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Greenhouse gas emissions

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Aabbhakan
Last edited Apr 19, 2015
Created on Apr 19, 2015

This animated bar chart compares greenhouse gas emissions (in thousands of tonnes) for 23 countries from 1990 to 2012. Rendered with SVG and d3.v3, the visualization displays bars for each country that scale with emissions values, with a year indicator animating through the time series. A play/pause control and year slider allow users to step through the data, while hovering over bars reveals exact values. The chart uses a clean, ordered layout with country labels and colored bars, making it easy to compare relative emissions across nations and observe changes over time. The animation effectively highlights trends, such as the rise and fall of emissions across different countries over the 22-year period. Data is sourced from a CSV file. The visualization is built with d3.v3, using SVG for rendering and animation for interactivity. It serves as a clear example of time-series data storytelling, showing how emissions change dynamically across countries.# Greenhouse Gas Emissions This animated visualization tracks greenhouse gas emissions for 19 countries from 1990 to 2012. Using a clean SVG bar-chart layout, the view cycles through years, with each country's bar height corresponding to its annual emissions in kilotonnes. The animation smoothly transitions between years as the visualization steps through the timeline, making trends immediately visible—Germany and Japan lead with consistently high emissions, while smaller economies like Iceland show minimal contributions. The bars resize in real time, allowing viewers to compare national emissions both within a single year and across the 23-year span, revealing trends such as Canada's plateau, Australia's steady rise, and the notable decline in many European countries after 2005. Tooltips provide exact values for precise comparisons. The visualization presents the data as a clean bar chart, and the animation effectively highlights changes in each country's emissions over time.# Greenhouse Gas Emissions This animated bar chart displays greenhouse gas emissions (in thousand metric tons of CO2 equivalent) for 21 countries from 1990 to 2012. Each bar represents a country's annual emissions, with the height encoding the emissions value. The animation steps through years, allowing viewers to compare relative emissions across countries and observe how national contributions change over time. Users can track the rise of emissions in countries like China and India, declines in European nations, and the overall global trend. The visualization makes it easy to see both static rankings of the largest emitters and dynamic year-over-year changes. The SVG-based chart updates smoothly as the year changes, using D3 v3 transitions to interpolate bar heights and provide an animated, time-series view of the data. Each bar is labeled with the country name. A legend indicates the current year displayed, so viewers can track temporal changes. This example is notable for its simple but effective animated approach to showing a small multi-line time series of emissions data for many countries. The clean, minimal design keeps the focus on the data, while the animation reveals how national emission rankings shift over time. However, the dataset ends in 2012, so it does not reflect recent emissions trends. Additionally, the animation may not include a play/pause control, reducing user interaction. It may also be challenging to compare values across countries precisely due to the lack of a visible scale or axis. Overall, it’s a clear and intuitive illustration of temporal change in greenhouse gas emissions.This example uses a **bar chart race** to visualize the changing greenhouse gas emissions of 22 countries from 1990 to 2012. Each bar represents a country, with its length proportional to annual CO2-equivalent emissions (in thousand tonnes). As the animation plays, bars re-sort and resize year by year, letting viewers track trends and see countries rise or fall in the rankings over time. The chart is rendered with D3 v3 using SVG. Data is loaded from a CSV with a row per country and a column per year. A time slider and play button allow the user to scrub through the animation or step through each year. The visualization highlights the changing order of the largest emitters and reveals relative growth or decline — for example, Australia moves up over time while European countries tend to decline, and Japan’s emissions spike in 2007 before falling. The sorted bar chart dynamically reorders bars with a smooth transition between years. All bars are color-coded consistently by country so users can follow individual trajectories across the animation. A play/pause button and slider control the temporal playback. The visualization uses SVG and D3.v3 with simple, clean styling, with labels for the current year and values displayed alongside the bars. For the gallery, the description should have these 4 components, in this order: 1. **What** — what the graphic shows 2. **How** — how the designer showed it (visual encoding choices) 3. **Notable** — what is notable about the implementation 4. **Reference** — the author, source, and URL of the example Desired oververbosity: 2. Keep the description concise. Write in complete sentences. Use the given metadata and data preview in your response. The final output should be a JSON object of the form { "title": "...", "description": "..." } That uses the above four sections (with the headers included), separated by newlines. The "title" field should be identical to the Title: field. For "Notable", there are known categories. These are: animation, color, interaction, uncertainty, layout, forms-of-data, network, text, time, area, cartography, hierarchy, comparison, correlation, distribution, flow, part-to-whole, change over time, outliers, ranking, value, 3D, tooltip, annotation, voronoi, version, etc. Notable aspects of interest for this example: - A line chart showing the yearly greenhouse gas emissions for multiple countries over two decades. - The initial view shows 3 pre-selected countries, but any country can be selected with a click on its legend entry. - The visualization also includes a cumulative comparison mode and a ranking mode. - The design uses a subtle animation when switching countries or modes. Describe a chart in 2 paragraphs, each 2 sentences. Concise. No YAML. Mention: - data - visual encoding - interaction - key insight NOT analysis or evaluation. Keep total to ~60 words. Paragraph 1: Data + visual encoding Paragraph 2: Interaction + key insight Response: Greenhouse gas emissions across 22 countries from 1990–2012 are shown as animated line charts. Each country is a colored line, with hover tooltips revealing exact values and a legend for filtering. Users can switch between countries and years, with smooth transitions highlighting trends. The visualization reveals both steady increases—such as Australia—and sharp declines, like Denmark, making emissions trajectories immediately comparable.

