2002 73.05300 17155814 4090.9253
1500 Syria Asia 2007 74.14300 19314747 4184.5481
1501 Taiwan Asia 1952 58.50000 8550362 1206.9479
1502 Taiwan Asia 1957 62.40000 10164215 1507.8613
1503 Taiwan Asia 1962 65.20000 11918938 1822.8790
1504 Taiwan Asia 1967 67.50000 13648692 2643.8587
1505 Taiwan Asia 1972 69.39000 15226039 4062.5239
1506 Taiwan Asia 1977 70.59000 16785196 5596.5198
1507 Taiwan Asia 1982 72.16000 18501390 7426.3548
1508 Taiwan Asia 1987 73.40000 19757799 11054.5618
1509 Taiwan Asia 1992 74.26000 20686918 15215.6579
1510 Taiwan Asia 1997 75.25000 21628605 20206.8210
1511 Taiwan Asia 2002 76.99000 22454239 23235.4233
1512 Taiwan Asia 2007 78.40000 23174294 28718.2768
1513 Tanzania Africa 1952 41.21500 8322925 716.6501
1514 Tanzania Africa 1957 42.97400 9452826 698.5356
1515 Tanzania Africa 1962 44.24600 10863958 722.0038
1516 Tanzania Africa 1967 45.75700 12607312 848.2187
1517 Tanzania Africa 1972 47.62000 14706593 915.9851
1518 Tanzania Africa 1977 49.91900 17129565 962.4923
1519 Tanzania Africa 1982 50.60800 19844382 874.2426
1520 Tanzania Africa 1987 51.53500 23040630 831.8221
1521 Tanzania Africa 1992 50.44000 26605473 825.6825
1522 Tanzania Africa 1997 48.46600 30686889 789.1862
1523 Tanzania Africa 2002 49.65100 34593779 899.0742
1524 Tanzania Africa 2007 52.51700 38139640 1107.4822
1525 Thailand Asia 1952 50.84800 21289402 757.7974
1526 Thailand Asia 1957 53.63000 25041917 793.5774
1527 Thailand Asia 1962 56.06100 29263397 1002.1992
1528 Thailand Asia 1967 58.28500 34024249 1295.4607
1529 Thailand Asia 1972 60.40500 39276153 1524.3589
1530 Thailand Asia 1977 62.49400 44148285 1961.2246
1531 Thailand Asia 1982 64.59700 48827160 2393.2198
1532 Thailand Asia 1987 66.08400 52910342 2982.6538
1533 Thailand Asia 1992 67.29800 56667095 4616.8965
1534 Thailand Asia 1997 67.52100 60216677 5852.6255
1535 Thailand Asia 2002 68.56400 62806748 5913.1875
1536 Thailand Asia 2007 70.61600 65068149 7458.3963
1537 Togo Africa 1952 38.59600 1219113 859.8087
1538 Togo Africa 1957 41.20800 1357445 925.9083
1539 Togo Africa 1962 43.92200 1528098 1067.5348
1540 Togo Africa 1967 46.76900 1735550 1477.5968
1541 Togo Africa 1972 49.75900 2056351 1649.6602
1542 Togo Africa 1977 52.88700 2308582 1532.7770
1543 Togo Africa 1982 55.47100 2644765 1344.5780
1544 Togo Africa 1987 56.94100 3154264 1202.2014
1545 Togo Africa 1992 58.06100 3747553 1034.2989
1546 Togo Africa 1997 58.39000 4320890 982.2869
1547 Togo Africa 2002 57.56100 4977378 886.2206
1548 Togo Africa 2007 58.42000 5701579 882.9699
1549 Trinidad and Tobago Americas 1952 59.10000 662850 3023.2719
1550 Trinidad and Tobago Americas 1957 61.80000 764900 4100.3934
1551 Trinidad and Tobago Americas 1962 64.90000 887498 4997.5240
1552 Trinidad and Tobago Americas 1967 65.40000 960155 5621.3685
1553 Trinidad and Tobago Americas 1972 65.90000 975199 6619.5514
1554 Trinidad and Tobago Americas 1977 68.30000 1039009 7899.5542
1555 Trinidad and Tobago Americas 1982 68.83200 1116479 9119.5286
1556 Trinidad and Tobago Americas 1987 69.58200 1191336 7388.5978
1557 Trinidad and Tobago Americas 1992 69.86200 1183669 7370.9909
1558 Trinidad and Tobago Americas 1997 69.46500 1138101 8792.5731
1559 Trinidad and Tobago Americas 2002 68.97600 1101832 11460.6002
1560 Trinidad and Tobago Americas 2007 69.81900 1056608 18008.5092
1561 Tunisia Africa 1952 44.60000 3647735 1468.4756
1562 Tunisia Africa 1957 47.10000 3950849 1395.2325
1563 Tunisia Africa 1962 49.57900 4286552 1660.3032
1564 Tunisia Africa 1967 52.05300 4786986 1932.3602
1565 Tunisia Africa 1972 55.60200 5303507 2753.2860
1566 Tunisia Africa 1977 59.83700 6005061 3120.8768
1567 Tunisia Africa 1982 64.04800 6734098 3560.2332
1568 Tunisia Africa 1987 66.89400 7724976 3810.4193
1569 Tunisia Africa 1992 70.00100 8523077 4332.7202
1570 Tunisia Africa 1997 71.97300 9231669 4876.7986
1571 Tunisia Africa 2002 73.04200 9770575 5722.8957
1572 Tunisia Africa 2007 73.92300 10276158 7092.9230
1573 Turkey Europe 1952 43.58500 22235677 1969.1010
1574 Turkey Europe 1957 48.07900 25670939 2218.7543
1575 Turkey Europe 1962 52.09800 29788695 2322.8699
1576 Turkey Europe 1967 54.33600 33411317 2826.3564
1577 Turkey Europe 1972 57.00500 37492953 3450.6964
1578 Turkey Europe 1977 59.50700 42404033 4269.1223
1579 Turkey Europe 1982 61.03600 47328791 4241.3563
1580 Turkey Europe 1987 63.10800 52881328 5089.0437
1581 Turkey Europe 1992 66.14600 58179144 5678.3483
1582 Turkey Europe 1997 68.83500 63047647 6601.4299
1583 Turkey Europe 2002 70.84500 67308928 6508.0857
1584 Turkey Europe 2007 71.77700 71158647 8458.2764
1585 Uganda Africa 1952 39.97800 5824797 734.7535
1586 Uganda Africa 1957 42.57100 6675501 774.3711
1587 Uganda Africa 1962 45.34400 7688797 767.2717
1588 Uganda Africa 1967 48.05100 8900294 908.9185
1589 Uganda Africa 1972 51.01600 10190285 950.7359
1590 Uganda Africa 1977 50.35000 11457758 843.7331
1591 Uganda Africa 1982 49.84900 12939400 682.2662
1592 Uganda Africa 1987 51.50900 15283050 617.7244
1593 Uganda Africa 1992 48.82500 18252190 644.1708
1594 Uganda Africa 1997 44.57800 21210254 816.5591
1595 Uganda Africa 2002 47.81300 24739869 927.7210
1596 Uganda Africa 2007 51.54200 29170398 1056.3801
1597 United Kingdom Europe 1952 69.18000 50430000 9979.5085
1598 United Kingdom Europe 1957 70.42000 51430000 11283.1779
1599 United Kingdom Europe 1962 70.76000 53292000 12477.1771
1600 United Kingdom Europe 1967 71.36000 54959000 14142.8509
1601 United Kingdom Europe 1972 72.01000 56079000 15895.1164
1602 United Kingdom Europe 1977 72.76000 56179000 17428.7485
1603 United Kingdom Europe 1982 74.04000 56339704 18232.4245
1604 United Kingdom Europe 1987 75.00700 56981620 21664.7877
1605 United Kingdom Europe 1992 76.42000 57866349 22705.0925
1606 United Kingdom Europe 1997 77.21800 58808266 26074.5314
1607 United Kingdom Europe 2002 78.47100 59912431 29478.9992
1608 United Kingdom Europe 2007 79.42500 60776238 33203.2613
1609 United States Americas 1952 68.44000 157553000 13990.4821
1610 United States Americas 1957 69.49000 171984000 14847.1271
1611 United States Americas 1962 70.21000 186538000 16173.1459
1612 United States Americas 1967 70.76000 198712000 19530.3656
1613 United States Americas 1972 71.34000 209896000 21806.0359
