2f:T7ac, 30:T6da,columnNames = [ 'alpha2', 'alpha3', 'country_name', 'continent', 'year', 'variable', 'Agriculture activity (meat): consumption_historic', 'Agriculture activity (meat): consumption_projected_current_policy_max', 'Agriculture activity (meat): consumption_projected_current_policy_min', 'Agriculture activity (total): consumption_historic', 'Agriculture activity (total): consumption_projected_current_policy_max', 'Agriculture activity (total): consumption_projected_current_policy_min', 'Electricity activity (per capita)_historic', 'Electricity activity (per capita)_projected_current_policy_max', 'Electricity activity (per capita)_projected_current_policy_min', 'Gas share in primary energy_historic', 'Gas share in primary energy_projected_current_policy_max', 'Gas share in primary energy_projected_current_policy_min', 'New EV sales per million capita_historic', 'Oil and Gas activity: production_historic', 'Oil and Gas activity: production_projected_current_policy_max', 'Oil and Gas activity: production_projected_current_policy_min', 'Total fossil CO2 emission_projected_current_policy_max', 'Total fossil CO2 emission_projected_current_policy_min', 'Waste generation (per capita)_CTI scenario', 'Waste generation (per capita)_historic', 'Waste generation (per capita)_projected_current_policy_max', 'Waste generation (per capita)_projected_current_policy_min', 'consumption_per_capita', 'Population', 'GDP (expenditure, multiple price benchmarks)', 'Consumption of households and government (single price benchmark)', 'Annual working hours per worker', 'GDP per capita (expenditure, multiple price benchmarks)', 'Productivity: output per hour worked', 'Gini coefficient' ]31:T4b8,diff_total_meat = { // Define the getPercentageChange function function getPercentageChange(oldNumber, newNumber) { var value = newNumber - oldNumber; return (value / oldNumber) * 100; } // Filter the array to get the elements with TIME equal to 1991 var countriesInitialYear = joined.filter(function(country) { return country.year == 1991; }); // Use the map method to transform the filtered array var data = joined.map(function(country) { // Find the object with the same country name and year equal to 1991 var initialYear = countriesInitialYear.find(function(initialYearCountry) { return initialYearCountry.country_name === country.country_name; }); if (initialYear) { // Calculate the percentage change in 'Agriculture activity (meat): consumption_historic' var percentageChange = getPercentageChange(initialYear['Agriculture activity (meat): consumption_historic'], country['Agriculture activity (meat): consumption_historic']); return { ...country, difference: percentageChange }; } else { // If there is no initial year data for this country, return the original object without the difference property return country; } }); return data } 32:Tc67,### 2.3 Main findings: **GDP per capita - Agriculture activity (meat) consumption:** There is correlation, more GDP => higher meat consumption - Asia eats less meat, some countries with high GDP, but don't eat more meat - POSSIBLE FEATURED STORY: Argentina has very high meat consumption, highest and the gap is widening. Maximum value in the latest year, 2018 (and upwards trend in the past few years). In 1990, Switzerland, France and Australia had high meat consumption, then they dropped. Also, India's meat consumption is super small! **Check which countries dropped meat consumption in past 30 years and if it's anything to do with their wealth (hypothesis: veggie trends expanding in wealthiest countries) **GDP per capita - Agriculture activity (total) consumption:** There is correlation, more GDP => higher meat consumption, very sharp trend **Total GDP - general consumption:** - POSSIBLE FEATURED STORY: The US and China are the two top economies in the world (clearly highest total GDPs), but the gral consumption per capita is almost 10 times higher in the US than in China. Yet, in the coming years China has overcome the US in meat consumption. - General consumption: Africa is by far the continent with lower general consumption. Asia, Americas and the other continents have seen it grow over the years, but it is not as clear for Africa. **Gini coefficient - Agriculture activity (meat) consumption:** - back then, more equality, more meat consumption - now it's more similar (less of a trend) - POSSIBLE FEATURED STORY: Countries that have a higher rate of consumption (general consumption, not only meat or agriculture) are usually those more equal. But, over the past three decades, the more they consume, the