├── .gitignore ├── API_GB.XPD.RSDV.GD.ZS_DS2_en_csv_v2.2011.csv ├── API_GB.XPD.RSDV.GD.ZS_DS2_en_csv_v2.csv ├── API_SE.TER.ENRR_DS2_en_csv_v2.2012.csv ├── API_SE.TER.ENRR_DS2_en_csv_v2.csv ├── API_SP.URB.TOTL.IN.ZS_DS2_en_v2.csv ├── LICENSE ├── README.md ├── country_downloads_population.csv ├── download_time_analysis.R ├── downloads_country.csv ├── expediture_education_percent_education2009.csv ├── graphs ├── download_time.pdf ├── download_time.png ├── download_time_germany_feb2016.pdf ├── download_time_germany_feb2016.png ├── download_time_hong_kong_feb2016.pdf ├── download_time_hong_kong_feb2016.png ├── downloads_per_population.pdf ├── downloads_per_population.png ├── downloads_per_population_ranked.pdf ├── downloads_per_population_ranked.png ├── downloads_per_population_residuals.pdf ├── downloads_per_population_residuals.png ├── gdp_vs_internet.pdf ├── gdp_vs_internet.png ├── normalized_downloads_gdp.pdf ├── normalized_downloads_gdp.png ├── normalized_downloads_internet_users.pdf ├── normalized_downloads_internet_users.png ├── normalized_downloads_life_expectancy.pdf ├── normalized_downloads_life_expectancy.png ├── normalized_downloads_percent_gdp_education.pdf ├── normalized_downloads_percent_gdp_education.png ├── normalized_downloads_unemployment.pdf ├── normalized_downloads_unemployment.png ├── university_downloads_country_per_time.pdf ├── university_downloads_country_per_time.png ├── university_downloads_per_enrolment.pdf ├── university_downloads_per_enrolment.png ├── university_downloads_per_researchspending.pdf ├── university_downloads_per_researchspending.png ├── university_downloads_per_weekday.pdf ├── university_downloads_per_weekday.png ├── university_downloads_ranking.pdf ├── university_downloads_ranking.png ├── university_downloads_vs_time.pdf └── university_downloads_vs_time.png ├── mapping_countrynames_iso_3166.csv ├── population_analysis.R ├── uni-stats-country.replaced.tab ├── uni-stats-country.tab ├── uni-stats-date.tab ├── university_analysis.R └── worldbank_2014_data.csv /.gitignore: -------------------------------------------------------------------------------- 1 | # History files 2 | .Rhistory 3 | .Rapp.history 4 | 5 | # Session Data files 6 | .RData 7 | 8 | # Example code in package build process 9 | *-Ex.R 10 | 11 | # RStudio files 12 | .Rproj.user/ 13 | 14 | # produced vignettes 15 | vignettes/*.html 16 | vignettes/*.pdf 17 | 18 | # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 19 | .httr-oauth 20 | -------------------------------------------------------------------------------- /API_GB.XPD.RSDV.GD.ZS_DS2_en_csv_v2.2011.csv: -------------------------------------------------------------------------------- 1 | Country Code,2011 2 | ABW, 3 | AND, 4 | AFG, 5 | AGO, 6 | ALB, 7 | ARB, 8 | ARE,0.49071 9 | ARG,0.51544 10 | ARM,0.27014 11 | ASM, 12 | ATG, 13 | AUS,2.24915 14 | AUT,2.68125 15 | AZE,0.21084 16 | BDI,0.12126 17 | BEL,2.15075 18 | BEN, 19 | BFA, 20 | BGD, 21 | BGR,0.54768 22 | BHR,0.03764 23 | BHS, 24 | BIH, 25 | BLR,0.7006 26 | BLZ, 27 | BMU,0.26122 28 | BOL, 29 | BRA,1.14008 30 | BRB, 31 | BRN, 32 | BTN, 33 | BWA, 34 | CAF, 35 | CAN,1.7788 36 | CEB,0.965154759185983 37 | CHE, 38 | CHI, 39 | CHL,0.35343 40 | CHN,1.79438 41 | CIV, 42 | CMR, 43 | COG, 44 | COL,0.21694 45 | COM, 46 | CPV,0.07264 47 | CRI,0.47403 48 | CSS, 49 | CUB,0.27192 50 | CUW, 51 | CYM, 52 | CYP,0.45612 53 | CZE,1.56009 54 | DEU,2.79725 55 | DJI, 56 | DMA, 57 | DNK,2.96625 58 | DOM, 59 | DZA, 60 | EAP,1.67730508973702 61 | EAS,2.42538073483319 62 | ECA,0.656511706594773 63 | ECS,1.80918937479295 64 | ECU,0.33991 65 | EGY,0.53154 66 | EMU,2.03994868847919 67 | ERI, 68 | ESP,1.31929 69 | EST,2.34365 70 | ETH, 71 | EUU,1.97029317846029 72 | FCS, 73 | FIN,3.63881 74 | FJI, 75 | FRA,2.19064 76 | FRO, 77 | FSM, 78 | GAB, 79 | GBR,1.69253 80 | GEO, 81 | GHA, 82 | GIN, 83 | GMB,0.13221 84 | GNB, 85 | GNQ, 86 | GRC,0.66962 87 | GRD, 88 | GRL, 89 | GTM,0.04814 90 | GUM, 91 | GUY, 92 | HIC,2.36287437626786 93 | HKG,0.72086 94 | HND, 95 | HPC, 96 | HRV,0.75237 97 | HTI, 98 | HUN,1.20042 99 | IDN, 100 | IMN, 101 | IND,0.82224 102 | INX, 103 | IRL,1.53301 104 | IRN, 105 | IRQ,0.03476 106 | ISL,2.49097 107 | ISR,4.10215 108 | ITA,1.20881 109 | JAM, 110 | JOR, 111 | JPN,3.38313 112 | KAZ,0.15723 113 | KEN, 114 | KGZ,0.15633 115 | KHM, 116 | KIR, 117 | KNA, 118 | KOR,3.74361 119 | KSV, 120 | KWT,0.09899 121 | LAC,0.817086956750516 122 | LAO, 123 | LBN, 124 | LBR, 125 | LBY, 126 | LCA, 127 | LCN,0.756424474533948 128 | LDC, 129 | LIC, 130 | LIE, 131 | LKA, 132 | LMC, 133 | LMY,1.19502383392017 134 | LSO,0.01283 135 | LTU,0.90471 136 | LUX,1.41092 137 | LVA,0.70027 138 | MAC,0.04453 139 | MAF, 140 | MAR, 141 | MCO, 142 | MDA,0.40499 143 | MDG,0.10588 144 | MDV, 145 | MEA, 146 | MEX,0.42497 147 | MHL, 148 | MIC,1.19584990938594 149 | MKD,0.22276 150 | MLI, 151 | MLT,0.72075 152 | MMR, 153 | MNA, 154 | MNE,0.40864 155 | MNG,0.23054 156 | MNP, 157 | MOZ, 158 | MRT, 159 | MUS, 160 | MWI, 161 | MYS,1.06422 162 | NAC,2.6627190665479 163 | NAM, 164 | NCL, 165 | NER, 166 | NGA, 167 | NIC, 168 | NLD,1.88839 169 | NOC,0.899550502032618 170 | NOR,1.62754 171 | NPL, 172 | NZL,1.24822 173 | OEC,2.48902054992195 174 | OED,2.41055682888702 175 | OMN,0.13645 176 | OSS, 177 | PAK,0.32917 178 | PAN,0.1804 179 | PER, 180 | PHL, 181 | PLW, 182 | PNG, 183 | POL,0.75224 184 | PRI, 185 | PRK, 186 | PRT,1.45683 187 | PRY,0.05671 188 | PSS, 189 | PYF, 190 | QAT, 191 | ROU,0.50059 192 | RUS,1.09069 193 | RWA, 194 | SAS,0.770816369324039 195 | SAU, 196 | SDN, 197 | SEN, 198 | SGP,2.15055 199 | SLB, 200 | SLE, 201 | SLV,0.03112 202 | SMR, 203 | SOM, 204 | SRB,0.72439 205 | SSA, 206 | SSD, 207 | SSF, 208 | SST, 209 | STP, 210 | SUR, 211 | SVK,0.66767 212 | SVN,2.42542 213 | SWE,3.22403 214 | SWZ, 215 | SXM, 216 | SYC, 217 | SYR, 218 | TCA, 219 | TCD, 220 | TGO, 221 | THA,0.38776 222 | TJK,0.12148 223 | TKM, 224 | TLS, 225 | TON, 226 | TTO,0.04348 227 | TUN,0.70645 228 | TUR,0.85952 229 | TUV, 230 | TZA, 231 | UGA, 232 | UKR,0.7378 233 | UMC,1.28872475051715 234 | URY,0.41679 235 | USA,2.76547 236 | UZB, 237 | VCT, 238 | VEN, 239 | VIR, 240 | VNM,0.19044 241 | VUT, 242 | PSE, 243 | WLD,2.05735657981616 244 | WSM, 245 | YEM, 246 | ZAF,0.7342 247 | COD, 248 | ZMB, 249 | ZWE, 250 | -------------------------------------------------------------------------------- /API_GB.XPD.RSDV.GD.ZS_DS2_en_csv_v2.csv: -------------------------------------------------------------------------------- 1 | "Country Name","Country Code","Indicator Name","Indicator Code","1960","1961","1962","1963","1964","1965","1966","1967","1968","1969","1970","1971","1972","1973","1974","1975","1976","1977","1978","1979","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015", 2 | "Aruba","ABW","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 3 | "Andorra","AND","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 4 | "Afghanistan","AFG","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 5 | "Angola","AGO","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 6 | "Albania","ALB","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.08737","0.15412","","","","","","","", 7 | "Arab World","ARB","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 8 | "United Arab Emirates","ARE","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.49071","","","","", 9 | "Argentina","ARG","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.41749","0.41959","0.41131","0.45337","0.43884","0.42461","0.38886","0.41013","0.36555","0.37868","0.40033","0.40169","0.42135","0.48292","0.49191","0.51544","0.57942","","","", 10 | "Armenia","ARM","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.18692","0.22601","0.214","0.18463","0.27987","0.25255","0.23971","0.21201","0.25761","0.24057","0.21107","0.22438","0.29093","0.24139","0.27014","0.25438","0.23686","","", 11 | "American Samoa","ASM","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 12 | "Antigua and Barbuda","ATG","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 13 | "Australia","AUS","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1.6619","","1.51483","","1.57648","","1.75338","","1.85341","","2.18215","","2.40144","","2.38592","2.24915","","","","", 14 | "Austria","AUT","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1.58431","1.65855","1.73613","1.84929","1.88965","1.99599","2.06993","2.18268","2.17368","2.38324","2.37115","2.4324","2.58557","2.61357","2.74175","2.68125","2.80978","2.81289","2.8345","", 15 | "Azerbaijan","AZE","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.23533","0.3695","0.42124","0.39289","0.336","0.34001","0.30155","0.32496","0.29831","0.21994","0.17165","0.17006","0.16554","0.24976","0.21848","0.21084","0.21724","0.21141","","", 16 | "Burundi","BDI","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.2007","0.15419","0.14434","0.13755","0.12126","","","","", 17 | "Belgium","BEL","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1.73312","1.79981","1.8291","1.89458","1.92696","2.02546","1.89417","1.83431","1.81112","1.7842","1.81035","1.84222","1.91871","1.97433","2.04719","2.15075","2.24321","2.2808","","", 18 | "Benin","BEN","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 19 | "Burkina Faso","BFA","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.15834","0.18104","","","","0.19057","0.33866","0.27119","0.24197","0.17057","","0.11062","0.18634","0.20005","","","","","","", 20 | "Bangladesh","BGD","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 21 | "Bulgaria","BGR","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.50866","0.47048","0.4954","0.53561","0.49116","0.44333","0.46642","0.47319","0.47518","0.45129","0.45176","0.43787","0.45709","0.51169","0.58636","0.54768","0.61988","0.64982","","", 22 | "Bahrain","BHR","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.03849","0.03858","0.03764","0.04099","0.04196","","", 23 | "Bahamas, The","BHS","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 24 | "Bosnia and Herzegovina","BIH","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.01854","0.01611","0.02776","0.02018","0.02576","0.01881","0.02148","","","0.27037","0.32721","","", 25 | "Belarus","BLR","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.99852","0.73","0.70578","0.84523","0.7221","0.70876","0.6209","0.61147","0.62759","0.67852","0.66065","0.96203","0.74147","0.64269","0.6935","0.7006","0.66705","0.67358","","", 26 | "Belize","BLZ","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 27 | "Bermuda","BMU","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.05885","","","","","","","","","","0.20526","0.2095","0.22217","0.22283","0.26122","0.23115","0.22606","","", 28 | "Bolivia","BOL","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.32544","0.31577","0.29495","0.29836","0.28744","0.29358","0.27663","","","","","","","0.157","","","","","","", 29 | "Brazil","BRA","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.99886","1.03166","0.9759","0.99816","0.96296","1.00193","0.98792","1.08227","1.12986","1.12029","1.15963","1.14008","1.15115","","","", 30 | "Barbados","BRB","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 31 | "Brunei Darussalam","BRN","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.01592","0.01842","0.03701","","","","","","","","","","","", 32 | "Bhutan","BTN","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 33 | "Botswana","BWA","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.53286","","","","","","","0.25229","","","", 34 | "Central African Republic","CAF","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 35 | "Canada","CAN","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1.6162","1.62374","1.71746","1.76045","1.87185","2.03845","1.99298","1.98508","2.0136","1.98645","1.95566","1.91787","1.86826","1.92265","1.83755","1.7788","1.70956","1.62365","","", 36 | "Central Europe and the Baltics","CEB","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.738845429805812","0.755416444399286","0.735550634105789","0.740956817963291","0.744949163726339","0.749760130759858","0.73971305702354","0.730646571920498","0.736754421573701","0.738802054397441","0.754793519015086","0.760799133020991","0.790493439681002","0.836793364866701","0.879973745283445","0.965154759185983","1.06463174319141","1.06358197594938","","", 37 | "Switzerland","CHE","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","2.45208","","","","2.32683","","","","2.67692","","","","2.72858","","","","2.96353","","","", 38 | "Channel Islands","CHI","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 39 | "Chile","CHL","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.3105","0.37499","0.35291","0.33099","0.35343","0.36479","","","", 40 | "China","CHN","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.56513","0.64102","0.64926","0.75276","0.89767","0.94539","1.06411","1.1274","1.22349","1.31793","1.37974","1.38432","1.4573","1.67871","1.7272","1.79438","1.9281","2.01466","","", 41 | "Cote d'Ivoire","CIV","Research and development expenditure (% of 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GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 226 | "Trinidad and Tobago","TTO","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.10325","0.11553","0.12715","0.12292","0.12614","0.11762","0.12933","0.11325","0.11496","0.09396","0.05526","0.04589","0.03072","0.05665","0.05137","0.04348","0.04856","","","", 227 | "Tunisia","TUN","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.304729998111725","0.387479990720749","0.39028999209404","0.397560000419617","0.414499998092651","0.481940001249313","0.53797","0.65021","0.73123","0.71171","0.67748","0.66591","0.63871","0.70726","0.68405","0.70645","0.67933","","","", 228 | "Turkey","TUR","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.45159","0.49169","0.37095","0.46767","0.47909","0.53779","0.52594","0.48311","0.51831","0.59104","0.58016","0.72241","0.72518","0.84902","0.84343","0.85952","0.92196","0.94477","","", 229 | "Tuvalu","TUV","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 230 | "Tanzania","TZA","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.33994","","","0.38025","","","","","", 231 | "Uganda","UGA","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.36756","0.25612","0.26056","0.21514","0.30033","0.38821","0.32632","0.35906","0.47567","","","","","", 232 | "Ukraine","UKR","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1.1923","1.06993","0.96968","0.96213","1.02329","0.9982","1.11179","1.08152","1.16896","0.94908","0.85319","0.84644","0.85644","0.83098","0.7378","0.75167","0.76175","","", 233 | "Upper middle income","UMC","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.669014049589643","0.688473112270297","0.731687663761767","0.772698685828159","0.810324298081794","0.884316983387482","0.910883607525806","0.966433141616161","1.04166803317005","1.17214441125721","1.23016788841972","1.28872475051715","1.44981693616485","1.64057333157307","","", 234 | "Uruguay","URY","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.26501","0.34994","0.19186","0.22417","0.20925","","0.23818","","","","0.35919","0.39948","0.36265","0.42266","0.39501","0.41679","0.23436","","","", 235 | "United States","USA","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","2.44182","2.47091","2.49675","2.54174","2.6205","2.63832","2.54968","2.55287","2.48995","2.50599","2.55002","2.62692","2.76683","2.81578","2.73716","2.76547","2.80604","","","", 236 | "Uzbekistan","UZB","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 237 | "St. Vincent and the Grenadines","VCT","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.04306","0.12028","","","","","","","","","","","","","", 238 | "Venezuela, RB","VEN","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 239 | "Virgin Islands (U.S.)","VIR","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 240 | "Vietnam","VNM","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.17808","","","","","","","","","0.19044","","","","", 241 | "Vanuatu","VUT","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 242 | "West Bank and Gaza","PSE","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 243 | "World","WLD","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1.98748898897237","1.98389840758757","2.00056450930969","2.07624938930922","2.0774564483069","2.09789467756826","2.06138253442277","2.04834590769575","1.99878203744452","1.98886409441585","1.995819196694","1.96566231519046","2.02803926212822","2.06326908796535","2.0551131910389","2.05735657981616","2.17617015035079","","","", 244 | "Samoa","WSM","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 245 | "Yemen, Rep.","YEM","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 246 | "South Africa","ZAF","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.58354","","","","0.71578","","0.76051","0.81334","0.86315","0.89815","0.88287","0.88816","0.83562","0.73704","0.7342","0.73168","","","", 247 | "Congo, Dem. Rep.","COD","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.26777","0.05799","0.0611","0.1392","0.08503","0.07987","","","","","","", 248 | "Zambia","ZMB","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","0.01104","0.00737","","","","","0.00544","0.00847","0.02223","0.02493","","","0.27819","","","","","","","", 249 | "Zimbabwe","ZWE","Research and development expenditure (% of GDP)","GB.XPD.RSDV.GD.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","", 250 | -------------------------------------------------------------------------------- /API_SE.TER.ENRR_DS2_en_csv_v2.2012.csv: -------------------------------------------------------------------------------- 1 | Country Code,2012 2 | ABW,37.75569 3 | AND, 4 | AFG, 5 | AGO, 6 | ALB,58.56549 7 | ARB,25.98412 8 | ARE,16.55895 9 | ARG,78.97288 10 | ARM,45.62385 11 | ASM, 12 | ATG,23.48624 13 | AUS,85.41392 14 | AUT,72.31488 15 | AZE,20.43846 16 | BDI,3.66913 17 | BEL,71.24437 18 | BEN,13.42122 19 | BFA,4.55828 20 | BGD,13.38656 21 | BGR,62.74134 22 | BHR,41.08677 23 | BHS, 24 | BIH, 25 | BLR,90.43713 26 | BLZ,24.3329 27 | BMU,30.8026 28 | BOL, 29 | BRA, 30 | BRB, 31 | BRN,22.5535 32 | BTN,9.4413 33 | BWA,18.07154 34 | CAF,2.77416 35 | CAN, 36 | CEB,64.60601 37 | CHE,55.4999 38 | CHI, 39 | CHL,79.44691 40 | CHN,27.18443 41 | CIV, 42 | CMR, 43 | COG,10.43946 44 | COL,46.21575 45 | COM,8.59989 46 | CPV,20.12141 47 | CRI,49.32474 48 | CSS,23.06213 49 | CUB,64.45275 50 | CUW, 51 | CYM, 52 | CYP,45.86359 53 | CZE,65.86197 54 | DEU, 55 | DJI, 56 | DMA, 57 | DNK,79.25263 58 | DOM,46.72693 59 | DZA,32.23133 60 | EAP,27.76401 61 | EAS,30.87508 62 | ECA,51.85919 63 | ECS,63.12709 64 | ECU,39.86351 65 | EGY,27.61831 66 | EMU,67.26799 67 | ERI, 68 | ESP,85.54599 69 | EST,72.10183 70 | ETH,8.14473 71 | EUU,66.17328 72 | FCS,11.23465 73 | FIN,93.28063 74 | FJI, 75 | FRA,59.98763 76 | FRO, 77 | FSM, 78 | GAB, 79 | GBR,59.22314 80 | GEO,29.20942 81 | GHA,12.18241 82 | GIN,9.89186 83 | GMB, 84 | GNB, 85 | GNQ, 86 | GRC,110.26312 87 | GRD, 88 | GRL, 89 | GTM, 90 | GUM, 91 | GUY,12.48062 92 | HIC,75.03963 93 | HKG,60.58629 94 | HND,20.49557 95 | HPC,7.67297 96 | HRV,61.66721 97 | HTI, 98 | HUN,59.79731 99 | IDN,30.65633 100 | IMN, 101 | IND,24.36533 102 | INX, 103 | IRL,67.63531 104 | IRN,54.89906 105 | IRQ, 106 | ISL,82.22758 107 | ISR,67.79533 108 | ITA,65.03444 109 | JAM,29.06177 110 | JOR,47.58631 111 | JPN,61.45817 112 | KAZ,51.32379 113 | KEN, 114 | KGZ, 115 | KHM, 116 | KIR, 117 | KNA, 118 | KOR, 119 | KSV, 120 | KWT, 121 | LAC,42.24999 122 | LAO,17.07804 123 | LBN,45.57901 124 | LBR,11.63857 125 | LBY, 126 | LCA,10.17779 127 | LCN,43.40965 128 | LDC,8.85019 129 | LIC,9.14096 130 | LIE,42.49668 131 | LKA,17.16244 132 | LMC,23.03523 133 | LMY,25.51285 134 | LSO,10.823 135 | LTU,77.23182 136 | LUX,19.40741 137 | LVA,65.56071 138 | MAC,56.03069 139 | MAF, 140 | MAR,19.28785 141 | MCO, 142 | MDA,40.10864 143 | MDG,4.08568 144 | MDV, 145 | MEA,34.44156 146 | MEX,28.30744 147 | MHL,42.86175 148 | MIC,28.35236 149 | MKD,40.84611 150 | MLI,6.87478 151 | MLT,45.53358 152 | MMR,13.52817 153 | MNA,33.53121 154 | MNE, 155 | MNG,58.73871 156 | MNP, 157 | MOZ,5.05311 158 | MRT,5.20095 159 | MUS,38.65997 160 | MWI, 161 | MYS,37.21254 162 | NAC,90.91132 163 | NAM, 164 | NCL, 165 | NER,1.71459 166 | NGA, 167 | NIC, 168 | NLD,78.50107 169 | NOC,64.39623 170 | NOR,73.25146 171 | NPL, 172 | NZL,80.84335 173 | OEC,77.78673 174 | OED,70.69429 175 | OMN, 176 | OSS,12.34655 177 | PAK,9.93023 178 | PAN,44.14989 179 | PER, 180 | PHL,31.29874 181 | PLW, 182 | PNG, 183 | POL,72.84839 184 | PRI,86.56501 185 | PRK, 186 | PRT,68.58554 187 | PRY, 188 | PSS, 189 | PYF, 190 | QAT,11.93219 191 | ROU, 192 | RUS,76.11465 193 | RWA,6.55378 194 | SAS,21.19963 195 | SAU,48.56231 196 | SDN,14.94187 197 | SEN, 198 | SGP, 199 | SLB, 200 | SLE, 201 | SLV,28.62897 202 | SMR,59.849 203 | SOM, 204 | SRB,52.37703 205 | SSA,8.32957 206 | SSD, 207 | SSF,8.32558 208 | SST,14.48543 209 | STP,8.25875 210 | SUR, 211 | SVK,56.04699 212 | SVN,86.32315 213 | SWE,69.61607 214 | SWZ, 215 | SXM, 216 | SYC,1.29534 217 | SYR,31.83422 218 | TCA, 219 | TCD, 220 | TGO,10.32146 221 | THA,51.56916 222 | TJK,22.41332 223 | TKM, 224 | TLS, 225 | TON, 226 | TTO, 227 | TUN,35.177 228 | TUR,69.30195 229 | TUV, 230 | TZA,3.93347 231 | UGA, 232 | UKR,82.12613 233 | UMC,34.20129 234 | URY, 235 | USA,94.84228 236 | UZB, 237 | VCT, 238 | VEN, 239 | VIR, 240 | VNM,25.02092 241 | VUT, 242 | PSE,48.17982 243 | WLD,32.27898 244 | WSM, 245 | YEM, 246 | ZAF,18.99404 247 | COD,7.92246 248 | ZMB, 249 | ZWE,5.86867 250 | -------------------------------------------------------------------------------- /API_SP.URB.TOTL.IN.ZS_DS2_en_v2.csv: -------------------------------------------------------------------------------- 1 | Country Code;2014 Percent Urban Population ABW;41,779 AND;85,633 AFG;26,282 AGO;43,274 ALB;56,409 ARB;57,38845654 ARE;85,266 ARG;91,604 ARM;62,812 ASM;87,264 ATG;24,19 AUS;89,289 AUT;65,919 AZE;54,355 BDI;11,761 BEL;97,818 BEN;43,514 BFA;29,024 BGD;33,516 BGR;73,626 BHR;88,719 BHS;82,801 BIH;39,616 BLR;76,277 BLZ;44,124 BMU;100 BOL;68,107 BRA;85,433 BRB;31,554 BRN;76,887 BTN;37,898 BWA;57,187 CAF;39,759 CAN;81,65 CEB;62,26936458 CHE;73,844 CHI;31,363 CHL;89,356 CHN;54,41 CIV;53,479 CMR;53,819 COG;64,957 COL;76,161 COM;28,193 CPV;64,84 CRI;75,915 CSS;42,08309589 CUB;76,97 CUW;89,43 CYM;100 CYP;67,019 CZE;73,019 DEU;75,094 DJI;77,262 DMA;69,256 DNK;87,502 DOM;78,061 DZA;70,129 EAP;51,9496476 EAS;55,72079895 ECA;59,93526451 ECS;70,67999786 ECU;63,516 EGY;43,069 EMU;75,69954773 ERI;22,193 ESP;79,355 EST;67,622 ETH;19,028 EUU;74,57523014 FCS;40,74676851 FIN;84,086 FJI;53,354 FRA;79,289 FRO;41,743 FSM;22,378 GAB;86,92 GBR;82,345 GEO;53,468 GHA;53,392 GIN;36,68 GMB;59,015 GNB;48,55 GNQ;39,756 GRC;77,678 GRD;35,58 GRL;86,045 GTM;51,115 GUM;94,437 GUY;28,459 HIC;80,65809502 HKG;100 HND;54,137 HPC;34,13133211 HRV;58,656 HTI;57,436 HUN;70,771 IDN;53,003 IMN;52,138 IND;32,366 INX; IRL;62,952 IRN;72,855 IRQ;69,361 ISL;94,042 ISR;92,076 ITA;68,821 JAM;54,556 JOR;83,447 JPN;93,021 KAZ;53,29 KEN;25,197 KGZ;35,585 KHM;20,514 KIR;44,173 KNA;31,96 KOR;82,36 KSV; KWT;98,326 LAC;77,78157837 LAO;37,551 LBN;87,67 LBR;49,308 LBY;78,359 LCA;18,477 LCN;79,61483658 LDC;31,01842042 LIC;29,76794504 LIE;14,305 LKA;18,32 LMC;38,52706738 LMY;46,98506528 LSO;26,789 LTU;66,522 LUX;89,872 LVA;67,421 MAC;100 MAF; MAR;59,699 MCO;100 MDA;44,925 MDG;34,468 MDV;44,494 MEA;63,74276204 MEX;78,971 MHL;72,424 MIC;49,02969359 MKD;57,029 MLI;39,143 MLT;95,277 MMR;33,551 MNA;60,01501824 MNE;63,832 MNG;71,222 MNP;89,272 MOZ;31,934 MRT;59,255 MUS;39,814 MWI;16,102 MYS;74,01 NAC;81,47076721 NAM;45,678 NCL;69,663 NER;18,469 NGA;46,942 NIC;58,459 NLD;89,91 NOC;80,58927715 NOR;80,208 NPL;18,243 NZL;86,251 OEC;80,67774012 OED;80,04415801 OMN;77,178 OSS;48,53192163 PAK;38,303 PAN;66,292 PER;78,285 PHL;44,488 PLW;86,452 PNG;12,985 POL;60,568 PRI;93,636 PRK;60,715 PRT;62,908 PRY;59,416 PSS;37,23226336 PYF;55,976 QAT;99,159 ROU;54,393 RUS;73,924 RWA;27,841 SAS;32,60337557 SAU;82,926 SDN;33,623 SEN;43,393 SGP;100 SLB;21,876 SLE;39,578 SLV;66,255 SMR;94,166 SOM;39,078 SRB;55,455 SSA;37,22805127 SSD;18,591 SSF;37,23171313 SST;46,17379323 STP;64,511 SUR;66,085 SVK;53,757 SVN;49,695 SWE;85,665 SWZ;21,315 SXM;100 SYC;53,559 SYR;57,255 TCA;91,847 TCD;22,341 TGO;39,469 THA;49,174 TJK;26,692 TKM;49,688 TLS;32,131 TON;23,632 TTO;8,55 TUN;66,645 TUR;72,891 TUV;58,782 TZA;30,901 UGA;15,766 UKR;69,482 UMC;61,82708863 URY;95,152 USA;81,447 UZB;36,278 VCT;50,2 VEN;88,941 VIR;95,203 VNM;32,951 VUT;25,817 PSE;75,026 WLD;53,3850865 WSM;19,26 YEM;34,027 ZAF;64,298 COD;41,976 ZMB;40,472 ZWE;32,501 -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | The MIT License (MIT) 2 | 3 | Copyright (c) 2016 Bastian Greshake 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![Analyzing the SciHub downloads](https://raw.githubusercontent.com/gedankenstuecke/scihub_analysis/master/graphs/downloads_per_population.png) 2 | 3 | # SciHub Data Analysis 4 | These are some scripts to analyse the data sets published by SciHub. You can find some analysis and texts about insights already won from the data at [Science](http://www.sciencemag.org/news/2016/04/whos-downloading-pirated-papers-everyone) and the [DataDryad Blog](https://blog.datadryad.org/2016/04/28/sci-hub-stories/). 5 | 6 | I wrote [a blogpost based on the analyses done with this code](http://ruleofthirds.de/analyzing-scihub-data/) which you might want to check out for some context. There's also a [follow up blogpost by me on how to integrate the information on whether access was made from universities or now](http://ruleofthirds.de/scihub-part2/). 7 | 8 | ## When are people downloading via SciHub? 9 | Run `download_time_analysis.R` and pass one of the `*.tab` files, yields a `graphs/download_time.pdf`. Watch out: The time zones are not taken into account, everything's UTC. For that reason there are two filtered files in `graphs/`, one for Hong Kong (UTC+7) and one for Germany (UTC+1) as examples. 10 | 11 | ## Aggregate by country 12 | Handling the complete files on my poor little notebook and in R was bit too much, so for looking at country-wise statistics I decided to calculate the aggregates the lazy bioinformatics-way: 13 | 14 | `cut -f 4 *.tab|sort|uniq -c|sort -n|awk '{out=$1"\t"; for(i=2;i<=NF;i++){out=out" "$i}; print out}' > countrywise.csv` 15 | 16 | ### Compare Downloads per Country to Population Size 17 | The [World Bank offers the Population Sizes for 214 countries/economies as of 2014](http://data.worldbank.org/data-catalog/Population-ranking-table), amongst other file types as a csv. I used those and for now manually matched those to the countries listed in the *SciHub* data. Results are in `country_downloads_population.csv` (If no match could be made I just removed the country for now). The population numbers are in 1.000's. 18 | 19 | This file is used by `population_analysis.R` to analyze how the population size and number of downloads are connected (`graphs/downloads_per_population.pdf`). Fitting a glm on the data the script also gives you the residuals, ranking which countries have how much more/less downloads than one would expect (`graphs/downloads_per_population_residuals.pdf`). 20 | 21 | Last but not least the script also gives you the number of downloads per 1.000 inhabitants (`graphs/downloads_per_population_ranked.pdf`). 22 | 23 | ## Are people inside universities using Sci-Hub? And to which extend? 24 | 25 | I was lucky enough to be put in touch with Sci-Hub and they used this list to classify their data on whether it comes from university IP ranges or not. For privacy reasons the data is more highly aggregated. [A blog post on these analyses can be found here](http://ruleofthirds.de/scihub-part2/). 26 | 27 | The first data set (*uni-stats-date.tab*) contains the percentage of academic downloads for a given day. 28 | 29 | ``` 30 | 2015-09-01 6.86 31 | 2015-09-02 7.14 32 | 2015-09-03 6.99 33 | 2015-09-04 6.62 34 | 2015-09-05 3.69 35 | 2015-09-06 3.58 36 | 2015-09-07 6.69 37 | 2015-09-08 6.93 38 | ``` 39 | 40 | The second one (*uni-stats-country.tab*) contains the percentage of academic downloads for each 10 days, split by country: 41 | 42 | ``` 43 | 2015-09-0* Colombia 13.49 44 | 2015-09-0* United States 5.98 45 | 2015-09-0* China 5.65 46 | 2015-09-0* Chile 11.88 47 | 2015-09-0* Portugal 0.01 48 | 2015-09-0* Morocco 0 49 | 2015-09-0* Croatia 0 50 | 2015-09-0* Egypt 8.2 51 | ``` 52 | 53 | ## Dependencies 54 | The R scripts use *ggplot2*, *ggrepel*, *ggthemes* and *lubridate*. 55 | 56 | ## Questions 57 | ### Where's the raw data? 58 | It's available at DataDryad, so it's not included here. [Download it from there](http://dx.doi.org/10.5061/dryad.q447c). 59 | 60 | ### What are the timezones? 61 | The times in the data set are all in UTC+3 (it said UTC earlier, but I was corrected on that), so you'll have to adjust them for the corresponding time zones if interested in analyzing those. Or you could just ignore it for now as I did above, because I'm lazy. 62 | 63 | ### What are the licenses? 64 | The Sci-Hub data is [CC-Zero, as indicated on DataDryad](http://datadryad.org/resource/doi:10.5061/dryad.q447c). The [World Bank uses their own ToS for the data](http://web.worldbank.org/WBSITE/EXTERNAL/0,,contentMDK:22547097~pagePK:50016803~piPK:50016805~theSitePK:13,00.html). My uglyish code is under MIT license, please take it. 65 | 66 | ### Can I Contribute? 67 | Most definitely, I'm happy for everyone who likes to join me or has suggestions in what to analyze next! Get in touch through the issues or via [Twitter](http://www.twitter.com/gedankenstuecke). 