├── .gitignore ├── R-Data-Visualization.Rproj ├── README.md ├── data ├── gapminder-FiveYearData.csv └── heart.csv ├── images ├── bars.png ├── circles.png ├── two-series-0.png └── two-series-1.png ├── lessons ├── R-Data-Visualization-Challenges.Rmd └── R-Data-Visualization.Rmd └── solutions └── R-Data-Visualization-Solutions.Rmd /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | 3 | # History files 4 | .Rhistory 5 | .Rapp.history 6 | 7 | # Session Data files 8 | .RData 9 | 10 | # Example code in package build process 11 | *-Ex.R 12 | 13 | # Output files from R CMD build 14 | /*.tar.gz 15 | 16 | # Output files from R CMD check 17 | /*.Rcheck/ 18 | 19 | # RStudio files 20 | .Rproj.user/ 21 | 22 | # produced vignettes 23 | vignettes/*.html 24 | vignettes/*.pdf 25 | 26 | # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 27 | .httr-oauth 28 | 29 | # knitr and R markdown default cache directories 30 | /*_cache/ 31 | /cache/ 32 | 33 | # Temporary files created by R markdown 34 | *.utf8.md 35 | *.knit.md 36 | .Rproj.user 37 | -------------------------------------------------------------------------------- /R-Data-Visualization.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # D-Lab's R Data Visualization Workshop 2 | 3 | [![Datahub](https://img.shields.io/badge/launch-datahub-blue)](http://dlab.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fdlab-berkeley%2FR-Data-Visualization&urlpath=rstudio%2F) [![Binder](http://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/dlab-berkeley/R-Data-Visualization/HEAD?urlpath=rstudio) 4 | 5 | 6 | This repository contains the materials for D-Lab's R Data Visualization workshop. Prior experience with [R Fundamentals](https://github.com/dlab-berkeley/R-Fundamentals) is assumed. 7 | 8 | Check D-Lab's [Learning Pathways](https://dlab-berkeley.github.io/dlab-workshops/R_path.html) to figure out which of our workshops to take! 9 | 10 | 11 | ## Workshop Goals 12 | 13 | In this workshop, we provide an introduction to data visualization in R. First, we'll cover some basics of visualization theory. Then, we'll explore how to plot data in R using base R functions as well as `ggplot2`. We aim to cover the following types of plots: 14 | 15 | * line plots 16 | * bar plots 17 | * scatter plots 18 | * boxplots 19 | * heat maps 20 | 21 | We will also explore the basic grammar of graphics, including the aesthetics and geometry layers, adding statistics, transforming scales, and coloring or paneling by groups. Throughout the workshop, we'll discuss the plot types best suited for particular types of data. 22 | 23 | Basic familiarity with R *is* assumed. If you are not familiar with material in [R Fundamentals](https://github.com/dlab-berkeley/R-Fundamentals), we recommend attending that workshop first. 24 | 25 | ## Installation Instructions 26 | 27 | We will use RStudio to go through the workshop materials, which requires installation of both the R language and the RStudio software. Complete the following steps: 28 | 29 | 1. [Download R](https://cloud.r-project.org/): Follow the links according to the operating system that you are running. Download the package, and install R onto your compute. You should install the most recent version (at least version 4.0). 30 | 2. [Download RStudio](https://rstudio.com/products/rstudio/download/#download): Install RStudio Desktop. This should be free. Do this after you have already installed R. The D-Lab strongly recommends an RStudio edition of 2022.02.0+443 "Prairie Trillium" or higher. 31 | 3. [Download these workshop materials](https://github.com/dlab-berkeley/R-Data-Visualization): 32 | 33 | * Click the green "Code" button in the top right of the repository information. 34 | * Click "Download Zip". 35 | * Extract this file to a folder on your computer where you can easily access it (we recommend Desktop). 36 | 37 | 4. Optional: if you're familiar with `git`, you can instead clone this repository by opening a terminal and entering `git clone git@github.com:dlab-berkeley/R-Data-Visualization.git`. 38 | 39 | ## Run the code 40 | 41 | Now that you have all the required software and materials, you need to run the code: 42 | 43 | 1. Launch the RStudio software. 44 | 45 | 2. Use the file navigator to find the `R-Data-Visualization` folder that you downloaded from Github. 46 | 47 | 3. Double click on the `R-Data-Visualization.Rproj` file, and click "yes" when RStudio asks you to confirm whether you want to open up the project. 48 | 49 | 4. Open up the `R-Data-Visualization.Rmd` file, located in the `lessons` folder. 50 | 51 | 5. If you do not have the `cowplot`, `dplyr`, `ggplot2`, and `here` packages installed, be sure to install them using the `install.packages()` function in the first code block of the `R-Data-Visualization.Rmd` file. 52 | 53 | 6. Run a chunk of code by clicking the green "play" button in the upper right hand corner of each code chunk. Alternatively, place your cursor on a given line and press "Command + Enter" (Mac) or "Control + Enter" (PC) to run an individual line of code. 54 | 55 | 7. The file `R-Data-Visualization-Challenges.Rmd` contains space for you to work on five challenge problems. The file `R-Data-Visualization-Challenges-Solutions.Rmd` contains the solutions to these challenges. 56 | 57 | ## Is R not working on your laptop? 58 | 59 | If you do not have R installed and the materials loaded on your workshop by the time it starts, we *strongly* recommend using the UC Berkeley Datahub to run the materials for these lessons. You can access the DataHub by clicking [this link](https://datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fdlab-berkeley%2FR-Data-Visualization&urlpath=rstudio%2F&branch=main). 60 | 61 | The DataHub downloads this repository, along with any necessary packages, and allows you to run the materials in an RStudio instance on UC Berkeley's servers. No installation is necessary from your end - you only need an internet browser and a CalNet ID to log in. By using the DataHub, you can save your work and come back to it at any time. When you want to return to your saved work, just go straight to [DataHub](https://datahub.berkeley.edu), sign in, and you click on the `R-Data-Visualization` folder. 62 | 63 | # Additional Resources 64 | 65 | Check out the following resources to learn more about data visualization and R: 66 | 67 | * [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/) 68 | * [The tidyverse style guide](http://style.tidyverse.org/) 69 | * [Software Carpentry](https://swcarpentry.github.io/) 70 | * [Quick-R](http://statmethods.net/) 71 | * [UCLA idre](https://stats.idre.ucla.edu/r/) 72 | * [R-bloggers](https://www.r-bloggers.com/) 73 | * [Stack Overflow - R](http://stackoverflow.com/questions/tagged/r) 74 | 75 | # About the UC Berkeley D-Lab 76 | 77 | D-Lab works with Berkeley faculty, research staff, and students to advance data-intensive social science and humanities research. Our goal at D-Lab is to provide practical training, staff support, resources, and space to enable you to use R for your own research applications. Our services cater to all skill levels and no programming, statistical, or computer science backgrounds are necessary. We offer these services in the form of workshops, one-to-one consulting, and working groups that cover a variety of research topics, digital tools, and programming languages. 78 | 79 | Visit the [D-Lab homepage](https://dlab.berkeley.edu/) to learn more about us. You can view our [calendar](https://dlab.berkeley.edu/events/calendar) for upcoming events, learn about how to utilize our [consulting](https://dlab.berkeley.edu/consulting) and [data](https://dlab.berkeley.edu/data) services, and check out upcoming [workshops](https://dlab.berkeley.edu/events/workshops). 80 | 81 | # Other D-Lab R Workshops 82 | 83 | Here are other R workshops offered by the D-Lab: 84 | 85 | ## Basic Competency 86 | 87 | * [Fast-R](https://github.com/dlab-berkeley/Fast-R) 88 | * [R Data Wrangling](https://github.com/dlab-berkeley/R-wrang) 89 | * [R Functional Programming](https://github.com/dlab-berkeley/R-functional-programming) 90 | * [Geospatial Fundamentals in R with sf](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-R-with-sf) 91 | * [Census Data in R](https://github.com/dlab-berkeley/Census-Data-in-R) 92 | 93 | ## Intermediate/Advanced Competency 94 | 95 | * [Advanced Data Wrangling in R](https://github.com/dlab-berkeley/advanced-data-wrangling-in-R) 96 | * [Unsupervised Learning in R](https://github.com/dlab-berkeley/Unsupervised-Learning-in-R) 97 | * [R Machine Learning with tidymodels](https://github.com/dlab-berkeley/Machine-Learning-with-tidymodels) 98 | * [Introduction to Deep Learning in R](https://github.com/dlab-berkeley/Deep-Learning-in-R) 99 | * [R Package Development](https://github.com/dlab-berkeley/R-package-development) 100 | 101 | # Contributors 102 | * [Alexander Stephenson](https://dlab.berkeley.edu/people/alex-stephenson) 103 | * [Pratik Sachdeva](https://dlab.berkeley.edu/people/pratik-sachdeva) 104 | * [Evan Muzzall](https://dlab.berkeley.edu/people/evan-muzzall) 105 | * Lawrence Yin Tello 106 | * Josh Quan 107 | 108 | Thanks to [Software Carpentry](http://software-carpentry.org/workshops/), Chris Paciorek, Rochelle Terman, and the [R-bootcamp](https://dlab.berkeley.edu/training/r-bootcamp-3) for inspiration. 109 | -------------------------------------------------------------------------------- /data/gapminder-FiveYearData.csv: -------------------------------------------------------------------------------- 1 | country,year,pop,continent,lifeExp,gdpPercap 2 | Afghanistan,1952,8425333,Asia,28.801,779.4453145 3 | Afghanistan,1957,9240934,Asia,30.332,820.8530296 4 | Afghanistan,1962,10267083,Asia,31.997,853.10071 5 | Afghanistan,1967,11537966,Asia,34.02,836.1971382 6 | Afghanistan,1972,13079460,Asia,36.088,739.9811058 7 | Afghanistan,1977,14880372,Asia,38.438,786.11336 8 | Afghanistan,1982,12881816,Asia,39.854,978.0114388 9 | Afghanistan,1987,13867957,Asia,40.822,852.3959448 10 | Afghanistan,1992,16317921,Asia,41.674,649.3413952 11 | Afghanistan,1997,22227415,Asia,41.763,635.341351 12 | Afghanistan,2002,25268405,Asia,42.129,726.7340548 13 | Afghanistan,2007,31889923,Asia,43.828,974.5803384 14 | Albania,1952,1282697,Europe,55.23,1601.056136 15 | Albania,1957,1476505,Europe,59.28,1942.284244 16 | Albania,1962,1728137,Europe,64.82,2312.888958 17 | Albania,1967,1984060,Europe,66.22,2760.196931 18 | Albania,1972,2263554,Europe,67.69,3313.422188 19 | Albania,1977,2509048,Europe,68.93,3533.00391 20 | Albania,1982,2780097,Europe,70.42,3630.880722 21 | Albania,1987,3075321,Europe,72,3738.932735 22 | Albania,1992,3326498,Europe,71.581,2497.437901 23 | Albania,1997,3428038,Europe,72.95,3193.054604 24 | Albania,2002,3508512,Europe,75.651,4604.211737 25 | Albania,2007,3600523,Europe,76.423,5937.029526 26 | Algeria,1952,9279525,Africa,43.077,2449.008185 27 | Algeria,1957,10270856,Africa,45.685,3013.976023 28 | Algeria,1962,11000948,Africa,48.303,2550.81688 29 | Algeria,1967,12760499,Africa,51.407,3246.991771 30 | Algeria,1972,14760787,Africa,54.518,4182.663766 31 | Algeria,1977,17152804,Africa,58.014,4910.416756 32 | Algeria,1982,20033753,Africa,61.368,5745.160213 33 | Algeria,1987,23254956,Africa,65.799,5681.358539 34 | Algeria,1992,26298373,Africa,67.744,5023.216647 35 | Algeria,1997,29072015,Africa,69.152,4797.295051 36 | Algeria,2002,31287142,Africa,70.994,5288.040382 37 | Algeria,2007,33333216,Africa,72.301,6223.367465 38 | Angola,1952,4232095,Africa,30.015,3520.610273 39 | Angola,1957,4561361,Africa,31.999,3827.940465 40 | Angola,1962,4826015,Africa,34,4269.276742 41 | Angola,1967,5247469,Africa,35.985,5522.776375 42 | Angola,1972,5894858,Africa,37.928,5473.288005 43 | Angola,1977,6162675,Africa,39.483,3008.647355 44 | Angola,1982,7016384,Africa,39.942,2756.953672 45 | Angola,1987,7874230,Africa,39.906,2430.208311 46 | Angola,1992,8735988,Africa,40.647,2627.845685 47 | Angola,1997,9875024,Africa,40.963,2277.140884 48 | Angola,2002,10866106,Africa,41.003,2773.287312 49 | Angola,2007,12420476,Africa,42.731,4797.231267 50 | Argentina,1952,17876956,Americas,62.485,5911.315053 51 | Argentina,1957,19610538,Americas,64.399,6856.856212 52 | Argentina,1962,21283783,Americas,65.142,7133.166023 53 | Argentina,1967,22934225,Americas,65.634,8052.953021 54 | Argentina,1972,24779799,Americas,67.065,9443.038526 55 | Argentina,1977,26983828,Americas,68.481,10079.02674 56 | Argentina,1982,29341374,Americas,69.942,8997.897412 57 | Argentina,1987,31620918,Americas,70.774,9139.671389 58 | Argentina,1992,33958947,Americas,71.868,9308.41871 59 | Argentina,1997,36203463,Americas,73.275,10967.28195 60 | Argentina,2002,38331121,Americas,74.34,8797.640716 61 | Argentina,2007,40301927,Americas,75.32,12779.37964 62 | Australia,1952,8691212,Oceania,69.12,10039.59564 63 | Australia,1957,9712569,Oceania,70.33,10949.64959 64 | Australia,1962,10794968,Oceania,70.93,12217.22686 65 | Australia,1967,11872264,Oceania,71.1,14526.12465 66 | Australia,1972,13177000,Oceania,71.93,16788.62948 67 | Australia,1977,14074100,Oceania,73.49,18334.19751 68 | Australia,1982,15184200,Oceania,74.74,19477.00928 69 | Australia,1987,16257249,Oceania,76.32,21888.88903 70 | Australia,1992,17481977,Oceania,77.56,23424.76683 71 | Australia,1997,18565243,Oceania,78.83,26997.93657 72 | Australia,2002,19546792,Oceania,80.37,30687.75473 73 | Australia,2007,20434176,Oceania,81.235,34435.36744 74 | Austria,1952,6927772,Europe,66.8,6137.076492 75 | Austria,1957,6965860,Europe,67.48,8842.59803 76 | Austria,1962,7129864,Europe,69.54,10750.72111 77 | Austria,1967,7376998,Europe,70.14,12834.6024 78 | Austria,1972,7544201,Europe,70.63,16661.6256 79 | Austria,1977,7568430,Europe,72.17,19749.4223 80 | Austria,1982,7574613,Europe,73.18,21597.08362 81 | Austria,1987,7578903,Europe,74.94,23687.82607 82 | Austria,1992,7914969,Europe,76.04,27042.01868 83 | Austria,1997,8069876,Europe,77.51,29095.92066 84 | Austria,2002,8148312,Europe,78.98,32417.60769 85 | Austria,2007,8199783,Europe,79.829,36126.4927 86 | Bahrain,1952,120447,Asia,50.939,9867.084765 87 | Bahrain,1957,138655,Asia,53.832,11635.79945 88 | Bahrain,1962,171863,Asia,56.923,12753.27514 89 | Bahrain,1967,202182,Asia,59.923,14804.6727 90 | Bahrain,1972,230800,Asia,63.3,18268.65839 91 | Bahrain,1977,297410,Asia,65.593,19340.10196 92 | Bahrain,1982,377967,Asia,69.052,19211.14731 93 | Bahrain,1987,454612,Asia,70.75,18524.02406 94 | Bahrain,1992,529491,Asia,72.601,19035.57917 95 | Bahrain,1997,598561,Asia,73.925,20292.01679 96 | Bahrain,2002,656397,Asia,74.795,23403.55927 97 | Bahrain,2007,708573,Asia,75.635,29796.04834 98 | Bangladesh,1952,46886859,Asia,37.484,684.2441716 99 | Bangladesh,1957,51365468,Asia,39.348,661.6374577 100 | Bangladesh,1962,56839289,Asia,41.216,686.3415538 101 | Bangladesh,1967,62821884,Asia,43.453,721.1860862 102 | Bangladesh,1972,70759295,Asia,45.252,630.2336265 103 | Bangladesh,1977,80428306,Asia,46.923,659.8772322 104 | Bangladesh,1982,93074406,Asia,50.009,676.9818656 105 | Bangladesh,1987,103764241,Asia,52.819,751.9794035 106 | Bangladesh,1992,113704579,Asia,56.018,837.8101643 107 | Bangladesh,1997,123315288,Asia,59.412,972.7700352 108 | Bangladesh,2002,135656790,Asia,62.013,1136.39043 109 | Bangladesh,2007,150448339,Asia,64.062,1391.253792 110 | Belgium,1952,8730405,Europe,68,8343.105127 111 | Belgium,1957,8989111,Europe,69.24,9714.960623 112 | Belgium,1962,9218400,Europe,70.25,10991.20676 113 | Belgium,1967,9556500,Europe,70.94,13149.04119 114 | Belgium,1972,9709100,Europe,71.44,16672.14356 115 | Belgium,1977,9821800,Europe,72.8,19117.97448 116 | Belgium,1982,9856303,Europe,73.93,20979.84589 117 | Belgium,1987,9870200,Europe,75.35,22525.56308 118 | Belgium,1992,10045622,Europe,76.46,25575.57069 119 | Belgium,1997,10199787,Europe,77.53,27561.19663 120 | Belgium,2002,10311970,Europe,78.32,30485.88375 121 | Belgium,2007,10392226,Europe,79.441,33692.60508 122 | Benin,1952,1738315,Africa,38.223,1062.7522 123 | Benin,1957,1925173,Africa,40.358,959.6010805 124 | Benin,1962,2151895,Africa,42.618,949.4990641 125 | Benin,1967,2427334,Africa,44.885,1035.831411 126 | Benin,1972,2761407,Africa,47.014,1085.796879 127 | Benin,1977,3168267,Africa,49.19,1029.161251 128 | Benin,1982,3641603,Africa,50.904,1277.897616 129 | Benin,1987,4243788,Africa,52.337,1225.85601 130 | Benin,1992,4981671,Africa,53.919,1191.207681 131 | Benin,1997,6066080,Africa,54.777,1232.975292 132 | Benin,2002,7026113,Africa,54.406,1372.877931 133 | Benin,2007,8078314,Africa,56.728,1441.284873 134 | Bolivia,1952,2883315,Americas,40.414,2677.326347 135 | Bolivia,1957,3211738,Americas,41.89,2127.686326 136 | Bolivia,1962,3593918,Americas,43.428,2180.972546 137 | Bolivia,1967,4040665,Americas,45.032,2586.886053 138 | Bolivia,1972,4565872,Americas,46.714,2980.331339 139 | Bolivia,1977,5079716,Americas,50.023,3548.097832 140 | Bolivia,1982,5642224,Americas,53.859,3156.510452 141 | Bolivia,1987,6156369,Americas,57.251,2753.69149 142 | Bolivia,1992,6893451,Americas,59.957,2961.699694 143 | Bolivia,1997,7693188,Americas,62.05,3326.143191 144 | Bolivia,2002,8445134,Americas,63.883,3413.26269 145 | Bolivia,2007,9119152,Americas,65.554,3822.137084 146 | Bosnia and Herzegovina,1952,2791000,Europe,53.82,973.5331948 147 | Bosnia and Herzegovina,1957,3076000,Europe,58.45,1353.989176 148 | Bosnia and Herzegovina,1962,3349000,Europe,61.93,1709.683679 149 | Bosnia and Herzegovina,1967,3585000,Europe,64.79,2172.352423 150 | Bosnia and Herzegovina,1972,3819000,Europe,67.45,2860.16975 151 | Bosnia and Herzegovina,1977,4086000,Europe,69.86,3528.481305 152 | Bosnia and Herzegovina,1982,4172693,Europe,70.69,4126.613157 153 | Bosnia and Herzegovina,1987,4338977,Europe,71.14,4314.114757 154 | Bosnia and Herzegovina,1992,4256013,Europe,72.178,2546.781445 155 | Bosnia and Herzegovina,1997,3607000,Europe,73.244,4766.355904 156 | Bosnia and Herzegovina,2002,4165416,Europe,74.09,6018.975239 157 | Bosnia and Herzegovina,2007,4552198,Europe,74.852,7446.298803 158 | Botswana,1952,442308,Africa,47.622,851.2411407 159 | Botswana,1957,474639,Africa,49.618,918.2325349 160 | Botswana,1962,512764,Africa,51.52,983.6539764 161 | Botswana,1967,553541,Africa,53.298,1214.709294 162 | Botswana,1972,619351,Africa,56.024,2263.611114 163 | Botswana,1977,781472,Africa,59.319,3214.857818 164 | Botswana,1982,970347,Africa,61.484,4551.14215 165 | Botswana,1987,1151184,Africa,63.622,6205.88385 166 | Botswana,1992,1342614,Africa,62.745,7954.111645 167 | Botswana,1997,1536536,Africa,52.556,8647.142313 168 | Botswana,2002,1630347,Africa,46.634,11003.60508 169 | Botswana,2007,1639131,Africa,50.728,12569.85177 170 | Brazil,1952,56602560,Americas,50.917,2108.944355 171 | Brazil,1957,65551171,Americas,53.285,2487.365989 172 | Brazil,1962,76039390,Americas,55.665,3336.585802 173 | Brazil,1967,88049823,Americas,57.632,3429.864357 174 | Brazil,1972,100840058,Americas,59.504,4985.711467 175 | Brazil,1977,114313951,Americas,61.489,6660.118654 176 | Brazil,1982,128962939,Americas,63.336,7030.835878 177 | Brazil,1987,142938076,Americas,65.205,7807.095818 178 | Brazil,1992,155975974,Americas,67.057,6950.283021 179 | Brazil,1997,168546719,Americas,69.388,7957.980824 180 | Brazil,2002,179914212,Americas,71.006,8131.212843 181 | Brazil,2007,190010647,Americas,72.39,9065.800825 182 | Bulgaria,1952,7274900,Europe,59.6,2444.286648 183 | Bulgaria,1957,7651254,Europe,66.61,3008.670727 184 | Bulgaria,1962,8012946,Europe,69.51,4254.337839 185 | Bulgaria,1967,8310226,Europe,70.42,5577.0028 186 | Bulgaria,1972,8576200,Europe,70.9,6597.494398 187 | Bulgaria,1977,8797022,Europe,70.81,7612.240438 188 | Bulgaria,1982,8892098,Europe,71.08,8224.191647 189 | Bulgaria,1987,8971958,Europe,71.34,8239.854824 190 | Bulgaria,1992,8658506,Europe,71.19,6302.623438 191 | Bulgaria,1997,8066057,Europe,70.32,5970.38876 192 | Bulgaria,2002,7661799,Europe,72.14,7696.777725 193 | Bulgaria,2007,7322858,Europe,73.005,10680.79282 194 | Burkina Faso,1952,4469979,Africa,31.975,543.2552413 195 | Burkina Faso,1957,4713416,Africa,34.906,617.1834648 196 | Burkina Faso,1962,4919632,Africa,37.814,722.5120206 197 | Burkina Faso,1967,5127935,Africa,40.697,794.8265597 198 | Burkina Faso,1972,5433886,Africa,43.591,854.7359763 199 | Burkina Faso,1977,5889574,Africa,46.137,743.3870368 200 | Burkina Faso,1982,6634596,Africa,48.122,807.1985855 201 | Burkina Faso,1987,7586551,Africa,49.557,912.0631417 202 | Burkina Faso,1992,8878303,Africa,50.26,931.7527731 203 | Burkina Faso,1997,10352843,Africa,50.324,946.2949618 204 | Burkina Faso,2002,12251209,Africa,50.65,1037.645221 205 | Burkina Faso,2007,14326203,Africa,52.295,1217.032994 206 | Burundi,1952,2445618,Africa,39.031,339.2964587 207 | Burundi,1957,2667518,Africa,40.533,379.5646281 208 | Burundi,1962,2961915,Africa,42.045,355.2032273 209 | Burundi,1967,3330989,Africa,43.548,412.9775136 210 | Burundi,1972,3529983,Africa,44.057,464.0995039 211 | Burundi,1977,3834415,Africa,45.91,556.1032651 212 | Burundi,1982,4580410,Africa,47.471,559.603231 213 | Burundi,1987,5126023,Africa,48.211,621.8188189 214 | Burundi,1992,5809236,Africa,44.736,631.6998778 215 | Burundi,1997,6121610,Africa,45.326,463.1151478 216 | Burundi,2002,7021078,Africa,47.36,446.4035126 217 | Burundi,2007,8390505,Africa,49.58,430.0706916 218 | Cambodia,1952,4693836,Asia,39.417,368.4692856 219 | Cambodia,1957,5322536,Asia,41.366,434.0383364 220 | Cambodia,1962,6083619,Asia,43.415,496.9136476 221 | Cambodia,1967,6960067,Asia,45.415,523.4323142 222 | Cambodia,1972,7450606,Asia,40.317,421.6240257 223 | Cambodia,1977,6978607,Asia,31.22,524.9721832 224 | Cambodia,1982,7272485,Asia,50.957,624.4754784 225 | Cambodia,1987,8371791,Asia,53.914,683.8955732 226 | Cambodia,1992,10150094,Asia,55.803,682.3031755 227 | Cambodia,1997,11782962,Asia,56.534,734.28517 228 | Cambodia,2002,12926707,Asia,56.752,896.2260153 229 | Cambodia,2007,14131858,Asia,59.723,1713.778686 230 | Cameroon,1952,5009067,Africa,38.523,1172.667655 231 | Cameroon,1957,5359923,Africa,40.428,1313.048099 232 | Cameroon,1962,5793633,Africa,42.643,1399.607441 233 | Cameroon,1967,6335506,Africa,44.799,1508.453148 234 | Cameroon,1972,7021028,Africa,47.049,1684.146528 235 | Cameroon,1977,7959865,Africa,49.355,1783.432873 236 | Cameroon,1982,9250831,Africa,52.961,2367.983282 237 | Cameroon,1987,10780667,Africa,54.985,2602.664206 238 | Cameroon,1992,12467171,Africa,54.314,1793.163278 239 | Cameroon,1997,14195809,Africa,52.199,1694.337469 240 | Cameroon,2002,15929988,Africa,49.856,1934.011449 241 | Cameroon,2007,17696293,Africa,50.43,2042.09524 242 | Canada,1952,14785584,Americas,68.75,11367.16112 243 | Canada,1957,17010154,Americas,69.96,12489.95006 244 | Canada,1962,18985849,Americas,71.3,13462.48555 245 | Canada,1967,20819767,Americas,72.13,16076.58803 246 | Canada,1972,22284500,Americas,72.88,18970.57086 247 | Canada,1977,23796400,Americas,74.21,22090.88306 248 | Canada,1982,25201900,Americas,75.76,22898.79214 249 | Canada,1987,26549700,Americas,76.86,26626.51503 250 | Canada,1992,28523502,Americas,77.95,26342.88426 251 | Canada,1997,30305843,Americas,78.61,28954.92589 252 | Canada,2002,31902268,Americas,79.77,33328.96507 253 | Canada,2007,33390141,Americas,80.653,36319.23501 254 | Central African Republic,1952,1291695,Africa,35.463,1071.310713 255 | Central African Republic,1957,1392284,Africa,37.464,1190.844328 256 | Central African Republic,1962,1523478,Africa,39.475,1193.068753 257 | Central African Republic,1967,1733638,Africa,41.478,1136.056615 258 | Central African Republic,1972,1927260,Africa,43.457,1070.013275 259 | Central African Republic,1977,2167533,Africa,46.775,1109.374338 260 | Central African Republic,1982,2476971,Africa,48.295,956.7529907 261 | Central African Republic,1987,2840009,Africa,50.485,844.8763504 262 | Central African Republic,1992,3265124,Africa,49.396,747.9055252 263 | Central African Republic,1997,3696513,Africa,46.066,740.5063317 264 | Central African Republic,2002,4048013,Africa,43.308,738.6906068 265 | Central African Republic,2007,4369038,Africa,44.741,706.016537 266 | Chad,1952,2682462,Africa,38.092,1178.665927 267 | Chad,1957,2894855,Africa,39.881,1308.495577 268 | Chad,1962,3150417,Africa,41.716,1389.817618 269 | Chad,1967,3495967,Africa,43.601,1196.810565 270 | Chad,1972,3899068,Africa,45.569,1104.103987 271 | Chad,1977,4388260,Africa,47.383,1133.98495 272 | Chad,1982,4875118,Africa,49.517,797.9081006 273 | Chad,1987,5498955,Africa,51.051,952.386129 274 | Chad,1992,6429417,Africa,51.724,1058.0643 275 | Chad,1997,7562011,Africa,51.573,1004.961353 276 | Chad,2002,8835739,Africa,50.525,1156.18186 277 | Chad,2007,10238807,Africa,50.651,1704.063724 278 | Chile,1952,6377619,Americas,54.745,3939.978789 279 | Chile,1957,7048426,Americas,56.074,4315.622723 280 | Chile,1962,7961258,Americas,57.924,4519.094331 281 | Chile,1967,8858908,Americas,60.523,5106.654313 282 | Chile,1972,9717524,Americas,63.441,5494.024437 283 | Chile,1977,10599793,Americas,67.052,4756.763836 284 | Chile,1982,11487112,Americas,70.565,5095.665738 285 | Chile,1987,12463354,Americas,72.492,5547.063754 286 | Chile,1992,13572994,Americas,74.126,7596.125964 287 | Chile,1997,14599929,Americas,75.816,10118.05318 288 | Chile,2002,15497046,Americas,77.86,10778.78385 289 | Chile,2007,16284741,Americas,78.553,13171.63885 290 | China,1952,556263527.999989,Asia,44,400.448610699994 291 | China,1957,637408000,Asia,50.54896,575.9870009 292 | China,1962,665770000,Asia,44.50136,487.6740183 293 | China,1967,754550000,Asia,58.38112,612.7056934 294 | China,1972,862030000,Asia,63.11888,676.9000921 295 | China,1977,943455000,Asia,63.96736,741.2374699 296 | China,1982,1000281000,Asia,65.525,962.4213805 297 | China,1987,1084035000,Asia,67.274,1378.904018 298 | China,1992,1164970000,Asia,68.69,1655.784158 299 | China,1997,1230075000,Asia,70.426,2289.234136 300 | China,2002,1280400000,Asia,72.028,3119.280896 301 | China,2007,1318683096,Asia,72.961,4959.114854 302 | Colombia,1952,12350771,Americas,50.643,2144.115096 303 | Colombia,1957,14485993,Americas,55.118,2323.805581 304 | Colombia,1962,17009885,Americas,57.863,2492.351109 305 | Colombia,1967,19764027,Americas,59.963,2678.729839 306 | Colombia,1972,22542890,Americas,61.623,3264.660041 307 | Colombia,1977,25094412,Americas,63.837,3815.80787 308 | Colombia,1982,27764644,Americas,66.653,4397.575659 309 | Colombia,1987,30964245,Americas,67.768,4903.2191 310 | Colombia,1992,34202721,Americas,68.421,5444.648617 311 | Colombia,1997,37657830,Americas,70.313,6117.361746 312 | Colombia,2002,41008227,Americas,71.682,5755.259962 313 | Colombia,2007,44227550,Americas,72.889,7006.580419 314 | Comoros,1952,153936,Africa,40.715,1102.990936 315 | Comoros,1957,170928,Africa,42.46,1211.148548 316 | Comoros,1962,191689,Africa,44.467,1406.648278 317 | Comoros,1967,217378,Africa,46.472,1876.029643 318 | Comoros,1972,250027,Africa,48.944,1937.577675 319 | Comoros,1977,304739,Africa,50.939,1172.603047 320 | Comoros,1982,348643,Africa,52.933,1267.100083 321 | Comoros,1987,395114,Africa,54.926,1315.980812 322 | Comoros,1992,454429,Africa,57.939,1246.90737 323 | Comoros,1997,527982,Africa,60.66,1173.618235 324 | Comoros,2002,614382,Africa,62.974,1075.811558 325 | Comoros,2007,710960,Africa,65.152,986.1478792 326 | Congo Dem. Rep.,1952,14100005,Africa,39.143,780.5423257 327 | Congo Dem. Rep.,1957,15577932,Africa,40.652,905.8602303 328 | Congo Dem. Rep.,1962,17486434,Africa,42.122,896.3146335 329 | Congo Dem. Rep.,1967,19941073,Africa,44.056,861.5932424 330 | Congo Dem. Rep.,1972,23007669,Africa,45.989,904.8960685 331 | Congo Dem. Rep.,1977,26480870,Africa,47.804,795.757282 332 | Congo Dem. Rep.,1982,30646495,Africa,47.784,673.7478181 333 | Congo Dem. Rep.,1987,35481645,Africa,47.412,672.774812 334 | Congo Dem. Rep.,1992,41672143,Africa,45.548,457.7191807 335 | Congo Dem. Rep.,1997,47798986,Africa,42.587,312.188423 336 | Congo Dem. Rep.,2002,55379852,Africa,44.966,241.1658765 337 | Congo Dem. Rep.,2007,64606759,Africa,46.462,277.5518587 338 | Congo Rep.,1952,854885,Africa,42.111,2125.621418 339 | Congo Rep.,1957,940458,Africa,45.053,2315.056572 340 | Congo Rep.,1962,1047924,Africa,48.435,2464.783157 341 | Congo Rep.,1967,1179760,Africa,52.04,2677.939642 342 | Congo Rep.,1972,1340458,Africa,54.907,3213.152683 343 | Congo Rep.,1977,1536769,Africa,55.625,3259.178978 344 | Congo Rep.,1982,1774735,Africa,56.695,4879.507522 345 | Congo Rep.,1987,2064095,Africa,57.47,4201.194937 346 | Congo Rep.,1992,2409073,Africa,56.433,4016.239529 347 | Congo Rep.,1997,2800947,Africa,52.962,3484.164376 348 | Congo Rep.,2002,3328795,Africa,52.97,3484.06197 349 | Congo Rep.,2007,3800610,Africa,55.322,3632.557798 350 | Costa Rica,1952,926317,Americas,57.206,2627.009471 351 | Costa Rica,1957,1112300,Americas,60.026,2990.010802 352 | Costa Rica,1962,1345187,Americas,62.842,3460.937025 353 | Costa Rica,1967,1588717,Americas,65.424,4161.727834 354 | Costa Rica,1972,1834796,Americas,67.849,5118.146939 355 | Costa Rica,1977,2108457,Americas,70.75,5926.876967 356 | Costa Rica,1982,2424367,Americas,73.45,5262.734751 357 | Costa Rica,1987,2799811,Americas,74.752,5629.915318 358 | Costa Rica,1992,3173216,Americas,75.713,6160.416317 359 | Costa Rica,1997,3518107,Americas,77.26,6677.045314 360 | Costa Rica,2002,3834934,Americas,78.123,7723.447195 361 | Costa Rica,2007,4133884,Americas,78.782,9645.06142 362 | "Cote d'Ivoire",1952,2977019,Africa,40.477,1388.594732 363 | "Cote d'Ivoire",1957,3300000,Africa,42.469,1500.895925 364 | "Cote d'Ivoire",1962,3832408,Africa,44.93,1728.869428 365 | "Cote d'Ivoire",1967,4744870,Africa,47.35,2052.050473 366 | "Cote d'Ivoire",1972,6071696,Africa,49.801,2378.201111 367 | "Cote d'Ivoire",1977,7459574,Africa,52.374,2517.736547 368 | "Cote d'Ivoire",1982,9025951,Africa,53.983,2602.710169 369 | "Cote d'Ivoire",1987,10761098,Africa,54.655,2156.956069 370 | "Cote d'Ivoire",1992,12772596,Africa,52.044,1648.073791 371 | "Cote d'Ivoire",1997,14625967,Africa,47.991,1786.265407 372 | "Cote d'Ivoire",2002,16252726,Africa,46.832,1648.800823 373 | "Cote d'Ivoire",2007,18013409,Africa,48.328,1544.750112 374 | Croatia,1952,3882229,Europe,61.21,3119.23652 375 | Croatia,1957,3991242,Europe,64.77,4338.231617 376 | Croatia,1962,4076557,Europe,67.13,5477.890018 377 | Croatia,1967,4174366,Europe,68.5,6960.297861 378 | Croatia,1972,4225310,Europe,69.61,9164.090127 379 | Croatia,1977,4318673,Europe,70.64,11305.38517 380 | Croatia,1982,4413368,Europe,70.46,13221.82184 381 | Croatia,1987,4484310,Europe,71.52,13822.58394 382 | Croatia,1992,4494013,Europe,72.527,8447.794873 383 | Croatia,1997,4444595,Europe,73.68,9875.604515 384 | Croatia,2002,4481020,Europe,74.876,11628.38895 385 | Croatia,2007,4493312,Europe,75.748,14619.22272 386 | Cuba,1952,6007797,Americas,59.421,5586.53878 387 | Cuba,1957,6640752,Americas,62.325,6092.174359 388 | Cuba,1962,7254373,Americas,65.246,5180.75591 389 | Cuba,1967,8139332,Americas,68.29,5690.268015 390 | Cuba,1972,8831348,Americas,70.723,5305.445256 391 | Cuba,1977,9537988,Americas,72.649,6380.494966 392 | Cuba,1982,9789224,Americas,73.717,7316.918107 393 | Cuba,1987,10239839,Americas,74.174,7532.924763 394 | Cuba,1992,10723260,Americas,74.414,5592.843963 395 | Cuba,1997,10983007,Americas,76.151,5431.990415 396 | Cuba,2002,11226999,Americas,77.158,6340.646683 397 | Cuba,2007,11416987,Americas,78.273,8948.102923 398 | Czech Republic,1952,9125183,Europe,66.87,6876.14025 399 | Czech Republic,1957,9513758,Europe,69.03,8256.343918 400 | Czech Republic,1962,9620282,Europe,69.9,10136.86713 401 | Czech Republic,1967,9835109,Europe,70.38,11399.44489 402 | Czech Republic,1972,9862158,Europe,70.29,13108.4536 403 | Czech Republic,1977,10161915,Europe,70.71,14800.16062 404 | Czech Republic,1982,10303704,Europe,70.96,15377.22855 405 | Czech Republic,1987,10311597,Europe,71.58,16310.4434 406 | Czech Republic,1992,10315702,Europe,72.4,14297.02122 407 | Czech Republic,1997,10300707,Europe,74.01,16048.51424 408 | Czech Republic,2002,10256295,Europe,75.51,17596.21022 409 | Czech Republic,2007,10228744,Europe,76.486,22833.30851 410 | Denmark,1952,4334000,Europe,70.78,9692.385245 411 | Denmark,1957,4487831,Europe,71.81,11099.65935 412 | Denmark,1962,4646899,Europe,72.35,13583.31351 413 | Denmark,1967,4838800,Europe,72.96,15937.21123 414 | Denmark,1972,4991596,Europe,73.47,18866.20721 415 | Denmark,1977,5088419,Europe,74.69,20422.9015 416 | Denmark,1982,5117810,Europe,74.63,21688.04048 417 | Denmark,1987,5127024,Europe,74.8,25116.17581 418 | Denmark,1992,5171393,Europe,75.33,26406.73985 419 | Denmark,1997,5283663,Europe,76.11,29804.34567 420 | Denmark,2002,5374693,Europe,77.18,32166.50006 421 | Denmark,2007,5468120,Europe,78.332,35278.41874 422 | Djibouti,1952,63149,Africa,34.812,2669.529475 423 | Djibouti,1957,71851,Africa,37.328,2864.969076 424 | Djibouti,1962,89898,Africa,39.693,3020.989263 425 | Djibouti,1967,127617,Africa,42.074,3020.050513 426 | Djibouti,1972,178848,Africa,44.366,3694.212352 427 | Djibouti,1977,228694,Africa,46.519,3081.761022 428 | Djibouti,1982,305991,Africa,48.812,2879.468067 429 | Djibouti,1987,311025,Africa,50.04,2880.102568 430 | Djibouti,1992,384156,Africa,51.604,2377.156192 431 | Djibouti,1997,417908,Africa,53.157,1895.016984 432 | Djibouti,2002,447416,Africa,53.373,1908.260867 433 | Djibouti,2007,496374,Africa,54.791,2082.481567 434 | Dominican Republic,1952,2491346,Americas,45.928,1397.717137 435 | Dominican Republic,1957,2923186,Americas,49.828,1544.402995 436 | Dominican Republic,1962,3453434,Americas,53.459,1662.137359 437 | Dominican Republic,1967,4049146,Americas,56.751,1653.723003 438 | Dominican Republic,1972,4671329,Americas,59.631,2189.874499 439 | Dominican Republic,1977,5302800,Americas,61.788,2681.9889 440 | Dominican Republic,1982,5968349,Americas,63.727,2861.092386 441 | Dominican Republic,1987,6655297,Americas,66.046,2899.842175 442 | Dominican Republic,1992,7351181,Americas,68.457,3044.214214 443 | Dominican Republic,1997,7992357,Americas,69.957,3614.101285 444 | Dominican Republic,2002,8650322,Americas,70.847,4563.808154 445 | Dominican Republic,2007,9319622,Americas,72.235,6025.374752 446 | Ecuador,1952,3548753,Americas,48.357,3522.110717 447 | Ecuador,1957,4058385,Americas,51.356,3780.546651 448 | Ecuador,1962,4681707,Americas,54.64,4086.114078 449 | Ecuador,1967,5432424,Americas,56.678,4579.074215 450 | Ecuador,1972,6298651,Americas,58.796,5280.99471 451 | Ecuador,1977,7278866,Americas,61.31,6679.62326 452 | Ecuador,1982,8365850,Americas,64.342,7213.791267 453 | Ecuador,1987,9545158,Americas,67.231,6481.776993 454 | Ecuador,1992,10748394,Americas,69.613,7103.702595 455 | Ecuador,1997,11911819,Americas,72.312,7429.455877 456 | Ecuador,2002,12921234,Americas,74.173,5773.044512 457 | Ecuador,2007,13755680,Americas,74.994,6873.262326 458 | Egypt,1952,22223309,Africa,41.893,1418.822445 459 | Egypt,1957,25009741,Africa,44.444,1458.915272 460 | Egypt,1962,28173309,Africa,46.992,1693.335853 461 | Egypt,1967,31681188,Africa,49.293,1814.880728 462 | Egypt,1972,34807417,Africa,51.137,2024.008147 463 | Egypt,1977,38783863,Africa,53.319,2785.493582 464 | Egypt,1982,45681811,Africa,56.006,3503.729636 465 | Egypt,1987,52799062,Africa,59.797,3885.46071 466 | Egypt,1992,59402198,Africa,63.674,3794.755195 467 | Egypt,1997,66134291,Africa,67.217,4173.181797 468 | Egypt,2002,73312559,Africa,69.806,4754.604414 469 | Egypt,2007,80264543,Africa,71.338,5581.180998 470 | El Salvador,1952,2042865,Americas,45.262,3048.3029 471 | El Salvador,1957,2355805,Americas,48.57,3421.523218 472 | El Salvador,1962,2747687,Americas,52.307,3776.803627 473 | El Salvador,1967,3232927,Americas,55.855,4358.595393 474 | El Salvador,1972,3790903,Americas,58.207,4520.246008 475 | El Salvador,1977,4282586,Americas,56.696,5138.922374 476 | El Salvador,1982,4474873,Americas,56.604,4098.344175 477 | El Salvador,1987,4842194,Americas,63.154,4140.442097 478 | El Salvador,1992,5274649,Americas,66.798,4444.2317 479 | El Salvador,1997,5783439,Americas,69.535,5154.825496 480 | El Salvador,2002,6353681,Americas,70.734,5351.568666 481 | El Salvador,2007,6939688,Americas,71.878,5728.353514 482 | Equatorial Guinea,1952,216964,Africa,34.482,375.6431231 483 | Equatorial Guinea,1957,232922,Africa,35.983,426.0964081 484 | Equatorial Guinea,1962,249220,Africa,37.485,582.8419714 485 | Equatorial Guinea,1967,259864,Africa,38.987,915.5960025 486 | Equatorial Guinea,1972,277603,Africa,40.516,672.4122571 487 | Equatorial Guinea,1977,192675,Africa,42.024,958.5668124 488 | Equatorial Guinea,1982,285483,Africa,43.662,927.8253427 489 | Equatorial Guinea,1987,341244,Africa,45.664,966.8968149 490 | Equatorial Guinea,1992,387838,Africa,47.545,1132.055034 491 | Equatorial Guinea,1997,439971,Africa,48.245,2814.480755 492 | Equatorial Guinea,2002,495627,Africa,49.348,7703.4959 493 | Equatorial Guinea,2007,551201,Africa,51.579,12154.08975 494 | Eritrea,1952,1438760,Africa,35.928,328.9405571 495 | Eritrea,1957,1542611,Africa,38.047,344.1618859 496 | Eritrea,1962,1666618,Africa,40.158,380.9958433 497 | Eritrea,1967,1820319,Africa,42.189,468.7949699 498 | Eritrea,1972,2260187,Africa,44.142,514.3242082 499 | Eritrea,1977,2512642,Africa,44.535,505.7538077 500 | Eritrea,1982,2637297,Africa,43.89,524.8758493 501 | Eritrea,1987,2915959,Africa,46.453,521.1341333 502 | Eritrea,1992,3668440,Africa,49.991,582.8585102 503 | Eritrea,1997,4058319,Africa,53.378,913.47079 504 | Eritrea,2002,4414865,Africa,55.24,765.3500015 505 | Eritrea,2007,4906585,Africa,58.04,641.3695236 506 | Ethiopia,1952,20860941,Africa,34.078,362.1462796 507 | Ethiopia,1957,22815614,Africa,36.667,378.9041632 508 | Ethiopia,1962,25145372,Africa,40.059,419.4564161 509 | Ethiopia,1967,27860297,Africa,42.115,516.1186438 510 | Ethiopia,1972,30770372,Africa,43.515,566.2439442 511 | Ethiopia,1977,34617799,Africa,44.51,556.8083834 512 | Ethiopia,1982,38111756,Africa,44.916,577.8607471 513 | Ethiopia,1987,42999530,Africa,46.684,573.7413142 514 | Ethiopia,1992,52088559,Africa,48.091,421.3534653 515 | Ethiopia,1997,59861301,Africa,49.402,515.8894013 516 | Ethiopia,2002,67946797,Africa,50.725,530.0535319 517 | Ethiopia,2007,76511887,Africa,52.947,690.8055759 518 | Finland,1952,4090500,Europe,66.55,6424.519071 519 | Finland,1957,4324000,Europe,67.49,7545.415386 520 | Finland,1962,4491443,Europe,68.75,9371.842561 521 | Finland,1967,4605744,Europe,69.83,10921.63626 522 | Finland,1972,4639657,Europe,70.87,14358.8759 523 | Finland,1977,4738902,Europe,72.52,15605.42283 524 | Finland,1982,4826933,Europe,74.55,18533.15761 525 | Finland,1987,4931729,Europe,74.83,21141.01223 526 | Finland,1992,5041039,Europe,75.7,20647.16499 527 | Finland,1997,5134406,Europe,77.13,23723.9502 528 | Finland,2002,5193039,Europe,78.37,28204.59057 529 | Finland,2007,5238460,Europe,79.313,33207.0844 530 | France,1952,42459667,Europe,67.41,7029.809327 531 | France,1957,44310863,Europe,68.93,8662.834898 532 | France,1962,47124000,Europe,70.51,10560.48553 533 | France,1967,49569000,Europe,71.55,12999.91766 534 | France,1972,51732000,Europe,72.38,16107.19171 535 | France,1977,53165019,Europe,73.83,18292.63514 536 | France,1982,54433565,Europe,74.89,20293.89746 537 | France,1987,55630100,Europe,76.34,22066.44214 538 | France,1992,57374179,Europe,77.46,24703.79615 539 | France,1997,58623428,Europe,78.64,25889.78487 540 | France,2002,59925035,Europe,79.59,28926.03234 541 | France,2007,61083916,Europe,80.657,30470.0167 542 | Gabon,1952,420702,Africa,37.003,4293.476475 543 | Gabon,1957,434904,Africa,38.999,4976.198099 544 | Gabon,1962,455661,Africa,40.489,6631.459222 545 | Gabon,1967,489004,Africa,44.598,8358.761987 546 | Gabon,1972,537977,Africa,48.69,11401.94841 547 | Gabon,1977,706367,Africa,52.79,21745.57328 548 | Gabon,1982,753874,Africa,56.564,15113.36194 549 | Gabon,1987,880397,Africa,60.19,11864.40844 550 | Gabon,1992,985739,Africa,61.366,13522.15752 551 | Gabon,1997,1126189,Africa,60.461,14722.84188 552 | Gabon,2002,1299304,Africa,56.761,12521.71392 553 | Gabon,2007,1454867,Africa,56.735,13206.48452 554 | Gambia,1952,284320,Africa,30,485.2306591 555 | Gambia,1957,323150,Africa,32.065,520.9267111 556 | Gambia,1962,374020,Africa,33.896,599.650276 557 | Gambia,1967,439593,Africa,35.857,734.7829124 558 | Gambia,1972,517101,Africa,38.308,756.0868363 559 | Gambia,1977,608274,Africa,41.842,884.7552507 560 | Gambia,1982,715523,Africa,45.58,835.8096108 561 | Gambia,1987,848406,Africa,49.265,611.6588611 562 | Gambia,1992,1025384,Africa,52.644,665.6244126 563 | Gambia,1997,1235767,Africa,55.861,653.7301704 564 | Gambia,2002,1457766,Africa,58.041,660.5855997 565 | Gambia,2007,1688359,Africa,59.448,752.7497265 566 | Germany,1952,69145952,Europe,67.5,7144.114393 567 | Germany,1957,71019069,Europe,69.1,10187.82665 568 | Germany,1962,73739117,Europe,70.3,12902.46291 569 | Germany,1967,76368453,Europe,70.8,14745.62561 570 | Germany,1972,78717088,Europe,71,18016.18027 571 | Germany,1977,78160773,Europe,72.5,20512.92123 572 | Germany,1982,78335266,Europe,73.8,22031.53274 573 | Germany,1987,77718298,Europe,74.847,24639.18566 574 | Germany,1992,80597764,Europe,76.07,26505.30317 575 | Germany,1997,82011073,Europe,77.34,27788.88416 576 | Germany,2002,82350671,Europe,78.67,30035.80198 577 | Germany,2007,82400996,Europe,79.406,32170.37442 578 | Ghana,1952,5581001,Africa,43.149,911.2989371 579 | Ghana,1957,6391288,Africa,44.779,1043.561537 580 | Ghana,1962,7355248,Africa,46.452,1190.041118 581 | Ghana,1967,8490213,Africa,48.072,1125.69716 582 | Ghana,1972,9354120,Africa,49.875,1178.223708 583 | Ghana,1977,10538093,Africa,51.756,993.2239571 584 | Ghana,1982,11400338,Africa,53.744,876.032569 585 | Ghana,1987,14168101,Africa,55.729,847.0061135 586 | Ghana,1992,16278738,Africa,57.501,925.060154 587 | Ghana,1997,18418288,Africa,58.556,1005.245812 588 | Ghana,2002,20550751,Africa,58.453,1111.984578 589 | Ghana,2007,22873338,Africa,60.022,1327.60891 590 | Greece,1952,7733250,Europe,65.86,3530.690067 591 | Greece,1957,8096218,Europe,67.86,4916.299889 592 | Greece,1962,8448233,Europe,69.51,6017.190733 593 | Greece,1967,8716441,Europe,71,8513.097016 594 | Greece,1972,8888628,Europe,72.34,12724.82957 595 | Greece,1977,9308479,Europe,73.68,14195.52428 596 | Greece,1982,9786480,Europe,75.24,15268.42089 597 | Greece,1987,9974490,Europe,76.67,16120.52839 598 | Greece,1992,10325429,Europe,77.03,17541.49634 599 | Greece,1997,10502372,Europe,77.869,18747.69814 600 | Greece,2002,10603863,Europe,78.256,22514.2548 601 | Greece,2007,10706290,Europe,79.483,27538.41188 602 | Guatemala,1952,3146381,Americas,42.023,2428.237769 603 | Guatemala,1957,3640876,Americas,44.142,2617.155967 604 | Guatemala,1962,4208858,Americas,46.954,2750.364446 605 | Guatemala,1967,4690773,Americas,50.016,3242.531147 606 | Guatemala,1972,5149581,Americas,53.738,4031.408271 607 | Guatemala,1977,5703430,Americas,56.029,4879.992748 608 | Guatemala,1982,6395630,Americas,58.137,4820.49479 609 | Guatemala,1987,7326406,Americas,60.782,4246.485974 610 | Guatemala,1992,8486949,Americas,63.373,4439.45084 611 | Guatemala,1997,9803875,Americas,66.322,4684.313807 612 | Guatemala,2002,11178650,Americas,68.978,4858.347495 613 | Guatemala,2007,12572928,Americas,70.259,5186.050003 614 | Guinea,1952,2664249,Africa,33.609,510.1964923 615 | Guinea,1957,2876726,Africa,34.558,576.2670245 616 | Guinea,1962,3140003,Africa,35.753,686.3736739 617 | Guinea,1967,3451418,Africa,37.197,708.7595409 618 | Guinea,1972,3811387,Africa,38.842,741.6662307 619 | Guinea,1977,4227026,Africa,40.762,874.6858643 620 | Guinea,1982,4710497,Africa,42.891,857.2503577 621 | Guinea,1987,5650262,Africa,45.552,805.5724718 622 | Guinea,1992,6990574,Africa,48.576,794.3484384 623 | Guinea,1997,8048834,Africa,51.455,869.4497668 624 | Guinea,2002,8807818,Africa,53.676,945.5835837 625 | Guinea,2007,9947814,Africa,56.007,942.6542111 626 | Guinea-Bissau,1952,580653,Africa,32.5,299.850319 627 | Guinea-Bissau,1957,601095,Africa,33.489,431.7904566 628 | Guinea-Bissau,1962,627820,Africa,34.488,522.0343725 629 | Guinea-Bissau,1967,601287,Africa,35.492,715.5806402 630 | Guinea-Bissau,1972,625361,Africa,36.486,820.2245876 631 | Guinea-Bissau,1977,745228,Africa,37.465,764.7259628 632 | Guinea-Bissau,1982,825987,Africa,39.327,838.1239671 633 | Guinea-Bissau,1987,927524,Africa,41.245,736.4153921 634 | Guinea-Bissau,1992,1050938,Africa,43.266,745.5398706 635 | Guinea-Bissau,1997,1193708,Africa,44.873,796.6644681 636 | Guinea-Bissau,2002,1332459,Africa,45.504,575.7047176 637 | Guinea-Bissau,2007,1472041,Africa,46.388,579.231743 638 | Haiti,1952,3201488,Americas,37.579,1840.366939 639 | Haiti,1957,3507701,Americas,40.696,1726.887882 640 | Haiti,1962,3880130,Americas,43.59,1796.589032 641 | Haiti,1967,4318137,Americas,46.243,1452.057666 642 | Haiti,1972,4698301,Americas,48.042,1654.456946 643 | Haiti,1977,4908554,Americas,49.923,1874.298931 644 | Haiti,1982,5198399,Americas,51.461,2011.159549 645 | Haiti,1987,5756203,Americas,53.636,1823.015995 646 | Haiti,1992,6326682,Americas,55.089,1456.309517 647 | Haiti,1997,6913545,Americas,56.671,1341.726931 648 | Haiti,2002,7607651,Americas,58.137,1270.364932 649 | Haiti,2007,8502814,Americas,60.916,1201.637154 650 | Honduras,1952,1517453,Americas,41.912,2194.926204 651 | Honduras,1957,1770390,Americas,44.665,2220.487682 652 | Honduras,1962,2090162,Americas,48.041,2291.156835 653 | Honduras,1967,2500689,Americas,50.924,2538.269358 654 | Honduras,1972,2965146,Americas,53.884,2529.842345 655 | Honduras,1977,3055235,Americas,57.402,3203.208066 656 | Honduras,1982,3669448,Americas,60.909,3121.760794 657 | Honduras,1987,4372203,Americas,64.492,3023.096699 658 | Honduras,1992,5077347,Americas,66.399,3081.694603 659 | Honduras,1997,5867957,Americas,67.659,3160.454906 660 | Honduras,2002,6677328,Americas,68.565,3099.72866 661 | Honduras,2007,7483763,Americas,70.198,3548.330846 662 | Hong Kong China,1952,2125900,Asia,60.96,3054.421209 663 | Hong Kong China,1957,2736300,Asia,64.75,3629.076457 664 | Hong Kong China,1962,3305200,Asia,67.65,4692.648272 665 | Hong Kong China,1967,3722800,Asia,70,6197.962814 666 | Hong Kong China,1972,4115700,Asia,72,8315.928145 667 | Hong Kong China,1977,4583700,Asia,73.6,11186.14125 668 | Hong Kong China,1982,5264500,Asia,75.45,14560.53051 669 | Hong Kong China,1987,5584510,Asia,76.2,20038.47269 670 | Hong Kong China,1992,5829696,Asia,77.601,24757.60301 671 | Hong Kong China,1997,6495918,Asia,80,28377.63219 672 | Hong Kong China,2002,6762476,Asia,81.495,30209.01516 673 | Hong Kong China,2007,6980412,Asia,82.208,39724.97867 674 | Hungary,1952,9504000,Europe,64.03,5263.673816 675 | Hungary,1957,9839000,Europe,66.41,6040.180011 676 | Hungary,1962,10063000,Europe,67.96,7550.359877 677 | Hungary,1967,10223422,Europe,69.5,9326.64467 678 | Hungary,1972,10394091,Europe,69.76,10168.65611 679 | Hungary,1977,10637171,Europe,69.95,11674.83737 680 | Hungary,1982,10705535,Europe,69.39,12545.99066 681 | Hungary,1987,10612740,Europe,69.58,12986.47998 682 | Hungary,1992,10348684,Europe,69.17,10535.62855 683 | Hungary,1997,10244684,Europe,71.04,11712.7768 684 | Hungary,2002,10083313,Europe,72.59,14843.93556 685 | Hungary,2007,9956108,Europe,73.338,18008.94444 686 | Iceland,1952,147962,Europe,72.49,7267.688428 687 | Iceland,1957,165110,Europe,73.47,9244.001412 688 | Iceland,1962,182053,Europe,73.68,10350.15906 689 | Iceland,1967,198676,Europe,73.73,13319.89568 690 | Iceland,1972,209275,Europe,74.46,15798.06362 691 | Iceland,1977,221823,Europe,76.11,19654.96247 692 | Iceland,1982,233997,Europe,76.99,23269.6075 693 | Iceland,1987,244676,Europe,77.23,26923.20628 694 | Iceland,1992,259012,Europe,78.77,25144.39201 695 | Iceland,1997,271192,Europe,78.95,28061.09966 696 | Iceland,2002,288030,Europe,80.5,31163.20196 697 | Iceland,2007,301931,Europe,81.757,36180.78919 698 | India,1952,3.72e+08,Asia,37.373,546.5657493 699 | India,1957,4.09e+08,Asia,40.249,590.061996 700 | India,1962,4.54e+08,Asia,43.605,658.3471509 701 | India,1967,5.06e+08,Asia,47.193,700.7706107 702 | India,1972,5.67e+08,Asia,50.651,724.032527 703 | India,1977,6.34e+08,Asia,54.208,813.337323 704 | India,1982,7.08e+08,Asia,56.596,855.7235377 705 | India,1987,7.88e+08,Asia,58.553,976.5126756 706 | India,1992,8.72e+08,Asia,60.223,1164.406809 707 | India,1997,9.59e+08,Asia,61.765,1458.817442 708 | India,2002,1034172547,Asia,62.879,1746.769454 709 | India,2007,1110396331,Asia,64.698,2452.210407 710 | Indonesia,1952,82052000,Asia,37.468,749.6816546 711 | Indonesia,1957,90124000,Asia,39.918,858.9002707 712 | Indonesia,1962,99028000,Asia,42.518,849.2897701 713 | Indonesia,1967,109343000,Asia,45.964,762.4317721 714 | Indonesia,1972,121282000,Asia,49.203,1111.107907 715 | Indonesia,1977,136725000,Asia,52.702,1382.702056 716 | Indonesia,1982,153343000,Asia,56.159,1516.872988 717 | Indonesia,1987,169276000,Asia,60.137,1748.356961 718 | Indonesia,1992,184816000,Asia,62.681,2383.140898 719 | Indonesia,1997,199278000,Asia,66.041,3119.335603 720 | Indonesia,2002,211060000,Asia,68.588,2873.91287 721 | Indonesia,2007,223547000,Asia,70.65,3540.651564 722 | Iran,1952,17272000,Asia,44.869,3035.326002 723 | Iran,1957,19792000,Asia,47.181,3290.257643 724 | Iran,1962,22874000,Asia,49.325,4187.329802 725 | Iran,1967,26538000,Asia,52.469,5906.731805 726 | Iran,1972,30614000,Asia,55.234,9613.818607 727 | Iran,1977,35480679,Asia,57.702,11888.59508 728 | Iran,1982,43072751,Asia,59.62,7608.334602 729 | Iran,1987,51889696,Asia,63.04,6642.881371 730 | Iran,1992,60397973,Asia,65.742,7235.653188 731 | Iran,1997,63327987,Asia,68.042,8263.590301 732 | Iran,2002,66907826,Asia,69.451,9240.761975 733 | Iran,2007,69453570,Asia,70.964,11605.71449 734 | Iraq,1952,5441766,Asia,45.32,4129.766056 735 | Iraq,1957,6248643,Asia,48.437,6229.333562 736 | Iraq,1962,7240260,Asia,51.457,8341.737815 737 | Iraq,1967,8519282,Asia,54.459,8931.459811 738 | Iraq,1972,10061506,Asia,56.95,9576.037596 739 | Iraq,1977,11882916,Asia,60.413,14688.23507 740 | Iraq,1982,14173318,Asia,62.038,14517.90711 741 | Iraq,1987,16543189,Asia,65.044,11643.57268 742 | Iraq,1992,17861905,Asia,59.461,3745.640687 743 | Iraq,1997,20775703,Asia,58.811,3076.239795 744 | Iraq,2002,24001816,Asia,57.046,4390.717312 745 | Iraq,2007,27499638,Asia,59.545,4471.061906 746 | Ireland,1952,2952156,Europe,66.91,5210.280328 747 | Ireland,1957,2878220,Europe,68.9,5599.077872 748 | Ireland,1962,2830000,Europe,70.29,6631.597314 749 | Ireland,1967,2900100,Europe,71.08,7655.568963 750 | Ireland,1972,3024400,Europe,71.28,9530.772896 751 | Ireland,1977,3271900,Europe,72.03,11150.98113 752 | Ireland,1982,3480000,Europe,73.1,12618.32141 753 | Ireland,1987,3539900,Europe,74.36,13872.86652 754 | Ireland,1992,3557761,Europe,75.467,17558.81555 755 | Ireland,1997,3667233,Europe,76.122,24521.94713 756 | Ireland,2002,3879155,Europe,77.783,34077.04939 757 | Ireland,2007,4109086,Europe,78.885,40675.99635 758 | Israel,1952,1620914,Asia,65.39,4086.522128 759 | Israel,1957,1944401,Asia,67.84,5385.278451 760 | Israel,1962,2310904,Asia,69.39,7105.630706 761 | Israel,1967,2693585,Asia,70.75,8393.741404 762 | Israel,1972,3095893,Asia,71.63,12786.93223 763 | Israel,1977,3495918,Asia,73.06,13306.61921 764 | Israel,1982,3858421,Asia,74.45,15367.0292 765 | Israel,1987,4203148,Asia,75.6,17122.47986 766 | Israel,1992,4936550,Asia,76.93,18051.52254 767 | Israel,1997,5531387,Asia,78.269,20896.60924 768 | Israel,2002,6029529,Asia,79.696,21905.59514 769 | Israel,2007,6426679,Asia,80.745,25523.2771 770 | Italy,1952,47666000,Europe,65.94,4931.404155 771 | Italy,1957,49182000,Europe,67.81,6248.656232 772 | Italy,1962,50843200,Europe,69.24,8243.58234 773 | Italy,1967,52667100,Europe,71.06,10022.40131 774 | Italy,1972,54365564,Europe,72.19,12269.27378 775 | Italy,1977,56059245,Europe,73.48,14255.98475 776 | Italy,1982,56535636,Europe,74.98,16537.4835 777 | Italy,1987,56729703,Europe,76.42,19207.23482 778 | Italy,1992,56840847,Europe,77.44,22013.64486 779 | Italy,1997,57479469,Europe,78.82,24675.02446 780 | Italy,2002,57926999,Europe,80.24,27968.09817 781 | Italy,2007,58147733,Europe,80.546,28569.7197 782 | Jamaica,1952,1426095,Americas,58.53,2898.530881 783 | Jamaica,1957,1535090,Americas,62.61,4756.525781 784 | Jamaica,1962,1665128,Americas,65.61,5246.107524 785 | Jamaica,1967,1861096,Americas,67.51,6124.703451 786 | Jamaica,1972,1997616,Americas,69,7433.889293 787 | Jamaica,1977,2156814,Americas,70.11,6650.195573 788 | Jamaica,1982,2298309,Americas,71.21,6068.05135 789 | Jamaica,1987,2326606,Americas,71.77,6351.237495 790 | Jamaica,1992,2378618,Americas,71.766,7404.923685 791 | Jamaica,1997,2531311,Americas,72.262,7121.924704 792 | Jamaica,2002,2664659,Americas,72.047,6994.774861 793 | Jamaica,2007,2780132,Americas,72.567,7320.880262 794 | Japan,1952,86459025,Asia,63.03,3216.956347 795 | Japan,1957,91563009,Asia,65.5,4317.694365 796 | Japan,1962,95831757,Asia,68.73,6576.649461 797 | Japan,1967,100825279,Asia,71.43,9847.788607 798 | Japan,1972,107188273,Asia,73.42,14778.78636 799 | Japan,1977,113872473,Asia,75.38,16610.37701 800 | Japan,1982,118454974,Asia,77.11,19384.10571 801 | Japan,1987,122091325,Asia,78.67,22375.94189 802 | Japan,1992,124329269,Asia,79.36,26824.89511 803 | Japan,1997,125956499,Asia,80.69,28816.58499 804 | Japan,2002,127065841,Asia,82,28604.5919 805 | Japan,2007,127467972,Asia,82.603,31656.06806 806 | Jordan,1952,607914,Asia,43.158,1546.907807 807 | Jordan,1957,746559,Asia,45.669,1886.080591 808 | Jordan,1962,933559,Asia,48.126,2348.009158 809 | Jordan,1967,1255058,Asia,51.629,2741.796252 810 | Jordan,1972,1613551,Asia,56.528,2110.856309 811 | Jordan,1977,1937652,Asia,61.134,2852.351568 812 | Jordan,1982,2347031,Asia,63.739,4161.415959 813 | Jordan,1987,2820042,Asia,65.869,4448.679912 814 | Jordan,1992,3867409,Asia,68.015,3431.593647 815 | Jordan,1997,4526235,Asia,69.772,3645.379572 816 | Jordan,2002,5307470,Asia,71.263,3844.917194 817 | Jordan,2007,6053193,Asia,72.535,4519.461171 818 | Kenya,1952,6464046,Africa,42.27,853.540919 819 | Kenya,1957,7454779,Africa,44.686,944.4383152 820 | Kenya,1962,8678557,Africa,47.949,896.9663732 821 | Kenya,1967,10191512,Africa,50.654,1056.736457 822 | Kenya,1972,12044785,Africa,53.559,1222.359968 823 | Kenya,1977,14500404,Africa,56.155,1267.613204 824 | Kenya,1982,17661452,Africa,58.766,1348.225791 825 | Kenya,1987,21198082,Africa,59.339,1361.936856 826 | Kenya,1992,25020539,Africa,59.285,1341.921721 827 | Kenya,1997,28263827,Africa,54.407,1360.485021 828 | Kenya,2002,31386842,Africa,50.992,1287.514732 829 | Kenya,2007,35610177,Africa,54.11,1463.249282 830 | Korea Dem. Rep.,1952,8865488,Asia,50.056,1088.277758 831 | Korea Dem. Rep.,1957,9411381,Asia,54.081,1571.134655 832 | Korea Dem. Rep.,1962,10917494,Asia,56.656,1621.693598 833 | Korea Dem. Rep.,1967,12617009,Asia,59.942,2143.540609 834 | Korea Dem. Rep.,1972,14781241,Asia,63.983,3701.621503 835 | Korea Dem. Rep.,1977,16325320,Asia,67.159,4106.301249 836 | Korea Dem. Rep.,1982,17647518,Asia,69.1,4106.525293 837 | Korea Dem. Rep.,1987,19067554,Asia,70.647,4106.492315 838 | Korea Dem. Rep.,1992,20711375,Asia,69.978,3726.063507 839 | Korea Dem. Rep.,1997,21585105,Asia,67.727,1690.756814 840 | Korea Dem. Rep.,2002,22215365,Asia,66.662,1646.758151 841 | Korea Dem. Rep.,2007,23301725,Asia,67.297,1593.06548 842 | Korea Rep.,1952,20947571,Asia,47.453,1030.592226 843 | Korea Rep.,1957,22611552,Asia,52.681,1487.593537 844 | Korea