├── .gitignore ├── data.Rproj ├── README.md ├── Assets.csv ├── student_data.csv ├── R └── speaking.R ├── speakingtime.csv ├── ManchesterByTheSea.csv └── countries2012.csv /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | -------------------------------------------------------------------------------- /data.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: knitr 13 | LaTeX: XeLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # data 2 | data used in class slides 3 | 4 | Sources 5 | 6 | Assets.csv: https://www.nytimes.com/interactive/2017/04/01/us/politics/how-much-people-in-the-trump-administration-are-worth-financial-disclosure.html 7 | 8 | countries2012.csv: http://databank.worldbank.org 9 | 10 | ManchesterByTheSea.csv: https://www.boxofficemojo.com/movies/?page=daily&view=chart&id=manchesterbythesea.htm 11 | 12 | speakingtime.csv: https://www.nytimes.com/interactive/2019/10/15/us/elections/debate-speaking-time.html web scraping script: [speaking.R](R/speaking.R) 13 | 14 | -------------------------------------------------------------------------------- /Assets.csv: -------------------------------------------------------------------------------- 1 | Name,Assets 2 | DeVos,579783484 3 | Ross,326224177 4 | Cohn,252952172 5 | Kushner,241037233 6 | Tillerson,239488353 7 | Cordish,197281047 8 | Mnuchin,154137166 9 | Liddell,75262171 10 | Lighthizer,18627275 11 | Bannon,11850014 12 | Chao,11269045 13 | Conway,11015017 14 | Perdue,11269045 15 | Rosen,9086316 16 | Coats,8770090 17 | Carson,8114059 18 | Price,8026265 19 | Mattis,3563022 20 | Spicer,3900059 21 | Mulvaney,3239057 22 | Sessions,2938056 23 | Eisenberg,2951034 24 | Wilson,2150150 25 | Zinke,1812011 26 | McGinley,1331036 27 | Perry,891027 28 | Hahn,1006009 29 | Priebus,604008 30 | Navarro,516013 31 | Acosta,431006 32 | Pruitt,210006 33 | Kelly,181005 34 | Bossert,140013 35 | Gorka,101010 36 | Pompeo,77021 37 | Haley,66003 -------------------------------------------------------------------------------- /student_data.csv: -------------------------------------------------------------------------------- 1 | School,Level,Affiliation 2 | CC ,U01,CCUNDC 3 | CC ,U01,CCUNDC 4 | CC ,U01,CCUNDC 5 | CC ,U01,CCUNDC 6 | CC ,U01,CCUNDC 7 | GS ,U03,GSUNDC 8 | CC ,U01,CCUNDC 9 | CC ,U01,CCUNDC 10 | CC ,U01,CCUNDC 11 | CC ,U01,CCUNDC 12 | CC ,U01,CCUNDC 13 | CC ,U02,CCUNDC 14 | CC ,U02,CCUNDC 15 | GS ,U03,GSUNDC 16 | CC ,U02,CCUNDC 17 | GS ,U03,GSUNDC 18 | BC ,U04,BCCR 19 | CC ,U04,CCCLAS 20 | CC ,U03,CCECON 21 | GS ,U03,GSUNDC 22 | BC ,U03,BCCR 23 | GN ,U05,GNPREM 24 | BC ,U02,BCCR 25 | GS ,U03,GSUNDC 26 | CC ,U01,CCUNDC 27 | CC ,U02,CCUNDC 28 | CC ,U01,CCUNDC 29 | GS ,U04,GSECON 30 | CC ,U01,CCUNDC 31 | SP ,U00,SPGRAD 32 | GS ,U04,GSUNDC 33 | CC ,U02,CCUNDC 34 | GS ,U03,GSUNDC 35 | CC ,U02,CCUNDC 36 | CC ,U02,CCUNDC 37 | CC ,U01,CCUNDC 38 | CC ,U01,CCUNDC 39 | GS ,U03,GSECON 40 | GS ,U03,GSUNDC 41 | CC ,U03,CCECON 42 | CC ,U03,CCECPO 43 | CC ,U01,CCUNDC 44 | CC ,U03,CCECON 45 | CC ,U02,CCUNDC -------------------------------------------------------------------------------- /R/speaking.R: -------------------------------------------------------------------------------- 1 | # In order to obtain script generated values, speaking.html was manually saved as a 2 | # complete web page from: https://www.nytimes.com/interactive/2019/10/15/us/elections/debate-speaking-time.html 3 | 4 | library(tidyverse) 5 | library(rvest) 6 | robotstxt::paths_allowed("https://www.nytimes.com/interactive/2019/10/15/us/elections/debate-speaking-time.html") 7 | 8 | ## [1] TRUE 9 | 10 | heatmap <- read_html("raw/speaking.html") %>% 11 | html_node("#palette") 12 | 13 | issue_name <- heatmap %>% 14 | html_nodes(".minlabel text") %>% 15 | html_attr("class") 16 | 17 | timelabel <- heatmap %>% 18 | html_nodes(".minlabel text") %>% 19 | html_text() 20 | 21 | # ex. O'Rourke 22 | names_human_readable <- heatmap %>% 23 | html_node(".yaxis") %>% 24 | html_nodes("g.tick") %>% 25 | html_text() 26 | 27 | # ex. Gun control 28 | issues_human_readable <- heatmap %>% 29 | html_node(".xaxis") %>% 30 | html_nodes("g.tick") %>% 31 | html_text() 32 | 33 | # create strings for use in separating JavaScript classes of the form 34 | # "gun-controlorourke" into issues and candidates from issue_name 35 | 36 | # ex. orourke 37 | names_machine_readable <- names_human_readable %>% 38 | tolower() %>% 39 | str_replace_all("[:punct:]", "") 40 | 41 | # ex. gun-control 42 | issues_machine_readable <- issues_human_readable %>% 43 | tolower() %>% 44 | str_replace_all("[:punct:]", "") %>% 45 | str_replace(" ", "-") 46 | 47 | dems <- tibble(issue_candidate, timelabel) %>% 48 | mutate(name = str_remove_all(issue_name, paste(issues_machine_readable, collapse = "|")), 49 | issue = str_remove_all(issue_name, paste(names_machine_readable, collapse = "|"))) %>% 50 | filter(name != "moderator") %>% 51 | separate(timelabel, into = c("minutes", "seconds"), 52 | sep = ":", remove = FALSE) %>% 53 | mutate(minutes = as.numeric(minutes), 54 | seconds = as.numeric(seconds), 55 | time = minutes*60 + seconds, 56 | candidate = map_chr(name, ~names_human_readable[names_machine_readable == .x]), 57 | topic = map_chr(issue, ~issues_human_readable[issues_machine_readable == .x])) %>% 58 | select(candidate, topic, time, timelabel) 59 | 60 | write_csv(dems, "speakingtime.csv") 61 | -------------------------------------------------------------------------------- /speakingtime.csv: -------------------------------------------------------------------------------- 1 | candidate,topic,time,timelabel 2 | Yang,Economy,153,2:33 3 | Yang,Income inequality,47,0:47 4 | Yang,Tech companies,81,1:21 5 | Warren,Impeachment,73,1:13 6 | Warren,Health care,239,3:59 7 | Warren,Economy,146,2:26 8 | Warren,Income inequality,200,3:20 9 | Warren,Middle East policy,58,0:58 10 | Warren,Gun control,75,1:15 11 | Warren,Tech companies,162,2:42 12 | Warren,Women’s rights,58,0:58 13 | Warren,Party strategy,156,2:36 14 | Steyer,Impeachment,48,0:48 15 | Steyer,Income inequality,88,1:28 16 | Steyer,Party strategy,65,1:05 17 | Sanders,Impeachment,56,0:56 18 | Sanders,Health care,101,1:41 19 | Sanders,Economy,66,1:06 20 | Sanders,Income