68 | -------------------------------------------------------------------------------- /country_downloads_population.csv: -------------------------------------------------------------------------------- 1 | population downloads country 2 | 104 1 Federated States of Micronesia 3 | 2076 2 Lesotho 4 | 13587 3 Chad 5 | 11911 3 South Sudan 6 | 259 3 Vanuatu 7 | 186 4 São Tomé and Príncipe 8 | 10817 5 Burundi 9 | 6316 5 Sierra Leone 10 | 184 6 Saint Lucia 11 | 109 6 Saint Vincent and the Grenadines 12 | 32 7 Collectivity of Saint Martin 13 | 192 7 Samoa 14 | 32 8 San Marino 15 | 59 9 Cayman Islands 16 | 91 12 Antigua and Barbuda 17 | 1928 16 The Gambia 18 | 1801 18 Guinea-Bissau 19 | 56 19 Greenland 20 | 19114 19 Niger 21 | 770 22 Comoros 22 | 87 22 Isle of Man 23 | 72 27 Dominica 24 | 514 30 Cape Verde 25 | 65 46 Bermuda 26 | 37 47 Liechtenstein 27 | 106 49 Grenada 28 | 55 50 Saint Kitts and Nevis 29 | 383 50 The Bahamas 30 | 104 52 U.S. Virgin Islands 31 | 1269 56 Swaziland 32 | 7464 72 Papua New Guinea 33 | 38 76 Monaco 34 | 55 76 Northern Mariana Islands 35 | 2403 77 Namibia 36 | 1212 78 Timor-Leste 37 | 572 81 Solomon Islands 38 | 156 89 Curaçao 39 | 48 102 Faroe Islands 40 | 103 104 Aruba 41 | 169 113 Guam 42 | 16695 158 Malawi 43 | 24229 160 Angola 44 | 2220 167 Botswana 45 | 8296 176 Tajikistan 46 | 352 181 Belize 47 | 10518 181 Somalia 48 | 821 192 Equatorial Guinea 49 | 4397 208 Liberia 50 | 876 211 Djibouti 51 | 280 234 French Polynesia 52 | 283 240 Barbados 53 | 17086 252 Mali 54 | 765 259 Bhutan 55 | 266 280 New Caledonia 56 | 7115 280 Togo 57 | 73 297 Andorra 58 | 31628 356 Afghanistan 59 | 401 438 Maldives 60 | 4505 464 Republic of the Congo 61 | 11342 552 Rwanda 62 | 37783 629 Uganda 63 | 1688 669 Gabon 64 | 5307 765 Turkmenistan 65 | 5836 776 Kyrgyzstan 66 | 38 829 Sint Maarten 67 | 2910 1026 Mongolia 68 | 7962 1030 Honduras 69 | 91 1090 Seychelles 70 | 74877 1144 Democratic Republic of the Congo 71 | 27216 1162 Mozambique 72 | 10598 1302 Benin 73 | 2721 1313 Jamaica 74 | 1362 1348 Bahrain 75 | 15246 1628 Zimbabwe 76 | 6014 1731 Nicaragua 77 | 23572 1779 Madagascar 78 | 3970 1852 Mauritania 79 | 6108 1992 El Salvador 80 | 417 2077 Brunei 81 | 1354 2195 Trinidad and Tobago 82 | 153278 2282 Cambodia 83 | 3727 2594 Georgia 84 | 622 2619 Montenegro 85 | 10406 3856 Dominican Republic 86 | 44864 4138 Kenya 87 | 26787 4449 Ghana 88 | 1154 4484 Cyprus 89 | 6553 4581 Paraguay 90 | 12276 4834 Guinea 91 | 51823 5173 Tanzania 92 | 9535 5753 Azerbaijan 93 | 327 5824 Iceland 94 | 30758 5945 Uzbekistan 95 | 2172 5958 Qatar 96 | 17589 5978 Burkina Faso 97 | 16015 6002 Guatemala 98 | 6259 6075 Libya 99 | 3753 6321 Kuwait 100 | 53437 6387 Myanmar (Burma) 101 | 3868 6430 Panama 102 | 427 6687 Malta 103 | 4236 6920 Oman 104 | 5137 7059 Norway 105 | 4510 7360 New Zealand 106 | 14673 7642 Senegal 107 | 4547 7668 Lebanon 108 | 2076 7776 Macedonia (FYROM) 109 | 1315 8072 Estonia 110 | 1261 8174 Mauritius 111 | 2894 8272 Albania 112 | 22157 8529 Côte d'Ivoire 113 | 26184 9065 Yemen 114 | 5639 10864 Denmark 115 | 22773 11021 Cameroon 116 | 28175 11893 Nepal 117 | 556 11958 Luxembourg 118 | 3556 11994 Moldova 119 | 3548 12162 Puerto Rico 120 | 3006 13449 Armenia 121 | 3818 13580 Bosnia and Herzegovina 122 | 10562 14194 Bolivia 123 | 96959 14525 Ethiopia 124 | 9086 14575 United Arab Emirates 125 | 39350 15109 Sudan 126 | 11379 18435 Cuba 127 | 5462 20402 Finland 128 | 54002 20441 South Africa 129 | 17289 20940 Kazakhstan 130 | 8216 24702 Israel 131 | 8188 25019 Nigeria 132 | 4758 26690 Costa Rica 133 | 5419 26930 Slovakia 134 | 20771 27841 Sri Lanka 135 | 22158 28510 Syria 136 | 9696 29373 Sweden 137 | 3420 30073 Uruguay 138 | 1994 35693 Latvia 139 | 23470 40545 Australia 140 | 2062 40809 Slovenia 141 | 2932 44260 Lithuania 142 | 67726 58423 Thailand 143 | 159078 60256 Bangladesh 144 | 8546 61566 Austria 145 | 10525 63983 Czech Republic 146 | 7129 64397 Serbia 147 | 7224 67289 Bulgaria 148 | 6607 67420 Jordan 149 | 30887 71393 Saudi Arabia 150 | 4616 74381 Ireland 151 | 9470 74564 Belarus 152 | 5470 74909 Singapore 153 | 4238 75514 Croatia 154 | 34812 85187 Iraq 155 | 99139 92100 Philippines 156 | 8188 97632 Switzerland 157 | 30694 105392 Venezuela 158 | 19904 108673 Romania 159 | 7242 111129 Hong Kong 160 | 9864 119731 Hungary 161 | 11231 129615 Belgium 162 | 15903 135175 Ecuador 163 | 38012 137888 Poland 164 | 35544 159390 Canada 165 | 16865 168876 Netherlands 166 | 50424 177023 South Korea 167 | 64559 218632 United Kingdom 168 | 127132 234755 Japan 169 | 10870 285452 Greece 170 | 60789 295015 Italy 171 | 38934 295478 Algeria 172 | 10997 339592 Tunisia 173 | 45363 367047 Ukraine 174 | 90729 369246 Vietnam 175 | 30973 373325 Peru 176 | 29902 373752 Malaysia 177 | 75932 393308 Turkey 178 | 42980 410986 Argentina 179 | 47791 414783 Colombia 180 | 17763 425596 Chile 181 | 46476 426412 Spain 182 | 10401 434721 Portugal 183 | 80971 462011 Germany 184 | 185044 493996 Pakistan 185 | 33921 497306 Morocco 186 | 125386 503093 Mexico 187 | 66218 511145 France 188 | 254455 780873 Indonesia 189 | 89580 801106 Egypt 190 | 206078 1021540 Brazil 191 | 318857 1150963 United States 192 | 143820 1521434 Russia 193 | 78144 2631035 Iran 194 | 1295292 3414582 India 195 | 1364270 4456076 China 196 | -------------------------------------------------------------------------------- /download_time_analysis.R: -------------------------------------------------------------------------------- 1 | library(ggplot2) 2 | library(ggthemes) 3 | library(lubridate) 4 | 5 | args <- commandArgs(trailingOnly = TRUE) 6 | 7 | df <- read.csv(file=args[1],sep="\t",head=F) 8 | df$dates <- as.POSIXct(df$V1) 9 | 10 | df$hour <- hour(df$dates) 11 | df$weekday <- weekdays(df$dates) 12 | df$weekday <- factor(df$weekday,levels = c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday")) 13 | p <- ggplot(df,aes(hour,fill=weekday)) + geom_histogram(binwidth=1) + facet_grid(weekday ~ .) + theme_minimal() + scale_y_continuous("Download Count") + scale_x_continuous("Hour") 14 | ggsave("graphs/download_time.pdf", width = 594, height = 220, units = "mm") 15 | ggsave("graphs/download_time.png", width = 594, height = 220, units = "mm") 16 | -------------------------------------------------------------------------------- /downloads_country.csv: -------------------------------------------------------------------------------- 1 | Downloads Country 2 | 1 Federated States of Micronesia 3 | 2 Caribbean Netherlands 4 | 2 Falkland Islands (Islas Malvinas) 5 | 2 Lesotho 6 | 2 Saint-Barthélemy 7 | 3 Chad 8 | 3 South Sudan 9 | 3 Vanuatu 10 | 4 São Tomé and Príncipe 11 | 5 Burundi 12 | 5 Guernsey 13 | 5 Sierra Leone 14 | 6 SaintLucia 15 | 6 SaintVincent and the Grenadines 16 | 7 Collectivity of SaintMartin 17 | 7 Samoa 18 | 8 San Marino 19 | 9 Anguilla 20 | 9 Cayman Islands 21 | 12 Antigua and Barbuda 22 | 14 Åland Islands 23 | 16 The Gambia 24 | 18 Guinea-Bissau 25 | 19 Greenland 26 | 19 Niger 27 | 21 Mayotte 28 | 22 Comoros 29 | 22 Isle of Man 30 | 27 Dominica 31 | 29 British Virgin Islands 32 | 30 Cape Verde 33 | 46 Bermuda 34 | 47 Liechtenstein 35 | 49 Grenada 36 | 50 SaintKitts and Nevis 37 | 50 The Bahamas 38 | 52 U.S. Virgin Islands 39 | 56 Swaziland 40 | 70 Jersey 41 | 72 Papua New Guinea 42 | 76 Monaco 43 | 76 Northern Mariana Islands 44 | 77 Namibia 45 | 78 Timor-Leste 46 | 81 Solomon Islands 47 | 89 Curaçao 48 | 102 Faroe Islands 49 | 104 Aruba 50 | 113 Guam 51 | 158 Malawi 52 | 160 Angola 53 | 167 Botswana 54 | 176 Tajikistan 55 | 181 Belize 56 | 181 Somalia 57 | 192 Equatorial Guinea 58 | 208 Liberia 59 | 211 Djibouti 60 | 215 Laos 61 | 234 French Polynesia 62 | 240 Barbados 63 | 252 Mali 64 | 259 Bhutan 65 | 280 New Caledonia 66 | 280 Togo 67 | 297 Andorra 68 | 356 Afghanistan 69 | 396 Martinique 70 | 438 Maldives 71 | 457 Guadeloupe 72 | 464 Republic of the Congo 73 | 467 Suriname 74 | 486 Guyana 75 | 501 Zambia 76 | 509 Fiji 77 | 536 Gibraltar 78 | 552 Rwanda 79 | 629 Uganda 80 | 669 Gabon 81 | 765 Turkmenistan 82 | 776 Kyrgyzstan 83 | 829 SintMaarten 84 | 1026 Mongolia 85 | 1030 Honduras 86 | 1090 Seychelles 87 | 1144 Democratic Republic of the Congo 88 | 1162 Mozambique 89 | 1165 Reunion 90 | 1281 French Guiana 91 | 1302 Benin 92 | 1313 Jamaica 93 | 1348 Bahrain 94 | 1628 Zimbabwe 95 | 1731 Nicaragua 96 | 1779 Madagascar 97 | 1852 Mauritania 98 | 1992 El Salvador 99 | 2077 Brunei 100 | 2195 Trinidad and Tobago 101 | 2282 Cambodia 102 | 2594 Georgia 103 | 2619 Montenegro 104 | 3856 Dominican Republic 105 | 4138 Kenya 106 | 4449 Ghana 107 | 4484 Cyprus 108 | 4581 Paraguay 109 | 4834 Guinea 110 | 4860 Macau 111 | 5173 Tanzania 112 | 5753 Azerbaijan 113 | 5824 Iceland 114 | 5945 Uzbekistan 115 | 5958 Qatar 116 | 5978 Burkina Faso 117 | 6002 Guatemala 118 | 6075 Libya 119 | 6321 Kuwait 120 | 6387 Myanmar (Burma) 121 | 6430 Panama 122 | 6687 Malta 123 | 6920 Oman 124 | 7059 Norway 125 | 7360 New Zealand 126 | 7642 Senegal 127 | 7668 Lebanon 128 | 7776 Macedonia (FYROM) 129 | 8072 Estonia 130 | 8174 Mauritius 131 | 8272 Albania 132 | 8529 Côte d'Ivoire 133 | 9065 Yemen 134 | 10864 Denmark 135 | 11021 Cameroon 136 | 11893 Nepal 137 | 11958 Luxembourg 138 | 11994 Moldova 139 | 12162 Puerto Rico 140 | 13449 Armenia 141 | 13580 Bosnia and Herzegovina 142 | 14194 Bolivia 143 | 14525 Ethiopia 144 | 14575 United Arab Emirates 145 | 15109 Sudan 146 | 18435 Cuba 147 | 20402 Finland 148 | 20441 South Africa 149 | 20940 Kazakhstan 150 | 24702 Israel 151 | 25019 Nigeria 152 | 26690 Costa Rica 153 | 26930 Slovakia 154 | 27841 Sri Lanka 155 | 28510 Syria 156 | 29373 Sweden 157 | 30073 Uruguay 158 | 35693 Latvia 159 | 40545 Australia 160 | 40809 Slovenia 161 | 44260 Lithuania 162 | 58423 Thailand 163 | 59091 Taiwan 164 | 60256 Bangladesh 165 | 61566 Austria 166 | 63983 Czech Republic 167 | 64397 Serbia 168 | 67289 Bulgaria 169 | 67420 Jordan 170 | 71393 Saudi Arabia 171 | 74381 Ireland 172 | 74564 Belarus 173 | 74909 Singapore 174 | 75514 Croatia 175 | 85187 Iraq 176 | 92100 Philippines 177 | 97632 Switzerland 178 | 105392 Venezuela 179 | 108673 Romania 180 | 111129 Hong Kong 181 | 119731 Hungary 182 | 129615 Belgium 183 | 135175 Ecuador 184 | 137888 Poland 185 | 159390 Canada 186 | 168876 Netherlands 187 | 177023 South Korea 188 | 218632 United Kingdom 189 | 234755 Japan 190 | 285452 Greece 191 | 295015 Italy 192 | 295478 Algeria 193 | 339592 Tunisia 194 | 367047 Ukraine 195 | 369246 Vietnam 196 | 373325 Peru 197 | 373752 Malaysia 198 | 393308 Turkey 199 | 410986 Argentina 200 | 414783 Colombia 201 | 425596 Chile 202 | 426412 Spain 203 | 434721 Portugal 204 | 462011 Germany 205 | 493996 Pakistan 206 | 497306 Morocco 207 | 503093 Mexico 208 | 511145 France 209 | 562692 N/A 210 | 780873 Indonesia 211 | 801106 Egypt 212 | 1021540 Brazil 213 | 1150963 United States 214 | 1521434 Russia 215 | 2631035 Iran 216 | 3414582 India 217 | 4456076 China 218 | -------------------------------------------------------------------------------- /expediture_education_percent_education2009.csv: -------------------------------------------------------------------------------- 1 | Country Code,2009 [YR2009] 2 | AFG,.. 