2a:[] 29:{"title":"Exploratory Data Analysis - Group 12","cells":[{"id":0,"value":"md`# Exploratory Data Analysis - Group 12 `","pinned":false,"hidden":false,"mode":"ojs"},{"id":4,"value":"html`

Part 1: Distribution of Variables

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Histogram #1 - Distributions of Playcounts Per User (Jonathan)

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":425,"value":"md`\nThis dataset stores a sample of last.fm's users, sourced by the [Music Listening Histories Dataset (MHLD)](https://tinyurl.com/yarbmfoj). It tracks playcount, gender, age, and country for each user. As the original dataset was too large to host online for free, we randomly sampled 100,000 users from the original set.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":477,"value":"hist1","pinned":false,"hidden":false,"mode":"ojs"},{"id":544,"value":"viewof hist1_select = text({\n placeholder: \"Type a country...\",\n description: \"Filter to a country\",\n submit: \"Go\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":540,"value":"currently_displayed_countries = {\n let vvv = Object.keys(hist1_user_data).map(d => {\n return hist1_user_data[d].country;\n });\n\n vvv = _.uniqBy(vvv);\n return vvv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":486,"value":"md`#### Pertinent Information\n* The average playcount for numerous users is significantly higher than expected - This data is documented in [this academic paper on the MLHD](https://dl.acm.org/doi/pdf/10.1145/2632188.2632203), which mentions that all users with less than 7300 listens logged are excluded. This may bias the results of our data, and we should mention it as a caveat.\n* Some countries have no listed users, as last.fm is not available in every country.\n* The user dataset is heavily skewed to the right, with most users having less than 50,000 plays over their account lifetime.\n* There are notable outliers with incredibly high lifetime playcounts. These may be bots or just particularly enthusiastic listeners. These should be filtered in the final project, as they may bias our results.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":548,"value":"md` \nThe Max is: **${d3\n .max(user_data, d => d.playcount)\n .toString()\n .replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\")}**
\nThe Min is: **${d3.min(user_data, d => d.playcount)}**
\nThe Mean is: **${d3.mean(user_data, d => d.playcount)}**
\nThe Median is: **${d3\n .median(user_data, d => d.playcount)\n .toString()\n .replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\")}**
\nThe Deviation is: **${d3.deviation(user_data, d => d.playcount)}**
\nThe Sum is: **${d3\n .sum(user_data, d => d.playcount)\n .toString()\n .replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\")}**
\nThe Range is: **${(\n d3.extent(user_data, d => d.playcount)[1] -\n d3.extent(user_data, d => d.playcount)[0]\n)\n .toString()\n .replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\")}**
\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":189,"value":"html`

Histogram #2 - Distribution of the # of listeners across the world (Linley's)

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":639,"value":"md`_This dataset came from last.fm and shows data related to topArtists around the world. It tracks each country, artist, and how many listeners per artist for every country. The data i'm using for the histogram is solely listeners data per country._ `","pinned":false,"hidden":false,"mode":"ojs"},{"id":192,"value":"md` ### Sample of Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":286,"value":"render_data_table(listeners_per_country.slice(0, 10))","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"md` ### Distribution of the # of listeners across the world`","pinned":false,"hidden":false,"mode":"ojs"},{"id":366,"value":"viewof choice","pinned":false,"hidden":false,"mode":"ojs"},{"id":199,"value":"histogram1","pinned":false,"hidden":false,"mode":"ojs"},{"id":273,"value":"md`_In the histogram above including all the countries listeners, it can be seen that there is an odd distribution because of the big number of countries that have 0 listeners. This is definitely skewing the mean and median. The mean for this chart is **${mean}**. When choosing to view the histogram excluding the countries with 0 listeners you can see that the mean is **${d3.mean(\n bin2_data\n)}** listeners. This chart is probably a better representation of the data because the countries with 0 listeners affected the mean greatly. It might be that those countries don't have any data that is being collected or that there aren't that many inhabitants on that country. For example a country such as \"Antarctica\" does not have that many people on it and of course it's main purpose is for research, not for living. _`","pinned":false,"hidden":false,"mode":"ojs"},{"id":233,"value":"md` ### Stats on total # of listeners for all countries`","pinned":false,"hidden":false,"mode":"ojs"},{"id":236,"value":"md` \nThe Max is: **${max}**
\nThe Min is: **${min}**
\nThe Mean is: **${mean}**
\nThe Median is: **${median}**
\nThe Deviation is: **${deviation}**
\nThe Sum is: **${sum}**
\nThe Range is: **${range}**
\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":406,"value":"md` ### Stats on total # of listeners excluding countries with 0 listeners`","pinned":false,"hidden":false,"mode":"ojs"},{"id":404,"value":"md` \nThe Max is: **${d3.max(bin2_data)}**
\nThe Min is: **${d3.min(bin2_data)}**
\nThe Mean is: **${d3.mean(bin2_data)}**
\nThe Median is: **${d3.median(bin2_data)}**
\nThe Deviation is: **${d3.deviation(bin2_data)}**
\nThe Sum is: **${d3.sum(bin2_data)}**
\nThe Range is: **${d3.max(bin2_data) - d3.min(bin2_data)}**
\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":433,"value":"html`