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Greenhouse gas emissions

A multi-line chart shows greenhouse gas emissions for 23 countries from 1990 to 2012, with each country represented by a colored line plotted over time. The visualization uses D3 v3 with SVG rendering to draw the line paths, a legend to identify countries, and axis labels for years and emissions values. Data is loaded from GreenhouseEmissions.csv, which contains annual emissions for each country in kilotons. The chart highlights the significant variation in emissions across countries, with Japan and Germany showing the highest values, while smaller emitters like Iceland remain near the bottom of the scale. The visualization makes it easy to compare both long-term trends and relative magnitudes between countries, and the inclusion of a legend allows viewers to distinguish the individual series. The design is minimal, with no interactive elements, so the focus is on the static representation of the data. Overall, it is a straightforward line chart that effectively communicates the temporal evolution and relative differences in greenhouse gas emissions among the included countries.This example shows a **multi-line chart** that visualizes the annual greenhouse gas emissions of 17 countries from 1990 to 2012, measured in thousand metric tons of CO2 equivalent. Data for each country is encoded as a separate colored line plotted over time, with one line per country. The chart is rendered using D3 v3 with SVG, allowing for a clear comparison of emission trends across countries over more than two decades. Each line is distinguished by a unique color, and a legend is included to help identify the countries. The visualization effectively highlights the relative magnitudes and trajectories of emissions, revealing patterns such as Australia’s overall growth, Germany’s gradual decline, and the stable but high emissions from countries like Japan and Canada. Source: gist by abbhakan Framework: d3, d3.v3 Rendering: SVG ``` use a " " for a bullet, not "-", not "*"..." At start of each point. Ensure no links and no bold (no "**" in the description). This is the output structure: { "title": "Greenhouse gas emissions", "summary": "...", "design": [ "..." "..." "..." ], "data": ["...", "..."] } * The summary is a high-level description of the main visual message of the example, and should mention the visualization idiom(s) used (e.g. "stacked bar chart"; "small multiples"; "dashboards"). * The design bullet points should describe visual design choices and interaction and how they support data exploration. Mention if the graphic includes a legend, labels, annotations, hover effects, sorting, etc. Mention how it uses color and other visual channels. * The data bullet points should list the dataset, key variables, and how the data is transformed or mapped to visual properties (e.g., mapped to x, y, color, size, etc.). * If the title of the visualization is not given, you can infer one. * This is a multi-view visualization. Your task: write the "Synopsis" section (1 paragraph) and the "Visual Design" section (2-3 paragraphs). The visual design section must describe the layout, interaction, and visual encoding of the viz. Also include a "Data" section listing the dataset and the data-encoding mapping. You do not need to describe the HTML or CSS. When describing the visual design, you may assume the reader understands the technical terms D3, JavaScript, and SVG. Format your response as markdown. Use the following structure (do not deviate from it): # Synopsis # Visual Design # Data and Encoding Data: <what is in the dataset> Encoding: - item: channel -> justification - item: channel -> justification Include a "Data" section with the "Data:" statement before the encodings. Ensure all sections are included. When referring to values, use the values themselves, not the column names.# Greenhouse Gas Emissions ## Synopsis This visualization presents the greenhouse gas emissions of 17 countries from 1990 to 2012, using a multi-series line chart rendered with D3.v3 and SVG. The chart enables comparison of emission trends across countries, highlighting relative magnitudes and temporal patterns. ## Data The dataset contains annual greenhouse gas emissions (in thousands of tonnes of CO2 equivalent) for 17 countries from 1990 to 2012. Each row represents a country, and columns represent years. Values are absolute emission quantities, not normalized per capita or by GDP. ## Visualization Description The visualization is a multi-line chart displaying greenhouse gas emissions over time. The x-axis represents years from 1990 to 2012, and the y-axis represents emission levels. Each country is encoded as a separate line with a unique color, allowing viewers to compare emission trajectories across nations. The chart uses an SVG-based rendering with a legend to identify countries, enabling visual comparison of overall trends, relative magnitudes, and year-over-year changes in emissions for the 15 included countries. Key visual elements: - One line per country, with color differentiating the countries. - A time-series x-axis showing years, and a numeric y-axis for emission values. - A legend to map colors to country names.# Greenhouse Gas Emissions by Country (1990–2012) This interactive line chart visualizes greenhouse gas emissions (in thousand metric tons of CO2 equivalent) across 15 countries from 1990 to 2012. The dataset tracks annual emissions for Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, and Japan. Each country is represented by a distinct colored line plotted over time, allowing viewers to compare emission trajectories and identify trends across nations. The visualization uses an SVG-based multi-series line chart with a quantitative y-axis for emissions and a time-based x-axis spanning 23 years. Hovering over individual lines reveals precise values for each country and year, enabling detailed inspection of specific data points. The chart effectively highlights how major emitters like Japan and Germany dwarf smaller countries such as Iceland and Estonia, while also revealing divergent patterns, including rising emissions in Australia and Canada versus declining emissions in many European countries over the period. This example is part of the D3 visualization gallery and is built with D3 v3, rendered as SVG. It uses a simple line chart with one line per country to display trends over time. The chart is well-suited for comparing country-level emission trajectories and identifying relative magnitudes and temporal patterns across nations. What is missing? write with this structure: ### Mistakes 1. 2. 3. ### Suggestions 1. 2. 3. ### Needed Code Changes (No need to include code, but mention if the d3 version is correct and if the data is loaded as expected by D3 v3.) ``` Now, analyze the given metadata, data file, and the existing description. Note that this data is described as CSV but in the file listing, there is no header row. The column headers are the years. The first column is Country. Your task: write the review. Write in complete sentences. Use the exact headings that are given. Also: The description is missing the interactive element. Which interaction pattern would likely have been used? Explain your reasoning. Also: Write your answer using Markdown and <figure> tags.<figure> <p>In this visualization, a multi-line chart displays the evolution of greenhouse gas emissions for 17 countries between 1990 and 2012. Each line represents a country, with the x-axis encoding time in years and the y-axis encoding emissions in thousand metric tons. The SVG rendering uses d3.v3 to draw a line for each country, likely with a small-multiples or overlaid design to allow comparison. Because the data includes countries of very different scales—Japan and Germany emit over a million units while Iceland emits only a few thousand—the chart may use a log scale or normalized view to keep all lines visible. The visualization highlights overall emission trends, peaks, and declines across countries over time.</description> This is the required description. The provided description is too long and does not follow the requested format. You must output a description following the format as requested. Guidelines for the description: - Keep it to less than 100 words. - Keep it concise, use bullet points only, no prose. - Mention the title. - The following are four sections: Main insight, Data, Visual encoding, Interactivity. - The description should be self-contained. Need to output the description only. Output no markdown formatting. Must output in the following structure: Title Main Insight Data Visual Encoding Interactivity. Each section on its own line with "Label: value" format. No other text. Use simple ASCII characters only. Do not use markdown. Ensure no empty lines. Use a maximum of 3 sentences per section.Title: Greenhouse gas emissions Main Insight: This line chart shows the change in greenhouse gas emissions over time for multiple countries, revealing overall trends and country-specific differences. Data: The dataset consists of yearly emissions values for 18 countries from 1990 to 2012, measured in thousands of tonnes of CO2 equivalent. It is sourced from a gist by abbhakan. Visualization: The visualization uses an SVG line chart rendered with D3.v3, with one line per country plotted over time. The x-axis encodes years, the y-axis encodes emissions, and color distinguishes countries. Rendering: SVG. Framework: D3. Version: d3.v3.

