Difference between revisions of "Group2 Proposal"
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<b>reshape</b>:Casts a molten data frame into the reshaped or aggregated form you want <br><br> | <b>reshape</b>:Casts a molten data frame into the reshaped or aggregated form you want <br><br> | ||
<b>readr </b>:The goal of 'readr' is to provide a fast and friendly way to read rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes <br><br> | <b>readr </b>:The goal of 'readr' is to provide a fast and friendly way to read rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes <br><br> | ||
− | <b> | + | <b>ggplot</b>:A system for 'declaratively' creating graphics. You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, |
− | <b> | + | and it takes care of the details <br><br> |
+ | <b>Plotly</b>:Easily translate 'ggplot2' graphs to an interactive web-based version and/or create custom web-based visualizations directly from R <br><br> | ||
+ | |||
Below are some of the Visualizations that we would be working on:<br><br> | Below are some of the Visualizations that we would be working on:<br><br> |
Revision as of 17:35, 5 August 2018
Analysis of World Energy Production & Consumption Through Interactive Visualizations
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Project Overview Properly functioning markets for electricity, natural gas, oil and pollution allowances are essential for the rational allocation of resources and cost-effective attainment of environmental goals. Rapidly changing energy dynamics determine the course of our economic development, geopolitics, technological breakthroughs, massive investments and trade flows.With renewable energy moving from niche to mainstream, the fuel mix is shifting.
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Motivation
Energy growth drives the well-being and prosperity across the globe. Growing demand for energy has to be met in a safe and environmentally conscious manner. Rapidly changing energy dynamics determine the course of our economic development, geopolitics, technological breakthroughs, massive investments and trade flows.
The main objective of this project is to study the dynamics of regional energy production and consumption of the different countries of the world to identify the dominant as well as weaker player in the world energy market
Though reports from various organizations such as EIA, IEA, OPEC, BP are available, most of the visualizations are static and do not aid exploration, limiting the scope of further drilling down to the areas of particular interests. Hence, we've decided to take up rich data-set provided by BP & explore the information related to energy production and consumption across countries, which might otherwise be hidden in the existing static visualizations.
Objective
Static visualization and reports do not allow for direct interaction with data, enhanced assimilation of information, quick access to relevant insights and drill-down analysis. By using interactive, visual data analytics techniques we will be bringing the above-mentioned capabilities to the interface.
We will be using the world energy datasets from BP along with the CO2 emissions to come up with more detailed visualizations including some level of interactivity to enable users to drill down in the world energy data & reveal specific insights.
About Data Source
BP plc, formerly British Petroleum, is a British multinational oil and gas company headquartered in London, England. It is the world's sixth-largest oil and gas company, the sixth-largest energy company by market capitalization and the company with the world's twelfth-largest revenue (turnover). It is a vertically integrated company operating in all areas of the oil and gas industry, including exploration and production, refining, distribution and marketing, petrochemicals, power generation and trading. It also has renewable energy interests in biofuels and wind power.
For 66 years, the BP Statistical Review of World Energy has provided high-quality objective and globally consistent data on world energy markets. The review is one of the most widely respected and authoritative publications in the field of energy economics, used for reference by the media, academia, world governments and energy companies. A new edition is published every June.
For the purpose of this project, we are using the BP Statistical Review of World Energy data.
Critique of Existing Visualization
We noticed that all the existing visualizations are static in nature. We would be enhancing these to make them interactive in order to gain deeper insights.
Visualization Deliverables and Packages
We plan to use the following R packages to come up with our visualizations:
dplyr: A Grammar of Data Manipulation. It is a fast, consistent tool for working with data frame like objects, both in memory and out of memory.
tidyr:It's designed specifically for data tidying (not general reshaping or aggregating) and works well with 'dplyr' data pipelines
reshape:Casts a molten data frame into the reshaped or aggregated form you want
readr :The goal of 'readr' is to provide a fast and friendly way to read rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes
ggplot:A system for 'declaratively' creating graphics. You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use,
and it takes care of the details
Plotly:Easily translate 'ggplot2' graphs to an interactive web-based version and/or create custom web-based visualizations directly from R
Below are some of the Visualizations that we would be working on:
• Energy consumption by Region
• Energy consumption by Fuel type
• Shares of global Energy Consumption
• Fuel Prices & Utilizations
• Reserve-to-Production Ratios
We also plan to come up with some interactive visualizations as we dig deeper into the dataset along the course of the project.
References
About BP: https://en.wikipedia.org/wiki/BP
Data Source: http://www.bp.com/statisticalreview
R Packages Description: https://cran.r-project.org