India.R
dell
Sat Oct 24 19:45:21 2015
require(maps)## Loading required package: mapslibrary(sp)
data(world.cities)
head(world.cities,4)## name country.etc pop lat long capital
## 1 'Abasan al-Jadidah Palestine 5629 31.31 34.34 0
## 2 'Abasan al-Kabirah Palestine 18999 31.32 34.35 0
## 3 'Abdul Hakim Pakistan 47788 30.55 72.11 0
## 4 'Abdullah-as-Salam Kuwait 21817 29.36 47.98 0table(world.cities$country.etc)##
## Afghanistan Albania
## 117 67
## Algeria American Samoa
## 316 35
## Andorra Angola
## 7 37
## Anguilla Antigua and Barbuda
## 8 13
## Argentina Armenia
## 82 402
## Aruba Australia
## 2 213
## Austria Azerbaijan
## 235 187
## Azores Bahamas
## 11 26
## Bahrain Bangladesh
## 11 143
## Barbados Belarus
## 11 102
## Belgium Belize
## 552 9
## Benin Bermuda
## 33 2
## Bhutan Bolivia
## 38 122
## Bosnia and Herzegovina Botswana
## 226 154
## Brazil British Virgin Islands
## 975 1
## Brunei Bulgaria
## 5 241
## Burkina Faso Burundi
## 49 17
## Cambodia Cameroon
## 25 135
## Canada Canary Islands
## 917 53
## Cape Verde Cayman Islands
## 15 5
## Central African Republic Chad
## 37 42
## Chile China
## 107 892
## Colombia Comoros
## 987 154
## Congo Congo Democratic Republic
## 19 74
## Cook Islands Costa Rica
## 11 141
## Croatia Cuba
## 545 129
## Cyprus Czech Republic
## 315 950
## Denmark Djibouti
## 310 11
## Dominica Dominican Republic
## 19 202
## East Timor Easter Island
## 19 1
## Ecuador Egypt
## 113 153
## El Salvador Equatorial Guinea
## 106 24
## Eritrea Estonia
## 14 955
## Ethiopia Falkland Islands
## 133 4
## Faroe Islands Fiji
## 116 22
## Finland France
## 430 1000
## French Guiana French Polynesia
## 15 116
## Gabon Gambia
## 33 302
## Georgia Germany
## 113 998
## Ghana Gibraltar
## 75 1
## Greece Greenland
## 1000 102
## Grenada Guadeloupe
## 7 27
## Guam Guatemala
## 27 331
## Guernsey and Alderney Guinea
## 3 37
## Guinea-Bissau Guyana
## 15 17
## Haiti Honduras
## 116 541
## Hungary Iceland
## 844 100
## India Indonesia
## 963 476
## Iran Iraq
## 266 80
## Ireland Isle of Man
## 165 8
## Israel Italy
## 193 985
## Ivory Coast Jamaica
## 66 103
## Japan Jersey
## 999 12
## Jordan Kazakhstan
## 151 320
## Kenya Kiribati
## 138 24
## Korea North Korea South
## 130 132
## Kuwait Kyrgyzstan
## 70 33
## Laos Latvia
## 18 77
## Lebanon Lesotho
## 19 11
## Liberia Libya
## 10 44
## Liechtenstein Lithuania
## 11 113
## Luxembourg Macedonia
## 473 117
## Madagascar Madeira
## 71 9
## Madiera Malawi
## 1 33
## Malaysia Maldives
## 163 201
## Mali Malta
## 37 66
## Marshall Islands Martinique
## 99 21
## Mauritania Mauritius
## 29 115
## Mayotte Mexico
## 17 939
## Micronesia Moldova
## 62 44
## Monaco Mongolia
## 4 25
## Montserrat Morocco
## 6 74
## Mozambique Myanmar
## 28 66
## Namibia Nauru
## 39 1
## Nepal Netherlands
## 52 318
## Netherlands Antilles New Caledonia
## 44 32
## New Zealand Nicaragua
## 276 147
## Niger Nigeria
## 42 521
## Niue Norfolk Island
## 13 1
## Northern Mariana Islands Norway
## 15 714
## Oman Pakistan
## 42 418
## Palau Palestine
## 17 244
## Panama Papua New Guinea
## 254 41
## Paraguay Peru
## 228 272