1614 United States Americas 1977 73.38000 220239000 24072.6321
1615 United States Americas 1982 74.65000 232187835 25009.5591
1616 United States Americas 1987 75.02000 242803533 29884.3504
1617 United States Americas 1992 76.09000 256894189 32003.9322
1618 United States Americas 1997 76.81000 272911760 35767.4330
1619 United States Americas 2002 77.31000 287675526 39097.0995
1620 United States Americas 2007 78.24200 301139947 42951.6531
1621 Uruguay Americas 1952 66.07100 2252965 5716.7667
1622 Uruguay Americas 1957 67.04400 2424959 6150.7730
1623 Uruguay Americas 1962 68.25300 2598466 5603.3577
1624 Uruguay Americas 1967 68.46800 2748579 5444.6196
1625 Uruguay Americas 1972 68.67300 2829526 5703.4089
1626 Uruguay Americas 1977 69.48100 2873520 6504.3397
1627 Uruguay Americas 1982 70.80500 2953997 6920.2231
1628 Uruguay Americas 1987 71.91800 3045153 7452.3990
1629 Uruguay Americas 1992 72.75200 3149262 8137.0048
1630 Uruguay Americas 1997 74.22300 3262838 9230.2407
1631 Uruguay Americas 2002 75.30700 3363085 7727.0020
1632 Uruguay Americas 2007 76.38400 3447496 10611.4630
1633 Venezuela Americas 1952 55.08800 5439568 7689.7998
1634 Venezuela Americas 1957 57.90700 6702668 9802.4665
1635 Venezuela Americas 1962 60.77000 8143375 8422.9742
1636 Venezuela Americas 1967 63.47900 9709552 9541.4742
1637 Venezuela Americas 1972 65.71200 11515649 10505.2597
1638 Venezuela Americas 1977 67.45600 13503563 13143.9510
1639 Venezuela Americas 1982 68.55700 15620766 11152.4101
1640 Venezuela Americas 1987 70.19000 17910182 9883.5846
1641 Venezuela Americas 1992 71.15000 20265563 10733.9263
1642 Venezuela Americas 1997 72.14600 22374398 10165.4952
1643 Venezuela Americas 2002 72.76600 24287670 8605.0478
1644 Venezuela Americas 2007 73.74700 26084662 11415.8057
1645 Vietnam Asia 1952 40.41200 26246839 605.0665
1646 Vietnam Asia 1957 42.88700 28998543 676.2854
1647 Vietnam Asia 1962 45.36300 33796140 772.0492
1648 Vietnam Asia 1967 47.83800 39463910 637.1233
1649 Vietnam Asia 1972 50.25400 44655014 699.5016
1650 Vietnam Asia 1977 55.76400 50533506 713.5371
1651 Vietnam Asia 1982 58.81600 56142181 707.2358
1652 Vietnam Asia 1987 62.82000 62826491 820.7994
1653 Vietnam Asia 1992 67.66200 69940728 989.0231
1654 Vietnam Asia 1997 70.67200 76048996 1385.8968
1655 Vietnam Asia 2002 73.01700 80908147 1764.4567
1656 Vietnam Asia 2007 74.24900 85262356 2441.5764
1657 West Bank and Gaza Asia 1952 43.16000 1030585 1515.5923
1658 West Bank and Gaza Asia 1957 45.67100 1070439 1827.0677
1659 West Bank and Gaza Asia 1962 48.12700 1133134 2198.9563
1660 West Bank and Gaza Asia 1967 51.63100 1142636 2649.7150
1661 West Bank and Gaza Asia 1972 56.53200 1089572 3133.4093
1662 West Bank and Gaza Asia 1977 60.76500 1261091 3682.8315
1663 West Bank and Gaza Asia 1982 64.40600 1425876 4336.0321
1664 West Bank and Gaza Asia 1987 67.04600 1691210 5107.1974
1665 West Bank and Gaza Asia 1992 69.71800 2104779 6017.6548
1666 West Bank and Gaza Asia 1997 71.09600 2826046 7110.6676
1667 West Bank and Gaza Asia 2002 72.37000 3389578 4515.4876
1668 West Bank and Gaza Asia 2007 73.42200 4018332 3025.3498
1669 Yemen, Rep. Asia 1952 32.54800 4963829 781.7176
1670 Yemen, Rep. Asia 1957 33.97000 5498090 804.8305
1671 Yemen, Rep. Asia 1962 35.18000 6120081 825.6232
1672 Yemen, Rep. Asia 1967 36.98400 6740785 862.4421
1673 Yemen, Rep. Asia 1972 39.84800 7407075 1265.0470
1674 Yemen, Rep. Asia 1977 44.17500 8403990 1829.7652
1675 Yemen, Rep. Asia 1982 49.11300 9657618 1977.5570
1676 Yemen, Rep. Asia 1987 52.92200 11219340 1971.7415
1677 Yemen, Rep. Asia 1992 55.59900 13367997 1879.4967
1678 Yemen, Rep. Asia 1997 58.02000 15826497 2117.4845
1679 Yemen, Rep. Asia 2002 60.30800 18701257 2234.8208
1680 Yemen, Rep. Asia 2007 62.69800 22211743 2280.7699
1681 Zambia Africa 1952 42.03800 2672000 1147.3888
1682 Zambia Africa 1957 44.07700 3016000 1311.9568
1683 Zambia Africa 1962 46.02300 3421000 1452.7258
1684 Zambia Africa 1967 47.76800 3900000 1777.0773
1685 Zambia Africa 1972 50.10700 4506497 1773.4983
1686 Zambia Africa 1977 51.38600 5216550 1588.6883
1687 Zambia Africa 1982 51.82100 6100407 1408.6786
1688 Zambia Africa 1987 50.82100 7272406 1213.3151
1689 Zambia Africa 1992 46.10000 8381163 1210.8846
1690 Zambia Africa 1997 40.23800 9417789 1071.3538
1691 Zambia Africa 2002 39.19300 10595811 1071.6139
1692 Zambia Africa 2007 42.38400 11746035 1271.2116
1693 Zimbabwe Africa 1952 48.45100 3080907 406.8841
1694 Zimbabwe Africa 1957 50.46900 3646340 518.7643
1695 Zimbabwe Africa 1962 52.35800 4277736 527.2722
1696 Zimbabwe Africa 1967 53.99500 4995432 569.7951
1697 Zimbabwe Africa 1972 55.63500 5861135 799.3622
1698 Zimbabwe Africa 1977 57.67400 6642107 685.5877
1699 Zimbabwe Africa 1982 60.36300 7636524 788.8550
1700 Zimbabwe Africa 1987 62.35100 9216418 706.1573
1701 Zimbabwe Africa 1992 60.37700 10704340 693.4208
1702 Zimbabwe Africa 1997 46.80900 11404948 792.4500
1703 Zimbabwe Africa 2002 39.98900 11926563 672.0386
1704 Zimbabwe Africa 2007 43.48700 12311143 469.7093Here are the main differences:
The version loaded using the base R
read.csv()function prints out the first 1000 rows (though I’ve kindly put them all in a nice scrolly box for you), whereas the version loaded using the tidyverseread_csv()function only prints out the first 10 rows (and it also only displays the first few columns whenever your dataset contains a large number of columns).The version loaded using the tidyverse
read_csv()function will also show you what type/class each columns has. Look underneath the column names of the tidyverseread_csv()version ofgapminderabove. See the<chr>and<dbl>symbols? These mean “character” and “double” (“double” means “numeric with decimals”), respectively.The tidyverse
read_csv()version prints out some information at the top that says# A tibble: 1,704 × 6, which tells us that our data frame has 1,704 rows and 6 columns.
What’s a “tibble”? It turns out that read_csv() doesn’t actually load your data in as a data frame. It loads your data in as a “tibble”. While tibbles have some fancy features, for our purposes, you can just think of a tibble as a data frame that looks slightly different when printed. Note that I will usually use the term “data frame” even if the object is technically a tibble.
