- Entering the tidyverse
- Piping: %>%
- Data manipulation: dplyr
select: select columnsfilter: filter to rows that satisfy certain conditionsmutate: add a new variablearrange: arrange the rows of the data frame in order a variablegroup_by: apply other dplyr functions separately within within a group defined by one or more variablessummarise/summarize: define a variable that is a summary of other variables- More dplyr functions
- Visualization: ggplot2
Most people who learned R before the tidyverse have likely started to feel a nibble of pressure to get aboard the tidyverse train. Sadly a fact of human nature is that once you’re comfortable doing something a certain way, it’s hard to find the motivation to learn a different way of doing something that you already know how to do. As someone first learnt R 10 years ago (long before the tidyverse) I’ve been there. Five years ago, I was pushed to stick my little toe into the shallow-end of the tidyverse pool by learning ggplot2, and I never went back.
While the tidyverse is primarily made up of a set of super useful R packages (ggplot2, dplyr, purrr, tidyr, readr, tibble), it is also a way of thinking about implementing “tidy” data analysis. If you combine tidy thinking with the tidy packages, you will inevitably become a master of tidy analysis. From where I float now substantially closer to the deep-end of the tidyverse pool, I would provide the following arguments you as well as to my past self for why it is a good idea to learn the tidyverse:
Regardless of whether you think the tidyverse or base R is “better”, it is always a good idea to keep up with what is current. It’s so easy to get left behind. What you learn today will most likely be out-of-date in a year, but next year’s iteration of what is current will almost certainly be built upon today’s iteration. If you make an effort keep up with things as they change, then you can take lots of little easy steps instead of finding yourself needing to take a big difficult jump in a few years.
Code written in the tidyverse style is much easier to read, and is more consistent than base R (e.g. the first argument of almost every tidyverse function is the data frame on which it acts, which allows us to make heavy use of something called “piping”). Base R, on the other hand, has a somewhat inconsistent mish-mash of function and argument styles.
The humans that make up the tidyverse community are amazing.
Much of the initial efforts of the tidyverse were the brainchild of Hadley Wickham, but these days there are a huge number of people who contribute to, maintain, and develop the tidyverse. The tidyverse is open-source and collaborative (which means that you - yes you - could contribute to it if you wanted to), and is hosted on the tidyverse github: https://github.com/tidyverse.
The goal of this post is to summarise the overall goals of the tidyverse, provide short tutorials on each of the packages that form the tidyverse (and how they play together), and to provide links to additional resources for learning them.
For new tidyverse practitioners, I’d recommend focusing on getting a handle on piping %>%, and learning the dplyr and ggplot2 packages (these form part one of this post). Once you feel comfortable with these core aspects of the tidyverse, you can move onto part two of this two-part series on the tidyverse to learn about the remaining packages.
It is important to remember that the tidyverse is constantly evolving. The best ways to keep up to date with the evolving tidyverse ecosystem is (1) to follow the RStudio blog (https://blog.rstudio.com/), and (2) start following R people on twitter. Mara Averick (@dataandme) and Hadley Wickham (@hadleywickham) are good people to follow. A great resource for learning about the tidyverse in more detail is R for Data Science by Garrett Grolemund and Hadley Wickham.
Entering the tidyverse
The fundamental object type of the tidyverse is the data frame (which, once you get a little deeper into the tidyverse ecosystem, becomes a “tibble” - more on that later in part 2). Thus the starting point for getting comfortable with the tidyverse is to always store your data as a data frame (rather than as a matrix or as vectors) with informative string-based column names where words are preferably separated by underscores (rather than periods).
The tidyverse is not simply a set of functions that replace base R functions. The tidyverse represents a way of thinking about how you conduct your data analysis:
Think of your data frame as the universe, and the columns of your data frame as the objects in your universe that you can explore, manipulate and model.
When coding in base R, it is very common to define many intermediate objects and modified versions of the same data frame (or data object). When coding in the tidyverse, the key is to minimize defining new data objects. Instead, focus on manipulating your current data frame and just printing the output of your manipulations (e.g. a summary or plot). Only create new data objects in your R environment if you will be using both the original data object and the new data object in your later analyses.
To load the tidyverse packages, you could install them all individually (once):
# only ever run once to install the individual packages on your system
install.packages("dplyr")
install.packages("ggplot2")
install.packages("purrr")
install.packages("tidyr")
install.packages("readr")
install.packages("tibble")and then load them all into your session individually every time:
library(dplyr)
library(ggplot2)
library(purrr)
library(tidyr)
library(readr)
library(tibble)or, you could just install and load the tidyverse package, which will do all of the above for you:
# only ever run once to install the tidyverse on your system
install.packages("tidyverse")library(tidyverse)which is much easier.
Throughout this tutorial, we will use the gapminder dataset that can be loaded directly if you’re connected to the internet.
My general workflow involves loading the original data and saving it as an object with a meaningful name and an _orig suffix. I then define a copy of the original dataset without the _orig suffix. Having an original copy of my data in my environment means that it is easy to check that my manipulations do what I expected. I will make direct data cleaning modifications to the gapminder data frame, but will never edit the gapminder_orig data frame.
# to download the data directly:
gapminder_orig <- read.csv("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder-FiveYearData.csv")
# define a copy of the original dataset that we will clean and play with
gapminder <- gapminder_origThe gapminder dataset has 1704 rows containing information on population, life expectancy and GDP per capita by year and country.
A “tidy” data frame is one where every row is a single observational unit (in this case, indexed by country and year), and every column corresponds to a variable that is measured for each observational unit (in this case, for each country and year, a measurement is made for population, continent, life expectancy and GDP). If you’d like to learn more about “tidy data”, I highly recommend reading Hadley Wickham’s tidy data article.
dim(gapminder)## [1] 1704 6head(gapminder)## country year pop continent lifeExp gdpPercap
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453
## 2 Afghanistan 1957 9240934 Asia 30.332 820.8530
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007
## 4 Afghanistan 1967 11537966 Asia 34.020 836.1971
## 5 Afghanistan 1972 13079460 Asia 36.088 739.9811
## 6 Afghanistan 1977 14880372 Asia 38.438 786.1134Now that you’ve loaded the tidyverse and the gapminder data, you’re are ready to learn about our first tidy analysis tool: the pipe.
Piping: %>%
Pipes are the workhorse of tidy analyses. Piping allows you to chain together many functions, eliminating the need to define multiple intermediate objects to use as the input to subsequent functions. In my eyes, pipes are also the primary reason that tidyverse code is fundamentally easier to read than base R code.
I always read the pipe symbol, %>%, in my head as “and then”. Consider the following code. Try to figure out what the following code will produce (even if you’ve never seen the filter and select dplyr functions before - I’ll formally introduce these later).
gapminder %>%
filter(continent == "Americas", year == "2007") %>%
select(country, lifeExp)I read this code in my head as: take the gapminder dataset and then filter to the “Americas” continents and the year 2007, and then select the country and life expectancy variables.
Running this code first filters the data frame only to the rows whose continent value is “Americas” and whose year value is “2007”, and then it shows you the country and lifeExp columns for those rows. Run it yourself to see.
# take the gapminder dataset
gapminder %>%
# and filter to the rows whose continent is Americas and year is 2007
filter(continent == "Americas", year == 2007) %>%
# show the country and lifeExp values for these rows
select(country, lifeExp)## country lifeExp
## 1 Argentina 75.320
## 2 Bolivia 65.554
## 3 Brazil 72.390
## 4 Canada 80.653
## 5 Chile 78.553
## 6 Colombia 72.889
## 7 Costa Rica 78.782
## 8 Cuba 78.273
## 9 Dominican Republic 72.235
## 10 Ecuador 74.994
## 11 El Salvador 71.878
## 12 Guatemala 70.259
## 13 Haiti 60.916
## 14 Honduras 70.198
## 15 Jamaica 72.567
## 16 Mexico 76.195
## 17 Nicaragua 72.899
## 18 Panama 75.537
## 19 Paraguay 71.752
## 20 Peru 71.421
## 21 Puerto Rico 78.746
## 22 Trinidad and Tobago 69.819
## 23 United States 78.242
## 24 Uruguay 76.384
## 25 Venezuela 73.747To become a piping expert you’ll need to have a firm grasp on what it’s actually doing. The pipe uses the object on the left-hand-side of the %>% as the first argument of the function on the right-hand-side.
For instance, the un-piped version of
gapminder %>%
filter(continent == "Americas", year == 2007)is
filter(gapminder, continent == "Americas", year == 2007)If you were determined not to use piping, but you wanted to do many manipulations, your code would very quickly get messy and difficult to read. In the style of base R, the common way of making code more readable is to define intermediate objects.
gapminder_filtered <- filter(gapminder, continent == "Americas", year == 2007)
gapminder_filtered_selected <- select(gapminder_filtered, country, lifeExp)
gapminder_filtered_selected## country lifeExp
## 1 Argentina 75.320
## 2 Bolivia 65.554
## 3 Brazil 72.390
## 4 Canada 80.653
## 5 Chile 78.553
## 6 Colombia 72.889
## 7 Costa Rica 78.782
## 8 Cuba 78.273
## 9 Dominican Republic 72.235
## 10 Ecuador 74.994
## 11 El Salvador 71.878
## 12 Guatemala 70.259
## 13 Haiti 60.916
## 14 Honduras 70.198
## 15 Jamaica 72.567
## 16 Mexico 76.195
## 17 Nicaragua 72.899
## 18 Panama 75.537
## 19 Paraguay 71.752
## 20 Peru 71.421
## 21 Puerto Rico 78.746
## 22 Trinidad and Tobago 69.819
## 23 United States 78.242
## 24 Uruguay 76.384
## 25 Venezuela 73.747To me, the piped version is infinitely more clear, and simultaneously got rid of the need to define any intermediate objects that I would have needed to keep track of while reading the code. Once I got more and more comfortable with piping, I started to find that pretty much all of my code uses pipes.
Data manipulation: dplyr
The filter() and select() functions that I just introduced are examples of data manipulation functions from the dplyr package.
In the tidyverse, you will almost never use the [,] indexing nor the $ data frame column indexing that are pervasive throughout base R code. Indexing in dplyr is done using filter() for rows and select() for columns.
You may have noticed that the variable names continent, year, country, lifeExp that were used inside the filter() and select() functions were unquoted. One of the key components of the tidyverse is thinking of your universe as the data frame, and the columns of the data frame as variables or objects that you can play with. Just like how you don’t need to quote variable names in your environment to play with them, you usually don’t need to quote data frame variables (columns) inside tidyverse functions.
Let’s contrast our piped dplyr code
# take the gapminder dataset
gapminder %>%
# and filter to the rows whose continent is Americas and year is 2007
filter(continent == "Americas", year == 2007) %>%
# show the country and lifeExp values for these rows
select(country, lifeExp)with one potential version of equivalent base R code:
# identify which rows correspond to the Americas and the year 2007
continent_year_index <- which(gapminder["continent"] == "Americas" & gapminder["year"] == 2007)
# pull only those rows and show the country and life expectency columns
gapminder[continent_year_index, c("country", "lifeExp")]There are a few key differences
The variable names are quoted in the base R version but not in the dplyr version
An intermediate row index variable was defined in the base R version but not in the dplyr version
The primary dplyr functions are
select: select columns
The arguments of the select function specify which data frame variables should be kept. select() is like indexing columns by name. You do not need to quote the column names (but you can if you want to).
gapminder %>%
select(country, gdpPercap) ## country gdpPercap
## 1 Afghanistan 779.4453
## 2 Afghanistan 820.8530
## 3 Afghanistan 853.1007
## 4 Afghanistan 836.1971
## 5 Afghanistan 739.9811
## 6 Afghanistan 786.1134
## 7 Afghanistan 978.0114
## 8 Afghanistan 852.3959
## 9 Afghanistan 649.3414
## 10 Afghanistan 635.3414
## 11 Afghanistan 726.7341
## 12 Afghanistan 974.5803
## 13 Albania 1601.0561
## 14 Albania 1942.2842
## 15 Albania 2312.8890
## 16 Albania 2760.1969
## 17 Albania 3313.4222
## 18 Albania 3533.0039
## 19 Albania 3630.8807
## 20 Albania 3738.9327
## 21 Albania 2497.4379
## 22 Albania 3193.0546
## 23 Albania 4604.2117
## 24 Albania 5937.0295
## 25 Algeria 2449.0082
## 26 Algeria 3013.9760
## 27 Algeria 2550.8169
## 28 Algeria 3246.9918
## 29 Algeria 4182.6638
## 30 Algeria 4910.4168
## 31 Algeria 5745.1602
## 32 Algeria 5681.3585
## 33 Algeria 5023.2166
## 34 Algeria 4797.2951
## 35 Algeria 5288.0404
## 36 Algeria 6223.3675
## 37 Angola 3520.6103
## 38 Angola 3827.9405
## 39 Angola 4269.2767
## 40 Angola 5522.7764
## 41 Angola 5473.2880
## 42 Angola 3008.6474
## 43 Angola 2756.9537
## 44 Angola 2430.2083
## 45 Angola 2627.8457
## 46 Angola 2277.1409
## 47 Angola 2773.2873
## 48 Angola 4797.2313
## 49 Argentina 5911.3151
## 50 Argentina 6856.8562
## 51 Argentina 7133.1660
## 52 Argentina 8052.9530
## 53 Argentina 9443.0385
## 54 Argentina 10079.0267
## 55 Argentina 8997.8974
## 56 Argentina 9139.6714
## 57 Argentina 9308.4187
## 58 Argentina 10967.2820
## 59 Argentina 8797.6407
## 60 Argentina 12779.3796
## 61 Australia 10039.5956
## 62 Australia 10949.6496
## 63 Australia 12217.2269
## 64 Australia 14526.1246
## 65 Australia 16788.6295
## 66 Australia 18334.1975
## 67 Australia 19477.0093
## 68 Australia 21888.8890
## 69 Australia 23424.7668
## 70 Australia 26997.9366
## 71 Australia 30687.7547
## 72 Australia 34435.3674
## 73 Austria 6137.0765
## 74 Austria 8842.5980
## 75 Austria 10750.7211
## 76 Austria 12834.6024
## 77 Austria 16661.6256
## 78 Austria 19749.4223
## 79 Austria 21597.0836
## 80 Austria 23687.8261
## 81 Austria 27042.0187
## 82 Austria 29095.9207
## 83 Austria 32417.6077
## 84 Austria 36126.4927
## 85 Bahrain 9867.0848
## 86 Bahrain 11635.7995
## 87 Bahrain 12753.2751
## 88 Bahrain 14804.6727
## 89 Bahrain 18268.6584
## 90 Bahrain 19340.1020
## 91 Bahrain 19211.1473
## 92 Bahrain 18524.0241
## 93 Bahrain 19035.5792
## 94 Bahrain 20292.0168
## 95 Bahrain 23403.5593
## 96 Bahrain 29796.0483
## 97 Bangladesh 684.2442
## 98 Bangladesh 661.6375
## 99 Bangladesh 686.3416
## 100 Bangladesh 721.1861
## 101 Bangladesh 630.2336
## 102 Bangladesh 659.8772
## 103 Bangladesh 676.9819
## 104 Bangladesh 751.9794
## 105 Bangladesh 837.8102
## 106 Bangladesh 972.7700
## 107 Bangladesh 1136.3904
## 108 Bangladesh 1391.2538
## 109 Belgium 8343.1051
## 110 Belgium 9714.9606
## 111 Belgium 10991.2068
## 112 Belgium 13149.0412
## 113 Belgium 16672.1436
## 114 Belgium 19117.9745
## 115 Belgium 20979.8459
## 116 Belgium 22525.5631
## 117 Belgium 25575.5707
## 118 Belgium 27561.1966
## 119 Belgium 30485.8838
## 120 Belgium 33692.6051
## 121 Benin 1062.7522
## 122 Benin 959.6011
## 123 Benin 949.4991
## 124 Benin 1035.8314
## 125 Benin 1085.7969
## 126 Benin 1029.1613
## 127 Benin 1277.8976
## 128 Benin 1225.8560
## 129 Benin 1191.2077
## 130 Benin 1232.9753
## 131 Benin 1372.8779
## 132 Benin 1441.2849
## 133 Bolivia 2677.3263
## 134 Bolivia 2127.6863
## 135 Bolivia 2180.9725
## 136 Bolivia 2586.8861
## 137 Bolivia 2980.3313
## 138 Bolivia 3548.0978
## 139 Bolivia 3156.5105
## 140 Bolivia 2753.6915
## 141 Bolivia 2961.6997
## 142 Bolivia 3326.1432
## 143 Bolivia 3413.2627
## 144 Bolivia 3822.1371
## 145 Bosnia and Herzegovina 973.5332
## 146 Bosnia and Herzegovina 1353.9892
## 147 Bosnia and Herzegovina 1709.6837
## 148 Bosnia and Herzegovina 2172.3524
## 149 Bosnia and Herzegovina 2860.1698
## 150 Bosnia and Herzegovina 3528.4813
## 151 Bosnia and Herzegovina 4126.6132
## 152 Bosnia and Herzegovina 4314.1148
## 153 Bosnia and Herzegovina 2546.7814
## 154 Bosnia and Herzegovina 4766.3559
## 155 Bosnia and Herzegovina 6018.9752
## 156 Bosnia and Herzegovina 7446.2988
## 157 Botswana 851.2411
## 158 Botswana 918.2325
## 159 Botswana 983.6540
## 160 Botswana 1214.7093
## 161 Botswana 2263.6111
## 162 Botswana 3214.8578
## 163 Botswana 4551.1421
## 164 Botswana 6205.8839
## 165 Botswana 7954.1116
## 166 Botswana 8647.1423
## 167 Botswana 11003.6051
## 168 Botswana 12569.8518
## 169 Brazil 2108.9444
## 170 Brazil 2487.3660
## 171 Brazil 3336.5858
## 172 Brazil 3429.8644
## 173 Brazil 4985.7115
## 174 Brazil 6660.1187
## 175 Brazil 7030.8359
## 176 Brazil 7807.0958
## 177 Brazil 6950.2830
## 178 Brazil 7957.9808
## 179 Brazil 8131.2128
## 180 Brazil 9065.8008
## 181 Bulgaria 2444.2866
## 182 Bulgaria 3008.6707
## 183 Bulgaria 4254.3378
## 184 Bulgaria 5577.0028
## 185 Bulgaria 6597.4944
## 186 Bulgaria 7612.2404
## 187 Bulgaria 8224.1916
## 188 Bulgaria 8239.8548
## 189 Bulgaria 6302.6234
## 190 Bulgaria 5970.3888
## 191 Bulgaria 7696.7777
## 192 Bulgaria 10680.7928
## 193 Burkina Faso 543.2552
## 194 Burkina Faso 617.1835
## 195 Burkina Faso 722.5120
## 196 Burkina Faso 794.8266
## 197 Burkina Faso 854.7360
## 198 Burkina Faso 743.3870
## 199 Burkina Faso 807.1986
## 200 Burkina Faso 912.0631
## 201 Burkina Faso 931.7528
## 202 Burkina Faso 946.2950
## 203 Burkina Faso 1037.6452
## 204 Burkina Faso 1217.0330
## 205 Burundi 339.2965
## 206 Burundi 379.5646
## 207 Burundi 355.2032
## 208 Burundi 412.9775
## 209 Burundi 464.0995
## 210 Burundi 556.1033
## 211 Burundi 559.6032
## 212 Burundi 621.8188
## 213 Burundi 631.6999
## 214 Burundi 463.1151
## 215 Burundi 446.4035
## 216 Burundi 430.0707
## 217 Cambodia 368.4693
## 218 Cambodia 434.0383
## 219 Cambodia 496.9136
## 220 Cambodia 523.4323
## 221 Cambodia 421.6240
## 222 Cambodia 524.9722
## 223 Cambodia 624.4755
## 224 Cambodia 683.8956
## 225 Cambodia 682.3032
## 226 Cambodia 734.2852
## 227 Cambodia 896.2260
## 228 Cambodia 1713.7787
## 229 Cameroon 1172.6677
## 230 Cameroon 1313.0481
## 231 Cameroon 1399.6074
## 232 Cameroon 1508.4531
## 233 Cameroon 1684.1465
## 234 Cameroon 1783.4329
## 235 Cameroon 2367.9833
## 236 Cameroon 2602.6642
## 237 Cameroon 1793.1633
## 238 Cameroon 1694.3375
## 239 Cameroon 1934.0114
## 240 Cameroon 2042.0952
## 241 Canada 11367.1611
## 242 Canada 12489.9501
## 243 Canada 13462.4855
## 244 Canada 16076.5880
## 245 Canada 18970.5709
## 246 Canada 22090.8831
## 247 Canada 22898.7921
## 248 Canada 26626.5150
## 249 Canada 26342.8843
## 250 Canada 28954.9259
## 251 Canada 33328.9651
## 252 Canada 36319.2350
## 253 Central African Republic 1071.3107
## 254 Central African Republic 1190.8443
## 255 Central African Republic 1193.0688
## 256 Central African Republic 1136.0566
## 257 Central African Republic 1070.0133
## 258 Central African Republic 1109.3743
## 259 Central African Republic 956.7530
## 260 Central African Republic 844.8764
## 261 Central African Republic 747.9055
## 262 Central African Republic 740.5063
## 263 Central African Republic 738.6906
## 264 Central African Republic 706.0165
## 265 Chad 1178.6659
## 266 Chad 1308.4956
## 267 Chad 1389.8176
## 268 Chad 1196.8106
## 269 Chad 1104.1040
## 270 Chad 1133.9850
## 271 Chad 797.9081
## 272 Chad 952.3861
## 273 Chad 1058.0643
## 274 Chad 1004.9614
## 275 Chad 1156.1819
## 276 Chad 1704.0637
## 277 Chile 3939.9788
## 278 Chile 4315.6227
## 279 Chile 4519.0943
## 280 Chile 5106.6543
## 281 Chile 5494.0244
## 282 Chile 4756.7638
## 283 Chile 5095.6657
## 284 Chile 5547.0638
## 285 Chile 7596.1260
## 286 Chile 10118.0532
## 287 Chile 10778.7838
## 288 Chile 13171.6388
## 289 China 400.4486
## 290 China 575.9870
## 291 China 487.6740
## 292 China 612.7057
## 293 China 676.9001
## 294 China 741.2375
## 295 China 962.4214
## 296 China 1378.9040
## 297 China 1655.7842
## 298 China 2289.2341
## 299 China 3119.2809
## 300 China 4959.1149
## 301 Colombia 2144.1151
## 302 Colombia 2323.8056
## 303 Colombia 2492.3511
## 304 Colombia 2678.7298
## 305 Colombia 3264.6600
## 306 Colombia 3815.8079
## 307 Colombia 4397.5757
## 308 Colombia 4903.2191
## 309 Colombia 5444.6486
## 310 Colombia 6117.3617
## 311 Colombia 5755.2600
## 312 Colombia 7006.5804
## 313 Comoros 1102.9909
## 314 Comoros 1211.1485
## 315 Comoros 1406.6483
## 316 Comoros 1876.0296
## 317 Comoros 1937.5777
## 318 Comoros 1172.6030
## 319 Comoros 1267.1001
## 320 Comoros 1315.9808
## 321 Comoros 1246.9074
## 322 Comoros 1173.6182
## 323 Comoros 1075.8116
## 324 Comoros 986.1479
## 325 Congo Dem. Rep. 780.5423
## 326 Congo Dem. Rep. 905.8602
## 327 Congo Dem. Rep. 896.3146
## 328 Congo Dem. Rep. 861.5932
## 329 Congo Dem. Rep. 904.8961
## 330 Congo Dem. Rep. 795.7573
## 331 Congo Dem. Rep. 673.7478
## 332 Congo Dem. Rep. 672.7748
## 333 Congo Dem. Rep. 457.7192
## 334 Congo Dem. Rep. 312.1884
## 335 Congo Dem. Rep. 241.1659
## 336 Congo Dem. Rep. 277.5519
## 337 Congo Rep. 2125.6214
## 338 Congo Rep. 2315.0566
## 339 Congo Rep. 2464.7832
## 340 Congo Rep. 2677.9396
## 341 Congo Rep. 3213.1527
## 342 Congo Rep. 3259.1790
## 343 Congo Rep. 4879.5075
## 344 Congo Rep. 4201.1949
## 345 Congo Rep. 4016.2395
## 346 Congo Rep. 3484.1644
## 347 Congo Rep. 3484.0620
## 348 Congo Rep. 3632.5578
## 349 Costa Rica 2627.0095
## 350 Costa Rica 2990.0108
## 351 Costa Rica 3460.9370
## 352 Costa Rica 4161.7278
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## 354 Costa Rica 5926.8770