## Philippines Pitcairn
## 980 1
## Poland Portugal
## 439 438
## Puerto Rico Qatar
## 80 27
## Reunion Romania
## 23 999
## Russia Rwanda
## 985 12
## Saint-Barthelemy Saint-Martin
## 1 1
## Saint Helena Saint Kitts and Nevis
## 9 16
## Saint Lucia Saint Pierre and Miquelon
## 12 2
## Saint Vincent and The Grenadines Samoa
## 8 198
## San Marino Sao Tome and Principe
## 9 6
## Saudi Arabia Senegal
## 186 59
## Serbia and Montenegro Seychelles
## 254 1
## Sicily Sierra Leone
## 2 97
## Singapore Slovakia
## 1 129
## Slovenia Solomon Islands
## 203 9
## Somalia South Africa
## 63 226
## Spain Sri Lanka
## 864 71
## Sudan Suriname
## 118 14
## Svalbard and Jan Mayen Swaziland
## 7 27
## Sweden Switzerland
## 104 826
## Syria Taiwan
## 105 98
## Tajikistan Tanzania
## 80 308
## Thailand Togo
## 293 21
## Tokelau Tonga
## 3 9
## Trinidad and Tobago Tunisia
## 22 200
## Turkey Turkmenistan
## 791 32
## Turks and Caicos Tuvalu
## 2 8
## Uganda UK
## 90 925
## Ukraine United Arab Emirates
## 959 9
## Uruguay US Virgin Islands
## 135 9
## USA Uzbekistan
## 997 153
## Vanuatu Vatican City
## 8 1
## Venezuela Vietnam
## 104 68
## Wallis and Futuna Western Sahara
## 23 4
## Yemen Zambia
## 30 73
## Zimbabwe
## 76citiesIND <- world.cities[world.cities$country.etc == "India",]
class(citiesIND)## [1] "data.frame"coordinates(citiesIND) <- ~long + lat
class(citiesIND)## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"str(citiesIND)## Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 963 obs. of 4 variables:
## .. ..$ name : chr [1:963] "Abohar" "Achalpur" "Adilabad" "Adityapur" ...
## .. ..$ country.etc: chr [1:963] "India" "India" "India" "India" ...
## .. ..$ pop : int [1:963] 132117 112173 121168 145045 165441 206666 1475091 59232 3769846 383784 ...
## .. ..$ capital : int [1:963] 0 0 0 0 0 0 0 0 0 0 ...
## ..@ coords.nrs : int [1:2] 5 4
## ..@ coords : num [1:963, 1:2] 74.2 77.5 78.5 86 77.3 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:963] "235" "315" "396" "399" ...
## .. .. ..$ : chr [1:2] "long" "lat"
## ..@ bbox : num [1:2, 1:2] 69.6 8.18 95.36 34.3
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "long" "lat"
## .. .. ..$ : chr [1:2] "min" "max"
## ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
## .. .. ..@ projargs: chr NAnrow(citiesIND@data)## [1] 963#creating random data for dengue deaths
citiesIND@data[["samp"]] <- sample(40:1000,963,T)
head(citiesIND@data)## name country.etc pop capital samp
## 235 Abohar India 132117 0 282
## 315 Achalpur India 112173 0 686
## 396 Adilabad India 121168 0 827
## 399 Adityapur India 145045 0 812
## 411 Adoni India 165441 0 217
## 467 Agartala India 206666 0 671summary(citiesIND@data$pop)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 51210 66420 99660 242800 180700 12880000par(mfrow=c(1,1))
plot(citiesIND, axes=T, asp=1, pch=16,main="Spatial Plot of Cities in India")
## Highlight big cities
plot(citiesIND[citiesIND@data$pop > 1000000, ], pch=1, col="red", cex=3, add=TRUE)
## Highlight cities with bigger dengue deaths
plot(citiesIND[citiesIND@data$samp > 960, ], pch=1, col="blue", cex=3, add=TRUE)