Rep.,1962,26420307,Asia,55.292,1536.344387 845 | Korea Rep.,1967,30131000,Asia,57.716,2029.228142 846 | Korea Rep.,1972,33505000,Asia,62.612,3030.87665 847 | Korea Rep.,1977,36436000,Asia,64.766,4657.22102 848 | Korea Rep.,1982,39326000,Asia,67.123,5622.942464 849 | Korea Rep.,1987,41622000,Asia,69.81,8533.088805 850 | Korea Rep.,1992,43805450,Asia,72.244,12104.27872 851 | Korea Rep.,1997,46173816,Asia,74.647,15993.52796 852 | Korea Rep.,2002,47969150,Asia,77.045,19233.98818 853 | Korea Rep.,2007,49044790,Asia,78.623,23348.13973 854 | Kuwait,1952,160000,Asia,55.565,108382.3529 855 | Kuwait,1957,212846,Asia,58.033,113523.1329 856 | Kuwait,1962,358266,Asia,60.47,95458.11176 857 | Kuwait,1967,575003,Asia,64.624,80894.88326 858 | Kuwait,1972,841934,Asia,67.712,109347.867 859 | Kuwait,1977,1140357,Asia,69.343,59265.47714 860 | Kuwait,1982,1497494,Asia,71.309,31354.03573 861 | Kuwait,1987,1891487,Asia,74.174,28118.42998 862 | Kuwait,1992,1418095,Asia,75.19,34932.91959 863 | Kuwait,1997,1765345,Asia,76.156,40300.61996 864 | Kuwait,2002,2111561,Asia,76.904,35110.10566 865 | Kuwait,2007,2505559,Asia,77.588,47306.98978 866 | Lebanon,1952,1439529,Asia,55.928,4834.804067 867 | Lebanon,1957,1647412,Asia,59.489,6089.786934 868 | Lebanon,1962,1886848,Asia,62.094,5714.560611 869 | Lebanon,1967,2186894,Asia,63.87,6006.983042 870 | Lebanon,1972,2680018,Asia,65.421,7486.384341 871 | Lebanon,1977,3115787,Asia,66.099,8659.696836 872 | Lebanon,1982,3086876,Asia,66.983,7640.519521 873 | Lebanon,1987,3089353,Asia,67.926,5377.091329 874 | Lebanon,1992,3219994,Asia,69.292,6890.806854 875 | Lebanon,1997,3430388,Asia,70.265,8754.96385 876 | Lebanon,2002,3677780,Asia,71.028,9313.93883 877 | Lebanon,2007,3921278,Asia,71.993,10461.05868 878 | Lesotho,1952,748747,Africa,42.138,298.8462121 879 | Lesotho,1957,813338,Africa,45.047,335.9971151 880 | Lesotho,1962,893143,Africa,47.747,411.8006266 881 | Lesotho,1967,996380,Africa,48.492,498.6390265 882 | Lesotho,1972,1116779,Africa,49.767,496.5815922 883 | Lesotho,1977,1251524,Africa,52.208,745.3695408 884 | Lesotho,1982,1411807,Africa,55.078,797.2631074 885 | Lesotho,1987,1599200,Africa,57.18,773.9932141 886 | Lesotho,1992,1803195,Africa,59.685,977.4862725 887 | Lesotho,1997,1982823,Africa,55.558,1186.147994 888 | Lesotho,2002,2046772,Africa,44.593,1275.184575 889 | Lesotho,2007,2012649,Africa,42.592,1569.331442 890 | Liberia,1952,863308,Africa,38.48,575.5729961 891 | Liberia,1957,975950,Africa,39.486,620.9699901 892 | Liberia,1962,1112796,Africa,40.502,634.1951625 893 | Liberia,1967,1279406,Africa,41.536,713.6036483 894 | Liberia,1972,1482628,Africa,42.614,803.0054535 895 | Liberia,1977,1703617,Africa,43.764,640.3224383 896 | Liberia,1982,1956875,Africa,44.852,572.1995694 897 | Liberia,1987,2269414,Africa,46.027,506.1138573 898 | Liberia,1992,1912974,Africa,40.802,636.6229191 899 | Liberia,1997,2200725,Africa,42.221,609.1739508 900 | Liberia,2002,2814651,Africa,43.753,531.4823679 901 | Liberia,2007,3193942,Africa,45.678,414.5073415 902 | Libya,1952,1019729,Africa,42.723,2387.54806 903 | Libya,1957,1201578,Africa,45.289,3448.284395 904 | Libya,1962,1441863,Africa,47.808,6757.030816 905 | Libya,1967,1759224,Africa,50.227,18772.75169 906 | Libya,1972,2183877,Africa,52.773,21011.49721 907 | Libya,1977,2721783,Africa,57.442,21951.21176 908 | Libya,1982,3344074,Africa,62.155,17364.27538 909 | Libya,1987,3799845,Africa,66.234,11770.5898 910 | Libya,1992,4364501,Africa,68.755,9640.138501 911 | Libya,1997,4759670,Africa,71.555,9467.446056 912 | Libya,2002,5368585,Africa,72.737,9534.677467 913 | Libya,2007,6036914,Africa,73.952,12057.49928 914 | Madagascar,1952,4762912,Africa,36.681,1443.011715 915 | Madagascar,1957,5181679,Africa,38.865,1589.20275 916 | Madagascar,1962,5703324,Africa,40.848,1643.38711 917 | Madagascar,1967,6334556,Africa,42.881,1634.047282 918 | Madagascar,1972,7082430,Africa,44.851,1748.562982 919 | Madagascar,1977,8007166,Africa,46.881,1544.228586 920 | Madagascar,1982,9171477,Africa,48.969,1302.878658 921 | Madagascar,1987,10568642,Africa,49.35,1155.441948 922 | Madagascar,1992,12210395,Africa,52.214,1040.67619 923 | Madagascar,1997,14165114,Africa,54.978,986.2958956 924 | Madagascar,2002,16473477,Africa,57.286,894.6370822 925 | Madagascar,2007,19167654,Africa,59.443,1044.770126 926 | Malawi,1952,2917802,Africa,36.256,369.1650802 927 | Malawi,1957,3221238,Africa,37.207,416.3698064 928 | Malawi,1962,3628608,Africa,38.41,427.9010856 929 | Malawi,1967,4147252,Africa,39.487,495.5147806 930 | Malawi,1972,4730997,Africa,41.766,584.6219709 931 | Malawi,1977,5637246,Africa,43.767,663.2236766 932 | Malawi,1982,6502825,Africa,45.642,632.8039209 933 | Malawi,1987,7824747,Africa,47.457,635.5173634 934 | Malawi,1992,10014249,Africa,49.42,563.2000145 935 | Malawi,1997,10419991,Africa,47.495,692.2758103 936 | Malawi,2002,11824495,Africa,45.009,665.4231186 937 | Malawi,2007,13327079,Africa,48.303,759.3499101 938 | Malaysia,1952,6748378,Asia,48.463,1831.132894 939 | Malaysia,1957,7739235,Asia,52.102,1810.066992 940 | Malaysia,1962,8906385,Asia,55.737,2036.884944 941 | Malaysia,1967,10154878,Asia,59.371,2277.742396 942 | Malaysia,1972,11441462,Asia,63.01,2849.09478 943 | Malaysia,1977,12845381,Asia,65.256,3827.921571 944 | Malaysia,1982,14441916,Asia,68,4920.355951 945 | Malaysia,1987,16331785,Asia,69.5,5249.802653 946 | Malaysia,1992,18319502,Asia,70.693,7277.912802 947 | Malaysia,1997,20476091,Asia,71.938,10132.90964 948 | Malaysia,2002,22662365,Asia,73.044,10206.97794 949 | Malaysia,2007,24821286,Asia,74.241,12451.6558 950 | Mali,1952,3838168,Africa,33.685,452.3369807 951 | Mali,1957,4241884,Africa,35.307,490.3821867 952 | Mali,1962,4690372,Africa,36.936,496.1743428 953 | Mali,1967,5212416,Africa,38.487,545.0098873 954 | Mali,1972,5828158,Africa,39.977,581.3688761 955 | Mali,1977,6491649,Africa,41.714,686.3952693 956 | Mali,1982,6998256,Africa,43.916,618.0140641 957 | Mali,1987,7634008,Africa,46.364,684.1715576 958 | Mali,1992,8416215,Africa,48.388,739.014375 959 | Mali,1997,9384984,Africa,49.903,790.2579846 960 | Mali,2002,10580176,Africa,51.818,951.4097518 961 | Mali,2007,12031795,Africa,54.467,1042.581557 962 | Mauritania,1952,1022556,Africa,40.543,743.1159097 963 | Mauritania,1957,1076852,Africa,42.338,846.1202613 964 | Mauritania,1962,1146757,Africa,44.248,1055.896036 965 | Mauritania,1967,1230542,Africa,46.289,1421.145193 966 | Mauritania,1972,1332786,Africa,48.437,1586.851781 967 | Mauritania,1977,1456688,Africa,50.852,1497.492223 968 | Mauritania,1982,1622136,Africa,53.599,1481.150189 969 | Mauritania,1987,1841240,Africa,56.145,1421.603576 970 | Mauritania,1992,2119465,Africa,58.333,1361.369784 971 | Mauritania,1997,2444741,Africa,60.43,1483.136136 972 | Mauritania,2002,2828858,Africa,62.247,1579.019543 973 | Mauritania,2007,3270065,Africa,64.164,1803.151496 974 | Mauritius,1952,516556,Africa,50.986,1967.955707 975 | Mauritius,1957,609816,Africa,58.089,2034.037981 976 | Mauritius,1962,701016,Africa,60.246,2529.067487 977 | Mauritius,1967,789309,Africa,61.557,2475.387562 978 | Mauritius,1972,851334,Africa,62.944,2575.484158 979 | Mauritius,1977,913025,Africa,64.93,3710.982963 980 | Mauritius,1982,992040,Africa,66.711,3688.037739 981 | Mauritius,1987,1042663,Africa,68.74,4783.586903 982 | Mauritius,1992,1096202,Africa,69.745,6058.253846 983 | Mauritius,1997,1149818,Africa,70.736,7425.705295 984 | Mauritius,2002,1200206,Africa,71.954,9021.815894 985 | Mauritius,2007,1250882,Africa,72.801,10956.99112 986 | Mexico,1952,30144317,Americas,50.789,3478.125529 987 | Mexico,1957,35015548,Americas,55.19,4131.546641 988 | Mexico,1962,41121485,Americas,58.299,4581.609385 989 | Mexico,1967,47995559,Americas,60.11,5754.733883 990 | Mexico,1972,55984294,Americas,62.361,6809.40669 991 | Mexico,1977,63759976,Americas,65.032,7674.929108 992 | Mexico,1982,71640904,Americas,67.405,9611.147541 993 | Mexico,1987,80122492,Americas,69.498,8688.156003 994 | Mexico,1992,88111030,Americas,71.455,9472.384295 995 | Mexico,1997,95895146,Americas,73.67,9767.29753 996 | Mexico,2002,102479927,Americas,74.902,10742.44053 997 | Mexico,2007,108700891,Americas,76.195,11977.57496 998 | Mongolia,1952,800663,Asia,42.244,786.5668575 999 | Mongolia,1957,882134,Asia,45.248,912.6626085 1000 | Mongolia,1962,1010280,Asia,48.251,1056.353958 1001 | Mongolia,1967,1149500,Asia,51.253,1226.04113 1002 | Mongolia,1972,1320500,Asia,53.754,1421.741975 1003 | Mongolia,1977,1528000,Asia,55.491,1647.511665 1004 | Mongolia,1982,1756032,Asia,57.489,2000.603139 1005 | Mongolia,1987,2015133,Asia,60.222,2338.008304 1006 | Mongolia,1992,2312802,Asia,61.271,1785.402016 1007 | Mongolia,1997,2494803,Asia,63.625,1902.2521 1008 | Mongolia,2002,2674234,Asia,65.033,2140.739323 1009 | Mongolia,2007,2874127,Asia,66.803,3095.772271 1010 | Montenegro,1952,413834,Europe,59.164,2647.585601 1011 | Montenegro,1957,442829,Europe,61.448,3682.259903 1012 | Montenegro,1962,474528,Europe,63.728,4649.593785 1013 | Montenegro,1967,501035,Europe,67.178,5907.850937 1014 | Montenegro,1972,527678,Europe,70.636,7778.414017 1015 | Montenegro,1977,560073,Europe,73.066,9595.929905 1016 | Montenegro,1982,562548,Europe,74.101,11222.58762 1017 | Montenegro,1987,569473,Europe,74.865,11732.51017 1018 | Montenegro,1992,621621,Europe,75.435,7003.339037 1019 | Montenegro,1997,692651,Europe,75.445,6465.613349 1020 | Montenegro,2002,720230,Europe,73.981,6557.194282 1021 | Montenegro,2007,684736,Europe,74.543,9253.896111 1022 | Morocco,1952,9939217,Africa,42.873,1688.20357 1023 | Morocco,1957,11406350,Africa,45.423,1642.002314 1024 | Morocco,1962,13056604,Africa,47.924,1566.353493 1025 | Morocco,1967,14770296,Africa,50.335,1711.04477 1026 | Morocco,1972,16660670,Africa,52.862,1930.194975 1027 | Morocco,1977,18396941,Africa,55.73,2370.619976 1028 | Morocco,1982,20198730,Africa,59.65,2702.620356 1029 | Morocco,1987,22987397,Africa,62.677,2755.046991 1030 | Morocco,1992,25798239,Africa,65.393,2948.047252 1031 | Morocco,1997,28529501,Africa,67.66,2982.101858 1032 | Morocco,2002,31167783,Africa,69.615,3258.495584 1033 | Morocco,2007,33757175,Africa,71.164,3820.17523 1034 | Mozambique,1952,6446316,Africa,31.286,468.5260381 1035 | Mozambique,1957,7038035,Africa,33.779,495.5868333 1036 | Mozambique,1962,7788944,Africa,36.161,556.6863539 1037 | Mozambique,1967,8680909,Africa,38.113,566.6691539 1038 | Mozambique,1972,9809596,Africa,40.328,724.9178037 1039 | Mozambique,1977,11127868,Africa,42.495,502.3197334 1040 | Mozambique,1982,12587223,Africa,42.795,462.2114149 1041 | Mozambique,1987,12891952,Africa,42.861,389.8761846 1042 | Mozambique,1992,13160731,Africa,44.284,410.8968239 1043 | Mozambique,1997,16603334,Africa,46.344,472.3460771 1044 | Mozambique,2002,18473780,Africa,44.026,633.6179466 1045 | Mozambique,2007,19951656,Africa,42.082,823.6856205 1046 | Myanmar,1952,20092996,Asia,36.319,331 1047 | Myanmar,1957,21731844,Asia,41.905,350 1048 | Myanmar,1962,23634436,Asia,45.108,388 1049 | Myanmar,1967,25870271,Asia,49.379,349 1050 | Myanmar,1972,28466390,Asia,53.07,357 1051 | Myanmar,1977,31528087,Asia,56.059,371 1052 | Myanmar,1982,34680442,Asia,58.056,424 1053 | Myanmar,1987,38028578,Asia,58.339,385 1054 | Myanmar,1992,40546538,Asia,59.32,347 1055 | Myanmar,1997,43247867,Asia,60.328,415 1056 | Myanmar,2002,45598081,Asia,59.908,611 1057 | Myanmar,2007,47761980,Asia,62.069,944 1058 | Namibia,1952,485831,Africa,41.725,2423.780443 1059 | Namibia,1957,548080,Africa,45.226,2621.448058 1060 | Namibia,1962,621392,Africa,48.386,3173.215595 1061 | Namibia,1967,706640,Africa,51.159,3793.694753 1062 | Namibia,1972,821782,Africa,53.867,3746.080948 1063 | Namibia,1977,977026,Africa,56.437,3876.485958 1064 | Namibia,1982,1099010,Africa,58.968,4191.100511 1065 | Namibia,1987,1278184,Africa,60.835,3693.731337 1066 | Namibia,1992,1554253,Africa,61.999,3804.537999 1067 | Namibia,1997,1774766,Africa,58.909,3899.52426 1068 | Namibia,2002,1972153,Africa,51.479,4072.324751 1069 | Namibia,2007,2055080,Africa,52.906,4811.060429 1070 | Nepal,1952,9182536,Asia,36.157,545.8657229 1071 | Nepal,1957,9682338,Asia,37.686,597.9363558 1072 | Nepal,1962,10332057,Asia,39.393,652.3968593 1073 | Nepal,1967,11261690,Asia,41.472,676.4422254 1074 | Nepal,1972,12412593,Asia,43.971,674.7881296 1075 | Nepal,1977,13933198,Asia,46.748,694.1124398 1076 | Nepal,1982,15796314,Asia,49.594,718.3730947 1077 | Nepal,1987,17917180,Asia,52.537,775.6324501 1078 | Nepal,1992,20326209,Asia,55.727,897.7403604 1079 | Nepal,1997,23001113,Asia,59.426,1010.892138 1080 | Nepal,2002,25873917,Asia,61.34,1057.206311 1081 | Nepal,2007,28901790,Asia,63.785,1091.359778 1082 | Netherlands,1952,10381988,Europe,72.13,8941.571858 1083 | Netherlands,1957,11026383,Europe,72.99,11276.19344 1084 | Netherlands,1962,11805689,Europe,73.23,12790.84956 1085 | Netherlands,1967,12596822,Europe,73.82,15363.25136 1086 | Netherlands,1972,13329874,Europe,73.75,18794.74567 1087 | Netherlands,1977,13852989,Europe,75.24,21209.0592 1088 | Netherlands,1982,14310401,Europe,76.05,21399.46046 1089 | Netherlands,1987,14665278,Europe,76.83,23651.32361 1090 | Netherlands,1992,15174244,Europe,77.42,26790.94961 1091 | Netherlands,1997,15604464,Europe,78.03,30246.13063 1092 | Netherlands,2002,16122830,Europe,78.53,33724.75778 1093 | Netherlands,2007,16570613,Europe,79.762,36797.93332 1094 | New Zealand,1952,1994794,Oceania,69.39,10556.57566 1095 | New Zealand,1957,2229407,Oceania,70.26,12247.39532 1096 | New Zealand,1962,2488550,Oceania,71.24,13175.678 1097 | New Zealand,1967,2728150,Oceania,71.52,14463.91893 1098 | New Zealand,1972,2929100,Oceania,71.89,16046.03728 1099 | New Zealand,1977,3164900,Oceania,72.22,16233.7177 1100 | New Zealand,1982,3210650,Oceania,73.84,17632.4104 1101 | New Zealand,1987,3317166,Oceania,74.32,19007.19129 1102 | New Zealand,1992,3437674,Oceania,76.33,18363.32494 1103 | New Zealand,1997,3676187,Oceania,77.55,21050.41377 1104 | New Zealand,2002,3908037,Oceania,79.11,23189.80135 1105 | New Zealand,2007,4115771,Oceania,80.204,25185.00911 1106 | Nicaragua,1952,1165790,Americas,42.314,3112.363948 1107 | Nicaragua,1957,1358828,Americas,45.432,3457.415947 1108 | Nicaragua,1962,1590597,Americas,48.632,3634.364406 1109 | Nicaragua,1967,1865490,Americas,51.884,4643.393534 1110 | Nicaragua,1972,2182908,Americas,55.151,4688.593267 1111 | Nicaragua,1977,2554598,Americas,57.47,5486.371089 1112 | Nicaragua,1982,2979423,Americas,59.298,3470.338156 1113 | Nicaragua,1987,3344353,Americas,62.008,2955.984375 1114 | Nicaragua,1992,4017939,Americas,65.843,2170.151724 1115 | Nicaragua,1997,4609572,Americas,68.426,2253.023004 1116 | Nicaragua,2002,5146848,Americas,70.836,2474.548819 1117 | Nicaragua,2007,5675356,Americas,72.899,2749.320965 1118 | Niger,1952,3379468,Africa,37.444,761.879376 1119 | Niger,1957,3692184,Africa,38.598,835.5234025 1120 | Niger,1962,4076008,Africa,39.487,997.7661127 1121 | Niger,1967,4534062,Africa,40.118,1054.384891 1122 | Niger,1972,5060262,Africa,40.546,954.2092363 1123 | Niger,1977,5682086,Africa,41.291,808.8970728 1124 | Niger,1982,6437188,Africa,42.598,909.7221354 1125 | Niger,1987,7332638,Africa,44.555,668.3000228 1126 | Niger,1992,8392818,Africa,47.391,581.182725 1127 | Niger,1997,9666252,Africa,51.313,580.3052092 1128 | Niger,2002,11140655,Africa,54.496,601.0745012 1129 | Niger,2007,12894865,Africa,56.867,619.6768924 1130 | Nigeria,1952,33119096,Africa,36.324,1077.281856 1131 | Nigeria,1957,37173340,Africa,37.802,1100.592563 1132 | Nigeria,1962,41871351,Africa,39.36,1150.927478 1133 | Nigeria,1967,47287752,Africa,41.04,1014.514104 1134 | Nigeria,1972,53740085,Africa,42.821,1698.388838 1135 | Nigeria,1977,62209173,Africa,44.514,1981.951806 1136 | Nigeria,1982,73039376,Africa,45.826,1576.97375 1137 | Nigeria,1987,81551520,Africa,46.886,1385.029563 1138 | Nigeria,1992,93364244,Africa,47.472,1619.848217 1139 | Nigeria,1997,106207839,Africa,47.464,1624.941275 1140 | Nigeria,2002,119901274,Africa,46.608,1615.286395 1141 | Nigeria,2007,135031164,Africa,46.859,2013.977305 1142 | Norway,1952,3327728,Europe,72.67,10095.42172 1143 | Norway,1957,3491938,Europe,73.44,11653.97304 1144 | Norway,1962,3638919,Europe,73.47,13450.40151 1145 | Norway,1967,3786019,Europe,74.08,16361.87647 1146 | Norway,1972,3933004,Europe,74.34,18965.05551 1147 | Norway,1977,4043205,Europe,75.37,23311.34939 1148 | Norway,1982,4114787,Europe,75.97,26298.63531 1149 | Norway,1987,4186147,Europe,75.89,31540.9748 1150 | Norway,1992,4286357,Europe,77.32,33965.66115 1151 | Norway,1997,4405672,Europe,78.32,41283.16433 1152 | Norway,2002,4535591,Europe,79.05,44683.97525 1153 | Norway,2007,4627926,Europe,80.196,49357.19017 1154 | Oman,1952,507833,Asia,37.578,1828.230307 1155 | Oman,1957,561977,Asia,40.08,2242.746551 1156 | Oman,1962,628164,Asia,43.165,2924.638113 1157 | Oman,1967,714775,Asia,46.988,4720.942687 1158 | Oman,1972,829050,Asia,52.143,10618.03855 1159 | Oman,1977,1004533,Asia,57.367,11848.34392 1160 | Oman,1982,1301048,Asia,62.728,12954.79101 1161 | Oman,1987,1593882,Asia,67.734,18115.22313 1162 | Oman,1992,1915208,Asia,71.197,18616.70691 1163 | Oman,1997,2283635,Asia,72.499,19702.05581 1164 | Oman,2002,2713462,Asia,74.193,19774.83687 1165 | Oman,2007,3204897,Asia,75.64,22316.19287 1166 | Pakistan,1952,41346560,Asia,43.436,684.5971438 1167 | Pakistan,1957,46679944,Asia,45.557,747.0835292 1168 | Pakistan,1962,53100671,Asia,47.67,803.3427418 1169 | Pakistan,1967,60641899,Asia,49.8,942.4082588 1170 | Pakistan,1972,69325921,Asia,51.929,1049.938981 1171 | Pakistan,1977,78152686,Asia,54.043,1175.921193 1172 | Pakistan,1982,91462088,Asia,56.158,1443.429832 1173 | Pakistan,1987,105186881,Asia,58.245,1704.686583 1174 | Pakistan,1992,120065004,Asia,60.838,1971.829464 1175 | Pakistan,1997,135564834,Asia,61.818,2049.350521 1176 | Pakistan,2002,153403524,Asia,63.61,2092.712441 1177 | Pakistan,2007,169270617,Asia,65.483,2605.94758 1178 | Panama,1952,940080,Americas,55.191,2480.380334 1179 | Panama,1957,1063506,Americas,59.201,2961.800905 1180 | Panama,1962,1215725,Americas,61.817,3536.540301 1181 | Panama,1967,1405486,Americas,64.071,4421.009084 1182 | Panama,1972,1616384,Americas,66.216,5364.249663 1183 | Panama,1977,1839782,Americas,68.681,5351.912144 1184 | Panama,1982,2036305,Americas,70.472,7009.601598 1185 | Panama,1987,2253639,Americas,71.523,7034.779161 1186 | Panama,1992,2484997,Americas,72.462,6618.74305 1187 | Panama,1997,2734531,Americas,73.738,7113.692252 1188 | Panama,2002,2990875,Americas,74.712,7356.031934 1189 | Panama,2007,3242173,Americas,75.537,9809.185636 