## 355 Costa Rica 5262.7348
## 356 Costa Rica 5629.9153
## 357 Costa Rica 6160.4163
## 358 Costa Rica 6677.0453
## 359 Costa Rica 7723.4472
## 360 Costa Rica 9645.0614
## 361 Cote d'Ivoire 1388.5947
## 362 Cote d'Ivoire 1500.8959
## 363 Cote d'Ivoire 1728.8694
## 364 Cote d'Ivoire 2052.0505
## 365 Cote d'Ivoire 2378.2011
## 366 Cote d'Ivoire 2517.7365
## 367 Cote d'Ivoire 2602.7102
## 368 Cote d'Ivoire 2156.9561
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## 370 Cote d'Ivoire 1786.2654
## 371 Cote d'Ivoire 1648.8008
## 372 Cote d'Ivoire 1544.7501
## 373 Croatia 3119.2365
## 374 Croatia 4338.2316
## 375 Croatia 5477.8900
## 376 Croatia 6960.2979
## 377 Croatia 9164.0901
## 378 Croatia 11305.3852
## 379 Croatia 13221.8218
## 380 Croatia 13822.5839
## 381 Croatia 8447.7949
## 382 Croatia 9875.6045
## 383 Croatia 11628.3890
## 384 Croatia 14619.2227
## 385 Cuba 5586.5388
## 386 Cuba 6092.1744
## 387 Cuba 5180.7559
## 388 Cuba 5690.2680
## 389 Cuba 5305.4453
## 390 Cuba 6380.4950
## 391 Cuba 7316.9181
## 392 Cuba 7532.9248
## 393 Cuba 5592.8440
## 394 Cuba 5431.9904
## 395 Cuba 6340.6467
## 396 Cuba 8948.1029
## 397 Czech Republic 6876.1403
## 398 Czech Republic 8256.3439
## 399 Czech Republic 10136.8671
## 400 Czech Republic 11399.4449
## 401 Czech Republic 13108.4536
## 402 Czech Republic 14800.1606
## 403 Czech Republic 15377.2285
## 404 Czech Republic 16310.4434
## 405 Czech Republic 14297.0212
## 406 Czech Republic 16048.5142
## 407 Czech Republic 17596.2102
## 408 Czech Republic 22833.3085
## 409 Denmark 9692.3852
## 410 Denmark 11099.6593
## 411 Denmark 13583.3135
## 412 Denmark 15937.2112
## 413 Denmark 18866.2072
## 414 Denmark 20422.9015
## 415 Denmark 21688.0405
## 416 Denmark 25116.1758
## 417 Denmark 26406.7399
## 418 Denmark 29804.3457
## 419 Denmark 32166.5001
## 420 Denmark 35278.4187
## 421 Djibouti 2669.5295
## 422 Djibouti 2864.9691
## 423 Djibouti 3020.9893
## 424 Djibouti 3020.0505
## 425 Djibouti 3694.2124
## 426 Djibouti 3081.7610
## 427 Djibouti 2879.4681
## 428 Djibouti 2880.1026
## 429 Djibouti 2377.1562
## 430 Djibouti 1895.0170
## 431 Djibouti 1908.2609
## 432 Djibouti 2082.4816
## 433 Dominican Republic 1397.7171
## 434 Dominican Republic 1544.4030
## 435 Dominican Republic 1662.1374
## 436 Dominican Republic 1653.7230
## 437 Dominican Republic 2189.8745
## 438 Dominican Republic 2681.9889
## 439 Dominican Republic 2861.0924
## 440 Dominican Republic 2899.8422
## 441 Dominican Republic 3044.2142
## 442 Dominican Republic 3614.1013
## 443 Dominican Republic 4563.8082
## 444 Dominican Republic 6025.3748
## 445 Ecuador 3522.1107
## 446 Ecuador 3780.5467
## 447 Ecuador 4086.1141
## 448 Ecuador 4579.0742
## 449 Ecuador 5280.9947
## 450 Ecuador 6679.6233
## 451 Ecuador 7213.7913
## 452 Ecuador 6481.7770
## 453 Ecuador 7103.7026
## 454 Ecuador 7429.4559
## 455 Ecuador 5773.0445
## 456 Ecuador 6873.2623
## 457 Egypt 1418.8224
## 458 Egypt 1458.9153
## 459 Egypt 1693.3359
## 460 Egypt 1814.8807
## 461 Egypt 2024.0081
## 462 Egypt 2785.4936
## 463 Egypt 3503.7296
## 464 Egypt 3885.4607
## 465 Egypt 3794.7552
## 466 Egypt 4173.1818
## 467 Egypt 4754.6044
## 468 Egypt 5581.1810
## 469 El Salvador 3048.3029
## 470 El Salvador 3421.5232
## 471 El Salvador 3776.8036
## 472 El Salvador 4358.5954
## 473 El Salvador 4520.2460
## 474 El Salvador 5138.9224
## 475 El Salvador 4098.3442
## 476 El Salvador 4140.4421
## 477 El Salvador 4444.2317
## 478 El Salvador 5154.8255
## 479 El Salvador 5351.5687
## 480 El Salvador 5728.3535
## 481 Equatorial Guinea 375.6431
## 482 Equatorial Guinea 426.0964
## 483 Equatorial Guinea 582.8420
## 484 Equatorial Guinea 915.5960
## 485 Equatorial Guinea 672.4123
## 486 Equatorial Guinea 958.5668
## 487 Equatorial Guinea 927.8253
## 488 Equatorial Guinea 966.8968
## 489 Equatorial Guinea 1132.0550
## 490 Equatorial Guinea 2814.4808
## 491 Equatorial Guinea 7703.4959
## 492 Equatorial Guinea 12154.0897
## 493 Eritrea 328.9406
## 494 Eritrea 344.1619
## 495 Eritrea 380.9958
## 496 Eritrea 468.7950
## 497 Eritrea 514.3242
## 498 Eritrea 505.7538
## 499 Eritrea 524.8758
## 500 Eritrea 521.1341
## 501 Eritrea 582.8585
## 502 Eritrea 913.4708
## 503 Eritrea 765.3500
## 504 Eritrea 641.3695
## 505 Ethiopia 362.1463
## 506 Ethiopia 378.9042
## 507 Ethiopia 419.4564
## 508 Ethiopia 516.1186
## 509 Ethiopia 566.2439
## 510 Ethiopia 556.8084
## 511 Ethiopia 577.8607
## 512 Ethiopia 573.7413
## 513 Ethiopia 421.3535
## 514 Ethiopia 515.8894
## 515 Ethiopia 530.0535
## 516 Ethiopia 690.8056
## 517 Finland 6424.5191
## 518 Finland 7545.4154
## 519 Finland 9371.8426
## 520 Finland 10921.6363
## 521 Finland 14358.8759
## 522 Finland 15605.4228
## 523 Finland 18533.1576
## 524 Finland 21141.0122
## 525 Finland 20647.1650
## 526 Finland 23723.9502
## 527 Finland 28204.5906
## 528 Finland 33207.0844
## 529 France 7029.8093
## 530 France 8662.8349
## 531 France 10560.4855
## 532 France 12999.9177
## 533 France 16107.1917
## 534 France 18292.6351
## 535 France 20293.8975
## 536 France 22066.4421
## 537 France 24703.7961
## 538 France 25889.7849
## 539 France 28926.0323
## 540 France 30470.0167
## 541 Gabon 4293.4765
## 542 Gabon 4976.1981
## 543 Gabon 6631.4592
## 544 Gabon 8358.7620
## 545 Gabon 11401.9484
## 546 Gabon 21745.5733
## 547 Gabon 15113.3619
## 548 Gabon 11864.4084
## 549 Gabon 13522.1575
## 550 Gabon 14722.8419
## 551 Gabon 12521.7139
## 552 Gabon 13206.4845
## 553 Gambia 485.2307
## 554 Gambia 520.9267
## 555 Gambia 599.6503
## 556 Gambia 734.7829
## 557 Gambia 756.0868
## 558 Gambia 884.7553
## 559 Gambia 835.8096
## 560 Gambia 611.6589
## 561 Gambia 665.6244
## 562 Gambia 653.7302
## 563 Gambia 660.5856
## 564 Gambia 752.7497
## 565 Germany 7144.1144
## 566 Germany 10187.8267
## 567 Germany 12902.4629
## 568 Germany 14745.6256
## 569 Germany 18016.1803
## 570 Germany 20512.9212
## 571 Germany 22031.5327
## 572 Germany 24639.1857
## 573 Germany 26505.3032
## 574 Germany 27788.8842
## 575 Germany 30035.8020
## 576 Germany 32170.3744
## 577 Ghana 911.2989
## 578 Ghana 1043.5615
## 579 Ghana 1190.0411
## 580 Ghana 1125.6972
## 581 Ghana 1178.2237
## 582 Ghana 993.2240
## 583 Ghana 876.0326
## 584 Ghana 847.0061
## 585 Ghana 925.0602
## 586 Ghana 1005.2458
## 587 Ghana 1111.9846
## 588 Ghana 1327.6089
## 589 Greece 3530.6901
## 590 Greece 4916.2999
## 591 Greece 6017.1907
## 592 Greece 8513.0970
## 593 Greece 12724.8296
## 594 Greece 14195.5243
## 595 Greece 15268.4209
## 596 Greece 16120.5284
## 597 Greece 17541.4963
## 598 Greece 18747.6981
## 599 Greece 22514.2548
## 600 Greece 27538.4119
## 601 Guatemala 2428.2378
## 602 Guatemala 2617.1560
## 603 Guatemala 2750.3644
## 604 Guatemala 3242.5311
## 605 Guatemala 4031.4083
## 606 Guatemala 4879.9927
## 607 Guatemala 4820.4948
## 608 Guatemala 4246.4860
## 609 Guatemala 4439.4508
## 610 Guatemala 4684.3138
## 611 Guatemala 4858.3475
## 612 Guatemala 5186.0500
## 613 Guinea 510.1965
## 614 Guinea 576.2670
## 615 Guinea 686.3737
## 616 Guinea 708.7595
## 617 Guinea 741.6662
## 618 Guinea 874.6859
## 619 Guinea 857.2504
## 620 Guinea 805.5725
## 621 Guinea 794.3484
## 622 Guinea 869.4498
## 623 Guinea 945.5836
## 624 Guinea 942.6542
## 625 Guinea-Bissau 299.8503
## 626 Guinea-Bissau 431.7905
## 627 Guinea-Bissau 522.0344
## 628 Guinea-Bissau 715.5806
## 629 Guinea-Bissau 820.2246
## 630 Guinea-Bissau 764.7260
## 631 Guinea-Bissau 838.1240
## 632 Guinea-Bissau 736.4154
## 633 Guinea-Bissau 745.5399
## 634 Guinea-Bissau 796.6645
## 635 Guinea-Bissau 575.7047
## 636 Guinea-Bissau 579.2317
## 637 Haiti 1840.3669
## 638 Haiti 1726.8879
## 639 Haiti 1796.5890
## 640 Haiti 1452.0577
## 641 Haiti 1654.4569
## 642 Haiti 1874.2989
## 643 Haiti 2011.1595
## 644 Haiti 1823.0160
## 645 Haiti 1456.3095
## 646 Haiti 1341.7269
## 647 Haiti 1270.3649
## 648 Haiti 1201.6372
## 649 Honduras 2194.9262
## 650 Honduras 2220.4877
## 651 Honduras 2291.1568
## 652 Honduras 2538.2694
## 653 Honduras 2529.8423
## 654 Honduras 3203.2081
## 655 Honduras 3121.7608
## 656 Honduras 3023.0967
## 657 Honduras 3081.6946
## 658 Honduras 3160.4549
## 659 Honduras 3099.7287
## 660 Honduras 3548.3308
## 661 Hong Kong China 3054.4212
## 662 Hong Kong China 3629.0765
## 663 Hong Kong China 4692.6483
## 664 Hong Kong China 6197.9628
## 665 Hong Kong China 8315.9281
## 666 Hong Kong China 11186.1413
## 667 Hong Kong China 14560.5305
## 668 Hong Kong China 20038.4727
## 669 Hong Kong China 24757.6030
## 670 Hong Kong China 28377.6322
## 671 Hong Kong China 30209.0152
## 672 Hong Kong China 39724.9787
## 673 Hungary 5263.6738
## 674 Hungary 6040.1800
## 675 Hungary 7550.3599
## 676 Hungary 9326.6447
## 677 Hungary 10168.6561
## 678 Hungary 11674.8374
## 679 Hungary 12545.9907
## 680 Hungary 12986.4800
## 681 Hungary 10535.6285
## 682 Hungary 11712.7768
## 683 Hungary 14843.9356
## 684 Hungary 18008.9444
## 685 Iceland 7267.6884
## 686 Iceland 9244.0014
## 687 Iceland 10350.1591
## 688 Iceland 13319.8957
## 689 Iceland 15798.0636
## 690 Iceland 19654.9625
## 691 Iceland 23269.6075
## 692 Iceland 26923.2063
## 693 Iceland 25144.3920
## 694 Iceland 28061.0997
## 695 Iceland 31163.2020
## 696 Iceland 36180.7892
## 697 India 546.5657
## 698 India 590.0620
## 699 India 658.3472
## 700 India 700.7706
## 701 India 724.0325
## 702 India 813.3373
## 703 India 855.7235
## 704 India 976.5127
## 705 India 1164.4068
## 706 India 1458.8174
## 707 India 1746.7695
## 708 India 2452.2104
## 709 Indonesia 749.6817
## 710 Indonesia 858.9003
## 711 Indonesia 849.2898
## 712 Indonesia 762.4318
## 713 Indonesia 1111.1079
## 714 Indonesia 1382.7021
## 715 Indonesia 1516.8730
## 716 Indonesia 1748.3570
## 717 Indonesia 2383.1409
## 718 Indonesia 3119.3356
## 719 Indonesia 2873.9129
## 720 Indonesia 3540.6516
## 721 Iran 3035.3260
## 722 Iran 3290.2576
## 723 Iran 4187.3298
## 724 Iran 5906.7318
## 725 Iran 9613.8186
## 726 Iran 11888.5951
## 727 Iran 7608.3346
## 728 Iran 6642.8814
## 729 Iran 7235.6532
## 730 Iran 8263.5903
## 731 Iran 9240.7620
## 732 Iran 11605.7145
## 733 Iraq 4129.7661
## 734 Iraq 6229.3336
## 735 Iraq 8341.7378
## 736 Iraq 8931.4598
## 737 Iraq 9576.0376
## 738 Iraq 14688.2351
## 739 Iraq 14517.9071
## 740 Iraq 11643.5727
## 741 Iraq 3745.6407
## 742 Iraq 3076.2398
## 743 Iraq 4390.7173
## 744 Iraq 4471.0619
## 745 Ireland 5210.2803
## 746 Ireland 5599.0779
## 747 Ireland 6631.5973
## 748 Ireland 7655.5690
## 749 Ireland 9530.7729
## 750 Ireland 11150.9811
## 751 Ireland 12618.3214
## 752 Ireland 13872.8665
## 753 Ireland 17558.8155
## 754 Ireland 24521.9471
## 755 Ireland 34077.0494
## 756 Ireland 40675.9964
## 757 Israel 4086.5221
## 758 Israel 5385.2785
## 759 Israel 7105.6307
## 760 Israel 8393.7414
## 761 Israel 12786.9322
## 762 Israel 13306.6192
## 763 Israel 15367.0292
## 764 Israel 17122.4799
## 765 Israel 18051.5225
## 766 Israel 20896.6092
## 767 Israel 21905.5951
## 768 Israel 25523.2771
## 769 Italy 4931.4042
## 770 Italy 6248.6562
## 771 Italy 8243.5823
## 772 Italy 10022.4013
## 773 Italy 12269.2738
## 774 Italy 14255.9847
## 775 Italy 16537.4835
## 776 Italy 19207.2348
## 777 Italy 22013.6449
## 778 Italy 24675.0245
## 779 Italy 27968.0982
## 780 Italy 28569.7197
## 781 Jamaica 2898.5309
## 782 Jamaica 4756.5258
## 783 Jamaica 5246.1075
## 784 Jamaica 6124.7035
## 785 Jamaica 7433.8893
## 786 Jamaica 6650.1956
## 787 Jamaica 6068.0513
## 788 Jamaica 6351.2375
## 789 Jamaica 7404.9237
## 790 Jamaica 7121.9247
## 791 Jamaica 6994.7749
## 792 Jamaica 7320.8803
## 793 Japan 3216.9563
## 794 Japan 4317.6944
## 795 Japan 6576.6495
## 796 Japan 9847.7886
## 797 Japan 14778.7864
## 798 Japan 16610.3770
## 799 Japan 19384.1057
## 800 Japan 22375.9419
## 801 Japan 26824.8951
## 802 Japan 28816.5850
## 803 Japan 28604.5919
## 804 Japan 31656.0681
## 805 Jordan 1546.9078
## 806 Jordan 1886.0806
## 807 Jordan 2348.0092
## 808 Jordan 2741.7963
## 809 Jordan 2110.8563
## 810 Jordan 2852.3516
## 811 Jordan 4161.4160
## 812 Jordan 4448.6799
## 813 Jordan 3431.5936
## 814 Jordan 3645.3796
## 815 Jordan 3844.9172
## 816 Jordan 4519.4612
## 817 Kenya 853.5409
## 818 Kenya 944.4383
## 819 Kenya 896.9664
## 820 Kenya 1056.7365
## 821 Kenya 1222.3600
## 822 Kenya 1267.6132
## 823 Kenya 1348.2258
## 824 Kenya 1361.9369
## 825 Kenya 1341.9217
## 826 Kenya 1360.4850
## 827 Kenya 1287.5147
## 828 Kenya 1463.2493
## 829 Korea Dem. Rep. 1088.2778
## 830 Korea Dem. Rep. 1571.1347
## 831 Korea Dem. Rep. 1621.6936
## 832 Korea Dem. Rep. 2143.5406
## 833 Korea Dem. Rep. 3701.6215
## 834 Korea Dem. Rep. 4106.3012
## 835 Korea Dem. Rep. 4106.5253
## 836 Korea Dem. Rep. 4106.4923
## 837 Korea Dem. Rep. 3726.0635
## 838 Korea Dem. Rep. 1690.7568
## 839 Korea Dem. Rep. 1646.7582
## 840 Korea Dem. Rep. 1593.0655
## 841 Korea Rep. 1030.5922
## 842 Korea Rep. 1487.5935
## 843 Korea Rep. 1536.3444
## 844 Korea Rep. 2029.2281
## 845 Korea Rep. 3030.8767
## 846 Korea Rep. 4657.2210
## 847 Korea Rep. 5622.9425
## 848 Korea Rep. 8533.0888
## 849 Korea Rep. 12104.2787
## 850 Korea Rep. 15993.5280
## 851 Korea Rep. 19233.9882
## 852 Korea Rep. 23348.1397
## 853 Kuwait 108382.3529
## 854 Kuwait 113523.1329
## 855 Kuwait 95458.1118
## 856 Kuwait 80894.8833
## 857 Kuwait 109347.8670
## 858 Kuwait 59265.4771
## 859 Kuwait 31354.0357
## 860 Kuwait 28118.4300
## 861 Kuwait 34932.9196
## 862 Kuwait 40300.6200
## 863 Kuwait 35110.1057
## 864 Kuwait 47306.9898
## 865 Lebanon 4834.8041
## 866 Lebanon 6089.7869
## 867 Lebanon 5714.5606
## 868 Lebanon 6006.9830
## 869 Lebanon 7486.3843
## 870 Lebanon 8659.6968
## 871 Lebanon 7640.5195
## 872 Lebanon 5377.0913
## 873 Lebanon 6890.8069
## 874 Lebanon 8754.9639
## 875 Lebanon 9313.9388
## 876 Lebanon 10461.0587
## 877 Lesotho 298.8462
## 878 Lesotho 335.9971
## 879 Lesotho 411.8006
## 880 Lesotho 498.6390
## 881 Lesotho 496.5816
## 882 Lesotho 745.3695
## 883 Lesotho 797.2631
## 884 Lesotho 773.9932
## 885 Lesotho 977.4863
## 886 Lesotho 1186.1480
## 887 Lesotho 1275.1846
## 888 Lesotho 1569.3314
## 889 Liberia 575.5730
## 890 Liberia 620.9700
## 891 Liberia 634.1952
## 892 Liberia 713.6036
## 893 Liberia 803.0055
## 894 Liberia 640.3224
## 895 Liberia 572.1996
## 896 Liberia 506.1139
## 897 Liberia 636.6229
## 898 Liberia 609.1740
## 899 Liberia 531.4824
## 900 Liberia 414.5073
## 901 Libya 2387.5481
## 902 Libya 3448.2844
## 903 Libya 6757.0308
## 904 Libya 18772.7517
## 905 Libya 21011.4972
## 906 Libya 21951.2118
## 907 Libya 17364.2754
## 908 Libya 11770.5898
## 909 Libya 9640.1385
## 910 Libya 9467.4461
## 911 Libya 9534.6775
## 912 Libya 12057.4993
## 913 Madagascar 1443.0117
## 914 Madagascar 1589.2027
## 915 Madagascar 1643.3871
## 916 Madagascar 1634.0473
## 917 Madagascar 1748.5630
## 918 Madagascar 1544.2286
## 919 Madagascar 1302.8787
## 920 Madagascar 1155.4419
## 921 Madagascar 1040.6762
## 922 Madagascar 986.2959
## 923 Madagascar 894.6371
## 924 Madagascar 1044.7701
## 925 Malawi 369.1651
## 926 Malawi 416.3698
## 927 Malawi 427.9011
## 928 Malawi 495.5148
## 929 Malawi 584.6220
## 930 Malawi 663.2237
## 931 Malawi 632.8039
## 932 Malawi 635.5174
## 933 Malawi 563.2000
## 934 Malawi 692.2758
## 935 Malawi 665.4231
## 936 Malawi 759.3499
## 937 Malaysia 1831.1329
## 938 Malaysia 1810.0670
## 939 Malaysia 2036.8849
## 940 Malaysia 2277.7424
## 941 Malaysia 2849.0948
## 942 Malaysia 3827.9216
## 943 Malaysia 4920.3560
## 944 Malaysia 5249.8027
## 945 Malaysia 7277.9128
## 946 Malaysia 10132.9096
## 947 Malaysia 10206.9779
## 948 Malaysia 12451.6558
## 949 Mali 452.3370
## 950 Mali 490.3822
## 951 Mali 496.1743
## 952 Mali 545.0099
## 953 Mali 581.3689
## 954 Mali 686.3953
## 955 Mali 618.0141
## 956 Mali 684.1716
## 957 Mali 739.0144
## 958 Mali 790.2580
## 959 Mali 951.4098
## 960 Mali 1042.5816
## 961 Mauritania 743.1159
## 962 Mauritania 846.1203
## 963 Mauritania 1055.8960
## 964 Mauritania 1421.1452
## 965 Mauritania 1586.8518
## 966 Mauritania 1497.4922
## 967 Mauritania 1481.1502
## 968 Mauritania 1421.6036
## 969 Mauritania 1361.3698
## 970 Mauritania 1483.1361
## 971 Mauritania 1579.0195
## 972 Mauritania 1803.1515
## 973 Mauritius 1967.9557
## 974 Mauritius 2034.0380
## 975 Mauritius 2529.0675
## 976 Mauritius 2475.3876
## 977 Mauritius 2575.4842
## 978 Mauritius 3710.9830
## 979 Mauritius 3688.0377
## 980 Mauritius 4783.5869
## 981 Mauritius 6058.2538
## 982 Mauritius 7425.7053
## 983 Mauritius 9021.8159
## 984 Mauritius 10956.9911
## 985 Mexico 3478.1255
## 986 Mexico 4131.5466
## 987 Mexico 4581.6094
## 988 Mexico 5754.7339
## 989 Mexico 6809.4067
## 990 Mexico 7674.9291
## 991 Mexico 9611.1475
## 992 Mexico 8688.1560
## 993 Mexico 9472.3843
## 994 Mexico 9767.2975
## 995 Mexico 10742.4405
## 996 Mexico 11977.5750
## 997 Mongolia 786.5669
## 998 Mongolia 912.6626
## 999 Mongolia 1056.3540
## 1000 Mongolia 1226.0411
## 1001 Mongolia 1421.7420
## 1002 Mongolia 1647.5117
## 1003 Mongolia 2000.6031
## 1004 Mongolia 2338.0083
## 1005 Mongolia 1785.4020
## 1006 Mongolia 1902.2521
## 1007 Mongolia 2140.7393
## 1008 Mongolia 3095.7723
## 1009 Montenegro 2647.5856
## 1010 Montenegro 3682.2599
## 1011 Montenegro 4649.5938
## 1012 Montenegro 5907.8509
## 1013 Montenegro 7778.4140
## 1014 Montenegro 9595.9299
## 1015 Montenegro 11222.5876
## 1016 Montenegro 11732.5102
## 1017 Montenegro 7003.3390
## 1018 Montenegro 6465.6133
## 1019 Montenegro 6557.1943
## 1020 Montenegro 9253.8961
## 1021 Morocco 1688.2036
## 1022 Morocco 1642.0023
## 1023 Morocco 1566.3535
## 1024 Morocco 1711.0448
## 1025 Morocco 1930.1950
## 1026 Morocco 2370.6200
## 1027 Morocco 2702.6204
## 1028 Morocco 2755.0470
## 1029 Morocco 2948.0473
## 1030 Morocco 2982.1019
## 1031 Morocco 3258.4956
## 1032 Morocco 3820.1752
## 1033 Mozambique 468.5260
## 1034 Mozambique 495.5868
## 1035 Mozambique 556.6864
## 1036 Mozambique 566.6692
## 1037 Mozambique 724.9178
## 1038 Mozambique 502.3197
## 1039 Mozambique 462.2114
## 1040 Mozambique 389.8762
## 1041 Mozambique 410.8968
## 1042 Mozambique 472.3461
## 1043 Mozambique 633.6179
## 1044 Mozambique 823.6856
## 1045 Myanmar 331.0000
## 1046 Myanmar 350.0000
## 1047 Myanmar 388.0000
## 1048 Myanmar 349.0000
## 1049 Myanmar 357.0000
## 1050 Myanmar 371.0000
## 1051 Myanmar 424.0000
## 1052 Myanmar 385.0000
## 