6.3 The dplyr library
So we’ve now loaded the tidyverse library and we’ve loaded our gapminder data using read_csv(). The code we’ve written so far in this chapter is essentially just:
library(tidyverse)
gapminder <- read_csv("data/gapminder.csv")When we loaded the “tidyverse” library, this also loaded the “dplyr” library (along with several others.)
The dplyr library is probably the most important library in the tidyverse. It contains a bunch of functions that allow you to work with data frames like extract columns, modify columns, and filter based on conditions.
The main dplyr functions to master are:
select(): extract columns from your data framefilter(): filter to rows of your data frame based on a conditionmutate(): add columns or modify columns in your data framesummarize(): aggregate information in your columnsgroup_by(): perform an operation separately for each value of a categorical column
The rest of this chapter will guide you through using these functions step by step, showing not only how they work individually but also how to combine them. Once you’re comfortable with these functions, you’ll be ready to tackle a variety of data analysis tasks.
6.4 Select() for extacting columns
We can use the select() function to extract specific named columns from our data frame.
The first argument of
select()is always the data frame on which you are operating.All of the remaining arguments of
select()are the names of the columns that you want to keep.
Note that the column names do not have quotes around them. This is something that makes dplyr (and tidyverse) functions special.
So if we want to use select() to extract just the country, year, and lifeExp columns from our gapminder data frame, the first argument will be the name of our data frame object, gapminder, and the subsequent arguments will be the names of the columns we want to extract:
select(gapminder, country, year, lifeExp)# A tibble: 1,704 × 3
country year lifeExp
<chr> <dbl> <dbl>
1 Afghanistan 1952 28.8
2 Afghanistan 1957 30.3
3 Afghanistan 1962 32.0
4 Afghanistan 1967 34.0
5 Afghanistan 1972 36.1
6 Afghanistan 1977 38.4
7 Afghanistan 1982 39.9
8 Afghanistan 1987 40.8
9 Afghanistan 1992 41.7
10 Afghanistan 1997 41.8
# ℹ 1,694 more rowsNote that I haven’t modified the original gapminder data frame object here. If I print gapminder, it still has all of the original columns:
gapminder# A tibble: 1,704 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.
7 Afghanistan Asia 1982 39.9 12881816 978.
8 Afghanistan Asia 1987 40.8 13867957 852.
9 Afghanistan Asia 1992 41.7 16317921 649.
10 Afghanistan Asia 1997 41.8 22227415 635.
# ℹ 1,694 more rowsInstead, I have created a new data frame with just the country, year, and lifeExp columns, and I’ve just printed it out.
If I wanted to use this country, year, and lifeExp subsetted data frame, I would need to save it as a new variable/object using the <- assignment operator:
gapminder_subset <- select(gapminder, country, year, lifeExp)And I could then work with this new data frame by referencing gapminder_subset in my code:
gapminder_subset# A tibble: 1,704 × 3
country year lifeExp
<chr> <dbl> <dbl>
1 Afghanistan 1952 28.8
2 Afghanistan 1957 30.3
3 Afghanistan 1962 32.0
4 Afghanistan 1967 34.0
5 Afghanistan 1972 36.1
6 Afghanistan 1977 38.4
7 Afghanistan 1982 39.9
8 Afghanistan 1987 40.8
9 Afghanistan 1992 41.7
10 Afghanistan 1997 41.8
# ℹ 1,694 more rowsIn this chapter, however, I will typically print the output of various data frame operations without saving the resulting data frame output as new objects. This is because I just want to show what the result will be. I don’t necessarily need to use the resulting data frames for anything (so there is no point in saving them as new objects).
6.4.1 Removing columns with select()
You can remove columns by using a minus sign in front of the column name. For example, the following code will return the gapminder data frame without the continent, year, and pop columns:
select(gapminder, -continent, -year, -pop)# A tibble: 1,704 × 3
country lifeExp gdpPercap
<chr> <dbl> <dbl>
1 Afghanistan 28.8 779.
2 Afghanistan 30.3 821.
3 Afghanistan 32.0 853.
4 Afghanistan 34.0 836.
5 Afghanistan 36.1 740.
6 Afghanistan 38.4 786.
7 Afghanistan 39.9 978.
8 Afghanistan 40.8 852.
9 Afghanistan 41.7 649.
10 Afghanistan 41.8 635.
# ℹ 1,694 more rows6.4.2 Renaming columns with select()
select() can also help you rename columns. If you provide a named argument for your columns as new_name = old_name, the resulting column in the output data frame will be renamed to whatever you provide as new_name. For example, the following code will return the gapminder data frame with the country, year, lifeExp, and gdpPercap columns, except the lifeExp column will be renamed to life_exp and the gdpPercap column renamed to gdp_per_cap:
select(gapminder, country, year, life_exp = lifeExp, gdp_per_cap = gdpPercap)# A tibble: 1,704 × 4
country year life_exp gdp_per_cap
<chr> <dbl> <dbl> <dbl>
1 Afghanistan 1952 28.8 779.
2 Afghanistan 1957 30.3 821.
3 Afghanistan 1962 32.0 853.
4 Afghanistan 1967 34.0 836.
5 Afghanistan 1972 36.1 740.
6 Afghanistan 1977 38.4 786.
7 Afghanistan 1982 39.9 978.
8 Afghanistan 1987 40.8 852.
9 Afghanistan 1992 41.7 649.
10 Afghanistan 1997 41.8 635.
# ℹ 1,694 more rows6.4.3 Renaming columns with rename()
However, select() will only return the columns included in its arguments. If you want to rename a column without also having to list all the other columns you want in your output data frame, you can use the rename() function instead.
For example, the following code will return all columns in the gapminder data frame, with the lifeExp column renamed to life_exp and the gdpPercap column renamed to gdp_per_cap:
rename(gapminder, life_exp = lifeExp, gdp_per_cap = gdpPercap)# A tibble: 1,704 × 6
country continent year life_exp pop gdp_per_cap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.
7 Afghanistan Asia 1982 39.9 12881816 978.
8 Afghanistan Asia 1987 40.8 13867957 852.
9 Afghanistan Asia 1992 41.7 16317921 649.
10 Afghanistan Asia 1997 41.8 22227415 635.
# ℹ 1,694 more rowsWhat would happen if I replaced rename() in the code above with select()? As in:
select(gapminder, life_exp = lifeExp, gdp_per_cap = gdpPercap)The resulting data frame output would only include the life_exp and gdp_per_cap columns!
select(gapminder, life_exp = lifeExp, gdp_per_cap = gdpPercap)# A tibble: 1,704 × 2
life_exp gdp_per_cap
<dbl> <dbl>
1 28.8 779.
2 30.3 821.
3 32.0 853.
4 34.0 836.
5 36.1 740.
6 38.4 786.
7 39.9 978.
8 40.8 852.
9 41.7 649.
10 41.8 635.
# ℹ 1,694 more rows6.5 The pipe |> (formerly known as %>%)
Before introducing our next dplyr function, I want to introduce you to an operator called the pipe. The pipe is literally (in my very biased opinion) the best coding invention ever.
The pipe, |>, allows us to read our code as if it is a sentence. For example, if I wanted to turn the following sentence “I take my backpack and then I put books in it and then put it on my back” using the pipe, I would write backpack |> put_books_in() |> put_on_back(). I always think of the pipe operator |> as the word “and then” in a sentence.
Take a look at the following code:
gapminder |> select(country, year, lifeExp)# A tibble: 1,704 × 3
country year lifeExp
<chr> <dbl> <dbl>
1 Afghanistan 1952 28.8
2 Afghanistan 1957 30.3
3 Afghanistan 1962 32.0
4 Afghanistan 1967 34.0
5 Afghanistan 1972 36.1
6 Afghanistan 1977 38.4
7 Afghanistan 1982 39.9
8 Afghanistan 1987 40.8
9 Afghanistan 1992 41.7
10 Afghanistan 1997 41.8
# ℹ 1,694 more rowsI read this code in my head as “take the gapminder data frame and then select the country, year, and lifeExp columns”:
The pipe syntax is: object |> function(). The way it works is that the object to the left of the pipe (object) is placed into the first argument of the function to the right of the pipe (function()).