least equal they are turning. Now all countries are steadily coming together to similar levels of equality. **GDP per capita - New electric vehicle sales per million:** - Not much data - POSSIBLE FEATURED STORY: The more GDP per capita, the more electric vehicles. But Norway has been by far the country with highest rate of EV sales for sales, with a big, big gap - No countries with a GDP per capita lower than 30,000 have significant sales of electric vehicles **GDP per capita - electricity consumption:** - Very clearly that the more GDP per capita, the more electricity consumption - POSSIBLE FEATURED STORY: Norway and Canada, both very northern (not a lot of light over the year, very cold weather and probably more in need for heating) are in the highest range of GDP per capita but have far more electricity activity than the rest. And over the years it has always been like this. **Annual working hours per worker - consumption per capita** - Annual working hours per worker may also be another indicator for wealth. The statistics is similar to that of GDP - consumption per capita. In here, the less hours per worker, the more consumption (they also have more time to consume!) - Similar trend with productivity per worker. In the old days, the difference in productivity was very high (Europe leading, Americas second and Asia trailing, with no figures for Africa), but it is a bit more even now33:[{"id":"da68c02f76274a2a4a451735f76638fe3e3bf0b769433fac555768657a87016c97a164b9bf03758716db869ff43530110ca928faf4d074152cba6b04555269d1","href":"https://static.observableusercontent.com/files/da68c02f76274a2a4a451735f76638fe3e3bf0b769433fac555768657a87016c97a164b9bf03758716db869ff43530110ca928faf4d074152cba6b04555269d1","name":"CAT_new_portal_data_05_2022@2.csv","type":"text/csv","lastModified":1672862812715.478,"size":3602243},{"id":"ebcedfcb3a8ee7e45f51b3f6427024eb5858a25259904872853f9b1d81e24b246fd8e675331ff0bfea89090d3c53d7c43e2f04ce82f913d5b7e54745419d75fc","href":"https://static.observableusercontent.com/files/ebcedfcb3a8ee7e45f51b3f6427024eb5858a25259904872853f9b1d81e24b246fd8e675331ff0bfea89090d3c53d7c43e2f04ce82f913d5b7e54745419d75fc","name":"joined@3.json","type":"application/json","lastModified":1672904147795.742,"size":2452136},{"id":"eb5dcced5ee76bdc696f3e4b470a43ba327249a1702c460550fb08e26648a9e891161aa462eba2e0f33d61cf0e0354d25697fd4274bbf5c016d7c2c93b977c2a","href":"https://static.observableusercontent.com/files/eb5dcced5ee76bdc696f3e4b470a43ba327249a1702c460550fb08e26648a9e891161aa462eba2e0f33d61cf0e0354d25697fd4274bbf5c016d7c2c93b977c2a","name":"World Inequality Database (WID) - Pretax income.csv","type":"text/csv","lastModified":1670323647458.062,"size":4353642},{"id":"a40d0224ae15dd3698f3cc8a63f104139ca36484c62930d7c420b21b3b152eedb74c13fdd55d71db13bc28cf5cffa47259538b1edfad50404fb9aa5b4a4d5a15","href":"https://static.observableusercontent.com/files/a40d0224ae15dd3698f3cc8a63f104139ca36484c62930d7c420b21b3b152eedb74c13fdd55d71db13bc28cf5cffa47259538b1edfad50404fb9aa5b4a4d5a15","name":"DP_LIVE_06122022114745787.csv","type":"text/csv","lastModified":1670323681892.094,"size":715947},{"id":"49bc082186026cf23ee8be9cb0cd101f3f156af176707117e532550c349cc431b78462e13165b6c986de8cc605c084988ea1a897427b2154653f9b3454d8ee19","href":"https://static.observableusercontent.com/files/49bc082186026cf23ee8be9cb0cd101f3f156af176707117e532550c349cc431b78462e13165b6c986de8cc605c084988ea1a897427b2154653f9b3454d8ee19","name":"Penn World Table version 10.0.csv","type":"text/csv","lastModified":1670323691594.021,"size":5356219},{"id":"94e752503fe8fa505c27d76ac2207d4524fa4e82fe6eee241fb996ddcadd302a335b13ff97305e0e20268533458e802c2a141b60bb2045cb75e7ce14383b6a1f","href":"https://static.observableusercontent.com/files/94e752503fe8fa505c27d76ac2207d4524fa4e82fe6eee241fb996ddcadd302a335b13ff97305e0e20268533458e802c2a141b60bb2045cb75e7ce14383b6a1f","name":"countries codes.csv","type":"text/csv","lastModified":1670450777828.247,"size":6281},{"id":"7b0886b665e72421baa7cf96a01c3aaa8a20d9431f1dec770619faf5c3cf803736720a0532c7d1b189462520dc9f5729892a6f491ae786647c0c486562ee2878","href":"https://static.observableusercontent.com/files/7b0886b665e72421baa7cf96a01c3aaa8a20d9431f1dec770619faf5c3cf803736720a0532c7d1b189462520dc9f5729892a6f491ae786647c0c486562ee2878","name":"joined@2.json","type":"application/json","lastModified":1670905906331.488,"size":6024457}] 2d:{"title":"Final project (Data exploration)","cells":[{"id":0,"value":"# Final project (Data exploration)","pinned":false,"hidden":false,"mode":"md"},{"id":471,"value":"