1053 Myanmar 347.0000
## 1054 Myanmar 415.0000
## 1055 Myanmar 611.0000
## 1056 Myanmar 944.0000
## 1057 Namibia 2423.7804
## 1058 Namibia 2621.4481
## 1059 Namibia 3173.2156
## 1060 Namibia 3793.6948
## 1061 Namibia 3746.0809
## 1062 Namibia 3876.4860
## 1063 Namibia 4191.1005
## 1064 Namibia 3693.7313
## 1065 Namibia 3804.5380
## 1066 Namibia 3899.5243
## 1067 Namibia 4072.3248
## 1068 Namibia 4811.0604
## 1069 Nepal 545.8657
## 1070 Nepal 597.9364
## 1071 Nepal 652.3969
## 1072 Nepal 676.4422
## 1073 Nepal 674.7881
## 1074 Nepal 694.1124
## 1075 Nepal 718.3731
## 1076 Nepal 775.6325
## 1077 Nepal 897.7404
## 1078 Nepal 1010.8921
## 1079 Nepal 1057.2063
## 1080 Nepal 1091.3598
## 1081 Netherlands 8941.5719
## 1082 Netherlands 11276.1934
## 1083 Netherlands 12790.8496
## 1084 Netherlands 15363.2514
## 1085 Netherlands 18794.7457
## 1086 Netherlands 21209.0592
## 1087 Netherlands 21399.4605
## 1088 Netherlands 23651.3236
## 1089 Netherlands 26790.9496
## 1090 Netherlands 30246.1306
## 1091 Netherlands 33724.7578
## 1092 Netherlands 36797.9333
## 1093 New Zealand 10556.5757
## 1094 New Zealand 12247.3953
## 1095 New Zealand 13175.6780
## 1096 New Zealand 14463.9189
## 1097 New Zealand 16046.0373
## 1098 New Zealand 16233.7177
## 1099 New Zealand 17632.4104
## 1100 New Zealand 19007.1913
## 1101 New Zealand 18363.3249
## 1102 New Zealand 21050.4138
## 1103 New Zealand 23189.8014
## 1104 New Zealand 25185.0091
## 1105 Nicaragua 3112.3639
## 1106 Nicaragua 3457.4159
## 1107 Nicaragua 3634.3644
## 1108 Nicaragua 4643.3935
## 1109 Nicaragua 4688.5933
## 1110 Nicaragua 5486.3711
## 1111 Nicaragua 3470.3382
## 1112 Nicaragua 2955.9844
## 1113 Nicaragua 2170.1517
## 1114 Nicaragua 2253.0230
## 1115 Nicaragua 2474.5488
## 1116 Nicaragua 2749.3210
## 1117 Niger 761.8794
## 1118 Niger 835.5234
## 1119 Niger 997.7661
## 1120 Niger 1054.3849
## 1121 Niger 954.2092
## 1122 Niger 808.8971
## 1123 Niger 909.7221
## 1124 Niger 668.3000
## 1125 Niger 581.1827
## 1126 Niger 580.3052
## 1127 Niger 601.0745
## 1128 Niger 619.6769
## 1129 Nigeria 1077.2819
## 1130 Nigeria 1100.5926
## 1131 Nigeria 1150.9275
## 1132 Nigeria 1014.5141
## 1133 Nigeria 1698.3888
## 1134 Nigeria 1981.9518
## 1135 Nigeria 1576.9738
## 1136 Nigeria 1385.0296
## 1137 Nigeria 1619.8482
## 1138 Nigeria 1624.9413
## 1139 Nigeria 1615.2864
## 1140 Nigeria 2013.9773
## 1141 Norway 10095.4217
## 1142 Norway 11653.9730
## 1143 Norway 13450.4015
## 1144 Norway 16361.8765
## 1145 Norway 18965.0555
## 1146 Norway 23311.3494
## 1147 Norway 26298.6353
## 1148 Norway 31540.9748
## 1149 Norway 33965.6611
## 1150 Norway 41283.1643
## 1151 Norway 44683.9753
## 1152 Norway 49357.1902
## 1153 Oman 1828.2303
## 1154 Oman 2242.7466
## 1155 Oman 2924.6381
## 1156 Oman 4720.9427
## 1157 Oman 10618.0385
## 1158 Oman 11848.3439
## 1159 Oman 12954.7910
## 1160 Oman 18115.2231
## 1161 Oman 18616.7069
## 1162 Oman 19702.0558
## 1163 Oman 19774.8369
## 1164 Oman 22316.1929
## 1165 Pakistan 684.5971
## 1166 Pakistan 747.0835
## 1167 Pakistan 803.3427
## 1168 Pakistan 942.4083
## 1169 Pakistan 1049.9390
## 1170 Pakistan 1175.9212
## 1171 Pakistan 1443.4298
## 1172 Pakistan 1704.6866
## 1173 Pakistan 1971.8295
## 1174 Pakistan 2049.3505
## 1175 Pakistan 2092.7124
## 1176 Pakistan 2605.9476
## 1177 Panama 2480.3803
## 1178 Panama 2961.8009
## 1179 Panama 3536.5403
## 1180 Panama 4421.0091
## 1181 Panama 5364.2497
## 1182 Panama 5351.9121
## 1183 Panama 7009.6016
## 1184 Panama 7034.7792
## 1185 Panama 6618.7431
## 1186 Panama 7113.6923
## 1187 Panama 7356.0319
## 1188 Panama 9809.1856
## 1189 Paraguay 1952.3087
## 1190 Paraguay 2046.1547
## 1191 Paraguay 2148.0271
## 1192 Paraguay 2299.3763
## 1193 Paraguay 2523.3380
## 1194 Paraguay 3248.3733
## 1195 Paraguay 4258.5036
## 1196 Paraguay 3998.8757
## 1197 Paraguay 4196.4111
## 1198 Paraguay 4247.4003
## 1199 Paraguay 3783.6742
## 1200 Paraguay 4172.8385
## 1201 Peru 3758.5234
## 1202 Peru 4245.2567
## 1203 Peru 4957.0380
## 1204 Peru 5788.0933
## 1205 Peru 5937.8273
## 1206 Peru 6281.2909
## 1207 Peru 6434.5018
## 1208 Peru 6360.9434
## 1209 Peru 4446.3809
## 1210 Peru 5838.3477
## 1211 Peru 5909.0201
## 1212 Peru 7408.9056
## 1213 Philippines 1272.8810
## 1214 Philippines 1547.9448
## 1215 Philippines 1649.5522
## 1216 Philippines 1814.1274
## 1217 Philippines 1989.3741
## 1218 Philippines 2373.2043
## 1219 Philippines 2603.2738
## 1220 Philippines 2189.6350
## 1221 Philippines 2279.3240
## 1222 Philippines 2536.5349
## 1223 Philippines 2650.9211
## 1224 Philippines 3190.4810
## 1225 Poland 4029.3297
## 1226 Poland 4734.2530
## 1227 Poland 5338.7521
## 1228 Poland 6557.1528
## 1229 Poland 8006.5070
## 1230 Poland 9508.1415
## 1231 Poland 8451.5310
## 1232 Poland 9082.3512
## 1233 Poland 7738.8812
## 1234 Poland 10159.5837
## 1235 Poland 12002.2391
## 1236 Poland 15389.9247
## 1237 Portugal 3068.3199
## 1238 Portugal 3774.5717
## 1239 Portugal 4727.9549
## 1240 Portugal 6361.5180
## 1241 Portugal 9022.2474
## 1242 Portugal 10172.4857
## 1243 Portugal 11753.8429
## 1244 Portugal 13039.3088
## 1245 Portugal 16207.2666
## 1246 Portugal 17641.0316
## 1247 Portugal 19970.9079
## 1248 Portugal 20509.6478
## 1249 Puerto Rico 3081.9598
## 1250 Puerto Rico 3907.1562
## 1251 Puerto Rico 5108.3446
## 1252 Puerto Rico 6929.2777
## 1253 Puerto Rico 9123.0417
## 1254 Puerto Rico 9770.5249
## 1255 Puerto Rico 10330.9891
## 1256 Puerto Rico 12281.3419
## 1257 Puerto Rico 14641.5871
## 1258 Puerto Rico 16999.4333
## 1259 Puerto Rico 18855.6062
## 1260 Puerto Rico 19328.7090
## 1261 Reunion 2718.8853
## 1262 Reunion 2769.4518
## 1263 Reunion 3173.7233
## 1264 Reunion 4021.1757
## 1265 Reunion 5047.6586
## 1266 Reunion 4319.8041
## 1267 Reunion 5267.2194
## 1268 Reunion 5303.3775
## 1269 Reunion 6101.2558
## 1270 Reunion 6071.9414
## 1271 Reunion 6316.1652
## 1272 Reunion 7670.1226
## 1273 Romania 3144.6132
## 1274 Romania 3943.3702
## 1275 Romania 4734.9976
## 1276 Romania 6470.8665
## 1277 Romania 8011.4144
## 1278 Romania 9356.3972
## 1279 Romania 9605.3141
## 1280 Romania 9696.2733
## 1281 Romania 6598.4099
## 1282 Romania 7346.5476
## 1283 Romania 7885.3601
## 1284 Romania 10808.4756
## 1285 Rwanda 493.3239
## 1286 Rwanda 540.2894
## 1287 Rwanda 597.4731
## 1288 Rwanda 510.9637
## 1289 Rwanda 590.5807
## 1290 Rwanda 670.0806
## 1291 Rwanda 881.5706
## 1292 Rwanda 847.9912
## 1293 Rwanda 737.0686
## 1294 Rwanda 589.9445
## 1295 Rwanda 785.6538
## 1296 Rwanda 863.0885
## 1297 Sao Tome and Principe 879.5836
## 1298 Sao Tome and Principe 860.7369
## 1299 Sao Tome and Principe 1071.5511
## 1300 Sao Tome and Principe 1384.8406
## 1301 Sao Tome and Principe 1532.9853
## 1302 Sao Tome and Principe 1737.5617
## 1303 Sao Tome and Principe 1890.2181
## 1304 Sao Tome and Principe 1516.5255
## 1305 Sao Tome and Principe 1428.7778
## 1306 Sao Tome and Principe 1339.0760
## 1307 Sao Tome and Principe 1353.0924
## 1308 Sao Tome and Principe 1598.4351
## 1309 Saudi Arabia 6459.5548
## 1310 Saudi Arabia 8157.5912
## 1311 Saudi Arabia 11626.4197
## 1312 Saudi Arabia 16903.0489
## 1313 Saudi Arabia 24837.4287
## 1314 Saudi Arabia 34167.7626
## 1315 Saudi Arabia 33693.1753
## 1316 Saudi Arabia 21198.2614
## 1317 Saudi Arabia 24841.6178
## 1318 Saudi Arabia 20586.6902
## 1319 Saudi Arabia 19014.5412
## 1320 Saudi Arabia 21654.8319
## 1321 Senegal 1450.3570
## 1322 Senegal 1567.6530
## 1323 Senegal 1654.9887
## 1324 Senegal 1612.4046
## 1325 Senegal 1597.7121
## 1326 Senegal 1561.7691
## 1327 Senegal 1518.4800
## 1328 Senegal 1441.7207
## 1329 Senegal 1367.8994
## 1330 Senegal 1392.3683
## 1331 Senegal 1519.6353
## 1332 Senegal 1712.4721
## 1333 Serbia 3581.4594
## 1334 Serbia 4981.0909
## 1335 Serbia 6289.6292
## 1336 Serbia 7991.7071
## 1337 Serbia 10522.0675
## 1338 Serbia 12980.6696
## 1339 Serbia 15181.0927
## 1340 Serbia 15870.8785
## 1341 Serbia 9325.0682
## 1342 Serbia 7914.3203
## 1343 Serbia 7236.0753
## 1344 Serbia 9786.5347
## 1345 Sierra Leone 879.7877
## 1346 Sierra Leone 1004.4844
## 1347 Sierra Leone 1116.6399
## 1348 Sierra Leone 1206.0435
## 1349 Sierra Leone 1353.7598
## 1350 Sierra Leone 1348.2852
## 1351 Sierra Leone 1465.0108
## 1352 Sierra Leone 1294.4478
## 1353 Sierra Leone 1068.6963
## 1354 Sierra Leone 574.6482
## 1355 Sierra Leone 699.4897
## 1356 Sierra Leone 862.5408
## 1357 Singapore 2315.1382
## 1358 Singapore 2843.1044
## 1359 Singapore 3674.7356
## 1360 Singapore 4977.4185
## 1361 Singapore 8597.7562
## 1362 Singapore 11210.0895
## 1363 Singapore 15169.1611
## 1364 Singapore 18861.5308
## 1365 Singapore 24769.8912
## 1366 Singapore 33519.4766
## 1367 Singapore 36023.1054
## 1368 Singapore 47143.1796
## 1369 Slovak Republic 5074.6591
## 1370 Slovak Republic 6093.2630
## 1371 Slovak Republic 7481.1076
## 1372 Slovak Republic 8412.9024
## 1373 Slovak Republic 9674.1676
## 1374 Slovak Republic 10922.6640
## 1375 Slovak Republic 11348.5459
## 1376 Slovak Republic 12037.2676
## 1377 Slovak Republic 9498.4677
## 1378 Slovak Republic 12126.2306
## 1379 Slovak Republic 13638.7784
## 1380 Slovak Republic 18678.3144
## 1381 Slovenia 4215.0417
## 1382 Slovenia 5862.2766
## 1383 Slovenia 7402.3034
## 1384 Slovenia 9405.4894
## 1385 Slovenia 12383.4862
## 1386 Slovenia 15277.0302
## 1387 Slovenia 17866.7218
## 1388 Slovenia 18678.5349
## 1389 Slovenia 14214.7168
## 1390 Slovenia 17161.1073
## 1391 Slovenia 20660.0194
## 1392 Slovenia 25768.2576
## 1393 Somalia 1135.7498
## 1394 Somalia 1258.1474
## 1395 Somalia 1369.4883
## 1396 Somalia 1284.7332
## 1397 Somalia 1254.5761
## 1398 Somalia 1450.9925
## 1399 Somalia 1176.8070
## 1400 Somalia 1093.2450
## 1401 Somalia 926.9603
## 1402 Somalia 930.5964
## 1403 Somalia 882.0818
## 1404 Somalia 926.1411
## 1405 South Africa 4725.2955
## 1406 South Africa 5487.1042
## 1407 South Africa 5768.7297
## 1408 South Africa 7114.4780
## 1409 South Africa 7765.9626
## 1410 South Africa 8028.6514
## 1411 South Africa 8568.2662
## 1412 South Africa 7825.8234
## 1413 South Africa 7225.0693
## 1414 South Africa 7479.1882
## 1415 South Africa 7710.9464
## 1416 South Africa 9269.6578
## 1417 Spain 3834.0347
## 1418 Spain 4564.8024
## 1419 Spain 5693.8439
## 1420 Spain 7993.5123
## 1421 Spain 10638.7513
## 1422 Spain 13236.9212
## 1423 Spain 13926.1700
## 1424 Spain 15764.9831
## 1425 Spain 18603.0645
## 1426 Spain 20445.2990
## 1427 Spain 24835.4717
## 1428 Spain 28821.0637
## 1429 Sri Lanka 1083.5320
## 1430 Sri Lanka 1072.5466
## 1431 Sri Lanka 1074.4720
## 1432 Sri Lanka 1135.5143
## 1433 Sri Lanka 1213.3955
## 1434 Sri Lanka 1348.7757
## 1435 Sri Lanka 1648.0798
## 1436 Sri Lanka 1876.7668
## 1437 Sri Lanka 2153.7392
## 1438 Sri Lanka 2664.4773
## 1439 Sri Lanka 3015.3788
## 1440 Sri Lanka 3970.0954
## 1441 Sudan 1615.9911
## 1442 Sudan 1770.3371
## 1443 Sudan 1959.5938
## 1444 Sudan 1687.9976
## 1445 Sudan 1659.6528
## 1446 Sudan 2202.9884
## 1447 Sudan 1895.5441
## 1448 Sudan 1507.8192
## 1449 Sudan 1492.1970
## 1450 Sudan 1632.2108
## 1451 Sudan 1993.3983
## 1452 Sudan 2602.3950
## 1453 Swaziland 1148.3766
## 1454 Swaziland 1244.7084
## 1455 Swaziland 1856.1821
## 1456 Swaziland 2613.1017
## 1457 Swaziland 3364.8366
## 1458 Swaziland 3781.4106
## 1459 Swaziland 3895.3840
## 1460 Swaziland 3984.8398
## 1461 Swaziland 3553.0224
## 1462 Swaziland 3876.7685
## 1463 Swaziland 4128.1169
## 1464 Swaziland 4513.4806
## 1465 Sweden 8527.8447
## 1466 Sweden 9911.8782
## 1467 Sweden 12329.4419
## 1468 Sweden 15258.2970
## 1469 Sweden 17832.0246
## 1470 Sweden 18855.7252
## 1471 Sweden 20667.3812
## 1472 Sweden 23586.9293
## 1473 Sweden 23880.0168
## 1474 Sweden 25266.5950
## 1475 Sweden 29341.6309
## 1476 Sweden 33859.7484
## 1477 Switzerland 14734.2327
## 1478 Switzerland 17909.4897
## 1479 Switzerland 20431.0927
## 1480 Switzerland 22966.1443
## 1481 Switzerland 27195.1130
## 1482 Switzerland 26982.2905
## 1483 Switzerland 28397.7151
## 1484 Switzerland 30281.7046
## 1485 Switzerland 31871.5303
## 1486 Switzerland 32135.3230
## 1487 Switzerland 34480.9577
## 1488 Switzerland 37506.4191
## 1489 Syria 1643.4854
## 1490 Syria 2117.2349
## 1491 Syria 2193.0371
## 1492 Syria 1881.9236
## 1493 Syria 2571.4230
## 1494 Syria 3195.4846
## 1495 Syria 3761.8377
## 1496 Syria 3116.7743
## 1497 Syria 3340.5428
## 1498 Syria 4014.2390
## 1499 Syria 4090.9253
## 1500 Syria 4184.5481
## 1501 Taiwan 1206.9479
## 1502 Taiwan 1507.8613
## 1503 Taiwan 1822.8790
## 1504 Taiwan 2643.8587
## 1505 Taiwan 4062.5239
## 1506 Taiwan 5596.5198
## 1507 Taiwan 7426.3548
## 1508 Taiwan 11054.5618
## 1509 Taiwan 15215.6579
## 1510 Taiwan 20206.8210
## 1511 Taiwan 23235.4233
## 1512 Taiwan 28718.2768
## 1513 Tanzania 716.6501
## 1514 Tanzania 698.5356
## 1515 Tanzania 722.0038
## 1516 Tanzania 848.2187
## 1517 Tanzania 915.9851
## 1518 Tanzania 962.4923
## 1519 Tanzania 874.2426
## 1520 Tanzania 831.8221
## 1521 Tanzania 825.6825
## 1522 Tanzania 789.1862
## 1523 Tanzania 899.0742
## 1524 Tanzania 1107.4822
## 1525 Thailand 757.7974
## 1526 Thailand 793.5774
## 1527 Thailand 1002.1992
## 1528 Thailand 1295.4607
## 1529 Thailand 1524.3589
## 1530 Thailand 1961.2246
## 1531 Thailand 2393.2198
## 1532 Thailand 2982.6538
## 1533 Thailand 4616.8965
## 1534 Thailand 5852.6255
## 1535 Thailand 5913.1875
## 1536 Thailand 7458.3963
## 1537 Togo 859.8087
## 1538 Togo 925.9083
## 1539 Togo 1067.5348
## 1540 Togo 1477.5968
## 1541 Togo 1649.6602
## 1542 Togo 1532.7770
## 1543 Togo 1344.5780
## 1544 Togo 1202.2014
## 1545 Togo 1034.2989
## 1546 Togo 982.2869
## 1547 Togo 886.2206
## 1548 Togo 882.9699
## 1549 Trinidad and Tobago 3023.2719
## 1550 Trinidad and Tobago 4100.3934
## 1551 Trinidad and Tobago 4997.5240
## 1552 Trinidad and Tobago 5621.3685
## 1553 Trinidad and Tobago 6619.5514
## 1554 Trinidad and Tobago 7899.5542
## 1555 Trinidad and Tobago 9119.5286
## 1556 Trinidad and Tobago 7388.5978
## 1557 Trinidad and Tobago 7370.9909
## 1558 Trinidad and Tobago 8792.5731
## 1559 Trinidad and Tobago 11460.6002
## 1560 Trinidad and Tobago 18008.5092
## 1561 Tunisia 1468.4756
## 1562 Tunisia 1395.2325
## 1563 Tunisia 1660.3032
## 1564 Tunisia 1932.3602
## 1565 Tunisia 2753.2860
## 1566 Tunisia 3120.8768
## 1567 Tunisia 3560.2332
## 1568 Tunisia 3810.4193
## 1569 Tunisia 4332.7202
## 1570 Tunisia 4876.7986
## 1571 Tunisia 5722.8957
## 1572 Tunisia 7092.9230
## 1573 Turkey 1969.1010
## 1574 Turkey 2218.7543
## 1575 Turkey 2322.8699
## 1576 Turkey 2826.3564
## 1577 Turkey 3450.6964
## 1578 Turkey 4269.1223
## 1579 Turkey 4241.3563
## 1580 Turkey 5089.0437
## 1581 Turkey 5678.3483
## 1582 Turkey 6601.4299
## 1583 Turkey 6508.0857
## 1584 Turkey 8458.2764
## 1585 Uganda 734.7535
## 1586 Uganda 774.3711
## 1587 Uganda 767.2717
## 1588 Uganda 908.9185
## 1589 Uganda 950.7359
## 1590 Uganda 843.7331
## 1591 Uganda 682.2662
## 1592 Uganda 617.7244
## 1593 Uganda 644.1708
## 1594 Uganda 816.5591
## 1595 Uganda 927.7210
## 1596 Uganda 1056.3801
## 1597 United Kingdom 9979.5085
## 1598 United Kingdom 11283.1779
## 1599 United Kingdom 12477.1771
## 1600 United Kingdom 14142.8509
## 1601 United Kingdom 15895.1164
## 1602 United Kingdom 17428.7485
## 1603 United Kingdom 18232.4245
## 1604 United Kingdom 21664.7877
## 1605 United Kingdom 22705.0925
## 1606 United Kingdom 26074.5314
## 1607 United Kingdom 29478.9992
## 1608 United Kingdom 33203.2613
## 1609 United States 13990.4821
## 1610 United States 14847.1271
## 1611 United States 16173.1459
## 1612 United States 19530.3656
## 1613 United States 21806.0359
## 1614 United States 24072.6321
## 1615 United States 25009.5591
## 1616 United States 29884.3504
## 1617 United States 32003.9322
## 1618 United States 35767.4330
## 1619 United States 39097.0995
## 1620 United States 42951.6531
## 1621 Uruguay 5716.7667
## 1622 Uruguay 6150.7730
## 1623 Uruguay 5603.3577
## 1624 Uruguay 5444.6196
## 1625 Uruguay 5703.4089
## 1626 Uruguay 6504.3397
## 1627 Uruguay 6920.2231
## 1628 Uruguay 7452.3990
## 1629 Uruguay 8137.0048
## 1630 Uruguay 9230.2407
## 1631 Uruguay 7727.0020
## 1632 Uruguay 10611.4630
## 1633 Venezuela 7689.7998
## 1634 Venezuela 9802.4665
## 1635 Venezuela 8422.9742
## 1636 Venezuela 9541.4742
## 1637 Venezuela 10505.2597
## 1638 Venezuela 13143.9510
## 1639 Venezuela 11152.4101
## 1640 Venezuela 9883.5846
## 1641 Venezuela 10733.9263
## 1642 Venezuela 10165.4952
## 1643 Venezuela 8605.0478
## 1644 Venezuela 11415.8057
## 1645 Vietnam 605.0665
## 1646 Vietnam 676.2854
## 1647 Vietnam 772.0492
## 1648 Vietnam 637.1233
## 1649 Vietnam 699.5016
## 1650 Vietnam 713.5371
## 1651 Vietnam 707.2358
## 1652 Vietnam 820.7994
## 1653 Vietnam 989.0231
## 1654 Vietnam 1385.8968
## 1655 Vietnam 1764.4567
## 1656 Vietnam 2441.5764
## 1657 West Bank and Gaza 1515.5923
## 1658 West Bank and Gaza 1827.0677
## 1659 West Bank and Gaza 2198.9563
## 1660 West Bank and Gaza 2649.7150
## 1661 West Bank and Gaza 3133.4093
## 1662 West Bank and Gaza 3682.8315
## 1663 West Bank and Gaza 4336.0321
## 1664 West Bank and Gaza 5107.1974
## 1665 West Bank and Gaza 6017.6548
## 1666 West Bank and Gaza 7110.6676
## 1667 West Bank and Gaza 4515.4876
## 1668 West Bank and Gaza 3025.3498
## 1669 Yemen Rep. 781.7176
## 1670 Yemen Rep. 804.8305
## 1671 Yemen Rep. 825.6232
## 1672 Yemen Rep. 862.4421
## 1673 Yemen Rep. 1265.0470
## 1674 Yemen Rep. 1829.7652
## 1675 Yemen Rep. 1977.5570
## 1676 Yemen Rep. 1971.7415
## 1677 Yemen Rep. 1879.4967
## 1678 Yemen Rep. 2117.4845
## 1679 Yemen Rep. 2234.8208
## 1680 Yemen Rep. 2280.7699
## 1681 Zambia 1147.3888
## 1682 Zambia 1311.9568
## 1683 Zambia 1452.7258
## 1684 Zambia 1777.0773
## 1685 Zambia 1773.4983
## 1686 Zambia 1588.6883
## 1687 Zambia 1408.6786
## 1688 Zambia 1213.3151
## 1689 Zambia 1210.8846
## 1690 Zambia 1071.3538
## 1691 Zambia 1071.6139
## 1692 Zambia 1271.2116
## 1693 Zimbabwe 406.8841
## 1694 Zimbabwe 518.7643
## 1695 Zimbabwe 527.2722
## 1696 Zimbabwe 569.7951
## 1697 Zimbabwe 799.3622
## 1698 Zimbabwe 685.5877
## 1699 Zimbabwe 788.8550
## 1700 Zimbabwe 706.1573
## 1701 Zimbabwe 693.4208
## 1702 Zimbabwe 792.4500
## 1703 Zimbabwe 672.0386
## 1704 Zimbabwe 469.7093You can also specify columns to remove using negative selection, select(-varname)
gapminder %>%
select(-continent) ## country year pop lifeExp gdpPercap