1190 | Paraguay,1952,1555876,Americas,62.649,1952.308701 1191 | Paraguay,1957,1770902,Americas,63.196,2046.154706 1192 | Paraguay,1962,2009813,Americas,64.361,2148.027146 1193 | Paraguay,1967,2287985,Americas,64.951,2299.376311 1194 | Paraguay,1972,2614104,Americas,65.815,2523.337977 1195 | Paraguay,1977,2984494,Americas,66.353,3248.373311 1196 | Paraguay,1982,3366439,Americas,66.874,4258.503604 1197 | Paraguay,1987,3886512,Americas,67.378,3998.875695 1198 | Paraguay,1992,4483945,Americas,68.225,4196.411078 1199 | Paraguay,1997,5154123,Americas,69.4,4247.400261 1200 | Paraguay,2002,5884491,Americas,70.755,3783.674243 1201 | Paraguay,2007,6667147,Americas,71.752,4172.838464 1202 | Peru,1952,8025700,Americas,43.902,3758.523437 1203 | Peru,1957,9146100,Americas,46.263,4245.256698 1204 | Peru,1962,10516500,Americas,49.096,4957.037982 1205 | Peru,1967,12132200,Americas,51.445,5788.09333 1206 | Peru,1972,13954700,Americas,55.448,5937.827283 1207 | Peru,1977,15990099,Americas,58.447,6281.290855 1208 | Peru,1982,18125129,Americas,61.406,6434.501797 1209 | Peru,1987,20195924,Americas,64.134,6360.943444 1210 | Peru,1992,22430449,Americas,66.458,4446.380924 1211 | Peru,1997,24748122,Americas,68.386,5838.347657 1212 | Peru,2002,26769436,Americas,69.906,5909.020073 1213 | Peru,2007,28674757,Americas,71.421,7408.905561 1214 | Philippines,1952,22438691,Asia,47.752,1272.880995 1215 | Philippines,1957,26072194,Asia,51.334,1547.944844 1216 | Philippines,1962,30325264,Asia,54.757,1649.552153 1217 | Philippines,1967,35356600,Asia,56.393,1814.12743 1218 | Philippines,1972,40850141,Asia,58.065,1989.37407 1219 | Philippines,1977,46850962,Asia,60.06,2373.204287 1220 | Philippines,1982,53456774,Asia,62.082,2603.273765 1221 | Philippines,1987,60017788,Asia,64.151,2189.634995 1222 | Philippines,1992,67185766,Asia,66.458,2279.324017 1223 | Philippines,1997,75012988,Asia,68.564,2536.534925 1224 | Philippines,2002,82995088,Asia,70.303,2650.921068 1225 | Philippines,2007,91077287,Asia,71.688,3190.481016 1226 | Poland,1952,25730551,Europe,61.31,4029.329699 1227 | Poland,1957,28235346,Europe,65.77,4734.253019 1228 | Poland,1962,30329617,Europe,67.64,5338.752143 1229 | Poland,1967,31785378,Europe,69.61,6557.152776 1230 | Poland,1972,33039545,Europe,70.85,8006.506993 1231 | Poland,1977,34621254,Europe,70.67,9508.141454 1232 | Poland,1982,36227381,Europe,71.32,8451.531004 1233 | Poland,1987,37740710,Europe,70.98,9082.351172 1234 | Poland,1992,38370697,Europe,70.99,7738.881247 1235 | Poland,1997,38654957,Europe,72.75,10159.58368 1236 | Poland,2002,38625976,Europe,74.67,12002.23908 1237 | Poland,2007,38518241,Europe,75.563,15389.92468 1238 | Portugal,1952,8526050,Europe,59.82,3068.319867 1239 | Portugal,1957,8817650,Europe,61.51,3774.571743 1240 | Portugal,1962,9019800,Europe,64.39,4727.954889 1241 | Portugal,1967,9103000,Europe,66.6,6361.517993 1242 | Portugal,1972,8970450,Europe,69.26,9022.247417 1243 | Portugal,1977,9662600,Europe,70.41,10172.48572 1244 | Portugal,1982,9859650,Europe,72.77,11753.84291 1245 | Portugal,1987,9915289,Europe,74.06,13039.30876 1246 | Portugal,1992,9927680,Europe,74.86,16207.26663 1247 | Portugal,1997,10156415,Europe,75.97,17641.03156 1248 | Portugal,2002,10433867,Europe,77.29,19970.90787 1249 | Portugal,2007,10642836,Europe,78.098,20509.64777 1250 | Puerto Rico,1952,2227000,Americas,64.28,3081.959785 1251 | Puerto Rico,1957,2260000,Americas,68.54,3907.156189 1252 | Puerto Rico,1962,2448046,Americas,69.62,5108.34463 1253 | Puerto Rico,1967,2648961,Americas,71.1,6929.277714 1254 | Puerto Rico,1972,2847132,Americas,72.16,9123.041742 1255 | Puerto Rico,1977,3080828,Americas,73.44,9770.524921 1256 | Puerto Rico,1982,3279001,Americas,73.75,10330.98915 1257 | Puerto Rico,1987,3444468,Americas,74.63,12281.34191 1258 | Puerto Rico,1992,3585176,Americas,73.911,14641.58711 1259 | Puerto Rico,1997,3759430,Americas,74.917,16999.4333 1260 | Puerto Rico,2002,3859606,Americas,77.778,18855.60618 1261 | Puerto Rico,2007,3942491,Americas,78.746,19328.70901 1262 | Reunion,1952,257700,Africa,52.724,2718.885295 1263 | Reunion,1957,308700,Africa,55.09,2769.451844 1264 | Reunion,1962,358900,Africa,57.666,3173.72334 1265 | Reunion,1967,414024,Africa,60.542,4021.175739 1266 | Reunion,1972,461633,Africa,64.274,5047.658563 1267 | Reunion,1977,492095,Africa,67.064,4319.804067 1268 | Reunion,1982,517810,Africa,69.885,5267.219353 1269 | Reunion,1987,562035,Africa,71.913,5303.377488 1270 | Reunion,1992,622191,Africa,73.615,6101.255823 1271 | Reunion,1997,684810,Africa,74.772,6071.941411 1272 | Reunion,2002,743981,Africa,75.744,6316.1652 1273 | Reunion,2007,798094,Africa,76.442,7670.122558 1274 | Romania,1952,16630000,Europe,61.05,3144.613186 1275 | Romania,1957,17829327,Europe,64.1,3943.370225 1276 | Romania,1962,18680721,Europe,66.8,4734.997586 1277 | Romania,1967,19284814,Europe,66.8,6470.866545 1278 | Romania,1972,20662648,Europe,69.21,8011.414402 1279 | Romania,1977,21658597,Europe,69.46,9356.39724 1280 | Romania,1982,22356726,Europe,69.66,9605.314053 1281 | Romania,1987,22686371,Europe,69.53,9696.273295 1282 | Romania,1992,22797027,Europe,69.36,6598.409903 1283 | Romania,1997,22562458,Europe,69.72,7346.547557 1284 | Romania,2002,22404337,Europe,71.322,7885.360081 1285 | Romania,2007,22276056,Europe,72.476,10808.47561 1286 | Rwanda,1952,2534927,Africa,40,493.3238752 1287 | Rwanda,1957,2822082,Africa,41.5,540.2893983 1288 | Rwanda,1962,3051242,Africa,43,597.4730727 1289 | Rwanda,1967,3451079,Africa,44.1,510.9637142 1290 | Rwanda,1972,3992121,Africa,44.6,590.5806638 1291 | Rwanda,1977,4657072,Africa,45,670.0806011 1292 | Rwanda,1982,5507565,Africa,46.218,881.5706467 1293 | Rwanda,1987,6349365,Africa,44.02,847.991217 1294 | Rwanda,1992,7290203,Africa,23.599,737.0685949 1295 | Rwanda,1997,7212583,Africa,36.087,589.9445051 1296 | Rwanda,2002,7852401,Africa,43.413,785.6537648 1297 | Rwanda,2007,8860588,Africa,46.242,863.0884639 1298 | Sao Tome and Principe,1952,60011,Africa,46.471,879.5835855 1299 | Sao Tome and Principe,1957,61325,Africa,48.945,860.7369026 1300 | Sao Tome and Principe,1962,65345,Africa,51.893,1071.551119 1301 | Sao Tome and Principe,1967,70787,Africa,54.425,1384.840593 1302 | Sao Tome and Principe,1972,76595,Africa,56.48,1532.985254 1303 | Sao Tome and Principe,1977,86796,Africa,58.55,1737.561657 1304 | Sao Tome and Principe,1982,98593,Africa,60.351,1890.218117 1305 | Sao Tome and Principe,1987,110812,Africa,61.728,1516.525457 1306 | Sao Tome and Principe,1992,125911,Africa,62.742,1428.777814 1307 | Sao Tome and Principe,1997,145608,Africa,63.306,1339.076036 1308 | Sao Tome and Principe,2002,170372,Africa,64.337,1353.09239 1309 | Sao Tome and Principe,2007,199579,Africa,65.528,1598.435089 1310 | Saudi Arabia,1952,4005677,Asia,39.875,6459.554823 1311 | Saudi Arabia,1957,4419650,Asia,42.868,8157.591248 1312 | Saudi Arabia,1962,4943029,Asia,45.914,11626.41975 1313 | Saudi Arabia,1967,5618198,Asia,49.901,16903.04886 1314 | Saudi Arabia,1972,6472756,Asia,53.886,24837.42865 1315 | Saudi Arabia,1977,8128505,Asia,58.69,34167.7626 1316 | Saudi Arabia,1982,11254672,Asia,63.012,33693.17525 1317 | Saudi Arabia,1987,14619745,Asia,66.295,21198.26136 1318 | Saudi Arabia,1992,16945857,Asia,68.768,24841.61777 1319 | Saudi Arabia,1997,21229759,Asia,70.533,20586.69019 1320 | Saudi Arabia,2002,24501530,Asia,71.626,19014.54118 1321 | Saudi Arabia,2007,27601038,Asia,72.777,21654.83194 1322 | Senegal,1952,2755589,Africa,37.278,1450.356983 1323 | Senegal,1957,3054547,Africa,39.329,1567.653006 1324 | Senegal,1962,3430243,Africa,41.454,1654.988723 1325 | Senegal,1967,3965841,Africa,43.563,1612.404632 1326 | Senegal,1972,4588696,Africa,45.815,1597.712056 1327 | Senegal,1977,5260855,Africa,48.879,1561.769116 1328 | Senegal,1982,6147783,Africa,52.379,1518.479984 1329 | Senegal,1987,7171347,Africa,55.769,1441.72072 1330 | Senegal,1992,8307920,Africa,58.196,1367.899369 1331 | Senegal,1997,9535314,Africa,60.187,1392.368347 1332 | Senegal,2002,10870037,Africa,61.6,1519.635262 1333 | Senegal,2007,12267493,Africa,63.062,1712.472136 1334 | Serbia,1952,6860147,Europe,57.996,3581.459448 1335 | Serbia,1957,7271135,Europe,61.685,4981.090891 1336 | Serbia,1962,7616060,Europe,64.531,6289.629157 1337 | Serbia,1967,7971222,Europe,66.914,7991.707066 1338 | Serbia,1972,8313288,Europe,68.7,10522.06749 1339 | Serbia,1977,8686367,Europe,70.3,12980.66956 1340 | Serbia,1982,9032824,Europe,70.162,15181.0927 1341 | Serbia,1987,9230783,Europe,71.218,15870.87851 1342 | Serbia,1992,9826397,Europe,71.659,9325.068238 1343 | Serbia,1997,10336594,Europe,72.232,7914.320304 1344 | Serbia,2002,10111559,Europe,73.213,7236.075251 1345 | Serbia,2007,10150265,Europe,74.002,9786.534714 1346 | Sierra Leone,1952,2143249,Africa,30.331,879.7877358 1347 | Sierra Leone,1957,2295678,Africa,31.57,1004.484437 1348 | Sierra Leone,1962,2467895,Africa,32.767,1116.639877 1349 | Sierra Leone,1967,2662190,Africa,34.113,1206.043465 1350 | Sierra Leone,1972,2879013,Africa,35.4,1353.759762 1351 | Sierra Leone,1977,3140897,Africa,36.788,1348.285159 1352 | Sierra Leone,1982,3464522,Africa,38.445,1465.010784 1353 | Sierra Leone,1987,3868905,Africa,40.006,1294.447788 1354 | Sierra Leone,1992,4260884,Africa,38.333,1068.696278 1355 | Sierra Leone,1997,4578212,Africa,39.897,574.6481576 1356 | Sierra Leone,2002,5359092,Africa,41.012,699.489713 1357 | Sierra Leone,2007,6144562,Africa,42.568,862.5407561 1358 | Singapore,1952,1127000,Asia,60.396,2315.138227 1359 | Singapore,1957,1445929,Asia,63.179,2843.104409 1360 | Singapore,1962,1750200,Asia,65.798,3674.735572 1361 | Singapore,1967,1977600,Asia,67.946,4977.41854 1362 | Singapore,1972,2152400,Asia,69.521,8597.756202 1363 | Singapore,1977,2325300,Asia,70.795,11210.08948 1364 | Singapore,1982,2651869,Asia,71.76,15169.16112 1365 | Singapore,1987,2794552,Asia,73.56,18861.53081 1366 | Singapore,1992,3235865,Asia,75.788,24769.8912 1367 | Singapore,1997,3802309,Asia,77.158,33519.4766 1368 | Singapore,2002,4197776,Asia,78.77,36023.1054 1369 | Singapore,2007,4553009,Asia,79.972,47143.17964 1370 | Slovak Republic,1952,3558137,Europe,64.36,5074.659104 1371 | Slovak Republic,1957,3844277,Europe,67.45,6093.26298 1372 | Slovak Republic,1962,4237384,Europe,70.33,7481.107598 1373 | Slovak Republic,1967,4442238,Europe,70.98,8412.902397 1374 | Slovak Republic,1972,4593433,Europe,70.35,9674.167626 1375 | Slovak Republic,1977,4827803,Europe,70.45,10922.66404 1376 | Slovak Republic,1982,5048043,Europe,70.8,11348.54585 1377 | Slovak Republic,1987,5199318,Europe,71.08,12037.26758 1378 | Slovak Republic,1992,5302888,Europe,71.38,9498.467723 1379 | Slovak Republic,1997,5383010,Europe,72.71,12126.23065 1380 | Slovak Republic,2002,5410052,Europe,73.8,13638.77837 1381 | Slovak Republic,2007,5447502,Europe,74.663,18678.31435 1382 | Slovenia,1952,1489518,Europe,65.57,4215.041741 1383 | Slovenia,1957,1533070,Europe,67.85,5862.276629 1384 | Slovenia,1962,1582962,Europe,69.15,7402.303395 1385 | Slovenia,1967,1646912,Europe,69.18,9405.489397 1386 | Slovenia,1972,1694510,Europe,69.82,12383.4862 1387 | Slovenia,1977,1746919,Europe,70.97,15277.03017 1388 | Slovenia,1982,1861252,Europe,71.063,17866.72175 1389 | Slovenia,1987,1945870,Europe,72.25,18678.53492 1390 | Slovenia,1992,1999210,Europe,73.64,14214.71681 1391 | Slovenia,1997,2011612,Europe,75.13,17161.10735 1392 | Slovenia,2002,2011497,Europe,76.66,20660.01936 1393 | Slovenia,2007,2009245,Europe,77.926,25768.25759 1394 | Somalia,1952,2526994,Africa,32.978,1135.749842 1395 | Somalia,1957,2780415,Africa,34.977,1258.147413 1396 | Somalia,1962,3080153,Africa,36.981,1369.488336 1397 | Somalia,1967,3428839,Africa,38.977,1284.73318 1398 | Somalia,1972,3840161,Africa,40.973,1254.576127 1399 | Somalia,1977,4353666,Africa,41.974,1450.992513 1400 | Somalia,1982,5828892,Africa,42.955,1176.807031 1401 | Somalia,1987,6921858,Africa,44.501,1093.244963 1402 | Somalia,1992,6099799,Africa,39.658,926.9602964 1403 | Somalia,1997,6633514,Africa,43.795,930.5964284 1404 | Somalia,2002,7753310,Africa,45.936,882.0818218 1405 | Somalia,2007,9118773,Africa,48.159,926.1410683 1406 | South Africa,1952,14264935,Africa,45.009,4725.295531 1407 | South Africa,1957,16151549,Africa,47.985,5487.104219 1408 | South Africa,1962,18356657,Africa,49.951,5768.729717 1409 | South Africa,1967,20997321,Africa,51.927,7114.477971 1410 | South Africa,1972,23935810,Africa,53.696,7765.962636 1411 | South Africa,1977,27129932,Africa,55.527,8028.651439 1412 | South Africa,1982,31140029,Africa,58.161,8568.266228 1413 | South Africa,1987,35933379,Africa,60.834,7825.823398 1414 | South Africa,1992,39964159,Africa,61.888,7225.069258 1415 | South Africa,1997,42835005,Africa,60.236,7479.188244 1416 | South Africa,2002,44433622,Africa,53.365,7710.946444 1417 | South Africa,2007,43997828,Africa,49.339,9269.657808 1418 | Spain,1952,28549870,Europe,64.94,3834.034742 1419 | Spain,1957,29841614,Europe,66.66,4564.80241 1420 | Spain,1962,31158061,Europe,69.69,5693.843879 1421 | Spain,1967,32850275,Europe,71.44,7993.512294 1422 | Spain,1972,34513161,Europe,73.06,10638.75131 1423 | Spain,1977,36439000,Europe,74.39,13236.92117 1424 | Spain,1982,37983310,Europe,76.3,13926.16997 1425 | Spain,1987,38880702,Europe,76.9,15764.98313 1426 | Spain,1992,39549438,Europe,77.57,18603.06452 1427 | Spain,1997,39855442,Europe,78.77,20445.29896 1428 | Spain,2002,40152517,Europe,79.78,24835.47166 1429 | Spain,2007,40448191,Europe,80.941,28821.0637 1430 | Sri Lanka,1952,7982342,Asia,57.593,1083.53203 1431 | Sri Lanka,1957,9128546,Asia,61.456,1072.546602 1432 | Sri Lanka,1962,10421936,Asia,62.192,1074.47196 1433 | Sri Lanka,1967,11737396,Asia,64.266,1135.514326 1434 | Sri Lanka,1972,13016733,Asia,65.042,1213.39553 1435 | Sri Lanka,1977,14116836,Asia,65.949,1348.775651 1436 | Sri Lanka,1982,15410151,Asia,68.757,1648.079789 1437 | Sri Lanka,1987,16495304,Asia,69.011,1876.766827 1438 | Sri Lanka,1992,17587060,Asia,70.379,2153.739222 1439 | Sri Lanka,1997,18698655,Asia,70.457,2664.477257 1440 | Sri Lanka,2002,19576783,Asia,70.815,3015.378833 1441 | Sri Lanka,2007,20378239,Asia,72.396,3970.095407 1442 | Sudan,1952,8504667,Africa,38.635,1615.991129 1443 | Sudan,1957,9753392,Africa,39.624,1770.337074 1444 | Sudan,1962,11183227,Africa,40.87,1959.593767 1445 | Sudan,1967,12716129,Africa,42.858,1687.997641 1446 | Sudan,1972,14597019,Africa,45.083,1659.652775 1447 | Sudan,1977,17104986,Africa,47.8,2202.988423 1448 | Sudan,1982,20367053,Africa,50.338,1895.544073 1449 | Sudan,1987,24725960,Africa,51.744,1507.819159 1450 | Sudan,1992,28227588,Africa,53.556,1492.197043 1451 | Sudan,1997,32160729,Africa,55.373,1632.210764 1452 | Sudan,2002,37090298,Africa,56.369,1993.398314 1453 | Sudan,2007,42292929,Africa,58.556,2602.394995 1454 | Swaziland,1952,290243,Africa,41.407,1148.376626 1455 | Swaziland,1957,326741,Africa,43.424,1244.708364 1456 | Swaziland,1962,370006,Africa,44.992,1856.182125 1457 | Swaziland,1967,420690,Africa,46.633,2613.101665 1458 | Swaziland,1972,480105,Africa,49.552,3364.836625 1459 | Swaziland,1977,551425,Africa,52.537,3781.410618 1460 | Swaziland,1982,649901,Africa,55.561,3895.384018 1461 | Swaziland,1987,779348,Africa,57.678,3984.839812 1462 | Swaziland,1992,962344,Africa,58.474,3553.0224 1463 | Swaziland,1997,1054486,Africa,54.289,3876.76846 1464 | Swaziland,2002,1130269,Africa,43.869,4128.116943 1465 | Swaziland,2007,1133066,Africa,39.613,4513.480643 1466 | Sweden,1952,7124673,Europe,71.86,8527.844662 1467 | Sweden,1957,7363802,Europe,72.49,9911.878226 1468 | Sweden,1962,7561588,Europe,73.37,12329.44192 1469 | Sweden,1967,7867931,Europe,74.16,15258.29697 1470 | Sweden,1972,8122293,Europe,74.72,17832.02464 1471 | Sweden,1977,8251648,Europe,75.44,18855.72521 1472 | Sweden,1982,8325260,Europe,76.42,20667.38125 1473 | Sweden,1987,8421403,Europe,77.19,23586.92927 1474 | Sweden,1992,8718867,Europe,78.16,23880.01683 1475 | Sweden,1997,8897619,Europe,79.39,25266.59499 1476 | Sweden,2002,8954175,Europe,80.04,29341.63093 1477 | Sweden,2007,9031088,Europe,80.884,33859.74835 1478 | Switzerland,1952,4815000,Europe,69.62,14734.23275 1479 | Switzerland,1957,5126000,Europe,70.56,17909.48973 1480 | Switzerland,1962,5666000,Europe,71.32,20431.0927 1481 | Switzerland,1967,6063000,Europe,72.77,22966.14432 1482 | Switzerland,1972,6401400,Europe,73.78,27195.11304 1483 | Switzerland,1977,6316424,Europe,75.39,26982.29052 1484 | Switzerland,1982,6468126,Europe,76.21,28397.71512 1485 | Switzerland,1987,6649942,Europe,77.41,30281.70459 1486 | Switzerland,1992,6995447,Europe,78.03,31871.5303 1487 | Switzerland,1997,7193761,Europe,79.37,32135.32301 1488 | Switzerland,2002,7361757,Europe,80.62,34480.95771 1489 | Switzerland,2007,7554661,Europe,81.701,37506.41907 1490 | Syria,1952,3661549,Asia,45.883,1643.485354 1491 | Syria,1957,4149908,Asia,48.284,2117.234893 1492 | Syria,1962,4834621,Asia,50.305,2193.037133 1493 | Syria,1967,5680812,Asia,53.655,1881.923632 1494 | Syria,1972,6701172,Asia,57.296,2571.423014 1495 | Syria,1977,7932503,Asia,61.195,3195.484582 1496 | Syria,1982,9410494,Asia,64.59,3761.837715 1497 | Syria,1987,11242847,Asia,66.974,3116.774285 1498 | Syria,1992,13219062,Asia,69.249,3340.542768 1499 | Syria,1997,15081016,Asia,71.527,4014.238972 1500 | Syria,2002,17155814,Asia,73.053,4090.925331 1501 | Syria,2007,19314747,Asia,74.143,4184.548089 1502 | Taiwan,1952,8550362,Asia,58.5,1206.947913 1503 | Taiwan,1957,10164215,Asia,62.4,1507.86129 1504 | Taiwan,1962,11918938,Asia,65.2,1822.879028 1505 | Taiwan,1967,13648692,Asia,67.5,2643.858681 1506 | Taiwan,1972,15226039,Asia,69.39,4062.523897 1507 | Taiwan,1977,16785196,Asia,70.59,5596.519826 1508 | Taiwan,1982,18501390,Asia,72.16,7426.354774 1509 | Taiwan,1987,19757799,Asia,73.4,11054.56175 1510 | Taiwan,1992,20686918,Asia,74.26,15215.6579 1511 | Taiwan,1997,21628605,Asia,75.25,20206.82098 1512 | Taiwan,2002,22454239,Asia,76.99,23235.42329 1513 | Taiwan,2007,23174294,Asia,78.4,28718.27684 1514 | Tanzania,1952,8322925,Africa,41.215,716.6500721 1515 | Tanzania,1957,9452826,Africa,42.974,698.5356073 1516 | Tanzania,1962,10863958,Africa,44.246,722.0038073 1517 | Tanzania,1967,12607312,Africa,45.757,848.2186575 1518 | Tanzania,1972,14706593,Africa,47.62,915.9850592 1519 | Tanzania,1977,17129565,Africa,49.919,962.4922932 1520 | Tanzania,1982,19844382,Africa,50.608,874.2426069 1521 | Tanzania,1987,23040630,Africa,51.535,831.8220794 1522 | Tanzania,1992,26605473,Africa,50.44,825.682454 1523 | Tanzania,1997,30686889,Africa,48.466,789.1862231 