Task Group 2 – Consumption

Overall question:

How is consumption (e.g food, waste, energy etc) related to wealth indicators like GDP, income\nor inequality?

How has consumption changed over time in different world regions?

","pinned":false,"hidden":false,"mode":"html"},{"id":1493,"value":"## Step 1: What data do we have, and which variables do we want to look into?","pinned":false,"hidden":false,"mode":"md"},{"id":1501,"value":"We were given four datasets, which are:\n1. [Climate Action Tracker](https://climateactiontracker.org/data-portal/)\n2. World inequality Database\n4. [Penn World Table](https://github.com/owid/owid-datasets/tree/master/datasets/Penn%20World%20Table%20version%2010.0)\n5. [OECD Meat Consumption](https://data.oecd.org/agroutput/meat-consumption.htm)\n\nThe first step of the exploration is to look at the indicators in each dataset and select which ones are interesting for our topic.","pinned":false,"hidden":false,"mode":"md"},{"id":303,"value":"### 1. CAT dataset - what are the indicators?\nDataset shows data per country and per regions, projections and historic data","pinned":false,"hidden":false,"mode":"md"},{"id":3301,"value":"cat","pinned":true,"hidden":false,"mode":"ojs"},{"id":1498,"value":"Printing the list of indicators in the CAT dataset below:","pinned":false,"hidden":false,"mode":"md"},{"id":294,"value":"new Set(cataq.array('indicator'))","pinned":false,"hidden":false,"mode":"ojs"},{"id":383,"value":"

We narrowed down some especially interesting rows for us:

\"Agriculture activity (meat): consumption\"
\"Agriculture activity (total): consumption\"
\"Electricity activity (per capita)\"
\"Electricity emissions intensity\"
\"Emissions intensity of primary energy\"
\"Fossil fuel share in primary energy\"
\"Nuclear share in primary energy\"
\"Total fossil CO2 emission\"
\"Gas share in primary energy\"
\"Solar and wind share in primary energy\"
\"Hydro share in Primary Energy\"
\"Waste generation (per capita)\"
\"Waste emissions intensity\"

\n
\n

* The indicators are in rows, not columns. It's different from other datasets

\n","pinned":false,"hidden":false,"mode":"html"},{"id":428,"value":"#### From here we shortlisted them even further:\n\n- \"Agriculture activity (total): consumption\"\n- \"Agriculture activity (meat): consumption\"\n- \"Electricity activity (per capita)\"\n- \"Waste generation (per capita)\"\n- \"Total fossil CO2 emission\"\n- \"Oil and Gas activity: production\"\n- \"Gas share in primary energy\"\n- \"New EV sales per million capita\"\n\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":305,"value":"### 2. World inequality database - which inequality indicators should we use?","pinned":false,"hidden":false,"mode":"md"},{"id":2743,"value":"Inputs.table(inequalityaq)","pinned":false,"hidden":false,"mode":"ojs"},{"id":308,"value":"inequalityaq.columnNames()","pinned":false,"hidden":false,"mode":"ojs"},{"id":386,"value":"

Especially interesting columns for us:

\"Entity\"
\"Year\"
\"Gini coefficient\" - higher means more unequal
\"Palma ratio (S90/S40 ratio) - higher means more unequal\"
\"P0-P10 - share of the bottom 10%\"
\"P90-P100 - share of the top 10%\"

* The indicators are in columns.