## 1 Afghanistan 1952 8425333 28.80100 779.4453
## 2 Afghanistan 1957 9240934 30.33200 820.8530
## 3 Afghanistan 1962 10267083 31.99700 853.1007
## 4 Afghanistan 1967 11537966 34.02000 836.1971
## 5 Afghanistan 1972 13079460 36.08800 739.9811
## 6 Afghanistan 1977 14880372 38.43800 786.1134
## 7 Afghanistan 1982 12881816 39.85400 978.0114
## 8 Afghanistan 1987 13867957 40.82200 852.3959
## 9 Afghanistan 1992 16317921 41.67400 649.3414
## 10 Afghanistan 1997 22227415 41.76300 635.3414
## 11 Afghanistan 2002 25268405 42.12900 726.7341
## 12 Afghanistan 2007 31889923 43.82800 974.5803
## 13 Albania 1952 1282697 55.23000 1601.0561
## 14 Albania 1957 1476505 59.28000 1942.2842
## 15 Albania 1962 1728137 64.82000 2312.8890
## 16 Albania 1967 1984060 66.22000 2760.1969
## 17 Albania 1972 2263554 67.69000 3313.4222
## 18 Albania 1977 2509048 68.93000 3533.0039
## 19 Albania 1982 2780097 70.42000 3630.8807
## 20 Albania 1987 3075321 72.00000 3738.9327
## 21 Albania 1992 3326498 71.58100 2497.4379
## 22 Albania 1997 3428038 72.95000 3193.0546
## 23 Albania 2002 3508512 75.65100 4604.2117
## 24 Albania 2007 3600523 76.42300 5937.0295
## 25 Algeria 1952 9279525 43.07700 2449.0082
## 26 Algeria 1957 10270856 45.68500 3013.9760
## 27 Algeria 1962 11000948 48.30300 2550.8169
## 28 Algeria 1967 12760499 51.40700 3246.9918
## 29 Algeria 1972 14760787 54.51800 4182.6638
## 30 Algeria 1977 17152804 58.01400 4910.4168
## 31 Algeria 1982 20033753 61.36800 5745.1602
## 32 Algeria 1987 23254956 65.79900 5681.3585
## 33 Algeria 1992 26298373 67.74400 5023.2166
## 34 Algeria 1997 29072015 69.15200 4797.2951
## 35 Algeria 2002 31287142 70.99400 5288.0404
## 36 Algeria 2007 33333216 72.30100 6223.3675
## 37 Angola 1952 4232095 30.01500 3520.6103
## 38 Angola 1957 4561361 31.99900 3827.9405
## 39 Angola 1962 4826015 34.00000 4269.2767
## 40 Angola 1967 5247469 35.98500 5522.7764
## 41 Angola 1972 5894858 37.92800 5473.2880
## 42 Angola 1977 6162675 39.48300 3008.6474
## 43 Angola 1982 7016384 39.94200 2756.9537
## 44 Angola 1987 7874230 39.90600 2430.2083
## 45 Angola 1992 8735988 40.64700 2627.8457
## 46 Angola 1997 9875024 40.96300 2277.1409
## 47 Angola 2002 10866106 41.00300 2773.2873
## 48 Angola 2007 12420476 42.73100 4797.2313
## 49 Argentina 1952 17876956 62.48500 5911.3151
## 50 Argentina 1957 19610538 64.39900 6856.8562
## 51 Argentina 1962 21283783 65.14200 7133.1660
## 52 Argentina 1967 22934225 65.63400 8052.9530
## 53 Argentina 1972 24779799 67.06500 9443.0385
## 54 Argentina 1977 26983828 68.48100 10079.0267
## 55 Argentina 1982 29341374 69.94200 8997.8974
## 56 Argentina 1987 31620918 70.77400 9139.6714
## 57 Argentina 1992 33958947 71.86800 9308.4187
## 58 Argentina 1997 36203463 73.27500 10967.2820
## 59 Argentina 2002 38331121 74.34000 8797.6407
## 60 Argentina 2007 40301927 75.32000 12779.3796
## 61 Australia 1952 8691212 69.12000 10039.5956
## 62 Australia 1957 9712569 70.33000 10949.6496
## 63 Australia 1962 10794968 70.93000 12217.2269
## 64 Australia 1967 11872264 71.10000 14526.1246
## 65 Australia 1972 13177000 71.93000 16788.6295
## 66 Australia 1977 14074100 73.49000 18334.1975
## 67 Australia 1982 15184200 74.74000 19477.0093
## 68 Australia 1987 16257249 76.32000 21888.8890
## 69 Australia 1992 17481977 77.56000 23424.7668
## 70 Australia 1997 18565243 78.83000 26997.9366
## 71 Australia 2002 19546792 80.37000 30687.7547
## 72 Australia 2007 20434176 81.23500 34435.3674
## 73 Austria 1952 6927772 66.80000 6137.0765
## 74 Austria 1957 6965860 67.48000 8842.5980
## 75 Austria 1962 7129864 69.54000 10750.7211
## 76 Austria 1967 7376998 70.14000 12834.6024
## 77 Austria 1972 7544201 70.63000 16661.6256
## 78 Austria 1977 7568430 72.17000 19749.4223
## 79 Austria 1982 7574613 73.18000 21597.0836
## 80 Austria 1987 7578903 74.94000 23687.8261
## 81 Austria 1992 7914969 76.04000 27042.0187
## 82 Austria 1997 8069876 77.51000 29095.9207
## 83 Austria 2002 8148312 78.98000 32417.6077
## 84 Austria 2007 8199783 79.82900 36126.4927
## 85 Bahrain 1952 120447 50.93900 9867.0848
## 86 Bahrain 1957 138655 53.83200 11635.7995
## 87 Bahrain 1962 171863 56.92300 12753.2751
## 88 Bahrain 1967 202182 59.92300 14804.6727
## 89 Bahrain 1972 230800 63.30000 18268.6584
## 90 Bahrain 1977 297410 65.59300 19340.1020
## 91 Bahrain 1982 377967 69.05200 19211.1473
## 92 Bahrain 1987 454612 70.75000 18524.0241
## 93 Bahrain 1992 529491 72.60100 19035.5792
## 94 Bahrain 1997 598561 73.92500 20292.0168
## 95 Bahrain 2002 656397 74.79500 23403.5593
## 96 Bahrain 2007 708573 75.63500 29796.0483
## 97 Bangladesh 1952 46886859 37.48400 684.2442
## 98 Bangladesh 1957 51365468 39.34800 661.6375
## 99 Bangladesh 1962 56839289 41.21600 686.3416
## 100 Bangladesh 1967 62821884 43.45300 721.1861
## 101 Bangladesh 1972 70759295 45.25200 630.2336
## 102 Bangladesh 1977 80428306 46.92300 659.8772
## 103 Bangladesh 1982 93074406 50.00900 676.9819
## 104 Bangladesh 1987 103764241 52.81900 751.9794
## 105 Bangladesh 1992 113704579 56.01800 837.8102
## 106 Bangladesh 1997 123315288 59.41200 972.7700
## 107 Bangladesh 2002 135656790 62.01300 1136.3904
## 108 Bangladesh 2007 150448339 64.06200 1391.2538
## 109 Belgium 1952 8730405 68.00000 8343.1051
## 110 Belgium 1957 8989111 69.24000 9714.9606
## 111 Belgium 1962 9218400 70.25000 10991.2068
## 112 Belgium 1967 9556500 70.94000 13149.0412
## 113 Belgium 1972 9709100 71.44000 16672.1436
## 114 Belgium 1977 9821800 72.80000 19117.9745
## 115 Belgium 1982 9856303 73.93000 20979.8459
## 116 Belgium 1987 9870200 75.35000 22525.5631
## 117 Belgium 1992 10045622 76.46000 25575.5707
## 118 Belgium 1997 10199787 77.53000 27561.1966
## 119 Belgium 2002 10311970 78.32000 30485.8838
## 120 Belgium 2007 10392226 79.44100 33692.6051
## 121 Benin 1952 1738315 38.22300 1062.7522
## 122 Benin 1957 1925173 40.35800 959.6011
## 123 Benin 1962 2151895 42.61800 949.4991
## 124 Benin 1967 2427334 44.88500 1035.8314
## 125 Benin 1972 2761407 47.01400 1085.7969
## 126 Benin 1977 3168267 49.19000 1029.1613
## 127 Benin 1982 3641603 50.90400 1277.8976
## 128 Benin 1987 4243788 52.33700 1225.8560
## 129 Benin 1992 4981671 53.91900 1191.2077
## 130 Benin 1997 6066080 54.77700 1232.9753
## 131 Benin 2002 7026113 54.40600 1372.8779
## 132 Benin 2007 8078314 56.72800 1441.2849
## 133 Bolivia 1952 2883315 40.41400 2677.3263
## 134 Bolivia 1957 3211738 41.89000 2127.6863
## 135 Bolivia 1962 3593918 43.42800 2180.9725
## 136 Bolivia 1967 4040665 45.03200 2586.8861
## 137 Bolivia 1972 4565872 46.71400 2980.3313
## 138 Bolivia 1977 5079716 50.02300 3548.0978
## 139 Bolivia 1982 5642224 53.85900 3156.5105
## 140 Bolivia 1987 6156369 57.25100 2753.6915
## 141 Bolivia 1992 6893451 59.95700 2961.6997
## 142 Bolivia 1997 7693188 62.05000 3326.1432
## 143 Bolivia 2002 8445134 63.88300 3413.2627
## 144 Bolivia 2007 9119152 65.55400 3822.1371
## 145 Bosnia and Herzegovina 1952 2791000 53.82000 973.5332
## 146 Bosnia and Herzegovina 1957 3076000 58.45000 1353.9892
## 147 Bosnia and Herzegovina 1962 3349000 61.93000 1709.6837
## 148 Bosnia and Herzegovina 1967 3585000 64.79000 2172.3524
## 149 Bosnia and Herzegovina 1972 3819000 67.45000 2860.1698
## 150 Bosnia and Herzegovina 1977 4086000 69.86000 3528.4813
## 151 Bosnia and Herzegovina 1982 4172693 70.69000 4126.6132
## 152 Bosnia and Herzegovina 1987 4338977 71.14000 4314.1148
## 153 Bosnia and Herzegovina 1992 4256013 72.17800 2546.7814
## 154 Bosnia and Herzegovina 1997 3607000 73.24400 4766.3559
## 155 Bosnia and Herzegovina 2002 4165416 74.09000 6018.9752
## 156 Bosnia and Herzegovina 2007 4552198 74.85200 7446.2988
## 157 Botswana 1952 442308 47.62200 851.2411
## 158 Botswana 1957 474639 49.61800 918.2325
## 159 Botswana 1962 512764 51.52000 983.6540
## 160 Botswana 1967 553541 53.29800 1214.7093
## 161 Botswana 1972 619351 56.02400 2263.6111
## 162 Botswana 1977 781472 59.31900 3214.8578
## 163 Botswana 1982 970347 61.48400 4551.1421
## 164 Botswana 1987 1151184 63.62200 6205.8839
## 165 Botswana 1992 1342614 62.74500 7954.1116
## 166 Botswana 1997 1536536 52.55600 8647.1423
## 167 Botswana 2002 1630347 46.63400 11003.6051
## 168 Botswana 2007 1639131 50.72800 12569.8518
## 169 Brazil 1952 56602560 50.91700 2108.9444
## 170 Brazil 1957 65551171 53.28500 2487.3660
## 171 Brazil 1962 76039390 55.66500 3336.5858
## 172 Brazil 1967 88049823 57.63200 3429.8644
## 173 Brazil 1972 100840058 59.50400 4985.7115
## 174 Brazil 1977 114313951 61.48900 6660.1187
## 175 Brazil 1982 128962939 63.33600 7030.8359
## 176 Brazil 1987 142938076 65.20500 7807.0958
## 177 Brazil 1992 155975974 67.05700 6950.2830
## 178 Brazil 1997 168546719 69.38800 7957.9808
## 179 Brazil 2002 179914212 71.00600 8131.2128
## 180 Brazil 2007 190010647 72.39000 9065.8008
## 181 Bulgaria 1952 7274900 59.60000 2444.2866
## 182 Bulgaria 1957 7651254 66.61000 3008.6707
## 183 Bulgaria 1962 8012946 69.51000 4254.3378
## 184 Bulgaria 1967 8310226 70.42000 5577.0028
## 185 Bulgaria 1972 8576200 70.90000 6597.4944
## 186 Bulgaria 1977 8797022 70.81000 7612.2404
## 187 Bulgaria 1982 8892098 71.08000 8224.1916
## 188 Bulgaria 1987 8971958 71.34000 8239.8548
## 189 Bulgaria 1992 8658506 71.19000 6302.6234
## 190 Bulgaria 1997 8066057 70.32000 5970.3888
## 191 Bulgaria 2002 7661799 72.14000 7696.7777
## 192 Bulgaria 2007 7322858 73.00500 10680.7928
## 193 Burkina Faso 1952 4469979 31.97500 543.2552
## 194 Burkina Faso 1957 4713416 34.90600 617.1835
## 195 Burkina Faso 1962 4919632 37.81400 722.5120
## 196 Burkina Faso 1967 5127935 40.69700 794.8266
## 197 Burkina Faso 1972 5433886 43.59100 854.7360
## 198 Burkina Faso 1977 5889574 46.13700 743.3870
## 199 Burkina Faso 1982 6634596 48.12200 807.1986
## 200 Burkina Faso 1987 7586551 49.55700 912.0631
## 201 Burkina Faso 1992 8878303 50.26000 931.7528
## 202 Burkina Faso 1997 10352843 50.32400 946.2950
## 203 Burkina Faso 2002 12251209 50.65000 1037.6452
## 204 Burkina Faso 2007 14326203 52.29500 1217.0330
## 205 Burundi 1952 2445618 39.03100 339.2965
## 206 Burundi 1957 2667518 40.53300 379.5646
## 207 Burundi 1962 2961915 42.04500 355.2032
## 208 Burundi 1967 3330989 43.54800 412.9775
## 209 Burundi 1972 3529983 44.05700 464.0995
## 210 Burundi 1977 3834415 45.91000 556.1033
## 211 Burundi 1982 4580410 47.47100 559.6032
## 212 Burundi 1987 5126023 48.21100 621.8188
## 213 Burundi 1992 5809236 44.73600 631.6999
## 214 Burundi 1997 6121610 45.32600 463.1151
## 215 Burundi 2002 7021078 47.36000 446.4035
## 216 Burundi 2007 8390505 49.58000 430.0707
## 217 Cambodia 1952 4693836 39.41700 368.4693
## 218 Cambodia 1957 5322536 41.36600 434.0383
## 219 Cambodia 1962 6083619 43.41500 496.9136
## 220 Cambodia 1967 6960067 45.41500 523.4323
## 221 Cambodia 1972 7450606 40.31700 421.6240
## 222 Cambodia 1977 6978607 31.22000 524.9722
## 223 Cambodia 1982 7272485 50.95700 624.4755
## 224 Cambodia 1987 8371791 53.91400 683.8956
## 225 Cambodia 1992 10150094 55.80300 682.3032
## 226 Cambodia 1997 11782962 56.53400 734.2852
## 227 Cambodia 2002 12926707 56.75200 896.2260
## 228 Cambodia 2007 14131858 59.72300 1713.7787
## 229 Cameroon 1952 5009067 38.52300 1172.6677
## 230 Cameroon 1957 5359923 40.42800 1313.0481
## 231 Cameroon 1962 5793633 42.64300 1399.6074
## 232 Cameroon 1967 6335506 44.79900 1508.4531
## 233 Cameroon 1972 7021028 47.04900 1684.1465
## 234 Cameroon 1977 7959865 49.35500 1783.4329
## 235 Cameroon 1982 9250831 52.96100 2367.9833
## 236 Cameroon 1987 10780667 54.98500 2602.6642
## 237 Cameroon 1992 12467171 54.31400 1793.1633
## 238 Cameroon 1997 14195809 52.19900 1694.3375
## 239 Cameroon 2002 15929988 49.85600 1934.0114
## 240 Cameroon 2007 17696293 50.43000 2042.0952
## 241 Canada 1952 14785584 68.75000 11367.1611
## 242 Canada 1957 17010154 69.96000 12489.9501
## 243 Canada 1962 18985849 71.30000 13462.4855
## 244 Canada 1967 20819767 72.13000 16076.5880
## 245 Canada 1972 22284500 72.88000 18970.5709
## 246 Canada 1977 23796400 74.21000 22090.8831
## 247 Canada 1982 25201900 75.76000 22898.7921
## 248 Canada 1987 26549700 76.86000 26626.5150
## 249 Canada 1992 28523502 77.95000 26342.8843
## 250 Canada 1997 30305843 78.61000 28954.9259
## 251 Canada 2002 31902268 79.77000 33328.9651
## 252 Canada 2007 33390141 80.65300 36319.2350
## 253 Central African Republic 1952 1291695 35.46300 1071.3107
## 254 Central African Republic 1957 1392284 37.46400 1190.8443
## 255 Central African Republic 1962 1523478 39.47500 1193.0688
## 256 Central African Republic 1967 1733638 41.47800 1136.0566
## 257 Central African Republic 1972 1927260 43.45700 1070.0133
## 258 Central African Republic 1977 2167533 46.77500 1109.3743
## 259 Central African Republic 1982 2476971 48.29500 956.7530
## 260 Central African Republic 1987 2840009 50.48500 844.8764
## 261 Central African Republic 1992 3265124 49.39600 747.9055
## 262 Central African Republic 1997 3696513 46.06600 740.5063
## 263 Central African Republic 2002 4048013 43.30800 738.6906
## 264 Central African Republic 2007 4369038 44.74100 706.0165
## 265 Chad 1952 2682462 38.09200 1178.6659
## 266 Chad 1957 2894855 39.88100 1308.4956
## 267 Chad 1962 3150417 41.71600 1389.8176
## 268 Chad 1967 3495967 43.60100 1196.8106
## 269 Chad 1972 3899068 45.56900 1104.1040
## 270 Chad 1977 4388260 47.38300 1133.9850
## 271 Chad 1982 4875118 49.51700 797.9081
## 272 Chad 1987 5498955 51.05100 952.3861
## 273 Chad 1992 6429417 51.72400 1058.0643
## 274 Chad 1997 7562011 51.57300 1004.9614
## 275 Chad 2002 8835739 50.52500 1156.1819
## 276 Chad 2007 10238807 50.65100 1704.0637
## 277 Chile 1952 6377619 54.74500 3939.9788
## 278 Chile 1957 7048426 56.07400 4315.6227
## 279 Chile 1962 7961258 57.92400 4519.0943
## 280 Chile 1967 8858908 60.52300 5106.6543
## 281 Chile 1972 9717524 63.44100 5494.0244
## 282 Chile 1977 10599793 67.05200 4756.7638
## 283 Chile 1982 11487112 70.56500 5095.6657
## 284 Chile 1987 12463354 72.49200 5547.0638
## 285 Chile 1992 13572994 74.12600 7596.1260
## 286 Chile 1997 14599929 75.81600 10118.0532
## 287 Chile 2002 15497046 77.86000 10778.7838
## 288 Chile 2007 16284741 78.55300 13171.6388
## 289 China 1952 556263528 44.00000 400.4486
## 290 China 1957 637408000 50.54896 575.9870
## 291 China 1962 665770000 44.50136 487.6740
## 292 China 1967 754550000 58.38112 612.7057
## 293 China 1972 862030000 63.11888 676.9001
## 294 China 1977 943455000 63.96736 741.2375
## 295 China 1982 1000281000 65.52500 962.4214
## 296 China 1987 1084035000 67.27400 1378.9040
## 297 China 1992 1164970000 68.69000 1655.7842
## 298 China 1997 1230075000 70.42600 2289.2341
## 299 China 2002 1280400000 72.02800 3119.2809
## 300 China 2007 1318683096 72.96100 4959.1149
## 301 Colombia 1952 12350771 50.64300 2144.1151
## 302 Colombia 1957 14485993 55.11800 2323.8056
## 303 Colombia 1962 17009885 57.86300 2492.3511
## 304 Colombia 1967 19764027 59.96300 2678.7298
## 305 Colombia 1972 22542890 61.62300 3264.6600
## 306 Colombia 1977 25094412 63.83700 3815.8079
## 307 Colombia 1982 27764644 66.65300 4397.5757
## 308 Colombia 1987 30964245 67.76800 4903.2191
## 309 Colombia 1992 34202721 68.42100 5444.6486
## 310 Colombia 1997 37657830 70.31300 6117.3617
## 311 Colombia 2002 41008227 71.68200 5755.2600
## 312 Colombia 2007 44227550 72.88900 7006.5804
## 313 Comoros 1952 153936 40.71500 1102.9909
## 314 Comoros 1957 170928 42.46000 1211.1485
## 315 Comoros 1962 191689 44.46700 1406.6483
## 316 Comoros 1967 217378 46.47200 1876.0296
## 317 Comoros 1972 250027 48.94400 1937.5777
## 318 Comoros 1977 304739 50.93900 1172.6030
## 319 Comoros 1982 348643 52.93300 1267.1001
## 320 Comoros 1987 395114 54.92600 1315.9808
## 321 Comoros 1992 454429 57.93900 1246.9074
## 322 Comoros 1997 527982 60.66000 1173.6182
## 323 Comoros 2002 614382 62.97400 1075.8116
## 324 Comoros 2007 710960 65.15200 986.1479
## 325 Congo Dem. Rep. 1952 14100005 39.14300 780.5423
## 326 Congo Dem. Rep. 1957 15577932 40.65200 905.8602
## 327 Congo Dem. Rep. 1962 17486434 42.12200 896.3146
## 328 Congo Dem. Rep. 1967 19941073 44.05600 861.5932
## 329 Congo Dem. Rep. 1972 23007669 45.98900 904.8961
## 330 Congo Dem. Rep. 1977 26480870 47.80400 795.7573
## 331 Congo Dem. Rep. 1982 30646495 47.78400 673.7478
## 332 Congo Dem. Rep. 1987 35481645 47.41200 672.7748
## 333 Congo Dem. Rep. 1992 41672143 45.54800 457.7192
## 334 Congo Dem. Rep. 1997 47798986 42.58700 312.1884
## 335 Congo Dem. Rep. 2002 55379852 44.96600 241.1659
## 336 Congo Dem. Rep. 2007 64606759 46.46200 277.5519
## 337 Congo Rep. 1952 854885 42.11100 2125.6214
## 338 Congo Rep. 1957 940458 45.05300 2315.0566
## 339 Congo Rep. 1962 1047924 48.43500 2464.7832
## 340 Congo Rep. 1967 1179760 52.04000 2677.9396
## 341 Congo Rep. 1972 1340458 54.90700 3213.1527
## 342 Congo Rep. 1977 1536769 55.62500 3259.1790
## 343 Congo Rep. 1982 1774735 56.69500 4879.5075
## 344 Congo Rep. 1987 2064095 57.47000 4201.1949
## 345 Congo Rep. 1992 2409073 56.43300 4016.2395
## 346 Congo Rep. 1997 2800947 52.96200 3484.1644
## 347 Congo Rep. 2002 3328795 52.97000 3484.0620
## 348 Congo Rep. 2007 3800610 55.32200 3632.5578
## 349 Costa Rica 1952 926317 57.20600 2627.0095
## 350 Costa Rica 1957 1112300 60.02600 2990.0108
## 351 Costa Rica 1962 1345187 62.84200 3460.9370
## 352 Costa Rica 1967 1588717 65.42400 4161.7278
## 353 Costa Rica 1972 1834796 67.84900 5118.1469
## 354 Costa Rica 1977 2108457 70.75000 5926.8770
## 355 Costa Rica 1982 2424367 73.45000 5262.7348
## 356 Costa Rica 1987 2799811 74.75200 5629.9153
## 357 Costa Rica 1992 3173216 75.71300 6160.4163
## 358 Costa Rica 1997 3518107 77.26000 6677.0453
## 359 Costa Rica 2002 3834934 78.12300 7723.4472
## 360 Costa Rica 2007 4133884 78.78200 9645.0614
## 361 Cote d'Ivoire 1952 2977019 40.47700 1388.5947
## 362 Cote d'Ivoire 1957 3300000 42.46900 1500.8959
## 363 Cote d'Ivoire 1962 3832408 44.93000 1728.8694
## 364 Cote d'Ivoire 1967 4744870 47.35000 2052.0505
## 365 Cote d'Ivoire 1972 6071696 49.80100 2378.2011
## 366 Cote d'Ivoire 1977 7459574 52.37400 2517.7365
## 367 Cote d'Ivoire 1982 9025951 53.98300 2602.7102
## 368 Cote d'Ivoire 1987 10761098 54.65500 2156.9561
## 369 Cote d'Ivoire 1992 12772596 52.04400 1648.0738
## 370 Cote d'Ivoire 1997 14625967 47.99100 1786.2654
## 371 Cote d'Ivoire 2002 16252726 46.83200 1648.8008
## 372 Cote d'Ivoire 2007 18013409 48.32800 1544.7501
## 373 Croatia 1952 3882229 61.21000 3119.2365
## 374 Croatia 1957 3991242 64.77000 4338.2316
## 375 Croatia 1962 4076557 67.13000 5477.8900
## 376 Croatia 1967 4174366 68.50000 6960.2979
## 377 Croatia 1972 4225310 69.61000 9164.0901
## 378 Croatia 1977 4318673 70.64000 11305.3852
## 379 Croatia 1982 4413368 70.46000 13221.8218
## 380 Croatia 1987 4484310 71.52000 13822.5839
## 381 Croatia 1992 4494013 72.52700 8447.7949
## 382 Croatia 1997 4444595 73.68000 9875.6045
## 383 Croatia 2002 4481020 74.87600 11628.3890
## 384 Croatia 2007 4493312 75.74800 14619.2227
## 385 Cuba 1952 6007797 59.42100 5586.5388
## 386 Cuba 1957 6640752 62.32500 6092.1744
## 387 Cuba 1962 7254373 65.24600 5180.7559
## 388 Cuba 1967 8139332 68.29000 5690.2680
## 389 Cuba 1972 8831348 70.72300 5305.4453
## 390 Cuba 1977 9537988 72.64900 6380.4950
## 391 Cuba 1982 9789224 73.71700 7316.9181
## 392 Cuba 1987 10239839 74.17400 7532.9248
## 393 Cuba 1992 10723260 74.41400 5592.8440
## 394 Cuba 1997 10983007 76.15100 5431.9904
## 395 Cuba 2002 11226999 77.15800 6340.6467
## 396 Cuba 2007 11416987 78.27300 8948.1029
## 397 Czech Republic 1952 9125183 66.87000 6876.1403
## 398 Czech Republic 1957 9513758 69.03000 8256.3439
## 399 Czech Republic 1962 9620282 69.90000 10136.8671
## 400 Czech Republic 1967 9835109 70.38000 11399.4449
## 401 Czech Republic 1972 9862158 70.29000 13108.4536
## 402 Czech Republic 1977 10161915 70.71000 14800.1606
## 403 Czech Republic 1982 10303704 70.96000 15377.2285
## 404 Czech Republic 1987 10311597 71.58000 16310.4434
## 405 Czech Republic 1992 10315702 72.40000 14297.0212
## 406 Czech Republic 1997 10300707 74.01000 16048.5142
## 407 Czech Republic 2002 10256295 75.51000 17596.2102
## 408 Czech Republic 2007 10228744 76.48600 22833.3085
## 409 Denmark 1952 4334000 70.78000 9692.3852
## 410 Denmark 1957 4487831 71.81000 11099.6593
## 411 Denmark 1962 4646899 72.35000 13583.3135
## 412 Denmark 1967 4838800 72.96000 15937.2112
## 413 Denmark 1972 4991596 73.47000 18866.2072
## 414 Denmark 1977 5088419 74.69000 20422.9015
## 415 Denmark 1982 5117810 74.63000 21688.0405
## 416 Denmark 1987 5127024 74.80000 25116.1758
## 417 Denmark 1992 5171393 75.33000 26406.7399
## 418 Denmark 1997 5283663 76.11000 29804.3457
## 419 Denmark 2002 5374693 77.18000 32166.5001
## 420 Denmark 2007 5468120 78.33200 35278.4187
## 421 Djibouti 1952 63149 34.81200 2669.5295
## 422 Djibouti 1957 71851 37.32800 2864.9691
## 423 Djibouti 1962 89898 39.69300 3020.9893
## 424 Djibouti 1967 127617 42.07400 3020.0505
## 425 Djibouti 1972 178848 44.36600 3694.2124
## 426 Djibouti 1977 228694 46.51900 3081.7610
## 427 Djibouti 1982 305991 48.81200 2879.4681
## 428 Djibouti 1987 311025 50.04000 2880.1026
## 429 Djibouti 1992 384156 51.60400 2377.1562
## 430 Djibouti 1997 417908 53.15700 1895.0170
## 431 Djibouti 2002 447416 53.37300 1908.2609
## 432 Djibouti 2007 496374 54.79100 2082.4816
## 433 Dominican Republic 1952 2491346 45.92800 1397.7171
## 434 Dominican Republic 1957 2923186 49.82800 1544.4030
## 435 Dominican Republic 1962 3453434 53.45900 1662.1374
## 436 Dominican Republic 1967 4049146 56.75100 1653.7230
## 437 Dominican Republic 1972 4671329 59.63100 2189.8745
## 438 Dominican Republic 1977 5302800 61.78800 2681.9889
## 439 Dominican Republic 1982 5968349 63.72700 2861.0924
## 440 Dominican Republic 1987 6655297 66.04600 2899.8422
## 441 Dominican Republic 1992 7351181 68.45700 3044.2142
## 442 Dominican Republic 1997 7992357 69.95700 3614.1013
## 443 Dominican Republic 2002 8650322 70.84700 4563.8082
## 444 Dominican Republic 2007 9319622 72.23500 6025.3748
## 445 Ecuador 1952 3548753 48.35700 3522.1107
## 446 Ecuador 1957 4058385 51.35600 3780.5467
## 447 Ecuador 1962 4681707 54.64000 4086.1141
## 448 Ecuador 1967 5432424 56.67800 4579.0742
## 449 Ecuador 1972 6298651 58.79600 5280.9947
## 450 Ecuador 1977 7278866 61.31000 6679.6233
## 451 Ecuador 1982 8365850 64.34200 7213.7913
## 452 Ecuador 1987 9545158 67.23100 6481.7770
## 453 Ecuador 1992 10748394 69.61300 7103.7026
## 454 Ecuador 1997 11911819 72.31200 7429.4559
## 455 Ecuador 2002 12921234 74.17300 5773.0445
## 456 Ecuador 2007 13755680 74.99400 6873.2623
## 457 Egypt 1952 22223309 41.89300 1418.8224
## 458 Egypt 1957 25009741 44.44400 1458.9153
## 459 Egypt 1962 28173309 46.99200 1693.3359
## 460 Egypt 1967 31681188 49.29300 1814.8807
## 461 Egypt 1972 34807417 51.13700 2024.0081
## 462 Egypt 1977 38783863 53.31900 2785.4936
## 463 Egypt 1982 45681811 56.00600 3503.7296