inequality,71,1:11 21 | Sanders,Middle East policy,60,1:00 22 | Sanders,Tech companies,52,0:52 23 | Sanders,Party strategy,114,1:54 24 | O’Rourke,Impeachment,63,1:03 25 | O’Rourke,Economy,73,1:13 26 | O’Rourke,Income inequality,75,1:15 27 | O’Rourke,Middle East policy,95,1:35 28 | O’Rourke,Gun control,206,3:26 29 | O’Rourke,Tech companies,74,1:14 30 | O’Rourke,Party strategy,56,0:56 31 | Klobuchar,Impeachment,80,1:20 32 | Klobuchar,Health care,108,1:48 33 | Klobuchar,Income inequality,101,1:41 34 | Klobuchar,Middle East policy,52,0:52 35 | Klobuchar,Gun control,55,0:55 36 | Klobuchar,Tech companies,62,1:02 37 | Klobuchar,Women’s rights,51,0:51 38 | Klobuchar,Party strategy,51,0:51 39 | Harris,Impeachment,72,1:12 40 | Harris,Health care,57,0:57 41 | Harris,Income inequality,82,1:22 42 | Harris,Middle East policy,65,1:05 43 | Harris,Gun control,65,1:05 44 | Harris,Tech companies,132,2:12 45 | Harris,Women’s rights,82,1:22 46 | Gabbard,Impeachment,52,0:52 47 | Gabbard,Economy,57,0:57 48 | Gabbard,Middle East policy,138,2:18 49 | Gabbard,Women’s rights,56,0:56 50 | Castro,Impeachment,54,0:54 51 | Castro,Economy,65,1:05 52 | Castro,Income inequality,65,1:05 53 | Castro,Middle East policy,59,0:59 54 | Castro,Gun control,64,1:04 55 | Castro,Tech companies,39,0:39 56 | Castro,Women’s rights,52,0:52 57 | Buttigieg,Impeachment,84,1:24 58 | Buttigieg,Health care,114,1:54 59 | Buttigieg,Income inequality,63,1:03 60 | Buttigieg,Middle East policy,175,2:55 61 | Buttigieg,Gun control,103,1:43 62 | Buttigieg,Women’s rights,71,1:11 63 | Buttigieg,Party strategy,81,1:21 64 | Booker,Impeachment,51,0:51 65 | Booker,Women’s rights,132,2:12 66 | Booker,Economy,82,1:22 67 | Booker,Income inequality,72,1:12 68 | Booker,Middle East policy,97,1:37 69 | Booker,Gun control,84,1:24 70 | Booker,Tech companies,53,0:53 71 | Biden,Impeachment,129,2:09 72 | Biden,Health care,98,1:38 73 | Biden,Income inequality,93,1:33 74 | Biden,Middle East policy,190,3:10 75 | Biden,Gun control,56,0:56 76 | Biden,Women’s rights,86,1:26 77 | Biden,Party strategy,128,2:08 78 | -------------------------------------------------------------------------------- /ManchesterByTheSea.csv: -------------------------------------------------------------------------------- 1 | Day,Date,Gross Fri,2016-11-18,72658.00 Sat,2016-11-19,90040.00 Sun,2016-11-20,93800.00 Mon,2016-11-21,32023.00 Tue,2016-11-22,29989.00 Wed,2016-11-23,41532.00 Thu,2016-11-24,41597.00 Fri,2016-11-25,450244.00 Sat,2016-11-26,457589.00 Sun,2016-11-27,318157.00 Mon,2016-11-28,96024.00 Tue,2016-11-29,106676.00 Wed,2016-11-30,117557.00 Thu,2016-12-01,112057.00 Fri,2016-12-02,665888.00 Sat,2016-12-03,999345.00 Sun,2016-12-04,605678.00 Mon,2016-12-05,197757.00 Tue,2016-12-06,231147.00 Wed,2016-12-07,215586.00 Thu,2016-12-08,194857.00 Fri,2016-12-09,922526.00 Sat,2016-12-10,1401072.00 Sun,2016-12-11,849326.00 Mon,2016-12-12,355794.00 Tue,2016-12-13,435857.00 Wed,2016-12-14,393703.00 Thu,2016-12-15,331826.00 Fri,2016-12-16,1275856.00 Sat,2016-12-17,1719724.00 Sun,2016-12-18,1228248.00 Mon,2016-12-19,606819.00 Tue,2016-12-20,728056.00 