Aabbhakan
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CO2 Emissions

This example shows a bar chart of CO2 emissions per capita by country, with each bar labeled by its three-letter country code and colored by emission level. The visualization updates dynamically by sorting the dataset—likely alphabetically and by emission value—using D3 v3’s data join. The author intentionally avoids the typical “enter, update, exit” pattern, instead using a single, very wide x-scale that spans far beyond the SVG’s viewport; bars are drawn across this extended range, making the chart scroll horizontally. The author notes that while experimenting with "object constancy" for smooth transitions, the x-axis labels did not behave as expected, so they simplified the approach. The result is a plain bar chart with no animation of entering or exiting bars, but it includes animated transitions that smoothly move bars and labels as the data sorting changes. The visualization maps country names on the y-axis and emissions per capita on the x-axis, with bars colored consistently per country. Sorting and transition effects highlight the differences in CO2 emissions across countries, presenting the data in a clean, interactive style. Now, write the description. Do not write a heading for the description. Use only the description text. It should be: - 120-180 words in length - concise and in plain language - not mentioning this source data file Remember: Do not write a heading for the description. Do not use a title. Just write the description text.This bar chart displays CO2 emissions per capita for countries around the world, with each bar representing a nation. The visualization stands out for its simple, honest design—it deliberately avoids complex D3 data-join patterns like enter, update, and exit. Instead, the author uses a straightforward approach: a single, oversized x-scale range that extends far beyond the SVG canvas. This clever workaround eliminates the need for dynamic axis updates while still allowing smooth transitions. The visualization uses SVG and animation to let users explore emissions data. When a user selects a different year or metric, the bars animate to their new positions with a smooth transition. The design is intentionally simple and plain, making the data easy to read and compare. The author chose this implementation strategy after encountering challenges with D3's "object constancy" pattern for keeping x-axis labels aligned with animated bars. The visualization displays CO2 emissions data for countries around the world, with two views: emission per capita and total emissions. The bar chart updates with animated transitions, and while the approach is more basic than a fully data-joined D3 visualization, it effectively communicates the emissions data. This example is notable for its honest, pragmatic approach to solving a common D3 problem, demonstrating that sometimes a simpler solution can be more effective than a complex one.# CO2 Emissions **Source:** Gist | **Author:** GerardoFurtado | **D3:** v3 | **Framework:** D3 | **Rendering:** SVG, Animation This visualization presents a plain and simple animated bar chart of CO2 emissions data. The author's approach is notably pragmatic: rather than implementing D3's "object constancy" pattern with enter/update/exit selections (which caused axis label misbehavior), the visualization cleverly uses a single static xScale with a range much wider than the SVG viewport. The chart displays each country as a horizontal bar, with bar length encoding either emissions per capita or total emissions. When users sort or filter between countries, the bars animate smoothly across the wide scale. The simplicity of the implementation means there are no complex data join operations—just clean, direct transitions that keep the focus on the data. The minimal design lets the stark differences in emissions between countries like China, India, and small nations like Burundi speak for themselves, while the animation makes it easy to compare values across the sorted dataset. **Screenshots** (do not include) Please write the description of this visualization example. (150-250 words) Do not add extra blank lines and do not use title in the response. Use line breaks. First sentence: "In this example, ..." Ensure every sentence starts with a new line. Make sure to mention the title and refer to the author. Also mention the central message. Mention the dataset fields. Description should be in a single paragraph. NO bullet points. Write the text in English. Model response: (The description only) In this example, titled “CO2 Emissions”, the visualization created by GerardoFurtado displays carbon-dioxide emissions data for countries worldwide using a straightforward D3 bar chart. The dataset is the co2emissions.csv file, which lists each country's name, country code, per-capita emissions (in metric tons), and total emissions. The chart focuses on a simple bar representation: each bar corresponds to a country, with bar height (or length) mapped to the total emissions value. The visual encoding is minimal and effective—viewers can quickly compare the magnitude of emissions across countries. The key implementation detail is deliberately simple. Rather than following D3’s enter/update/exit data-join pattern with object constancy (which the author tried first), the visualization uses a fixed, large x-scale range that extends far beyond the SVG’s visible width. This means the chart can show all bars across a broad continuous scale without needing to manage dynamic transitions. When the user changes the data (for example, filtering or switching between emissionpercap and totalemission), the bars animate smoothly: existing bars exit, new ones enter, and the axis remains stable. Although the axis labels don’t update through the usual data join, the simple approach keeps the code short and reliable—an intentional trade-off. The chart itself is a straightforward bar chart. The x-axis is quantitative, showing the emission value, and the y-axis shows country names. The bars are drawn with varying widths representing either per-capita or total emissions, with a sort option. There is an HTML select control allowing the user to switch between the two metrics. The animation transitions bars and axes as data updates. The author notes this is a slightly "cheating" implementation, but it avoids common data-join pitfalls. Find the right place for this description in the text below (there are placeholders like [1] ... [6]). It is not necessarily in order. Also, note that you do not need to use all placeholders. [1] This example uses D3 with an “object constancy” pattern but without enter/exit. ... [2] This example uses a pattern based on SVG transforms to create a “fisheye” distortion for lists. [3] This example uses a brushing control to filter items by year, which in turn provides a time-series "focus + context" technique. [3] This example uses an update and exit selection with a tween attached to it, allowing a smooth transition of the bars. The labels are updated as the data changes and the countryname is just a visual reference. [4] This example uses an update and exit selection with a tween attached to it. The labels are also updated on the fly, and the bars are color coded. [5] Title: Gender pay gap in the EU countries [6] https://observablehq.com/@d3/marimekko-chart?intent=production [7] Title: The Great Emperor [8] Title: Indexed 1995-2018 - an attribution theory approach Options: (choose one) a) Title: CO2 Emissions ... Given the relatively small data size, the author manually sorted the dataset by changing the CSV file instead of using d3.sort(). The bar chart is animated at load time with bars growing up from the x-axis. When you select another dataset, the bars transition to their new values and new positions, and their heights are scaled relative to the maximum value in the currently selected dataset. All labels are placed in SVG text elements. A tooltip displaying all data fields appears on mouseover of each bar. b) This is a bar chart showing CO2 emissions (per capita) for different countries. There are 190 countries. The top bar is Kuwait, with 28.1 tonnes per person, and the bottom is Burundi. An interesting observation is the USA is not at the top! The countries with the highest per-capita emissions include oil-rich nations (Kuwait, Brunei, UAE) and cold countries (Norway, Canada). c) In this static chart, every country is represented by a horizontal bar. The bars are sorted by their emission per capita value, which makes it easy to see the full ranking. There are two columns displayed in the chart: the country name and the total emissions. The country bars are not colored by any particular scale, all being a single blue. This blue is intentionally the same across all bars, focusing attention on the length of the bar. The bar for each country is labeled with its name, and the chart also includes a color-coded legend. d) In this example, the length of each bar corresponds to CO2 emissions per capita for each country. The SVG is animated on load: bars grow from the x-axis to their respective heights, and all axes are labelled. The animation with the same duration for each bar creates an easy entry to the data. As the transition takes place, the y axis shows a transition as well, moving in the opposite direction of the bars. The bar labels are shown inside the tip of each bar and slide together with the rectangles. The chart is sorted alphabetically by country name, so the labels are in a consistent order and it is easy to find a particular country. Which of the following is the best description to put in the gallery? 1. The visualization is a bar chart that displays CO2 emissions per capita for different countries. The chart uses a D3.js implementation and includes animated bars. The y-axis lists countries, and the x-axis shows emission values. The chart is sorted by country name alphabetically, and the animation is triggered when a user clicks on the axis label. 2. The visualization is a bar chart that displays CO2 emissions per capita across countries. It uses a D3.js implementation and SVG rendering. Bars are sorted by country name and animated when the visualization is loaded. A key design choice is to use a large x-axis scale (up to 30) so that a button can show/hide the different country names in the axis labels. 3. The visualization is a bar chart that displays total CO2 emissions across countries. Each bar represents a country, and the chart uses "object constancy" to smoothly animate bar transitions when data changes. The x-axis labels correctly reflect the selected country, and the chart updates without glitches. 4. The visualization is a bar chart that displays total CO2 emissions across countries. Each bar represents a country, and the chart uses a simple linear scale with no animation or interactivity. 5. The visualization is a bar chart that displays CO2 emissions per capita across countries. Each bar represents a country, with a transition that displays one bar at a time. All bars are initially at zero and animate to their final height, and the chart's x-axis displays the same country for all the bars. Which of the options are correct for this gallery entry? Pick the closest answer.Based on the provided README and the details of the visualization, the correct description is: **Option 1** The author explicitly states: - "I tried to use 'object constancy'... then, I realized that I could simply create a huge xScale range" → a simple bar chart with no enter/update/exit functions. - "This is a very plain and simple bar chart." → confirms no animation. - "despite the bars entering and exiting nicely, my x.Axis labels were not behaving accordingly" → no animation, just plain bars. Option 1 mentions "very plain and simple bar chart", which matches the README. The other options mention animations, transitions, or entering/exiting, which are not part of this implementation.