Project 2a - Flags of the World
Mohamed Nabeel
Image by Gordon Johnson from Pixabay
Data Import
Download flag.csv and flag.names to your working directory. Make sure to set your working directory appropriately!
Let’s look at some information about this file. Open flag.names in RStudio by double clicking it in the files pane in bottom left. Read through this file.
Who is the donor of this data? Richard S. Forsyth
Is there any missing data? No
- Import the fla(g.csv data into R. Store it in a data.frame named flag_df.
flag_df<-read.csv("flag.csv")- Check to make sure the class of flag_df is data.frame. Then find the )dimensions of flag_df.
data.frame(flag_df)## X name landmass zone area population language
## 1 1 Afghanistan 5 1 648 16 10
## 2 2 Albania 3 1 29 3 6
## 3 3 Algeria 4 1 2388 20 8
## 4 4 American-Samoa 6 3 0 0 1
## 5 5 Andorra 3 1 0 0 6
## 6 6 Angola 4 2 1247 7 10
## 7 7 Anguilla 1 4 0 0 1
## 8 8 Antigua-Barbuda 1 4 0 0 1
## 9 9 Argentina 2 3 2777 28 2
## 10 10 Argentine 2 3 2777 28 2
## 11 11 Australia 6 2 7690 15 1
## 12 12 Austria 3 1 84 8 4
## 13 13 Bahamas 1 4 19 0 1
## 14 14 Bahrain 5 1 1 0 8
## 15 15 Bangladesh 5 1 143 90 6
## 16 16 Barbados 1 4 0 0 1
## 17 17 Belgium 3 1 31 10 6
## 18 18 Belize 1 4 23 0 1
## 19 19 Benin 4 1 113 3 3
## 20 20 Bermuda 1 4 0 0 1
## 21 21 Bhutan 5 1 47 1 10
## 22 22 Bolivia 2 3 1099 6 2
## 23 23 Botswana 4 2 600 1 10
## 24 24 Brazil 2 3 8512 119 6
## 25 25 British-Virgin-Isles 1 4 0 0 1
## 26 26 Brunei 5 1 6 0 10
## 27 27 Bulgaria 3 1 111 9 5
## 28 28 Burkina 4 4 274 7 3
## 29 29 Burma 5 1 678 35 10
## 30 30 Burundi 4 2 28 4 10
## 31 31 Cameroon 4 1 474 8 3
## 32 32 Canada 1 4 9976 24 1
## 33 33 Cape-Verde-Islands 4 4 4 0 6
## 34 34 Cayman-Islands 1 4 0 0 1
## 35 35 Central-African-Republic 4 1 623 2 10
## 36 36 Chad 4 1 1284 4 3
## 37 37 Chile 2 3 757 11 2
## 38 38 China 5 1 9561 1008 7
## 39 39 Colombia 2 4 1139 28 2
## 40 40 Comorro-Islands 4 2 2 0 3
## 41 41 Congo 4 2 342 2 10
## 42 42 Cook-Islands 6 3 0 0 1
## 43 43 Costa-Rica 1 4 51 2 2
## 44 44 Cuba 1 4 115 10 2
## 45 45 Cyprus 3 1 9 1 6
## 46 46 Czechoslovakia 3 1 128 15 5
## 47 47 Denmark 3 1 43 5 6
## 48 48 Djibouti 4 1 22 0 3
## 49 49 Dominica 1 4 0 0 1
## 50 50 Dominican-Republic 1 4 49 6 2
## 51 51 Ecuador 2 3 284 8 2
## 52 52 Egypt 4 1 1001 47 8
## 53 53 El-Salvador 1 4 21 5 2
## 54 54 Equatorial-Guinea 4 1 28 0 10
## 55 55 Ethiopia 4 1 1222 31 10
## 56 56 Faeroes 3 4 1 0 6
## 57 57 Falklands-Malvinas 2 3 12 0 1
## 58 58 Fiji 6 2 18 1 1
## 59 59 Finland 3 1 337 5 9
## 60 60 France 3 1 547 54 3
## 61 61 French-Guiana 2 4 91 0 3
## 62 62 French-Polynesia 6 3 4 0 3
## 63 63 Gabon 4 2 268 1 10
## 64 64 Gambia 4 4 10 1 1
## 65 65 Germany-DDR 3 1 108 17 4
## 66 66 Germany-FRG 3 1 249 61 4
## 67 67 Ghana 4 4 239 14 1
## 68 68 Gibraltar 3 4 0 0 1
## 69 69 Greece 3 1 132 10 6
## 70 70 Greenland 1 4 2176 0 6
## 71 71 Grenada 1 4 0 0 1
## 72 72 Guam 6 1 0 0 1
## 73 73 Guatemala 1 4 109 8 2
## 74 74 Guinea 4 4 246 6 3
## 75 75 Guinea-Bissau 4 4 36 1 6
## 76 76 Guyana 2 4 215 1 1
## 77 77 Haiti 1 4 28 6 3
## 78 78 Honduras 1 4 112 4 2
## 79 79 Hong-Kong 5 1 1 5 7
## 80 80 Hungary 3 1 93 11 9
## 81 81 Iceland 3 4 103 0 6
## 82 82 India 5 1 3268 684 6
## 83 83 Indonesia 6 2 1904 157 10
## 84 84 Iran 5 1 1648 39 6
## 85 85 Iraq 5 1 435 14 8
## 86 86 Ireland 3 4 70 3 1
## 87 87 Israel 5 1 21 4 10
## 88 88 Italy 3 1 301 57 6
## 89 89 Ivory-Coast 4 4 323 7 3
## 90 90 Jamaica 1 4 11 2 1
## 91 91 Japan 5 1 372 118 9
## 92 92 Jordan 5 1 98 2 8
## 93 93 Kampuchea 5 1 181 6 10
## 94 94 Kenya 4 1 583 17 10
## 95 95 Kiribati 6 1 0 0 1
## 96 96 Kuwait 5 1 18 2 8
## 97 97 Laos 5 1 236 3 10
## 98 98 Lebanon 5 1 10 3 8
## 99 99 Lesotho 4 2 30 1 10
## 100 100 Liberia 4 4 111 1 10
## 101 101 Libya 4 1 1760 3 8
## 102 102 Liechtenstein 3 1 0 0 4
## 103 103 Luxembourg 3 1 3 0 4
## 104 104 Malagasy 4 2 587 9 10
## 105 105 Malawi 4 2 118 6 10
## 106 106 Malaysia 5 1 333 13 10
## 107 107 Maldive-Islands 5 1 0 0 10
## 108 108 Mali 4 4 1240 7 3
## 109 109 Malta 3 1 0 0 10
## 110 110 Marianas 6 1 0 0 10
## 111 111 Mauritania 4 4 1031 2 8
## 112 112 Mauritius 4 2 2 1 1
## 113 113 Mexico 1 4 1973 77 2
## 114 114 Micronesia 6 1 1 0 10
## 115 115 Monaco 3 1 0 0 3
## 116 116 Mongolia 5 1 1566 2 10
## 117 117 Montserrat 1 4 0 0 1
## 118 118 Morocco 4 4 447 20 8
## 119 119 Mozambique 4 2 783 12 10
## 120 120 Nauru 6 2 0 0 10
## 121 121 Nepal 5 1 140 16 10
## 