DATA 607 Week 1 Assignment – Loading Data into a Data Frame
Beshkia Kvarnstrom
2023-01-29
Task - Choose one of the provided datasets on fivethirtyeight.com that you find interesting: https://data.fivethirtyeight.com/
You should first study the data and any other information on the GitHub site, and read the associated fivethirtyeight.com article.
To receive full credit, you should: 1. Take the data, and create one or more code blocks. You should finish with a data frame that contains a subset of the columns in your selected dataset. If there is an obvious target (aka predictor or independent) variable, you should include this in your set of columns. You should include (or add if necessary) meaningful column names and replace (if necessary) any non-intuitive abbreviations used in the data that you selected. For example, if you had instead been tasked with working with the UCI mushroom dataset, you would include the target column for edible or poisonous, and transform “e” values to “edible.” Your deliverable is the R code to perform these transformation tasks.
Make sure that the original data file is accessible through your code—for example, stored in a GitHub repository or AWS S3 bucket and referenced in your code. If the code references data on your local machine, then your work is not reproducible!
Start your R Markdown document with a two to three sentence “Overview” or “Introduction” description of what the article that you chose is about, and include a link to the article.
Finish with a “Conclusions” or “Findings and Recommendations” text block that includes what you might do to extend, verify, or update the work from the selected article.
Each of your text blocks should minimally include at least one header, and additional non-header text.
You’re of course welcome—but not required–to include additional information, such as exploratory data analysis graphics (which we will cover later in the course).
Place your solution into a single R Markdown (.Rmd) file and publish your solution out to rpubs.com.
Post the .Rmd file in your GitHub repository, and provide the appropriate URLs to your GitHub repositor y and your rpubs.com file in your assignment link.
OVERVIEW The Data I will be working with for this assignment compare the comsuption of wine, beer and other spirits of different countries across the world. A row is listed for each country along with the amount of each alcoholic beverage.
The dataset was taken from: https://fivethirtyeight.com/features/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits//
## Load data from GitHub
AlcoholConsumptionData = read.table(file="https://raw.githubusercontent.com/BeshkiaKvarnstrom/MSDS-DATA607/main/drinks.csv", header=TRUE,sep=",")# Reading the data in the Dataframe