## 464 Egypt 1987 52799062 59.79700 3885.4607
## 465 Egypt 1992 59402198 63.67400 3794.7552
## 466 Egypt 1997 66134291 67.21700 4173.1818
## 467 Egypt 2002 73312559 69.80600 4754.6044
## 468 Egypt 2007 80264543 71.33800 5581.1810
## 469 El Salvador 1952 2042865 45.26200 3048.3029
## 470 El Salvador 1957 2355805 48.57000 3421.5232
## 471 El Salvador 1962 2747687 52.30700 3776.8036
## 472 El Salvador 1967 3232927 55.85500 4358.5954
## 473 El Salvador 1972 3790903 58.20700 4520.2460
## 474 El Salvador 1977 4282586 56.69600 5138.9224
## 475 El Salvador 1982 4474873 56.60400 4098.3442
## 476 El Salvador 1987 4842194 63.15400 4140.4421
## 477 El Salvador 1992 5274649 66.79800 4444.2317
## 478 El Salvador 1997 5783439 69.53500 5154.8255
## 479 El Salvador 2002 6353681 70.73400 5351.5687
## 480 El Salvador 2007 6939688 71.87800 5728.3535
## 481 Equatorial Guinea 1952 216964 34.48200 375.6431
## 482 Equatorial Guinea 1957 232922 35.98300 426.0964
## 483 Equatorial Guinea 1962 249220 37.48500 582.8420
## 484 Equatorial Guinea 1967 259864 38.98700 915.5960
## 485 Equatorial Guinea 1972 277603 40.51600 672.4123
## 486 Equatorial Guinea 1977 192675 42.02400 958.5668
## 487 Equatorial Guinea 1982 285483 43.66200 927.8253
## 488 Equatorial Guinea 1987 341244 45.66400 966.8968
## 489 Equatorial Guinea 1992 387838 47.54500 1132.0550
## 490 Equatorial Guinea 1997 439971 48.24500 2814.4808
## 491 Equatorial Guinea 2002 495627 49.34800 7703.4959
## 492 Equatorial Guinea 2007 551201 51.57900 12154.0897
## 493 Eritrea 1952 1438760 35.92800 328.9406
## 494 Eritrea 1957 1542611 38.04700 344.1619
## 495 Eritrea 1962 1666618 40.15800 380.9958
## 496 Eritrea 1967 1820319 42.18900 468.7950
## 497 Eritrea 1972 2260187 44.14200 514.3242
## 498 Eritrea 1977 2512642 44.53500 505.7538
## 499 Eritrea 1982 2637297 43.89000 524.8758
## 500 Eritrea 1987 2915959 46.45300 521.1341
## 501 Eritrea 1992 3668440 49.99100 582.8585
## 502 Eritrea 1997 4058319 53.37800 913.4708
## 503 Eritrea 2002 4414865 55.24000 765.3500
## 504 Eritrea 2007 4906585 58.04000 641.3695
## 505 Ethiopia 1952 20860941 34.07800 362.1463
## 506 Ethiopia 1957 22815614 36.66700 378.9042
## 507 Ethiopia 1962 25145372 40.05900 419.4564
## 508 Ethiopia 1967 27860297 42.11500 516.1186
## 509 Ethiopia 1972 30770372 43.51500 566.2439
## 510 Ethiopia 1977 34617799 44.51000 556.8084
## 511 Ethiopia 1982 38111756 44.91600 577.8607
## 512 Ethiopia 1987 42999530 46.68400 573.7413
## 513 Ethiopia 1992 52088559 48.09100 421.3535
## 514 Ethiopia 1997 59861301 49.40200 515.8894
## 515 Ethiopia 2002 67946797 50.72500 530.0535
## 516 Ethiopia 2007 76511887 52.94700 690.8056
## 517 Finland 1952 4090500 66.55000 6424.5191
## 518 Finland 1957 4324000 67.49000 7545.4154
## 519 Finland 1962 4491443 68.75000 9371.8426
## 520 Finland 1967 4605744 69.83000 10921.6363
## 521 Finland 1972 4639657 70.87000 14358.8759
## 522 Finland 1977 4738902 72.52000 15605.4228
## 523 Finland 1982 4826933 74.55000 18533.1576
## 524 Finland 1987 4931729 74.83000 21141.0122
## 525 Finland 1992 5041039 75.70000 20647.1650
## 526 Finland 1997 5134406 77.13000 23723.9502
## 527 Finland 2002 5193039 78.37000 28204.5906
## 528 Finland 2007 5238460 79.31300 33207.0844
## 529 France 1952 42459667 67.41000 7029.8093
## 530 France 1957 44310863 68.93000 8662.8349
## 531 France 1962 47124000 70.51000 10560.4855
## 532 France 1967 49569000 71.55000 12999.9177
## 533 France 1972 51732000 72.38000 16107.1917
## 534 France 1977 53165019 73.83000 18292.6351
## 535 France 1982 54433565 74.89000 20293.8975
## 536 France 1987 55630100 76.34000 22066.4421
## 537 France 1992 57374179 77.46000 24703.7961
## 538 France 1997 58623428 78.64000 25889.7849
## 539 France 2002 59925035 79.59000 28926.0323
## 540 France 2007 61083916 80.65700 30470.0167
## 541 Gabon 1952 420702 37.00300 4293.4765
## 542 Gabon 1957 434904 38.99900 4976.1981
## 543 Gabon 1962 455661 40.48900 6631.4592
## 544 Gabon 1967 489004 44.59800 8358.7620
## 545 Gabon 1972 537977 48.69000 11401.9484
## 546 Gabon 1977 706367 52.79000 21745.5733
## 547 Gabon 1982 753874 56.56400 15113.3619
## 548 Gabon 1987 880397 60.19000 11864.4084
## 549 Gabon 1992 985739 61.36600 13522.1575
## 550 Gabon 1997 1126189 60.46100 14722.8419
## 551 Gabon 2002 1299304 56.76100 12521.7139
## 552 Gabon 2007 1454867 56.73500 13206.4845
## 553 Gambia 1952 284320 30.00000 485.2307
## 554 Gambia 1957 323150 32.06500 520.9267
## 555 Gambia 1962 374020 33.89600 599.6503
## 556 Gambia 1967 439593 35.85700 734.7829
## 557 Gambia 1972 517101 38.30800 756.0868
## 558 Gambia 1977 608274 41.84200 884.7553
## 559 Gambia 1982 715523 45.58000 835.8096
## 560 Gambia 1987 848406 49.26500 611.6589
## 561 Gambia 1992 1025384 52.64400 665.6244
## 562 Gambia 1997 1235767 55.86100 653.7302
## 563 Gambia 2002 1457766 58.04100 660.5856
## 564 Gambia 2007 1688359 59.44800 752.7497
## 565 Germany 1952 69145952 67.50000 7144.1144
## 566 Germany 1957 71019069 69.10000 10187.8267
## 567 Germany 1962 73739117 70.30000 12902.4629
## 568 Germany 1967 76368453 70.80000 14745.6256
## 569 Germany 1972 78717088 71.00000 18016.1803
## 570 Germany 1977 78160773 72.50000 20512.9212
## 571 Germany 1982 78335266 73.80000 22031.5327
## 572 Germany 1987 77718298 74.84700 24639.1857
## 573 Germany 1992 80597764 76.07000 26505.3032
## 574 Germany 1997 82011073 77.34000 27788.8842
## 575 Germany 2002 82350671 78.67000 30035.8020
## 576 Germany 2007 82400996 79.40600 32170.3744
## 577 Ghana 1952 5581001 43.14900 911.2989
## 578 Ghana 1957 6391288 44.77900 1043.5615
## 579 Ghana 1962 7355248 46.45200 1190.0411
## 580 Ghana 1967 8490213 48.07200 1125.6972
## 581 Ghana 1972 9354120 49.87500 1178.2237
## 582 Ghana 1977 10538093 51.75600 993.2240
## 583 Ghana 1982 11400338 53.74400 876.0326
## 584 Ghana 1987 14168101 55.72900 847.0061
## 585 Ghana 1992 16278738 57.50100 925.0602
## 586 Ghana 1997 18418288 58.55600 1005.2458
## 587 Ghana 2002 20550751 58.45300 1111.9846
## 588 Ghana 2007 22873338 60.02200 1327.6089
## 589 Greece 1952 7733250 65.86000 3530.6901
## 590 Greece 1957 8096218 67.86000 4916.2999
## 591 Greece 1962 8448233 69.51000 6017.1907
## 592 Greece 1967 8716441 71.00000 8513.0970
## 593 Greece 1972 8888628 72.34000 12724.8296
## 594 Greece 1977 9308479 73.68000 14195.5243
## 595 Greece 1982 9786480 75.24000 15268.4209
## 596 Greece 1987 9974490 76.67000 16120.5284
## 597 Greece 1992 10325429 77.03000 17541.4963
## 598 Greece 1997 10502372 77.86900 18747.6981
## 599 Greece 2002 10603863 78.25600 22514.2548
## 600 Greece 2007 10706290 79.48300 27538.4119
## 601 Guatemala 1952 3146381 42.02300 2428.2378
## 602 Guatemala 1957 3640876 44.14200 2617.1560
## 603 Guatemala 1962 4208858 46.95400 2750.3644
## 604 Guatemala 1967 4690773 50.01600 3242.5311
## 605 Guatemala 1972 5149581 53.73800 4031.4083
## 606 Guatemala 1977 5703430 56.02900 4879.9927
## 607 Guatemala 1982 6395630 58.13700 4820.4948
## 608 Guatemala 1987 7326406 60.78200 4246.4860
## 609 Guatemala 1992 8486949 63.37300 4439.4508
## 610 Guatemala 1997 9803875 66.32200 4684.3138
## 611 Guatemala 2002 11178650 68.97800 4858.3475
## 612 Guatemala 2007 12572928 70.25900 5186.0500
## 613 Guinea 1952 2664249 33.60900 510.1965
## 614 Guinea 1957 2876726 34.55800 576.2670
## 615 Guinea 1962 3140003 35.75300 686.3737
## 616 Guinea 1967 3451418 37.19700 708.7595
## 617 Guinea 1972 3811387 38.84200 741.6662
## 618 Guinea 1977 4227026 40.76200 874.6859
## 619 Guinea 1982 4710497 42.89100 857.2504
## 620 Guinea 1987 5650262 45.55200 805.5725
## 621 Guinea 1992 6990574 48.57600 794.3484
## 622 Guinea 1997 8048834 51.45500 869.4498
## 623 Guinea 2002 8807818 53.67600 945.5836
## 624 Guinea 2007 9947814 56.00700 942.6542
## 625 Guinea-Bissau 1952 580653 32.50000 299.8503
## 626 Guinea-Bissau 1957 601095 33.48900 431.7905
## 627 Guinea-Bissau 1962 627820 34.48800 522.0344
## 628 Guinea-Bissau 1967 601287 35.49200 715.5806
## 629 Guinea-Bissau 1972 625361 36.48600 820.2246
## 630 Guinea-Bissau 1977 745228 37.46500 764.7260
## 631 Guinea-Bissau 1982 825987 39.32700 838.1240
## 632 Guinea-Bissau 1987 927524 41.24500 736.4154
## 633 Guinea-Bissau 1992 1050938 43.26600 745.5399
## 634 Guinea-Bissau 1997 1193708 44.87300 796.6645
## 635 Guinea-Bissau 2002 1332459 45.50400 575.7047
## 636 Guinea-Bissau 2007 1472041 46.38800 579.2317
## 637 Haiti 1952 3201488 37.57900 1840.3669
## 638 Haiti 1957 3507701 40.69600 1726.8879
## 639 Haiti 1962 3880130 43.59000 1796.5890
## 640 Haiti 1967 4318137 46.24300 1452.0577
## 641 Haiti 1972 4698301 48.04200 1654.4569
## 642 Haiti 1977 4908554 49.92300 1874.2989
## 643 Haiti 1982 5198399 51.46100 2011.1595
## 644 Haiti 1987 5756203 53.63600 1823.0160
## 645 Haiti 1992 6326682 55.08900 1456.3095
## 646 Haiti 1997 6913545 56.67100 1341.7269
## 647 Haiti 2002 7607651 58.13700 1270.3649
## 648 Haiti 2007 8502814 60.91600 1201.6372
## 649 Honduras 1952 1517453 41.91200 2194.9262
## 650 Honduras 1957 1770390 44.66500 2220.4877
## 651 Honduras 1962 2090162 48.04100 2291.1568
## 652 Honduras 1967 2500689 50.92400 2538.2694
## 653 Honduras 1972 2965146 53.88400 2529.8423
## 654 Honduras 1977 3055235 57.40200 3203.2081
## 655 Honduras 1982 3669448 60.90900 3121.7608
## 656 Honduras 1987 4372203 64.49200 3023.0967
## 657 Honduras 1992 5077347 66.39900 3081.6946
## 658 Honduras 1997 5867957 67.65900 3160.4549
## 659 Honduras 2002 6677328 68.56500 3099.7287
## 660 Honduras 2007 7483763 70.19800 3548.3308
## 661 Hong Kong China 1952 2125900 60.96000 3054.4212
## 662 Hong Kong China 1957 2736300 64.75000 3629.0765
## 663 Hong Kong China 1962 3305200 67.65000 4692.6483
## 664 Hong Kong China 1967 3722800 70.00000 6197.9628
## 665 Hong Kong China 1972 4115700 72.00000 8315.9281
## 666 Hong Kong China 1977 4583700 73.60000 11186.1413
## 667 Hong Kong China 1982 5264500 75.45000 14560.5305
## 668 Hong Kong China 1987 5584510 76.20000 20038.4727
## 669 Hong Kong China 1992 5829696 77.60100 24757.6030
## 670 Hong Kong China 1997 6495918 80.00000 28377.6322
## 671 Hong Kong China 2002 6762476 81.49500 30209.0152
## 672 Hong Kong China 2007 6980412 82.20800 39724.9787
## 673 Hungary 1952 9504000 64.03000 5263.6738
## 674 Hungary 1957 9839000 66.41000 6040.1800
## 675 Hungary 1962 10063000 67.96000 7550.3599
## 676 Hungary 1967 10223422 69.50000 9326.6447
## 677 Hungary 1972 10394091 69.76000 10168.6561
## 678 Hungary 1977 10637171 69.95000 11674.8374
## 679 Hungary 1982 10705535 69.39000 12545.9907
## 680 Hungary 1987 10612740 69.58000 12986.4800
## 681 Hungary 1992 10348684 69.17000 10535.6285
## 682 Hungary 1997 10244684 71.04000 11712.7768
## 683 Hungary 2002 10083313 72.59000 14843.9356
## 684 Hungary 2007 9956108 73.33800 18008.9444
## 685 Iceland 1952 147962 72.49000 7267.6884
## 686 Iceland 1957 165110 73.47000 9244.0014
## 687 Iceland 1962 182053 73.68000 10350.1591
## 688 Iceland 1967 198676 73.73000 13319.8957
## 689 Iceland 1972 209275 74.46000 15798.0636
## 690 Iceland 1977 221823 76.11000 19654.9625
## 691 Iceland 1982 233997 76.99000 23269.6075
## 692 Iceland 1987 244676 77.23000 26923.2063
## 693 Iceland 1992 259012 78.77000 25144.3920
## 694 Iceland 1997 271192 78.95000 28061.0997
## 695 Iceland 2002 288030 80.50000 31163.2020
## 696 Iceland 2007 301931 81.75700 36180.7892
## 697 India 1952 372000000 37.37300 546.5657
## 698 India 1957 409000000 40.24900 590.0620
## 699 India 1962 454000000 43.60500 658.3472
## 700 India 1967 506000000 47.19300 700.7706
## 701 India 1972 567000000 50.65100 724.0325
## 702 India 1977 634000000 54.20800 813.3373
## 703 India 1982 708000000 56.59600 855.7235
## 704 India 1987 788000000 58.55300 976.5127
## 705 India 1992 872000000 60.22300 1164.4068
## 706 India 1997 959000000 61.76500 1458.8174
## 707 India 2002 1034172547 62.87900 1746.7695
## 708 India 2007 1110396331 64.69800 2452.2104
## 709 Indonesia 1952 82052000 37.46800 749.6817
## 710 Indonesia 1957 90124000 39.91800 858.9003
## 711 Indonesia 1962 99028000 42.51800 849.2898
## 712 Indonesia 1967 109343000 45.96400 762.4318
## 713 Indonesia 1972 121282000 49.20300 1111.1079
## 714 Indonesia 1977 136725000 52.70200 1382.7021
## 715 Indonesia 1982 153343000 56.15900 1516.8730
## 716 Indonesia 1987 169276000 60.13700 1748.3570
## 717 Indonesia 1992 184816000 62.68100 2383.1409
## 718 Indonesia 1997 199278000 66.04100 3119.3356
## 719 Indonesia 2002 211060000 68.58800 2873.9129
## 720 Indonesia 2007 223547000 70.65000 3540.6516
## 721 Iran 1952 17272000 44.86900 3035.3260
## 722 Iran 1957 19792000 47.18100 3290.2576
## 723 Iran 1962 22874000 49.32500 4187.3298
## 724 Iran 1967 26538000 52.46900 5906.7318
## 725 Iran 1972 30614000 55.23400 9613.8186
## 726 Iran 1977 35480679 57.70200 11888.5951
## 727 Iran 1982 43072751 59.62000 7608.3346
## 728 Iran 1987 51889696 63.04000 6642.8814
## 729 Iran 1992 60397973 65.74200 7235.6532
## 730 Iran 1997 63327987 68.04200 8263.5903
## 731 Iran 2002 66907826 69.45100 9240.7620
## 732 Iran 2007 69453570 70.96400 11605.7145
## 733 Iraq 1952 5441766 45.32000 4129.7661
## 734 Iraq 1957 6248643 48.43700 6229.3336
## 735 Iraq 1962 7240260 51.45700 8341.7378
## 736 Iraq 1967 8519282 54.45900 8931.4598
## 737 Iraq 1972 10061506 56.95000 9576.0376
## 738 Iraq 1977 11882916 60.41300 14688.2351
## 739 Iraq 1982 14173318 62.03800 14517.9071
## 740 Iraq 1987 16543189 65.04400 11643.5727
## 741 Iraq 1992 17861905 59.46100 3745.6407
## 742 Iraq 1997 20775703 58.81100 3076.2398
## 743 Iraq 2002 24001816 57.04600 4390.7173
## 744 Iraq 2007 27499638 59.54500 4471.0619
## 745 Ireland 1952 2952156 66.91000 5210.2803
## 746 Ireland 1957 2878220 68.90000 5599.0779
## 747 Ireland 1962 2830000 70.29000 6631.5973
## 748 Ireland 1967 2900100 71.08000 7655.5690
## 749 Ireland 1972 3024400 71.28000 9530.7729
## 750 Ireland 1977 3271900 72.03000 11150.9811
## 751 Ireland 1982 3480000 73.10000 12618.3214
## 752 Ireland 1987 3539900 74.36000 13872.8665
## 753 Ireland 1992 3557761 75.46700 17558.8155
## 754 Ireland 1997 3667233 76.12200 24521.9471
## 755 Ireland 2002 3879155 77.78300 34077.0494
## 756 Ireland 2007 4109086 78.88500 40675.9964
## 757 Israel 1952 1620914 65.39000 4086.5221
## 758 Israel 1957 1944401 67.84000 5385.2785
## 759 Israel 1962 2310904 69.39000 7105.6307
## 760 Israel 1967 2693585 70.75000 8393.7414
## 761 Israel 1972 3095893 71.63000 12786.9322
## 762 Israel 1977 3495918 73.06000 13306.6192
## 763 Israel 1982 3858421 74.45000 15367.0292
## 764 Israel 1987 4203148 75.60000 17122.4799
## 765 Israel 1992 4936550 76.93000 18051.5225
## 766 Israel 1997 5531387 78.26900 20896.6092
## 767 Israel 2002 6029529 79.69600 21905.5951
## 768 Israel 2007 6426679 80.74500 25523.2771
## 769 Italy 1952 47666000 65.94000 4931.4042
## 770 Italy 1957 49182000 67.81000 6248.6562
## 771 Italy 1962 50843200 69.24000 8243.5823
## 772 Italy 1967 52667100 71.06000 10022.4013
## 773 Italy 1972 54365564 72.19000 12269.2738
## 774 Italy 1977 56059245 73.48000 14255.9847
## 775 Italy 1982 56535636 74.98000 16537.4835
## 776 Italy 1987 56729703 76.42000 19207.2348
## 777 Italy 1992 56840847 77.44000 22013.6449
## 778 Italy 1997 57479469 78.82000 24675.0245
## 779 Italy 2002 57926999 80.24000 27968.0982
## 780 Italy 2007 58147733 80.54600 28569.7197
## 781 Jamaica 1952 1426095 58.53000 2898.5309
## 782 Jamaica 1957 1535090 62.61000 4756.5258
## 783 Jamaica 1962 1665128 65.61000 5246.1075
## 784 Jamaica 1967 1861096 67.51000 6124.7035
## 785 Jamaica 1972 1997616 69.00000 7433.8893
## 786 Jamaica 1977 2156814 70.11000 6650.1956
## 787 Jamaica 1982 2298309 71.21000 6068.0513
## 788 Jamaica 1987 2326606 71.77000 6351.2375
## 789 Jamaica 1992 2378618 71.76600 7404.9237
## 790 Jamaica 1997 2531311 72.26200 7121.9247
## 791 Jamaica 2002 2664659 72.04700 6994.7749
## 792 Jamaica 2007 2780132 72.56700 7320.8803
## 793 Japan 1952 86459025 63.03000 3216.9563
## 794 Japan 1957 91563009 65.50000 4317.6944
## 795 Japan 1962 95831757 68.73000 6576.6495
## 796 Japan 1967 100825279 71.43000 9847.7886
## 797 Japan 1972 107188273 73.42000 14778.7864
## 798 Japan 1977 113872473 75.38000 16610.3770
## 799 Japan 1982 118454974 77.11000 19384.1057
## 800 Japan 1987 122091325 78.67000 22375.9419
## 801 Japan 1992 124329269 79.36000 26824.8951
## 802 Japan 1997 125956499 80.69000 28816.5850
## 803 Japan 2002 127065841 82.00000 28604.5919
## 804 Japan 2007 127467972 82.60300 31656.0681
## 805 Jordan 1952 607914 43.15800 1546.9078
## 806 Jordan 1957 746559 45.66900 1886.0806
## 807 Jordan 1962 933559 48.12600 2348.0092
## 808 Jordan 1967 1255058 51.62900 2741.7963
## 809 Jordan 1972 1613551 56.52800 2110.8563
## 810 Jordan 1977 1937652 61.13400 2852.3516
## 811 Jordan 1982 2347031 63.73900 4161.4160
## 812 Jordan 1987 2820042 65.86900 4448.6799
## 813 Jordan 1992 3867409 68.01500 3431.5936
## 814 Jordan 1997 4526235 69.77200 3645.3796
## 815 Jordan 2002 5307470 71.26300 3844.9172
## 816 Jordan 2007 6053193 72.53500 4519.4612
## 817 Kenya 1952 6464046 42.27000 853.5409
## 818 Kenya 1957 7454779 44.68600 944.4383
## 819 Kenya 1962 8678557 47.94900 896.9664
## 820 Kenya 1967 10191512 50.65400 1056.7365
## 821 Kenya 1972 12044785 53.55900 1222.3600
## 822 Kenya 1977 14500404 56.15500 1267.6132
## 823 Kenya 1982 17661452 58.76600 1348.2258
## 824 Kenya 1987 21198082 59.33900 1361.9369
## 825 Kenya 1992 25020539 59.28500 1341.9217
## 826 Kenya 1997 28263827 54.40700 1360.4850
## 827 Kenya 2002 31386842 50.99200 1287.5147
## 828 Kenya 2007 35610177 54.11000 1463.2493
## 829 Korea Dem. Rep. 1952 8865488 50.05600 1088.2778
## 830 Korea Dem. Rep. 1957 9411381 54.08100 1571.1347
## 831 Korea Dem. Rep. 1962 10917494 56.65600 1621.6936
## 832 Korea Dem. Rep. 1967 12617009 59.94200 2143.5406
## 833 Korea Dem. Rep. 1972 14781241 63.98300 3701.6215
## 834 Korea Dem. Rep. 1977 16325320 67.15900 4106.3012
## 835 Korea Dem. Rep. 1982 17647518 69.10000 4106.5253
## 836 Korea Dem. Rep. 1987 19067554 70.64700 4106.4923
## 837 Korea Dem. Rep. 1992 20711375 69.97800 3726.0635
## 838 Korea Dem. Rep. 1997 21585105 67.72700 1690.7568
## 839 Korea Dem. Rep. 2002 22215365 66.66200 1646.7582
## 840 Korea Dem. Rep. 2007 23301725 67.29700 1593.0655
## 841 Korea Rep. 1952 20947571 47.45300 1030.5922
## 842 Korea Rep. 1957 22611552 52.68100 1487.5935
## 843 Korea Rep. 1962 26420307 55.29200 1536.3444
## 844 Korea Rep. 1967 30131000 57.71600 2029.2281
## 845 Korea Rep. 1972 33505000 62.61200 3030.8767
## 846 Korea Rep. 1977 36436000 64.76600 4657.2210
## 847 Korea Rep. 1982 39326000 67.12300 5622.9425
## 848 Korea Rep. 1987 41622000 69.81000 8533.0888
## 849 Korea Rep. 1992 43805450 72.24400 12104.2787
## 850 Korea Rep. 1997 46173816 74.64700 15993.5280
## 851 Korea Rep. 2002 47969150 77.04500 19233.9882
## 852 Korea Rep. 2007 49044790 78.62300 23348.1397
## 853 Kuwait 1952 160000 55.56500 108382.3529
## 854 Kuwait 1957 212846 58.03300 113523.1329
## 855 Kuwait 1962 358266 60.47000 95458.1118
## 856 Kuwait 1967 575003 64.62400 80894.8833
## 857 Kuwait 1972 841934 67.71200 109347.8670
## 858 Kuwait 1977 1140357 69.34300 59265.4771
## 859 Kuwait 1982 1497494 71.30900 31354.0357
## 860 Kuwait 1987 1891487 74.17400 28118.4300
## 861 Kuwait 1992 1418095 75.19000 34932.9196
## 862 Kuwait 1997 1765345 76.15600 40300.6200
## 863 Kuwait 2002 2111561 76.90400 35110.1057
## 864 Kuwait 2007 2505559 77.58800 47306.9898
## 865 Lebanon 1952 1439529 55.92800 4834.8041
## 866 Lebanon 1957 1647412 59.48900 6089.7869
## 867 Lebanon 1962 1886848 62.09400 5714.5606
## 868 Lebanon 1967 2186894 63.87000 6006.9830
## 869 Lebanon 1972 2680018 65.42100 7486.3843
## 870 Lebanon 1977 3115787 66.09900 8659.6968
## 871 Lebanon 1982 3086876 66.98300 7640.5195
## 872 Lebanon 1987 3089353 67.92600 5377.0913
## 873 Lebanon 1992 3219994 69.29200 6890.8069
## 874 Lebanon 1997 3430388 70.26500 8754.9639
## 875 Lebanon 2002 3677780 71.02800 9313.9388
## 876 Lebanon 2007 3921278 71.99300 10461.0587
## 877 Lesotho 1952 748747 42.13800 298.8462
## 878 Lesotho 1957 813338 45.04700 335.9971
## 879 Lesotho 1962 893143 47.74700 411.8006
## 880 Lesotho 1967 996380 48.49200 498.6390
## 881 Lesotho 1972 1116779 49.76700 496.5816
## 882 Lesotho 1977 1251524 52.20800 745.3695
## 883 Lesotho 1982 1411807 55.07800 797.2631
## 884 Lesotho 1987 1599200 57.18000 773.9932
## 885 Lesotho 1992 1803195 59.68500 977.4863
## 886 Lesotho 1997 1982823 55.55800 1186.1480
## 887 Lesotho 2002 2046772 44.59300 1275.1846
## 888 Lesotho 2007 2012649 42.59200 1569.3314
## 889 Liberia 1952 863308 38.48000 575.5730
## 890 Liberia 1957 975950 39.48600 620.9700
## 