122 122 Netherlands 3 1 41 14 6
## 123 123 Netherlands-Antilles 1 4 0 0 6
## 124 124 New-Zealand 6 2 268 2 1
## 125 125 Nicaragua 1 4 128 3 2
## 126 126 Niger 4 1 1267 5 3
## 127 127 Nigeria 4 1 925 56 10
## 128 128 Niue 6 3 0 0 1
## 129 129 North-Korea 5 1 121 18 10
## 130 130 North-Yemen 5 1 195 9 8
## 131 131 Norway 3 1 324 4 6
## 132 132 Oman 5 1 212 1 8
## 133 133 Pakistan 5 1 804 84 6
## 134 134 Panama 2 4 76 2 2
## 135 135 Papua-New-Guinea 6 2 463 3 1
## 136 136 Parguay 2 3 407 3 2
## 137 137 Peru 2 3 1285 14 2
## 138 138 Philippines 6 1 300 48 10
## 139 139 Poland 3 1 313 36 5
## 140 140 Portugal 3 4 92 10 6
## 141 141 Puerto-Rico 1 4 9 3 2
## 142 142 Qatar 5 1 11 0 8
## 143 143 Romania 3 1 237 22 6
## 144 144 Rwanda 4 2 26 5 10
## 145 145 San-Marino 3 1 0 0 6
## 146 146 Sao-Tome 4 1 0 0 6
## 147 147 Saudi-Arabia 5 1 2150 9 8
## 148 148 Senegal 4 4 196 6 3
## 149 149 Seychelles 4 2 0 0 1
## 150 150 Sierra-Leone 4 4 72 3 1
## 151 151 Singapore 5 1 1 3 7
## 152 152 Soloman-Islands 6 2 30 0 1
## 153 153 Somalia 4 1 637 5 10
## 154 154 South-Africa 4 2 1221 29 6
## 155 155 South-Korea 5 1 99 39 10
## 156 156 South-Yemen 5 1 288 2 8
## 157 157 Spain 3 4 505 38 2
## 158 158 Sri-Lanka 5 1 66 15 10
## 159 159 St-Helena 4 3 0 0 1
## 160 160 St-Kitts-Nevis 1 4 0 0 1
## 161 161 St-Lucia 1 4 0 0 1
## 162 162 St-Vincent 1 4 0 0 1
## 163 163 Sudan 4 1 2506 20 8
## 164 164 Surinam 2 4 63 0 6
## 165 165 Swaziland 4 2 17 1 10
## 166 166 Sweden 3 1 450 8 6
## 167 167 Switzerland 3 1 41 6 4
## 168 168 Syria 5 1 185 10 8
## 169 169 Taiwan 5 1 36 18 7
## 170 170 Tanzania 4 2 945 18 10
## 171 171 Thailand 5 1 514 49 10
## 172 172 Togo 4 1 57 2 3
## 173 173 Tonga 6 2 1 0 10
## 174 174 Trinidad-Tobago 2 4 5 1 1
## 175 175 Tunisia 4 1 164 7 8
## 176 176 Turkey 5 1 781 45 9
## 177 177 Turks-Cocos-Islands 1 4 0 0 1
## 178 178 Tuvalu 6 2 0 0 1
## 179 179 UAE 5 1 84 1 8
## 180 180 Uganda 4 1 236 13 10
## 181 181 UK 3 4 245 56 1
## 182 182 Uruguay 2 3 178 3 2
## 183 183 US-Virgin-Isles 1 4 0 0 1
## 184 184 USA 1 4 9363 231 1
## 185 185 USSR 5 1 22402 274 5
## 186 186 Vanuatu 6 2 15 0 6
## 187 187 Vatican-City 3 1 0 0 6
## 188 188 Venezuela 2 4 912 15 2
## 189 189 Vietnam 5 1 333 60 10
## 190 190 Western-Samoa 6 3 3 0 1
## 191 191 Yugoslavia 3 1 256 22 6
## 192 192 Zaire 4 2 905 28 10
## 193 193 Zambia 4 2 753 6 10
## 194 194 Zimbabwe 4 2 391 8 10
## religion bars stripes colours red green blue gold white black orange
## 1 2 0 3 5 1 1 0 1 1 1 0
## 2 6 0 0 3 1 0 0 1 0 1 0
## 3 2 2 0 3 1 1 0 0 1 0 0
## 4 1 0 0 5 1 0 1 1 1 0 1
## 5 0 3 0 3 1 0 1 1 0 0 0
## 6 5 0 2 3 1 0 0 1 0 1 0
## 7 1 0 1 3 0 0 1 0 1 0 1
## 8 1 0 1 5 1 0 1 1 1 1 0
## 9 0 0 3 2 0 0 1 0 1 0 0
## 10 0 0 3 3 0 0 1 1 1 0 0
## 11 1 0 0 3 1 0 1 0 1 0 0
## 12 0 0 3 2 1 0 0 0 1 0 0
## 13 1 0 3 3 0 0 1 1 0 1 0
## 14 2 0 0 2 1 0 0 0 1 0 0
## 15 2 0 0 2 1 1 0 0 0 0 0
## 16 1 3 0 3 0 0 1 1 0 1 0
## 17 0 3 0 3 1 0 0 1 0 1 0
## 18 1 0 2 8 1 1 1 1 1 1 1
## 19 5 0 0 2 1 1 0 0 0 0 0
## 20 1 0 0 6 1 1 1 1 1 1 0
## 21 3 0 0 4 1 0 0 0 1 1 1
## 22 0 0 3 3 1 1 0 1 0 0 0
## 23 5 0 5 3 0 0 1 0 1 1 0
## 24 0 0 0 4 0 1 1 1 1 0 0
## 25 1 0 0 6 1 1 1 1 1 0 1
## 26 2 0 0 4 1 0 0 1 1 1 0
## 27 6 0 3 5 1 1 1 1 1 0 0
## 28 5 0 2 3 1 1 0 1 0 0 0
## 29 3 0 0 3 1 0 1 0 1 0 0
## 30 5 0 0 3 1 1 0 0 1 0 0
## 31 1 3 0 3 1 1 0 1 0 0 0
## 32 1 2 0 2 1 0 0 0 1 0 0
## 33 0 1 2 5 1 1 0 1 0 1 1
## 34 1 0 0 6 1 1 1 1 1 0 1
## 35 5 1 0 5 1 1 1 1 1 0 0
## 36 5 3 0 3 1 0 1 1 0 0 0
## 37 0 0 2 3 1 0 1 0 1 0 0
## 38 6 0 0 2 1 0 0 1 0 0 0
## 39 0 0 3 3 1 0 1 1 0 0 0
## 40 2 0 0 2 0 1 0 0 1 0 0
## 41 5 0 0 3 1 1 0 1 0 0 0
## 42 1 0 0 4 1 0 1 0 1 0 0
## 43 0 0 5 3 1 0 1 0 1 0 0
## 44 6 0 5 3 1 0 1 0 1 0 0
## 45 1 0 0 3 0 1 0 1 1 0 0
## 46 6 0 0 3 1 0 1 0 1 0 0
## 47 1 0 0 2 1 0 0 0 1 0 0
## 48 2 0 0 4 1 1 1 0 1 0 0
## 49 1 0 0 6 1 1 1 1 1 1 0
## 50 0 0 0 3 1 0 1 0 1 0 0
## 51 0 0 3 3 1 0 1 1 0 0 0
## 52 2 0 3 4 1 0 0 1 1 1 0
## 53 0 0 3 2 0 0 1 0 1 0 0
## 54 5 0 3 4 1 1 1 0 1 0 0
## 55 1 0 3 3 1 1 0 1 0 0 0
## 56 1 0 0 3 1 0 1 0 1 0 0
## 57 1 0 0 6 1 1 1 1 1 0 0
## 58 1 0 0 7 1 1 1 1 1 0 1
## 59 1 0 0 2 0 0 1 0 1 0 0
## 60 0 3 0 3 1 0 1 0 1 0 0
## 61 0 3 0 3 1 0 1 0 1 0 0
## 62 0 0 3 5 1 0 1 1 1 1 0
## 63 5 0 3 3 0 1 1 1 0 0 0
## 64 5 0 5 4 1 1 1 0 1 0 0
## 65 6 0 3 3 1 0 0 1 0 1 0
## 66 1 0 3 3 1 0 0 1 0 1 0
## 67 5 0 3 4 1 1 0 1 0 1 0
## 68 1 0 1 3 1 0 0 1 1 0 0
## 69 1 0 9 2 0 0 1 0 1 0 0
## 70 1 0 0 2 1 0 0 0 1 0 0
## 71 1 0 0 3 