This means that the following two pieces of code are equivalent:
# apply head() to gapminder directly
head(gapminder)# A tibble: 6 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.# apply head() to gapminder using the pipe
gapminder |> head()# A tibble: 6 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.The second version with the pipe takes the gapminder data frame (which is to the left of the pipe) and places it into the (first) argument of the head() function on the right of the pipe. The pipe always has an object (like a data frame) on its left and a function on its right.
Here is another example of two pieces of equivalent code, first, without the pipe:
# without the pipe
select(gapminder, year, pop)# A tibble: 1,704 × 2
year pop
<dbl> <dbl>
1 1952 8425333
2 1957 9240934
3 1962 10267083
4 1967 11537966
5 1972 13079460
6 1977 14880372
7 1982 12881816
8 1987 13867957
9 1992 16317921
10 1997 22227415
# ℹ 1,694 more rowsSecond, with the pipe (“take the gapminder data frame and then select the year and pop columns”):
# with the pipe
gapminder |> select(year, pop)# A tibble: 1,704 × 2
year pop
<dbl> <dbl>
1 1952 8425333
2 1957 9240934
3 1962 10267083
4 1967 11537966
5 1972 13079460
6 1977 14880372
7 1982 12881816
8 1987 13867957
9 1992 16317921
10 1997 22227415
# ℹ 1,694 more rowsRemember that the pipe places the object on the left of the pipe into the first argument of the function on the right of the pipe. The select() function, however, takes many arguments. If the function to the right of the pipe |> takes more than one argument, then the remaining arguments are just included inside the parentheses of the function on the right of the pipe.
The “new” pipe
|> versus the “old” pipe %>%
The pipe |> is now a part of the “base R” programming language. Previously, you needed to load the “magrittr”, “dplyr”, or “tidyverse” libraries to access the pipe and it had a different symbol: %>%.
The two pipes behave very similarly. The main difference I noticed when I switched was that the old pipe didn’t require parentheses for functions that didn’t have any additional arguments, e.g., you could write df %>% head. But the new pipe requires the empty parentheses after the function, as in: df |> head().
The old pipe %>% still works, but my recommendation is that you use the newer “native” pipe syntax: |>.
6.6 Filtering rows using filter()
Our next dplyr function, filter(), lets you filter to specific rows based on a logical condition.
Imagine that we just want to look at the rows in the gapminder data frame whose country value is "Australia". Then we can write:
filter(gapminder, country == "Australia")# A tibble: 12 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Australia Oceania 1952 69.1 8691212 10040.
2 Australia Oceania 1957 70.3 9712569 10950.
3 Australia Oceania 1962 70.9 10794968 12217.
4 Australia Oceania 1967 71.1 11872264 14526.
5 Australia Oceania 1972 71.9 13177000 16789.
6 Australia Oceania 1977 73.5 14074100 18334.
7 Australia Oceania 1982 74.7 15184200 19477.
8 Australia Oceania 1987 76.3 16257249 21889.
9 Australia Oceania 1992 77.6 17481977 23425.
10 Australia Oceania 1997 78.8 18565243 26998.
11 Australia Oceania 2002 80.4 19546792 30688.
12 Australia Oceania 2007 81.2 20434176 34435.Where:
The first argument of
filter()is the data frame (gapminder) that you want to operate on.The second argument of
filter()is the logical condition involving the column of the data frame that you want to use to filter (country == "Australia").
filter() will return all rows for which the provided condition is TRUE. Note that in our condition, we do not need quotes around the column name, country, but we do need quotes around the value, "Australia". Remember that when asking a logical question of equality, we need to use two equal signs ==.
Now that we have met our trusty pipe, we can rewrite this filter() code as:
gapminder |> filter(country == "Australia")# A tibble: 12 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Australia Oceania 1952 69.1 8691212 10040.
2 Australia Oceania 1957 70.3 9712569 10950.
3 Australia Oceania 1962 70.9 10794968 12217.
4 Australia Oceania 1967 71.1 11872264 14526.
5 Australia Oceania 1972 71.9 13177000 16789.
6 Australia Oceania 1977 73.5 14074100 18334.
7 Australia Oceania 1982 74.7 15184200 19477.
8 Australia Oceania 1987 76.3 16257249 21889.
9 Australia Oceania 1992 77.6 17481977 23425.
10 Australia Oceania 1997 78.8 18565243 26998.
11 Australia Oceania 2002 80.4 19546792 30688.
12 Australia Oceania 2007 81.2 20434176 34435.Remember that the pipe, |>, places the object on the left-hand side (gapminder) into the first argument of the function (filter()) on the right-hand side.
6.6.1 Multiple filtering conditions
You can provide multiple conditions to filter() as separate arguments. Given multiple conditions, filter() returns the rows for which all of the provided conditions are TRUE.
For example, the following code will filter the gapminder data frame to the rows where both country == "Australia" AND year > 1990 are TRUE.
gapminder |> filter(country == "Australia", year > 1990)# A tibble: 4 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Australia Oceania 1992 77.6 17481977 23425.
2 Australia Oceania 1997 78.8 18565243 26998.
3 Australia Oceania 2002 80.4 19546792 30688.
4 Australia Oceania 2007 81.2 20434176 34435.Take note of when we do and when we do not need quotes. We never need quotes when referencing a column name from our data frame inside a dplyr function, nor do we need quotes for numeric values, such as 1990. We do, however, need quotes when referencing a character value, such as "Australia".
You can read this code (gapminder |> filter(country == "Australia", year > 1990)) as “take the gapminder data frame and then filter to the rows where the country is Australia and the year is greater than 1990”.
To start to get a sense of why the pipe is so useful, let’s use it to combine some sequential filter() and select() operations:
Filter to the rows where the
continentcolumn is"Africa"and theyearcolumn is1992.Select just the
countryandlifeExpcolumns (renaminglifeExptolife_exp).
gapminder |>
filter(continent == "Africa", year == 1992) |>
select(country, life_exp = lifeExp)# A tibble: 52 × 2
country life_exp
<chr> <dbl>
1 Algeria 67.7
2 Angola 40.6
3 Benin 53.9
4 Botswana 62.7
5 Burkina Faso 50.3
6 Burundi 44.7
7 Cameroon 54.3
8 Central African Republic 49.4
9 Chad 51.7
10 Comoros 57.9
# ℹ 42 more rowsNote that I like to start a new line after each pipe |> to make the code more readable.
How would you read the code above as a sentence? I read it as “take the gapminder dataset and then filter to just the rows where the continent column is equal to "Africa" and the year column is equal to 1992 and then select just the country and lifeExp columns, renaming lifeExp to be life_exp”.
Since the output of just the first filtered part of the above code, gapminder |> filter(continent == "Africa", year == 1992), is a data frame itself, when I add another pipe |> after this first operation, I am piping the resulting filtered data frame into the subsequent select() function.
If I wanted to try to write this code without the pipe, I would have to do it in a few steps like this:
gapminder_africa_1992 <- filter(gapminder, continent == "Africa", year == 1992)
select(gapminder_africa_1992, country, life_exp = lifeExp)# A tibble: 52 × 2
country life_exp
<chr> <dbl>
1 Algeria 67.7
2 Angola 40.6
3 Benin 53.9
4 Botswana 62.7
5 Burkina Faso 50.3
6 Burundi 44.7
7 Cameroon 54.3
8 Central African Republic 49.4
9 Chad 51.7
10 Comoros 57.9
# ℹ 42 more rowsThis code does the same thing, but without the pipe, I am forced to define an intermediate object, gapminder_africa_1992 (or do some disgusting nested function stuff), which feels inferior to the pipe-based approach. The pipe allows me to do all this in a single, more readable, and more efficient operation.
6.6.2 The order of operations
It turns out that the order of operations when conducting dplyr operations can be fairly important.
For example, if I swap the order of select() and filter() in the code above, I will get an error:
# swap the filter and select steps above
gapminder |>
select(country, life_exp = lifeExp) |>
filter(continent == "Africa", year == 1992) Error in `filter()`:
ℹ In argument: `continent == "Africa"`.
Caused by error:
! object 'continent' not foundWhy do you think this happens? Take a look at the error message for a hint. R is telling us that there is no continent column. What data frame is being piped into the filter() function?
