4) Load the PRB data and do some stuff.
install.packages("readr", repos = "http://cran.us.r-project.org")
## Installing package into 'C:/Users/Uriel/Documents/R/win-library/3.4'
## (as 'lib' is unspecified)
## package 'readr' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\Uriel\AppData\Local\Temp\RtmpiysxWL\downloaded_packages
library(readr)
prb<-read_csv(file = "https://raw.githubusercontent.com/coreysparks/data/master/PRB2008_All.csv")
## Parsed with column specification:
## cols(
## .default = col_integer(),
## Country = col_character(),
## Continent = col_character(),
## Region = col_character(),
## Population. = col_double(),
## Rate.of.natural.increase = col_double(),
## ProjectedPopMid2025 = col_double(),
## ProjectedPopMid2050 = col_double(),
## IMR = col_double(),
## TFR = col_double(),
## PercPop1549HIVAIDS2001 = col_double(),
## PercPop1549HIVAIDS2007 = col_double(),
## PercPpUnderNourished0204 = col_double(),
## PopDensPerSqMile = col_double()
## )
## See spec(...) for full column specifications.
head(prb, 10)
## # A tibble: 10 x 35
## Y X ID Country Continent Region
## <int> <int> <int> <chr> <chr> <chr>
## 1 1 1 115 Afghanistan Asia South Central Asia
## 2 2 2 178 Albania Europe Southern Europe
## 3 3 3 1 Algeria Africa NORTHERN AFRICA
## 4 4 4 179 Andorra Europe Southern Europe
## 5 5 5 43 Angola Africa MIDDLE AFRICA
## 6 6 6 67 Antigua and Barbuda North America Carribean
## 7 7 7 84 Argentina South America South America
## 8 8 8 97 Armenia Asia Western Asia
## 9 9 9 192 Australia Oceania Oceania
## 10 10 10 159 Austria Europe Western Europe
## # ... with 29 more variables: Year <int>, Population. <dbl>, CBR <int>,
## # CDR <int>, Rate.of.natural.increase <dbl>, Net.Migration.Rate <int>,
## # ProjectedPopMid2025 <dbl>, ProjectedPopMid2050 <dbl>,
## # ProjectedPopChange_08_50Perc <int>, IMR <dbl>,
## # WomandLifeTimeRiskMaternalDeath <int>, TFR <dbl>, PercPopLT15 <int>,
## # PercPopGT65 <int>, e0Total <int>, e0Male <int>, e0Female <int>,
## # PercUrban <int>, PercPopinUrbanGT750k <int>,
## # PercPop1549HIVAIDS2001 <dbl>, PercPop1549HIVAIDS2007 <dbl>,
## # PercMarWomContraALL <int>, PercMarWomContraModern <int>,
## # PercPpUnderNourished0204 <dbl>, MotorVehper1000Pop0005 <int>,
## # PercPopwAccessImprovedWaterSource <int>,
## # GNIPPPperCapitaUSDollars <int>, PopDensPerSqKM <int>,
## # PopDensPerSqMile <dbl>
length(prb$Country)
## [1] 209
summary(prb$Country)
## Length Class Mode
## 209 character character
summary(prb$e0Total) #There are two NA's in the e0Total variable.
