cemgundogan / single_visualizations

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Single Visualizations

In this folder I display assorted single visualizations I created in different contexts and from datatsets. Some of these have been recreated in a blog post or shared in some format on my website which I invite you to explore:

Anyways, enjoy!

A revisit to #TidyTuesday

01.06
On behalf of Almog Simchon's course at Ben-Gurion university of the Negev We were required to explore and visualize findings from a #TidyTuesday dataset. My partner and I chose a dataset about spotify songs which I explore in a sub-folder of this repository. Below is the final visualization from the exploration:

(Link to code and exploration)

Choosing an appropriate font

11.05
I decided to plot all my available fonts so that I don't have to try a font just to see how it looks. You can always find fonts on Google fonts, download and install them to windowns. My personal favorite? "Roboto Condensed". Plot idea comes from Hadley Wickham's ggplot 2 book, specifically ch.8.

(Link to plot code)

A street map of Be'er-Sheva, IL (left) and the city's light posts (right)

23.11.2019
I mapped the streets of Be'er Sheva thanks to Christian Burkhart blog. Once I mapped the streets (left) I could map the city lights of the city (right) thanks to the municipality's free open data.

Link to street map code | Link to light posts code

Mapping bomb shelters in Be'er-Sheva, Israel

12.11.2019

In this visualization I mapped the bomb shelters near my neighborhood in Be'er-Sheva. I discovered open data sets from our municipality's website and took the next day's opportunity (missiles fired towards Israel) to plot bomb shelters. I've since created a blog post on my website explaining how I made this map along with an interactive leaflet package; you can find it here.

Link to code

Eliud Kipchoge unformal marathon record

12.10.2019

In this visualization I took Eliud Kipchoge's marathon score of under 2 hours (1:59:40) and situated it in comparison to previous yearly records. I've since created a blog post where I scrape the data from Wikipedia and explain how I create this plot; you can find it here

Link to code

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