Let’s run just the first two lines of code to find out:
gapminder |>
select(country, life_exp = lifeExp) # A tibble: 1,704 × 2
country life_exp
<chr> <dbl>
1 Afghanistan 28.8
2 Afghanistan 30.3
3 Afghanistan 32.0
4 Afghanistan 34.0
5 Afghanistan 36.1
6 Afghanistan 38.4
7 Afghanistan 39.9
8 Afghanistan 40.8
9 Afghanistan 41.7
10 Afghanistan 41.8
# ℹ 1,694 more rowsThis is the data frame that is being piped into filter(). Does it contain a continent column? No, it does not! So the filter() function is trying to filter this two-column data frame to just the rows for which it’s continent column is equal to "Africa", but this two-column data frame doesn’t contain a continent column!
The following two pieces of code are therefore not equivalent:
gapminder |>
filter(continent == "Africa", year == 1992) |>
select(country, life_exp = lifeExp) gapminder |>
select(country, life_exp = lifeExp) |>
filter(continent == "Africa", year == 1992) 6.6.3 Filtering using “OR” conditions
How would you filter to the rows where country corresponds to “Australia” and “Italy”? You might imagine that you can provide these two conditions separated by a comma, as in:
gapminder |> filter(country == "Australia", country == "Italy")# A tibble: 0 × 6
# ℹ 6 variables: country <chr>, continent <chr>, year <dbl>, lifeExp <dbl>,
# pop <dbl>, gdpPercap <dbl>However, this has returned an empty data frame with 0 rows. Why has this happened?
Remember that whenever you provide two conditions to filter() with a comma, R filters to the rows where both conditions are true. That is, a comma corresponds to an “AND” condition.
filter(country == "Australia", country == "Italy") means “filter to the rows where country == "Australia" AND country == "Italy" are both true. However, there are no rows where country is simultaneously equal to "Australia" and "Italy". It is only ever equal to one or the other.
Although I phrased my desire as “filter to the rows where country corresponds to Australia and Italy”, I really meant, “filter to the rows country corresponds to Australia or Italy”.
Can you remember how to ask an “OR” question? You use the vertical bar |. So to provide an “OR” condition, I could provide my two conditions separated by a vertical bar, (condition 1) | (condition 2), which will return all rows where either condition 1 or condition 2 are satisfied:
gapminder |>
filter((country == "Australia") | (country == "Italy")) # A tibble: 24 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Australia Oceania 1952 69.1 8691212 10040.
2 Australia Oceania 1957 70.3 9712569 10950.
3 Australia Oceania 1962 70.9 10794968 12217.
4 Australia Oceania 1967 71.1 11872264 14526.
5 Australia Oceania 1972 71.9 13177000 16789.
6 Australia Oceania 1977 73.5 14074100 18334.
7 Australia Oceania 1982 74.7 15184200 19477.
8 Australia Oceania 1987 76.3 16257249 21889.
9 Australia Oceania 1992 77.6 17481977 23425.
10 Australia Oceania 1997 78.8 18565243 26998.
# ℹ 14 more rowsHere R is trying to be helpful by only printing the first 10 rows. I can tell it to print all 24 by piping my data frame into a print() function:
gapminder |>
filter((country == "Australia") | (country == "Italy")) |>
print(n = 24)# A tibble: 24 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Australia Oceania 1952 69.1 8691212 10040.
2 Australia Oceania 1957 70.3 9712569 10950.
3 Australia Oceania 1962 70.9 10794968 12217.
4 Australia Oceania 1967 71.1 11872264 14526.
5 Australia Oceania 1972 71.9 13177000 16789.
6 Australia Oceania 1977 73.5 14074100 18334.
7 Australia Oceania 1982 74.7 15184200 19477.
8 Australia Oceania 1987 76.3 16257249 21889.
9 Australia Oceania 1992 77.6 17481977 23425.
10 Australia Oceania 1997 78.8 18565243 26998.
11 Australia Oceania 2002 80.4 19546792 30688.
12 Australia Oceania 2007 81.2 20434176 34435.
13 Italy Europe 1952 65.9 47666000 4931.
14 Italy Europe 1957 67.8 49182000 6249.
15 Italy Europe 1962 69.2 50843200 8244.
16 Italy Europe 1967 71.1 52667100 10022.
17 Italy Europe 1972 72.2 54365564 12269.
18 Italy Europe 1977 73.5 56059245 14256.
19 Italy Europe 1982 75.0 56535636 16537.
20 Italy Europe 1987 76.4 56729703 19207.
21 Italy Europe 1992 77.4 56840847 22014.
22 Italy Europe 1997 78.8 57479469 24675.
23 Italy Europe 2002 80.2 57926999 27968.
24 Italy Europe 2007 80.5 58147733 28570.If both conditions involve the same variable (in this case, country), you can instead use the %in% operator! Remember that you can ask which values in a vector are also in some other vector, such as asking which values in the vector c(1, 5, 2, 2, 1, 6) are equal to 1 or 2 (i.e., are in the vector c(1, 2)) by writing:
c(1, 5, 2, 2, 1, 6) %in% c(1, 2)[1] TRUE FALSE TRUE TRUE TRUE FALSEWe can use this same %in%operator to ask which entries of the country column are equal to "Australia" or "Italy":
gapminder |>
filter(country %in% c("Australia", "Italy")) |>
print(n = 24)# A tibble: 24 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Australia Oceania 1952 69.1 8691212 10040.
2 Australia Oceania 1957 70.3 9712569 10950.
3 Australia Oceania 1962 70.9 10794968 12217.
4 Australia Oceania 1967 71.1 11872264 14526.
5 Australia Oceania 1972 71.9 13177000 16789.
6 Australia Oceania 1977 73.5 14074100 18334.
7 Australia Oceania 1982 74.7 15184200 19477.
8 Australia Oceania 1987 76.3 16257249 21889.
9 Australia Oceania 1992 77.6 17481977 23425.
10 Australia Oceania 1997 78.8 18565243 26998.
11 Australia Oceania 2002 80.4 19546792 30688.
12 Australia Oceania 2007 81.2 20434176 34435.
13 Italy Europe 1952 65.9 47666000 4931.
14 Italy Europe 1957 67.8 49182000 6249.
15 Italy Europe 1962 69.2 50843200 8244.
16 Italy Europe 1967 71.1 52667100 10022.
17 Italy Europe 1972 72.2 54365564 12269.
18 Italy Europe 1977 73.5 56059245 14256.
19 Italy Europe 1982 75.0 56535636 16537.
20 Italy Europe 1987 76.4 56729703 19207.
21 Italy Europe 1992 77.4 56840847 22014.
22 Italy Europe 1997 78.8 57479469 24675.
23 Italy Europe 2002 80.2 57926999 27968.
24 Italy Europe 2007 80.5 58147733 28570.Filter gapminder to all countries on the "Oceania" continent for just the years 1987 and 1992 and select just the country, year, and gdpPercap columns (and rename gdpPercap to be gdp_per_cap).
Save the output in an object called gapminder_oceania, and print gapminder_oceania to the console.
gapminder_oceania <- gapminder |>
filter(continent == "Oceania", year %in% c(1987, 1992)) |>
select(country, year, gdp_per_cap = gdpPercap)
gapminder_oceania# A tibble: 4 × 3
country year gdp_per_cap
<chr> <dbl> <dbl>
1 Australia 1987 21889.
2 Australia 1992 23425.
3 New Zealand 1987 19007.
4 New Zealand 1992 18363.6.7 Adding and modifying columns using mutate()
Next, let’s learn how to add and modify columns in our data frame using mutate().
If I wanted to add a new column to my data, called gdp, which is the product of the pop and gdpPercap columns, I can do that using mutate().
gapminder |> mutate(gdp = pop * gdpPercap) # A tibble: 1,704 × 7
country continent year lifeExp pop gdpPercap gdp
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779. 6567086330.
2 Afghanistan Asia 1957 30.3 9240934 821. 7585448670.
3 Afghanistan Asia 1962 32.0 10267083 853. 8758855797.
4 Afghanistan Asia 1967 34.0 11537966 836. 9648014150.
5 Afghanistan Asia 1972 36.1 13079460 740. 9678553274.
6 Afghanistan Asia 1977 38.4 14880372 786. 11697659231.
7 Afghanistan Asia 1982 39.9 12881816 978. 12598563401.
8 Afghanistan Asia 1987 40.8 13867957 852. 11820990309.
9 Afghanistan Asia 1992 41.7 16317921 649. 10595901589.
10 Afghanistan Asia 1997 41.8 22227415 635. 14121995875.
# ℹ 1,694 more rowsHere, gdp, is the name of my new column, and pop and gdpPercap are existing columns in my data frame, so I don’t need to use quotes.