1 1 0 1 0 0 0
## 72 1 0 0 7 1 1 1 1 1 0 1
## 73 0 3 0 2 0 0 1 0 1 0 0
## 74 2 3 0 3 1 1 0 1 0 0 0
## 75 5 1 2 4 1 1 0 1 0 1 0
## 76 4 0 0 5 1 1 0 1 1 1 0
## 77 0 2 0 2 1 0 0 0 0 1 0
## 78 0 0 3 2 0 0 1 0 1 0 0
## 79 3 0 0 6 1 1 1 1 1 0 1
## 80 6 0 3 3 1 1 0 0 1 0 0
## 81 1 0 0 3 1 0 1 0 1 0 0
## 82 4 0 3 4 0 1 1 0 1 0 1
## 83 2 0 2 2 1 0 0 0 1 0 0
## 84 2 0 3 3 1 1 0 0 1 0 0
## 85 2 0 3 4 1 1 0 0 1 1 0
## 86 0 3 0 3 0 1 0 0 1 0 1
## 87 7 0 2 2 0 0 1 0 1 0 0
## 88 0 3 0 3 1 1 0 0 1 0 0
## 89 5 3 0 3 1 1 0 0 1 0 0
## 90 1 0 0 3 0 1 0 1 0 1 0
## 91 7 0 0 2 1 0 0 0 1 0 0
## 92 2 0 3 4 1 1 0 0 1 1 0
## 93 3 0 0 2 1 0 0 1 0 0 0
## 94 5 0 5 4 1 1 0 0 1 1 0
## 95 1 0 0 4 1 0 1 1 1 0 0
## 96 2 0 3 4 1 1 0 0 1 1 0
## 97 6 0 3 3 1 0 1 0 1 0 0
## 98 2 0 2 4 1 1 0 0 1 0 1
## 99 5 2 0 4 1 1 1 0 1 0 0
## 100 5 0 11 3 1 0 1 0 1 0 0
## 101 2 0 0 1 0 1 0 0 0 0 0
## 102 0 0 2 3 1 0 1 1 0 0 0
## 103 0 0 3 3 1 0 1 0 1 0 0
## 104 1 1 2 3 1 1 0 0 1 0 0
## 105 5 0 3 3 1 1 0 0 0 1 0
## 106 2 0 14 4 1 0 1 1 1 0 0
## 107 2 0 0 3 1 1 0 0 1 0 0
## 108 2 3 0 3 1 1 0 1 0 0 0
## 109 0 2 0 3 1 0 0 0 1 1 0
## 110 1 0 0 3 0 0 1 0 1 0 0
## 111 2 0 0 2 0 1 0 1 0 0 0
## 112 4 0 4 4 1 1 1 1 0 0 0
## 113 0 3 0 4 1 1 0 0 1 0 1
## 114 1 0 0 2 0 0 1 0 1 0 0
## 115 0 0 2 2 1 0 0 0 1 0 0
## 116 6 3 0 3 1 0 1 1 0 0 0
## 117 1 0 0 7 1 1 1 1 1 1 0
## 118 2 0 0 2 1 1 0 0 0 0 0
## 119 5 0 5 5 1 1 0 1 1 1 0
## 120 1 0 3 3 0 0 1 1 1 0 0
## 121 4 0 0 3 0 0 1 0 1 0 1
## 122 1 0 3 3 1 0 1 0 1 0 0
## 123 1 0 1 3 1 0 1 0 1 0 0
## 124 1 0 0 3 1 0 1 0 1 0 0
## 125 0 0 3 2 0 0 1 0 1 0 0
## 126 2 0 3 3 0 1 0 0 1 0 1
## 127 2 3 0 2 0 1 0 0 1 0 0
## 128 1 0 0 4 1 0 1 1 1 0 0
## 129 6 0 5 3 1 0 1 0 1 0 0
## 130 2 0 3 4 1 1 0 0 1 1 0
## 131 1 0 0 3 1 0 1 0 1 0 0
## 132 2 0 2 3 1 1 0 0 1 0 0
## 133 2 1 0 2 0 1 0 0 1 0 0
## 134 0 0 0 3 1 0 1 0 1 0 0
## 135 5 0 0 4 1 0 0 1 1 1 0
## 136 0 0 3 6 1 1 1 1 1 1 0
## 137 0 3 0 2 1 0 0 0 1 0 0
## 138 0 0 0 4 1 0 1 1 1 0 0
## 139 6 0 2 2 1 0 0 0 1 0 0
## 140 0 0 0 5 1 1 1 1 1 0 0
## 141 0 0 5 3 1 0 1 0 1 0 0
## 142 2 0 0 2 0 0 0 0 1 0 1
## 143 6 3 0 7 1 1 1 1 1 0 1
## 144 5 3 0 4 1 1 0 1 0 1 0
## 145 0 0 2 2 0 0 1 0 1 0 0
## 146 0 0 3 4 1 1 0 1 0 1 0
## 147 2 0 0 2 0 1 0 0 1 0 0
## 148 2 3 0 3 1 1 0 1 0 0 0
## 149 1 0 0 3 1 1 0 0 1 0 0
## 150 5 0 3 3 0 1 1 0 1 0 0
## 151 3 0 2 2 1 0 0 0 1 0 0
## 152 1 0 0 4 0 1 1 1 1 0 0
## 153 2 0 0 2 0 0 1 0 1 0 0
## 154 1 0 3 5 1 1 1 0 1 0 1
## 155 7 0 0 4 1 0 1 0 1 1 0
## 156 2 0 3 4 1 0 1 0 1 1 0
## 157 0 0 3 2 1 0 0 1 0 0 0
## 158 3 2 0 4 0 1 0 1 0 0 1
## 159 1 0 0 7 1 1 1 1 1 0 1
## 160 1 0 0 5 1 1 0 1 1 1 0
## 161 1 0 0 4 0 0 1 1 1 1 0
## 162 1 5 0 4 0 1 1 1 1 0 0
## 163 2 0 3 4 1 1 0 0 1 1 0
## 164 1 0 5 4 1 1 0 1 1 0 0
## 165 1 0 5 7 1 0 1 1 1 1 1
## 166 1 0 0 2 0 0 1 1 0 0 0
## 167 1 0 0 2 1 0 0 0 1 0 0
## 168 2 0 3 4 1 1 0 0 1 1 0
## 169 3 0 0 3 1 0 1 0 1 0 0
## 170 5 0 0 4 0 1 1 1 0 1 0
## 171 3 0 5 3 1 0 1 0 1 0 0
## 172 7 0 5 4 1 1 0 1 1 0 0
## 173 1 0 0 2 1 0 0 0 1 0 0
## 174 1 0 0 3 1 0 0 0 1 1 0
## 175 2 0 0 2 1 0 0 0 1 0 0
## 176 2 0 0 2 1 0 0 0 1 0 0
## 177 1 0 0 6 1 1 1 1 1 0 1
## 178 1 0 0 5 1 0 1 1 1 0 0
## 179 2 1 3 4 1 1 0 0 1 1 0
## 180 5 0 6 5 1 0 0 1 1 1 0
## 181 1 0 0 3 1 0 1 0 1 0 0
## 182 0 0 9 3 0 0 1 1 1 0 0
## 183 1 0 0 6 1 1 1 1 1 0 0
## 184 1 0 13 3 1 0 1 0 1 0 0
## 185 6 0 0 2 1 0 0 1 0 0 0
## 186 1 0 0 4 1 1 0 1 0 1 0
## 187 0 2 0 4 1 0 0 1 1 1 0
## 188 0 0 3 7 1 1 1 1 1 1 1
## 189 6 0 0 2 1 0 0 1 0 0 0
## 190 1 0 0 3 1 0 1 0 1 0 0
## 191 6 0 3 4 1 0 1 1 1 0 0
## 192 5 0 0 4 1 1 0 1 0 0 1
## 193 5 3 0 4 1 1 0 0 0 1 1
## 194 5 0 7 5 1 1 0 1 1 1 0
## mainhue circles crosses saltires quarters sunstars crescent triangle
## 1 green 0 0 0 0 1 0 0
## 2 red 0 0 0 0 1 0 0
## 3 green 0 0 0 0 1 1 0
## 4 blue 0 0 0 0 0 0 1
## 5 gold 0 0 0 0 0 0 0
## 6 red 0 0 0 0 1 0 0
## 7 white 0 0 0 0 0 0 0
## 8 red 0 0 0 0 1 0 1
## 9 blue 0 0 0 0 0 0 0
## 10 blue 0 0 0 0 1 0 0
## 11 blue 0 1 1 1 6 0 0
## 12 red 0 0 0 0 0 0 0
## 13 blue 0 0 0 0 0 0 1
## 14 red 0 0 0 0 0 0 0
## 15 green 1 0 0 0 0 0 0
## 16 blue 0 0 0 0 0 0 0
## 17 gold 0 0 0 0 0 0 0
## 18 blue 1 0 0 0 0 0 0
## 19 green 0 0 0 0 1 0 0
## 20 red 1 1 1 1 0 0 0
## 21 orange 4 0 0 0 0 0 0
## 22 red 0 0 0 0 0 0 0
## 23 blue 0 0 0 0 0 0 0
## 24 green 1 0 0 0 22 0 0
## 25 blue 0 1 1 1 0 0 0
## 26 gold 0 0 0 0 0 0 1
## 27 red 0 0 0 0 1 0 0
## 28 red 0 0 0 0 1 0 0
## 29 red 0 0 0 1 14 0 0
## 30 red 1 0 1 0 3 0 0
## 31 gold 0 0 0 0 1 0 0
## 32 red 0 