1524 | Tanzania,2002,34593779,Africa,49.651,899.0742111 1525 | Tanzania,2007,38139640,Africa,52.517,1107.482182 1526 | Thailand,1952,21289402,Asia,50.848,757.7974177 1527 | Thailand,1957,25041917,Asia,53.63,793.5774148 1528 | Thailand,1962,29263397,Asia,56.061,1002.199172 1529 | Thailand,1967,34024249,Asia,58.285,1295.46066 1530 | Thailand,1972,39276153,Asia,60.405,1524.358936 1531 | Thailand,1977,44148285,Asia,62.494,1961.224635 1532 | Thailand,1982,48827160,Asia,64.597,2393.219781 1533 | Thailand,1987,52910342,Asia,66.084,2982.653773 1534 | Thailand,1992,56667095,Asia,67.298,4616.896545 1535 | Thailand,1997,60216677,Asia,67.521,5852.625497 1536 | Thailand,2002,62806748,Asia,68.564,5913.187529 1537 | Thailand,2007,65068149,Asia,70.616,7458.396327 1538 | Togo,1952,1219113,Africa,38.596,859.8086567 1539 | Togo,1957,1357445,Africa,41.208,925.9083202 1540 | Togo,1962,1528098,Africa,43.922,1067.53481 1541 | Togo,1967,1735550,Africa,46.769,1477.59676 1542 | Togo,1972,2056351,Africa,49.759,1649.660188 1543 | Togo,1977,2308582,Africa,52.887,1532.776998 1544 | Togo,1982,2644765,Africa,55.471,1344.577953 1545 | Togo,1987,3154264,Africa,56.941,1202.201361 1546 | Togo,1992,3747553,Africa,58.061,1034.298904 1547 | Togo,1997,4320890,Africa,58.39,982.2869243 1548 | Togo,2002,4977378,Africa,57.561,886.2205765 1549 | Togo,2007,5701579,Africa,58.42,882.9699438 1550 | Trinidad and Tobago,1952,662850,Americas,59.1,3023.271928 1551 | Trinidad and Tobago,1957,764900,Americas,61.8,4100.3934 1552 | Trinidad and Tobago,1962,887498,Americas,64.9,4997.523971 1553 | Trinidad and Tobago,1967,960155,Americas,65.4,5621.368472 1554 | Trinidad and Tobago,1972,975199,Americas,65.9,6619.551419 1555 | Trinidad and Tobago,1977,1039009,Americas,68.3,7899.554209 1556 | Trinidad and Tobago,1982,1116479,Americas,68.832,9119.528607 1557 | Trinidad and Tobago,1987,1191336,Americas,69.582,7388.597823 1558 | Trinidad and Tobago,1992,1183669,Americas,69.862,7370.990932 1559 | Trinidad and Tobago,1997,1138101,Americas,69.465,8792.573126 1560 | Trinidad and Tobago,2002,1101832,Americas,68.976,11460.60023 1561 | Trinidad and Tobago,2007,1056608,Americas,69.819,18008.50924 1562 | Tunisia,1952,3647735,Africa,44.6,1468.475631 1563 | Tunisia,1957,3950849,Africa,47.1,1395.232468 1564 | Tunisia,1962,4286552,Africa,49.579,1660.30321 1565 | Tunisia,1967,4786986,Africa,52.053,1932.360167 1566 | Tunisia,1972,5303507,Africa,55.602,2753.285994 1567 | Tunisia,1977,6005061,Africa,59.837,3120.876811 1568 | Tunisia,1982,6734098,Africa,64.048,3560.233174 1569 | Tunisia,1987,7724976,Africa,66.894,3810.419296 1570 | Tunisia,1992,8523077,Africa,70.001,4332.720164 1571 | Tunisia,1997,9231669,Africa,71.973,4876.798614 1572 | Tunisia,2002,9770575,Africa,73.042,5722.895655 1573 | Tunisia,2007,10276158,Africa,73.923,7092.923025 1574 | Turkey,1952,22235677,Europe,43.585,1969.10098 1575 | Turkey,1957,25670939,Europe,48.079,2218.754257 1576 | Turkey,1962,29788695,Europe,52.098,2322.869908 1577 | Turkey,1967,33411317,Europe,54.336,2826.356387 1578 | Turkey,1972,37492953,Europe,57.005,3450.69638 1579 | Turkey,1977,42404033,Europe,59.507,4269.122326 1580 | Turkey,1982,47328791,Europe,61.036,4241.356344 1581 | Turkey,1987,52881328,Europe,63.108,5089.043686 1582 | Turkey,1992,58179144,Europe,66.146,5678.348271 1583 | Turkey,1997,63047647,Europe,68.835,6601.429915 1584 | Turkey,2002,67308928,Europe,70.845,6508.085718 1585 | Turkey,2007,71158647,Europe,71.777,8458.276384 1586 | Uganda,1952,5824797,Africa,39.978,734.753484 1587 | Uganda,1957,6675501,Africa,42.571,774.3710692 1588 | Uganda,1962,7688797,Africa,45.344,767.2717398 1589 | Uganda,1967,8900294,Africa,48.051,908.9185217 1590 | Uganda,1972,10190285,Africa,51.016,950.735869 1591 | Uganda,1977,11457758,Africa,50.35,843.7331372 1592 | Uganda,1982,12939400,Africa,49.849,682.2662268 1593 | Uganda,1987,15283050,Africa,51.509,617.7244065 1594 | Uganda,1992,18252190,Africa,48.825,644.1707969 1595 | Uganda,1997,21210254,Africa,44.578,816.559081 1596 | Uganda,2002,24739869,Africa,47.813,927.7210018 1597 | Uganda,2007,29170398,Africa,51.542,1056.380121 1598 | United Kingdom,1952,50430000,Europe,69.18,9979.508487 1599 | United Kingdom,1957,51430000,Europe,70.42,11283.17795 1600 | United Kingdom,1962,53292000,Europe,70.76,12477.17707 1601 | United Kingdom,1967,54959000,Europe,71.36,14142.85089 1602 | United Kingdom,1972,56079000,Europe,72.01,15895.11641 1603 | United Kingdom,1977,56179000,Europe,72.76,17428.74846 1604 | United Kingdom,1982,56339704,Europe,74.04,18232.42452 1605 | United Kingdom,1987,56981620,Europe,75.007,21664.78767 1606 | United Kingdom,1992,57866349,Europe,76.42,22705.09254 1607 | United Kingdom,1997,58808266,Europe,77.218,26074.53136 1608 | United Kingdom,2002,59912431,Europe,78.471,29478.99919 1609 | United Kingdom,2007,60776238,Europe,79.425,33203.26128 1610 | United States,1952,157553000,Americas,68.44,13990.48208 1611 | United States,1957,171984000,Americas,69.49,14847.12712 1612 | United States,1962,186538000,Americas,70.21,16173.14586 1613 | United States,1967,198712000,Americas,70.76,19530.36557 1614 | United States,1972,209896000,Americas,71.34,21806.03594 1615 | United States,1977,220239000,Americas,73.38,24072.63213 1616 | United States,1982,232187835,Americas,74.65,25009.55914 1617 | United States,1987,242803533,Americas,75.02,29884.35041 1618 | United States,1992,256894189,Americas,76.09,32003.93224 1619 | United States,1997,272911760,Americas,76.81,35767.43303 1620 | United States,2002,287675526,Americas,77.31,39097.09955 1621 | United States,2007,301139947,Americas,78.242,42951.65309 1622 | Uruguay,1952,2252965,Americas,66.071,5716.766744 1623 | Uruguay,1957,2424959,Americas,67.044,6150.772969 1624 | Uruguay,1962,2598466,Americas,68.253,5603.357717 1625 | Uruguay,1967,2748579,Americas,68.468,5444.61962 1626 | Uruguay,1972,2829526,Americas,68.673,5703.408898 1627 | Uruguay,1977,2873520,Americas,69.481,6504.339663 1628 | Uruguay,1982,2953997,Americas,70.805,6920.223051 1629 | Uruguay,1987,3045153,Americas,71.918,7452.398969 1630 | Uruguay,1992,3149262,Americas,72.752,8137.004775 1631 | Uruguay,1997,3262838,Americas,74.223,9230.240708 1632 | Uruguay,2002,3363085,Americas,75.307,7727.002004 1633 | Uruguay,2007,3447496,Americas,76.384,10611.46299 1634 | Venezuela,1952,5439568,Americas,55.088,7689.799761 1635 | Venezuela,1957,6702668,Americas,57.907,9802.466526 1636 | Venezuela,1962,8143375,Americas,60.77,8422.974165 1637 | Venezuela,1967,9709552,Americas,63.479,9541.474188 1638 | Venezuela,1972,11515649,Americas,65.712,10505.25966 1639 | Venezuela,1977,13503563,Americas,67.456,13143.95095 1640 | Venezuela,1982,15620766,Americas,68.557,11152.41011 1641 | Venezuela,1987,17910182,Americas,70.19,9883.584648 1642 | Venezuela,1992,20265563,Americas,71.15,10733.92631 1643 | Venezuela,1997,22374398,Americas,72.146,10165.49518 1644 | Venezuela,2002,24287670,Americas,72.766,8605.047831 1645 | Venezuela,2007,26084662,Americas,73.747,11415.80569 1646 | Vietnam,1952,26246839,Asia,40.412,605.0664917 1647 | Vietnam,1957,28998543,Asia,42.887,676.2854478 1648 | Vietnam,1962,33796140,Asia,45.363,772.0491602 1649 | Vietnam,1967,39463910,Asia,47.838,637.1232887 1650 | Vietnam,1972,44655014,Asia,50.254,699.5016441 1651 | Vietnam,1977,50533506,Asia,55.764,713.5371196 1652 | Vietnam,1982,56142181,Asia,58.816,707.2357863 1653 | Vietnam,1987,62826491,Asia,62.82,820.7994449 1654 | Vietnam,1992,69940728,Asia,67.662,989.0231487 1655 | Vietnam,1997,76048996,Asia,70.672,1385.896769 1656 | Vietnam,2002,80908147,Asia,73.017,1764.456677 1657 | Vietnam,2007,85262356,Asia,74.249,2441.576404 1658 | West Bank and Gaza,1952,1030585,Asia,43.16,1515.592329 1659 | West Bank and Gaza,1957,1070439,Asia,45.671,1827.067742 1660 | West Bank and Gaza,1962,1133134,Asia,48.127,2198.956312 1661 | West Bank and Gaza,1967,1142636,Asia,51.631,2649.715007 1662 | West Bank and Gaza,1972,1089572,Asia,56.532,3133.409277 1663 | West Bank and Gaza,1977,1261091,Asia,60.765,3682.831494 1664 | West Bank and Gaza,1982,1425876,Asia,64.406,4336.032082 1665 | West Bank and Gaza,1987,1691210,Asia,67.046,5107.197384 1666 | West Bank and Gaza,1992,2104779,Asia,69.718,6017.654756 1667 | West Bank and Gaza,1997,2826046,Asia,71.096,7110.667619 1668 | West Bank and Gaza,2002,3389578,Asia,72.37,4515.487575 1669 | West Bank and Gaza,2007,4018332,Asia,73.422,3025.349798 1670 | Yemen Rep.,1952,4963829,Asia,32.548,781.7175761 1671 | Yemen Rep.,1957,5498090,Asia,33.97,804.8304547 1672 | Yemen Rep.,1962,6120081,Asia,35.18,825.6232006 1673 | Yemen Rep.,1967,6740785,Asia,36.984,862.4421463 1674 | Yemen Rep.,1972,7407075,Asia,39.848,1265.047031 1675 | Yemen Rep.,1977,8403990,Asia,44.175,1829.765177 1676 | Yemen Rep.,1982,9657618,Asia,49.113,1977.55701 1677 | Yemen Rep.,1987,11219340,Asia,52.922,1971.741538 1678 | Yemen Rep.,1992,13367997,Asia,55.599,1879.496673 1679 | Yemen Rep.,1997,15826497,Asia,58.02,2117.484526 1680 | Yemen Rep.,2002,18701257,Asia,60.308,2234.820827 1681 | Yemen Rep.,2007,22211743,Asia,62.698,2280.769906 1682 | Zambia,1952,2672000,Africa,42.038,1147.388831 1683 | Zambia,1957,3016000,Africa,44.077,1311.956766 1684 | Zambia,1962,3421000,Africa,46.023,1452.725766 1685 | Zambia,1967,3900000,Africa,47.768,1777.077318 1686 | Zambia,1972,4506497,Africa,50.107,1773.498265 1687 | Zambia,1977,5216550,Africa,51.386,1588.688299 1688 | Zambia,1982,6100407,Africa,51.821,1408.678565 1689 | Zambia,1987,7272406,Africa,50.821,1213.315116 1690 | Zambia,1992,8381163,Africa,46.1,1210.884633 1691 | Zambia,1997,9417789,Africa,40.238,1071.353818 1692 | Zambia,2002,10595811,Africa,39.193,1071.613938 1693 | Zambia,2007,11746035,Africa,42.384,1271.211593 1694 | Zimbabwe,1952,3080907,Africa,48.451,406.8841148 1695 | Zimbabwe,1957,3646340,Africa,50.469,518.7642681 1696 | Zimbabwe,1962,4277736,Africa,52.358,527.2721818 1697 | Zimbabwe,1967,4995432,Africa,53.995,569.7950712 1698 | Zimbabwe,1972,5861135,Africa,55.635,799.3621758 1699 | Zimbabwe,1977,6642107,Africa,57.674,685.5876821 1700 | Zimbabwe,1982,7636524,Africa,60.363,788.8550411 1701 | Zimbabwe,1987,9216418,Africa,62.351,706.1573059 1702 | Zimbabwe,1992,10704340,Africa,60.377,693.4207856 1703 | Zimbabwe,1997,11404948,Africa,46.809,792.4499603 1704 | Zimbabwe,2002,11926563,Africa,39.989,672.0386227 1705 | Zimbabwe,2007,12311143,Africa,43.487,469.7092981 1706 | -------------------------------------------------------------------------------- /data/heart.csv: -------------------------------------------------------------------------------- 1 | age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target 2 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 3 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 4 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 5 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 6 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 7 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 8 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 9 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1 10 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 11 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 12 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1 13 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1 14 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1 15 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1 16 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1 17 | 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65,0,2,160,360,0,0,151,0,0.8,2,0,2,1 42 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1 43 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1 44 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1 45 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1 46 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1 47 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1 48 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1 49 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1 50 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1 51 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1 52 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1 53 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1 54 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1 55 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1 56 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1 57 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1 58 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1 59 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1 60 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1 61 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1 62 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1 63 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1 64 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1 65 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1 66 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1 67 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1 68 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1 69 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1 70 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1 71 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1 72 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1 73 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1 74 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1 75 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1 76 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1 77 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1 78 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1 79 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1 80 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1 81 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1 82 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1 83 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1 84 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1 85 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1 86 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1 87 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1 88 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1 89 | 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51,1,0,140,298,0,1,122,1,4.2,1,3,3,0 253 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0 254 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0 255 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0 256 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0 257 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0 258 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0 259 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0 260 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0 261 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0 262 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0 263 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0 264 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0 265 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0 266 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0 267 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0 268 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0 269 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0 270 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0 271 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0 272 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0 273 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0 274 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0 275 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0 276 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0 277 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0 278 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0 279 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0 280 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0 281 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0 282 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0 283 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0 284 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0 285 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0 286 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0 287 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0 288 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0 289 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0 290 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0 291 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0 292 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0 293 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0 294 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0 295 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0 296 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0 297 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0 298 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0 299 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0 300 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0 301 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0 302 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0 303 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0 304 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0 305 | -------------------------------------------------------------------------------- /images/bars.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dlab-berkeley/R-Data-Visualization-Legacy/HEAD/images/bars.png -------------------------------------------------------------------------------- /images/circles.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dlab-berkeley/R-Data-Visualization-Legacy/HEAD/images/circles.png -------------------------------------------------------------------------------- /images/two-series-0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dlab-berkeley/R-Data-Visualization-Legacy/HEAD/images/two-series-0.png -------------------------------------------------------------------------------- /images/two-series-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dlab-berkeley/R-Data-Visualization-Legacy/HEAD/images/two-series-1.png -------------------------------------------------------------------------------- /lessons/R-Data-Visualization-Challenges.