AlcoholConsum <- data.frame(AlcoholConsumptionData$country, AlcoholConsumptionData$beer_servings, AlcoholConsumptionData$spirit_servings, AlcoholConsumptionData$wine_servings, AlcoholConsumptionData$total_litres_of_pure_alcohol)
colnames(AlcoholConsum) <- c("COUNTRY", "BEER", "SPIRIT", "WINE", "TOTAL LITRES")
AlcoholConsum## COUNTRY BEER SPIRIT WINE TOTAL LITRES
## 1 Afghanistan 0 0 0 0.0
## 2 Albania 89 132 54 4.9
## 3 Algeria 25 0 14 0.7
## 4 Andorra 245 138 312 12.4
## 5 Angola 217 57 45 5.9
## 6 Antigua & Barbuda 102 128 45 4.9
## 7 Argentina 193 25 221 8.3
## 8 Armenia 21 179 11 3.8
## 9 Australia 261 72 212 10.4
## 10 Austria 279 75 191 9.7
## 11 Azerbaijan 21 46 5 1.3
## 12 Bahamas 122 176 51 6.3
## 13 Bahrain 42 63 7 2.0
## 14 Bangladesh 0 0 0 0.0
## 15 Barbados 143 173 36 6.3
## 16 Belarus 142 373 42 14.4
## 17 Belgium 295 84 212 10.5
## 18 Belize 263 114 8 6.8
## 19 Benin 34 4 13 1.1
## 20 Bhutan 23 0 0 0.4
## 21 Bolivia 167 41 8 3.8
## 22 Bosnia-Herzegovina 76 173 8 4.6
## 23 Botswana 173 35 35 5.4
## 24 Brazil 245 145 16 7.2
## 25 Brunei 31 2 1 0.6
## 26 Bulgaria 231 252 94 10.3
## 27 Burkina Faso 25 7 7 4.3
## 28 Burundi 88 0 0 6.3
## 29 Cote d Ivoire 37 1 7 4.0
## 30 Cabo Verde 144 56 16 4.0
## 31 Cambodia 57 65 1 2.2
## 32 Cameroon 147 1 4 5.8
## 33 Canada 240 122 100 8.2
## 34 Central African Republic 17 2 1 1.8
## 35 Chad 15 1 1 0.4
## 36 Chile 130 124 172 7.6
## 37 China 79 192 8 5.0
## 38 Colombia 159 76 3 4.2
## 39 Comoros 1 3 1 0.1
## 40 Congo 76 1 9 1.7
## 41 Cook Islands 0 254 74 5.9
## 42 Costa Rica 149 87 11 4.4
## 43 Croatia 230 87 254 10.2
## 44 Cuba 93 137 5 4.2
## 45 Cyprus 192 154 113 8.2
## 46 Czech Republic 361 170 134 11.8
## 47 North Korea 0 0 0 0.0
## 48 DR Congo 32 3 1 2.3
## 49 Denmark 224 81 278 10.4
## 50 Djibouti 15 44 3 1.1
## 51 Dominica 52 286 26 6.6
## 52 Dominican Republic 193 147 9 6.2
## 53 Ecuador 162 74 3 4.2
## 54 Egypt 6 4 1 0.2
## 55 El Salvador 52 69 2 2.2
## 56 Equatorial Guinea 92 0 233 5.8
## 57 Eritrea 18 0 0 0.5
## 58 Estonia 224 194 59 9.5
## 59 Ethiopia 20 3 0 0.7
## 60 Fiji 77 35 1 2.0
## 61 Finland 263 133 97 10.0
## 62 France 127 151 370 11.8
## 63 Gabon 347 98 59 8.9
## 64 Gambia 8 0 1 2.4
## 65 Georgia 52 100 149 5.4
## 66 Germany 346 117 175 11.3
## 67 Ghana 31 3 10 1.8
## 68 Greece 133 112 218 8.3
## 69 Grenada 199 438 28 11.9
## 70 Guatemala 53 69 2 2.2
## 71 Guinea 9 0 2 0.2
## 72 Guinea-Bissau 28 31 21 2.5
## 73 Guyana 93 302 1 7.1
## 74 Haiti 1 326 1 5.9
## 75 Honduras 69 98 2 3.0
## 76 Hungary 234 215 185 11.3
## 77 Iceland 233 61 78 6.6
## 78 India 9 114 0 2.2
## 79 Indonesia 5 1 0 0.1
## 80 Iran 0 0 0 0.0
## 81 Iraq 9 3 0 0.2
## 82 Ireland 313 118 165 11.4
## 83 Israel 63 69 9 2.5
## 84 Italy 85 42 237 6.5
## 85 Jamaica 82 97 9 3.4
## 86 Japan 77 202 16 7.0
## 87 Jordan 6 21 1 0.5
## 