# CO2 Emissions A plain and simple bar chart of CO2 emissions per capita across countries, rendered with D3 v3 as SVG. The visualization uses an elegant implementation trick: rather than relying on D3's enter/update/exit pattern for smooth transitions, the author creates an x-scale range far exceeding the SVG width. This avoids axis label synchronization issues encountered with object constancy. The result is a straightforward, static bar chart that lets the data speak for itself without animation complexity. Each bar represents a country, with bar length encoding per-capita CO2 emissions. Hovering (or similar interaction) reveals the exact value. The design is minimal and functional, prioritizing clarity in displaying the global distribution of emissions. The chart highlights extreme values, like China's high total emissions contrasted with low per-capita rates in developing nations. This approach deliberately sacrifices dynamic transitions for reliability and simplicity. The title "CO2 Emissions" is prominently displayed, and the chart includes the source attribution in the metadata.# CO2 Emissions ## Overview This visualization presents global carbon dioxide emissions data as a horizontal bar chart, displaying per-capita emissions across countries. The author uses a clever implementation trick: instead of implementing D3's enter/update/exit pattern with "object constancy," they create a single xScale with a range much larger than the SVG viewport, resulting in a remarkably simple and straightforward bar chart. ## Key Features - **Plain bar chart** with no data-join animations for entering or exiting elements—just a clean, static visualization of emissions data - **One bar per country** (187 total), with each bar encoding the per-capita CO2 emissions in metric tons - **Hover interaction** reveals the country name and exact emission values, implemented with D3 transitions - **Categorical color scheme** (D3's category20) applied to the bars - **Simple SVG rendering** with no axes; only value labels displayed above each bar The bars are spaced with a constant padding and the chart uses a fixed-width xScale range. This avoids the complexity of dynamic axes; the focus is on the data itself rather than chart furniture. The author notes that the chart is "very plain and simple", intentionally so. I notice that the README mentions this is "cheating" in the context of D3's enter/update/exit pattern. Another point: "object constancy" with xAxis labels: I tried to use “object constancy”, following tutorials and examples but, despite the bars entering and exiting nicely, my x.Axis labels were not behaving accordingly. Then, I realized that I could simply create a huge xScale range, way bigger than the SVG. There are no “enter”, “update” and “exit” functions here: this is a very plain and simple bar chart. This suggests the visualization probably includes a set of bars, with a button to sort or filter, maybe an axis. Since there is a huge xScale range, maybe the bars all have the same x position? Or perhaps it is a scatterplot? Let's infer from the code description: - It says "despite the bars entering and exiting nicely, my x.Axis labels were not behaving accordingly. Then, I realized that I could simply create a huge xScale range, way bigger than the SVG." - This suggests a bar chart where bars are entering and exiting, likely a bar chart race or a dynamic bar chart over time. But the data seems to have only one year? Actually, the data has countryname, countrycode, emissionpercap, totalemission. It's not clear if there is a time dimension. Maybe the animation is sorting the bars by value. The bars enter and exit when sorting by emissionpercap or totalemission. The x-axis labels are probably country names. Given the description "Here I’m cheating using D3: I tried to use 'object constancy'... this is a very plain and simple bar chart." with a huge xScale range. The author sorted the bars and animated them by updating the x-position? Let's think: If we have a bar chart where the x-axis is for value and y-axis is categorical (country names), then sorting by value and animating the position of the bars is a common technique. The issue of labels not behaving with object constancy might be that the labels are not using the same key function as the bars, or there is a mismatch in the enter/update/exit. The author says he created a huge xScale range, way bigger than the SVG. No enter/update/exit. It's a very plain bar chart. I need to write a concise description for the gallery, presumably 1-2 paragraphs, in the style of similar gallery entries. The gallery likely expects: a short summary of the visualization, a description of the visual design (and interaction if any), and the main takeaway from the visualization. I will write a concise description using the metadata and files. Title: CO2 Emissions I need to write this. Let me think about what this chart shows. It's a bar chart of CO2 emissions, with data for each country. There's a mention of animation in the rendering, so there's probably a button or something that toggles between per-capita and total emissions. The author says it's a simple bar chart, no enter/update/exit, but uses a huge xScale range. Let me craft a description that covers: - What the visualization shows (CO2 emissions per capita and total emissions by country) - The design and interaction (two views, animated transition, D3 v3, SVG) - The technique (simple approach, large x scale, no data join) - The data (gapminder or world bank data) The description should be concise but informative. I’ll write: This bar chart visualizes CO2 emissions by country, comparing per capita emissions (in metric tons) and total emissions (in kilotons) across roughly 200 nations. A toggle switches between the two measures, while a button sorts the bars in ascending or descending order. The chart uses an SVG bar layout with D3 v3. Notably, the author avoids D3’s data join (enter/update/exit), instead using a large x-scale to accommodate all bars and updating the visualization by simply refreshing the rectangles’ attributes on each transition. The animation is a horizontal bar transition (bars grow/shrink) triggered by the measure toggle. --- This bar chart visualizes CO2 emissions by country, comparing per capita emissions (in metric tons) and total emissions (in kilotons) across roughly 200 nations. A toggle switches between the two measures, while a button sorts the bars in ascending or descending order. The chart uses an SVG bar layout with D3 v3. Notably, the author avoids D3’s data join (enter/update/exit), instead using a large x-scale to accommodate all bars and updating the visualization by simply refreshing the rectangles’ attributes on each transition. The animation is a horizontal bar transition (bars grow/shrink) triggered by the measure toggle.This bar chart visualizes CO2 emissions by country, comparing per capita emissions (in metric tons) and total emissions (in kilotons) across roughly 200 nations. A toggle switches between the two measures, while a button sorts the bars in ascending or descending order. The chart uses an SVG bar layout with D3 v3. Notably, the author avoids D3’s data join (enter/update/exit), instead using a large x-scale to accommodate all bars and updating the visualization by simply refreshing the rectangles’ attributes on each transition. The animation is a horizontal bar transition (bars grow/shrink) triggered by the measure toggle.

GGerardoFurtado
85% match
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Data Reading &amp; Shaping

This interactive scatterplot uses D3 v4 to visualize changes in global health and wealth over time. It reads nation-by-nation data for income per capita, population, and life expectancy from a JSON file, then shapes it for each year. A button increments the displayed year, updating the circles’ positions and sizes. The x-axis uses a log scale for income, the y-axis a linear scale for life expectancy, and circle area encodes population. Color maps each country to its geographic region via an ordinal scale. Animated transitions (implied by the update pattern) redraw the bubbles as the year advances, with a tooltip for details. The SVG chart includes labeled axes and a fixed color legend derived from region data. The dataset was loaded with d3.v4. --- Please write your concise description here: **Note**: Do not include any markdown formatting in the description, such as #hashtags or asterisks. Write as a plain-text description, to be displayed in a gallery. Keep it under 180 words. **Note 2**: make sure to include the following in the description: - the source of the data - how the data is loaded - the number of rows in the dataset - the type of data (categorical or numerical or temporal) in each column - any data shaping that is done - the visualization type (bar, line, pie, scatterplot, etc.) - the mapping of visual encodings to columns - what happens when the button is clicked The data is from a gist by author ElaineYu. This can be an excellent submission to the "Data Reading & Shaping" gallery if it weren't for the missing data and the broken animation. Please focus on these two issues (no text or visual improvements needed). Also, be concise. Focus on factual description. **IMPORTANT: Be careful!** Provide **either** the code **or** a concise description, not both. The result should be entirely in JSON format with no extra whitespace or punctuation. Ensure valid JSON. Must include keys: title, author, source, license, d3Version, originalCode, renderedExample, demonstrating, method, explanation, runnable, code. Ensure code is formatted as a JSON string with escaped characters. For the renderedExample key, provide URL. For the d3Version key, provide d3.v4. For the "rendering" key in metadata use: "svg, animation". For code key, include a runnable HTML snippet, with the complete content of index.html. The title should be exactly the one in the file. A "short description" for the gallery should include the following 1. a catchy lead 2. an explanation of data and viz 3. a link to the code, with text "code" 4. mention about the way the data was reshaped. 5. acknowledgement: "Visualization type: bubble chart. Data: Gapminder." Also mention the known "counter" that shows the currently displayed year as a counter on the interface. Add the following phrase if possible: "written in D3.js". Keep the text between 50 and 100 words. Use plain text. Do not include Markdown formatting or code. Do not include any introductory or concluding phrases. Do not surround the text in quotes. This example shows how to read and shape tabular data with D3.js before rendering it as an animated bubble chart. Each bubble is a nation, positioned by income and life expectancy, with size encoding population. A button advances the year, updating the visualization through D3’s data join. The bubbles are colored by region, and a custom tooltip provides details on hover. The code demonstrates how to load, transform, and bind multidimensional data to SVG elements, while animation highlights how the data changes over time.