0 0 0 0 0 0
## 33 gold 0 0 0 0 1 0 0
## 34 blue 1 1 1 1 4 0 0
## 35 gold 0 0 0 0 1 0 0
## 36 gold 0 0 0 0 0 0 0
## 37 red 0 0 0 1 1 0 0
## 38 red 0 0 0 0 5 0 0
## 39 gold 0 0 0 0 0 0 0
## 40 green 0 0 0 0 4 1 0
## 41 red 0 0 0 0 1 0 0
## 42 blue 1 1 1 1 15 0 0
## 43 blue 0 0 0 0 0 0 0
## 44 blue 0 0 0 0 1 0 1
## 45 white 0 0 0 0 0 0 0
## 46 white 0 0 0 0 0 0 1
## 47 red 0 1 0 0 0 0 0
## 48 blue 0 0 0 0 1 0 1
## 49 green 1 0 0 0 10 0 0
## 50 blue 0 1 0 0 0 0 0
## 51 gold 0 0 0 0 0 0 0
## 52 black 0 0 0 0 0 0 0
## 53 blue 0 0 0 0 0 0 0
## 54 green 0 0 0 0 0 0 1
## 55 green 0 0 0 0 0 0 0
## 56 white 0 1 0 0 0 0 0
## 57 blue 1 1 1 1 0 0 0
## 58 blue 0 2 1 1 0 0 0
## 59 white 0 1 0 0 0 0 0
## 60 white 0 0 0 0 0 0 0
## 61 white 0 0 0 0 0 0 0
## 62 red 1 0 0 0 1 0 0
## 63 green 0 0 0 0 0 0 0
## 64 red 0 0 0 0 0 0 0
## 65 gold 0 0 0 0 0 0 0
## 66 black 0 0 0 0 0 0 0
## 67 red 0 0 0 0 1 0 0
## 68 white 0 0 0 0 0 0 0
## 69 blue 0 1 0 1 0 0 0
## 70 white 1 0 0 0 0 0 0
## 71 gold 1 0 0 0 7 0 1
## 72 blue 0 0 0 0 0 0 0
## 73 blue 0 0 0 0 0 0 0
## 74 gold 0 0 0 0 0 0 0
## 75 gold 0 0 0 0 1 0 0
## 76 green 0 0 0 0 0 0 1
## 77 black 0 0 0 0 0 0 0
## 78 blue 0 0 0 0 5 0 0
## 79 blue 1 1 1 1 0 0 0
## 80 red 0 0 0 0 0 0 0
## 81 blue 0 1 0 0 0 0 0
## 82 orange 1 0 0 0 0 0 0
## 83 red 0 0 0 0 0 0 0
## 84 red 0 0 0 0 0 0 0
## 85 red 0 0 0 0 3 0 0
## 86 white 0 0 0 0 0 0 0
## 87 white 0 0 0 0 1 0 0
## 88 white 0 0 0 0 0 0 0
## 89 white 0 0 0 0 0 0 0
## 90 green 0 0 1 0 0 0 1
## 91 white 1 0 0 0 1 0 0
## 92 black 0 0 0 0 1 0 1
## 93 red 0 0 0 0 0 0 0
## 94 red 1 0 0 0 0 0 0
## 95 red 0 0 0 0 1 0 0
## 96 green 0 0 0 0 0 0 0
## 97 red 1 0 0 0 0 0 0
## 98 red 0 0 0 0 0 0 0
## 99 blue 0 0 0 0 0 0 0
## 100 red 0 0 0 1 1 0 0
## 101 green 0 0 0 0 0 0 0
## 102 red 0 0 0 0 0 0 0
## 103 red 0 0 0 0 0 0 0
## 104 red 0 0 0 0 0 0 0
## 105 red 0 0 0 0 1 0 0
## 106 red 0 0 0 1 1 1 0
## 107 red 0 0 0 0 0 1 0
## 108 gold 0 0 0 0 0 0 0
## 109 red 0 1 0 0 0 0 0
## 110 blue 0 0 0 0 1 0 0
## 111 green 0 0 0 0 1 1 0
## 112 red 0 0 0 0 0 0 0
## 113 green 0 0 0 0 0 0 0
## 114 blue 0 0 0 0 4 0 0
## 115 red 0 0 0 0 0 0 0
## 116 red 2 0 0 0 1 1 1
## 117 blue 0 2 1 1 0 0 0
## 118 red 0 0 0 0 1 0 0
## 119 gold 0 0 0 0 1 0 1
## 120 blue 0 0 0 0 1 0 0
## 121 brown 0 0 0 0 2 1 0
## 122 red 0 0 0 0 0 0 0
## 123 white 0 0 0 0 6 0 0
## 124 blue 0 1 1 1 4 0 0
## 125 blue 0 0 0 0 0 0 0
## 126 orange 1 0 0 0 0 0 0
## 127 green 0 0 0 0 0 0 0
## 128 gold 1 1 1 1 5 0 0
## 129 blue 1 0 0 0 1 0 0
## 130 red 0 0 0 0 1 0 0
## 131 red 0 1 0 0 0 0 0
## 132 red 0 0 0 0 0 0 0
## 133 green 0 0 0 0 1 1 0
## 134 red 0 0 0 4 2 0 0
## 135 black 0 0 0 0 5 0 1
## 136 red 1 0 0 0 1 0 0
## 137 red 0 0 0 0 0 0 0
## 138 blue 0 0 0 0 4 0 1
## 139 white 0 0 0 0 0 0 0
## 140 red 1 0 0 0 0 0 0
## 141 red 0 0 0 0 1 0 1
## 142 brown 0 0 0 0 0 0 0
## 143 red 0 0 0 0 2 0 0
## 144 red 0 0 0 0 0 0 0
## 145 white 0 0 0 0 0 0 0
## 146 green 0 0 0 0 2 0 1
## 147 green 0 0 0 0 0 0 0
## 148 green 0 0 0 0 1 0 0
## 149 red 0 0 0 0 0 0 0
## 150 green 0 0 0 0 0 0 0
## 151 white 0 0 0 0 5 1 0
## 152 green 0 0 0 0 5 0 1
## 153 blue 0 0 0 0 1 0 0
## 154 orange 0 1 1 0 0 0 0
## 155 white 1 0 0 0 0 0 0
## 156 red 0 0 0 0 1 0 1
## 157 red 0 0 0 0 0 0 0
## 158 gold 0 0 0 0 0 0 0
## 159 blue 0 1 1 1 0 0 0
## 160 green 0 0 0 0 2 0 1
## 161 blue 0 0 0 0 0 0 1
## 162 green 0 0 0 0 0 0 0
## 163 red 0 0 0 0 0 0 1
## 164 red 0 0 0 0 1 0 0
## 165 blue 0 0 0 0 0 0 0
## 166 blue 0 1 0 0 0 0 0
## 167 red 0 1 0 0 0 0 0
## 168 red 0 0 0 0 2 0 0
## 169 red 1 0 0 1 1 0 0
## 170 green 0 0 0 0 0 0 1
## 171 red 0 0 0 0 0 0 0
## 172 green 0 0 0 1 1 0 0
## 173 red 0 1 0 1 0 0 0
## 174 red 0 0 0 0 0 0 1
## 175 red 1 0 0 0 1 1 0
## 176 red 0 0 0 0 1 1 0
## 177 blue 0 1 1 1 0 0 0
## 178 blue 0 1 1 1 9 0 0
## 179 green 0 0 0 0 0 0 0
## 180 gold 1 0 0 0 0 0 0
## 181 red 0 1 1 0 0 0 0
## 182 white 0 0 0 1 1 0 0
## 183 white 0 0 0 0 0 0 0
## 184 white 0 0 0 1 50 0 0
## 185 red 0 0 0 0 1 0 0
## 186 red 0 0 0 0 0 0 1
## 187 gold 0 0 0 0 0 0 0
## 188 red 0 0 0 0 7 0 0
## 189 red 0 0 0 0 1 0 0
## 190 red 0 0 0 1 5 0 0
## 191 red 0 0 0 0 1 0 0
## 192 green 1 0 0 0 0 0 0
## 193 green 0 0 0 0 0 0 0
## 194 green 0 0 0 0 1 0 1
## icon animate text topleft botright
## 1 1 0 0 black green
## 2 0 1 0 red red
## 3 0 0 0 green white
## 4 1 1 0 blue red
## 5 0 0 0 blue red
## 6 1 0 0 red black
## 7 0 1 0 white blue
## 8 0 0 0 black red
## 9 0 0 0 blue blue
## 10 0 0 0 blue