![]() John B Murdoch: Ggplot2 as a creative engine. You can find the data and R code behind the animations here. John also shared these animated versions (along with the ggplot2 code to produce them): The final work included 128 charts in total, telling the story of dozens of soccer teams in four countries. The librarys main capability is the creation and manipulation of multi-dimensional data types like array and matrices. ![]() Python Library 1: NumPy NumPy (pronounced 'Numb Pie') is arguably the most important library for quantitative finance. John presented a case study (you can see the slides with animations here) on creating this FT article, Explore the changing tides of European footballing power. This article explores the 5 most important Python libraries for quantitative finance today. ![]() That's where R and the ggplot2 package come in. The FT needed to be able to "audition" several different visual treatments quickly, to be able to create stunning visuals before deadline. Until recently, charts were the realm of an information designer using tools like Adobe Illustrator: the output was beautiful, but the process was a long and winding one. ![]() At the 2016 EARL London conference senior data-visualisation journalist John Burn-Murdoch, described how the Financial Times uses R to produce high-quality, striking data visualisations.
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