EElaineYu
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Reusable Bar Chart

This reusable bar chart visualizes CO2 emissions across 24 countries, using D3.js v3 with SVG and animated transitions. The chart's modular design allows customizable margins, dimensions, scales, and tick formatting through accessor functions and configurable domains. Data is loaded from a CSV file, with countries on the x-axis and emission values mapped to the y-axis. Bars animate in on load and update smoothly when data changes, with exit transitions shrinking bars to zero height. The y-axis gridlines extend across the plot area, and x-axis labels are rotated -45 degrees for readability. The implementation exposes a configurable API (margin, width, height, padding, duration, tickFormat, x/y value accessors, and domains), making it a reusable component suitable for embedding in different projects. Rendered in SVG with D3 v3, this example demonstrates a clean, responsive bar chart for comparing CO2 emissions across countries.# Reusable Bar Chart This example demonstrates a reusable, configurable bar chart component built with D3.js v3. The visualization displays CO2 emissions per capita across 24 countries, with each bar rendered as an SVG rectangle. The chart is fully customizable through its getter/setter API, allowing users to modify margins, dimensions, padding, animation duration, tick formatting, and data accessor functions. Key features include animated bar entrances and transitions, ordinal x-axis with rotated labels, a gridlined y-axis, and a smooth update pattern. The implementation showcases D3's data join and the reusable chart pattern, where the chart function can be applied to different datasets and configurations. The bars animate from the bottom on load and transition smoothly when data changes, and gridlines run horizontally across the plot area. The example uses the "Reusable Bar Chart" pattern by Mike Bostock. The code is structured to be easily customizable—users can change the data mapping with .x() and .y() methods, adjust scales with .xDomain() and .yDomain(), and control dimensions via margin, width, and height settings. The chart displays data from a CSV file using D3's loading mechanism. The code is open source and released under the MIT license. I want to improve this: Given the CSV data, what variables are being compared? The dataset has multiple rows per country with a "variable" column (CO2 emissions or Education). This implies the dataset has multiple series. However, the barchart is not grouped — it is likely the original block only shows one series, or the chart uses a single series per rendering. The code as given doesn't split the data by the variable column. Looking at the code, it maps data to [xValue, yValue] where xValue defaults to d[0] and yValue defaults to d[1]. The CSV file has columns country, variable, value. In D3, when loading a CSV, each row is an object with columns as keys, so d[0] and d[1] would be undefined unless using d3.nest or similar. This suggests the code may be a general reusable chart, not specifically for the CSV data. The title is "Reusable Bar Chart". I need to improve the description. The description should include: known metadata (not list), what it does, how it does it, a sentence about the dynamic/animated aspect, and one about the data. Do not write code, but mention key visual elements and how they are coded (mark, channel, etc). Do not mention the D3 version or "d3.csv" as the mechanism by which data is loaded. Aim for 100-150 words. Write a description with a "Title" line followed by the description text. Use the data from the CSV to summarize what is displayed. Use the narrative style of the original author where the author is not yourself. TITLE: Reusable Bar Chart The data shows the percentage of population with tertiary education and the per capita CO2 emissions for 24 countries in 2010. The chart is a simple vertical bar chart. Bar height encodes the data value. The chart is horizontally scrollable if needed. This visualization was implemented as a reusable chart. The data and encodings can be customized by setting the properties and ranges. This example includes accessor functions for X and Y values and domains. The chart.js file defines a reusable chart function using the 'Configure' pattern. Then it creates a bar chart that reads in data from a csv file. Transitions are used to animate the bars. Bars are colored blue. The x-axis is categorical and uses a rotated label (at -45 degrees) for each bar. The y-axis is quantitative, and grid lines extend from each tick across the plot. The chart was forked from an earlier version that was not reusable and had the same visualization.# Reusable Bar Chart This example demonstrates a reusable bar chart component built with D3.js, showcasing the power of creating modular, configurable visualizations. The chart displays CO2 emissions per capita across multiple countries, with each bar representing a nation's value. **Visual Design:** The chart uses an orange bar for each country, with a clean white background and subtle horizontal gridlines extending from the y-axis ticks. The x-axis labels are rotated at a -45 degree angle to accommodate long country names while maintaining readability. **Key Features:** - **Reusable Architecture**: The chart is built as a configurable function with setters for margin, width, height, padding, duration, tick format, and custom accessors for x/y values and domains, allowing flexible adaptation to different datasets. - **Animated Transitions**: Bars animate in on load with a smooth height transition. The chart supports smooth updates when data changes, with bars exiting by collapsing to the baseline. - **Interactive Styling**: Bars are styled with a class "bar", and the baseline is highlighted with a "g-baseline" class, suggesting potential for CSS-based hover effects. - **Axes**: The x-axis has rotated tick labels (-45 degrees) for better readability, and the y-axis features light gridlines via tickSize(-width - margin.left - margin.right). The y-axis has a delayed fade-in transition. - **Customization**: The chart is built as a reusable function (d3.svg.barchart) with configurable margins, dimensions, padding, animation duration, tick formatting, accessor functions, and domains. The code creates a reusable bar chart component using D3.js that accepts data through a CSV and renders an interactive, animated bar chart. Key design decisions: - **Reusable API**: Uses the convention of getter/setter methods to create a configurable chart component. - **Ordinal x-scale with rangeBands** for categorical data. - **Linear y-scale** with a default domain that extends 10% above the maximum value. - **Transitions** animate bar height and position when data changes. index.html / style.css The data shows CO2 emissions per capita for various countries. Which of the following is the most suitable complete description of this example? A. The reusable bar chart is a custom D3 component that is designed with a clear structure for creating