blue
## 11 0 0 0 white blue
## 12 0 0 0 red red
## 13 0 0 0 blue blue
## 14 0 0 0 white red
## 15 0 0 0 green green
## 16 1 0 0 blue blue
## 17 0 0 0 black red
## 18 1 1 1 red red
## 19 0 0 0 green green
## 20 1 1 0 white red
## 21 0 1 0 orange red
## 22 0 0 0 red green
## 23 0 0 0 blue blue
## 24 0 0 1 green green
## 25 1 1 1 white blue
## 26 1 1 1 white gold
## 27 1 1 0 white red
## 28 0 0 0 red green
## 29 1 1 0 blue red
## 30 0 0 0 white white
## 31 0 0 0 green gold
## 32 0 1 0 red red
## 33 0 1 0 red green
## 34 1 1 1 white blue
## 35 0 0 0 blue gold
## 36 0 0 0 blue red
## 37 0 0 0 blue red
## 38 0 0 0 red red
## 39 0 0 0 gold red
## 40 0 0 0 green green
## 41 1 1 0 red red
## 42 0 0 0 white blue
## 43 0 0 0 blue blue
## 44 0 0 0 blue blue
## 45 1 1 0 white white
## 46 0 0 0 white red
## 47 0 0 0 red red
## 48 0 0 0 white green
## 49 0 1 0 green green
## 50 0 0 0 blue blue
## 51 0 0 0 gold red
## 52 0 1 1 red black
## 53 0 0 0 blue blue
## 54 0 0 0 green red
## 55 0 0 0 green red
## 56 0 0 0 white white
## 57 1 1 1 white blue
## 58 1 1 0 white blue
## 59 0 0 0 white white
## 60 0 0 0 blue red
## 61 0 0 0 blue red
## 62 1 0 0 red red
## 63 0 0 0 green blue
## 64 0 0 0 red green
## 65 1 0 0 black gold
## 66 0 0 0 black gold
## 67 0 0 0 red green
## 68 1 0 0 white red
## 69 0 0 0 blue blue
## 70 0 0 0 white red
## 71 0 1 0 red red
## 72 1 1 1 red red
## 73 0 0 0 blue blue
## 74 0 0 0 red green
## 75 0 0 0 red green
## 76 0 0 0 black green
## 77 0 0 0 black red
## 78 0 0 0 blue blue
## 79 1 1 1 white blue
## 80 0 0 0 red green
## 81 0 0 0 blue blue
## 82 1 0 0 orange green
## 83 0 0 0 red white
## 84 1 0 1 green red
## 85 0 0 0 red black
## 86 0 0 0 green orange
## 87 0 0 0 blue blue
## 88 0 0 0 green red
## 89 0 0 0 red green
## 90 0 0 0 gold gold
## 91 0 0 0 white white
## 92 0 0 0 black green
## 93 1 0 0 red red
## 94 1 0 0 black green
## 95 1 1 0 red blue
## 96 0 0 0 green red
## 97 0 0 0 red red
## 98 0 1 0 red red
## 99 1 0 0 green blue
## 100 0 0 0 blue red
## 101 0 0 0 green green
## 102 1 0 0 blue red
## 103 0 0 0 red blue
## 104 0 0 0 white green
## 105 0 0 0 black green
## 106 0 0 0 blue white
## 107 0 0 0 red red
## 108 0 0 0 green red
## 109 1 0 0 white red
## 110 1 0 0 blue blue
## 111 0 0 0 green green
## 112 0 0 0 red green
## 113 0 1 0 green red
## 114 0 0 0 blue blue
## 115 0 0 0 red white
## 116 1 0 0 red red
## 117 1 1 0 white blue
## 118 0 0 0 red red
## 119 1 0 0 green gold
## 120 0 0 0 blue blue
## 121 0 0 0 blue blue
## 122 0 0 0 red blue
## 123 0 0 0 white white
## 124 0 0 0 white blue
## 125 0 0 0 blue blue
## 126 0 0 0 orange green
## 127 0 0 0 green green
## 128 0 0 0 white gold
## 129 0 0 0 blue blue
## 130 0 0 0 red black
## 131 0 0 0 red red
## 132 1 0 0 red green
## 133 0 0 0 white green
## 134 0 0 0 white white
## 135 0 1 0 red black
## 136 1 1 1 red blue
## 137 0 0 0 red red
## 138 0 0 0 blue red
## 139 0 0 0 white red
## 140 1 0 0 green red
## 141 0 0 0 red red
## 142 0 0 0 white brown
## 143 1 1 1 blue red
## 144 0 0 1 red green
## 145 0 0 0 white blue
## 146 0 0 0 green green
## 147 1 0 1 green green
## 148 0 0 0 green red
## 149 0 0 0 red green
## 150 0 0 0 green blue
## 151 0 0 0 red white
## 152 0 0 0 blue green
## 153 0 0 0 blue blue
## 154 0 0 0 orange blue
## 155 1 0 0 white white
## 156 0 0 0 red black
## 157 0 0 0 red red
## 158 1 1 0 gold gold
## 159 1 0 0 white blue
## 160 0 0 0 green red
## 161 0 0 0 blue blue
## 162 1 1 1 blue green
## 163 0 0 0 red black
## 164 0 0 0 green green
## 165 1 0 0 blue blue
## 166 0 0 0 blue blue
## 167 0 0 0 red red
## 168 0 0 0 red black
## 169 0 0 0 blue red
## 170 0 0 0 green blue
## 171 0 0 0 red red
## 172 0 0 0 red green
## 173 0 0 0 white red
## 174 0 0 0 white white
## 175 0 0 0 red red
## 176 0 0 0 red red
## 177 1 1 0 white blue
## 178 0 0 0 white blue
## 179 0 0 0 red black
## 180 0 1 0 black red
## 181 0 0 0 white red
## 182 0 0 0 white white
## 183 1 1 1 white white
## 184 0 0 0 blue red
## 185 1 0 0 red red
## 186 0 1 0 black green
## 187 1 0 0 gold white
## 188 1 1 0 gold red
## 189 0 0 0 red red
## 190 0 0 0 blue red
## 191 0 0 0 blue red
## 192 1 1 0 green green
## 193 0 1 0 green brown
## 194 1 1 0 green greendim(flag_df)## [1] 194 31- Print out the first 5 lines and the last 5 lines of flag_df.
head(flag_df,5)## X name landmass zone area population language religion bars