Wed,2016-12-21,619771.00 Thu,2016-12-22,676337.00 Fri,2016-12-23,951413.00 Sat,2016-12-24,594526.00 Sun,2016-12-25,1222881.00 Mon,2016-12-26,1534324.00 Tue,2016-12-27,1105513.00 Wed,2016-12-28,1071162.00 Thu,2016-12-29,1029716.00 Fri,2016-12-30,1379010.00 Sat,2016-12-31,1451378.00 Sun,2017-01-01,1415658.00 Mon,2017-01-02,1276660.00 Tue,2017-01-03,607168.00 Wed,2017-01-04,514954.00 Thu,2017-01-05,470878.00 Fri,2017-01-06,743626.00 Sat,2017-01-07,1063558.00 Sun,2017-01-08,711053.00 Mon,2017-01-09,301192.00 Tue,2017-01-10,377666.00 Wed,2017-01-11,320448.00 Thu,2017-01-12,303878.00 Fri,2017-01-13,557697.00 Sat,2017-01-14,831142.00 Sun,2017-01-15,534388.00 Mon,2017-01-16,363998.00 Tue,2017-01-17,187062.00 Wed,2017-01-18,158659.00 Thu,2017-01-19,153772.00 Fri,2017-01-20,244180.00 Sat,2017-01-21,457423.00 Sun,2017-01-22,247307.00 Mon,2017-01-23,115837.00 Tue,2017-01-24,181157.00 Wed,2017-01-25,157323.00 Thu,2017-01-26,162416.00 Fri,2017-01-27,528971.00 Sat,2017-01-28,934697.00 Sun,2017-01-29,607428.00 Mon,2017-01-30,188870.00 Tue,2017-01-31,279459.00 Wed,2017-02-01,213755.00 Thu,2017-02-02,207494.00 Fri,2017-02-03,392857.00 Sat,2017-02-04,695339.00 Sun,2017-02-05,238145.00 Mon,2017-02-06,136581.00 Tue,2017-02-07,189010.00 Wed,2017-02-08,145279.00 Thu,2017-02-09,127964.00 Fri,2017-02-10,181931.00 Sat,2017-02-11,334433.00 Sun,2017-02-12,185890.00 Mon,2017-02-13,76331.00 Tue,2017-02-14,144346.00 Wed,2017-02-15,79989.00 Thu,2017-02-16,77364.00 Fri,2017-02-17,146727.00 Sat,2017-02-18,212327.00 Sun,2017-02-19,166693.00 Mon,2017-02-20,122598.00 Tue,2017-02-21,97191.00 Wed,2017-02-22,72474.00 Thu,2017-02-23,96131.00 Fri,2017-02-24,122441.00 Sat,2017-02-25,267589.00 Sun,2017-02-26,90948.00 Mon,2017-02-27,51992.00 Tue,2017-02-28,66404.00 Wed,2017-03-01,48163.00 Thu,2017-03-02,42482.00 Fri,2017-03-03,67844.00 Sat,2017-03-04,120821.00 Sun,2017-03-05,73519.00 Mon,2017-03-06,29703.00 Tue,2017-03-07,35468.00 Wed,2017-03-08,28331.00 Thu,2017-03-09,24644.00 Fri,2017-03-10,16475.00 Sat,2017-03-11,30697.00 Sun,2017-03-12,20202.00 Mon,2017-03-13,14108.00 Tue,2017-03-14,18449.00 Wed,2017-03-15,14462.00 Thu,2017-03-16,13684.00 Fri,2017-03-17,7818.00 Sat,2017-03-18,12387.00 Sun,2017-03-19,8391.00 Mon,2017-03-20,5785.00 Tue,2017-03-21,8425.00 Wed,2017-03-22,6723.00 Thu,2017-03-23,5126.00 Fri,2017-03-24,4344.00 Sat,2017-03-25,5189.00 Sun,2017-03-26,3552.00 Mon,2017-03-27,2716.00 Tue,2017-03-28,3847.00 Wed,2017-03-29,3121.00 Thu,2017-03-30,3280.00 Fri,2017-03-31,1226.00 Sat,2017-04-01,1710.00 Sun,2017-04-02,1284.00 Mon,2017-04-03,614.00 Tue,2017-04-04,1357.00 Wed,2017-04-05,1467.00 Thu,2017-04-06,1130.00 Fri,2017-04-07,1399.00 Sat,2017-04-08,1540.00 Sun,2017-04-09,2129.00 Mon,2017-04-10,961.00 Tue,2017-04-11,1579.00 Wed,2017-04-12,1249.00 Thu,2017-04-13,1050.00 -------------------------------------------------------------------------------- /countries2012.csv: -------------------------------------------------------------------------------- 1 | COUNTRY,CONTINENT,GDP,TFR,LIFEEXP,CHMORT Afghanistan,Asia,690.842629,5.272,59.67960976,99.5 