2d:null 2e:["$","title",null,{"children":"Project 1 | Jackson Levitt | Observable"}] 33:Te47,md` Introduction -------- Magic the Gathering is a popular Trading Card Game. It features glorious heroes, hateful villains, magical spells, ancient artifacts, and monsters all fighting against one another in a grand battle. The game is played by planeswalkers (players) who use the magical powers of the lands that they play to manifest various effects in an effort to reduce their opponents' life total down to zero. The game can be played in many different formats, from one player versus one player with a limited card pool to a free-for-all wherein a bunch of players all compete to be the last one standing. The most popular format is known as EDH. EDH is a free-for-all format, wherein players are limited to 99 unique cards in their decks and one special legendary card that can be continuously replayed known as the commander. In recent years, WOTC (Wizards of the Coast) the owner of MTG (Magic the Gathering) has engaged in trying to sell more cards to EDH players. One easy way to do this is to print new and exciting cards that players desire to have in their decks to be able to more effectively win. While this determination may not be popular with the player base at large, it still poses a significant question... can one suitably determine the characteristics that influence whether a card is successful or not? Let's take a look into some data. Methodology ------- Notice: The following Methodology is for the purposes of visualization, it is not regression or the usage of complex data modelling techniques that allow for the construction of firm conclusions. All conclusions grabbed should be taken with a grain of salt as more complex methodologies have not been performed. [MtgJson](https://mtgjson.com/) is an open-source project that catalogs all MTG data as it relates to the game. It provides data from card-pricing to card information. The data is verified as accurate and is used by many MTG applications. For this project, we will be cleaning up the data from AllPrintingsCSVFiles.csv which is a CSV version of the data provided by AllPrintings. For the cleanup, we need to remove all duplicates so that we have 1 unique card per name, remove unimportant columns of data, and normalize all data within columns to be valid. For this purpose some data had to be cut. Non-standard values for convertedManaCost, toughness, power, and loyalty were converted to null-data. Our goal is to visualize some variables effect on EdhRank. EdhRank is a ranking for how popular a card is by how many decks use that particular card. Terms ------- - Power: How much damage a creature card can do to another creature. - Toughness: How much damage a creature card can take without dying. - Converted Mana Cost: The amount of mana/resources required to play a card. - Loyalty: How much damage a planeswalker card can take without dying. (Planeswalkers are special cards that have Loyalty) - Rarity: How Rare a card is to find in a booster pack. (Booster Packs typical contains 10 commons, 3 uncommons and one rare or mythic with mythics only appearing in 1 in 8 packs.) - Color: Refers to what types of mana are required to play a card. - Color Identity: Refers to what types of mana are required to both play a card or use it's abilities. Questions to Answer: ------- - Are bigger cards on average more popular to use? (higher power or toughness) - Are cards with higher Converted Mana Costs on average more popular to use? - Are rarer cards more popular to use? - What is the most popular planeswalker loyalty amount for usage? - What are the most and least popular colors? - What are the most and least popular color identities? `34:T58a,md` Conclusions ------ We can clearly see that as Power increases, the average popularity of cards seems to increase, however, initially it goes down. Cards that have 0 or 1 power are more popular than cards that have 2 or 3. Similar results occur with Toughness. For Converted Mana Cost, we find that cheap, cards are incredibly popular, but there is an increase in popularity up until a cost of 13 at which point popularity diminishes. The one thing that I dislike is that I couldn't show how many cards are within each group. When viewed with that in mind, higher costed cards are rarer than lower costed ones so there is less competition for the higher costed ones for ranking and less purely bad cards in those groups. For Loyalty, we find that cards with Loyalty tend to be very popular compared to other measures, though there is not a clear pattern for whether high loyalty is more popular than low loyalty. We find that more rare cards (and by tendency, expensive in currency) are more popular for players to use. The data is rather striking on the differences. The least popular colors are W (white) and R (red), the combination of WBR (White-Black-Red) is the most popular in terms of average Rank. The least popular color identities are similarly W (white), R (red) and additionally BRG (Black-Red-Green). The Most popular color identities are WUBG (White-Blue-Black-Green) and URG (Blue-Red-Green) `35:Td74,function makeChart(database){ database = [...database]; const w = 1200; const h = 500; const svg = d3.create('svg') .attr('width', w) .attr('height', h); const xAxisGroup = svg.append('g') .attr('class', 'axis') .attr('transform', `translate(0, ${h - 60})`); const yAxisGroup = svg.append('g') .attr('class', 'axis-left1') .attr('transform', `translate(80,20)`); const xLabel = svg.append("text") // text label for the x axis .attr("x", w/2) .attr("y", h-20) .style("text-anchor", "middle") const yLabel = svg.append("text") // text label for the x axis .attr("x", 20 - h/2) .attr("y", 20) .attr("transform", "rotate(-90)") .style("text-anchor", "middle") .text("Average Rank"); const updateGraph = (newtype) =>{ console.log(newtype); const secondType = newtype; const datas4 = getFromDatabase(dataset, newtype); const dataMin = d3.min(datas4, d => d[newtype]); const dataMax = d3.max(datas4, d => d[newtype]) + 1; console.log(dataMin); console.log(dataMax); console.log(datas4[0]); const uxScale = d3.scaleLinear() .domain([dataMin,dataMax]) .rangeRound([80, w - 80]); const colorScale = d3.scaleLinear() .domain([0, 20000]) .range(['#393', '#d0d']); const uyScale = d3.scaleLinear() .domain([0, 20000]) .rangeRound([20, h - 80]); svg.selectAll('rect') .data(datas4, d => d[newtype]) .join( enter => enter.append('rect') .attr('x', d => uxScale(d[newtype]) - 800) .attr('y', d=> h-uyScale(d.avgRank) - 60) .attr('width', (w-160)/(dataMax-dataMin)) .attr('height', d => uyScale(d.avgRank)) .attr('fill', d => colorScale(d.avgRank)) .transition() .attr('x', d=>uxScale(d[newtype])), exit => exit.transition() .attr('x',d=>uxScale(d[newtype])) .remove()); function capitalize(str) { return str.charAt(0).toUpperCase() + str.slice(1); } xLabel.text(capitalize(newtype)); const xAxis = d3.axisBottom(uxScale); const yAxis = d3.axisLeft(uyScale); yAxis.ticks(d3.tickStep(0,20000,1000)); xAxis.tickValues(datas4.map(function(d){ return d[newtype]})); xAxis.tickFormat((d,i) => { if(newtype == 'rarity') return getRarity(d); if(newtype == 'colors' || newtype == 'colorIdentity') return getColor(d); return d; }); xAxisGroup.call(xAxis); xAxisGroup.selectAll("text") .attr("x", (w-160)/(2*(dataMax-dataMin))) .style("text-anchor", "middle"); yAxisGroup.call(yAxis); } updateGraph("power"); const typeSelector = html` `; d3.select(typeSelector).on('change', () => { updateGraph(typeSelector.value); }); return html`

Chart Type: ${typeSelector} ${svg.node()}

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