2d:null 2e:["$","title",null,{"children":"PSET 3 | Dylan | Observable"}] 33:T4f0,var_group_sums = { // fundamentally the same loop and adding to a new DF as above, but with some added complexity let grouping = [] for (let i=0;i r['country'] == country, var_grouped)[0][measures[m]]; // pulling the array from var_grouped -- I was having some trouble just slicing it var_grouped[`${country}`][0][measures[m] so I just filtered it if(filtered == undefined) { // some of the rows have no data, so here is the first part of my error handling currRow[`Sum of ${measures[m]}`] = 0; } else { let sum_num = z.gbSum("Value", z.groupBy(d => d['Variable'], filtered)); // grouping by sum for Value based on variable from the filtered DF created in the loop above currRow[`Sum of ${measures[m]}`] = sum_num[0].sum; // adding to the current row Sum of the measure = the number } } grouping.push(currRow); } return grouping; }34:T534,df_summed = { // again, much the same toolkit as before let grouping = [] for (let i=0;i x['Country of birth/nationality'], var_grouped[i][curr_measure])); console.log(`${country} filtered ${measures[m]}`) if (filtered == undefined) { } else { for (let y=0;y x['Country'], data_parsed) // simple group, nothing to see here","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":285,"value":"var_grouped = { // as per above, looping through unique countries to group by them\n let grouping = [] \n for (var i=0;i x['Variable'], data_grouped[`${country}`]); //grouping by variable within country slice of data_grouped\n currRow.country = country;\n currRow.CO2 = unique_cou[i]; //adding co2 here for convenience\n grouping.push(currRow);\n }\n return grouping;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":524,"value":"measures = ['Inflows of foreign population by nationality', 'Outflows of foreign population by nationality', 'Stock of foreign-born population by country of birth'] // declaring three things we're looking at for future convenience","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":542,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":894,"value":"has_foreign = { // this is a tangent, I worked out a better way to do this and avoid type errors in df_summed, but I left this in for posterity\n var foreign_born_co = []\n for (var i=0; i r.country == country, var_grouped)[0]['Stock of foreign-born population by country of birth'] == undefined) {} else {foreign_born_co.push(country)}\n }\n return foreign_born_co;\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":746,"value":"foreign_origin_sums = { \n let grouping = [] \n for (var i=0; i r.country == country, var_grouped)[0]['Stock of foreign-born population by country of birth'];\n let currRow = {};\n currRow.Country = country;\n currRow.Foreign_Born = z.gbSum(\"Value\", z.groupBy(x => x['Country of birth/nationality'], filtered));\n grouping.push(currRow);\n }\n return grouping;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1092,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":560,"value":"{ return `Variables by Country by Country` + z.head(5, df_summed)}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":114,"value":"md`## 4. Joining data\nFor this question, we want to compare how the immigration stats differ across continents. You can find a list of Countries, County Codes, and Continents [here](https://gist.githubusercontent.com/cesandoval/b834ac93c07e03ec5205843b97f68017/raw/0c14fa1b1f65a610491417b2894fceecde5f4260/country-and-continent-codes-list-csv_csv.csv)\n\n- Load this data.\n- Create a data frame that only keeps the \\`Country Codes \\` and the \\`Continent\\`.\n- Join your datasets by \\`Country Code\\`\n- Create a new aggregated dataset that counts the number of \\`Inflows of foreign population by nationality, Outflows of foreign population by nationality, Stock of foreign-born population by country of birth\\` by \\`Continent\\`\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":91,"value":"// Your code here\npset_data_url_2 = \"https://gist.githubusercontent.com/cesandoval/b834ac93c07e03ec5205843b97f68017/raw/0c14fa1b1f65a610491417b2894fceecde5f4260/country-and-continent-codes-list-csv_csv.csv\" // new url","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":92,"value":"// Your code here\ndata_2 = d3.csv(pset_data_url_2); // d3 pulling in CSV as object array yada yada","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":584,"value":"con_codes = z.pickCols([\"Continent_Name\",\"Three_Letter_Country_Code\"], data_2) // snagging just the continent and country code columns","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"// Your code here\ncontinents_grouped = z.groupBy(x => x.Continent_Name, z.merge(var_grouped, con_codes, \"CO2\", \"Three_Letter_Country_Code\", '_1', '_2')) // merging continents with the var_grouped ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1209,"value":"z.gbSum(\"Value\", z.groupBy(x => x.Continent_Name, z.merge(continents_grouped.Oceania[0]['Inflows of foreign population by nationality'], con_codes, \"CO2\", \"Three_Letter_Country_Code\", '_1', '_2'))) // testing the order/syntax for the next iteration","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":613,"value":"continent_totals = { // same strategies as above\n \n let grouping = [] // declare object array\n for (let i=0;i x.Continent_Name, z.merge(continents_grouped[continent][0][measures[m]], con_codes, \"CO2\", \"Three_Letter_Country_Code\", '_1', '_2'))) // iterating through continent and measures\n for (let y=0;y 100,000\n - \\`Medium Immigration\\` > 50,000 & < 100,000\n - \\`Low Immigration\\` < 50,000\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"// Your code here\ncob_grouped = z.groupBy(x => x['Country of birth/nationality'], data_parsed) // simple group by COB","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1284,"value":"z.filter(r => r.Year == 2016, cob_grouped.Afghanistan) // testing filter","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":126,"value":"// Your code here\ncob_grouped_vars = { \n let grouping = [];\n for(let i=0;i x['Variable'], cob_grouped[country_of_birth])) // grouping by variable the sums from the loop country of birth\n for(let y=0;y x['Variable'], z.filter(r => r.Year == 2016, cob_grouped[country_of_birth]))) // only different between this and above -- filtering for 2016 as the DF for the gbSum -> groupBy call. \n for(let y=0;y x['Stock of foreign-born population by country of birth'] < 50000, cob_grouped_vars_2016); \n \n let full_table = z.pipe([ // looking back, this could have been done using z.pickCols. The reason for me doing this was the the order of variables was not the same in each row, and zebras displayed the wrong values in the columns-- ex = [{\"country\":\"usa\",\"a\":1,\"b\":2}{\"country\":\"mex\",\"b\":2,\"a\":1}] the display rows would be USA | A | B // MEX | B | A. change the below to returning the init_table for a clearer demo\n z.dropCol(\"Inflows of foreign population by nationality\"),\n z.dropCol(\"Inflows of asylum seekers by nationality\"),\n z.dropCol(\"Outflows of foreign population by nationality\"),\n z.dropCol(\"Stock of foreign population by nationality\"),\n z.dropCol(\"Acquisition of nationality by country of former nationality\"),\n ])(init_table)\n \n return full_table;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1332,"value":"{return `Low Immigration Table 2016` + z.head(5,low_table)}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1329,"value":"medium_table = {\n let init_table = z.filter(x => x['Stock of foreign-born population by country of birth'] > 50000 && x['Stock of foreign-born population by country of birth'] < 100000 , cob_grouped_vars_2016)\n \n let full_table = z.pipe([\n z.dropCol(\"Inflows of foreign population by nationality\"),\n z.dropCol(\"Inflows of asylum seekers by nationality\"),\n z.dropCol(\"Outflows of foreign population by nationality\"),\n z.dropCol(\"Stock of foreign population by nationality\"),\n z.dropCol(\"Acquisition of nationality by country of former nationality\"),\n ])(init_table)\n \n return full_table;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1334,"value":"{return `Medium Immigration Table 2016` + z.head(5,medium_table)}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1337,"value":"high_table = {\n let init_table = z.filter(x => x['Stock of foreign-born population by country of birth'] > 100000 , cob_grouped_vars_2016);\n \n let full_table = z.pipe([\n z.dropCol(\"Inflows of foreign population by nationality\"),\n z.dropCol(\"Inflows of asylum seekers by nationality\"),\n z.dropCol(\"Outflows of foreign population by nationality\"),\n z.dropCol(\"Stock of foreign population by nationality\"),\n z.dropCol(\"Acquisition of nationality by country of former nationality\"),\n ])(init_table)\n \n return full_table;\n}\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1339,"value":"{return `High Immigration Table 2016` + z.head(5, high_table)}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":151,"value":"md `## 6. Comparing data\n\nFor this question, we ask you to compare the inflow and outflow of population by nationality.\n\n- Create a data frame that has a column called \\`inflow-outflow\\`, which subtracts the aggregated number of \\`Inflows of foreign population by nationality\\`** - **\\`Outflows of foreign population by nationality\\` by \\`Country of birth/nationality\\`.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":171,"value":"// Your code here\ninflow_outflow = {\n let outflow=[];\n let a = z.getCol('country_of_birth', cob_grouped_vars); // getting from previous step in alphabetical order as an array\n let b = z.deriveCol((r) => r['Inflows of foreign population by nationality']-r['Outflows of foreign population by nationality'], cob_grouped_vars); // subtracting the two columns, outputting an array\n for(let i=0;i