Albania,Europe,4247.485437,1.76,77.35046341,15.5 Algeria,Africa,5583.61616,2.909,74.32409756,26.1 Angola,Africa,5531.776299,6.251,51.464,172.2 Antigua and Barbuda,North America,13525.61622,2.102,75.62180488,9.1 Argentina,South America,14357.41159,2.347,75.8162439,13.8 Armenia,Europe,3565.517575,1.581,74.45209756,16.3 Australia,Oceania,67646.10385,1.921,82.04634146,4.3 Austria,Europe,48324.25404,1.44,80.93658537,4 Azerbaijan,Europe,7393.771877,2,70.62495122,35.7 "Bahamas, The",North America,22112.60835,1.893,74.91446341,13.2 Bahrain,Asia,23063.13229,2.095,76.40763415,7.3 Bangladesh,Asia,858.9333626,2.245,70.86026829,44 Barbados,North America,15317.139,1.788,75.17102439,14.1 Belarus,Europe,6721.834908,1.62,71.96585366,5.3 Belgium,Europe,44731.21948,1.79,80.38536585,4.3 Belize,North America,4674.293377,2.643,69.90514634,18 Benin,Africa,807.688451,4.927,59.12197561,107 Bhutan,Asia,2452.151588,2.152,68.72290244,37.8 Bolivia,South America,2645.290274,3.073,67.44546341,43.1 Bosnia and Herzegovina,Europe,4415.923592,1.28,76.12017073,6.5 Botswana,Africa,6935.593653,2.881,64.22273171,49.5 Brazil,South America,11922.51306,1.812,73.83958537,16 Brunei Darussalam,Asia,41807.65334,1.913,78.25258537,9.7 Bulgaria,Europe,7333.355073,1.5,74.31463415,12 Burkina Faso,Africa,673.0267834,5.693,57.87931707,101.4 Burundi,Africa,244.1964862,6.123,55.78929268,91.2 Cambodia,Asia,946.4766787,2.739,67.32887805,35.8 Cameroon,Africa,1222.192142,4.859,54.5875122,97.4 Canada,North America,52733.47369,1.61,81.23804878,5.3 Cape Verde,Africa,3497.691141,2.376,72.82821951,26.6 Central African Republic,Africa,469.6842871,4.451,49.10529268,142.1 Chad,Africa,972.6793451,6.374,50.78139024,151.6 Chile,South America,15253.33083,1.789,80.89485366,8.6 China,Asia,6264.643878,1.663,75.1995122,13.4 Colombia,South America,7885.061292,1.948,73.63078049,17.4 Comoros,Africa,750.3146086,4.628,62.58297561,80.9 "Congo, Dem. Rep.",Africa,390.7066035,6.199,57.85407317,108.5 "Congo, Rep.",Africa,3191.164299,4.961,60.92390244,52.6 Costa Rica,North America,9733.396931,1.866,79.05353659,10.1 Cote d'Ivoire,Africa,1281.382865,5.121,50.86334146,102.5 Croatia,Europe,13235.97757,1.51,76.92439024,4.9 Cuba,North America,6448.155635,1.628,79.14160976,5.8 Cyprus,Europe,28868.27382,1.464,79.76226829,3.2 Czech Republic,Europe,19640.92866,1.45,78.07560976,3.8 Denmark,Europe,57636.12531,1.73,80.05121951,3.8 Djibouti,Africa,1586.780133,3.332,61.29597561,71.7 Dominican Republic,North America,5967.000984,2.539,73.13531707,32.7 Ecuador,South America,5702.168288,2.599,75.433,23.7 "Egypt, Arab Rep.",Africa,3068.193883,3.306,70.72914634,26.8 El Salvador,North America,3921.720395,1.991,72.23185366,18.6 Equatorial Guinea,Africa,23278.23006,5.011,56.89026829,103.7 Estonia,Europe,17490.99313,1.56,76.32682927,3.8 Ethiopia,Africa,469.7923039,4.642,62.79353659,67.7 Fiji,Oceania,4550.267095,2.615,69.74321951,23.5 Finland,Europe,47415.55987,1.8,80.62682927,2.7 France,Europe,40850.35237,2.01,81.96829268,4.3 Gabon,Africa,10642.43225,4.01,63.28073171,57.7 "Gambia, The",Africa,504.9890138,5.775,59.77814634,76.1 Georgia,Europe,3528.731511,1.82,73.94487805,14.4 