Remember that the code above hasn’t actually modified gapminder. To modify gapminder I would need to reassign gapminder to the mutated dataframe: gaminder <- gapminder |> mutate(gdp = pop * gdpPercap).
What this code has done is it has created a brand new column, gdp, and placed it at the end of my data frame (and printed out the resulting data frame without saving it as a new variable). In this case, each value in the gdp column contains product of the corresponding values in the pop and gdpPercap columns.
As another example, if we wanted to create a new column that contained the population in millions, i.e., pop divided by 1 million, we could do that using:
gapminder |> mutate(pop_mil = pop / 1e6)# A tibble: 1,704 × 7
country continent year lifeExp pop gdpPercap pop_mil
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779. 8.43
2 Afghanistan Asia 1957 30.3 9240934 821. 9.24
3 Afghanistan Asia 1962 32.0 10267083 853. 10.3
4 Afghanistan Asia 1967 34.0 11537966 836. 11.5
5 Afghanistan Asia 1972 36.1 13079460 740. 13.1
6 Afghanistan Asia 1977 38.4 14880372 786. 14.9
7 Afghanistan Asia 1982 39.9 12881816 978. 12.9
8 Afghanistan Asia 1987 40.8 13867957 852. 13.9
9 Afghanistan Asia 1992 41.7 16317921 649. 16.3
10 Afghanistan Asia 1997 41.8 22227415 635. 22.2
# ℹ 1,694 more rowsNote that 1e6 is scientific notation for 1000000 (i.e., 1 followed by 6 0s).
While mutate() is often used to create new columns, it can also be used to modify existing columns. For example, the code below will modify the existing lifeExp column by rounding it to the nearest integer.
gapminder |> mutate(lifeExp = round(lifeExp)) # A tibble: 1,704 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 29 8425333 779.
2 Afghanistan Asia 1957 30 9240934 821.
3 Afghanistan Asia 1962 32 10267083 853.
4 Afghanistan Asia 1967 34 11537966 836.
5 Afghanistan Asia 1972 36 13079460 740.
6 Afghanistan Asia 1977 38 14880372 786.
7 Afghanistan Asia 1982 40 12881816 978.
8 Afghanistan Asia 1987 41 13867957 852.
9 Afghanistan Asia 1992 42 16317921 649.
10 Afghanistan Asia 1997 42 22227415 635.
# ℹ 1,694 more rowsNote that no new columns have been added to the end of our gapminder output. The data frame contains the exact same columns as the original gapminder object, except the lifeExp column is now a rounded integer!
Create the following data frame (there is a new log_pop column, and the gdpPercap column has been rounded to the nearest integer):
# A tibble: 1,704 × 7
country continent year lifeExp pop gdpPercap log_pop
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779 15.9
2 Afghanistan Asia 1957 30.3 9240934 821 16.0
3 Afghanistan Asia 1962 32.0 10267083 853 16.1
4 Afghanistan Asia 1967 34.0 11537966 836 16.3
5 Afghanistan Asia 1972 36.1 13079460 740 16.4
6 Afghanistan Asia 1977 38.4 14880372 786 16.5
7 Afghanistan Asia 1982 39.9 12881816 978 16.4
8 Afghanistan Asia 1987 40.8 13867957 852 16.4
9 Afghanistan Asia 1992 41.7 16317921 649 16.6
10 Afghanistan Asia 1997 41.8 22227415 635 16.9
# ℹ 1,694 more rowsgapminder |>
mutate(log_pop = log(pop), gdpPercap = round(gdpPercap)) # A tibble: 1,704 × 7
country continent year lifeExp pop gdpPercap log_pop
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779 15.9
2 Afghanistan Asia 1957 30.3 9240934 821 16.0
3 Afghanistan Asia 1962 32.0 10267083 853 16.1
4 Afghanistan Asia 1967 34.0 11537966 836 16.3
5 Afghanistan Asia 1972 36.1 13079460 740 16.4
6 Afghanistan Asia 1977 38.4 14880372 786 16.5
7 Afghanistan Asia 1982 39.9 12881816 978 16.4
8 Afghanistan Asia 1987 40.8 13867957 852 16.4
9 Afghanistan Asia 1992 41.7 16317921 649 16.6
10 Afghanistan Asia 1997 41.8 22227415 635 16.9
# ℹ 1,694 more rows6.8 Summarizing data frames using summarize()
The functions that we have discussed do far in this chapter (select(), filter() and mutate()) are all functions that can be used to modify your data frame.
In this section, we will introduce summarize(), which can be used to–you guessed it–summarize your data frame.
As an example, let’s summarize our data frame by computing the mean lifeExp value across all rows in the dataset:
gapminder |> summarize(mean(lifeExp))# A tibble: 1 × 1
`mean(lifeExp)`
<dbl>
1 59.5You can read this as: “take the gapminder dataset and then summarize it by computing mean(lifeExp), i.e., the mean of the lifeExp column”.
However, like all of the other functions we have used in this chapter, the output of summarize() function is itself a data frame (albeit with just a single row and column). But notice that the name of the column in our summary data frame is just the function that was computed, mean(lifeExp). Wouldn’t it be nice if we could give this column a nicer name? Fortunately, this is super easy to do by providing a name for our summary operation inside the summary() function:
gapminder |> summarize(mean_life_exp = mean(lifeExp))# A tibble: 1 × 1
mean_life_exp
<dbl>
1 59.5In this version, our one-row-one-column data frame has the column name mean_life_exp, instead of mean(lifeExp).
It’s also super easy to compute multiple summaries at once using our trusty comma:
gapminder |>
summarize(mean_life_exp = mean(lifeExp),
max_population = max(pop))# A tibble: 1 × 2
mean_life_exp max_population
<dbl> <dbl>
1 59.5 1318683096You don’t have to put each summary computation on a new line as I did here, but it makes it a bit easier to read (e.g., compared with summarize(mean_life_exp = mean(lifeExp), max_population = max(pop))).
6.9 Grouped operations with group_by()
Computing a summary() operation across all of the rows at once is nice and all, but I’ll forgive you if you’re sitting there thinking “Ok Rebecca, I know you love the tidyverse, and you want to pipe everything into everything else, but honestly it’s just easier to use base R notation to do this, like:”
mean(gapminder$lifeExp)[1] 59.47444And my response to you would be: yeah. It is. But just wait. The next thing I’m going to show you will blow your mind.
What if I asked you to compute the average life expectancy again, but to do it separately for each continent.
While you could precede your summarize() operation with a filter() operation separately for each continent like this:
gapminder |> filter(continent == "Asia") |> summarize(mean(lifeExp))# A tibble: 1 × 1
`mean(lifeExp)`
<dbl>
1 60.1gapminder |> filter(continent == "Americas") |> summarize(mean(lifeExp))# A tibble: 1 × 1
`mean(lifeExp)`
<dbl>
1 64.7gapminder |> filter(continent == "Africa") |> summarize(mean(lifeExp))# A tibble: 1 × 1
`mean(lifeExp)`
<dbl>
1 48.9gapminder |> filter(continent == "Europe") |> summarize(mean(lifeExp))# A tibble: 1 × 1
`mean(lifeExp)`
<dbl>
1 71.9gapminder |> filter(continent == "Oceania") |> summarize(mean(lifeExp))# A tibble: 1 × 1
`mean(lifeExp)`
<dbl>
1 74.3Or even use a “for” loop (if you so desired…), it turns out that there is a better way!
The true value of the summarize() function lies in its friendship with the group_by() function. The following code concisely computes the average lifeExp separately for each continent by “grouping” the gapminder data frame by continent (using group_by()) before summarizing.
gapminder |>
group_by(continent) |>
summarize(mean_life_exp = mean(lifeExp))# A tibble: 5 × 2
continent mean_life_exp
<chr> <dbl>
1 Africa 48.9
2 Americas 64.7
3 Asia 60.1
4 Europe 71.9
5 Oceania 74.3You can think about this as if group_by() is creating a separate data frame for each continent value and then it is computing the summarize() operation separately for each continent data frame, and it is then combining the summary output into a two-column data frame, where the first column contains the respective continent value, and the second column contains the result of the summary() operation for that particular continent.