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "R Data Visualization Challenges" 3 | output: html_document 4 | --- 5 | 6 | # Load the [heart dataset](http://search.r-project.org/library/kmed/html/heart.html) 7 | 8 | Use the heart dataset for the following challenges. Import it and save it in a variable named `heart`. Make sure to review the data dictionary linked above! 9 | 10 | ```{r import} 11 | ## YOUR CODE HERE 12 | ``` 13 | 14 | **For each challenge, use ggplot2** 15 | 16 | # Challenge 1 17 | 18 | Using the heart dataset, create a histogram of the Serum cholesterol. Save it in a variable named `A`. 19 | 20 | ```{r challenge_1} 21 | ## YOUR CODE HERE 22 | ``` 23 | 24 | # Challenge 2 25 | 26 | Using the heart dataset, create a boxplot of Serum cholesterol based on patient self-described sex. Save this figure in a variable named `B`. 27 | 28 | Hint: The variable that identifies patient sex needs to be coerced into a different variable type in order to make this visualization. 29 | 30 | ```{r challenge_2} 31 | ## YOUR CODE HERE 32 | ``` 33 | 34 | # Challenge 3 35 | 36 | Using the heart dataset, create a scatterplot that visualizes the relationship between patient age and Serum cholesterol. Save it in a variable named `C` 37 | 38 | ```{r challenge_3} 39 | ## YOUR CODE HERE 40 | ``` 41 | 42 | # Challenge 4 43 | 44 | Create a line plot of the mean cholesterol for each age, separated by sex using the heart data. You will need to do some data manipulation with `dplyr` to make this plot. Save your plot to a variable named `D`. 45 | 46 | ```{r challenge_4} 47 | ## YOUR CODE HERE 48 | ``` 49 | 50 | # Challenge 5 51 | 52 | Use cowplot to create a compound figure named `compound_figure` that contains subplots `A`, `B`, `C`, and `D` that you created above. 53 | 54 | ```{r challenge_5} 55 | ## YOUR CODE HERE 56 | ``` 57 | -------------------------------------------------------------------------------- /lessons/R-Data-Visualization.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "R Data Visualization" 3 | theme: readable 4 | output: 5 | html_document: 6 | number_sections: yes 7 | toc: yes 8 | toc_float: yes 9 | fig_width: 12 10 | fig_height: 7 11 | --- 12 | 13 | # Theory of Data Visualization 14 | 15 | Visualization is meant to convey information. 16 | 17 | > The power of a graph is its ability to enable one to take in the quantitative information, organize it, and see patterns and structure not readily revealed by other means of studying the data. 18 | 19 | \- Cleveland and McGill, 1984 20 | 21 | Certain techniques make that information easier to interpret and understand. In their 1984 paper titled, "[Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods](https://www-jstor-org.libproxy.berkeley.edu/stable/2288400?seq=1#page_scan_tab_contents)," Cleveland and McGill identify 10 elementary perceptual tasks that are used to "extract quantitative information from graphs." Their premise is: 22 | 23 | > A graphical form that involves elementary perceptual tasks that lead to more accurate judgments than another graphical form (with the same quantitative information) will result in better organization and increase the chances of a correct perception of patterns and behavior. 24 | 25 | Whereas graph design had, up to that point, been "largely unscientific," Cleveland and McGill took a systematic approach in analyzing human graphical perception through experimentation. Their researched helped identify the most and least accurate elementary perceptual tasks, ordered below: 26 | 27 | 1. Position along a common scale 28 | 2. Position along non-aligned scales 29 | 3. Length, direction, angle 30 | 4. Area 31 | 5. Volume, curvature 32 | 6. Shading, color saturation 33 | 34 | In 2010, [Heer and Bostock](http://vis.stanford.edu/files/2010-MTurk-CHI.pdf) confirmed these results using Amazon's Mechanical Turk. 35 | 36 | Let's take a look at a few examples. Because we're only interested in relative sizes, we don't include a legend with size information or reference points. 37 | 38 | ![](../images/circles.png) 39 | 40 | For circles of distinctly different sizes, the comparison is simple. For example, "A" is smaller than "B." However, for circles, such as "L" and "M," that are almost the same size, it's difficult to tell which is smaller. Area, according to Cleveland and McGill's research, is less accurate than, say, length, which we consider next. 41 | 42 | ![](../images/bars.png) 43 | 44 | Focusing on "L" and "M," it is clear to see which is larger. You might be wondering whether scale makes a difference -- that is, if the small circle sizes make it difficult to compare -- it doesn't. 45 | 46 | Next, we consider a case where we want to plot two series. For this example, let's suppose we're working with student English and math test scores. Here, we'll want to use bars, which we arbitrarily label Z-L. The question is, which bars should we use? This is a case where the answer depends on what we're trying to communicate. If we're interested in showing total scores, we could use a stacked bar chart. 47 | 48 | ![](../images/two-series-0.png) 49 | 50 | We can tell that "Y" and "L" had the highest cumulative scores. What if we want to know which students scored highest on the math exam? Because the math portions of each bar are on "non-aligned scales," as Cleveland and McGill call it, it's difficult to tell. One solution is to plot these on opposite sides of the x-axis. 51 | 52 | ![](../images/two-series-1.png) 53 | 54 | Now, it's easier to see that "R" scored quite well on the math exam. The tradeoff with this layout is that it's difficult to compare cumulative scores. Comparing "Z" and "O," for example, is a challenge. Again, it depends on what the message is. 55 | 56 | These findings are a *guide* for what works when the goal is to make accurate judgments. Sometimes, however, the goal might not be to allow for precise comparisons but, rather, to facilitate the perception of larger patterns. In those cases, we'll be required to use elements lower on the list of perceptual tasks - particularly color and shading - in order to efficiently convey a point. 57 | 58 | ## Form and Function 59 | 60 | > A good graphic realizes two basic goals: It **presents** information, and it allows users to **explore** that information. 61 | 62 | \- Alberto Cairo 63 | 64 | ------------------------------------------------------------------------ 65 | 66 | > A data visualization should only be beautiful when beauty can promote understanding in some way without undermining it in another. Is beauty sometimes useful? Certainly. Is beauty always useful? Certainly not. 67 | 68 | \- Stephen Few 69 | 70 | ------------------------------------------------------------------------ 71 | 72 | > Good displays of data help to reveal knowledge relevant to understanding mechanism, process and dynamics, cause and effect. 73 | 74 | \- Edward Tufte 75 | 76 | ------------------------------------------------------------------------ 77 | 78 | > A figure is ineffective if it "wastes too much real estate (and the designer's time) on things that don't help readers understand [it]." 79 | 80 | \- Alberto Cairo 81 | 82 | ------------------------------------------------------------------------ 83 | 84 | > The important criterion for a graph is not simply how fast we can see a result; rather it is whether through the use of the graph we can see something that would have been harder to see otherwise or that could not have been seen at all. 85 | 86 | \- William Cleveland 87 | 88 | ------------------------------------------------------------------------ 89 | 90 | > [A]lways take advantage of the space you have available to seek depth within reasonable limits. After that, *and only after that*, worry about how to make the presentation prettier. 91 | 92 | \- Alberto Cairo 93 | 94 | Ultimately, identify your audience and their needs and interests. The same data should be visualized differently for a scientific publication versus a magazine. 95 | 96 | # Plotting in R 97 | 98 | There are two major sets of tools for creating plots in R: 99 | 100 | 1. base graphics, which come with all R installations\ 101 | 2. `ggplot2`, a stand-alone package. 102 | 103 | Note that other plotting facilities do exist (notably, `lattice`), but base graphics and `ggplot2` are by far the most popular. Check out [this post](https://stackoverflow.com/questions/2759556/r-what-are-the-pros-and-cons-of-using-lattice-versus-ggplot2) on comparisons between base, `ggplot2`, and `lattice` graphics to learn more. In this workshop, we are going to use `ggplot2` to make our graphics because we think that `ggplot2` provides a nice framework to think about how to make visualizations. 104 | 105 | ## Package installation 106 | 107 | First, we'll install and import the necessary packages. Let's get started! 108 | 109 | ```{r import, message=F, warning = F} 110 | # Uncomment this to install packages, if necessary. 111 | # install.packages(c("here", "cowplot", "dplyr", "ggplot2", "viridis")) 112 | 113 | library(dplyr) 114 | library(cowplot) 115 | library(ggplot2) 116 | library(here) 117 | ``` 118 | 119 | ## Dataset Details 120 | 121 | For the following examples, we will using the gapminder dataset. Gapminder is a country-year dataset with information on life expectancy and GDP per-capita. Check out more on gapminder in this [Github repository](https://github.com/jennybc/gapminder). 122 | 123 | ```{r import_gap} 124 | gap <- read.csv(here::here("data/gapminder-FiveYearData.csv"), stringsAsFactors = TRUE) 125 | head(gap) 126 | glimpse(gap) 127 | ``` 128 | 129 | # Using `ggplot2` 130 | 131 | Base-R provides graphical capabilities, but we are going to focus on a package called `ggplot2` inspired by Leland Wilkinson's **g**rammar of **g**raphics. 132 | 133 | > NOTE: `ggplot2` is the name of the package, but `ggplot` is the main function call. 134 | 135 | ## Scatter plots 136 | 137 | Scatter plots are useful for visualizing the relationship between two numeric quantities. Typically, these quantities are continuous, but scatterplots can sometimes effectively convey comparisons of discrete quantities as well. 138 | 139 | In the following example, we will look at the relationship between GDP per capita and life expectancy in country of Portugal. Notice that we can also view these plots as line plots. However, line plots are typically used when one axis is ordered (e.g., it might be a year). 140 | 141 | ```{r scatter_plot} 142 | portugal <- dplyr::filter(gap, country == "Portugal") 143 | 144 | # Points 145 | plot(x = portugal$gdpPercap, y = portugal$lifeExp, 146 | type = "p", 147 | xlab = "GDP per capita", 148 | ylab = "Life Expectancy", 149 | main = "GDP per-capita vs. Life Expectancy in Portugal") 150 | 151 | # Connected lines (not a smoothing line) 152 | plot(x = portugal$gdpPercap, y = portugal$lifeExp, 153 | type = "l", 154 | xlab = "GDP per capita", 155 | ylab = "Life Expectancy", 156 | main = "GDP per-capita vs. Life Expectancy in Portugal") 157 | 158 | # Both 159 | plot(x = portugal$gdpPercap, y = portugal$lifeExp, 160 | type = "b", 161 | xlab = "GDP per capita", 162 | ylab = "Life Expectancy", 163 | main = "GDP per-capita vs. Life Expectancy in Portugal") 164 | ``` 165 | 166 | # The `ggplot2` way 167 | 168 | Base plotting is just fine, but it takes some slightly complicated code to map colors to points and shapes of a given variable. And, adding a legend gets even trickier. Thankfully, a package called `ggplot2` handles these complexities with ease using more compact code inspired by Leland Wilkinson's **g**rammar of **g**raphics. 169 | 170 | > NOTE: `ggplot2` is the name of the package, but `ggplot` is the main function call. A ggplot is comprised of three fundamental building blocks: 171 | 172 | 1. **Data:** typically as a dataframe. 173 | 2. **"aes"thetics:** to define your x and y axes, map colors to factor levels, etc.\ 174 | 3. **"geom\_"s:** the visual marks to represent your data - points, bars, lines, ribbons, polygons, etc. 175 | 176 | One thing to remember is that `ggplot2` works in layers, similar to photoimaging software such as Photoshop, Illustrator, Inkscape, GIMP, ImageJ, etc. We create a base layer, and then stack layers on top of that base layer. New layers are added to a plot using the addition **`+`** symbol. Lastly, plots can be further customized using a host of **"theme\_"** functions. 177 | 178 | ## Building a histogram layer by layer 179 | 180 | We'll demonstrate the layer-based approach in `ggplot2` by building a histogram of life expectancy globally. 181 | 182 | Histograms are useful to illustrate the distribution of a single continuous (i.e., numeric or integer) variable. In the gapminder dataset, the histogram is a natural choice for visualizing `lifeExp`: 183 | 184 | ### Define the base layer 185 | 186 | First, to construct the base layer, we pass in two arguments to the `ggplot` function: the data and the global aesthetics (the ones that apply to all layers of the plot) defined within `aes()`. Once we do this, we see our coordinate system, but no data! 187 | 188 | Notice that the output of the `ggplot` function can be stored in a variable, which we've called `plot`. This allows us to continually add layers to `plot` in downstream code, and visualize how it looks at different points. 189 | 190 | ```{r base_layer} 191 | plot <- ggplot(data = gap, aes(x = lifeExp)) 192 | print(plot) 193 | ``` 194 | 195 | ### Add the histogram layer 196 | 197 | We need the last building block: a `geom_` function, which in this case, is `geom_histogram`. Add `geom_histogram` to see the data! 198 | 199 | ```{r histogram_layer} 200 | plot <- ggplot(data = gap, aes(x = lifeExp)) + 201 | geom_histogram() 202 | print(plot) 203 | ``` 204 | 205 | `ggplot` prints a note when using `geom_histogram` that the default number of bins is 30. We can eliminate that note by setting explicitly the number of bins to any number. Here we picked `bins=30` to replicate the default behavior. 206 | 207 | ```{r} 208 | plot <- ggplot(data = gap, aes(x = lifeExp)) + 209 | geom_histogram(bins = 30) 210 | print(plot) 211 | ``` 212 | 213 | Note that we can also make a density plot with a similar structure as a histogram. This time we will make sure to avoid the ugly coloring. 214 | 215 | ```{r density_layer} 216 | plot <- ggplot(data = gap, aes(x=lifeExp)) + 217 | geom_density() 218 | print(plot) 219 | ``` 220 | 221 | A density plot computes a kernel density estimate, which you can think of as the smoothed version of the histogram. We like using them when we have continuous data that comes from a smooth distribution. Importantly for this workshop, note that the only difference we made between the histogram and the density plot was changing which geom\_\* we were interested in. 222 | 223 | ### Customize with theme layers 224 | 225 | Themes in `ggplot2` are top-level layers which can be use to stylize and customize plots. These themes contain many components, which can be used to change every part of the plot, including the fonts, label sizes, label placements, and more. `ggplot2` comes with some built-in themes that you can easily add to a plot to give it a clean, recognizable, and consistent style. One of the most commonly used themes is a black and white theme, denoted by `theme_bw()`: 226 | 227 | ```{r hist_w_theme} 228 | plot <- 229 | ggplot(data = gap, aes(x = lifeExp)) + 230 | geom_histogram(color = "black", 231 | fill = "gray80", 232 | bins = 10) + 233 | theme_bw() 234 | print(plot) 235 | ``` 236 | 237 | We can also use label and title layers to customize the axis and title labels. Note, however, the syntax differences of each layer compared to base plotting arguments from earlier. 238 | 239 | ```{r hist_w_labels} 240 | hist <- 241 | ggplot(data = gap, aes(x = lifeExp)) + 242 | geom_histogram(color = "black", 243 | fill = "gray80", 244 | bins = 10) + 245 | theme_bw() + 246 | ggtitle("Histogram of Life Expectancy") + 247 | xlab("Years") + 248 | ylab("Frequency") 249 | print(hist) 250 | ``` 251 | 252 | We can also assign this visualization to a variable for later use 253 | 254 | > **Challenge 1:** Open `R-Data-Visualization-Challenges.Rmd`. Create a histogram of cholesterol in the heart dataset using `ggplot`. Save it in a variable named `A`. 255 | 256 | ## Barplots, `ggplot` style 257 | 258 | Barplots are useful to visualize counts and or averages of a single continuous variable - which can be parsed by levels of a factor (if needed). For example, we can use a bar plot to learn how many countries there are per continent that we have data for. 259 | 260 | In `ggplot`, we simply specify the aesthetic and tack on a `geom_col`: 261 | 262 | ```{r barplot_ggplot} 263 | ## Calculate the number of countries per continent 264 | countries_by_continent <- gap %>% 265 | dplyr::distinct(country, .keep_all = TRUE) %>% 266 | dplyr::group_by(continent) %>% 267 | dplyr::count() %>% 268 | ## by default count() will give us a column named n 269 | ## change to n_countries to be more informative 270 | dplyr::rename(n_countries = n) 271 | 272 | bar <- ggplot(countries_by_continent, aes(x = continent, y = n_countries)) + 273 | geom_col() + 274 | theme_bw() + 275 | xlab("Continent") + 276 | ylab("Number of Countries") + 277 | ggtitle("Number of Countries per Continent") 278 | print(bar) 279 | ``` 280 | 281 | There also exists a `geom_bar` function. This can be used to count the number of entries per group for us, so we don't have to do it ourselves: 282 | 283 | ```{r geom_bar} 284 | bar2 <- ggplot(gap |> dplyr::distinct(country, .keep_all = TRUE), aes(x = continent)) + 285 | geom_bar() + 286 | theme_bw() + 287 | xlab("Continent") + 288 | ylab("Number of Countries") + 289 | ggtitle("Number of Countries per Continent") 290 | print(bar2) 291 | ``` 292 | 293 | ## Boxplots 294 | 295 | Boxplots are useful to visualize the distribution of a single continuous variable that can be parsed by levels of a factor (i.e., a categorical feature). 296 | 297 | Let's repeat the previous exercise with boxplots. We have two variables: the continent status and the life expectancy. We need to provide both of these to the aesthetic, so that `ggplot2` knows how to structure the boxplot. We can also provide an optional `fill` variable, which we'll assign to be the diabetes status. 