","pinned":false,"hidden":false,"mode":"html"},{"id":440,"value":"#### From there, we shorlisted them even further\n- \"Entity\"\n- \"Year\"\n- \"Gini coefficient\" - higher means more unequal","pinned":false,"hidden":false,"mode":"md"},{"id":310,"value":"### 3. Penn World dataset - what are the indicators?","pinned":false,"hidden":false,"mode":"md"},{"id":2751,"value":"Inputs.table(pennWorld)","pinned":false,"hidden":false,"mode":"ojs"},{"id":314,"value":"pennworldaq.columnNames()","pinned":false,"hidden":false,"mode":"ojs"},{"id":399,"value":"

Especially interesting columns for us:

\"Entity\"
\"Year\"
\"GDP (expenditure, multiple price benchmarks)\"
\"Consumption of households and government (single price benchmark)\"
\"Annual working hours per worker\"
\"GDP per capita (expenditure, multiple price benchmarks)\"
\"Productivity: output per hour worked\"

\n
\n* The indicators are in columns.","pinned":false,"hidden":false,"mode":"html"},{"id":443,"value":"#### From there, we shortlisted them even further.\n\n- \"Entity\"\n- \"Year\"\n- \"GDP (output, multiple price benchmarks)\"\n- \"Consumption of households and government (single price benchmark)\"\n- \"GDP per capita (output, multiple price benchmarks)\"\n- Population\n","pinned":false,"hidden":false,"mode":"md"},{"id":2760,"value":"### 4. OECD data","pinned":false,"hidden":false,"mode":"md"},{"id":36,"value":"Inputs.table(oecd)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2765,"value":"This dataset breaks down the type of meat consumption for each country.","pinned":false,"hidden":false,"mode":"md"},{"id":451,"value":"## Step 2: What should we do with the data?\nFinding the relationship between consumption of these factors to inequality and GDP: \"Agriculture activity (total): consumption\", \"Agriculture activity (meat): consumption\", \"Electricity activity (per capita)\", \"Waste generation (per capita)\", EV sales per million\n \nPotential visualization: Evolution throughout the year (Gapminder-style chart with interactivity and ability to change variables)","pinned":false,"hidden":false,"mode":"md"},{"id":1536,"value":"Then, we got to work on the initial tasks:\n1. Data cleanup\n2. Exploration of consumption variables compared with inequality (Gini coefficient) and wealth (GDP)","pinned":false,"hidden":false,"mode":"md"},{"id":326,"value":"### 2.1 Data cleanup tasks\n1. Standardise the country names, so we should have a country name, alpha3 and alpha 2 for each, because each data source has different way of referring to countries (country list is below)\n2. Filter out the rows that are not countries, such as regions or global figures (then manual check for rows that are not part of the countries list)\n3. Joining them into one dataset","pinned":false,"hidden":false,"mode":"md"},{"id":332,"value":"List of countries with 2 and 3 digit codes - source IBAN.com","pinned":false,"hidden":false,"mode":"md"},{"id":349,"value":"countriesCodes = FileAttachment(\"countries codes.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2770,"value":"These tasks are done directly in a js file in the [Github repo](https://github.com/stephadeline/mvtec2022-final).\n\nThe result of the initial joining of the data is below, although this is not the final data we end up using. Further filtering and selecting were done in the js file for the final output.","pinned":false,"hidden":false,"mode":"md"},{"id":523,"value":"joined = FileAttachment(\"joined@2.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":71,"value":"### 2.2 Exploration of consumption variables compared to equality and wealth","pinned":false,"hidden":false,"mode":"md"},{"id":60,"value":"$2f","pinned":false,"hidden":false,"mode":"html"},{"id":115,"value":"","pinned":false,"hidden":false,"mode":"md"},{"id":169,"value":"### Quick chart of Gini coefficient","pinned":false,"hidden":false,"mode":"md"},{"id":734,"value":"viewof form = Inputs.form({\n countrySel: Inputs.checkbox(joined.map(d => d.continent), {sort:true, unique:true,label: \"continent\"}),\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":737,"value":"filterCountry = joined.filter(d => form.countrySel.includes(d.continent))\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":726,"value":"Plot.plot({\n y: {\n grid: true,\n },\n\n color: {\n legend: true\n },\n\n marks: [\n Plot.line(filterCountry, {\n x: \"consumption_per_capita\",\n y: \"Gini coefficient\",\n opacity: 0.5,\n stroke: \"#555\",\n fill:\"continent\"\n })\n ]\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":401,"value":"Questions/hypothesis:\n\n1. What is the relationship between income and consumption of meat? Hypothesis: consumption increases with income increase.