3 | ALB,.. 4 | DZA,.. 5 | ASM,.. 6 | ADO,3.16387 7 | AGO,.. 8 | ATG,2.55447 9 | ARG,4.89 10 | ARM,3.84079 11 | ABW,5.92467 12 | AUS,5.0942 13 | AUT,5.7674 14 | AZE,3.22431 15 | BHS,.. 16 | BHR,.. 17 | BGD,1.93942 18 | BRB,5.32773 19 | BLR,4.51672 20 | BEL,6.39845 21 | BLZ,6.10014 22 | BEN,4.5508 23 | BMU,2.61519 24 | BTN,4.62672 25 | BOL,8.0812 26 | BIH,.. 27 | BWA,9.63293 28 | BRA,5.47154 29 | BRN,.. 30 | BGR,4.43296 31 | BFA,.. 32 | BDI,6.31979 33 | CPV,5.30896 34 | KHM,.. 35 | CMR,3.46009 36 | CAN,4.87313 37 | CYM,.. 38 | CAF,1.30356 39 | TCD,2.31174 40 | CHI,.. 41 | CHL,4.24129 42 | CHN,.. 43 | COL,4.74743 44 | COM,.. 45 | ZAR,.. 46 | COG,.. 47 | CRI,.. 48 | CIV,.. 49 | HRV,4.38925 50 | CUB,13.12516 51 | CUW,.. 52 | CYP,7.30415 53 | CZE,4.18041 54 | DNK,8.48796 55 | DJI,.. 56 | DMA,.. 57 | DOM,.. 58 | ECU,.. 59 | EGY,.. 60 | SLV,3.96954 61 | GNQ,.. 62 | ERI,.. 63 | EST,5.95738 64 | ETH,4.42207 65 | FRO,.. 66 | FJI,4.49848 67 | FIN,6.48518 68 | FRA,5.73849 69 | PYF,.. 70 | GAB,.. 71 | GMB,3.07235 72 | GEO,3.22238 73 | DEU,4.88767 74 | GHA,5.31744 75 | GRC,.. 76 | GRL,.. 77 | GRD,.. 78 | GUM,.. 79 | GTM,.. 80 | GIN,3.21608 81 | GNB,.. 82 | GUY,3.36104 83 | HTI,.. 84 | HND,.. 85 | HKG,4.39213 86 | HUN,5.01172 87 | ISL,7.3752 88 | IND,3.21076 89 | IDN,3.52513 90 | IRN,4.45065 91 | IRQ,.. 92 | IRL,6.20758 93 | IMY,.. 94 | ISR,5.52088 95 | ITA,4.53407 96 | JAM,6.19928 97 | JPN,.. 98 | JOR,.. 99 | KAZ,3.06072 100 | KEN,.. 101 | KIR,.. 102 | PRK,.. 103 | KOR,4.66983 104 | KSV,.. 105 | KWT,.. 106 | KGZ,6.23268 107 | LAO,.. 108 | LVA,3.91929 109 | LBN,1.77877 110 | LSO,.. 111 | LBR,.. 112 | LBY,.. 113 | LIE,.. 114 | LTU,19.25768 115 | LUX,.. 116 | MAC,2.56586 117 | MKD,.. 118 | MDG,3.17751 119 | MWI,.. 120 | MYS,5.97418 121 | MDV,7.0041 122 | MLI,4.39837 123 | MLT,5.44377 124 | MHL,.. 125 | MRT,.. 126 | MUS,3.15771 127 | MEX,5.21749 128 | FSM,.. 129 | MDA,9.50976 130 | MCO,1.32877 131 | MNG,5.14485 132 | MNE,.. 133 | MAR,5.3758 134 | MOZ,.. 135 | MMR,.. 136 | NAM,.. 137 | NPL,4.66376 138 | NLD,5.49481 139 | NCL,.. 140 | NZL,6.34519 141 | NIC,.. 142 | NER,4.52603 143 | NGA,.. 144 | MNP,.. 145 | NOR,7.09982 146 | OMN,4.18775 147 | PAK,2.59078 148 | PLW,.. 149 | PAN,.. 150 | PNG,.. 151 | PRY,.. 152 | PER,3.13037 153 | PHL,2.65295 154 | POL,5.02705 155 | PRT,5.56214 156 | PRI,.. 157 | QAT,3.41315 158 | ROM,4.23975 159 | RUS,.. 160 | RWA,.. 161 | WSM,.. 162 | SMR,.. 163 | STP,10.20764 164 | SAU,.. 165 | SEN,5.53451 166 | SRB,4.75188 167 | SYC,.. 168 | SLE,2.77814 169 | SGP,3.03184 170 | SXM,.. 171 | SVK,4.02503 172 | SVN,5.56859 173 | SLB,8.97326 174 | SOM,.. 175 | ZAF,5.24869 176 | SSD,.. 177 | ESP,4.86779 178 | LKA,2.0559 179 | KNA,.. 180 | LCA,3.76522 181 | MAF,.. 182 | VCT,5.68978 183 | SDN,2.21868 184 | SUR,.. 185 | SWZ,7.0592 186 | SWE,6.85779 187 | CHE,5.05967 188 | SYR,5.13014 189 | TJK,4.09831 190 | TZA,4.04365 191 | THA,4.12553 192 | TMP,11.46614 193 | TGO,4.11576 194 | TON,.. 195 | TTO,.. 196 | TUN,6.52848 197 | TUR,.. 198 | TKM,.. 199 | TCA,.. 200 | TUV,.. 201 | UGA,3.25429 202 | UKR,7.31088 203 | ARE,.. 204 | GBR,5.3124 205 | USA,5.24958 206 | URY,.. 207 | UZB,.. 208 | VUT,5.01287 209 | VEN,6.87468 210 | VNM,.. 211 | VIR,.. 212 | WBG,.. 213 | YEM,.. 214 | ZMB,.. 215 | ZWE,.. 216 | , 217 | , 218 | , 219 | , 220 | , 221 | -------------------------------------------------------------------------------- /graphs/download_time.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gedankenstuecke/scihub_analysis/505bc634a419528b62ac0c2dc9d159f5eed38c1d/graphs/download_time.pdf -------------------------------------------------------------------------------- /graphs/download_time.png: 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-------------------------------------------------------------------------------- /mapping_countrynames_iso_3166.csv: -------------------------------------------------------------------------------- 1 | Country Name SciHub ISO 3166-1 alpha-3 2 | Afghanistan AFG 3 | Åland Islands ALA 4 | Albania ALB 5 | Algeria DZA 6 | Andorra AND 7 | Angola AGO 8 | Anguilla AIA 9 | Antigua and Barbuda ATG 10 | Argentina ARG 11 | Armenia ARM 12 | Aruba ABW 13 | Australia AUS 14 | Austria AUT 15 | Azerbaijan AZE 16 | Bahrain BHR 17 | Bangladesh BGD 18 | Barbados BRB 19 | Belarus BLR 20 | Belgium BEL 21 | Belize BLZ 22 | Benin BEN 23 | Bermuda BMU 24 | Bhutan BTN 25 | Bolivia BOL 26 | Bosnia and Herzegovina BIH 27 | Botswana BWA 28 | Brazil BRA 29 | British Virgin Islands VGB 30 | Brunei BRN 31 | Bulgaria BGR 32 | Burkina Faso BFA 33 | Burundi BDI 34 | Cambodia KHM 35 | Cameroon CMR 36 | Canada CAN 37 | Cape Verde CPV 38 | Caribbean Netherlands BES 39 | Cayman Islands CYM 40 | Chad TCD 41 | Chile CHL 42 | China CHN 43 | Collectivity of Saint Martin MAF 44 | Colombia COL 45 | Comoros COM 46 | Costa Rica CRI 47 | Côte d'Ivoire CIV 48 | Croatia HRV 49 | Cuba CUB 50 | Curaçao CUW 51 | Cyprus CYP 52 | Czech Republic CZE 53 | Democratic Republic of the Congo COD 54 | Denmark DNK 55 | Djibouti DJI 56 | Dominica DMA 57 | Dominican Republic DOM 58 | Ecuador ECU 59 | Egypt EGY 60 | El Salvador SLV 61 | Equatorial Guinea GNQ 62 | Estonia EST 63 | Ethiopia ETH 64 | Falkland Islands (Islas Malvinas) FLK 65 | Faroe Islands FRO 66 | Federated States of Micronesia FSM 67 | Fiji FJI 68 | Finland FIN 69 | France FRA 70 | French Guiana GUF 71 | French Polynesia PYF 72 | Gabon GAB 73 | Georgia GEO 74 | Germany DEU 75 | Ghana GHA 76 | Gibraltar GIB 77 | Greece GRC 78 | Greenland GRL 79 | Grenada GRD 80 | Guadeloupe GLP 81 | Guam GUM 82 | Guatemala GTM 83 | Guernsey GGY 84 | Guinea GIN 85 | Guinea-Bissau GNB 86 | Guyana GUY 87 | Honduras HND 88 | Hong Kong HKG 89 | Hungary HUN 90 | Iceland ISL 91 | India IND 92 | Indonesia IDN 93 | Iran IRN 94 | Iraq IRQ 95 | Ireland IRL 96 | Isle of Man IMN 97 | Israel ISR 98 | Italy ITA 99 | Jamaica JAM 100 | Japan JPN 101 | Jersey JEY 102 | Jordan JOR 103 | Kazakhstan KAZ 104 | Kenya KEN 105 | Kuwait KWT 106 | Kyrgyzstan KGZ 107 | Laos LAO 108 | Latvia LVA 109 | Lebanon LBN 110 | Lesotho LSO 111 | Liberia LBR 112 | Libya LBY 113 | Liechtenstein LIE 114 | Lithuania LTU 115 | Luxembourg LUX 116 | Macau MAC 117 | Macedonia (FYROM) MKD 118 | Madagascar MDG 119 | Malawi MWI 120 | Malaysia MYS 121 | Maldives MDV 122 | Mali MLI 123 | Malta MLT 124 | Martinique MTQ 125 | Mauritania MRT 126 | Mauritius MUS 127 | Mayotte MYT 128 | Mexico MEX 129 | Moldova MDA 130 | Monaco MCO 131 | Mongolia MNG 132 | Montenegro MNE 133 | Morocco MAR 134 | Mozambique MOZ 135 | Myanmar (Burma) MMR 136 | N/A 137 | Namibia NAM 138 | Nepal NPL 139 | Netherlands NLD 140 | New Caledonia NCL 141 | New Zealand NZL 142 | Nicaragua NIC 143 | Niger NER 144 | Nigeria NGA 145 | Northern Mariana Islands MNP 146 | Norway NOR 147 | Oman OMN 148 | Pakistan PAK 149 | Panama PAN 150 | Papua New Guinea PNG 151 | Paraguay PRY 152 | Peru PER 153 | Philippines PHL 154 | Poland POL 155 | Portugal PRT 156 | Puerto Rico PRI 157 | Qatar QAT 158 | Republic of the Congo COG 159 | Reunion REU 160 | Romania ROU 161 | Russia RUS 162 | Rwanda RWA 163 | Saint Kitts and Nevis KNA 164 | Saint Lucia LCA 165 | Saint Vincent and the Grenadines VCT 166 | Saint-Barthélemy BLM 167 | Samoa WSM 168 | San Marino SMR 169 | São Tomé and Príncipe STP 170 | Saudi Arabia SAU 171 | Senegal SEN 172 | Serbia SRB 173 | Seychelles SYC 174 | Sierra Leone SLE 175 | Singapore SGP 176 | Sint Maarten SXM 177 | Slovakia SVK 178 | Slovenia SVN 179 | Solomon Islands SLB 180 | Somalia SOM 181 | South Africa ZAF 182 | South Korea KOR 183 | South Sudan SSD 184 | Spain ESP 185 | Sri Lanka LKA 186 | Sudan SDN 187 | Suriname SUR 188 | Swaziland SWZ 189 | Sweden SWE 190 | Switzerland CHE 191 | Syria SYR 192 | Taiwan TWN 193 | Tajikistan TJK 194 | Tanzania TZA 195 | Thailand THA 196 | The Bahamas BHS 197 | The Gambia GMB 198 | Timor-Leste TLS 199 | Togo TGO 200 | Trinidad and Tobago TTO 201 | Tunisia TUN 202 | Turkey TUR 203 | Turkmenistan TKM 204 | U.S. Virgin Islands VIR 205 | Uganda UGA 206 | Ukraine UKR 207 | United Arab Emirates ARE 208 | United Kingdom GBR 209 | United States USA 210 | Uruguay URY 211 | Uzbekistan UZB 212 | Vanuatu VUT 213 | Venezuela VEN 214 | Vietnam VNM 215 | Yemen YEM 216 | Zambia ZMB 217 | Zimbabwe ZWE 218 | -------------------------------------------------------------------------------- /population_analysis.R: -------------------------------------------------------------------------------- 1 | library(ggplot2) 2 | library(ggrepel) 3 | library(ggthemes) 4 | 5 | # now revised for the better mapping and world bank data 6 | 7 | # read scihub aggregate data 8 | sh <- read.csv(file="downloads_country.csv",sep="\t",stringsAsFactors=FALSE) 9 | sh$Country <- trimws(sh$Country) 10 | 11 | # read country mapping 12 | mapping <- read.csv(file="mapping_countrynames_iso_3166.csv",sep="\t",stringsAsFactors=FALSE) 13 | mapping$ISO.3166.1.alpha.3 <- trimws(mapping$ISO.3166.1.alpha.3) 14 | 15 | # read worldbank data 16 | wb <- read.csv(file="worldbank_2014_data.csv",sep=",",stringsAsFactors=FALSE) 17 | 18 | # merge all 3 19 | sh <- merge(sh,mapping,by.x=c("Country"),by.y=c("Country.Name.SciHub")) 20 | sh <- merge(sh,wb,by.x=c("ISO.3166.1.alpha.3"),by.y=c("Country.Code")) 21 | sh$Downloads.per.capita <- sh$Downloads/sh$Total.Population 22 | sh$Downloads.per.1000capita <- sh$Downloads.per.capita * 1000 23 | # general plot 24 | 25 | p <- ggplot(sh,aes(x=sh$Total.Population,y=sh$Downloads,label=sh$Country)) + stat_smooth(method="glm")+ geom_point(color="red") + geom_text_repel() + scale_x_log10("Population Size (log10)") + scale_y_log10("# of Downloads (log10)") + theme_minimal() + ggtitle("# of Sci-Hub Downloads / 2014 Population Size (data from worldbank.org)") 26 | ggsave("graphs/downloads_per_population.pdf", width = 594, height = 210, units = "mm") 27 | ggsave("graphs/downloads_per_population.png", width = 594, height = 210, units = "mm") 28 | 29 | # residuals 30 | 31 | sh$residuals_population <- residuals(glm(sh$Downloads ~ sh$Total.Population)) 32 | p <- ggplot(sh,aes(x = reorder(Country, -residuals_population), y=residuals_population,fill=Country)) + geom_bar(stat="identity") + theme_minimal() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_y_continuous("Residuals (difference observed downloads from predicted downloads)") + scale_fill_discrete(guide = FALSE) + scale_x_discrete("Countries, sorted by ∆ in residuals") 33 | 34 | ggsave("graphs/downloads_per_population_residuals.pdf", width = 594, height = 220, units = "mm") 35 | ggsave("graphs/downloads_per_population_residuals.png", width = 594, height = 220, units = "mm") 36 | 37 | # sorted by downloads / 1.000 inhabitants 38 | 39 | p <- ggplot(sh,aes(x=reorder(Country, -Downloads.per.capita),y=Downloads.per.capita)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_x_discrete("Country, sorted by Rank") + scale_y_continuous("Downloads / Inhabitants") 40 | 41 | ggsave("graphs/downloads_per_population_ranked.pdf", width = 594, height = 220, units = "mm") 42 | ggsave("graphs/downloads_per_population_ranked.png", width = 594, height = 220, units = "mm") 43 | 44 | # correlation downloads per 1000 capita / life expectancy 45 | 46 | p <- ggplot(sh,aes(x=Life.Expectancy.at.Birth,y=Downloads.per.capita,label=Country)) + stat_smooth(method="glm") + geom_point(color="red") + geom_text_repel() + scale_x_log10("Life Expectancy at Birth") + scale_y_log10("Downloads per 1000 Capita") + theme_minimal() + ggtitle("# of Sci-Hub Downloads per 1000 Capita / Life Expectancy at Birth (data from World Bank)") 47 | ggsave("graphs/normalized_downloads_life_expectancy.pdf", width = 594, height = 220, units = "mm") 48 | ggsave("graphs/normalized_downloads_life_expectancy.png", width = 594, height = 220, units = "mm") 49 | 50 | # correlation downloads per 1000 capita / internet users amongst 100 people 51 | 52 | p <- ggplot(sh,aes(x=Internet.Users..per.100.People.,y=Downloads.per.capita,label=Country)) + stat_smooth(method="glm") + geom_point(color="red") + geom_text_repel() + scale_x_log10("Internet Users per 100 People") + scale_y_log10("Downloads per 1000 Capita") + theme_minimal() + ggtitle("# of Sci-Hub Downloads per 1000 Capita / Internet Users per 100 People (data from World Bank)") 53 | ggsave("graphs/normalized_downloads_internet_users.pdf", width = 594, height = 220, units = "mm") 54 | ggsave("graphs/normalized_downloads_internet_users.png", width = 594, height = 220, units = "mm") 55 | 56 | # correlation downloads per 1000 capita / % unemployed workforce 57 | p <- ggplot(sh,aes(x=Total.Unemployment..,y=Downloads.per.capita,label=Country)) + stat_smooth(method="glm") + geom_point(color="red") + geom_text_repel() + scale_x_log10("Percent Unemployed Workforce") + scale_y_log10("Downloads per 1000 Capita") + theme_minimal() + ggtitle("# of Sci-Hub Downloads per 1000 Capita / Percent Unemployed Workforce (data from World Bank)") 58 | ggsave("graphs/normalized_downloads_unemployment.pdf", width = 594, height = 220, units = "mm") 59 | ggsave("graphs/normalized_downloads_unemployment.png", width = 594, height = 220, units = "mm") 60 | 61 | # correlation downloads per 1000 capita / GDP per Capita 62 | p <- ggplot(sh,aes(x=GDP.per.capita..current.USD.,y=Downloads.per.capita,label=Country)) + stat_smooth(method="glm") + geom_point(color="red") + geom_text_repel() + scale_x_log10("GDP per Capita") + scale_y_log10("Downloads per 1000 Capita") + theme_minimal() + ggtitle("# of Sci-Hub Downloads per 1000 Capita / GDP per Capita (data from World Bank)") 63 | ggsave("graphs/normalized_downloads_gdp.pdf", width = 594, height = 220, units = "mm") 64 | ggsave("graphs/normalized_downloads_gdp.png", width = 594, height = 220, units = "mm") 65 | 66 | # internet users / GDP 67 | p <- ggplot(sh,aes(x=GDP.per.capita..current.USD.,y=Internet.Users..per.100.People.,label=Country)) + stat_smooth(method="glm") + geom_point(color="red") + geom_text_repel() + scale_x_log10("GDP per Capita") + scale_y_log10("Internet Users per 100 Capita") + theme_minimal() + ggtitle("# of Internet Users amongst 100 People / GDP per Capita (data from World Bank)") 68 | ggsave("graphs/gdp_vs_internet.pdf", width = 594, height = 220, units = "mm") 69 | ggsave("graphs/gdp_vs_internet.png", width = 594, height = 220, units = "mm") 70 | 71 | # % GDP spent on education vs. downloads per 1000 capita normalized for GDP 72 | edu <- read.csv(file="expediture_education_percent_education2009.csv",sep=",",stringsAsFactor=FALSE) 73 | sh <- merge(sh,edu,by.x=c("ISO.3166.1.alpha.3"),by.y="Country.Code") 74 | 75 | sh$downloads_per_pop_per_gdp <- sh$Downloads.per.1000capita / sh$GDP.per.capita..current.USD. 76 | sh$education_expediture_percent_gdp <- as.numeric(sh$X2009..YR2009.) 77 | sh$X2009..YR2009. <- NULL 78 | 79 | p <- ggplot(sh,aes(x=education_expediture_percent_gdp,y=downloads_per_pop_per_gdp,label=Country)) + stat_smooth(method="glm") + geom_point(color="red") + geom_text_repel() + scale_x_log10("% GDP spent on Education") + scale_y_log10("downloads per 1000 capita, corrected for GDP size") + theme_minimal() 80 | ggsave("graphs/normalized_downloads_percent_gdp_education.pdf", width = 594, height = 220, units = "mm") 81 | ggsave("graphs/normalized_downloads_percent_gdp_education.png", width = 594, height = 220, units = "mm") 82 | -------------------------------------------------------------------------------- /uni-stats-date.tab: -------------------------------------------------------------------------------- 1 | 2015-09-01 6.86 2 | 2015-09-02 7.14 3 | 2015-09-03 6.99 4 | 2015-09-04 6.62 5 | 2015-09-05 3.69 6 | 2015-09-06 3.58 7 | 2015-09-07 6.69 8 | 2015-09-08 6.93 9 | 2015-09-09 7.84 10 | 2015-09-10 7.75 11 | 2015-09-11 6.6 12 | 2015-09-12 4.43 13 | 2015-09-13 4.03 14 | 2015-09-14 7.01 15 | 2015-09-15 8.45 16 | 2015-09-16 8.29 17 | 2015-09-17 9.05 18 | 2015-09-18 8.45 19 | 2015-09-19 5 20 | 2015-09-20 4.48 21 | 2015-09-21 7.44 22 | 2015-09-22 8.83 23 | 2015-09-23 9.2 24 | 2015-09-24 8.83 25 | 2015-09-25 9.04 26 | 2015-09-26 5.46 27 | 2015-09-27 4.92 28 | 2015-09-28 7.82 29 | 2015-09-29 8.78 30 | 2015-09-30 8.63 31 | 2015-11-01 4.72 32 | 2015-11-02 8.26 33 | 2015-11-03 8.7 34 | 2015-11-04 9.98 35 | 2015-11-22 4.03 36 | 2015-11-23 8.12 37 | 2015-11-24 8.88 38 | 2015-11-25 8.89 39 | 2015-11-26 7.59 40 | 2015-11-27 6.98 41 | 2015-11-28 4.12 42 | 2015-11-29 3.92 43 | 2015-11-30 8.34 44 | 2015-12-01 8.8 45 | 2015-12-02 9.31 46 | 2015-12-03 9.33 47 | 2015-12-04 8.26 48 | 2015-12-05 5.11 49 | 2015-12-06 4.06 50 | 2015-12-07 7.68 51 | 2015-12-08 7.62 52 | 2015-12-09 8.4 53 | 2015-12-10 9.29 54 | 2015-12-11 7.98 55 | 2015-12-12 4.57 56 | 2015-12-13 4.49 57 | 2015-12-14 8.14 58 | 2015-12-15 8.11 59 | 2015-12-16 7.22 60 | 2015-12-17 7.53 61 | 2015-12-18 7.71 62 | 2015-12-19 4.33 63 | 2015-12-20 4.41 64 | 2015-12-21 7.5 65 | 2015-12-22 5.85 66 | 2015-12-23 3.11 67 | 2015-12-24 4.31 68 | 2015-12-25 3.41 69 | 2015-12-26 4.63 70 | 2015-12-28 5.31 71 | 2015-12-29 5.25 72 | 2015-12-30 5.87 73 | 2015-12-31 4.97 74 | 2016-01-01 2.51 75 | 2016-01-02 3.87 76 | 2016-01-03 3.94 77 | 2016-01-04 7.53 78 | 2016-01-05 7.69 79 | 2016-01-06 6.55 80 | 2016-01-07 7.15 81 | 2016-01-08 7.19 82 | 2016-01-09 4.37 83 | 2016-01-10 3.39 84 | 2016-01-11 7.83 85 | 2016-01-12 8 86 | 2016-01-13 8.65 87 | 2016-01-14 8.29 88 | 2016-01-15 10.26 89 | 2016-01-16 6.96 90 | 2016-01-17 5.98 91 | 2016-01-18 9.22 92 | 2016-01-19 8.44 93 | 2016-01-20 8.51 94 | 2016-01-21 8.06 95 | 2016-01-22 8.21 96 | 2016-01-23 4.28 97 | 2016-01-24 3.88 98 | 2016-01-25 7.87 99 | 2016-01-26 8.6 100 | 2016-01-27 7.93 101 | 2016-01-28 7.41 102 | 2016-01-29 7.01 103 | 2016-01-30 3.92 104 | 2016-01-31 3.25 105 | 2016-02-01 6.97 106 | 2016-02-02 7.45 107 | 2016-02-03 6.99 108 | 2016-02-04 7.04 109 | 2016-02-05 6.71 110 | 2016-02-06 3.14 111 | 2016-02-07 2.87 112 | 2016-02-08 5.66 113 | 2016-02-09 6.68 114 | 2016-02-10 7.34 115 | 2016-02-11 7.69 116 | 2016-02-12 7.43 117 | 2016-02-13 3.16 118 | 2016-02-14 2.95 119 | 2016-02-15 7.63 120 | 2016-02-16 8.28 121 | 2016-02-17 7.95 122 | 2016-02-18 8.17 123 | 2016-02-19 7.8 124 | 2016-02-20 3.97 125 | 2016-02-21 3.93 126 | 2016-02-22 8.33 127 | 2016-02-23 8.79 128 | 2016-02-24 8.67 129 | 2016-02-25 8.72 130 | 2016-02-26 8.11 131 | 2016-02-27 4.71 132 | 2016-02-28 3.85 133 | 2016-02-29 8.81 134 | 2016-03-01 0 135 | 2015-10-01 8.76 136 | 2015-10-02 8.81 137 | 2015-10-03 4.83 138 | 2015-10-04 4.28 139 | 2015-10-05 8.39 140 | 2015-10-06 7.81 141 | 2015-10-07 8.77 142 | 2015-10-08 8.69 143 | 2015-10-09 8.56 144 | 2015-10-10 5.68 145 | 2015-10-11 5 146 | 2015-10-12 7.15 147 | 2015-10-13 8.79 148 | 2015-10-14 8.42 149 | 2015-10-15 9.14 150 | 2015-10-16 7.84 151 | 2015-10-17 5.2 152 | 2015-10-18 4.44 153 | 2015-10-19 8.71 154 | 2015-10-20 9.82 155 | 2015-10-21 9.17 156 | 2015-10-22 9.33 157 | 2015-10-23 9 158 | 2015-10-24 4.86 159 | 2015-10-25 4.64 160 | 2015-10-26 8.78 161 | 2015-10-27 9.86 162 | 2015-10-28 9.61 163 | 2015-10-29 9.2 164 | 2015-10-30 8.47 165 | -------------------------------------------------------------------------------- /university_analysis.R: -------------------------------------------------------------------------------- 1 | library(ggplot2) 2 | library(ggrepel) 3 | library(ggthemes) 4 | 5 | # read in the data needed to plot nicely 6 | unistats <- read.csv(file="uni-stats-date.tab",sep="\t",head=F) 7 | unistats$V1 <- as.Date(unistats$V1) 8 | unistats$weekday <- weekdays(unistats$V1) 9 | unistats$weekday <- factor(unistats$weekday, levels = c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday")) 10 | 11 | p <- ggplot(unistats,aes(x=V1,y=V2)) + geom_point(aes(color=weekday)) + stat_smooth(method="glm") + geom_vline(xintercept=as.numeric(as.Date("2015-12-24"))) + geom_text(aes(x=as.Date("2015-12-20"),y=2.5,label="Christmas Eve")) + theme_minimal() + scale_x_date("Date") + scale_y_continuous("% Downloads from Universities") + ggtitle("% of Sci-Hub Downloads from Universities") 12 | 13 | ggsave("graphs/university_downloads_vs_time.pdf", width = 594, height = 220, units = "mm") 14 | ggsave("graphs/university_downloads_vs_time.png", width = 594, height = 220, units = "mm") 15 | 16 | p <- ggplot(unistats,aes(x=unistats$weekday,y=unistats$V2,fill=unistats$weekday)) + geom_boxplot() + geom_jitter(alpha=0.2) + scale_x_discrete("Weekday") + scale_y_continuous("% Downloads from Universities") + scale_fill_discrete(guide=FALSE) + theme_minimal() + ggtitle("% of Sci-Hub Downloads from Universities per Weekday") 17 | 18 | ggsave("graphs/university_downloads_per_weekday.pdf", width = 594, height = 220, units = "mm") 19 | ggsave("graphs/university_downloads_per_weekday.png", width = 594, height = 220, units = "mm") 20 | 21 | 22 | # let's read the country-wise data 23 | uni_country <- read.csv(file="uni-stats-country.tab",sep="\t",head=F) 24 | uni_ag <- aggregate(uni_country$V3,by=list(uni_country$V2),FUN=mean) 25 | 26 | # read scihub aggregate data 27 | sh <- read.csv(file="downloads_country.csv",sep="\t",stringsAsFactors=FALSE) 28 | sh$Country <- trimws(sh$Country) 29 | mapping <- read.csv(file="mapping_countrynames_iso_3166.csv",sep="\t",stringsAsFactors=FALSE) 30 | mapping$ISO.3166.1.alpha.3 <- trimws(mapping$ISO.3166.1.alpha.3) 31 | wb <- read.csv(file="worldbank_2014_data.csv",sep=",",stringsAsFactors=FALSE) 32 | sh <- merge(sh,mapping,by.x=c("Country"),by.y=c("Country.Name.SciHub")) 33 | sh <- merge(sh,wb,by.x=c("ISO.3166.1.alpha.3"),by.y=c("Country.Code")) 34 | sh$Downloads.per.Internet.Users..per.100.People. <- sh$Downloads / (sh$Total.Population / sh$Internet.Users..per.100.People.) 