298 | 299 | Take a look at the plot: how did our inputs to the aesthetic correspond to the outputs? What did the fill variable do? How is it different from a color option? Try removing the fill to see how the plot looks without it. 300 | 301 | ```{r boxplot_gap} 302 | box <- ggplot(data = gap, aes(x = continent, y = lifeExp, fill = continent)) + 303 | geom_boxplot() + 304 | theme_minimal() 305 | print(box) 306 | ``` 307 | 308 | Notice that the fill in this case is a **redundant encoding** of continent. It doesn't tell us any new information. In general, this is considered bad practice - we want to keep our plots as simple as possible. If the continent is already captured by the $x$-axis, we shouldn't need to use color to convey it as well. However, there are exceptions to every rule, and sometimes it may be useful to redundantly encode a variable. 309 | 310 | In the above plot, we have a legend, now that we've used the `fill` option. The legend can be moved around by adding the `legend.position` argument of a theme layer that is applied on top of the minimal theme we're working with: 311 | 312 | ```{r boxplot_gap_legend} 313 | boxplot_legend <- ggplot(data = gap, aes(x = continent, y = lifeExp, fill = continent)) + 314 | geom_boxplot() + 315 | theme_minimal() + 316 | theme(legend.position = "top") 317 | print(boxplot_legend) 318 | ``` 319 | 320 | > **Challenge 2:** Open `R-Data-Visualization-Challenges.Rmd`. Create a boxplot of Serum cholesterol based on patient self-described sex using `ggplot`. Save it in a variable named `B`. 321 | 322 | ## Scatter plots 323 | 324 | Scatter plots are useful for visualizing the relationship between two numeric quantities. Typically, these quantities are continuous, but scatterplots can sometimes effectively convey comparisons of discrete quantities as well. 325 | 326 | In the following example, we will look at the relationship between GDP per capita and life expectancy in country of Portugal. Notice that we can also view these plots as line plots. However, line plots are typically used when one axis is ordered (e.g., it might be a year). 327 | 328 | Scatter plots in `ggplot2` are very similar as above, but this time, we use the `geom_point` function. Let's examine life expectancy by GDP per capita, but also include information about the continent, using the color option: 329 | 330 | ```{r scatter_ggplot} 331 | scatter <- ggplot(data = gap, aes(x = gdpPercap, y = lifeExp, color = continent)) + 332 | geom_point() + 333 | theme_test() 334 | print(scatter) 335 | ``` 336 | 337 | Notice that the `color` argument in the aesthetic allowed us to capture another dimension of the data. This is a key idea: the aesthetic captures the relationships amongst the data, while the geometry specifies how they're visualized. 338 | 339 | Now, let's take a look at a variety of ways we can further customize the scatter plot. 340 | 341 | ### Custom scale breaks 342 | 343 | Suppose we want to adjust the points at which the axis ticks appear. We can use the `scale_` theme layers to do so, by specifying "custom breaks". Specifically, to create a custom scale that goes *from* a start point *to* some end point *by* some interval, we do: 344 | 345 | ```{r custom_scale} 346 | scale_break <- ggplot(data = gap, aes(x = gdpPercap, 347 | y = lifeExp, 348 | color = continent)) + 349 | geom_point() + 350 | theme_bw() + 351 | theme(legend.position = "top", 352 | axis.text.x = element_text(angle = 45, hjust = 1)) + 353 | scale_x_continuous(breaks = seq(from = 0, to = 120000, 354 | by = 20000), 355 | limits = c(0, 120000)) + 356 | scale_y_continuous(breaks = seq(from = 20, to = 90, by = 10), limits = c(20, 90)) 357 | print(scale_break) 358 | ``` 359 | 360 | ### Point sizes, shapes, transparencies 361 | 362 | Let's push how far we can take the aesthetic and theme customizations we can do: 363 | 364 | ```{r point_sizes_theme} 365 | fancy_plot <- 366 | ggplot(data = gap, 367 | aes(x = gdpPercap, 368 | y = lifeExp, 369 | color = continent, 370 | shape = continent)) + 371 | # We add the alpha argument to control the opacity of each data point that is displayed. 372 | # Alpha accepts values between 0 and 1, with lower values indicating more transparency. 373 | geom_point(alpha = 0.50, size = 2) + 374 | theme_bw() + # Does it still work if you add this theme after the other theme? 375 | theme(legend.position = "top", 376 | axis.text.x = element_text(angle = 45, hjust = 1)) + 377 | scale_x_continuous(breaks = seq(from = 0, to = 120000, by = 20000), limits = c(0, 120000)) + 378 | scale_y_continuous(breaks = seq(from = 0, to = 90, by = 10), limits = c(20, 90)) + 379 | xlab("GDP Per Capita") + 380 | ylab("Life Expectancy") + 381 | ggtitle("Life Expectancy vs. GDP Per Capita across Years and Countries") 382 | print(fancy_plot) 383 | ``` 384 | 385 | Notice that we are redundantly encoding continent via shape and color. In this plot, it probably doesn't serve much purpose - but we are demonstrating how you can easily use both aspects of the aesthetic to provide more information in `ggplot`. 386 | 387 | ### Log-transforming axes 388 | 389 | Alternatively, you can [log transform](https://stats.stackexchange.com/questions/298/in-linear-regression-when-is-it-appropriate-to-use-the-log-of-an-independent-va) an axis. This is useful in the plot we are examining, because GDP per capita varies over several orders of magnitude: 390 | 391 | ```{r log_transform} 392 | scatter <- ggplot(data = gap, aes(x = gdpPercap, y = lifeExp, 393 | color = continent)) + 394 | geom_point(alpha = 0.50, size = 2) + 395 | theme_bw() + 396 | theme(legend.position = "top", 397 | axis.text.x = element_text(angle = 45, hjust = 1)) + 398 | scale_x_log10() + 399 | scale_y_continuous(breaks = seq(from = 0, to = 90, by = 10), limits = c(20, 90)) + 400 | xlab("GDP Per Capita") + 401 | ylab("Life Expectancy") + 402 | ggtitle("Life Expectancy vs. GDP Per Capita across Years and Countries") 403 | show(scatter) 404 | ``` 405 | 406 | > **Challenge 3:** Open `R-Data-Visualization-Challenges.Rmd`. Create a scatterplot that visualizes the relationship between patient age and Serum cholesterol using `ggplot`. Save it in a variable named `C`. 407 | 408 | ## Line plots, `ggplot` style 409 | 410 | Line plots are useful for visualizing change in some variable on the y-axis plotted against time 411 | 412 | We'll calculate the the mean `lifeExp` for each continent by year. Check out D-Lab's [Data Wrangling and Manipulation in R](https://github.com/dlab-berkeley/R-wrang) to learn more! 413 | 414 | ```{r mean_life_exp} 415 | gap_mean_life_exp = gap |> 416 | dplyr::group_by(year, continent) |> 417 | dplyr::mutate(mean_lifeExp = mean(lifeExp)) 418 | 419 | head(gap_mean_life_exp) 420 | ``` 421 | 422 | Next, we'll plot the mean life expectancy using `geom_line`: 423 | 424 | ```{r lineplot} 425 | line <- ggplot(gap_mean_life_exp, aes(x = year, 426 | y = mean_lifeExp, 427 | color = continent)) + 428 | geom_line(lwd = 1) + 429 | theme_bw() + 430 | theme(legend.position = "top") 431 | print(line) 432 | ``` 433 | 434 | Let's customize the legend. We can: 435 | 436 | - remove the legend title\ 437 | - increase the spacing of the legend items\ 438 | - right align the legend text\ 439 | - move labels to left of glyphs 440 | 441 | ```{r line_plot_legend} 442 | line <- ggplot(gap_mean_life_exp, 443 | aes(x = year, 444 | y = mean_lifeExp, 445 | color = continent)) + 446 | geom_line(lwd = 1) + 447 | theme_bw() + 448 | theme(legend.position = "right", 449 | legend.title = element_blank(), 450 | legend.text = element_text(color = "black", size = 10, face = "bold"), 451 | legend.key.width = unit(2.54, "cm"), 452 | legend.text.align = 1, 453 | legend.key = element_rect(size = 3, fill = "white", colour = NA), 454 | legend.key.size = unit(1, "cm")) + 455 | guides(color = guide_legend(label.position = "left")) + 456 | xlab("Year") + 457 | ylab("Mean Life Expectancy") 458 | print(line) 459 | ``` 460 | 461 | ## Heatmaps, `ggplot` style 462 | 463 | Heatmaps are useful when you want to plot three variables: one (usually) continuous variable (as the color on the heatmap) by two factors (each of the axes). Let's make a simple heatmap, examining the life expectancy as a function of continent and year: 464 | 465 | ```{r heatmap} 466 | heat <- ggplot(gap, aes(x = continent, y = year, fill = lifeExp)) + 467 | geom_tile() + 468 | scale_fill_gradient(low = "white", 469 | high = "gray20", 470 | limits = c(20,90), 471 | breaks = seq(20, 90, 10)) + 472 | theme_bw() + 473 | theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 474 | scale_y_continuous(breaks = seq(from = 1952, to = 2007, by = 5), 475 | limits = c(1947, 2012)) + 476 | guides(fill = guide_colourbar(label.position = "left")) 477 | print(heat) 478 | ``` 479 | 480 | ## Subplots with a facet wrap 481 | 482 | An alternative approach to showing the previous plot is to separate each of the individual curves into their own subplots. This can be done with a `facet_wrap`, in which we choose a variable (i.e., a facet) by which to split the plot into individual subplots: 483 | 484 | ```{r facet_wrap} 485 | facet_line <- line + 486 | facet_wrap(~continent) + 487 | guides(linetype = "none") + 488 | theme(axis.text.x = element_text(angle = 45, hjust = 1)) 489 | print(facet_line) 490 | ``` 491 | 492 | A word about the colors here. While these colors are arguably distinct, it's not the case that the base colors necessarily will be. It's estimated that, "globally, 1 in 12 males and 1 in 200 females are colorblind." The most common form of color blindness is red-green color blindness. **Viridis** is a color palette with an emphasis on accessibility for those of us who experience colorblindness. 493 | 494 | To show the difference with a colorblind friendly palette, let's remake the same `facet_line` chart. 495 | 496 | ```{r, facet_colorblind} 497 | facet_line_cb <- line + 498 | facet_wrap(~continent) + 499 | guides(linetype = "none") + 500 | theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 501 | scale_color_viridis_d() 502 | print(facet_line_cb) 503 | ``` 504 | 505 | Note that we have overridden the default colors from our line graph and replaced them with a colorblind friendly palette. Every line color is now clearly distinct for users. 506 | 507 | > **Challenge 4:** Open `R-Data-Visualization-Challenges.Rmd`. Create a line plot of the mean cholesterol for each age, separated by sex using the heart data. You will need to do some data manipulation with `dplyr` to make this plot. Save your plot to a variable named `D`. 508 | 509 | # Creating compound figures using `cowplot` 510 | 511 | Publication quality figures often incorporate several subplots, which together tell a narrative about the problem being studied. We saw above that `ggplot2` can create something like a subplot using facets, but it can be quite limited. A package called `cowplot` makes it easy to stitch together individual plots into a bigger figure: 512 | 513 | ```{r compound_figure} 514 | compound <- plot_grid(hist, scatter, 515 | facet_line, heat, 516 | nrow = 2, 517 | ncol = 2, 518 | scale = 0.85, 519 | labels = c("A)", "B)", "C)", "D)")) 520 | print(compound) 521 | ``` 522 | 523 | # Exporting 524 | 525 | Exporting graphs in R is straightforward. Start by clicking the "Export" button: 526 | 527 | 1. Click **Copy to clipboard...** if you want to quickly copy/paste a figure into a slideshow presentation or text document. 528 | 529 | 2. Click **Save as image...** (Raster/Bitmap formats such as .png, .jpeg, .tiff) if you want to explort to this format. 530 | 531 | > NOTE: Not recommended because every pixel of a plot contains its own separate coding; not so great if you want to resize the image 532 | 533 | 3. Click **Save as PDF...** (Vector format such as .pdf, .ps) to export to .pdf. 534 | 535 | > NOTE: Recommended! Every element of a plot is encoded with a function that gives its coding conditional on several factors, which makes it great for resizing. 536 | 537 | 4. Or, **export with `ggsave`** 538 | 539 | ```{r eval = FALSE} 540 | ggsave(filename = "../images/compound.pdf", 541 | plot = compound, 542 | width = 12, 543 | height = 8, 544 | units = "in", 545 | dpi = 600) 546 | ``` 547 | 548 | > **Challenge 5:** Open `R-Data-Visualization-Challenges.Rmd`. Use `cowplot` to create a compound figure named `compound_figure` that contains subplots `A`, `B`, `C`, and `D` that you created above. 549 | 550 | # Resources for data visualization 551 | 552 | Here, we provide some useful resources that may deepen your data visualization skills: 553 | 554 | - Wickham et al. 2019. [ggplot2](https://ggplot2.tidyverse.org/): The `ggplot2` documentation. 555 | 556 | - [ggplot2 cheatsheet](https://rstudio.com/wp-content/uploads/2019/01/Cheatsheets_2019.pdf): A helpful cheatsheet to `ggplot2`. 557 | 558 | - Wilke CO. 2019. [Introduction to cowplot](https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html): An introductory vignette (and accompanying documentation) to `cowplot`. 559 | 560 | - Wilke CO. 2019. [Arranging plots in a grid](https://wilkelab.org/cowplot/articles/plot_grid.html): A vignette detailing subplotting in `cowplot`. 561 | 562 | - Tufte ER. 2001. [The Visual Display of Quantitative Information](https://www.amazon.com/Visual-Display-Quantitative-Information/dp/1930824130): One of the definitive books on data visualization. 563 | 564 | - Wilke CO. 2019. [Fundamentals of Data Visualization](https://serialmentor.com/dataviz/): Online (i.e., free) textbook covering data visualization, written by the creator of `cowplot`. 565 | 566 | - Wilkinson L. 1999. [The Grammar of Graphics](https://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448): A classic text on data visualization, which served as the inspiration for the structure of `ggplot2`. 567 | 568 | - [The R Graph Gallery](https://www.r-graph-gallery.com/index.html): A handy repository of example visualizations in R. 569 | 570 | - [Accessibility Design](https://www.storytellingwithdata.com/blog/2018/6/26/accessible-data-viz-is-better-data-viz): A nice blog post on considering accessibility in data visualization. Also consider the accessibility principles laid out in the [CFPB Design System](https://cfpb.github.io/design-system/guidelines/accessibility-principles). 571 | -------------------------------------------------------------------------------- /solutions/R-Data-Visualization-Solutions.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "R Data Visualization Solutions" 3 | output: html_document 4 | --- 5 | 6 | # Load the [heart dataset](http://search.r-project.org/library/kmed/html/heart.html) 7 | 8 | Use the heart dataset for the following challenges. Import it and save it in a variable named `heart`. Make sure to review the data dictionary linked above! 9 | 10 | ```{r import} 11 | library(here) 12 | library(ggplot2) 13 | heart <- read.csv(here::here("data/heart.csv")) 14 | names(heart) 15 | ``` 16 | 17 | **For each challenge, use** `ggplot2` 18 | 19 | # Challenge 1 20 | 21 | Using the heart dataset, create a histogram of the Serum cholesterol. Save it in a variable named `A`. 22 | 23 | ```{r challenge_1} 24 | A <- ggplot(heart, aes(x = chol)) + 25 | geom_histogram(binwidth = 15) + 26 | labs(x = "Serum cholestorol in mg/dl", 27 | y = "Count", 28 | title = "Histogram of Serum Cholestorol (mg/dl)") + 29 | theme_minimal() 30 | 31 | print(A) 32 | ``` 33 | 34 | # Challenge 2 35 | 36 | Using the heart dataset, create a boxplot of Serum cholesterol based on patient self-described sex. Save this figure in a variable named `B`. 37 | 38 | Hint: The variable that identifies patient sex needs to be coerced into a different variable type in order to make this visualization. 39 | 40 | ```{r challenge_2} 41 | library(dplyr) 42 | # treat sex as a factor instad of continuous variable for plotting 43 | heart <- heart %>% 44 | mutate(sex = as.factor(sex)) 45 | 46 | B <- ggplot(heart, aes(x = sex, y = chol, fill = sex)) + 47 | geom_boxplot() + 48 | scale_x_discrete(labels = c("Female", "Male")) + # change x labels 49 | scale_fill_discrete(name = "Biological Sex", # change fill legend labels 50 | labels = c("Female", "Male")) + 51 | labs(x = element_blank(), 52 | y = "Serum cholestoral in mg/dl", 53 | title = "Boxplot of Serum Cholesteral (mg/dl)", 54 | subtitle = "by Biological Sex") + 55 | theme_bw() 56 | 57 | print(B) 58 | ``` 59 | 60 | # Challenge 3 61 | 62 | Using the heart dataset, create a scatterplot that visualizes the relationship between patient age and Serum cholesterol. Save it in a variable named `C` 63 | 64 | ```{r challenge_3} 65 | C <- ggplot(heart, aes(x = age, y = chol, 66 | color = as.factor(sex), 67 | shape = as.factor(sex))) + 68 | geom_point(alpha = 0.7) + 69 | geom_smooth(method = "lm", se = TRUE, lwd = 1) + 70 | labs(x = "Age", 71 | y = "Serum cholestoral in mg/dl", 72 | title = "Serum Cholesteral (mg/dl) by Biological Sex") + 73 | theme_bw() 74 | 75 | print(C) 76 | ``` 77 | 78 | # Challenge 4 79 | 80 | Create a line plot of the mean cholesterol for each age, separated by sex using the heart data. You will need to do some data manipulation with `dplyr` to make this plot. Save your plot to a variable named `D`. 81 | 82 | ```{r challenge_4} 83 | # Sometimes we will want to re-shape our data for plotting 84 | heart_plot <- heart %>% 85 | mutate(sex = ifelse(sex == 0, "Female", "Male"), # we can also change labels in the data instead of in ggplot 86 | slope = as.factor(slope)) %>% 87 | group_by(age, sex, slope) %>% 88 | summarize(chol = mean(chol)) 89 | 90 | D <- ggplot(heart_plot, aes(x = age, y = chol, color = sex)) + 91 | facet_wrap(~sex) + 92 | geom_line() + 93 | scale_color_manual(values=c("#CC6666", "#9999CC"), name = "Biological Sex") + 94 | labs(x = "Age", 95 | y = "Serum cholestoral in mg/dl", 96 | title = "Serum Cholesteral (mg/dl) by Biological Sex") + 97 | theme_minimal() 98 | 99 | print(D) 100 | ``` 101 | 102 | # Challenge 5 103 | 104 | Use cowplot to create a compound figure named `compound_figure` that contains subplots `A`, `B`, `C`, and `D` that you created above. 105 | 106 | ```{r challenge_5} 107 | library(cowplot) 108 | compound = plot_grid(A, B, C, D, 109 | nrow = 2, ncol = 2, 110 | scale = 0.85, 111 | labels = c("A)", "B)", "C)", "D)")) 112 | print(compound) 113 | ``` 114 | --------------------------------------------------------------------------------