Part 2: Relationships between variables

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Bar chart - Artists appers on the most top 50 lists/By Country (Kitty)

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":628,"value":"md` ### Sample of data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":630,"value":"render_data_table(nested_artists.slice(0, 10))","pinned":false,"hidden":false,"mode":"ojs"},{"id":617,"value":"barChart","pinned":false,"hidden":false,"mode":"ojs"},{"id":717,"value":"md`\n* This is a chart showing the information on the Artists appears on the most - Top 50 list (by country). We aggregated the number of countries artists appear in the [last.fm Top Artists by country dataset](https://www.last.fm/api/show/geo.getTopArtists). In this dataset, it has information about the top 50 artists in each country where last.fm offer service with. \n* Some countries have no listed artists, as last.fm is not available in every country.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":686,"value":"html`

Chloropleth - World Map (Wenjia's)

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":647,"value":"chloropleth","pinned":false,"hidden":false,"mode":"ojs"},{"id":741,"value":"md`\n* This is a chart showing the information on total musica play counts around the world. Trough the map, we could easily tell which countries played largest number of music.\n* Some countries have no listed users, as last.fm is not available in every country`","pinned":true,"hidden":false,"mode":"ojs"},{"id":711,"value":"html`

Appendix

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":220,"value":"md` ## Import data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":185,"value":"import {\n geo_data,\n user_data,\n similar_tracks\n} from '@info474/last-fm-data-source-group-12'","pinned":false,"hidden":false,"mode":"ojs"},{"id":187,"value":"geo_data","pinned":false,"hidden":false,"mode":"ojs"},{"id":297,"value":"import { select, slider, radio } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"d3 = require(\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":28,"value":"_ = require(\"lodash\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":287,"value":"import { render_data_table, table_styles } from \"@info474/utilities\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":448,"value":"import { projectionInput } from \"@d3/projection-comparison\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":227,"value":"md` ### Linley's Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":230,"value":"import {\n listeners_per_country,\n listener_data,\n listeners2_per_country,\n bin2_data,\n min,\n max,\n median,\n deviation,\n mean,\n sum,\n range\n} from '@info474/histogram-of-play-counts-per-country'","pinned":false,"hidden":false,"mode":"ojs"},{"id":282,"value":"import {\n viewof choice,\n histogram_chart as histogram1\n} from '@info474/histogram-of-play-counts-per-country'","pinned":false,"hidden":false,"mode":"ojs"},{"id":416,"value":"md` ### Kitty's Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":614,"value":"import { chart as barChart } from \"@info474/artists-vs-countries-bar-chart-kitty-fixed\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":623,"value":"import { nested_artists } from '@info474/artists-vs-countries-bar-chart-kitty-fixed'","pinned":false,"hidden":false,"mode":"ojs"},{"id":418,"value":"md` ### Jonathan's Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":493,"value":"hist1_user_data = user_data.filter(d =>\n d.country.toUpperCase().startsWith(hist1_select.toUpperCase())\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":495,"value":"import { chart as hist1 } with {\n hist1_user_data as hist_user\n} from \"@info474/part-2-exploratory-data-analysis/3\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":500,"value":"import { text } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":419,"value":"md` ### Wenjia's Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":340,"value":"import { chart1 as chloropleth } from '@info474/chloropleth-map-wenjia'","pinned":false,"hidden":false,"mode":"ojs"},{"id":707,"value":"md`## CSS`","pinned":false,"hidden":false,"mode":"ojs"},{"id":690,"value":"html`\n`","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W2a","stdlib":"1","titling":"auto"}