\n2. What is the relationship between inequality and consumption of meat?\n3. On electricity: Does greater income lead to a more diverse source of energy? e.g. more renewable energy sources?\n4. Do richer countries generate more waste?","pinned":false,"hidden":false,"mode":"md"},{"id":500,"value":"Plot.plot({\n marks: [\n Plot.dot(joined, {x:\"GDP per capita (expenditure, multiple price benchmarks)\", y:\"consumption_per_capita\", fill:\"blue\", opacity:0.2, xlim: 200})\n ] \n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":329,"value":"clm.getAllCountries()","pinned":false,"hidden":false,"mode":"ojs"},{"id":527,"value":"Inputs.table(joined.filter(d => d.country_name === \"India\" && d[\"Gini coefficient\"]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":709,"value":"filtered = joined.filter(d => d.year === year2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":792,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":543,"value":"### Plotting the data in a scatterplot to see the correlation, with dropdowns to explore variables","pinned":false,"hidden":false,"mode":"md"},{"id":704,"value":"viewof year2 = Inputs.range([1990, 2018], {label: \"Amount\", step: 1})","pinned":false,"hidden":false,"mode":"ojs"},{"id":787,"value":"viewof xVar = Inputs.select([ 'GDP (expenditure, multiple price benchmarks)',\n 'Annual working hours per worker',\n 'GDP per capita (expenditure, multiple price benchmarks)',\n 'Productivity: output per hour worked',\n 'Gini coefficient' ], {label: \"Select x variable\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":807,"value":"viewof yVar = Inputs.select([\n 'Agriculture activity (meat): consumption_historic',\n 'Agriculture activity (total): consumption_historic',\n 'Electricity activity (per capita)_historic',\n 'Gas share in primary energy_historic',\n 'New EV sales per million capita_historic',\n 'Oil and Gas activity: production_historic',\n 'Waste generation (per capita)_CTI scenario',\n 'Waste generation (per capita)_historic',\n 'consumption_per_capita'], {label: \"Select y variable\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":535,"value":"Plot.plot({\n color: {\n legend: true\n },\n x: {\n type: \"log\"\n },\n y: {\n },\n marks: [\n Plot.dot(filtered, {x:xVar, y:yVar, fill:\"continent\", opacity:0.7, xlim: 200}),\n Plot.linearRegressionX(filtered, {x:xVar, y:yVar}),\n Plot.text(filtered, {x: xVar, y: yVar, text: d => d.country_name}),\n\n ] \n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":560,"value":"### Chart to show that productivity and GDP are closely linked, therefore productivity can also be used as an alternative key indicator of country wealth","pinned":false,"hidden":false,"mode":"md"},{"id":551,"value":"Plot.plot({\n color: {\n legend: true\n },\n x: {\n domain: [0, 100000],\n },\n y: {\n domain: [0,120],\n },\n marks: [\n Plot.dot(joined, {x:\"GDP per capita (expenditure, multiple price benchmarks)\", y:\"Productivity: output per hour worked\", fill:\"continent\", opacity:0.7}),\n Plot.linearRegressionX(joined, {x:\"GDP per capita (expenditure, multiple price benchmarks)\", y:\"Productivity: output per hour worked\"})\n ] \n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":570,"value":"### Unclear trend between sales of electric vehicles and GDP per capita","pinned":false,"hidden":false,"mode":"md"},{"id":556,"value":"Plot.plot({\n color: {\n legend: true\n },\n x: {\n domain: [0, 80000],\n // type: \"log\"\n },\n y: {\n domain: [1, 15000],\n type: \"log\"\n },\n marks: [\n Plot.dot(joined, {x:\"GDP per capita (expenditure, multiple price benchmarks)\", y:\"New EV sales per million capita_historic\", fill:\"continent\", opacity:0.7}),\n ] \n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":606,"value":"### Chart showing that Europe and Oceania have