Germany,Europe,44010.93139,1.38,80.89268293,4 Ghana,Africa,1641.825922,4.238,60.97702439,69.2 Greece,Europe,22146.91592,1.34,80.63414634,4.7 Grenada,North America,7583.546304,2.194,73.00226829,12.7 Guatemala,North America,3278.629083,3.317,71.24939024,32.1 Guinea,Africa,487.3457142,5.175,57.63763415,104 Guinea-Bissau,Africa,559.224752,4.972,54.5035122,105.2 Guyana,South America,3759.377095,2.615,66.21726829,41.2 Haiti,North America,766.8722334,3.169,62.03339024,75.2 Honduras,North America,2395.073442,2.514,72.75502439,22.6 Hungary,Europe,12819.71206,1.34,75.06341463,6.3 Iceland,Europe,44258.84279,2.04,82.91707317,2.2 India,Asia,1449.664875,2.51,67.28987805,54.5 Indonesia,Asia,3700.523538,2.5,68.51956098,30.4 "Iran, Islamic Rep.",Asia,7710.513314,1.742,74.79934146,17.5 Iraq,Asia,6650.228867,4.086,69.24192683,34.8 Ireland,Europe,48976.92975,2.01,80.89512195,4 Israel,Asia,32818.85838,3.05,81.70487805,4.3 Italy,Europe,34844.49809,1.43,82.23902439,3.8 Jamaica,North America,5445.894718,2.284,73.2824878,17.2 Japan,Asia,46679.26543,1.41,83.09609756,3 Jordan,Asia,4896.688447,3.314,73.74739024,19.6 Kazakhstan,Asia,12120.30534,2.62,69.61,17.9 Kenya,Africa,1184.923256,4.481,60.27278049,55.6 Kiribati,Oceania,1641.197419,3.796,65.59607317,60.4 "Korea, Rep.",Asia,24453.97191,1.297,81.21341463,3.8 Kuwait,Asia,50903.9046,2.626,74.359,9.9 Kyrgyz Republic,Asia,1177.974735,3.2,70.00243902,25.7 Lao PDR,Asia,1445.86945,3.138,65.2485122,74 Latvia,Europe,13775.26158,1.44,73.77804878,8.7 Lebanon,Asia,9729.282193,1.498,79.84636585,9.3 Lesotho,Africa,1158.804222,3.253,48.836,94.1 Liberia,Africa,414.1851554,4.868,60.20436585,80.3 Libya,Africa,13035.1922,2.543,71.64956098,15.1 Lithuania,Europe,14342.52348,1.6,73.86341463,5.6 Luxembourg,Europe,105447.0932,1.57,81.39268293,2.1 "Macedonia, FYR",Europe,4709.511628,1.497,75.03126829,7.4 Madagascar,Africa,444.9584938,4.528,64.24665854,55.5 Malawi,Africa,270.0875313,5.318,60.05029268,77.3 Malaysia,Asia,10834.65908,1.968,74.42331707,7.7 Maldives,Asia,6529.978071,2.175,76.46234146,10.7 Mali,Africa,641.7938201,6.396,57.095,127 Malta,Europe,21176.30998,1.43,80.74634146,6.7 Mauritania,Africa,1282.785101,4.721,62.56017073,92.7 Mauritius,Africa,9113.640643,1.54,73.86341463,14.6 Mexico,North America,9703.371017,2.3,76.35409756,15.3 "Micronesia, Fed. Sts.",Oceania,3147.679202,3.347,68.85070732,38.2 Moldova,Europe,2046.536787,1.462,68.69341463,16.7 Mongolia,Asia,4377.23887,2.641,68.6135122,26.2 Montenegro,Europe,6586.721279,1.676,74.64987805,5.7 Morocco,Africa,2931.4002,2.545,73.36465854,30.7 Mozambique,Africa,564.8124631,5.472,54.21212195,90.9 Myanmar,Asia,1421.497351,2.28,65.42778049,55.3 Namibia,Africa,5679.958215,3.586,63.88114634,49.9 Nepal,Asia,685.4967586,2.381,68.82331707,40.9 Netherlands,Europe,49474.70561,1.72,81.10487805,4.2 Nicaragua,North America,1779.867088,2.34,74.21246341,24.4 Niger,Africa,393.643423,7.642,60.07253659,109.6 Nigeria,Africa,2739.852189,5.758,52.105,120.9 Norway,Europe,101563.7027,1.85,81.45121951,3 Oman,Asia,21533.8076,2.857,76.58956098,11.6 Pakistan,Asia,1266.380758,3.744,65.71687805,87.8 