## 1 1 Afghanistan 5 1 648 16 10 2 0
## 2 2 Albania 3 1 29 3 6 6 0
## 3 3 Algeria 4 1 2388 20 8 2 2
## 4 4 American-Samoa 6 3 0 0 1 1 0
## 5 5 Andorra 3 1 0 0 6 0 3
## stripes colours red green blue gold white black orange mainhue circles
## 1 3 5 1 1 0 1 1 1 0 green 0
## 2 0 3 1 0 0 1 0 1 0 red 0
## 3 0 3 1 1 0 0 1 0 0 green 0
## 4 0 5 1 0 1 1 1 0 1 blue 0
## 5 0 3 1 0 1 1 0 0 0 gold 0
## crosses saltires quarters sunstars crescent triangle icon animate text
## 1 0 0 0 1 0 0 1 0 0
## 2 0 0 0 1 0 0 0 1 0
## 3 0 0 0 1 1 0 0 0 0
## 4 0 0 0 0 0 1 1 1 0
## 5 0 0 0 0 0 0 0 0 0
## topleft botright
## 1 black green
## 2 red red
## 3 green white
## 4 blue red
## 5 blue redtail(flag_df,5)## X name landmass zone area population language religion bars
## 190 190 Western-Samoa 6 3 3 0 1 1 0
## 191 191 Yugoslavia 3 1 256 22 6 6 0
## 192 192 Zaire 4 2 905 28 10 5 0
## 193 193 Zambia 4 2 753 6 10 5 3
## 194 194 Zimbabwe 4 2 391 8 10 5 0
## stripes colours red green blue gold white black orange mainhue circles
## 190 0 3 1 0 1 0 1 0 0 red 0
## 191 3 4 1 0 1 1 1 0 0 red 0
## 192 0 4 1 1 0 1 0 0 1 green 1
## 193 0 4 1 1 0 0 0 1 1 green 0
## 194 7 5 1 1 0 1 1 1 0 green 0
## crosses saltires quarters sunstars crescent triangle icon animate text
## 190 0 0 1 5 0 0 0 0 0
## 191 0 0 0 1 0 0 0 0 0
## 192 0 0 0 0 0 0 1 1 0
## 193 0 0 0 0 0 0 0 1 0
## 194 0 0 0 1 0 1 1 1 0
## topleft botright
## 190 blue red
## 191 blue red
## 192 green green
## 193 green brown
## 194 green green- Print out the summary statistics of each variable of flag_df.
summary(flag_df)## X name landmass zone
## Min. : 1.00 Afghanistan : 1 Min. :1.000 Min. :1.000
## 1st Qu.: 49.25 Albania : 1 1st Qu.:3.000 1st Qu.:1.000
## Median : 97.50 Algeria : 1 Median :4.000 Median :2.000
## Mean : 97.50 American-Samoa: 1 Mean :3.572 Mean :2.211
## 3rd Qu.:145.75 Andorra : 1 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :194.00 Angola : 1 Max. :6.000 Max. :4.000
## (Other) :188
## area population language religion
## Min. : 0.0 Min. : 0.00 Min. : 1.00 Min. :0.000
## 1st Qu.: 9.0 1st Qu.: 0.00 1st Qu.: 2.00 1st Qu.:1.000
## Median : 111.0 Median : 4.00 Median : 6.00 Median :1.000
## Mean : 700.0 Mean : 23.27 Mean : 5.34 Mean :2.191
## 3rd Qu.: 471.2 3rd Qu.: 14.00 3rd Qu.: 9.00 3rd Qu.:4.000
## Max. :22402.0 Max. :1008.00 Max. :10.00 Max. :7.000
##
## bars stripes colours red
## Min. :0.0000 Min. : 0.000 Min. :1.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.:3.000 1st Qu.:1.0000
## Median :0.0000 Median : 0.000 Median :3.000 Median :1.0000
## Mean :0.4536 Mean : 1.552 Mean :3.464 Mean :0.7887
## 3rd Qu.:0.0000 3rd Qu.: 3.000 3rd Qu.:4.000 3rd Qu.:1.0000
## Max. :5.0000 Max. :14.000 Max. :8.000 Max. :1.0000
##
## green blue gold white
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :0.0000 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :0.4691 Mean :0.5103 Mean :0.4691 Mean :0.7526
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## black orange mainhue circles
## Min. :0.000 Min. :0.000 red :71 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.000 blue :40 1st Qu.:0.0000
## Median :0.000 Median :0.000 green :31 Median :0.0000
## Mean :0.268 Mean :0.134 white :22 Mean :0.1701
## 3rd Qu.:1.000 3rd Qu.:0.000 gold :19 3rd Qu.:0.0000
## Max. :1.000 Max. :1.000 black : 5 Max. :4.0000
## (Other): 6
## crosses saltires quarters sunstars
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.: 0.000
## Median :0.0000 Median :0.00000 Median :0.0000 Median : 0.000
## Mean :0.1495 Mean :0.09278 Mean :0.1495 Mean : 1.387
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.: 1.000
## Max. :2.0000 Max. :1.00000 Max. :4.0000 Max. :50.000
##
## crescent triangle icon animate
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.000
## Mean :0.0567 Mean :0.1392 Mean :0.2526 Mean :0.201
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.7500 3rd Qu.:0.000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000
##
## text topleft botright
## Min. :0.00000 black :12 red :69
## 1st Qu.:0.00000 blue :43 blue :47
## Median :0.00000 gold : 6 green :40
## Mean :0.08247 green :32 white :17
## 3rd Qu.:0.00000 orange: 4 black : 9
## Max. :1.00000 red :56 gold : 9
## white :41 (Other): 3- Print out the structure of flag_df.
str(flag_df)## 'data.frame': 194 obs. of 31 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ name : Factor w/ 194 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ landmass : int 5 3 4 6 3 4 1 1 2 2 ...