35 | enrollment <- read.csv(file="API_SE.TER.ENRR_DS2_en_csv_v2.2012.csv",sep=",",head=T) 36 | shc <- merge(sh,uni_ag,by.x=c("Country"),by.y=c("Group.1")) 37 | shc <- merge(shc,enrollment,by.x=c("ISO.3166.1.alpha.3"),by.y=c("Country.Code")) 38 | shc$enrollment <- shc$X2012 39 | print(colnames(shc)) 40 | 41 | spending <- read.csv(file="API_GB.XPD.RSDV.GD.ZS_DS2_en_csv_v2.2011.csv",sep=",",head=T) 42 | shc <- merge(shc,spending,by.x=c("ISO.3166.1.alpha.3"),by.y=c("Country.Code")) 43 | shc$research_spending <- shc$X2011 44 | 45 | shc_sub <- subset(shc,shc$x > 0) 46 | 47 | p <- ggplot(shc_sub,aes(x=as.numeric(as.character(enrollment)),y=x,label=Country)) + stat_smooth(method="glm") + geom_point(aes(size=Downloads),color="red") + geom_text_repel() + scale_x_log10("Gross enrolment ratio, tertiary, both sexes (%)") + scale_y_continuous("% Downloads from universities") + theme_minimal() + ggtitle("% of Sci-Hub Downloads by Gross Enrolment Ratio (tertiary, both sexes)") 48 | ggsave("graphs/university_downloads_per_enrolment.pdf", width = 594, height = 220, units = "mm") 49 | ggsave("graphs/university_downloads_per_enrolment.png", width = 594, height = 220, units = "mm") 50 | 51 | p <- ggplot(shc_sub,aes(x=as.numeric(as.character(research_spending)),y=x,label=Country)) + stat_smooth(method="glm") + geom_point(aes(size=Downloads),color="red") + geom_text_repel() + scale_x_log10("% GDP spent on Research") + scale_y_continuous("% Downloads from universities") + theme_minimal() + ggtitle("% of Sci-Hub Downloads from Universities by % GDP spent on Research/Development") 52 | ggsave("graphs/university_downloads_per_researchspending.pdf", width = 594, height = 220, units = "mm") 53 | ggsave("graphs/university_downloads_per_researchspending.png", width = 594, height = 220, units = "mm") 54 | 55 | p <- ggplot(shc_sub,aes(x= reorder(Country, -x),y=x,fill=Country)) + geom_bar(stat="identity")+ theme_minimal() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_fill_discrete(guide = FALSE) + scale_y_continuous("% Downloads from Universities") + scale_x_discrete("Countries by % Downloads from Universities") 56 | ggsave("graphs/university_downloads_ranking.pdf", width = 594, height = 220, units = "mm") 57 | ggsave("graphs/university_downloads_ranking.png", width = 594, height = 220, units = "mm") 58 | 59 | countries <- read.csv(file="uni-stats-country.replaced.tab",sep="\t",head=F) 60 | countries$date <- as.Date(as.character(countries$V1)) 61 | csub <- subset(countries,as.character(countries$V2) == "Greece" | as.character(countries$V2) == "Germany" | as.character(countries$V2) == "France"| as.character(countries$V2) == "Spain"| as.character(countries$V2) == "Italy"| as.character(countries$V2) =="Belgium"| as.character(countries$V2) =="United Kingdom"| as.character(countries$V2) =="Sweden"| as.character(countries$V2) =="Iran"| as.character(countries$V2) =="Netherlands"| as.character(countries$V2) =="Poland"| as.character(countries$V2) =="Czech Republic"| as.character(countries$V2) =="Austria"| as.character(countries$V2) =="Switzerland"| as.character(countries$V2) =="Israel"| as.character(countries$V2) =="China"| as.character(countries$V2) == "United States") 62 | p <- ggplot(csub,aes(x=date,y=V3,color=V2)) + geom_line() + scale_color_discrete(guide=FALSE) + theme_minimal() + scale_x_date("Date") + scale_y_continuous("% Academic Downloads") + geom_text_repel(data = subset(csub, csub$date == min(date)|csub$date == max(date)),aes(label=V2)) 63 | ggsave("graphs/university_downloads_country_per_time.pdf", width = 594, height = 220, units = "mm") 64 | ggsave("graphs/university_downloads_country_per_time.png", width = 594, height = 220, units = "mm") 65 | -------------------------------------------------------------------------------- /worldbank_2014_data.csv: -------------------------------------------------------------------------------- 1 | Country Code,GDP per capita (current USD),Total Population,Total Unemployment %,Life Expectancy at Birth,Internet Users (per 100 People) 2 | AFG,633.5692469519,31627506,9.1000003815,60.3744634146,6.39 3 | ALB,4564.3903386929,2894475,16.1000003815,77.8304634146,60.1 4 | DZA,5484.0668056148,38934334,9.5,74.808097561,18.09 5 | ASM,,55434,,, 6 | ADO,,72786,,,95.9 7 | AGO,,24227524,6.8000001907,52.2668780488,21.26 8 | ATG,13432.0793301552,90900,,75.9376341463,64 9 | ARG,12509.5311180615,42980026,8.1999998093,76.1586097561,64.7 10 | ARM,3873.5335658068,3006154,17.1000003815,74.6757073171,46.3 11 | ABW,,103441,,75.451097561,83.78 12 | AUS,61979.8962947626,23470118,6,82.2512195122,84.56 13 | AUT,51122.4253136062,8545908,5,81.3365853659,81 14 | AZE,7886.4591436727,9535079,5.1999998093,70.7632195122,61 15 | BHS,22217.4941392075,383054,15.3999996185,75.2336585366,76.92 16 | BHR,24855.2156350086,1361930,3.9000000954,76.6832682927,90.99998 17 | BGD,1086.8070785352,159077513,4.3000001907,71.625902439,9.6 18 | BRB,15366.2926106288,283380,12,75.4964146341,76.67 19 | BLR,8040.0475569713,9470000,5.9000000954,72.9756097561,59.02 20 | BEL,47327.6204608156,11231213,8.5,80.587804878,85 21 | BLZ,4831.1775518618,351706,11.5,70.0774390244,38.7 22 | BEN,903.4649240077,10598482,1,59.5105853659,5.3 23 | BMU,,65181,,80.7973170732,96.8 24 | BTN,2560.5011541718,765008,2.7999999523,69.4712439024,34.37 25 | BOL,3124.0807621235,10561887,2.7000000477,68.344,39.02 26 | BIH,4851.6603501811,3817554,27.8999996185,76.4332439024,60.8 27 | BWA,7123.3392413015,2219937,18.2000007629,64.4292439024,18.5 28 | BRA,11726.8058803488,206077898,6.8000001907,74.4018780488,57.6 29 | BRN,40979.6419433378,417394,3.7999999523,78.8095853659,68.77 30 | BGR,7851.2654280423,7223938,11.6000003815,75.4073170732,55.49 31 | BFA,713.0638896588,17589198,3.0999999046,58.5884634146,9.4 32 | BDI,286.002335873,10816860,6.9000000954,56.6920243902,1.38 33 | KHM,1094.576687779,15328136,0.400000006,68.2122926829,9 34 | CMR,1407.4034132223,22773014,4.3000001907,55.4927560976,11 35 | CAN,50230.8076901423,35543658,6.9000000954,81.9566097561,87.12 36 | CPV,3641.1076558658,513906,9.1999998093,73.147,40.26 37 | CYM,,59172,,,74.1 38 | CAF,358.5378358576,4804316,7.4000000954,50.6577804878,4.03 39 | TCD,1024.6684517087,13587053,7,51.555804878,2.5 40 | CHI,,162917,,80.6064878049, 41 | CHL,14528.3258112673,17762647,6.4000000954,81.496195122,72.35 42 | CHN,7590.0164405436,1364270000,4.6999998093,75.7822682927,49.3 43 | COL,7903.9257731165,47791393,10.1000003815,73.9931463415,52.57 44 | COM,810.0757667622,769991,6.5,63.2568536585,6.98 45 | ZAR,442.3395393673,74877030,8,58.659195122,3 46 | COG,3147.0721606121,4504962,6.5,62.3111463415,7.11 47 | CRI,10415.4443817218,4757606,8.3000001907,79.4027073171,49.41 48 | CIV,1545.942249248,22157107,4,51.5595853659,14.6 49 | HRV,13475.2589621781,4238389,16.7000007629,77.3292682927,68.57 50 | CUB,,11379111,3.2999999523,79.3908292683,30 51 | CUW,,155909,,77.8243902439, 52 | CYP,27194.3918374826,1153658,15.6000003815,80.1315609756,69.33 53 | CZE,19502.4173306083,10525347,6.1999998093,78.2756097561,79.71 54 | DNK,60718.3926958809,5638530,6.5999999046,80.5487804878,95.99 55 | DJI,1813.5965663026,876174,,62.0155121951,10.71 56 | DMA,7244.4958470864,72341,,,62.86 57 | DOM,6163.5758566516,10405943,15,73.5000243902,49.58 58 | ECU,6345.8407250595,15902916,4.5999999046,75.8724878049,43 59 | EGY,3365.7074205748,89579670,13.1999998093,71.1217073171,31.7 60 | SLV,4119.9920231917,6107706,6.1999998093,72.7545609756,29.7 61 | GNQ,18918.2768313328,820885,7.9000000954,57.6470487805,18.86 62 | ERI,,5110444,7.1999998093,63.6630243902,0.99 63 | EST,20147.7782167553,1314545,7.6999998093,77.2390243902,84.24 64 | ETH,573.5659603461,96958732,5.1999998093,64.0350243902,2.9 65 | FRO,,48221,,81.6926829268,94.66 66 | FJI,5112.3221174052,886450,7.9000000954,70.0891219512,41.8 67 | FIN,49842.7130622895,5461512,8.6000003815,81.1292682927,92.38 68 | FRA,42725.7394894127,66217509,9.8999996185,82.3731707317,83.75 69 | PYF,,279781,,76.5416829268,60.68 70 | GAB,10772.0617537637,1687673,19.7000007629,64.3833902439,9.81 71 | GMB,441.2934114986,1928201,7,60.2284390244,15.56 72 | GEO,4435.1926985255,3727000,13.3999996185,74.6686341463,48.9 73 | DEU,47773.9441928693,80970732,5,80.843902439,86.19 74 | GHA,1441.6364531117,26786598,2.4000000954,61.3116341463,18.9 75 | GRC,21672.6717735205,10869637,26.2999992371,81.2853658537,63.21 76 | GRL,43364.8828499176,56295,,,66.7 77 | GRD,8573.6940673666,106349,,73.3663170732,37.38 78 | GUM,,167543,,79.1260243902,69.27 79 | GTM,3673.1358425127,16015494,2.9000000954,71.7224146341,23.4 80 | GIN,539.6157749888,12275527,1.7999999523,58.7334390244,1.72 81 | GNB,567.8226102976,1800513,6.9000000954,55.1600487805,3.32 82 | GUY,4053.9019037796,763893,11.1000003815,66.4064146341,37.35 83 | HTI,824.1597732045,10572029,6.8000001907,62.7474390244,11.4 84 | HND,2434.8271619068,7961680,3.9000000954,73.1357073171,19.08 85 | HKG,40169.5436383441,7241700,3.2000000477,83.9804878049,74.56 86 | HUN,14026.574374109,9863183,7.8000001907,75.8731707317,76.13 87 | ISL,52036.7318144531,327386,5,82.0609756098,98.16 88 | IND,1581.5107030877,1295291543,3.5999999046,68.013804878,18 89 | IDN,3491.9297173714,254454778,6.1999998093,68.8884878049,17.14 90 | IRN,5442.8747707604,78143644,12.8000001907,75.3893170732,39.35 91 | IRQ,6420.139809101,34812326,16.3999996185,69.3996829268,11.3 92 | IRL,54339.3175599219,4615693,11.6000003815,81.1536585366,79.69 93 | IMY,,87127,,, 94 | ISR,37206.1829418069,8215700,6.0999999046,82.1536585366,71.45 95 | ITA,35222.7606011111,60789140,12.5,82.6902439024,61.96 96 | JAM,5106.0774635319,2720554,13.1999998093,75.6535121951,40.5 97 | JPN,36194.4156134427,127131800,3.7000000477,83.587804878,90.58 98 | JOR,5422.5708755332,6607000,11.1000003815,74.0521463415,44 99 | KAZ,12601.6211150605,17289224,4.0999999046,71.62,54.89 100 | KEN,1358.2622185563,44863583,9.1999998093,61.5763658537,43.4 101 | KIR,1509.5211865705,110470,,65.9516829268,12.25 102 | PRK,,25026772,4.0999999046,70.0746829268,0 103 | KOR,27970.4951469293,50423955,3.5,82.1558536585,84.33 104 | KSV,4051.6483607707,1823149,,71.0975609756, 105 | KWT,43593.7020176514,3753121,3,74.5850243902,78.7 106 | KGZ,1268.8566035996,5835500,8.1000003815,70.4024390244,28.3 107 | LAO,1793.4704941761,6689300,1.3999999762,66.1173658537,14.26 108 | LVA,15692.1915611781,1993782,10,74.187804878,75.83 109 | LBN,10057.8883563669,4546774,6.4000000954,79.373097561,74.7 110 | LSO,1034.1852875122,2109197,26.2000007629,49.7005853659,11 111 | LBR,457.8585865203,4396554,3.7999999523,60.8344146341,5.41 112 | LBY,6573.386737262,6258984,19.2000007629,71.7161219512,17.76 113 | LIE,,37286,,82.2609756098,95.21 114 | LTU,16489.7289835331,2932367,11.3000001907,73.9658536585,72.13 115 | LUX,116612.884151875,556319,6.0999999046,82.2073170732,94.67 116 | MAC,96038.0507240687,577914,1.5,80.553097561,69.78 117 | MKD,5455.5948890016,2075625,27.8999996185,75.3423902439,68.06 118 | MDG,449.4008297456,23571713,3.5999999046,65.0856097561,3.7 119 | MWI,255.0445695732,16695253,7.5,62.7216341463,5.83 120 | MYS,11307.064952828,29901997,2,74.7182926829,67.5 121 | MDV,7635.4841513313,401000,11.6000003815,76.7728292683,49.28 122 | MLI,704.5074400243,17086022,8.1000003815,57.9862682927,7 123 | MLT,,427364,5.9000000954,81.7463414634,73.17 124 | MHL,3529.7483033974,52898,,,16.8 125 | MRT,1274.9769489686,3969625,31,63.0165853659,10.7 126 | MUS,10016.6486405725,1260934,7.6999998093,74.1943902439,41.44 127 | MEX,10325.6460658759,125385833,4.9000000954,76.7218536585,44.39 128 | FSM,3057.0910247179,104044,,69.1010731707,29.65 129 | MDA,2238.9017737728,3556397,3.4000000954,71.4558780488,46.6 130 | MCO,,37623,,,92.4 131 | MNG,4129.3735483622,2909871,4.8000001907,69.463902439,27 132 | MNE,7378.3452890295,621810,19.1000003815,76.1807073171,61 133 | MAR,3190.3104441402,33921203,10.1999998093,74.016097561,56.8 134 | MOZ,585.6226826367,27216276,22.6000003815,55.0259512195,5.94 135 | MMR,1203.8446629383,53437159,3.2999999523,65.8578536585,2.1 136 | NAM,5408.2434909387,2402858,18.6000003815,64.680195122,14.84 137 | NPL,701.6800633995,28174724,2.7000000477,69.6046829268,15.44 138 | NLD,52138.6839244096,16865008,6.9000000954,81.3048780488,93.17 139 | NCL,,266000,,77.5731707317,70 140 | NZL,44342.1644241792,4509700,5.5999999046,81.4048780488,85.5 141 | NIC,1963.0548840336,6013913,5.3000001907,74.8101463415,17.6 142 | NER,427.3732403154,19113728,5.0999999046,61.4584878049,1.95 143 | NGA,3203.2968244943,177475986,7.5,52.7542682927,42.68 144 | MNP,,54541,,, 145 | NOR,97299.636068076,5136886,3.4000000954,81.7512195122,96.3 146 | OMN,19309.6124499023,4236057,7.1999998093,77.085097561,70.22 147 | PAK,1316.6141096974,185044286,5.1999998093,66.1833658537,13.8 148 | PLW,11879.6778117315,21097,,, 149 | PAN,11948.8511416186,3867535,4.3000001907,77.5951463415,44.92 150 | PNG,2268.1587169891,7463577,2.5,62.6069268293,9.38 151 | PRY,4712.8233115125,6552518,4.5,72.9217073171,43 152 | PER,6541.0305636068,30973148,4.1999998093,74.5255365854,40.2 153 | PHL,2872.5121647166,99138690,7.0999999046,68.2656341463,39.69 154 | POL,14336.7977208633,38011735,9.1999998093,77.2536585366,66.6 155 | PRT,22124.3669649875,10401062,14.1999998093,80.7219512195,64.59 156 | PRI,,3548397,14.3000001907,79.3747317073,78.78 157 | QAT,96732.4025450739,2172065,0.3000000119,78.596804878,91.49 158 | ROM,10000.0026233174,19904360,7,75.0634146341,54.08 159 | RUS,12735.9184024927,143819569,5.0999999046,70.3658536585,70.52 160 | RWA,695.6892586009,11341544,0.6000000238,63.9656585366,10.6 161 | WSM,4172.2171003766,191845,,73.5118292683,21.2 162 | SMR,,31595,,, 163 | STP,1810.7215665104,186342,,66.3846097561,24.41 164 | SAU,24406.4764554706,30886545,5.5999999046,74.3372195122,63.7 165 | SEN,1067.131753327,14672557,10,66.3725853659,17.7 166 | SRB,6152.9206337474,7129366,22.2000007629,75.5341463415,53.5 167 | SYC,15564.641970499,91400,,73.2292682927,54.26 168 | SLE,765.9591972967,6315627,3.2999999523,50.8787804878,2.1 169 | SGP,56284.3314404291,5469724,3,82.6463414634,82 170 | SXM,,37685,,, 171 | SVK,18500.6646092218,5418649,13.3000001907,76.7146341463,79.98 172 | SVN,24001.9013862278,2061980,9.5,80.5195121951,71.59 173 | SLB,2024.1904146843,572171,3.9000000954,67.930804878,9 174 | SOM,542.6158839557,10517569,6.9000000954,55.354804878,1.63 175 | ZAF,6483.8545747284,54001953,25.1000003815,57.1821219512,49 176 | SSD,1115.0935156088,11911184,,55.6822195122,15.9 177 | ESP,29721.6014855933,46476032,24.7000007629,83.0780487805,76.19 178 | LKA,3794.8875863911,20771000,4.5999999046,74.794804878,25.8 179 | KNA,15510.3939262063,54944,,,65.4 180 | LCA,7647.5295478495,183645,,75.0468536585,51 181 | MAF,,31530,,79.3219512195, 182 | VCT,6668.886105015,109360,,72.9368536585,56.48 183 | SDN,1875.8432874165,39350274,14.8000001907,63.4585365854,24.64 184 | SUR,9680.1159136737,538248,5.5999999046,71.1514390244,40.08 185 | SWZ,3477.1492429559,1269112,22.2999992371,48.9347317073,27.1 186 | SWE,58898.9275256779,9696110,8,81.956097561,92.52 187 | CHE,85616.5611964835,8188102,4.5,82.8487804878,87 188 | SYR,,22157800,10.8000001907,70.0710243902,28.09 189 | TJK,1114.007483342,8295840,10.8999996185,69.5979756098,17.49 190 | TZA,955.1413113922,51822621,3.0999999046,64.943902439,4.86 191 | THA,5977.3805871146,67725979,0.8999999762,74.4220243902,34.89 192 | TMP,1169.0387069788,1212107,4.6999998093,68.2591463415,1.14 193 | TGO,635.0442676625,7115163,6.9000000954,59.655804878,5.7 194 | TON,4113.9934930679,105586,,72.792195122,40 195 | TTO,21323.754712196,1354483,4,70.4405609756,65.1 196 | TUN,4420.6984351606,10996600,13.3000001907,74.143902439,46.16 197 | TUR,10515.007820334,75932348,9.1999998093,75.1635121951,51.04 198 | TKM,9031.5115696978,5307188,10.5,65.5985365854,12.2 199 | TCA,,33740,,, 200 | TUV,3826.9029012988,9893,,, 201 | UGA,714.5673454013,37782971,3.7999999523,58.4664146341,17.71 202 | UKR,3082.4614470702,45362900,7.6999998093,71.1865853659,43.4 203 | ARE,43962.7136932011,9086139,3.5999999046,77.3681707317,90.4 204 | GBR,46296.9846725052,64559135,6.3000001907,81.056097561,91.61 205 | USA,54629.4951678912,318857056,6.1999998093,78.9414634146,87.36 206 | URY,16806.7732671439,3419516,7,76.9861463415,61.46 207 | UZB,2036.6917234305,30757700,10.6000003815,68.3390243902,43.55 208 | VUT,3147.9637788925,258883,,71.9183170732,18.8 209 | VEN,,30693827,8.6000003815,74.236195122,57 210 | VNM,2052.3190838009,90728900,2.2999999523,75.6291219512,48.31 211 | VIR,,104170,,79.7731707317,50.07 212 | WBG,2965.9036745951,4294682,26.2000007629,72.9040243902,53.67 213 | YEM,,26183676,17.3999996185,63.818195122,22.55 214 | ZMB,1721.6232741122,15721343,13.3000001907,60.0470487805,17.34 215 | ZWE,931.1981869586,15245855,5.4000000954,57.4983170732,19.89 216 | ARB,7446.7669199684,385272539,11.5246568678,70.5332693097,34.5173747337 217 | CSS,10165.3326281428,7012857,10.4051788239,72.8446998092,49.2739489479 218 | EAS,9482.0797327263,2264058207,4.5221502409,74.9285246169,46.8912750396 219 | EAP,6240.4071477738,2020656037,4.5837861102,73.985723037,42.1320603779 220 | EMU,39639.8123622324,338302110,11.5260280476,81.6481033605,78.3000360116 221 | ECS,25703.245825993,901978575,9.0069077569,76.8931116309,69.2034365478 222 | ECA,6895.6690382066,263565607,9.2627727264,72.7091078819,48.1822381678 223 | EUU,36447.8550869944,507962837,10.2073359679,80.6728746791,78.1021476801 224 | HPC,898.0442810245,701714118,6.5964140126,60.7854836139,8.8652911821 225 | HIC,37792.7561528723,1398429060,7.165141909,79.2404621229,80.6075498547 226 | OEC,43697.2196239015,1069655987,7.4826800492,80.8846106048,83.8455755786 227 | NOC,18617.6136492259,328773073,6.0501844777,73.8826972688,69.8993263469 228 | LCN,9985.3064025717,626269527,6.5773602094,74.9420601757,50.1533204189 229 | LAC,9225.603363969,525172706,6.3056614898,74.6275211492,47.466033327 230 | LDC,951.999439311,931955068,6.1263179001,63.1904459419,8.6363921502 231 | LMY,4290.6113311149,5861262709,5.6165483509,69.5940790227,31.1365897394 232 | LIC,639.6519620419,621977594,5.7124740405,61.325097296,6.2909866251 233 | LMC,2008.3843157096,2878467298,5.3087122648,67.2282647562,22.555151452 234 | MEA,8432.3295476394,417451788,11.2691864894,72.8166596303,38.2614233289 235 | MNA,4356.8756359945,357312867,12.8887794707,72.2250608815,32.6617750664 236 | MIC,4723.6307735074,5239285115,5.6058159539,70.5757643162,34.0523027842 237 | NAC,54194.6053820553,354465895,6.2763852498,79.2441449936,87.3379213412 238 | INX,,,,, 239 | OED,38422.5838587063,1270974168,7.3280784962,80.1321427681,78.0513737077 240 | OSS,4799.4063168169,20937048,14.0599058309,62.286620696,18.392568245 241 | PSS,3760.707986468,2313337,,69.9190322185,26.6331225128 242 | SST,5963.445999493,30263242,12.743997947,65.271922179,26.3799180756 243 | SAS,1504.0433104744,1721152580,3.898770718,68.1210445968,16.6099246937 244 | SSF,1791.2494583042,974315197,7.9691534231,58.5589350669,19.2212447812 245 | SSA,1775.546494709,973402912,7.9692312137,58.5583265679,19.2181330694 246 | UMC,8030.3310439836,2360817817,5.8825822795,74.6576260859,47.7213465385 247 | WLD,10738.831492412,7259691769,5.9319980107,71.4517450293,40.6891366054 248 | --------------------------------------------------------------------------------