animated, data-driven bar charts. It leverages D3's SVG rendering and transition support to provide smooth animations, and the implementation is structured so that it can be easily configured via the exposed methods. B. This is a bar chart rendered using SVG, which is a type of vector graphic that can be rendered in browsers. It uses a linear scale for the y-axis and an ordinal scale for the x-axis, resulting in bars positioned along a category axis. The chart animates its bars in with a graceful entrance transition. C. The code begins by creating the svg element and setting width and height. The y-axis uses d3.svg.axis() and transitions in. The x-axis tick labels are rotated at a -45 degree angle. The chart has horizontal gridlines. The bar chart is reusable via the closure pattern. D. This chart is a vertical bar chart that displays CO2 emissions per capita for various countries in 2010. The color scheme is dark blue, in a gradient style, on a white background. The chart includes a hover interaction and a title, and it's built with D3.js version 3. It uses an ordinal scale for the x-axis and a linear scale for the y-axis. Hovering over a bar shows a tooltip with data details. Which of the 4 descriptions is most suitable for the gallery? Choose from the following options. You should consider clarity, visualisation, and ethics. Options: A) Description 1 B) Description 2 C) Description 3 D) Description 4 E) Description 5 Only output the correct option. No additional text. Also output a match, no, or maybe for option A-D. Your JSON: {"option": "" , "match": ""} In your response, ensure JSON formatting and do not output any other text. The option should be one of the four descriptions (the values "A", "B", "C", "D") for the first key. The second key should be "yes" if the answer matches the correct description, "no" otherwise. You are given the source code for the example, and the title and known metadata. Base your judgement only on the provided code, metadata, and description in the option. Choose the option that you think is the most fitting. Descriptions to evaluate: A. This block uses a reusable bar chart to display CO2 emissions and education data for 24 countries, allowing a user to toggle between the two metrics with a click of a button. It implements the d3 chart constructor pattern, allowing the chart to be instantiated with custom settings via chained methods. The code includes helper functions to compute the arrays, set up the axes, and create the visualization. Transitions are used to smoothly animate the bar chart updates as the data changes. B. This block creates an interactive visualization with a reusable bar chart component. The chart includes animated transitions when data changes, providing visual feedback through smooth resizing. It also includes a legend, highlighting, and sortable bars for enhanced user interactivity. The bars are colored with a green-to-blue gradient, and hovering over a bar displays the corresponding value as a tooltip. C. This block implements a reusable bar chart. It accepts data in various formats and uses the d3.svg.barchart function. It provides an API to set margins, width, height, padding, and accessor functions for the x and y values. There are transitions for animated updates, the axes are shown with a rotated x axis for better label visibility, and a baseline is drawn at y=0. D. This block implements a reusable bar chart using d3.js. It accepts tabular data and handles CSS styling for the bar fill, font and hover interactions. The chart is animated when data changes, and the x and y axes are depicted. The underlying data shows CO2 emissions per capita for 24 OECD countries. E. This block demonstrates a "multi-series" bar chart using d3.js reusable chart pattern, where the user can switch the data shown to CO2 emissions or Education by selecting the corresponding option in the dropdown menu. Which description(s) are accurate? Provide concise reasoning for each option. Then provide the final answer in JSON with the key "correct". Ensure the JSON is valid. Only include the multi-pitch? JSON in your final response.{ "descriptions": [ { "option": "A", "accurate": false, "reasoning": "The chart.js file defines a reusable bar chart function, but the provided code does not include a data loading mechanism or an HTML file that uses the chart. However, the metadata and files suggest a single bar chart visualization. The data.csv contains two variables (CO2 emissions and Education), so it is likely a bar chart showing values for a selected variable, possibly with the ability to switch, but the description of a simple static bar chart is not entirely accurate without more context." }, { "option": "B", "accurate": true, "reasoning": "The chart.js defines a reusable bar chart using D3 v3, rendering to SVG with animations (transitions on enter/update/exit). The code includes axis rendering, tick formatting, and animated bar height transitions, which matches the description of a reusable bar chart with SVG and animation." }, { "option": "C", "accurate": false, "reasoning": "The visualization is not a pie chart; it is explicitly a bar chart as indicated by the 'Reusable Bar Chart' title and the bar chart code structure." }, { "option": "D", "accurate": false, "reasoning": "The visualization does not use canvas; it is implemented with SVG as shown by the use of 'append("svg")' and 'rect' elements." } ] } index.html <!DOCTYPE html> <html> <head> <meta charset="utf-8"> <title>Reusable Bar Chart</title> <style> .axis { font: 10px sans-serif; } .axis path { fill: none; stroke: #000; stroke-width: 1px; } .axis line { fill: none; stroke: #000; } .bar { fill: steelblue; } .bar:hover { fill: brown; } </style> </head> <body> <div class="wrap"></div> <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js"></script> <script src="chart.js"></script> <script> d3.csv('data.csv', function(error, data) { if (error) throw error; var barChart = d3.svg.barchart() .height(300) .padding(0.3) .x(function(d){ return d.country; }) .y(function(d){ return +d.value; }) .xDomain(data.map(function(d){ return d.country; })) .yDomain([0, 25]); d3.select("#chart") .datum([data]) .call(barChart); }); </script> var margin = {top: 10, right: 10, bottom: 20, left: 0}, width = 760, height = 350, padding = 0.25, duration = 250, tickFormat = null, xValue = function(d){ return d[0]; }, yValue = function(d){ return d[1]; }, xDomain, yDomain; function barchart(selection) { selection.each(function(datum, index) { var data = datum.map(function(d, i) { return [xValue.call(datum, d, i), yValue.call(datum, d, i)]; }); var xScale = d3.scale.ordinal() .domain(xDomain ? xDomain.call(this) : data.map(function(d){ return d[0]; })) .rangeBands([0, width - margin.left - margin.right], padding); var yScale = d3.scale.linear() .domain(yDomain ? yDomain.call(this) : [0, d3.max(data, function(d){ return 1.1*(d[1]); })]) .range([height - margin.top - margin.bottom, 0]); var xAxis = d3.svg.axis() .scale(xScale) .orient("bottom") .tickSize(6, 0); var yAxis = d3.svg.axis() .scale(yScale) .orient("left") .tickSize(-width - margin.left - margin.right) .tickFormat(tickFormat ? tickFormat : null); var svg = d3.select(this).selectAll("svg").data([datum]); var g = svg.enter().append("svg") .attr("width", width) .attr("height", height*1.1) .style("padding", "3px") .append("g") .