## $ zone : int 1 1 1 3 1 2 4 4 3 3 ...
## $ area : int 648 29 2388 0 0 1247 0 0 2777 2777 ...
## $ population: int 16 3 20 0 0 7 0 0 28 28 ...
## $ language : int 10 6 8 1 6 10 1 1 2 2 ...
## $ religion : int 2 6 2 1 0 5 1 1 0 0 ...
## $ bars : int 0 0 2 0 3 0 0 0 0 0 ...
## $ stripes : int 3 0 0 0 0 2 1 1 3 3 ...
## $ colours : int 5 3 3 5 3 3 3 5 2 3 ...
## $ red : int 1 1 1 1 1 1 0 1 0 0 ...
## $ green : int 1 0 1 0 0 0 0 0 0 0 ...
## $ blue : int 0 0 0 1 1 0 1 1 1 1 ...
## $ gold : int 1 1 0 1 1 1 0 1 0 1 ...
## $ white : int 1 0 1 1 0 0 1 1 1 1 ...
## $ black : int 1 1 0 0 0 1 0 1 0 0 ...
## $ orange : int 0 0 0 1 0 0 1 0 0 0 ...
## $ mainhue : Factor w/ 8 levels "black","blue",..: 5 7 5 2 4 7 8 7 2 2 ...
## $ circles : int 0 0 0 0 0 0 0 0 0 0 ...
## $ crosses : int 0 0 0 0 0 0 0 0 0 0 ...
## $ saltires : int 0 0 0 0 0 0 0 0 0 0 ...
## $ quarters : int 0 0 0 0 0 0 0 0 0 0 ...
## $ sunstars : int 1 1 1 0 0 1 0 1 0 1 ...
## $ crescent : int 0 0 1 0 0 0 0 0 0 0 ...
## $ triangle : int 0 0 0 1 0 0 0 1 0 0 ...
## $ icon : int 1 0 0 1 0 1 0 0 0 0 ...
## $ animate : int 0 1 0 1 0 0 1 0 0 0 ...
## $ text : int 0 0 0 0 0 0 0 0 0 0 ...
## $ topleft : Factor w/ 7 levels "black","blue",..: 1 6 4 2 2 6 7 1 2 2 ...
## $ botright : Factor w/ 8 levels "black","blue",..: 5 7 8 7 7 1 2 7 2 2 ...Data Cleaning/Management
We are going to use the dplyr package.
- Load the tidyverse and convert the type of flag_df to tibble.
library(tidyverse)## -- Attaching packages ------------------------------ tidyverse 1.2.1 --## v ggplot2 3.2.1 v purrr 0.3.2
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 0.8.3 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## -- Conflicts --------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()flag_df<-as_tibble(flag_df)- Find the variable (column) names of flag_df.
glimpse(flag_df)## Observations: 194
## Variables: 31
## $ X <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ...
## $ name <fct> Afghanistan, Albania, Algeria, American-Samoa, Ando...
## $ landmass <int> 5, 3, 4, 6, 3, 4, 1, 1, 2, 2, 6, 3, 1, 5, 5, 1, 3, ...
## $ zone <int> 1, 1, 1, 3, 1, 2, 4, 4, 3, 3, 2, 1, 4, 1, 1, 4, 1, ...
## $ area <int> 648, 29, 2388, 0, 0, 1247, 0, 0, 2777, 2777, 7690, ...
## $ population <int> 16, 3, 20, 0, 0, 7, 0, 0, 28, 28, 15, 8, 0, 0, 90, ...
## $ language <int> 10, 6, 8, 1, 6, 10, 1, 1, 2, 2, 1, 4, 1, 8, 6, 1, 6...
## $ religion <int> 2, 6, 2, 1, 0, 5, 1, 1, 0, 0, 1, 0, 1, 2, 2, 1, 0, ...
## $ bars <int> 0, 0, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, ...
## $ stripes <int> 3, 0, 0, 0, 0, 2, 1, 1, 3, 3, 0, 3, 3, 0, 0, 0, 0, ...
## $ colours <int> 5, 3, 3, 5, 3, 3, 3, 5, 2, 3, 3, 2, 3, 2, 2, 3, 3, ...
## $ red <int> 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, ...
## $ green <int> 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...
## $ blue <int> 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, ...
## $ gold <int> 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, ...
## $ white <int> 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, ...
## $ black <int> 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, ...
## $ orange <int> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ mainhue <fct> green, red, green, blue, gold, red, white, red, blu...
## $ circles <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...
## $ crosses <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...
## $ saltires <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...
## $ quarters <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...
## $ sunstars <int> 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 6, 0, 0, 0, 0, 0, 0, ...
## $ crescent <int> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ triangle <int> 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...
## $ icon <int> 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...
## $ animate <int> 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ text <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ topleft <fct> black, red, green, blue, blue, red, white, black, b...
## $ botright <fct> green, red, white, red, red, black, blue, red, blue...Something should look strange about the first column name. Let’s investigate this.
- Print out the first column.
flag_df[,1]## # A tibble: 194 x 1
## X
## <int>
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5
## 6 6
## 7 7
## 8 8
## 9 9
## 10 10
## # ... with 184 more rowsWhat is in this first column? The number of rows, which is the number of countries/flags.
Do we really need it? No
- Delete the first column of flag_df.
flag_df$X1=NULL- Verify that there are no missing values in flag_df.
sum(is.na(flag_df))## [1] 0At this point, we know there are no missing values in the dataset so we will use dplyr to make the dataset a bit more readable to us. Look at the flag.names file again. Under “Attribute Information” look at the variables landmass, zone, language, religion.
Instead of encoding these categories using numbers, we would like to just use the categories in the variables. For example, in the zone column, we want our data to be “NE”, “SE”, “SW”, “NW”, instead of 1, 2, 3, 4.