891 Liberia 1962 1112796 40.50200 634.1952
## 892 Liberia 1967 1279406 41.53600 713.6036
## 893 Liberia 1972 1482628 42.61400 803.0055
## 894 Liberia 1977 1703617 43.76400 640.3224
## 895 Liberia 1982 1956875 44.85200 572.1996
## 896 Liberia 1987 2269414 46.02700 506.1139
## 897 Liberia 1992 1912974 40.80200 636.6229
## 898 Liberia 1997 2200725 42.22100 609.1740
## 899 Liberia 2002 2814651 43.75300 531.4824
## 900 Liberia 2007 3193942 45.67800 414.5073
## 901 Libya 1952 1019729 42.72300 2387.5481
## 902 Libya 1957 1201578 45.28900 3448.2844
## 903 Libya 1962 1441863 47.80800 6757.0308
## 904 Libya 1967 1759224 50.22700 18772.7517
## 905 Libya 1972 2183877 52.77300 21011.4972
## 906 Libya 1977 2721783 57.44200 21951.2118
## 907 Libya 1982 3344074 62.15500 17364.2754
## 908 Libya 1987 3799845 66.23400 11770.5898
## 909 Libya 1992 4364501 68.75500 9640.1385
## 910 Libya 1997 4759670 71.55500 9467.4461
## 911 Libya 2002 5368585 72.73700 9534.6775
## 912 Libya 2007 6036914 73.95200 12057.4993
## 913 Madagascar 1952 4762912 36.68100 1443.0117
## 914 Madagascar 1957 5181679 38.86500 1589.2027
## 915 Madagascar 1962 5703324 40.84800 1643.3871
## 916 Madagascar 1967 6334556 42.88100 1634.0473
## 917 Madagascar 1972 7082430 44.85100 1748.5630
## 918 Madagascar 1977 8007166 46.88100 1544.2286
## 919 Madagascar 1982 9171477 48.96900 1302.8787
## 920 Madagascar 1987 10568642 49.35000 1155.4419
## 921 Madagascar 1992 12210395 52.21400 1040.6762
## 922 Madagascar 1997 14165114 54.97800 986.2959
## 923 Madagascar 2002 16473477 57.28600 894.6371
## 924 Madagascar 2007 19167654 59.44300 1044.7701
## 925 Malawi 1952 2917802 36.25600 369.1651
## 926 Malawi 1957 3221238 37.20700 416.3698
## 927 Malawi 1962 3628608 38.41000 427.9011
## 928 Malawi 1967 4147252 39.48700 495.5148
## 929 Malawi 1972 4730997 41.76600 584.6220
## 930 Malawi 1977 5637246 43.76700 663.2237
## 931 Malawi 1982 6502825 45.64200 632.8039
## 932 Malawi 1987 7824747 47.45700 635.5174
## 933 Malawi 1992 10014249 49.42000 563.2000
## 934 Malawi 1997 10419991 47.49500 692.2758
## 935 Malawi 2002 11824495 45.00900 665.4231
## 936 Malawi 2007 13327079 48.30300 759.3499
## 937 Malaysia 1952 6748378 48.46300 1831.1329
## 938 Malaysia 1957 7739235 52.10200 1810.0670
## 939 Malaysia 1962 8906385 55.73700 2036.8849
## 940 Malaysia 1967 10154878 59.37100 2277.7424
## 941 Malaysia 1972 11441462 63.01000 2849.0948
## 942 Malaysia 1977 12845381 65.25600 3827.9216
## 943 Malaysia 1982 14441916 68.00000 4920.3560
## 944 Malaysia 1987 16331785 69.50000 5249.8027
## 945 Malaysia 1992 18319502 70.69300 7277.9128
## 946 Malaysia 1997 20476091 71.93800 10132.9096
## 947 Malaysia 2002 22662365 73.04400 10206.9779
## 948 Malaysia 2007 24821286 74.24100 12451.6558
## 949 Mali 1952 3838168 33.68500 452.3370
## 950 Mali 1957 4241884 35.30700 490.3822
## 951 Mali 1962 4690372 36.93600 496.1743
## 952 Mali 1967 5212416 38.48700 545.0099
## 953 Mali 1972 5828158 39.97700 581.3689
## 954 Mali 1977 6491649 41.71400 686.3953
## 955 Mali 1982 6998256 43.91600 618.0141
## 956 Mali 1987 7634008 46.36400 684.1716
## 957 Mali 1992 8416215 48.38800 739.0144
## 958 Mali 1997 9384984 49.90300 790.2580
## 959 Mali 2002 10580176 51.81800 951.4098
## 960 Mali 2007 12031795 54.46700 1042.5816
## 961 Mauritania 1952 1022556 40.54300 743.1159
## 962 Mauritania 1957 1076852 42.33800 846.1203
## 963 Mauritania 1962 1146757 44.24800 1055.8960
## 964 Mauritania 1967 1230542 46.28900 1421.1452
## 965 Mauritania 1972 1332786 48.43700 1586.8518
## 966 Mauritania 1977 1456688 50.85200 1497.4922
## 967 Mauritania 1982 1622136 53.59900 1481.1502
## 968 Mauritania 1987 1841240 56.14500 1421.6036
## 969 Mauritania 1992 2119465 58.33300 1361.3698
## 970 Mauritania 1997 2444741 60.43000 1483.1361
## 971 Mauritania 2002 2828858 62.24700 1579.0195
## 972 Mauritania 2007 3270065 64.16400 1803.1515
## 973 Mauritius 1952 516556 50.98600 1967.9557
## 974 Mauritius 1957 609816 58.08900 2034.0380
## 975 Mauritius 1962 701016 60.24600 2529.0675
## 976 Mauritius 1967 789309 61.55700 2475.3876
## 977 Mauritius 1972 851334 62.94400 2575.4842
## 978 Mauritius 1977 913025 64.93000 3710.9830
## 979 Mauritius 1982 992040 66.71100 3688.0377
## 980 Mauritius 1987 1042663 68.74000 4783.5869
## 981 Mauritius 1992 1096202 69.74500 6058.2538
## 982 Mauritius 1997 1149818 70.73600 7425.7053
## 983 Mauritius 2002 1200206 71.95400 9021.8159
## 984 Mauritius 2007 1250882 72.80100 10956.9911
## 985 Mexico 1952 30144317 50.78900 3478.1255
## 986 Mexico 1957 35015548 55.19000 4131.5466
## 987 Mexico 1962 41121485 58.29900 4581.6094
## 988 Mexico 1967 47995559 60.11000 5754.7339
## 989 Mexico 1972 55984294 62.36100 6809.4067
## 990 Mexico 1977 63759976 65.03200 7674.9291
## 991 Mexico 1982 71640904 67.40500 9611.1475
## 992 Mexico 1987 80122492 69.49800 8688.1560
## 993 Mexico 1992 88111030 71.45500 9472.3843
## 994 Mexico 1997 95895146 73.67000 9767.2975
## 995 Mexico 2002 102479927 74.90200 10742.4405
## 996 Mexico 2007 108700891 76.19500 11977.5750
## 997 Mongolia 1952 800663 42.24400 786.5669
## 998 Mongolia 1957 882134 45.24800 912.6626
## 999 Mongolia 1962 1010280 48.25100 1056.3540
## 1000 Mongolia 1967 1149500 51.25300 1226.0411
## 1001 Mongolia 1972 1320500 53.75400 1421.7420
## 1002 Mongolia 1977 1528000 55.49100 1647.5117
## 1003 Mongolia 1982 1756032 57.48900 2000.6031
## 1004 Mongolia 1987 2015133 60.22200 2338.0083
## 1005 Mongolia 1992 2312802 61.27100 1785.4020
## 1006 Mongolia 1997 2494803 63.62500 1902.2521
## 1007 Mongolia 2002 2674234 65.03300 2140.7393
## 1008 Mongolia 2007 2874127 66.80300 3095.7723
## 1009 Montenegro 1952 413834 59.16400 2647.5856
## 1010 Montenegro 1957 442829 61.44800 3682.2599
## 1011 Montenegro 1962 474528 63.72800 4649.5938
## 1012 Montenegro 1967 501035 67.17800 5907.8509
## 1013 Montenegro 1972 527678 70.63600 7778.4140
## 1014 Montenegro 1977 560073 73.06600 9595.9299
## 1015 Montenegro 1982 562548 74.10100 11222.5876
## 1016 Montenegro 1987 569473 74.86500 11732.5102
## 1017 Montenegro 1992 621621 75.43500 7003.3390
## 1018 Montenegro 1997 692651 75.44500 6465.6133
## 1019 Montenegro 2002 720230 73.98100 6557.1943
## 1020 Montenegro 2007 684736 74.54300 9253.8961
## 1021 Morocco 1952 9939217 42.87300 1688.2036
## 1022 Morocco 1957 11406350 45.42300 1642.0023
## 1023 Morocco 1962 13056604 47.92400 1566.3535
## 1024 Morocco 1967 14770296 50.33500 1711.0448
## 1025 Morocco 1972 16660670 52.86200 1930.1950
## 1026 Morocco 1977 18396941 55.73000 2370.6200
## 1027 Morocco 1982 20198730 59.65000 2702.6204
## 1028 Morocco 1987 22987397 62.67700 2755.0470
## 1029 Morocco 1992 25798239 65.39300 2948.0473
## 1030 Morocco 1997 28529501 67.66000 2982.1019
## 1031 Morocco 2002 31167783 69.61500 3258.4956
## 1032 Morocco 2007 33757175 71.16400 3820.1752
## 1033 Mozambique 1952 6446316 31.28600 468.5260
## 1034 Mozambique 1957 7038035 33.77900 495.5868
## 1035 Mozambique 1962 7788944 36.16100 556.6864
## 1036 Mozambique 1967 8680909 38.11300 566.6692
## 1037 Mozambique 1972 9809596 40.32800 724.9178
## 1038 Mozambique 1977 11127868 42.49500 502.3197
## 1039 Mozambique 1982 12587223 42.79500 462.2114
## 1040 Mozambique 1987 12891952 42.86100 389.8762
## 1041 Mozambique 1992 13160731 44.28400 410.8968
## 1042 Mozambique 1997 16603334 46.34400 472.3461
## 1043 Mozambique 2002 18473780 44.02600 633.6179
## 1044 Mozambique 2007 19951656 42.08200 823.6856
## 1045 Myanmar 1952 20092996 36.31900 331.0000
## 1046 Myanmar 1957 21731844 41.90500 350.0000
## 1047 Myanmar 1962 23634436 45.10800 388.0000
## 1048 Myanmar 1967 25870271 49.37900 349.0000
## 1049 Myanmar 1972 28466390 53.07000 357.0000
## 1050 Myanmar 1977 31528087 56.05900 371.0000
## 1051 Myanmar 1982 34680442 58.05600 424.0000
## 1052 Myanmar 1987 38028578 58.33900 385.0000
## 1053 Myanmar 1992 40546538 59.32000 347.0000
## 1054 Myanmar 1997 43247867 60.32800 415.0000
## 1055 Myanmar 2002 45598081 59.90800 611.0000
## 1056 Myanmar 2007 47761980 62.06900 944.0000
## 1057 Namibia 1952 485831 41.72500 2423.7804
## 1058 Namibia 1957 548080 45.22600 2621.4481
## 1059 Namibia 1962 621392 48.38600 3173.2156
## 1060 Namibia 1967 706640 51.15900 3793.6948
## 1061 Namibia 1972 821782 53.86700 3746.0809
## 1062 Namibia 1977 977026 56.43700 3876.4860
## 1063 Namibia 1982 1099010 58.96800 4191.1005
## 1064 Namibia 1987 1278184 60.83500 3693.7313
## 1065 Namibia 1992 1554253 61.99900 3804.5380
## 1066 Namibia 1997 1774766 58.90900 3899.5243
## 1067 Namibia 2002 1972153 51.47900 4072.3248
## 1068 Namibia 2007 2055080 52.90600 4811.0604
## 1069 Nepal 1952 9182536 36.15700 545.8657
## 1070 Nepal 1957 9682338 37.68600 597.9364
## 1071 Nepal 1962 10332057 39.39300 652.3969
## 1072 Nepal 1967 11261690 41.47200 676.4422
## 1073 Nepal 1972 12412593 43.97100 674.7881
## 1074 Nepal 1977 13933198 46.74800 694.1124
## 1075 Nepal 1982 15796314 49.59400 718.3731
## 1076 Nepal 1987 17917180 52.53700 775.6325
## 1077 Nepal 1992 20326209 55.72700 897.7404
## 1078 Nepal 1997 23001113 59.42600 1010.8921
## 1079 Nepal 2002 25873917 61.34000 1057.2063
## 1080 Nepal 2007 28901790 63.78500 1091.3598
## 1081 Netherlands 1952 10381988 72.13000 8941.5719
## 1082 Netherlands 1957 11026383 72.99000 11276.1934
## 1083 Netherlands 1962 11805689 73.23000 12790.8496
## 1084 Netherlands 1967 12596822 73.82000 15363.2514
## 1085 Netherlands 1972 13329874 73.75000 18794.7457
## 1086 Netherlands 1977 13852989 75.24000 21209.0592
## 1087 Netherlands 1982 14310401 76.05000 21399.4605
## 1088 Netherlands 1987 14665278 76.83000 23651.3236
## 1089 Netherlands 1992 15174244 77.42000 26790.9496
## 1090 Netherlands 1997 15604464 78.03000 30246.1306
## 1091 Netherlands 2002 16122830 78.53000 33724.7578
## 1092 Netherlands 2007 16570613 79.76200 36797.9333
## 1093 New Zealand 1952 1994794 69.39000 10556.5757
## 1094 New Zealand 1957 2229407 70.26000 12247.3953
## 1095 New Zealand 1962 2488550 71.24000 13175.6780
## 1096 New Zealand 1967 2728150 71.52000 14463.9189
## 1097 New Zealand 1972 2929100 71.89000 16046.0373
## 1098 New Zealand 1977 3164900 72.22000 16233.7177
## 1099 New Zealand 1982 3210650 73.84000 17632.4104
## 1100 New Zealand 1987 3317166 74.32000 19007.1913
## 1101 New Zealand 1992 3437674 76.33000 18363.3249
## 1102 New Zealand 1997 3676187 77.55000 21050.4138
## 1103 New Zealand 2002 3908037 79.11000 23189.8014
## 1104 New Zealand 2007 4115771 80.20400 25185.0091
## 1105 Nicaragua 1952 1165790 42.31400 3112.3639
## 1106 Nicaragua 1957 1358828 45.43200 3457.4159
## 1107 Nicaragua 1962 1590597 48.63200 3634.3644
## 1108 Nicaragua 1967 1865490 51.88400 4643.3935
## 1109 Nicaragua 1972 2182908 55.15100 4688.5933
## 1110 Nicaragua 1977 2554598 57.47000 5486.3711
## 1111 Nicaragua 1982 2979423 59.29800 3470.3382
## 1112 Nicaragua 1987 3344353 62.00800 2955.9844
## 1113 Nicaragua 1992 4017939 65.84300 2170.1517
## 1114 Nicaragua 1997 4609572 68.42600 2253.0230
## 1115 Nicaragua 2002 5146848 70.83600 2474.5488
## 1116 Nicaragua 2007 5675356 72.89900 2749.3210
## 1117 Niger 1952 3379468 37.44400 761.8794
## 1118 Niger 1957 3692184 38.59800 835.5234
## 1119 Niger 1962 4076008 39.48700 997.7661
## 1120 Niger 1967 4534062 40.11800 1054.3849
## 1121 Niger 1972 5060262 40.54600 954.2092
## 1122 Niger 1977 5682086 41.29100 808.8971
## 1123 Niger 1982 6437188 42.59800 909.7221
## 1124 Niger 1987 7332638 44.55500 668.3000
## 1125 Niger 1992 8392818 47.39100 581.1827
## 1126 Niger 1997 9666252 51.31300 580.3052
## 1127 Niger 2002 11140655 54.49600 601.0745
## 1128 Niger 2007 12894865 56.86700 619.6769
## 1129 Nigeria 1952 33119096 36.32400 1077.2819
## 1130 Nigeria 1957 37173340 37.80200 1100.5926
## 1131 Nigeria 1962 41871351 39.36000 1150.9275
## 1132 Nigeria 1967 47287752 41.04000 1014.5141
## 1133 Nigeria 1972 53740085 42.82100 1698.3888
## 1134 Nigeria 1977 62209173 44.51400 1981.9518
## 1135 Nigeria 1982 73039376 45.82600 1576.9738
## 1136 Nigeria 1987 81551520 46.88600 1385.0296
## 1137 Nigeria 1992 93364244 47.47200 1619.8482
## 1138 Nigeria 1997 106207839 47.46400 1624.9413
## 1139 Nigeria 2002 119901274 46.60800 1615.2864
## 1140 Nigeria 2007 135031164 46.85900 2013.9773
## 1141 Norway 1952 3327728 72.67000 10095.4217
## 1142 Norway 1957 3491938 73.44000 11653.9730
## 1143 Norway 1962 3638919 73.47000 13450.4015
## 1144 Norway 1967 3786019 74.08000 16361.8765
## 1145 Norway 1972 3933004 74.34000 18965.0555
## 1146 Norway 1977 4043205 75.37000 23311.3494
## 1147 Norway 1982 4114787 75.97000 26298.6353
## 1148 Norway 1987 4186147 75.89000 31540.9748
## 1149 Norway 1992 4286357 77.32000 33965.6611
## 1150 Norway 1997 4405672 78.32000 41283.1643
## 1151 Norway 2002 4535591 79.05000 44683.9753
## 1152 Norway 2007 4627926 80.19600 49357.1902
## 1153 Oman 1952 507833 37.57800 1828.2303
## 1154 Oman 1957 561977 40.08000 2242.7466
## 1155 Oman 1962 628164 43.16500 2924.6381
## 1156 Oman 1967 714775 46.98800 4720.9427
## 1157 Oman 1972 829050 52.14300 10618.0385
## 1158 Oman 1977 1004533 57.36700 11848.3439
## 1159 Oman 1982 1301048 62.72800 12954.7910
## 1160 Oman 1987 1593882 67.73400 18115.2231
## 1161 Oman 1992 1915208 71.19700 18616.7069
## 1162 Oman 1997 2283635 72.49900 19702.0558
## 1163 Oman 2002 2713462 74.19300 19774.8369
## 1164 Oman 2007 3204897 75.64000 22316.1929
## 1165 Pakistan 1952 41346560 43.43600 684.5971
## 1166 Pakistan 1957 46679944 45.55700 747.0835
## 1167 Pakistan 1962 53100671 47.67000 803.3427
## 1168 Pakistan 1967 60641899 49.80000 942.4083
## 1169 Pakistan 1972 69325921 51.92900 1049.9390
## 1170 Pakistan 1977 78152686 54.04300 1175.9212
## 1171 Pakistan 1982 91462088 56.15800 1443.4298
## 1172 Pakistan 1987 105186881 58.24500 1704.6866
## 1173 Pakistan 1992 120065004 60.83800 1971.8295
## 1174 Pakistan 1997 135564834 61.81800 2049.3505
## 1175 Pakistan 2002 153403524 63.61000 2092.7124
## 1176 Pakistan 2007 169270617 65.48300 2605.9476
## 1177 Panama 1952 940080 55.19100 2480.3803
## 1178 Panama 1957 1063506 59.20100 2961.8009
## 1179 Panama 1962 1215725 61.81700 3536.5403
## 1180 Panama 1967 1405486 64.07100 4421.0091
## 1181 Panama 1972 1616384 66.21600 5364.2497
## 1182 Panama 1977 1839782 68.68100 5351.9121
## 1183 Panama 1982 2036305 70.47200 7009.6016
## 1184 Panama 1987 2253639 71.52300 7034.7792
## 1185 Panama 1992 2484997 72.46200 6618.7431
## 1186 Panama 1997 2734531 73.73800 7113.6923
## 1187 Panama 2002 2990875 74.71200 7356.0319
## 1188 Panama 2007 3242173 75.53700 9809.1856
## 1189 Paraguay 1952 1555876 62.64900 1952.3087
## 1190 Paraguay 1957 1770902 63.19600 2046.1547
## 1191 Paraguay 1962 2009813 64.36100 2148.0271
## 1192 Paraguay 1967 2287985 64.95100 2299.3763
## 1193 Paraguay 1972 2614104 65.81500 2523.3380
## 1194 Paraguay 1977 2984494 66.35300 3248.3733
## 1195 Paraguay 1982 3366439 66.87400 4258.5036
## 1196 Paraguay 1987 3886512 67.37800 3998.8757
## 1197 Paraguay 1992 4483945 68.22500 4196.4111
## 1198 Paraguay 1997 5154123 69.40000 4247.4003
## 1199 Paraguay 2002 5884491 70.75500 3783.6742
## 1200 Paraguay 2007 6667147 71.75200 4172.8385
## 1201 Peru 1952 8025700 43.90200 3758.5234
## 1202 Peru 1957 9146100 46.26300 4245.2567
## 1203 Peru 1962 10516500 49.09600 4957.0380
## 1204 Peru 1967 12132200 51.44500 5788.0933
## 1205 Peru 1972 13954700 55.44800 5937.8273
## 1206 Peru 1977 15990099 58.44700 6281.2909
## 1207 Peru 1982 18125129 61.40600 6434.5018
## 1208 Peru 1987 20195924 64.13400 6360.9434
## 1209 Peru 1992 22430449 66.45800 4446.3809
## 1210 Peru 1997 24748122 68.38600 5838.3477
## 1211 Peru 2002 26769436 69.90600 5909.0201
## 1212 Peru 2007 28674757 71.42100 7408.9056
## 1213 Philippines 1952 22438691 47.75200 1272.8810
## 1214 Philippines 1957 26072194 51.33400 1547.9448
## 1215 Philippines 1962 30325264 54.75700 1649.5522
## 1216 Philippines 1967 35356600 56.39300 1814.1274
## 1217 Philippines 1972 40850141 58.06500 1989.3741
## 1218 Philippines 1977 46850962 60.06000 2373.2043
## 1219 Philippines 1982 53456774 62.08200 2603.2738
## 1220 Philippines 1987 60017788 64.15100 2189.6350
## 1221 Philippines 1992 67185766 66.45800 2279.3240
## 1222 Philippines 1997 75012988 68.56400 2536.5349
## 1223 Philippines 2002 82995088 70.30300 2650.9211
## 1224 Philippines 2007 91077287 71.68800 3190.4810
## 1225 Poland 1952 25730551 61.31000 4029.3297
## 1226 Poland 1957 28235346 65.77000 4734.2530
## 1227 Poland 1962 30329617 67.64000 5338.7521
## 1228 Poland 1967 31785378 69.61000 6557.1528
## 1229 Poland 1972 33039545 70.85000 8006.5070
## 1230 Poland 1977 34621254 70.67000 9508.1415
## 1231 Poland 1982 36227381 71.32000 8451.5310
## 1232 Poland 1987 37740710 70.98000 9082.3512
## 1233 Poland 1992 38370697 70.99000 7738.8812
## 1234 Poland 1997 38654957 72.75000 10159.5837
## 1235 Poland 2002 38625976 74.67000 12002.2391
## 1236 Poland 2007 38518241 75.56300 15389.9247
## 1237 Portugal 1952 8526050 59.82000 3068.3199
## 1238 Portugal 1957 8817650 61.51000 3774.5717
## 1239 Portugal 1962 9019800 64.39000 4727.9549
## 1240 Portugal 1967 9103000 66.60000 6361.5180
## 1241 Portugal 1972 8970450 69.26000 9022.2474
## 1242 Portugal 1977 9662600 70.41000 10172.4857
## 1243 Portugal 1982 9859650 72.77000 11753.8429
## 1244 Portugal 1987 9915289 74.06000 13039.3088
## 1245 Portugal 1992 9927680 74.86000 16207.2666
## 1246 Portugal 1997 10156415 75.97000 17641.0316
## 1247 Portugal 2002 10433867 77.29000 19970.9079
## 1248 Portugal 2007 10642836 78.09800 20509.6478
## 1249 Puerto Rico 1952 2227000 64.28000 3081.9598
## 1250 Puerto Rico 1957 2260000 68.54000 3907.1562
## 1251 Puerto Rico 1962 2448046 69.62000 5108.3446
## 1252 Puerto Rico 1967 2648961 71.10000 6929.2777
## 1253 Puerto Rico 1972 2847132 72.16000 9123.0417
## 1254 Puerto Rico 1977 3080828 73.44000 9770.5249
## 1255 Puerto Rico 1982 3279001 73.75000 10330.9891
## 1256 Puerto Rico 1987 3444468 74.63000 12281.3419
## 1257 Puerto Rico 1992 3585176 73.91100 14641.5871
## 1258 Puerto Rico 1997 3759430 74.91700 16999.4333
## 1259 Puerto Rico 2002 3859606 77.77800 18855.6062
## 1260 Puerto Rico 2007 3942491 78.74600 19328.7090
## 1261 Reunion 1952 257700 52.72400 2718.8853
## 1262 Reunion 1957 308700 55.09000 2769.4518
## 1263 Reunion 1962 358900 57.66600 3173.7233
## 1264 Reunion 1967 414024 60.54200 4021.1757
## 1265 Reunion 1972 461633 64.27400 5047.6586
## 1266 Reunion 1977 492095 67.06400 4319.8041
## 1267 Reunion 1982 517810 69.88500 5267.2194
## 1268 Reunion 1987 562035 71.91300 5303.3775
## 1269 Reunion 1992 622191 73.61500 6101.2558
## 1270 Reunion 1997 684810 74.77200 6071.9414
## 1271 Reunion 2002 743981 75.74400 6316.1652
## 1272 Reunion 2007 798094 76.44200 7670.1226
## 1273 Romania 1952 16630000 61.05000 3144.6132
## 1274 Romania 1957 17829327 64.10000 3943.3702
## 1275 Romania 1962 18680721 66.80000 4734.9976
## 1276 Romania 1967 19284814 66.80000 6470.8665
## 1277 Romania 1972 20662648 69.21000 8011.4144
## 1278 Romania 1977 21658597 69.46000 9356.3972
## 1279 Romania 1982 22356726 69.66000 9605.3141
## 1280 Romania 1987 22686371 69.53000 9696.2733
## 1281 Romania 1992 22797027 69.36000 6598.4099
## 1282 Romania 1997 22562458 69.72000 7346.5476
## 1283 Romania 2002 22404337 71.32200 7885.3601
## 1284 Romania 2007 22276056 72.47600 10808.4756
## 1285 Rwanda 1952 2534927 40.00000 493.3239
## 1286 Rwanda 1957 2822082 41.50000 540.2894
## 1287 Rwanda 1962 3051242 43.00000 597.4731
## 1288 Rwanda 1967 3451079 44.10000 510.9637
## 1289 Rwanda 1972 3992121 44.60000 590.5807
## 1290 Rwanda 1977 4657072 45.00000 670.0806
## 1291 Rwanda 1982 5507565 46.21800 881.5706
## 1292 Rwanda 1987 6349365 44.02000 847.9912
## 1293 Rwanda 1992 7290203 23.59900 737.0686
## 1294 Rwanda 1997 7212583 36.08700 589.9445
## 1295 Rwanda 2002 7852401 43.41300 785.6538
## 1296 Rwanda 2007 8860588 46.24200 863.0885
## 1297 Sao Tome and Principe 1952 60011 46.47100 879.5836
## 1298 Sao Tome and Principe 1957 61325 48.94500 860.7369
## 1299 Sao Tome and Principe 1962 65345 51.89300 1071.5511
## 1300 Sao Tome and Principe 1967 70787 54.42500 1384.8406
## 1301 Sao Tome and Principe 1972 76595 56.48000 1532.9853
## 1302 Sao Tome and Principe 1977 86796 58.55000 1737.5617
## 1303 Sao Tome and Principe 1982 98593 60.35100 1890.2181
## 1304 Sao Tome and Principe 1987 110812 61.72800 1516.5255
## 1305 Sao Tome and Principe 1992 125911 62.74200 1428.7778
## 1306 Sao Tome and Principe 1997 145608 63.30600 1339.0760
## 1307 Sao Tome and Principe 2002 170372 64.33700 1353.0924
## 1308 Sao Tome and Principe 2007 199579 65.52800 1598.4351
## 1309 Saudi Arabia 1952 4005677 39.87500 6459.5548