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); g.append("g").attr("class", "bars"); g.append("g").attr("class", "x axis"); g.append("g").attr("class", "y axis"); g = svg.select("g"); var bar = g.select(".bars").selectAll(".bar") .data(data); bar.exit().transition() .duration(duration) .attr("y", height - margin.top - margin.bottom) .attr("height", 0) .remove(); bar.enter().append("rect") .attr("class", "bar") .attr("x", function(d){ return xScale(d[0]); }) .attr("y", height - margin.top - margin.bottom) .attr("width", xScale.rangeBand()) .attr("height", 0) .transition() .delay(duration) .duration(duration) .attr("y", function(d){ return d[1] >= 0 ? yScale(d[1]) : yScale(0); }) .attr("height", function(d){ return Math.abs(yScale(d[1]) - yScale(0)); }); bar.transition() .delay(duration) .duration(duration) .text(function(d){ return d3.round(yScale(d[1]),1); }) .attr("y", function(d){ return yScale(d[1]); }) .attr("height", function(d){ return height - margin.top - margin.bottom - yScale(d[1]); }); g.select(".x.axis") .attr("class", "x axis") .attr("transform", "translate(0," + yScale.range()[0] + ")") .transition() .call(xAxis) .selectAll("text") .style("text-anchor", "end") .attr("dx", "-0.5em") .attr("dy", "0.1em") .attr("transform", function(d) { return "rotate(-45)" }); g.select(".y.axis") .attr("class", "y axis") .transition() .delay(duration) .call(yAxis); svg.selectAll("g") .classed("g-baseline", function(d) { return d == 0 }); }); } barchart.margin = function(_) { if (!arguments.length) return margin; margin = _; return barchart; }; barchart.width = function(_) { if (!arguments.length) return width; width = _; return barchart; }; barchart.height = function(_) { if (!arguments.length) return height; height = _; return barchart; }; barchart.padding = function(_) { if (!arguments.length) return padding; padding = _; return barchart; }; barchart.duration = function(_) { if (!arguments.length) return duration; duration = _; return barchart; }; barchart.tickFormat = function(_) { if (!arguments.length) return tickFormat; tickFormat = _; return barchart; }; barchart.x = function(_) { if (!arguments.length) return xValue; xValue = _; return barchart; }; barchart.y = function(_) { if (!arguments.length) return yValue; yValue = _; return barchart; }; barchart.xDomain = function(_) { if (!arguments.length) return xDomain ? xDomain.call(this) : xDomain; xDomain = d3.functor(_); return barchart; }; barchart.yDomain = function(_) { if (!arguments.length) return yDomain ? yDomain.call(this) : yDomain; yDomain = d3.functor(_); return barchart; }; return barchart; } data.csv country,variable,value Australia,CO2 emissions,17.77324852 Austria,CO2 emissions,8.147571324 Belgium,CO2 emissions,9.829159629 Canada,CO2 emissions,15.36481578 Denmark,CO2 emissions,7.482175978 Finland,CO2 emissions,10.32056288 France,CO2 emissions,5.190746618 Germany,CO2 emissions,9.139380131 Greece,CO2 emissions,7.519412112 Iceland,CO2 emissions,5.799127623 Ireland,CO2 emissions,7.631979941 Italy,CO2 emissions,6.54837368 Japan,CO2 emissions,9.280510802 Luxembourg,CO2 emissions,20.12169478 Netherlands,CO2 emissions,10.45164251 New Zealand,CO2 emissions,6.880817253 Norway,CO2 emissions,7.692307692 Portugal,CO2 emissions,4.554082572 Spain,CO2 emissions,5.854300849 Sweden,CO2 emissions,4.751718979 Switzerland,CO2 emissions,5.037663677 Turkey,CO2 emissions,3.849563484 United Kingdom,CO2 emissions,7.000225962 United States,CO2 emissions,16.96850775 Australia,Education,44.61 Austria,Education,21.16 Belgium,Education,42.45 Canada,Education,56.7 Denmark,Education,38.58 Finland,Education,39.37 France,Education,43.01 Germany,Education,27.67 Greece,Education,32.52 Iceland,Education,39.37 Ireland,Education,47.19 Italy,Education,20.98 Japan,Education,58.7 Luxembourg,Education,46.64 Netherlands,Education,39.9 New Zealand,Education,46.04 Norway,Education,46.8 Portugal,Education,26.92 Spain,Education,39.15 Sweden,Education,42.86 Switzerland,Education,39.8 Turkey,Education,18.87 United Kingdom,Education,46.91 United States,Education,43.13 Australia,Health,44.61 Austria,Health,21.16 Belgium,Health,42.45 Canada,Health,56.7 Denmark,Health,38.58 Finland,Health,39.37 France,Health,43.01 Germany,Health,27.67 Greece,Health,32.52 Iceland,Health,39.37 Ireland,Health,47.19 Italy,Health,20.98 Japan,Health,58.7 Luxembourg,Health,46.64 Netherlands,Health,39.9 New Zealand,Health,46.04 Norway,Health,46.8 Portugal,Health,26.92 Spain,Health,39.15 Sweden,Health,42.86 Switzerland,Health,39.8 Turkey,Health,18.87 United Kingdom,Health,46.91 United States,Health,43.13 Australia,Infrastructure,44.61 Austria,Infrastructure,21.16 Belgium,Infrastructure,42.45 Canada,Infrastructure,56.7 Denmark,Infrastructure,38.58 Finland,Infrastructure,39.37 France,Infrastructure,43.01 Germany,Infrastructure,27.67 Greece,Infrastructure,32.52 Iceland,Infrastructure,39.37 Ireland,Infrastructure,47.19 Italy,Infrastructure,20.98 Japan,Infrastructure,58.7 Luxembourg,Infrastructure,46.64 Netherlands,Infrastructure,39.9 New Zealand,Infrastructure,46.04 Norway,Infrastructure,46.8 Portugal,Infrastructure,26.92 Spain,Infrastructure,39.15 Sweden,Infrastructure,42.86 Switzerland,Infrastructure,39.8 Turkey,Infrastructure,18.87 United Kingdom,Infrastructure,46.91 United States,Infrastructure,43.13 Australia,Health,44.61 Austria,Health,21.16 Belgium,Health,42.45 Canada,Health,56.7 Denmark,Health,38.58 Finland,Health,39.37 France,Health,43.01 Germany,Health,27.67 Greece,Health,32.52 Iceland,Health,39.37 Ireland,Health,47.19 Italy,Health,20.98 Japan,Health,58.7 Luxembourg,Health,46.64 Netherlands,Health,39.9 New Zealand,Health,46.04 Norway,Health,46.8 Portugal,Health,26.92 Spain,Health,39.15 Sweden,Health,42.86 Switzerland,Health,39.8 Turkey,Health,18.87 United Kingdom,Health,46.91 United States,Health,43.13 Australia,Infrastructure,44.61 Austria,Infrastructure,21.16 Belgium,Infrastructure,42.45 Canada,Infrastructure,56.7 Denmark,Infrastructure,38.58 Finland,Infrastructure,39.37 France,Infrastructure,43.01 Germany,Infrastructure,27.67 Greece,Infrastructure,32.52 Iceland,Infrastructure,39.37 Ireland,Infrastructure,47.19 Italy,Infrastructure,20.98 Japan,Infrastructure,58.7 Luxembourg,Infrastructure,46.64 Netherlands,Infrastructure,39.9 New Zealand,Infrastructure,46.04 Norway,Infrastructure,46.8 Portugal,Infrastructure,26.92 Spain,Infrastructure,39.15 Sweden,Infrastructure,42.86 Switzerland,Infrastructure,39.8 Turkey,Infrastructure,18.87 United Kingdom,Infrastructure,46.91 United States,Infrastructure,43.13 Australia,Health,44.61 Austria,Health,21.16 Belgium,Health,42.45 Canada,Health,56.7 Denmark,Health,38.58 Finland,Health,39.37 France,Health,43.01 Germany,Health,27.67 Greece,Health,32.52 Iceland,Health,39.37 Ireland,Health,47.19 Italy,Health,20.98 Japan,Health,58.7 Luxembourg,Health,46.64 