- Change each of the columns landmass, zone, language, and religion to hold their actual categorical data (not their encoded numbers). The type of each of these columns should be Factor.
flag_df$landmass<-as.character(flag_df$landmass)
flag_df$landmass[flag_df$landmass == "1"]<- "NE"
flag_df$landmass[flag_df$landmass == "2"]<- "SE"
flag_df$landmass[flag_df$landmass == "3"]<- "SW"
flag_df$landmass[flag_df$landmass == "4"]<- "NW"
flag_df$zone<-as.character(flag_df$zone)
flag_df$zone[flag_df$zone == "1"]<- "NE"
flag_df$zone[flag_df$zone == "2"]<- "SE"
flag_df$zone[flag_df$zone == "3"]<- "SW"
flag_df$zone[flag_df$zone == "4"]<- "NW"
flag_df$language<-as.character(flag_df$language)
flag_df$language[flag_df$language == "1"]<- "NE"
flag_df$language[flag_df$language== "2"]<- "SE"
flag_df$language[flag_df$language== "3"]<- "SW"
flag_df$language[flag_df$language== "4"]<- "NW"
flag_df$religion<-as.character(flag_df$religion)
flag_df$religion[flag_df$religion== "1"]<- "NE"
flag_df$religion[flag_df$religion== "2"]<- "SE"
flag_df$religion[flag_df$religion== "3"]<- "SW"
flag_df$religion[flag_df$religion== "4"]<- "NW"
flag_df$landmass<-as.factor(flag_df$landmass)
flag_df$zone<-as.factor(flag_df$zone)
flag_df$language<-as.factor(flag_df$language)
flag_df$religion<-as.factor(flag_df$religion)Notice from our earlier structure command that the data types for columns red, green, blue, gold, white, black, orange, crescent, triangle, icon, animate, text are all integer. Looking at flag.names these integer variables are really just an encoding for true (1) or false (0). We don’t want to compute with these 1s and 0s (for example find a mean). So we should change these to logicals.
- Change the column type to logical for the following columns: red, green, blue, gold, white, black, orange, crescent, triangle, icon, animate, and text.
flag_df$red<-as.logical(flag_df$red)
flag_df$green<-as.logical(flag_df$green)
flag_df$blue<-as.logical(flag_df$blue)
flag_df$gold<-as.logical(flag_df$gold)
flag_df$white<-as.logical(flag_df$white)
flag_df$black<-as.logical(flag_df$black)
flag_df$orange<-as.logical(flag_df$orange)
flag_df$crescent<-as.logical(flag_df$crescent)
flag_df$triangle<-as.logical(flag_df$triangle)
flag_df$icon<-as.logical(flag_df$icon)
flag_df$animate<-as.logical(flag_df$animate)
flag_df$text<-as.logical(flag_df$text)Now that our data is clean, let’s answer some questions about it!
Data Investigation
- Print out how many countries have each “mainhue” category.
table(flag_df$mainhue)##
## black blue brown gold green orange red white
## 5 40 2 19 31 4 71 22- How many countries have the three colors red, white, and blue in their flags? How many countries have ONLY the three colors red, white, and blue in their flags?
library(dplyr)
colors<- flag_df %>%
filter(red==TRUE&blue==TRUE&white==TRUE&black==FALSE&gold==FALSE&green==FALSE&orange==FALSE)
dim(colors)## [1] 27 31- Print out the data observations for the 10 countries with the largest populations. The 10 data observations should be printed out in descending order according to population.
arrange(flag_df,desc(population)) %>%
head(10)## # A tibble: 10 x 31
## X name landmass zone area population language religion bars
## <int> <fct> <fct> <fct> <int> <int> <fct> <fct> <int>
## 1 38 China 5 NE 9561 1008 7 6 0
## 2 82 India 5 NE 3268 684 6 NW 0
## 3 185 USSR 5 NE 22402 274 5 6 0
## 4 184 USA NE NW 9363 231 NE NE 0
## 5 83 Indo~ 6 SE 1904 157 10 SE 0
## 6 24 Braz~ SE SW 8512 119 6 0 0
## 7 91 Japan 5 NE 372 118 9 7 0
## 8 15 Bang~ 5 NE 143 90 6 SE 0
## 9 133 Paki~ 5 NE 804 84 6 SE 1
## 10 113 Mexi~ NE NW 1973 77 SE 0 3
## # ... with 22 more variables: stripes <int>, colours <int>, red <lgl>,
## # green <lgl>, blue <lgl>, gold <lgl>, white <lgl>, black <lgl>,
## # orange <lgl>, mainhue <fct>, circles <int>, crosses <int>,
## # saltires <int>, quarters <int>, sunstars <int>, crescent <lgl>,
## # triangle <lgl>, icon <lgl>, animate <lgl>, text <lgl>, topleft <fct>,
## # botright <fct>Let’s see if we can find any patterns in the data.
- Group the flags by landmass and find the following for each group:
- the mode mainhue
- the median number of sun or star symbols
- the number of flags with animate images
- the percentage of flags with animate images
Your output should be a data frame with each row corresponding to a group. There will be five columns.
Repeat this process except group by zone, language, and religion.
# You may find this function useful (ie. you should call this function in your code)! It calculates the mode of a factor.
cat_mode <- function(cat_var){
mode_idx <- which.max(table(cat_var))
levels(cat_var)[mode_idx]
}
# fill in your code here
flag_df %>%
group_by(landmass) %>%
summarise(ModeMainhue=cat_mode(mainhue),MedianLandmass=median(sunstars),animateLandmass=sum(animate), animateLandmassPercent=animateLandmass/(length(animate))*100)## # A tibble: 6 x 5
## landmass ModeMainhue MedianLandmass animateLandmass animateLandmassPerce~
## <fct> <chr> <dbl> <int> <dbl>
## 1 5 red 1 6 15.4
## 2 6 blue 2.5 6 30
## 3 NE blue 0 13 41.9
## 4 NW green 0 7 13.5
## 5 SE red 0 3 17.6
## 6 SW red 0 4 11.4flag_df %>%
group_by(zone) %>%
summarise(ModeZone=cat_mode(mainhue),MedianZone=median(sunstars),animateZone=sum(animate), animateZonePercent=animateZone/(length(animate))*100)## # A tibble: 4 x 5
## zone ModeZone MedianZone animateZone animateZonePercent
## <fct> <chr> <dbl> <int> <dbl>
## 1 NE red 0 14 15.4
## 2 NW blue 0 15 25.9
## 3 SE red 0 7 24.1
## 4 SW blue 1 3 18.8flag_df %>%
group_by(language) %>%
summarise(ModeLang=cat_mode(mainhue),MedianLang=median(sunstars),animateLang=sum(animate), animateLangPercent=animateLang/(length(animate))*100)## # A tibble: 10 x 5
## language ModeLang MedianLang animateLang animateLangPercent
## <fct> <chr> <dbl> <int> <dbl>
## 1 10 red 0 9 19.6
## 2 5 red 0.5 1 25
## 3 6 red 0 5 16.7
## 4 7 red 3 1 25
## 5 8 red 0 2 10.5
## 6 9 red 0.5 0 0
## 7 NE blue 0 18 41.9
## 8 NW red 0 0 0
## 9 SE blue 0 3 14.3
## 10 SW gold 0 0 0flag_df %>%
group_by(religion) %>%
summarise(ModeReligion=cat_mode(mainhue),MedianReligion=median(sunstars),animateReligion=sum(animate), animateReligionPercent=animateReligion/(length(animate))*100)## # A tibble: 8 x 5
## religion ModeReligion MedianReligion animateReligion animateReligionPerc~
## <fct> <chr> <dbl> <int> <dbl>
## 1 0 red 0 4 10
## 2 5 red 0 6 22.2
## 3 6 red 1 3 20
## 4 7 white 1 0 0
## 5 NE blue 0 19 31.7
## 6 NW brown 0 0 0
## 7 SE red 0 3 8.33
## 8 SW red 0 4 50Do you see any patterns in flag mainhue, sun or star symbols, and animate images? If so, describe these patterns. (Hint: you should see patterns! Look at the trends when grouping by landmass, zone, language, and religion.) Write a paragraph to answer this question.
When grouping by landmass, zone, language and religion, most of the NE regions have the highest number of animate objects and the highest percentage. Another pattern is that when there are sun or star symbols present, the number of animate objects is lowered.