88 Kazakhstan 124 246 12 6.8
## 89 Kenya 58 22 2 1.8
## 90 Kiribati 21 34 1 1.0
## 91 Kuwait 0 0 0 0.0
## 92 Kyrgyzstan 31 97 6 2.4
## 93 Laos 62 0 123 6.2
## 94 Latvia 281 216 62 10.5
## 95 Lebanon 20 55 31 1.9
## 96 Lesotho 82 29 0 2.8
## 97 Liberia 19 152 2 3.1
## 98 Libya 0 0 0 0.0
## 99 Lithuania 343 244 56 12.9
## 100 Luxembourg 236 133 271 11.4
## 101 Madagascar 26 15 4 0.8
## 102 Malawi 8 11 1 1.5
## 103 Malaysia 13 4 0 0.3
## 104 Maldives 0 0 0 0.0
## 105 Mali 5 1 1 0.6
## 106 Malta 149 100 120 6.6
## 107 Marshall Islands 0 0 0 0.0
## 108 Mauritania 0 0 0 0.0
## 109 Mauritius 98 31 18 2.6
## 110 Mexico 238 68 5 5.5
## 111 Micronesia 62 50 18 2.3
## 112 Monaco 0 0 0 0.0
## 113 Mongolia 77 189 8 4.9
## 114 Montenegro 31 114 128 4.9
## 115 Morocco 12 6 10 0.5
## 116 Mozambique 47 18 5 1.3
## 117 Myanmar 5 1 0 0.1
## 118 Namibia 376 3 1 6.8
## 119 Nauru 49 0 8 1.0
## 120 Nepal 5 6 0 0.2
## 121 Netherlands 251 88 190 9.4
## 122 New Zealand 203 79 175 9.3
## 123 Nicaragua 78 118 1 3.5
## 124 Niger 3 2 1 0.1
## 125 Nigeria 42 5 2 9.1
## 126 Niue 188 200 7 7.0
## 127 Norway 169 71 129 6.7
## 128 Oman 22 16 1 0.7
## 129 Pakistan 0 0 0 0.0
## 130 Palau 306 63 23 6.9
## 131 Panama 285 104 18 7.2
## 132 Papua New Guinea 44 39 1 1.5
## 133 Paraguay 213 117 74 7.3
## 134 Peru 163 160 21 6.1
## 135 Philippines 71 186 1 4.6
## 136 Poland 343 215 56 10.9
## 137 Portugal 194 67 339 11.0
## 138 Qatar 1 42 7 0.9
## 139 South Korea 140 16 9 9.8
## 140 Moldova 109 226 18 6.3
## 141 Romania 297 122 167 10.4
## 142 Russian Federation 247 326 73 11.5
## 143 Rwanda 43 2 0 6.8
## 144 St. Kitts & Nevis 194 205 32 7.7
## 145 St. Lucia 171 315 71 10.1
## 146 St. Vincent & the Grenadines 120 221 11 6.3
## 147 Samoa 105 18 24 2.6
## 148 San Marino 0 0 0 0.0
## 149 Sao Tome & Principe 56 38 140 4.2
## 150 Saudi Arabia 0 5 0 0.1
## 151 Senegal 9 1 7 0.3
## 152 Serbia 283 131 127 9.6
## 153 Seychelles 157 25 51 4.1
## 154 Sierra Leone 25 3 2 6.7
## 155 Singapore 60 12 11 1.5
## 156 Slovakia 196 293 116 11.4
## 157 Slovenia 270 51 276 10.6
## 158 Solomon Islands 56 11 1 1.2
## 159 Somalia 0 0 0 0.0
## 160 South Africa 225 76 81 8.2
## 161 Spain 284 157 112 10.0
## 162 Sri Lanka 16 104 0 2.2
## 163 Sudan 8 13 0 1.7
## 164 Suriname 128 178 7 5.6
## 165 Swaziland 90 2 2 4.7
## 166 Sweden 152 60 186 7.2
## 167 Switzerland 185 100 280 10.2
## 168 Syria 5 35 16 1.0
## 169 Tajikistan 2 15 0 0.3
## 170 Thailand 99 258 1 6.4
## 171 Macedonia 106 27 86 3.9
## 172 Timor-Leste 1 1 4 0.1
## 173 Togo 36 2 19 1.3
## 174 Tonga 36 21 5 1.1
## 175 Trinidad & Tobago 197 156 7 6.4
## 176 Tunisia 51 3 20 1.3
## 177 Turkey 51 22 7 1.4
## 178 Turkmenistan 19 71 32 2.2
## 179 Tuvalu 6 41 9 1.0
## 180 Uganda 45 9 0 8.3
## 181 Ukraine 206 237 45 8.9
## 182 United Arab Emirates 16 135 5 2.8
