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Visualization and statistical inference of SpaceX missions from 2006 to 2017 with GG plots in R

Visualization and statistical inference of SpaceX missions from 2006 to 2017 with GG plots in R

Space X, Space Exploration Technologies Corporation is American company aerospace company started in 2002 by entrepreneur Elon Musk and was originally based in El Segundo and later relocated to Hawthorne, California. It was founded on a vision and mission to reduce the cost of space transportation and enabling the colonization of Mars, it has since then tested and developed the Dragon launch vehicle and the Falcon spacecraft family, which both currently deliver payloads into Earth’s orbit.


In 2001, Elon Musk came with the idea and motivation regarding a project to land a miniature greenhouse to grow certain plants on Mars, this would be the farthest that life’s ever been traveled since Neil Armstrong and Buzz Aldrin set foot on the Moon. Musk tried to buy rockets from Russia but returned empty handed as the cost was too high, he realised that he could build a company that could build affordable rockets as the raw materials for building rockets were around 3% of the sales price at that time. SpaceX started with the smallest orbital rocket to test their design as a more costlier and riskier vehicle could have failed and bankrupted the company.



Space X designed and funded the first privately owned liquid propellant rocket to reach orbit in the year 2008 [Falcon 1], the first private company to successfully launch, orbit, and recover a spacecraft in the year 2010 [Dragon]. Also, the first private company to send a spacecraft to the International space station in the year 2012 from the Dragon family of spacecraft family. NASA awarded SpaceX a development contract in 2011 to develop a spaceship that would be used to transport astronauts to the International Space Station.

Visualization and Statistics of SpaceX missions

Big data in the aerospace and aeronautics industry

Data analytics helps the defence and aerospace industry by helping them optimizing the flow of resources and business processes while maintaining and regulating precise details which help in business decisions as information technology enabled the digitation of business procedures with the help of automation of some manual tasks to improve overall efficiency. Analytics in its intrinsic form has been helping to collect data on spaceships, rockets, and aircraft for years ranging from binary data such as altitude and speed, it even deals with very minute and slow progressions details such as crack growth progression, temperature variations.

The data is not a problem, aside from its ability to ease the launch of rockets, the astronomical data collected over several launches of SpaceX has been the driving force behind the decisions they make. Moulded by their state of their art intelligence systems, the information produced has changed the direction of traditional business such as research and sales. It has helped SpaceX to return from catastrophic failures such as the explosion in September 2016. And ultimately allowed them to take decisions based on data which was proved when they launched their reusable rocket Falcon 9 and could land it on a 91 x 52-meter landing pad, 350km away, from 80km up.

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GG plots in r

The ggplot2 package is created by Hadley Wickham and provides powerful graphics processing interface for creating elegant and sharp complex plots, it’s been popular in the recent years, it allows to plot graphs that represent both multivariate and univariate data in a straightforward manner, plus grouping can be represented by colour, symbol, and size which results in steady and neat representation.

Future of big data in aerospace engineering and cosmology

Data science should not be complicated as rocket science, yes it has its challenges but the industry trends will only make the tools and algorithms easier to divest, often organizations spend up huge amounts of financial and economic resources in endless cycles to model. In the field of cosmology, measurements must be taken of the structures and evolution of the universe as it’s a big place, plus to make theoretical predictions of what the universe looks like, big simulations are run.

The science required for these simulations is less of an issue than the technology required to run the simulations as big data is quite tough to move around that’s why processing frameworks like Hadoop exist. Getting information from a metaphorical slob of data is the first critical step that is why organizations analyse data at its source because then there is no waiting period to download data in a separate analytical environment, in an overall gist; data will continue to grow and new patterns will emerge, rapid changes in the stream of big data ecosystem will be driven by advanced predicting insights which will eventually be the common norm in industry and academia alike and will foster the growth of machine learning with the time to come.

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