## 1310 Saudi Arabia 1957 4419650 42.86800 8157.5912
## 1311 Saudi Arabia 1962 4943029 45.91400 11626.4197
## 1312 Saudi Arabia 1967 5618198 49.90100 16903.0489
## 1313 Saudi Arabia 1972 6472756 53.88600 24837.4287
## 1314 Saudi Arabia 1977 8128505 58.69000 34167.7626
## 1315 Saudi Arabia 1982 11254672 63.01200 33693.1753
## 1316 Saudi Arabia 1987 14619745 66.29500 21198.2614
## 1317 Saudi Arabia 1992 16945857 68.76800 24841.6178
## 1318 Saudi Arabia 1997 21229759 70.53300 20586.6902
## 1319 Saudi Arabia 2002 24501530 71.62600 19014.5412
## 1320 Saudi Arabia 2007 27601038 72.77700 21654.8319
## 1321 Senegal 1952 2755589 37.27800 1450.3570
## 1322 Senegal 1957 3054547 39.32900 1567.6530
## 1323 Senegal 1962 3430243 41.45400 1654.9887
## 1324 Senegal 1967 3965841 43.56300 1612.4046
## 1325 Senegal 1972 4588696 45.81500 1597.7121
## 1326 Senegal 1977 5260855 48.87900 1561.7691
## 1327 Senegal 1982 6147783 52.37900 1518.4800
## 1328 Senegal 1987 7171347 55.76900 1441.7207
## 1329 Senegal 1992 8307920 58.19600 1367.8994
## 1330 Senegal 1997 9535314 60.18700 1392.3683
## 1331 Senegal 2002 10870037 61.60000 1519.6353
## 1332 Senegal 2007 12267493 63.06200 1712.4721
## 1333 Serbia 1952 6860147 57.99600 3581.4594
## 1334 Serbia 1957 7271135 61.68500 4981.0909
## 1335 Serbia 1962 7616060 64.53100 6289.6292
## 1336 Serbia 1967 7971222 66.91400 7991.7071
## 1337 Serbia 1972 8313288 68.70000 10522.0675
## 1338 Serbia 1977 8686367 70.30000 12980.6696
## 1339 Serbia 1982 9032824 70.16200 15181.0927
## 1340 Serbia 1987 9230783 71.21800 15870.8785
## 1341 Serbia 1992 9826397 71.65900 9325.0682
## 1342 Serbia 1997 10336594 72.23200 7914.3203
## 1343 Serbia 2002 10111559 73.21300 7236.0753
## 1344 Serbia 2007 10150265 74.00200 9786.5347
## 1345 Sierra Leone 1952 2143249 30.33100 879.7877
## 1346 Sierra Leone 1957 2295678 31.57000 1004.4844
## 1347 Sierra Leone 1962 2467895 32.76700 1116.6399
## 1348 Sierra Leone 1967 2662190 34.11300 1206.0435
## 1349 Sierra Leone 1972 2879013 35.40000 1353.7598
## 1350 Sierra Leone 1977 3140897 36.78800 1348.2852
## 1351 Sierra Leone 1982 3464522 38.44500 1465.0108
## 1352 Sierra Leone 1987 3868905 40.00600 1294.4478
## 1353 Sierra Leone 1992 4260884 38.33300 1068.6963
## 1354 Sierra Leone 1997 4578212 39.89700 574.6482
## 1355 Sierra Leone 2002 5359092 41.01200 699.4897
## 1356 Sierra Leone 2007 6144562 42.56800 862.5408
## 1357 Singapore 1952 1127000 60.39600 2315.1382
## 1358 Singapore 1957 1445929 63.17900 2843.1044
## 1359 Singapore 1962 1750200 65.79800 3674.7356
## 1360 Singapore 1967 1977600 67.94600 4977.4185
## 1361 Singapore 1972 2152400 69.52100 8597.7562
## 1362 Singapore 1977 2325300 70.79500 11210.0895
## 1363 Singapore 1982 2651869 71.76000 15169.1611
## 1364 Singapore 1987 2794552 73.56000 18861.5308
## 1365 Singapore 1992 3235865 75.78800 24769.8912
## 1366 Singapore 1997 3802309 77.15800 33519.4766
## 1367 Singapore 2002 4197776 78.77000 36023.1054
## 1368 Singapore 2007 4553009 79.97200 47143.1796
## 1369 Slovak Republic 1952 3558137 64.36000 5074.6591
## 1370 Slovak Republic 1957 3844277 67.45000 6093.2630
## 1371 Slovak Republic 1962 4237384 70.33000 7481.1076
## 1372 Slovak Republic 1967 4442238 70.98000 8412.9024
## 1373 Slovak Republic 1972 4593433 70.35000 9674.1676
## 1374 Slovak Republic 1977 4827803 70.45000 10922.6640
## 1375 Slovak Republic 1982 5048043 70.80000 11348.5459
## 1376 Slovak Republic 1987 5199318 71.08000 12037.2676
## 1377 Slovak Republic 1992 5302888 71.38000 9498.4677
## 1378 Slovak Republic 1997 5383010 72.71000 12126.2306
## 1379 Slovak Republic 2002 5410052 73.80000 13638.7784
## 1380 Slovak Republic 2007 5447502 74.66300 18678.3144
## 1381 Slovenia 1952 1489518 65.57000 4215.0417
## 1382 Slovenia 1957 1533070 67.85000 5862.2766
## 1383 Slovenia 1962 1582962 69.15000 7402.3034
## 1384 Slovenia 1967 1646912 69.18000 9405.4894
## 1385 Slovenia 1972 1694510 69.82000 12383.4862
## 1386 Slovenia 1977 1746919 70.97000 15277.0302
## 1387 Slovenia 1982 1861252 71.06300 17866.7218
## 1388 Slovenia 1987 1945870 72.25000 18678.5349
## 1389 Slovenia 1992 1999210 73.64000 14214.7168
## 1390 Slovenia 1997 2011612 75.13000 17161.1073
## 1391 Slovenia 2002 2011497 76.66000 20660.0194
## 1392 Slovenia 2007 2009245 77.92600 25768.2576
## 1393 Somalia 1952 2526994 32.97800 1135.7498
## 1394 Somalia 1957 2780415 34.97700 1258.1474
## 1395 Somalia 1962 3080153 36.98100 1369.4883
## 1396 Somalia 1967 3428839 38.97700 1284.7332
## 1397 Somalia 1972 3840161 40.97300 1254.5761
## 1398 Somalia 1977 4353666 41.97400 1450.9925
## 1399 Somalia 1982 5828892 42.95500 1176.8070
## 1400 Somalia 1987 6921858 44.50100 1093.2450
## 1401 Somalia 1992 6099799 39.65800 926.9603
## 1402 Somalia 1997 6633514 43.79500 930.5964
## 1403 Somalia 2002 7753310 45.93600 882.0818
## 1404 Somalia 2007 9118773 48.15900 926.1411
## 1405 South Africa 1952 14264935 45.00900 4725.2955
## 1406 South Africa 1957 16151549 47.98500 5487.1042
## 1407 South Africa 1962 18356657 49.95100 5768.7297
## 1408 South Africa 1967 20997321 51.92700 7114.4780
## 1409 South Africa 1972 23935810 53.69600 7765.9626
## 1410 South Africa 1977 27129932 55.52700 8028.6514
## 1411 South Africa 1982 31140029 58.16100 8568.2662
## 1412 South Africa 1987 35933379 60.83400 7825.8234
## 1413 South Africa 1992 39964159 61.88800 7225.0693
## 1414 South Africa 1997 42835005 60.23600 7479.1882
## 1415 South Africa 2002 44433622 53.36500 7710.9464
## 1416 South Africa 2007 43997828 49.33900 9269.6578
## 1417 Spain 1952 28549870 64.94000 3834.0347
## 1418 Spain 1957 29841614 66.66000 4564.8024
## 1419 Spain 1962 31158061 69.69000 5693.8439
## 1420 Spain 1967 32850275 71.44000 7993.5123
## 1421 Spain 1972 34513161 73.06000 10638.7513
## 1422 Spain 1977 36439000 74.39000 13236.9212
## 1423 Spain 1982 37983310 76.30000 13926.1700
## 1424 Spain 1987 38880702 76.90000 15764.9831
## 1425 Spain 1992 39549438 77.57000 18603.0645
## 1426 Spain 1997 39855442 78.77000 20445.2990
## 1427 Spain 2002 40152517 79.78000 24835.4717
## 1428 Spain 2007 40448191 80.94100 28821.0637
## 1429 Sri Lanka 1952 7982342 57.59300 1083.5320
## 1430 Sri Lanka 1957 9128546 61.45600 1072.5466
## 1431 Sri Lanka 1962 10421936 62.19200 1074.4720
## 1432 Sri Lanka 1967 11737396 64.26600 1135.5143
## 1433 Sri Lanka 1972 13016733 65.04200 1213.3955
## 1434 Sri Lanka 1977 14116836 65.94900 1348.7757
## 1435 Sri Lanka 1982 15410151 68.75700 1648.0798
## 1436 Sri Lanka 1987 16495304 69.01100 1876.7668
## 1437 Sri Lanka 1992 17587060 70.37900 2153.7392
## 1438 Sri Lanka 1997 18698655 70.45700 2664.4773
## 1439 Sri Lanka 2002 19576783 70.81500 3015.3788
## 1440 Sri Lanka 2007 20378239 72.39600 3970.0954
## 1441 Sudan 1952 8504667 38.63500 1615.9911
## 1442 Sudan 1957 9753392 39.62400 1770.3371
## 1443 Sudan 1962 11183227 40.87000 1959.5938
## 1444 Sudan 1967 12716129 42.85800 1687.9976
## 1445 Sudan 1972 14597019 45.08300 1659.6528
## 1446 Sudan 1977 17104986 47.80000 2202.9884
## 1447 Sudan 1982 20367053 50.33800 1895.5441
## 1448 Sudan 1987 24725960 51.74400 1507.8192
## 1449 Sudan 1992 28227588 53.55600 1492.1970
## 1450 Sudan 1997 32160729 55.37300 1632.2108
## 1451 Sudan 2002 37090298 56.36900 1993.3983
## 1452 Sudan 2007 42292929 58.55600 2602.3950
## 1453 Swaziland 1952 290243 41.40700 1148.3766
## 1454 Swaziland 1957 326741 43.42400 1244.7084
## 1455 Swaziland 1962 370006 44.99200 1856.1821
## 1456 Swaziland 1967 420690 46.63300 2613.1017
## 1457 Swaziland 1972 480105 49.55200 3364.8366
## 1458 Swaziland 1977 551425 52.53700 3781.4106
## 1459 Swaziland 1982 649901 55.56100 3895.3840
## 1460 Swaziland 1987 779348 57.67800 3984.8398
## 1461 Swaziland 1992 962344 58.47400 3553.0224
## 1462 Swaziland 1997 1054486 54.28900 3876.7685
## 1463 Swaziland 2002 1130269 43.86900 4128.1169
## 1464 Swaziland 2007 1133066 39.61300 4513.4806
## 1465 Sweden 1952 7124673 71.86000 8527.8447
## 1466 Sweden 1957 7363802 72.49000 9911.8782
## 1467 Sweden 1962 7561588 73.37000 12329.4419
## 1468 Sweden 1967 7867931 74.16000 15258.2970
## 1469 Sweden 1972 8122293 74.72000 17832.0246
## 1470 Sweden 1977 8251648 75.44000 18855.7252
## 1471 Sweden 1982 8325260 76.42000 20667.3812
## 1472 Sweden 1987 8421403 77.19000 23586.9293
## 1473 Sweden 1992 8718867 78.16000 23880.0168
## 1474 Sweden 1997 8897619 79.39000 25266.5950
## 1475 Sweden 2002 8954175 80.04000 29341.6309
## 1476 Sweden 2007 9031088 80.88400 33859.7484
## 1477 Switzerland 1952 4815000 69.62000 14734.2327
## 1478 Switzerland 1957 5126000 70.56000 17909.4897
## 1479 Switzerland 1962 5666000 71.32000 20431.0927
## 1480 Switzerland 1967 6063000 72.77000 22966.1443
## 1481 Switzerland 1972 6401400 73.78000 27195.1130
## 1482 Switzerland 1977 6316424 75.39000 26982.2905
## 1483 Switzerland 1982 6468126 76.21000 28397.7151
## 1484 Switzerland 1987 6649942 77.41000 30281.7046
## 1485 Switzerland 1992 6995447 78.03000 31871.5303
## 1486 Switzerland 1997 7193761 79.37000 32135.3230
## 1487 Switzerland 2002 7361757 80.62000 34480.9577
## 1488 Switzerland 2007 7554661 81.70100 37506.4191
## 1489 Syria 1952 3661549 45.88300 1643.4854
## 1490 Syria 1957 4149908 48.28400 2117.2349
## 1491 Syria 1962 4834621 50.30500 2193.0371
## 1492 Syria 1967 5680812 53.65500 1881.9236
## 1493 Syria 1972 6701172 57.29600 2571.4230
## 1494 Syria 1977 7932503 61.19500 3195.4846
## 1495 Syria 1982 9410494 64.59000 3761.8377
## 1496 Syria 1987 11242847 66.97400 3116.7743
## 1497 Syria 1992 13219062 69.24900 3340.5428
## 1498 Syria 1997 15081016 71.52700 4014.2390
## 1499 Syria 2002 17155814 73.05300 4090.9253
## 1500 Syria 2007 19314747 74.14300 4184.5481
## 1501 Taiwan 1952 8550362 58.50000 1206.9479
## 1502 Taiwan 1957 10164215 62.40000 1507.8613
## 1503 Taiwan 1962 11918938 65.20000 1822.8790
## 1504 Taiwan 1967 13648692 67.50000 2643.8587
## 1505 Taiwan 1972 15226039 69.39000 4062.5239
## 1506 Taiwan 1977 16785196 70.59000 5596.5198
## 1507 Taiwan 1982 18501390 72.16000 7426.3548
## 1508 Taiwan 1987 19757799 73.40000 11054.5618
## 1509 Taiwan 1992 20686918 74.26000 15215.6579
## 1510 Taiwan 1997 21628605 75.25000 20206.8210
## 1511 Taiwan 2002 22454239 76.99000 23235.4233
## 1512 Taiwan 2007 23174294 78.40000 28718.2768
## 1513 Tanzania 1952 8322925 41.21500 716.6501
## 1514 Tanzania 1957 9452826 42.97400 698.5356
## 1515 Tanzania 1962 10863958 44.24600 722.0038
## 1516 Tanzania 1967 12607312 45.75700 848.2187
## 1517 Tanzania 1972 14706593 47.62000 915.9851
## 1518 Tanzania 1977 17129565 49.91900 962.4923
## 1519 Tanzania 1982 19844382 50.60800 874.2426
## 1520 Tanzania 1987 23040630 51.53500 831.8221
## 1521 Tanzania 1992 26605473 50.44000 825.6825
## 1522 Tanzania 1997 30686889 48.46600 789.1862
## 1523 Tanzania 2002 34593779 49.65100 899.0742
## 1524 Tanzania 2007 38139640 52.51700 1107.4822
## 1525 Thailand 1952 21289402 50.84800 757.7974
## 1526 Thailand 1957 25041917 53.63000 793.5774
## 1527 Thailand 1962 29263397 56.06100 1002.1992
## 1528 Thailand 1967 34024249 58.28500 1295.4607
## 1529 Thailand 1972 39276153 60.40500 1524.3589
## 1530 Thailand 1977 44148285 62.49400 1961.2246
## 1531 Thailand 1982 48827160 64.59700 2393.2198
## 1532 Thailand 1987 52910342 66.08400 2982.6538
## 1533 Thailand 1992 56667095 67.29800 4616.8965
## 1534 Thailand 1997 60216677 67.52100 5852.6255
## 1535 Thailand 2002 62806748 68.56400 5913.1875
## 1536 Thailand 2007 65068149 70.61600 7458.3963
## 1537 Togo 1952 1219113 38.59600 859.8087
## 1538 Togo 1957 1357445 41.20800 925.9083
## 1539 Togo 1962 1528098 43.92200 1067.5348
## 1540 Togo 1967 1735550 46.76900 1477.5968
## 1541 Togo 1972 2056351 49.75900 1649.6602
## 1542 Togo 1977 2308582 52.88700 1532.7770
## 1543 Togo 1982 2644765 55.47100 1344.5780
## 1544 Togo 1987 3154264 56.94100 1202.2014
## 1545 Togo 1992 3747553 58.06100 1034.2989
## 1546 Togo 1997 4320890 58.39000 982.2869
## 1547 Togo 2002 4977378 57.56100 886.2206
## 1548 Togo 2007 5701579 58.42000 882.9699
## 1549 Trinidad and Tobago 1952 662850 59.10000 3023.2719
## 1550 Trinidad and Tobago 1957 764900 61.80000 4100.3934
## 1551 Trinidad and Tobago 1962 887498 64.90000 4997.5240
## 1552 Trinidad and Tobago 1967 960155 65.40000 5621.3685
## 1553 Trinidad and Tobago 1972 975199 65.90000 6619.5514
## 1554 Trinidad and Tobago 1977 1039009 68.30000 7899.5542
## 1555 Trinidad and Tobago 1982 1116479 68.83200 9119.5286
## 1556 Trinidad and Tobago 1987 1191336 69.58200 7388.5978
## 1557 Trinidad and Tobago 1992 1183669 69.86200 7370.9909
## 1558 Trinidad and Tobago 1997 1138101 69.46500 8792.5731
## 1559 Trinidad and Tobago 2002 1101832 68.97600 11460.6002
## 1560 Trinidad and Tobago 2007 1056608 69.81900 18008.5092
## 1561 Tunisia 1952 3647735 44.60000 1468.4756
## 1562 Tunisia 1957 3950849 47.10000 1395.2325
## 1563 Tunisia 1962 4286552 49.57900 1660.3032
## 1564 Tunisia 1967 4786986 52.05300 1932.3602
## 1565 Tunisia 1972 5303507 55.60200 2753.2860
## 1566 Tunisia 1977 6005061 59.83700 3120.8768
## 1567 Tunisia 1982 6734098 64.04800 3560.2332
## 1568 Tunisia 1987 7724976 66.89400 3810.4193
## 1569 Tunisia 1992 8523077 70.00100 4332.7202
## 1570 Tunisia 1997 9231669 71.97300 4876.7986
## 1571 Tunisia 2002 9770575 73.04200 5722.8957
## 1572 Tunisia 2007 10276158 73.92300 7092.9230
## 1573 Turkey 1952 22235677 43.58500 1969.1010
## 1574 Turkey 1957 25670939 48.07900 2218.7543
## 1575 Turkey 1962 29788695 52.09800 2322.8699
## 1576 Turkey 1967 33411317 54.33600 2826.3564
## 1577 Turkey 1972 37492953 57.00500 3450.6964
## 1578 Turkey 1977 42404033 59.50700 4269.1223
## 1579 Turkey 1982 47328791 61.03600 4241.3563
## 1580 Turkey 1987 52881328 63.10800 5089.0437
## 1581 Turkey 1992 58179144 66.14600 5678.3483
## 1582 Turkey 1997 63047647 68.83500 6601.4299
## 1583 Turkey 2002 67308928 70.84500 6508.0857
## 1584 Turkey 2007 71158647 71.77700 8458.2764
## 1585 Uganda 1952 5824797 39.97800 734.7535
## 1586 Uganda 1957 6675501 42.57100 774.3711
## 1587 Uganda 1962 7688797 45.34400 767.2717
## 1588 Uganda 1967 8900294 48.05100 908.9185
## 1589 Uganda 1972 10190285 51.01600 950.7359
## 1590 Uganda 1977 11457758 50.35000 843.7331
## 1591 Uganda 1982 12939400 49.84900 682.2662
## 1592 Uganda 1987 15283050 51.50900 617.7244
## 1593 Uganda 1992 18252190 48.82500 644.1708
## 1594 Uganda 1997 21210254 44.57800 816.5591
## 1595 Uganda 2002 24739869 47.81300 927.7210
## 1596 Uganda 2007 29170398 51.54200 1056.3801
## 1597 United Kingdom 1952 50430000 69.18000 9979.5085
## 1598 United Kingdom 1957 51430000 70.42000 11283.1779
## 1599 United Kingdom 1962 53292000 70.76000 12477.1771
## 1600 United Kingdom 1967 54959000 71.36000 14142.8509
## 1601 United Kingdom 1972 56079000 72.01000 15895.1164
## 1602 United Kingdom 1977 56179000 72.76000 17428.7485
## 1603 United Kingdom 1982 56339704 74.04000 18232.4245
## 1604 United Kingdom 1987 56981620 75.00700 21664.7877
## 1605 United Kingdom 1992 57866349 76.42000 22705.0925
## 1606 United Kingdom 1997 58808266 77.21800 26074.5314
## 1607 United Kingdom 2002 59912431 78.47100 29478.9992
## 1608 United Kingdom 2007 60776238 79.42500 33203.2613
## 1609 United States 1952 157553000 68.44000 13990.4821
## 1610 United States 1957 171984000 69.49000 14847.1271
## 1611 United States 1962 186538000 70.21000 16173.1459
## 1612 United States 1967 198712000 70.76000 19530.3656
## 1613 United States 1972 209896000 71.34000 21806.0359
## 1614 United States 1977 220239000 73.38000 24072.6321
## 1615 United States 1982 232187835 74.65000 25009.5591
## 1616 United States 1987 242803533 75.02000 29884.3504
## 1617 United States 1992 256894189 76.09000 32003.9322
## 1618 United States 1997 272911760 76.81000 35767.4330
## 1619 United States 2002 287675526 77.31000 39097.0995
## 1620 United States 2007 301139947 78.24200 42951.6531
## 1621 Uruguay 1952 2252965 66.07100 5716.7667
## 1622 Uruguay 1957 2424959 67.04400 6150.7730
## 1623 Uruguay 1962 2598466 68.25300 5603.3577
## 1624 Uruguay 1967 2748579 68.46800 5444.6196
## 1625 Uruguay 1972 2829526 68.67300 5703.4089
## 1626 Uruguay 1977 2873520 69.48100 6504.3397
## 1627 Uruguay 1982 2953997 70.80500 6920.2231
## 1628 Uruguay 1987 3045153 71.91800 7452.3990
## 1629 Uruguay 1992 3149262 72.75200 8137.0048
## 1630 Uruguay 1997 3262838 74.22300 9230.2407
## 1631 Uruguay 2002 3363085 75.30700 7727.0020
## 1632 Uruguay 2007 3447496 76.38400 10611.4630
## 1633 Venezuela 1952 5439568 55.08800 7689.7998
## 1634 Venezuela 1957 6702668 57.90700 9802.4665
## 1635 Venezuela 1962 8143375 60.77000 8422.9742
## 1636 Venezuela 1967 9709552 63.47900 9541.4742
## 1637 Venezuela 1972 11515649 65.71200 10505.2597
## 1638 Venezuela 1977 13503563 67.45600 13143.9510
## 1639 Venezuela 1982 15620766 68.55700 11152.4101
## 1640 Venezuela 1987 17910182 70.19000 9883.5846
## 1641 Venezuela 1992 20265563 71.15000 10733.9263
## 1642 Venezuela 1997 22374398 72.14600 10165.4952
## 1643 Venezuela 2002 24287670 72.76600 8605.0478
## 1644 Venezuela 2007 26084662 73.74700 11415.8057
## 1645 Vietnam 1952 26246839 40.41200 605.0665
## 1646 Vietnam 1957 28998543 42.88700 676.2854
## 1647 Vietnam 1962 33796140 45.36300 772.0492
## 1648 Vietnam 1967 39463910 47.83800 637.1233
## 1649 Vietnam 1972 44655014 50.25400 699.5016
## 1650 Vietnam 1977 50533506 55.76400 713.5371
## 1651 Vietnam 1982 56142181 58.81600 707.2358
## 1652 Vietnam 1987 62826491 62.82000 820.7994
## 1653 Vietnam 1992 69940728 67.66200 989.0231
## 1654 Vietnam 1997 76048996 70.67200 1385.8968
## 1655 Vietnam 2002 80908147 73.01700 1764.4567
## 1656 Vietnam 2007 85262356 74.24900 2441.5764
## 1657 West Bank and Gaza 1952 1030585 43.16000 1515.5923
## 1658 West Bank and Gaza 1957 1070439 45.67100 1827.0677
## 1659 West Bank and Gaza 1962 1133134 48.12700 2198.9563
## 1660 West Bank and Gaza 1967 1142636 51.63100 2649.7150
## 1661 West Bank and Gaza 1972 1089572 56.53200 3133.4093
## 1662 West Bank and Gaza 1977 1261091 60.76500 3682.8315
## 1663 West Bank and Gaza 1982 1425876 64.40600 4336.0321
## 1664 West Bank and Gaza 1987 1691210 67.04600 5107.1974
## 1665 West Bank and Gaza 1992 2104779 69.71800 6017.6548
## 1666 West Bank and Gaza 1997 2826046 71.09600 7110.6676
## 1667 West Bank and Gaza 2002 3389578 72.37000 4515.4876
## 1668 West Bank and Gaza 2007 4018332 73.42200 3025.3498
## 1669 Yemen Rep. 1952 4963829 32.54800 781.7176
## 1670 Yemen Rep. 1957 5498090 33.97000 804.8305
## 1671 Yemen Rep. 1962 6120081 35.18000 825.6232
## 1672 Yemen Rep. 1967 6740785 36.98400 862.4421
## 1673 Yemen Rep. 1972 7407075 39.84800 1265.0470
## 1674 Yemen Rep. 1977 8403990 44.17500 1829.7652
## 1675 Yemen Rep. 1982 9657618 49.11300 1977.5570
## 1676 Yemen Rep. 1987 11219340 52.92200 1971.7415
## 1677 Yemen Rep. 1992 13367997 55.59900 1879.4967
## 1678 Yemen Rep. 1997 15826497 58.02000 2117.4845
## 1679 Yemen Rep. 2002 18701257 60.30800 2234.8208
## 1680 Yemen Rep. 2007 22211743 62.69800 2280.7699
## 1681 Zambia 1952 2672000 42.03800 1147.3888
## 1682 Zambia 1957 3016000 44.07700 1311.9568
## 1683 Zambia 1962 3421000 46.02300 1452.7258
## 1684 Zambia 1967 3900000 47.76800 1777.0773
## 1685 Zambia 1972 4506497 50.10700 1773.4983
## 1686 Zambia 1977 5216550 51.38600 1588.6883
## 1687 Zambia 1982 6100407 51.82100 1408.6786
## 1688 Zambia 1987 7272406 50.82100 1213.3151
## 1689 Zambia 1992 8381163 46.10000 1210.8846
## 1690 Zambia 1997 9417789 40.23800 1071.3538
## 1691 Zambia 2002 10595811 39.19300 1071.6139
## 1692 Zambia 2007 11746035 42.38400 1271.2116
## 1693 Zimbabwe 1952 3080907 48.45100 406.8841
## 1694 Zimbabwe 1957 3646340 50.46900 518.7643
## 1695 Zimbabwe 1962 4277736 52.35800 527.2722
## 1696 Zimbabwe 1967 4995432 53.99500 569.7951
## 1697 Zimbabwe 1972 5861135 55.63500 799.3622
## 1698 Zimbabwe 1977 6642107 57.67400 685.5877
## 1699 Zimbabwe 1982 7636524 60.36300 788.8550
## 1700 Zimbabwe 1987 9216418 62.35100 706.1573
## 1701 Zimbabwe 1992 10704340 60.37700 693.4208
## 1702 Zimbabwe 1997 11404948 46.80900 792.4500
## 1703 Zimbabwe 2002 11926563 39.98900 672.0386
## 1704 Zimbabwe 2007 12311143 43.48700 469.7093Note that there are some other packages (e.g. the MASS package) that also have a function called select(). If you happen to load one of those packages after loading the tidyverse package in your session, you may end up with an error that says Error in select(., x) : unused argument (x). To fix this, you will either need to directly call the select() function from the dplyr package using dplyr::select() or ensure that you load such packages before the tidyverse package (which automatically loads the dplyr package).