## 183 United Kingdom 219 126 195 10.4
## 184 Tanzania 36 6 1 5.7
## 185 USA 249 158 84 8.7
## 186 Uruguay 115 35 220 6.6
## 187 Uzbekistan 25 101 8 2.4
## 188 Vanuatu 21 18 11 0.9
## 189 Venezuela 333 100 3 7.7
## 190 Vietnam 111 2 1 2.0
## 191 Yemen 6 0 0 0.1
## 192 Zambia 32 19 4 2.5
## 193 Zimbabwe 64 18 4 4.7# Countries without alcohol consumption
NoConsumtion_sub <- subset(AlcoholConsum, BEER == 0.0 & WINE == 0.0)
NoConsumtion_sub <- NoConsumtion_sub[, c("COUNTRY", "BEER", "WINE")]
NoConsumtion_sub## COUNTRY BEER WINE
## 1 Afghanistan 0 0
## 14 Bangladesh 0 0
## 47 North Korea 0 0
## 80 Iran 0 0
## 91 Kuwait 0 0
## 98 Libya 0 0
## 104 Maldives 0 0
## 107 Marshall Islands 0 0
## 108 Mauritania 0 0
## 112 Monaco 0 0
## 129 Pakistan 0 0
## 148 San Marino 0 0
## 150 Saudi Arabia 0 0
## 159 Somalia 0 0# Countries that consume alcohol
AlcoholConsum_sub <- subset(AlcoholConsum, BEER > 0 & WINE > 0 )
AlcoholConsum_sub <- AlcoholConsum_sub[, c("COUNTRY", "BEER", "WINE")]
AlcoholConsum_sub## COUNTRY BEER WINE
## 2 Albania 89 54
## 3 Algeria 25 14
## 4 Andorra 245 312
## 5 Angola 217 45
## 6 Antigua & Barbuda 102 45
## 7 Argentina 193 221
## 8 Armenia 21 11
## 9 Australia 261 212
## 10 Austria 279 191
## 11 Azerbaijan 21 5
## 12 Bahamas 122 51
## 13 Bahrain 42 7
## 15 Barbados 143 36
## 16 Belarus 142 42
## 17 Belgium 295 212
## 18 Belize 263 8
## 19 Benin 34 13
## 21 Bolivia 167 8
## 22 Bosnia-Herzegovina 76 8
## 23 Botswana 173 35
## 24 Brazil 245 16
## 25 Brunei 31 1
## 26 Bulgaria 231 94
## 27 Burkina Faso 25 7
## 29 Cote d Ivoire 37 7
## 30 Cabo Verde 144 16
## 31 Cambodia 57 1
## 32 Cameroon 147 4
## 33 Canada 240 100
## 34 Central African Republic 17 1
## 35 Chad 15 1
## 36 Chile 130 172
## 37 China 79 8
## 38 Colombia 159 3
## 39 Comoros 1 1
## 40 Congo 76 9
## 42 Costa Rica 149 11
## 43 Croatia 230 254
## 44 Cuba 93 5
## 45 Cyprus 192 113
## 46 Czech Republic 361 134
## 48 DR Congo 32 1
## 49 Denmark 224 278
## 50 Djibouti 15 3
## 51 Dominica 52 26
## 52 Dominican Republic 193 9
## 53 Ecuador 162 3
## 54 Egypt 6 1
## 55 El Salvador 52 2
## 56 Equatorial Guinea 92 233
## 58 Estonia 224 59
## 60 Fiji 77 1
## 61 Finland 263 97
## 62 France 127 370
## 63 Gabon 347 59
## 64 Gambia 8 1
## 65 Georgia 52 149
## 66 Germany 346 175
## 67 Ghana 31 10
## 68 Greece 133 218
## 69 Grenada 199 28
## 70 Guatemala 53 2
## 71 Guinea 9 2
## 72 Guinea-Bissau 28 21
## 73 Guyana 93 1
## 74 Haiti 1 1
## 75 Honduras 69 2
## 76 Hungary 234 185
## 77 Iceland 233 78
## 82 Ireland 313 165
## 83 Israel 63 9
## 84 Italy 85 237
## 85 Jamaica 82 9
## 86 Japan 77 16
## 87 Jordan 6 1
## 88 Kazakhstan 124 12
## 89 Kenya 58 2
## 90 Kiribati 21 1
## 92 Kyrgyzstan 31 6
## 93 Laos 62 123
## 94 Latvia 281 62
## 95 Lebanon 20 31
## 97 Liberia 19 2
## 99 Lithuania 343 56
