Year: 2018
Tool: Excel
Description: Portland Water Cycles.
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Data can tell us many different stories, and depending on how we use the information, it is possible to identify what we are looking for and interpret data in many different ways. In this example, we can see an excel sheet with essential details about Portland water cycles.

Year: 2018
Tool: Tableau
Description: Portland Water Cycles.
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In this example, I was able to import the excel sheet into Tableau to display the effects of water cycles during each day of the year.

Year: 2018
Tool: R Studio
Description: Portland Water Cycles.
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While using the same data source, I was able to display the distribution of the cycle that happens each day because of tides. To better display this information, I used candlestick to show distribution, mean, and out layers.

Year: 2018
Tool: R Studio
Description: Portland Water Cycles.
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While using the same candlestick format, I was able to display the difference between water cycles between January through December. This information could be useful for many purposes, like fishing or flooding prevention.

Year: 2017
Tool: R Studio
Description: Chicago Population.
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R Studio is a powerful tool useful for statistics and data visualization. Here is an example of how the code looks when plotting population in Chicago neighborhoods.

Year: 2017
Tool: R Studio
Description: Chicago Buildings.
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Using the same data, I was able to display the distribution of building age base on commercial, industrial, residential, and vacant buildings. The format that I used to display these data is called the violin plot and is one of the best to display distribution, median, mean, and out layers — all in one graphic.

Year: 2017
Tool: Tableau
Description: NY Stock Exchange History.
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In this graph, I wanted to show the progress of the NY stock from 1991 to 2001. The light color in the trending line represents lower volumes, while darker areas represent a higher volume of exchange.

Year: 2017
Tool: Tableau
Description: NY Stock Exchange History.
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This graph shows the average adjusted close next to the difference in average at the closing price, from 1991 to 2000. All this data visualization was created using Tableau.