Published
Edited
Sep 27, 2019
1 fork
//this section is by Theo
minHP = d3.min(data2017, d => d["Engine HP"]); //minimum HP of all cars in 2017
maxHP = d3.max(data2017, d => d["Engine HP"]); //maximum HP of all cars in 2017
minEngineCyl = d3.min(data2017, d => d["Engine Cylinders"]); //least amount of cylinders of all cars in 2017; 0 represents Electric cars
maxEngineCyl = d3.max(data2017, d => d["Engine Cylinders"]); //most amount of cylinders of all cars in 2017
minDoors = d3.min(data2017, d => d["Number of Doors"]); //minimum number of doors of all cars in 2017
maxDoors = d3.max(data2017, d => d["Number of Doors"]); //maximum number of doors of all cars in 2017
minHwyMPG = d3.min(data2017, d => d["highway MPG"]); //minimum highway miles per gallon (MPG) of all cars in 2017
maxHwyMPG = d3.max(data2017, d => d["highway MPG"]); //maximum highway MPG of all cars in 2017//this section is by David
minCityMPG = d3.min(data2017, d => d["city mpg"]); //minimum city MPG of all cars in 2017
maxCityMPG = d3.max(data2017, d => d["city mpg"]); //maximum city MPG of all cars in 2017
minPop = d3.min(data2017, d => d.Popularity); //least popular rating of all cars in 2017
maxPop = d3.max(data2017, d => d.Popularity); //most popular rating of all cars in 2017
minMSRP = d3.min(data2017, d => d.MSRP); //cheapest price of all cars in 2017
maxMSRP = d3.max(data2017, d => d.MSRP); //most expensive price of all cars in 2017
//this section is by David
meanMSRP = d3.mean(data2017, d => d.MSRP); //average = $70,375,479 / 1668 cars
meanEngineHP = d3.mean(data2017, d => d["Engine HP"]); //average = 453664 (sum of HP) / 1653 cars (some cars do not have recorded data)
meanHMPG = d3.mean(data2017, d => d["highway MPG"]); //average = 47667 HMPG / 1668 cars
meanCMPG = d3.mean(data2017, d => d["city mpg"]); //average = 35569 CMPG / 1668 cars
//this section is by Theo
acuraHPAboveAvg = acuraData.filter(d => d["Engine HP"] >= meanEngineHP);
acuraMSRPAboveAvg = acuraData.filter(d => d.MSRP >= meanMSRP);
//this section is by David
bmwCMPGAboveAvg = bmwData.filter(d => d["city mpg"] >= meanCMPG);
bmwHMPGAboveAvg = bmwData.filter(d => d["highway MPG"] >= meanHMPG);
bmwMSRPAboveAvg = bmwData.filter(d => d.MSRP >= meanMSRP);
bmw_MSRP_HMPG_CMPG_AboveAvg = bmwData.filter(function(d) {
return (d.MSRP >= meanMSRP &&
d["city mpg"] >= meanCMPG &&
d["highway MPG"] >= meanHMPG
)
});
//this section is by Theo
twoDoorCarAvgMSRP = data2017.filter(function(d) {
return (d["Number of Doors"] == 2 &&
d.MSRP >= meanMSRP
)
});
fourDoorCarAvgMSRP = data2017.filter(function(d) {
return (d["Number of Doors"] == 4 &&
d.MSRP >= meanMSRP
)
});
//this section is by David
//we chose to implement a slider for this histogram because it allows us to see the data from a general point of view to a more zoomed in point of view
//we chose to use a slider for the bin size because it allowed us to see general highs and lows in data, then be able to take a closer look at specific data regions
//this section is by Theo
//we chose to have a fixed bin size of 5000 because it allowed us to show the maximum and minimum highway MPG for each make of car
//for the y axis of the graph we have so many categories due to the number of manufactures for cars there are
//this section is by David
//we chose to have a fixed bin size because it allowed us to show the minimum and maximum city MPG for each make of car
//a fixed bin size was used because it allowed us to best demonstrate the minimum and maximum average values of MSRP for each make of car
//we chose to limit the bin size for this graph because we wanted to see the average popularity at the lowest, highest and all the values in between
//https://gist.githubusercontent.com/tdore98/fc20a7d8472fd73932cd9abd907927df/raw/8e9aa9e157b67197a57c80b93b669c72411e8850/data.csv
//the remaining sections are done collaboratively by Theo and David
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