filter: filter to rows that satisfy certain conditions
Filtering is a very simple way of only keeping rows that satisfy certain conditions. These conditions are always based on logical statements involving variables/columns of the data frame.
For instance, to keep only the rows that have a recorded population of at least 1 billion, you can use a filtering with a logical statement involving the pop variable (again unquoted).
gapminder %>%
filter(pop > 1000000000) ## country year pop continent lifeExp gdpPercap
## 1 China 1982 1000281000 Asia 65.525 962.4214
## 2 China 1987 1084035000 Asia 67.274 1378.9040
## 3 China 1992 1164970000 Asia 68.690 1655.7842
## 4 China 1997 1230075000 Asia 70.426 2289.2341
## 5 China 2002 1280400000 Asia 72.028 3119.2809
## 6 China 2007 1318683096 Asia 72.961 4959.1149
## 7 India 2002 1034172547 Asia 62.879 1746.7695
## 8 India 2007 1110396331 Asia 64.698 2452.2104You can specify multiple filter conditions using a comma (and in this case the filter function will return rows that satisfy all of the conditions specified). Below I filter to rows from 1992 that have a population of at least 100 million in that year.
gapminder %>%
filter(pop > 100000000, year == 1992) ## country year pop continent lifeExp gdpPercap
## 1 Bangladesh 1992 113704579 Asia 56.018 837.8102
## 2 Brazil 1992 155975974 Americas 67.057 6950.2830
## 3 China 1992 1164970000 Asia 68.690 1655.7842
## 4 India 1992 872000000 Asia 60.223 1164.4068
## 5 Indonesia 1992 184816000 Asia 62.681 2383.1409
## 6 Japan 1992 124329269 Asia 79.360 26824.8951
## 7 Pakistan 1992 120065004 Asia 60.838 1971.8295
## 8 United States 1992 256894189 Americas 76.090 32003.9322mutate: add a new variable
Mutating the data frame involves adding a new variable. This new variable is usually a function of existing variables, but it can also be defined based on external objects.
For instance below I add a new variable, gdp, to the gapminder data frame. gdp is equal to gdpPercap multiplied by pop, and then look at the first 6 rows of the data frame using the classic head() function.
gapminder %>%
mutate(gdp = gdpPercap * pop) %>%
head## country year pop continent lifeExp gdpPercap gdp
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453 6567086330
## 2 Afghanistan 1957 9240934 Asia 30.332 820.8530 7585448670
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007 8758855797
## 4 Afghanistan 1967 11537966 Asia 34.020 836.1971 9648014150
## 5 Afghanistan 1972 13079460 Asia 36.088 739.9811 9678553274
## 6 Afghanistan 1977 14880372 Asia 38.438 786.1134 11697659231Note that I haven’t re-defined the gapminder data frame, so all I have done here is print out the data frame with the additional gdp variable.
If you wanted to be able to use this gdp variable down the line, you would need to re-define the gapminder data frame so that gapminder now corresponds to the version with the gdp variable.
gapminder <- gapminder %>%
mutate(gdp = gdpPercap * pop)arrange: arrange the rows of the data frame in order a variable
The arrange function allows you to easily reorder the rows of the data frame in increasing or decreasing order of one (or more) of the variables of the data frame.
For instance, you could arrange all rows in the data frame in order of increasing life expectancy.
gapminder %>%
arrange(lifeExp) %>%
head## country year pop continent lifeExp gdpPercap gdp
## 1 Rwanda 1992 7290203 Africa 23.599 737.0686 5373379682
## 2 Afghanistan 1952 8425333 Asia 28.801 779.4453 6567086330
## 3 Gambia 1952 284320 Africa 30.000 485.2307 137960781
## 4 Angola 1952 4232095 Africa 30.015 3520.6103 14899557133
## 5 Sierra Leone 1952 2143249 Africa 30.331 879.7877 1885604185
## 6 Afghanistan 1957 9240934 Asia 30.332 820.8530 7585448670To arrange in descending order, you need to wrap the variable name in the desc() function.
gapminder %>%
arrange(desc(gdpPercap)) %>%
head## country year pop continent lifeExp gdpPercap gdp
## 1 Kuwait 1957 212846 Asia 58.033 113523.13 24162944745
## 2 Kuwait 1972 841934 Asia 67.712 109347.87 92063687055
## 3 Kuwait 1952 160000 Asia 55.565 108382.35 17341176464
## 4 Kuwait 1962 358266 Asia 60.470 95458.11 34199395868
## 5 Kuwait 1967 575003 Asia 64.624 80894.88 46514800559
## 6 Kuwait 1977 1140357 Asia 69.343 59265.48 67583801715Again, if you wanted your data frame to actually be arranged as specified, you would need to re-define the gapminder data frame. But if you only need it for one quick analysis (e.g. creating a summary table), then you don’t need to redefine the data frame.
Below I re-define the gapminder dataset so that the rows are in order of increasing year, and the countries are in alphabetical order within each year (the secondary arrange variable).
gapminder %>%
arrange(year, country) %>%
head## country year pop continent lifeExp gdpPercap gdp
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453 6567086330
## 2 Albania 1952 1282697 Europe 55.230 1601.0561 2053669902
## 3 Algeria 1952 9279525 Africa 43.077 2449.0082 22725632678
## 4 Angola 1952 4232095 Africa 30.015 3520.6103 14899557133
## 5 Argentina 1952 17876956 Americas 62.485 5911.3151 105676319105
## 6 Australia 1952 8691212 Oceania 69.120 10039.5956 87256254102group_by: apply other dplyr functions separately within within a group defined by one or more variables
The group_by() function can be really useful if you want to apply a function independently within groups of observations (where the groups are specified by a categorical variable in your data frame). Think of group_by() as splitting your data frame into several separate data frames based on the categorical variable you specify. All functions that you apply to the grouped data frame are applied separately to each group until you specify an ungroup() function.
The code below filters the data frame to only the country-years that have life expectancy above the average life expectancy for their continent.
gapminder %>%
group_by(continent) %>%
filter(lifeExp > mean(lifeExp)) %>%
ungroup() ## # A tibble: 873 x 7
## country year pop continent lifeExp gdpPercap gdp
## <fct> <int> <dbl> <fct> <dbl> <dbl> <dbl>
## 1 Albania 1987 3075321 Europe 72 3739. 11498418358.
## 2 Albania 1997 3428038 Europe 73.0 3193. 10945912519.
## 3 Albania 2002 3508512 Europe 75.7 4604. 16153932130.
## 4 Albania 2007 3600523 Europe 76.4 5937. 21376411360.
## 5 Algeria 1967 12760499 Africa 51.4 3247. 41433235247.
## 6 Algeria 1972 14760787 Africa 54.5 4183. 61739408943.
## 7 Algeria 1977 17152804 Africa 58.0 4910. 84227416174.
## 8 Algeria 1982 20033753 Africa 61.4 5745. 115097120653.
## 9 Algeria 1987 23254956 Africa 65.8 5681. 132119742845.
## 10 Algeria 1992 26298373 Africa 67.7 5023. 132102425043.
## # … with 863 more rowsTo check that this does something different than it would without the group_by() (i.e. filtering to the country-years that have life expectancy above the average global life expectancy), compare the distribution of continents from each filter() command using the count() function (another handly dplyr function):
The number of countries from each continent included post-filtering when grouping by continent is:
gapminder %>%
group_by(continent) %>%
filter(lifeExp > mean(lifeExp)) %>%
ungroup() %>%
count(continent)## # A tibble: 5 x 2
## continent n
## <fct> <int>
## 1 Africa 282
## 2 Americas 176
## 3 Asia 216
## 4 Europe 189
## 5 Oceania 10The number of countries from each continent included post-filtering when not grouping by continent is:
gapminder %>%
filter(lifeExp > mean(lifeExp)) %>%
count(continent)## # A tibble: 5 x 2
## continent n
## <fct> <int>
## 1 Africa 80
## 2 Americas 218
## 3 Asia 224
## 4 Europe 349
## 5 Oceania 24Notice that when you don’t group by continent, substantially fewer African countries are included since they tend to have lower life expectencies than the global average.
To combine some of the things you’ve just learnt, the code below first filters to the year 2007, and then splits the data frame into groups by continent and adds a row to each group corresponding to the average life expectancy of all of the countries in that group/continent.
gapminder %>%
# first filter to 2007
filter(year == 2007) %>%
# group by continent
group_by(continent) %>%
# add a column within each continent corresponding to the average life expectancy
mutate(continent_lifeExp = mean(lifeExp)) %>%
# ungroup the data frame
ungroup() %>%
# only show a few variables
select(country, continent, lifeExp, continent_lifeExp) %>%
head(10)## # A tibble: 10 x 4
## country continent lifeExp continent_lifeExp
## <fct> <fct> <dbl> <dbl>
## 1 Afghanistan Asia 43.8 70.7
## 2 Albania Europe 76.4 77.6
## 3 Algeria Africa 72.3 54.8
## 4 Angola Africa 42.7 54.8
## 5 Argentina Americas 75.3 73.6
## 6 Australia Oceania 81.2 80.7
## 7 Austria Europe 79.8 77.6
## 8 Bahrain Asia 75.6 70.7
## 9 Bangladesh Asia 64.1 70.7
## 10 Belgium Europe 79.4 77.6Notice that all rows from the same continent have the same value for continent_lifeExp. Note that even though this example defines a single value for each continent, this value is repeated for all rows within the continent.
summarise/summarize: define a variable that is a summary of other variables
The summarise() (or summarize()) function aggregates across the rows of the data frame. For instance, you can calculate the average life expectancy, as well as the total GDP.
gapminder %>%
summarise(mean_lifeExp = mean(lifeExp),
total_gdp = sum(gdp))## mean_lifeExp total_gdp
## 1 59.47444 3.183235e+14The summarise function plays very nicely with the group_by() function. For instance, by first grouping by year and then calculating the average life expectancy and total GDP for each year.
gapminder %>%
group_by(year) %>%
summarise(mean_lifeExp = mean(lifeExp),
total_gdp = sum(gdp)) ## # A tibble: 12 x 3
## year mean_lifeExp total_gdp
## <int> <dbl> <dbl>
## 1 1952 49.1 7.04e12
## 2 1957 51.5 8.90e12
## 3 1962 53.6 1.10e13
## 4 1967 55.7 1.42e13
## 5 1972 57.6 1.84e13
## 6 1977 59.6 2.23e13
## 7 1982 61.5 2.54e13
## 8 1987 63.2 3.01e13
## 9 1992 64.2 3.45e13
## 10 1997 65.0 4.10e13
## 11 2002 65.7 4.73e13
## 12 2007 67.0 5.81e13Note that since these are summaries of the data frame itself, I just want to print them out, rather than re-defining the gapminder data frame to be equal to these summaries. And since I won’t be using them for anything other than to look at, I don’t need to define them as a variable.
More dplyr functions
Other dplyr functions that are incredibly useful include:
rename() for renaming variables of the data frame
gapminder %>%
rename(gdp_per_capita = gdpPercap,
life_exp = lifeExp) %>%
head## country year pop continent life_exp gdp_per_capita gdp
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453 6567086330
## 2 Afghanistan 1957 9240934 Asia 30.332 820.8530 7585448670
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007 8758855797
## 4 Afghanistan 1967 11537966 Asia 34.020 836.1971 9648014150
## 5 Afghanistan 1972 13079460 Asia 36.088 739.9811 9678553274
## 6 Afghanistan 1977 14880372 Asia 38.438 786.1134 11697659231distinct() for extracting the distinct values of a variable
gapminder %>%
distinct(continent)## continent
## 1 Asia
## 2 Europe
## 3 Africa
## 4 Americas
## 5 Oceaniasample_n() and sample_frac() for taking random samples of rows
gapminder %>%
sample_n(2)## country year pop continent lifeExp gdpPercap
## 1 South Africa 1997 42835005 Africa 60.236 7479.188
## 2 Central African Republic 1967 1733638 Africa 41.478 1136.057
## gdp
## 1 320371065828
## 2 1969510918count() for counting the number of rows with each value of a categorical variable
gapminder %>%
count(continent)## # A tibble: 5 x 2
## continent n
## <fct> <int>
## 1 Africa 624
## 2 Americas 300
## 3 Asia 396
## 4 Europe 360
## 5 Oceania 24transmute() for doing a mutate and select at the same time: only the variables defined in the mutation are retained.
gapminder %>%
transmute(gdp = gdpPercap * pop) %>%
head## gdp
## 1 6567086330
## 2 7585448670
## 3 8758855797
## 4 9648014150
## 5 9678553274
## 6 11697659231Advanced dplyr practitioners will eventually want to learn about scoped verbs.
Visualization: ggplot2
The first tidyverse package I ever learnt was ggplot2 (the more popular older sibling of the non-existent ggplot1). Ggplot2 is the data visualization package made by Hadley Wickham, and it is based a set of principles called the layered grammar of graphics. The basic idea is that a ggplot graphic layers geometric objects (circles, lines, etc), themes, and scales ontop of data. The form of the geometric object is defined by a geom_xxx() function and the properties (position, size, colour) of the geometric objects that based on the data variables are specified by the aesthetic (aes()) function (within the geom_xxx() function).
The base layer of any ggplot graph is the empty ggplot layer defined by the ggplot() function, which describes the data frame that the plot will be based on. I haven’t told ggplot what type of geometric object(s) I want yet, nor how the variables should be mapped to the geometric objects, so I just have a blank plot.
ggplot(gapminder)
Since you now know about pipes, you could pipe in the data that you want to plot. Piping makes it easy to do intermediate manipulations that you don’t necessarily want to save in the data frame itself, such as only plotting one year’s worth of data
gapminder %>%
filter(year == 2007) %>%
ggplot()
Adding geom layers
Next, I will add a “geom” layer to our ggplot object.
Layers are added to ggplot objects using +, instead of %>%, since you are not explicitly piping the LHS into each subsequent layer (we are actually adding a layer on top). The error messages have recently been improved to warn you if you are accidentally using a pipe %>% to add layers to ggplot objects (which, once you start piping everything into everything, becomes an easy mistake to make).
Probably the most common geom layer is geom_point. Inside geom_point(), you will specify the aesthetic mappings from the variables to the geometric objects that you want. For instance, if you want to plot a scatterplot with gdpPercap on the x-axis and lifeExp on the y-axis, then you would add a geom_point() geometric layer with relevant aesthetic function: geom_point(aes(x = gdpPercap, y = lifeExp)).
# describe the base ggplot object and tell it what data we are interested in along with the aesthetic mapping
gapminder %>%
filter(year == 2007) %>%
ggplot() +
# add a points layer on top
geom_point(aes(x = gdpPercap, y = lifeExp))
We could also add a smoothed trend line layer on top of the points using geom_smooth().
# describe the base ggplot object and tell it what data we are interested in along with the aesthetic mapping
gapminder %>%
filter(year == 2007) %>%
ggplot() +
# add a points layer on top
geom_point(aes(x = gdpPercap, y = lifeExp)) +
# add a smoothed LOESS layer
geom_smooth(aes(x = gdpPercap, y = lifeExp), method = "loess")
Note that since the aesthetics for geom_point() and geom_smooth() are the same, you might want to just specify global aesthetics in the ggplot() function, rather than layer-specific aesthetics.
# describe the base ggplot object and tell it what data we are interested in along with the aesthetic mapping
gapminder %>%
filter(year == 2007) %>%
# specify global aesthetic mappings
ggplot(aes(x = gdpPercap, y = lifeExp)) +
# add a points layer on top
geom_point() +
# add a smoothed LOESS layer
geom_smooth(method = "loess")
We could also combine a points geom layer with a line geom layer, or any other type of geom layer. Line plots work well for plotting time series, so below we plot the average life expectancy over time using both point and line layers.
Here you can do some clever combinations of dplyr manipulations with ggplot2 by summarising life expectancy by year and piping the results into a ggplot without having to define any intermediate variables.
gapminder %>%
# calcualte the average life expectency for each year
group_by(year) %>%
summarise(avg_lifeExp = mean(lifeExp)) %>%
ungroup() %>%
# specify global aesthetic mappings
ggplot(aes(x = year, y = avg_lifeExp)) +
# add a points layer on top
geom_point() +
# add a line layer on top
geom_line()
If you wanted to have a separate line on our plot for each continent (rather than an aggregated line across all continents), you don’t need to add an individual layer for each continent to get the following plot:
Instead, start by also grouping by continent when you calculate the average life expectency by year.
gapminder %>%
group_by(continent, year) %>%
summarise(avg_lifeExp = mean(lifeExp))## # A tibble: 60 x 3
## # Groups: continent [5]
## continent year avg_lifeExp
## <fct> <int> <dbl>
## 1 Africa 1952 39.1
## 2 Africa 1957 41.3
## 3 Africa 1962 43.3
## 4 Africa 1967 45.3
## 5 Africa 1972 47.5
## 6 Africa 1977 49.6
## 7 Africa 1982 51.6
## 8 Africa 1987 53.3
## 9 Africa 1992 53.6
## 10 Africa 1997 53.6
## # … with 50 more rowsHowever if you try to use the same code as above to plot a line on the country-year grouped data frame, you get a weird zig-zag pattern.
gapminder %>%
group_by(continent, year) %>%
summarise(avg_lifeExp = mean(lifeExp)) %>%
ungroup() %>%
ggplot() +
# add a points layer on top
geom_point(aes(x = year, y = avg_lifeExp)) +
# add a lines layer ontop
geom_line(aes(x = year, y = avg_lifeExp))
This happens because you now have multiple average life expectancy values for each year, but you haven’t specified which ones go together. To fix this plot, you need to specify how the rows are grouped together (i.e. which variable defines the individual lines) by specifying the group = continent argument in the aes() function of the geom_line() layer.
gapminder %>%
group_by(continent, year) %>%
summarise(avg_lifeExp = mean(lifeExp)) %>%
ggplot() +
# add a points layer on top
geom_point(aes(x = year, y = avg_lifeExp)) +
# add a lines layer on top that is grouped by continent
geom_line(aes(x = year, y = avg_lifeExp, group = continent))
More aesthetic mappings based on variables
So far we have only specified the x- and y-position aesthetic mappings from the data to the geom objects. But you can also specify other types of aesthetic mappings, such as using a variable to specify the colour of the points.
If you want all of the points to be the same colour, you can specify a global point colour argument (that lies outside the aes() function).
gapminder %>%
ggplot() +
geom_point(aes(x = gdpPercap, y = lifeExp),
col = "cornflowerblue")
However, if you wanted to use a variable from the data frame to define the colour (or any other aesthetic feature) of the geoms, you will need to include it inside the aes() function.
gapminder %>%
ggplot() +
geom_point(aes(x = gdpPercap,
y = lifeExp,
col = continent))
Note that the continent variable does not specify the colours themselves: this is done automatically. You can specify the colours you want yourself by adding a scale layer for colour.
gapminder %>%
ggplot() +
geom_point(aes(x = gdpPercap,
y = lifeExp,
col = continent)) +
scale_colour_manual(values = c("orange", "red4", "purple", "darkgreen", "blue"))
There are lots of types of scales that you can use for every type of aesthetic mapping (including x- and y-positions), and typically scales are specific to whether your variable using in the aesthetic mapping is discrete or continuous.
We could also add aesthetic mappings for other features such as shape, size, transparency (alpha) and more! For example, changing the size based on population:
gapminder %>%
ggplot() +
geom_point(aes(x = gdpPercap, y = lifeExp,
col = continent, size = pop),
alpha = 0.5)
For the line plot example above where we plotted an average life expectancy time line for each continent, instead of specifying a group argument, you could instead specify a colour argument to be continent. This will will automatically group and colour by continent.
gapminder %>%
group_by(continent, year) %>%
summarise(avg_lifeExp = mean(lifeExp)) %>%
# specify global aesthetic mappings
ggplot() +
# add a points layer on top
geom_line(aes(x = year, y = avg_lifeExp, colour = continent))
Other types of layers
So far, we have only seen scatterplots (points) and line plots, however, there are many other geoms you could add, including:
Histograms
Histograms only require an x-aesthetic (the y-aesthetic is a count by default, but you can force it to be a density by specifying y = ..density..).
gapminder %>%
ggplot() +
geom_histogram(aes(x = lifeExp), binwidth = 3)
Boxplots
Boxplots are automatically grouped by the x-aesthetic provided (e.g. continent in the plot below). To colour boxplots, use the fill argument instead of the col (or color/colour) argument.
gapminder %>%
ggplot() +
geom_boxplot(aes(x = continent, y = lifeExp, fill = continent))
Faceting
You can create a grid (or “facet”) of plots separated by a categorical variable of your choosing (e.g. continent) by adding a facet layer.
gapminder %>%
ggplot() +
geom_point(aes(x = gdpPercap, y = lifeExp)) +
facet_wrap(~continent, ncol = 2)
Customizing ggplot2
While we have stayed within the default ggplot2 functionalities here, there is a lot you can do with ggplot2. For instance, with practice, you will learn how to produce highly-customized plots by combining many layers together. As motivation, here is a much more beautiful plot that can be made with ggplot2:
gapminder %>%
filter(year == 2007) %>%
ggplot() +
# add scatter points
geom_point(aes(x = gdpPercap, y = lifeExp, col = continent, size = pop),
alpha = 0.5) +
# add some text annotations for the very large countries
geom_text(aes(x = gdpPercap, y = lifeExp + 3, label = country),
col = "grey50",
data = filter(gapminder, year == 2007, pop > 1000000000 | country %in% c("Nigeria", "United States"))) +
# clean the axes names and breaks
scale_x_log10(limits = c(200, 60000)) +
# change labels
labs(title = "GDP versus life expectancy in 2007",
x = "GDP per capita (log scale)",
y = "Life expectancy",
size = "Population",
col = "Continent") +
# change the size scale
scale_size(range = c(0.1, 10),
# remove size legend
guide = "none") +
# add a nicer theme
theme_classic() +
# place legend at top and grey axis lines
theme(legend.position = "top")
In part two of this post on the tidyverse, you will see some ggplot2 code (under the guise of learning about factors) that makes this plot:
If you’d like to learn more about ggplot2, such as themes, scales and advanced geoms, check out my more detailed ggplot2 blog post.
If the tidyverse is new to you, I suggest that you stop here for now. Focus on incorporating piping, dplyr, and ggplot2 into every analysis that you do for the next few months (even if it would initially be quicker to use base R versions). When you feel comfortable with your new skills, move onto part two of this blog post and start to incorporate the remaining tidyverse packages (below) into your analytic workflow. Trying to learn everything at once is a sure-fire way to become discouraged. First get comfortable with the main ideas, then learn some more.
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