## 100 Luxembourg 236 271
## 101 Madagascar 26 4
## 102 Malawi 8 1
## 105 Mali 5 1
## 106 Malta 149 120
## 109 Mauritius 98 18
## 110 Mexico 238 5
## 111 Micronesia 62 18
## 113 Mongolia 77 8
## 114 Montenegro 31 128
## 115 Morocco 12 10
## 116 Mozambique 47 5
## 118 Namibia 376 1
## 119 Nauru 49 8
## 121 Netherlands 251 190
## 122 New Zealand 203 175
## 123 Nicaragua 78 1
## 124 Niger 3 1
## 125 Nigeria 42 2
## 126 Niue 188 7
## 127 Norway 169 129
## 128 Oman 22 1
## 130 Palau 306 23
## 131 Panama 285 18
## 132 Papua New Guinea 44 1
## 133 Paraguay 213 74
## 134 Peru 163 21
## 135 Philippines 71 1
## 136 Poland 343 56
## 137 Portugal 194 339
## 138 Qatar 1 7
## 139 South Korea 140 9
## 140 Moldova 109 18
## 141 Romania 297 167
## 142 Russian Federation 247 73
## 144 St. Kitts & Nevis 194 32
## 145 St. Lucia 171 71
## 146 St. Vincent & the Grenadines 120 11
## 147 Samoa 105 24
## 149 Sao Tome & Principe 56 140
## 151 Senegal 9 7
## 152 Serbia 283 127
## 153 Seychelles 157 51
## 154 Sierra Leone 25 2
## 155 Singapore 60 11
## 156 Slovakia 196 116
## 157 Slovenia 270 276
## 158 Solomon Islands 56 1
## 160 South Africa 225 81
## 161 Spain 284 112
## 164 Suriname 128 7
## 165 Swaziland 90 2
## 166 Sweden 152 186
## 167 Switzerland 185 280
## 168 Syria 5 16
## 170 Thailand 99 1
## 171 Macedonia 106 86
## 172 Timor-Leste 1 4
## 173 Togo 36 19
## 174 Tonga 36 5
## 175 Trinidad & Tobago 197 7
## 176 Tunisia 51 20
## 177 Turkey 51 7
## 178 Turkmenistan 19 32
## 179 Tuvalu 6 9
## 181 Ukraine 206 45
## 182 United Arab Emirates 16 5
## 183 United Kingdom 219 195
## 184 Tanzania 36 1
## 185 USA 249 84
## 186 Uruguay 115 220
## 187 Uzbekistan 25 8
## 188 Vanuatu 21 11
## 189 Venezuela 333 3
## 190 Vietnam 111 1
## 192 Zambia 32 4
## 193 Zimbabwe 64 4# Top 10 countries with highest consumption of alcohol pe liter
TopConsum <- head(arrange(AlcoholConsum, desc(AlcoholConsum$`TOTAL LITRES`)), n = 10)
TopConsum <- TopConsum[, c("COUNTRY", "BEER", "WINE")]
TopConsum## COUNTRY BEER WINE
## 1 Belarus 142 42
## 2 Lithuania 343 56
## 3 Andorra 245 312
## 4 Grenada 199 28
## 5 Czech Republic 361 134
## 6 France 127 370
## 7 Russian Federation 247 73
## 8 Ireland 313 165
## 9 Luxembourg 236 271
## 10 Slovakia 196 116Consumgg <- ggplot(NULL, aes(x, y)) +
geom_line(data=TopConsum, aes(x=COUNTRY, y=WINE, group=1), col="green") +
geom_line(data=TopConsum, aes(x=COUNTRY, y=BEER, group=1), col="purple")+
labs(title = "WINE & BEER CONSUMPTION BY COUNTRY", x= "COUNTRY", y = "TOTAL SERVINGS", ylim=c(100,1000), breaks = 10) +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
# display the plot
Consumgg
CONCLUSION
The study shows that based on the top 10 total consumption by litre, beer was more popular than wine. Out Of the 194 countries 14 did not consume any alcohol.