higher GDP per capita than Asia, the Americas and Africa. It also shows that in all cases, the more GDP, the more consumption of meat, but only slightly","pinned":false,"hidden":false,"mode":"md"},{"id":830,"value":"filtered3 = joined.filter(d => d.year === year3)","pinned":false,"hidden":false,"mode":"ojs"},{"id":827,"value":"viewof year3 = Inputs.range([1990, 2018], {label: \"Amount\", step: 1})","pinned":false,"hidden":false,"mode":"ojs"},{"id":590,"value":"Plot.plot({\n marginTop: 22,\n x: {\n domain: [0, 100000],\n },\n y: {\n domain: [0, 650],\n },\n facet:{\n data:filtered3,\n x: \"continent\"\n },\n marks: [\n Plot.frame(),\n Plot.dot(filtered3, {x:\"GDP per capita (expenditure, multiple price benchmarks)\", y:\"Agriculture activity (meat): consumption_historic\", fill:\"continent\"}),\n // Plot.linearRegressionX(filtered3, {x:\"GDP per capita (expenditure, multiple price benchmarks)\", y:\"Agriculture activity (meat): consumption_historic\"})\n ]\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":651,"value":"### Bar chart showing that African and Asian countries have had a smaller consumption per capita over the years, while Europe is predominant in the highest ranges of consumption with a big difference","pinned":false,"hidden":false,"mode":"md"},{"id":617,"value":"Plot.plot({\n color: {\n legend: true\n },\n marks: [\n Plot.rectY(joined, Plot.binX({y:\"count\"}, {x:\"consumption_per_capita\", fill:\"continent\"}))\n ]\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":685,"value":"### Visualization with waste per capita and GDP per capita","pinned":false,"hidden":false,"mode":"md"},{"id":882,"value":"### Spotlight in meat consumption","pinned":false,"hidden":false,"mode":"md"},{"id":1156,"value":"filtered7 = joined.filter(d => d.year === year7)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1158,"value":"viewof year7 = Inputs.range([1990, 2018], {label: \"Amount\", step: 1})","pinned":false,"hidden":false,"mode":"ojs"},{"id":865,"value":"Plot.plot({\n color: {\n legend: true\n },\n x: {\n // domain: [100, 150000],\n type: \"log\"\n },\n y: {\n domain: [0, 660]\n },\n marks: [\n Plot.dot(filtered7, {x:xVar, y:`Agriculture activity (meat): consumption_historic`, fill:\"continent\", opacity:0.7}),\n //Plot.text(filtered7, {x: xVar, y: yVar, text: d => d.country_name}),\n\n ] \n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":868,"value":"joined","pinned":false,"hidden":false,"mode":"ojs"},{"id":1144,"value":"filtered6 = joined.filter(d => d.year === year6)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1188,"value":"**Gini coefficient - consumption per capita over the years**","pinned":false,"hidden":false,"mode":"md"},{"id":1146,"value":"viewof year6 = Inputs.range([1990, 2017], {label: \"Amount\", step: 1})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1134,"value":"Plot.plot({\n x: {\n domain: [0.3, 0.9]\n },\n y: {\n domain: [0, 40000]\n },\n marks: [\n Plot.dot(filtered6, {x:\"Gini coefficient\", y:\"consumption_per_capita\", fill:\"continent\", opacity:0.8})\n ] \n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":1255,"value":"### Types of meat","pinned":false,"hidden":false,"mode":"md"},{"id":1261,"value":"filtered5 = oecd.filter(d => d.SUBJECT === \"BEEF\")\n .filter (g => g.MEASURE === \"KG_CAP\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1263,"value":"// Define the getPercentageChange function\nbeef = {\n function getPercentageChange(oldNumber, newNumber) {\n var value = newNumber - oldNumber;\n return (value / oldNumber) * 100;\n}\n\n// Filter the array to get the elements with TIME equal to 1991\nvar countriesInitialYear = filtered5.filter(function(country) {\n return country.TIME == '1991';\n});\n\n// Use the map method to transform the filtered5 array\nvar data = filtered5.map(function(country) {\n var initialYear = countriesInitialYear.find(function(initialYearCountry) {\n return initialYearCountry.LOCATION === country.LOCATION;\n });\n if (initialYear && initialYear.Value) {\n country.difference = getPercentageChange(Number(initialYear.Value), Number(country.Value));\n } else {\n country.difference = 0;\n }\n return country;\n});\n return data\n }","pinned":false,"hidden":false,"mode":"ojs"},{"id":1270,"value":"filtered8 = oecd.filter(d => d.SUBJECT === \"PIG\")\n.filter (g => g.MEASURE === \"KG_CAP\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1273,"value":"// Define the getPercentageChange function\npig = {\n function getPercentageChange(oldNumber, newNumber) {\n var value = newNumber - oldNumber;\n return (value / oldNumber) * 100;\n}\n\n// Filter the array to get the elements with TIME equal to 1991\nvar countriesInitialYear = filtered8.filter(function(country) {\n return country.TIME == '1991';\n});\n\n// Use the map method to transform the filtered5 array\nvar data = filtered8.map(function(country) {\n var initialYear = countriesInitialYear.find(function(initialYearCountry) {\n return initialYearCountry.LOCATION === country.LOCATION;\n });\n if (initialYear && initialYear.Value) {\n country.difference = getPercentageChange(Number(initialYear.Value), Number(country.Value));\n } else {\n country.difference = 0;\n }\n return country;\n});\n return data\n }","pinned":false,"hidden":false,"mode":"ojs"},{"id":1278,"value":"filtered9 = oecd.filter(d => d.SUBJECT === \"POULTRY\")\n.filter (g => g.MEASURE === \"KG_CAP\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1280,"value":"// Define the getPercentageChange function\npoultry = {\n function getPercentageChange(oldNumber, newNumber) {\n var value = newNumber - oldNumber;\n return (value / oldNumber) * 100;\n}\n\n// Filter the array to get the elements with TIME equal to 1991\nvar countriesInitialYear = filtered9.filter(function(country) {\n return country.TIME == '1991';\n});\n\n// Use the map method to transform the filtered5 array\nvar data = filtered9.map(function(country) {\n var initialYear = countriesInitialYear.find(function(initialYearCountry) {\n return initialYearCountry.LOCATION === country.LOCATION;\n });\n if (initialYear && initialYear.Value) {\n country.difference = getPercentageChange(Number(initialYear.Value), Number(country.Value));\n } else {\n country.difference = 0;\n }\n return country;\n});\n return data\n }","pinned":false,"hidden":false,"mode":"ojs"},{"id":1284,"value":"filtered10 = oecd.filter(d => d.SUBJECT === \"SHEEP\")\n.filter (g => g.MEASURE === \"KG_CAP\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1286,"value":"// Define the getPercentageChange function\nsheep = {\n function getPercentageChange(oldNumber, newNumber) {\n var value = newNumber - oldNumber;\n return (value / oldNumber) * 100;\n}\n\n// Filter the array to get the elements with TIME equal to 1991\nvar countriesInitialYear = filtered10.filter(function(country) {\n return country.TIME == '1991';\n});\n\n// Use the map method to transform the filtered5 array\nvar data = filtered10.map(function(country) {\n var initialYear = countriesInitialYear.find(function(initialYearCountry) {\n return initialYearCountry.LOCATION === country.LOCATION;\n });\n if (initialYear && initialYear.Value) {\n country.difference = getPercentageChange(Number(initialYear.Value), Number(country.Value));\n } else {\n country.difference = 0;\n }\n return country;\n});\n return data\n }","pinned":false,"hidden":false,"mode":"ojs"},{"id":1359,"value":"merged_data = beef.concat(pig, poultry, sheep)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1295,"value":"merged_2018 = merged_data.filter(d => d.TIME === \"2018\")\n .filter (d => d.MEASURE === \"KG_CAP\")\n .filter (g => g.LOCATION === \"USA\" || g.LOCATION === \"IND\" || g.LOCATION === \"OECD\" || g.LOCATION === \"CHN\" || g.LOCATION === \"ARG\" || g.LOCATION === \"GBR\" || g.LOCATION === \"BRICS\" )","pinned":false,"hidden":false,"mode":"ojs"},{"id":1298,"value":"Plot.plot({\n facet:{\n data:merged_2018,\n y: \"SUBJECT\"\n },\n\n marks: [\n Plot.frame(),\n Plot.barY(merged_2018, {x:\"LOCATION\", y:\"difference\", fill: \"darkblue\"}),\n ]\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":1395,"value":"### Difference in total meat consumption between 1991 and 2018","pinned":false,"hidden":false,"mode":"md"},{"id":1399,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1427,"value":"total_meat_2018 = diff_total_meat.filter (d => d.year==2018)\n .filter (g => g['Agriculture activity (meat): consumption_historic'])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1435,"value":"Plot.plot({\n color: {\n legend: true\n },\n height: 400,\n width: 1000,\n style: {\n fontSize:10.5\n },\n marks: [\n Plot.barY(total_meat_2018, {x:\"alpha3\", y:\"difference\", fill: \"continent\"}),\n ]\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":1480,"value":"selected_meat_2018 = total_meat_2018.filter (d => d.country_name == \"India\" || d.country_name == \"United States\" || d.country_name == \"China\" || d.country_name == \"Argentina\" || d.country_name == \"France\")\n ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1482,"value":"Plot.plot({\n color: {\n legend: true\n },\n height: 400,\n width: 1000,\n style: {\n fontSize:10.5\n },\n marks: [\n Plot.barY(selected_meat_2018, {x:\"alpha3\", y:\"difference\", fill: \"continent\"}),\n ]\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":109,"value":"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3325,"value":"$32","pinned":false,"hidden":false,"mode":"md"},{"id":3323,"value":"### Arquero imports","pinned":false,"hidden":false,"mode":"md"},{"id":105,"value":"cataq = aq.fromCSV(await FileAttachment('CAT_new_portal_data_05_2022@2.csv').text())","pinned":false,"hidden":false,"mode":"ojs"},{"id":129,"value":"inequalityaq = aq.fromCSV(await FileAttachment('World Inequality Database (WID) - Pretax income.csv').text())","pinned":false,"hidden":false,"mode":"ojs"},{"id":250,"value":"pennworldaq = aq.fromCSV(await FileAttachment('Penn World Table version 10.0.csv').text())","pinned":false,"hidden":false,"mode":"ojs"},{"id":255,"value":"pennWorldClean = pennworldaq\n .rename({\"Entity\": \"country_name\", \"Year\": \"year\", \"GDP (expenditure, multiple price benchmarks)\": \"gdp\", \"Population\": \"population\", \"Consumption of households and government (single price benchmark)\": \"consumption_households_and_govt\", \"GDP per capita (output, multiple price benchmarks)\": \"GDP per capita\"})\n .select(\"country_name\", \"year\", \"gdp\", \"population\", \"consumption_households_and_govt\", \"GDP per capita\"\n)\n .filter(d => d.gdp)\n .derive({country: aq.escape( d => clm.getAlpha2ByName(d.country_name))})","pinned":false,"hidden":false,"mode":"ojs"},{"id":139,"value":"inequalityDataClean = inequalityaq\n .select(\"Entity\", \"Year\", \"Gini coefficient\")\n .rename({\"Entity\" : \"country_name\", \"Year\": \"year\", \"Gini coefficient\": \"gini_coefficient\"})\n .derive({country: aq.escape( d => clm.getAlpha2ByName(d.country_name))})\n .filter(d => d.year >= 1990)\n // .join(catDataClean)","pinned":false,"hidden":false,"mode":"ojs"},{"id":271,"value":"catData = cataq.filter(d => d.indicator === \"Agriculture activity (meat): consumption\" && d.unit === \"meat kcal / cap / day\")\n .select(\"country\", \"year\", \"variable\", \"value\" )\n .groupby(\"country\", \"year\")\n .pivot('variable', 'value')\n .derive({country_name: aq.escape( d => clm.getCountryNameByAlpha2(d.country))})","pinned":false,"hidden":false,"mode":"ojs"},{"id":414,"value":"countries = aq.fromCSV(await FileAttachment('countries codes.csv').text())","pinned":false,"hidden":false,"mode":"ojs"},{"id":3377,"value":"countries.objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":12,"value":"cat = FileAttachment(\"CAT_new_portal_data_05_2022@2.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"owid = FileAttachment(\"World Inequality Database (WID) - Pretax income.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":8,"value":"oecd = FileAttachment(\"DP_LIVE_06122022114745787.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"pennWorld = FileAttachment(\"Penn World Table version 10.0.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":77,"value":"### Library imports","pinned":false,"hidden":false,"mode":"md"},{"id":81,"value":"import { aq, op } from '@uwdata/arquero'","pinned":false,"hidden":false,"mode":"ojs"},{"id":84,"value":"import {Plot} from \"@mkfreeman/plot-tooltip\"\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":96,"value":"clmImport =(import('https://cdn.skypack.dev/country-locale-map@1.8.11?min'));","pinned":false,"hidden":false,"mode":"ojs"},{"id":98,"value":"clm = clmImport.default;","pinned":false,"hidden":false,"mode":"ojs"},{"id":495,"value":"d3 = require(\"d3@7\");","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}