Panama,North America,10138.52113,2.484,77.23704878,18.7 Papua New Guinea,Oceania,2151.210277,3.869,62.29990244,62.5 Paraguay,South America,3858.036492,2.625,72.654,22.6 Peru,South America,6388.845098,2.504,74.05765854,19.1 Philippines,Asia,2604.655997,3.048,68.00707317,30.4 Poland,Europe,13142.04599,1.3,76.79756098,5.3 Portugal,Europe,20577.40264,1.28,80.37317073,3.8 Qatar,Asia,94407.40692,2.059,78.22765854,8.6 Romania,Europe,8577.289214,1.53,74.46341463,12.5 Russian Federation,Asia,14078.83057,1.7,70.36585366,10.8 Rwanda,Africa,667.4145823,4.143,62.79936585,52.1 Samoa,Oceania,4257.060935,4.212,72.98004878,18.5 Sao Tome and Principe,Africa,1488.048003,4.689,66.13390244,52.4 Saudi Arabia,Asia,24883.18971,2.873,74.01602439,16 Senegal,Africa,1019.27223,5.161,65.31887805,55.9 Serbia,Europe,5659.380204,1.45,74.83658537,7.1 Seychelles,Africa,12844.85853,2.4,74.22682927,14.2 Sierra Leone,Africa,618.9472529,4.874,49.74909756,141.6 Singapore,Asia,54577.13737,1.29,81.99512195,2.8 Slovak Republic,Europe,17207.27921,1.34,76.1097561,7.9 Slovenia,Europe,22477.59756,1.58,80.12439024,3 Solomon Islands,Oceania,1866.707246,4.098,67.50658537,30.7 South Africa,Africa,7592.157997,2.412,56.09831707,47.7 South Sudan,Africa,944.2828116,5.197,54.727,102.8 Spain,Europe,28647.83524,1.32,82.42682927,4.4 Sri Lanka,Asia,3366.51036,2.346,74.06804878,10.4 St. Lucia,North America,7248.234505,1.936,74.77763415,15.4 St. Vincent and the Grenadines,North America,6337.770112,2.021,72.67185366,19.8 Sudan,Africa,1662.287641,4.491,62.83219512,76.3 Suriname,South America,9422.270994,2.411,70.80631707,23.4 Swaziland,Africa,3988.667162,3.407,48.85063415,72.6 Sweden,Europe,57134.07707,1.91,81.70487805,3 Switzerland,Europe,83208.68654,1.52,82.69756098,4.3 Tajikistan,Asia,962.4391249,3.525,69.16697561,49.3 Tanzania,Africa,827.5288808,5.287,63.52090244,55.7 Thailand,Asia,5917.917934,1.534,74.07190244,13.5 Timor-Leste,Asia,1127.108215,5.3,67.02058537,58.7 Togo,Africa,580.4950618,4.727,58.55385366,85.8 Tonga,Oceania,4364.309244,3.815,72.488,17.5 Trinidad and Tobago,North America,18322.3238,1.798,70.15139024,22.2 Tunisia,Africa,4187.543531,2.2,73.99512195,15.8 Turkey,Asia,10646.03553,2.06,74.86243902,16.5 Turkmenistan,Asia,6797.721166,2.353,65.31158537,56.5 Uganda,Africa,656.3980727,5.964,57.10019512,64.1 Ukraine,Europe,3855.42128,1.531,70.94414634,10.6 United Arab Emirates,Asia,41712.12421,1.82,77.02414634,7.8 United Kingdom,Europe,41294.5148,1.92,80.90487805,4.8 United States,North America,51456.65873,1.8805,78.74146341,7.1 Uruguay,South America,15127.64415,2.048,76.68839024,11.6 Uzbekistan,Asia,1719.036196,2.3,68.104,43.2 Vanuatu,Oceania,3158.420974,3.419,71.40817073,28.6 "Venezuela, RB",South America,12771.59504,2.417,73.92573171,16.1 Vietnam,Asia,1755.265424,1.768,75.60668293,23.5 West Bank and Gaza,Asia,2782.905026,4.076,73.01787805,22.8 "Yemen, Rep.",Asia,1289.034078,4.416,63.32729268,48.4 Zambia,Africa,1686.618024,5.511,58.36331707,74.4 Zimbabwe,Africa,850.827694,4.016,53.64307317,78.5 --------------------------------------------------------------------------------