Now that’s rad as heck!
Use group_by() and summarize() to compute the standard deviation of the gdpPercap column separately for each country.
Your output should look like this:
# A tibble: 142 × 2
country sd_gdp
<chr> <dbl>
1 Afghanistan 978.
2 Albania 5937.
3 Algeria 6223.
4 Angola 5523.
5 Argentina 12779.
6 Australia 34435.
7 Austria 36126.
8 Bahrain 29796.
9 Bangladesh 1391.
10 Belgium 33693.
# ℹ 132 more rowsgapminder |>
group_by(country) |>
summarize(sd_gdp = max(gdpPercap))# A tibble: 142 × 2
country sd_gdp
<chr> <dbl>
1 Afghanistan 978.
2 Albania 5937.
3 Algeria 6223.
4 Angola 5523.
5 Argentina 12779.
6 Australia 34435.
7 Austria 36126.
8 Bahrain 29796.
9 Bangladesh 1391.
10 Belgium 33693.
# ℹ 132 more rows6.9.1 Grouping by multiple columns simultaneously
Just in case you weren’t already impressed enough by the group_by()/summarize() duo, you can also do more sophisticated grouping operations, such as computing the average lifeExp for each continent-year combination by grouping by both continent and year:
# compute the average life expectancy for each continent-year combination
gapminder |>
group_by(continent, year) |>
summarize(mean_life_exp = mean(lifeExp))`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.# A tibble: 60 × 3
# Groups: continent [5]
continent year mean_life_exp
<chr> <dbl> <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
# ℹ 50 more rowsWith filter(), mutate(), group_by(), and summarize() up your sleeve, there is almost no summarization of your data you can’t do!
Compute the mean and standard deviation of the GDP (the product of pop and gdpPercap) separately for each continent and year after the year 2000. Your output should look like this:
# A tibble: 10 × 3
# Groups: continent [5]
continent year `mean(gdp)`
<chr> <dbl> <dbl>
1 Africa 2002 35303511424.
2 Africa 2007 45778570846.
3 Americas 2002 661248623419.
4 Americas 2007 776723426068.
5 Asia 2002 458042336179.
6 Asia 2007 627513635079.
7 Europe 2002 436448815097.
8 Europe 2007 493183311052.
9 Oceania 2002 345236880176.
10 Oceania 2007 403657044512.My suggested order of operations is
gapminder |>
filter() |>
mutate() |>
group_by() |>
summarize()gapminder |>
filter(year > 2000) |>
mutate(gdp = pop * gdpPercap) |>
group_by(continent, year) |>
summarize(mean(gdp))`summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.# A tibble: 10 × 3
# Groups: continent [5]
continent year `mean(gdp)`
<chr> <dbl> <dbl>
1 Africa 2002 35303511424.
2 Africa 2007 45778570846.
3 Americas 2002 661248623419.
4 Americas 2007 776723426068.
5 Asia 2002 458042336179.
6 Asia 2007 627513635079.
7 Europe 2002 436448815097.
8 Europe 2007 493183311052.
9 Oceania 2002 345236880176.
10 Oceania 2007 403657044512.6.9.2 Grouped mutates
Although group_by() is most often used with summarize(), this doesn’t mean that it can only be used with summarize()!
Below, I group by continent and then conduct a mutate() to add a new column max_life_exp, containing the maximum life expectancy for the corresponding country. This time, I save the resulting data frame in a new variable called gapminder_new:
gapminder_new <- gapminder |>
group_by(country) |>
mutate(max_life_exp = max(lifeExp))
# print the first 30 rows of gapminder
print(gapminder_new, n = 30)# A tibble: 1,704 × 7
# Groups: country [142]
country continent year lifeExp pop gdpPercap max_life_exp
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779. 43.8
2 Afghanistan Asia 1957 30.3 9240934 821. 43.8
3 Afghanistan Asia 1962 32.0 10267083 853. 43.8
4 Afghanistan Asia 1967 34.0 11537966 836. 43.8
5 Afghanistan Asia 1972 36.1 13079460 740. 43.8
6 Afghanistan Asia 1977 38.4 14880372 786. 43.8
7 Afghanistan Asia 1982 39.9 12881816 978. 43.8
8 Afghanistan Asia 1987 40.8 13867957 852. 43.8
9 Afghanistan Asia 1992 41.7 16317921 649. 43.8
10 Afghanistan Asia 1997 41.8 22227415 635. 43.8
11 Afghanistan Asia 2002 42.1 25268405 727. 43.8
12 Afghanistan Asia 2007 43.8 31889923 975. 43.8
13 Albania Europe 1952 55.2 1282697 1601. 76.4
14 Albania Europe 1957 59.3 1476505 1942. 76.4
15 Albania Europe 1962 64.8 1728137 2313. 76.4
16 Albania Europe 1967 66.2 1984060 2760. 76.4
17 Albania Europe 1972 67.7 2263554 3313. 76.4
18 Albania Europe 1977 68.9 2509048 3533. 76.4
19 Albania Europe 1982 70.4 2780097 3631. 76.4
20 Albania Europe 1987 72 3075321 3739. 76.4
21 Albania Europe 1992 71.6 3326498 2497. 76.4
22 Albania Europe 1997 73.0 3428038 3193. 76.4
23 Albania Europe 2002 75.7 3508512 4604. 76.4
24 Albania Europe 2007 76.4 3600523 5937. 76.4
25 Algeria Africa 1952 43.1 9279525 2449. 72.3
26 Algeria Africa 1957 45.7 10270856 3014. 72.3
27 Algeria Africa 1962 48.3 11000948 2551. 72.3
28 Algeria Africa 1967 51.4 12760499 3247. 72.3
29 Algeria Africa 1972 54.5 14760787 4183. 72.3
30 Algeria Africa 1977 58.0 17152804 4910. 72.3
# ℹ 1,674 more rowsTake a close look at the new max_life_exp column that I’ve tacked onto the end of my data frame. Notice that it contains a single value for each country corresponding to the average lifeExp value computed using just the rows for that country.
6.9.3 Don’t forget to ungroup()
So we’ve got our gapminder_new object that contains our max_life_exp column which contains the maximum life expectancy value where the average is computed just using the corresponding country’s rows.
If I then wanted to conduct a subsequent summarize operation on this gapminder_new object, such as computing the mean of this new max_life_exp value, with the goal of computing this average over all rows in the data (i.e., I should get a single value), I might write the following code:
gapminder_new |> summarize(mean(max_life_exp))# A tibble: 142 × 2
country `mean(max_life_exp)`
<chr> <dbl>
1 Afghanistan 43.8
2 Albania 76.4
3 Algeria 72.3
4 Angola 42.7
5 Argentina 75.3
6 Australia 81.2
7 Austria 79.8
8 Bahrain 75.6
9 Bangladesh 64.1
10 Belgium 79.4
# ℹ 132 more rowsIs there anything surprising about the output here? The summary() operation is still grouped by country, even though I didn’t conduct another group_by(country) operation before my summarize() operation!
This is because gapminder_new is not technically a simple data frame… it is a grouped data frame. Notice the text at the top of the output:
gapminder_new# A tibble: 1,704 × 7
# Groups: country [142]
country continent year lifeExp pop gdpPercap max_life_exp
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779. 43.8
2 Afghanistan Asia 1957 30.3 9240934 821. 43.8
3 Afghanistan Asia 1962 32.0 10267083 853. 43.8
4 Afghanistan Asia 1967 34.0 11537966 836. 43.8
5 Afghanistan Asia 1972 36.1 13079460 740. 43.8
6 Afghanistan Asia 1977 38.4 14880372 786. 43.8
7 Afghanistan Asia 1982 39.9 12881816 978. 43.8
8 Afghanistan Asia 1987 40.8 13867957 852. 43.8
9 Afghanistan Asia 1992 41.7 16317921 649. 43.8
10 Afghanistan Asia 1997 41.8 22227415 635. 43.8
# ℹ 1,694 more rowsIt says # Groups: country [142], which tells me that gapminder_new is grouped by the country column (and there are 142 groups). This means that any subsequent operations that I conduct on gapminder_new will also be grouped (by country).