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 33.00 61.50 72.00 67.85 75.00 82.00 2
table(is.na(prb$e0Total),prb$Country)
##
## Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda
## FALSE 1 1 1 0 1 1
## TRUE 0 0 0 1 0 0
##
## Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain
## FALSE 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0
##
## Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia
## FALSE 1 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0 0
##
## Bosnia-Herzegovina Botswana Brazil Brunei Bulgaria Burkina Faso
## FALSE 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0
##
## Burundi C<U+FFFD>te d'Ivoire Cambodia Cameroon Canada Cape Verde
## FALSE 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0
##
## Central African Republic Chad Channel Islands Chile China
## FALSE 1 1 1 1 1
## TRUE 0 0 0 0 0
##
## China Hong Kong SARe China Macao SARe Colombia Comoros Congo
## FALSE 1 1 1 1 1
## TRUE 0 0 0 0 0
##
## Congo Dem. Rep. Costa Rica Croatia Cuba Cyprus Czech Republic
## FALSE 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0
##
## Denmark Djibouti Dominica Dominican Republic Ecuador Egypt
## FALSE 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0
##
## El Salvador Equatorial Guinea Eritrea Estonia Ethiopia
## FALSE 1 1 1 1 1
## TRUE 0 0 0 0 0
##
## Federated States of Micronesia Fiji Finland France French Guiana
## FALSE 1 1 1 1 1
## TRUE 0 0 0 0 0
##
## French Polynesia Gabon Gambia Georgia Germany Ghana Greece Grenada
## FALSE 1 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0 0
##
## Guadeloupe Guam Guatemala Guinea Guinea-Bissau Guyana Haiti
## FALSE 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0
##
## Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel
## FALSE 1 1 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0 0 0
##
## Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea North
## FALSE 1 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0 0
##
## Korea South Kosovof Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho
## FALSE 1 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0 0
##
## Liberia Libya Liechtenstein Lithuania Luxembourg Macedoniag
## FALSE 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0
##
## Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands
## FALSE 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0
##
## Martinique Mauritania Mauritius Mayotte Mexico Moldova Monaco
## FALSE 1 1 1 1 1 1 0
## TRUE 0 0 0 0 0 0 1
##
## Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nauru Nepal
## FALSE 1 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0 0
##
## Netherlands Netherlands Antilles New Caledonia New Zealand
## FALSE 1 1 1 1
## TRUE 0 0 0 0
##
## Nicaragua Niger Nigeria Norway Oman Pakistan Palau
## FALSE 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0
##
## Palestinian Territory Panama Papua New Guinea Paraguay Peru
## FALSE 1 1 1 1 1
## TRUE 0 0 0 0 0
##
## Philippines Poland Portugal Puerto Rico Qatar Reunion Romania
## FALSE 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0
##
## Russia Rwanda Saint Lucia Samoa San Marino Sao Tome and Principe
## FALSE 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0
##
## Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore
## FALSE 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0
##
## Slovakia Slovenia Solomon Islands Somalia South Africa Spain
## FALSE 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0
##
## Sri Lanka St. Kitts-Nevis St. Vincent & the Grenadines Sudan
## FALSE 1 1 1 1
## TRUE 0 0 0 0
##
## Suriname Swaziland Sweden Switzerland Syria Taiwan Tajikistan
## FALSE 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0
##
## Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago
## FALSE 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0
##
## Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine
## FALSE 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0
##
## United Arab Emirates United Kingdom United States Uruguay
## FALSE 1 1 1 1
## TRUE 0 0 0 0
##
## Uzbekistan Vanuatu Venezuela Vietnam Western Sahara Yemen Zambia
## FALSE 1 1 1 1 1 1 1
## TRUE 0 0 0 0 0 0 0
##
## Zimbabwe
## FALSE 1
## TRUE 0
ifelse(test=is.na(prb$e0Total), yes=prb$Country, no=prb$e0Total) #More compact view but not so compact.
## [1] "43" "75" "72" "Andorra" "43" "73" "75"
## [8] "71" "81" "80" "72" "72" "75" "63"
## [15] "76" "70" "80" "73" "56" "66" "65"
## [22] "74" "49" "72" "75" "73" "51" "49"
## [29] "62" "52" "80" "71" "43" "47" "78"
## [36] "78" "73" "82" "79" "72" "64" "53"
## [43] "53" "78" "52" "76" "77" "78" "77"
## [50] "78" "54" "75" "72" "75" "72" "71"
## [57] "59" "57" "73" "49" "67" "68" "79"
## [64] "81" "75" "75" "57" "58" "74" "79"
## [71] "59" "79" "68" "79" "78" "69" "54"
## [78] "45" "65" "58" "72" "73" "81" "65"
## [85] "70" "71" "58" "79" "80" "81" "72"
## [92] "82" "72" "66" "53" "61" "71" "79"
## [99] "69" "78" "66" "61" "72" "72" "36"
## [106] "46" "73" "80" "71" "80" "74" "58"
## [113] "46" "74" "73" "56" "79" "66" "80"
## [120] "60" "72" "74" "75" "69" "Monaco" "64"
## [127] "73" "70" "43" "61" "47" "55" "64"
## [134] "80" "75" "76" "80" "71" "57" "47"
## [141] "80" "74" "63" "71" "72" "57" "71"
## [148] "71" "69" "75" "79" "78" "75" "76"
## [155] "71" "67" "47" "73" "73" "82" "64"
## [162] "76" "62" "73" "72" "48" "81" "74"
## [169] "78" "62" "48" "80" "71" "70" "72"
## [176] "58" "69" "33" "82" "73" "78" "67"
## [183] "51" "72" "60" "58" "71" "69" "74"
## [190] "72" "62" "64" "48" "68" "78" "79"
## [197] "78" "76" "67" "67" "73" "73" "64"
## [204] "61" "38" "40" "75" "50" "81"