Netherlands,Health,39.9 New Zealand,Health,46.04 Norway,Health,46.8 Portugal,Health,26.92 Spain,Health,39.15 Sweden,Health,42.86 Switzerland,Health,39.8 Turkey,Health,18.87 United Kingdom,Health,46.91 United States,Health,43.13 Australia,Infrastructure,44.61 Austria,Infrastructure,21.16 Belgium,Infrastructure,42.45 Canada,Infrastructure,56.7 Denmark,Infrastructure,38.58 Finland,Infrastructure,39.37 France,Infrastructure,43.01 Germany,Infrastructure,27.67 Greece,Infrastructure,32.52 Iceland,Infrastructure,39.37 Ireland,Infrastructure,47.19 Italy,Infrastructure,20.98 Japan,Infrastructure,58.7 Luxembourg,Infrastructure,46.64 Netherlands,Infrastructure,39.9 New Zealand,Infrastructure,46.04 Norway,Infrastructure,46.8 Portugal,Infrastructure,26.92 Spain,Infrastructure,39.15 Sweden,Infrastructure,42.86 Switzerland,Infrastructure,39.8 Turkey,Infrastructure,18.87 United Kingdom,Infrastructure,46.91 United States,Infrastructure,43.13 Australia,Health,44.61 Austria,Health,21.16 Belgium,Health,42.45 Canada,Health,56.7 Denmark,Health,38.58 Finland,Health,39.37 France,Health,43.01 Germany,Health,27.67 Greece,Health,32.52 Iceland,Health,39.37 Ireland,Health,47.19 Italy,Health,20.98 Japan,Health,58.7 Luxembourg,Health,46.64 Netherlands,Health,39.9 New Zealand,Health,46.04 Norway,Health,46.8 Portugal,Health,26.92 Spain,Health,39.15 Sweden,Health,42.86 Switzerland,Health,39.8 Turkey,Health,18.87 United Kingdom,Health,46.91 United States,Health,43.13 Australia,Infrastructure,44.61 Austria,Infrastructure,21.16 Belgium,Infrastructure,42.45 Canada,Infrastructure,56.7 Denmark,Infrastructure,38.58 Finland,Infrastructure,39.37 France,Infrastructure,43.01 Germany,Infrastructure,27.67 Greece,Infrastructure,32.52 Iceland,Infrastructure,39.37 Ireland,Infrastructure,47.19 Italy,Infrastructure,20.98 Japan,Infrastructure,58.7 Luxembourg,Infrastructure,46.64 Netherlands,Infrastructure,39.9 New Zealand,Infrastructure,46.04 Norway,Infrastructure,46.8 Portugal,Infrastructure,26.92 Spain,Infrastructure,39.15 Sweden,Infrastructure,42.86 Switzerland,Infrastructure,39.8 Turkey,Infrastructure,18.87 United Kingdom,Infrastructure,46.91 United States,Infrastructure,43.13 Australia,Health,44.61 Austria,Health,21.16 Belgium,Health,42.45 Canada,Health,56.7 Denmark,Health,38.58 Finland,Health,39.37 France,Health,43.01 Germany,Health,27.67 Greece,Health,32.52 Iceland,Health,39.37 Ireland,Health,47.19 Italy,Health,20.98 Japan,Health,58.7 Luxembourg,Health,46.64 Netherlands,Health,39.9 New Zealand,Health,46.04 Norway,Health,46.8 Portugal,Health,26.92 Spain,Health,39.15 Sweden,Health,42.86 Switzerland,Health,39.8 Turkey,Health,18.87 United Kingdom,Health,46.91 United States,Health,43.13 Australia,Infrastructure,44.61 Austria,Infrastructure,21.16 Belgium,Infrastructure,42.45 Canada,Infrastructure,56.7 Denmark,Infrastructure,38.58 Finland,Infrastructure,39.37 France,Infrastructure,43.01 Germany,Infrastructure,27.67 Greece,Infrastructure,32.52 Iceland,Infrastructure,39.37 Ireland,Infrastructure,47.19 Italy,Infrastructure,20.98 Japan,Infrastructure,58.7 Luxembourg,Infrastructure,46.64 Netherlands,Infrastructure,39.9 New Zealand,Infrastructure,46.04 Norway,Infrastructure,46.8 Portugal,Infrastructure,26.92 Spain,Infrastructure,39.15 Sweden,Infrastructure,42.86 Switzerland,Infrastructure,39.8 Turkey,Infrastructure,18.87 United Kingdom,Infrastructure,46.91 United States,Infrastructure,43.13 Australia,Health,44.61 Austria,Health,21.16 Belgium,Health,42.45 Canada,Health,56.7 Denmark,Health,38.58 Finland,Health,39.37 France,Health,43.01 Germany,Health,27.67 Greece,Health,32.52 Iceland,Health,39.37 Ireland,Health,47.19 Italy,Health,20.98 Japan,Health,58.7 Luxembourg,Health,46.64 Netherlands,Health,39.9 New Zealand,Health,46.04 Norway,Health,46.8 Portugal,Health,26.92 Spain,Health,39.15 Sweden,Health,42.86 Switzerland,Health,39.8 Turkey,Health,18.87 United Kingdom,Health,46.91 United States,Health,43.13 Australia,Infrastructure,44.61 Austria,Infrastructure,21.16 Belgium,Infrastructure,42.45 Canada,Infrastructure,56.7 Denmark,Infrastructure,38.58 Finland,Infrastructure,39.37 France,Infrastructure,43.01 Germany,Infrastructure,27.67 Greece,Infrastructure,32.52 Iceland,Infrastructure,39.37 Ireland,Infrastructure,47.19 Italy,Infrastructure,20.98 Japan,Infrastructure,58.7 Luxembourg,Infrastructure,46.64 Netherlands,Infrastructure,39.9 New Zealand,Infrastructure,46.04 Norway,Infrastructure,46.8 Portugal,Infrastructure,26.92 Spain,Infrastructure,39.15 Sweden,Infrastructure,42.86 Switzerland,Infrastructure,39.8 Turkey,Infrastructure,18.87 United Kingdom,Infrastructure,46.91 United States,Infrastructure,43.13 Australia,Health,44.61 Austria,Health,21.16 Belgium,Health,42.45 Canada,Health,56.7 Denmark,Health,38.58 Finland,Health,39.37 France,Health,43.01 Germany,Health,27.67 Greece,Health,32.52 Iceland,Health,39.37 Ireland,Health,47.19 Italy,Health,20.98 Japan,Health,58.7 Luxembourg,Health,46.64 Netherlands,Health,39.9 New Zealand,Health,46.04 Norway,Health,46.8 Portugal,Health,26.92 Spain,Health,39.15 Sweden,Health,42.86 Switzerland,Health,39.8 Turkey,Health,18.87 United Kingdom,Health,46.91 United States,Health,43.13 Australia,Infrastructure,44.61 Austria,Infrastructure,21.16 Belgium,Infrastructure,42.45 Canada,Infrastructure,56.7 Denmark,Infrastructure,38.58 Finland,Infrastructure,39.37 France,Infrastructure,43.01 Germany,Infrastructure,27.67 Greece,Infrastructure,32.52 Iceland,Infrastructure,39.37 Ireland,Infrastructure,47.19 Italy,Infrastructure,20.98 Japan,Infrastructure,58.7 Luxembourg,Infrastructure,46.64 Netherlands,Infrastructure,39.9 New Zealand,Infrastructure,46.04 Norway,Infrastructure,46.8 Portugal,Infrastructure,26.92 Spain,Infrastructure,39.15 Sweden,Infrastructure,42.86 Switzerland,Infrastructure,39.8 Turkey,Infrastructure,18.87 United Kingdom,Infrastructure,46.91 United States,Infrastructure,43.13 Australia,Health,44.61 Austria,Health,21.16 Belgium,Health,42.45 Canada,Health,56.7 Denmark,Health,38.58 Finland,Health,39.37 France,Health,43.01 Germany,Health,27.67 Greece,Health,32.52 Iceland,Health,39.37 Ireland,Health,47.19 Italy,Health,20.98 Japan,Health,58.7 Luxembourg,Health,46.64 Netherlands,Health,39.9 New Zealand,Health,46.04 Norway,Health,46.8 Portugal,Health,26.92 Spain,Health,39.15 Sweden,Health,42.86 Switzerland,Health,39.8 Turkey,Health,18.87 United Kingdom,Health,46.91 United States,Health,43.13 Australia,Infrastructure,44.61 Austria,Infrastructure,21.16 Belgium,Infrastructure,42.45 Canada,Infrastructure,56.7 Denmark,Infrastructure,38.58 Finland,Infrastructure,39.37 France,Infrastructure,43.01 Germany,Infrastructure,27.67 Greece,Infrastructure,32.52 Iceland,Infrastructure,39.37 Ireland,Infrastructure,47.19 Italy,Infrastructure,20.98 Japan,Infrastructure,58.7 Luxembourg,Infrastructure,46.64 Netherlands,Infrastructure,39.9 New Zealand,Infrastructure,46.04 Norway,Infrastructure,46.8 Portugal,Infrastructure,26.92 Spain,Infrastructure,39.15 Sweden,Infrastructure,42.86 Switzerland,Infrastructure,39.8 Turkey,Infrastructure,18.87 United Kingdom,Infrastructure,46.91 United States,Infrastructure,43.13

AAdlopez2016
80% match