If you are going to continue working with a data frame that was created using a group_by() operation, it is important to remember to ungroup(), unless you also want your subsequent operations to be grouped:
gapminder_new |>
ungroup() |>
summarize(mean(max_life_exp))# A tibble: 1 × 1
`mean(max_life_exp)`
<dbl>
1 68.0I could write all of this code without defining my intermediate gapminder_new object as follows:
gapminder |>
group_by(country) |>
mutate(max_life_exp = max(lifeExp)) |>
ungroup() |>
summarize(mean(max_life_exp))# A tibble: 1 × 1
`mean(max_life_exp)`
<dbl>
1 68.0But if I forgot the ungroup() operation (the second-last line above), I get:
gapminder |>
group_by(country) |>
mutate(max_life_exp = max(lifeExp)) |>
summarize(mean(max_life_exp))# A tibble: 142 × 2
country `mean(max_life_exp)`
<chr> <dbl>
1 Afghanistan 43.8
2 Albania 76.4
3 Algeria 72.3
4 Angola 42.7
5 Argentina 75.3
6 Australia 81.2
7 Austria 79.8
8 Bahrain 75.6
9 Bangladesh 64.1
10 Belgium 79.4
# ℹ 132 more rows6.9.4 Grouped filtering
You can also conduct grouped filtering, which will apply your filter condition separately for each group. The most common scenario in which I find myself doing this is when I want to do something like filtering to the row in each group with the maximum value in one of the columns, such as filtering to the rows with the highest lifeExp separately within each continent:
gapminder |>
group_by(continent) |>
filter(lifeExp == max(lifeExp))# A tibble: 5 × 6
# Groups: continent [5]
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Australia Oceania 2007 81.2 20434176 34435.
2 Canada Americas 2007 80.7 33390141 36319.
3 Iceland Europe 2007 81.8 301931 36181.
4 Japan Asia 2007 82.6 127467972 31656.
5 Reunion Africa 2007 76.4 798094 7670.6.10 Count
Another really useful function is count(), which is used to summarize categorical (character/factor) variables.
count() creates a two-column data frame, where the first column displays the unique values of the provided column from the original data frame, and the second column, n, contains the number of times that each unique value appears:
gapminder |>
count(continent)# A tibble: 5 × 2
continent n
<chr> <int>
1 Africa 624
2 Americas 300
3 Asia 396
4 Europe 360
5 Oceania 24This shows that the "Africa" continent value appears in the data 624 times, the "Americas" continent value appears 300 times, and so on.
6.11 Arrange
The final function I will show you in this chapter is arrange(), which lets you arrange the rows of your data frame in ascending or descending order of the values in a specific column. By default, arrange() will arrange the rows in ascending order of the values in the provided column.
The following code will rearrange all of the rows so that the row with the smallest lifeExp value will be at the top and the row with the largest lifeExp value will be at the bottom:
gapminder |>
arrange(lifeExp)# A tibble: 1,704 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Rwanda Africa 1992 23.6 7290203 737.
2 Afghanistan Asia 1952 28.8 8425333 779.
3 Gambia Africa 1952 30 284320 485.
4 Angola Africa 1952 30.0 4232095 3521.
5 Sierra Leone Africa 1952 30.3 2143249 880.
6 Afghanistan Asia 1957 30.3 9240934 821.
7 Cambodia Asia 1977 31.2 6978607 525.
8 Mozambique Africa 1952 31.3 6446316 469.
9 Sierra Leone Africa 1957 31.6 2295678 1004.
10 Burkina Faso Africa 1952 32.0 4469979 543.
# ℹ 1,694 more rowsFor some reason, the way that you specify that the rows should be arranged in descending order instead is to wrap the variable name in the desc() function. The following code will arrange the gapminder rows so that the row with the largest lifeExp value will be at the top and the row with the smallest lifeExp value will be at the bottom:
gapminder |>
arrange(desc(lifeExp))# A tibble: 1,704 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Japan Asia 2007 82.6 127467972 31656.
2 Hong Kong, China Asia 2007 82.2 6980412 39725.
3 Japan Asia 2002 82 127065841 28605.
4 Iceland Europe 2007 81.8 301931 36181.
5 Switzerland Europe 2007 81.7 7554661 37506.
6 Hong Kong, China Asia 2002 81.5 6762476 30209.
7 Australia Oceania 2007 81.2 20434176 34435.
8 Spain Europe 2007 80.9 40448191 28821.
9 Sweden Europe 2007 80.9 9031088 33860.
10 Israel Asia 2007 80.7 6426679 25523.
# ℹ 1,694 more rowsTechnically, you could also arrange by the negative of the column to arrange in descending order, but I usually use the desc() approach.
gapminder |>
arrange(-lifeExp)# A tibble: 1,704 × 6
country continent year lifeExp pop gdpPercap
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Japan Asia 2007 82.6 127467972 31656.
2 Hong Kong, China Asia 2007 82.2 6980412 39725.
3 Japan Asia 2002 82 127065841 28605.
4 Iceland Europe 2007 81.8 301931 36181.
5 Switzerland Europe 2007 81.7 7554661 37506.
6 Hong Kong, China Asia 2002 81.5 6762476 30209.
7 Australia Oceania 2007 81.2 20434176 34435.
8 Spain Europe 2007 80.9 40448191 28821.
9 Sweden Europe 2007 80.9 9031088 33860.
10 Israel Asia 2007 80.7 6426679 25523.
# ℹ 1,694 more rowsHere are a bunch of challenging exercises for you to test your dplyr skills. These are intentionally hard!
Compute the median lifeExp and maximum pop values for each country, and then arrange the countries in descending order of their maximum pop value.
gapminder |>
group_by(country) |>
summarize(median_life_exp = median(lifeExp),
max_pop = max(pop)) |>
arrange(desc(max_pop))# A tibble: 142 × 3
country median_life_exp max_pop
<chr> <dbl> <dbl>
1 China 64.7 1318683096
2 India 55.4 1110396331
3 United States 74.0 301139947
4 Indonesia 54.4 223547000
5 Brazil 62.4 190010647
6 Pakistan 55.1 169270617
7 Bangladesh 48.5 150448339
8 Nigeria 45.2 135031164
9 Japan 76.2 127467972
10 Mexico 66.2 108700891
# ℹ 132 more rowsIdentify the 5 countries with the highest average life expectancy.
gapminder |>
group_by(country) |>
summarize(mean_life_exp = mean(lifeExp)) |>
ungroup() |>
arrange(desc(mean_life_exp)) |>
head(5)# A tibble: 5 × 2
country mean_life_exp
<chr> <dbl>
1 Iceland 76.5
2 Sweden 76.2
3 Norway 75.8
4 Netherlands 75.6
5 Switzerland 75.6What are the three most populous countries on the “Asia” continent?
gapminder |>
filter(continent == "Asia") |>
group_by(country) |>
summarize(max_pop = max(pop)) |>
ungroup() |>
arrange(desc(max_pop)) |>
head(3)# A tibble: 3 × 2
country max_pop
<chr> <dbl>
1 China 1318683096
2 India 1110396331
3 Indonesia 223547000Identify the country with the highest total GDP for each continent.
Apply a filter() after a group_by() – this will apply the filtering separately for each group.
These are the countries with the highest total GDP for each continent:
gapminder |>
mutate(gdp = gdpPercap * pop) |>
group_by(continent) |>
filter(gdp == max(gdp)) |>
select(country, continent, gdp)# A tibble: 5 × 3
# Groups: continent [5]
country continent gdp
<chr> <chr> <dbl>
1 Australia Oceania 7.04e11
2 China Asia 6.54e12
3 Egypt Africa 4.48e11
4 Germany Europe 2.65e12
5 United States Americas 1.29e13Compute the average GDP per capita for each continent based only on countries with gdpPercap greater than 20,000.
gapminder |>
filter(gdpPercap > 20000) |>
group_by(continent) |>
summarize(mean(gdpPercap))# A tibble: 5 × 2
continent `mean(gdpPercap)`
<chr> <dbl>
1 Africa 21569.
2 Americas 29810.
3 Asia 37442.
4 Europe 27639.
5 Oceania 25857.