Using the TidyTuesday Art History Data, I’ve created two plots. One is a ggstream plot organized by artist_race in the dataset. {width = 100%}
The seocnd is using ggwaffle organized by country.
I have always been very interested in keeping track of my workout activities, especially since I got an Apple Watch. This has really propelled me to become very obsessed with tracking nearly all my walks and hikes.
This week for #TidyTuesday the dataset comes from Flavors of Cacao. Interesting set of information on chocolate ratings from across the world. Code below the figures. I first wanted to look at the data statistically.
This is my contribution for #TidyTuesday, after a long time of not participating. I created an interactive plot using ggplot and plotly packages that compares the calories vs caffeine content of Starbucks drinks.
I came across this dataset from NYC from Urban Park Ranger Animal Condition Response datasource. From May 2018 - June 2019, data was collected on animal calls received by the Urban Park Rangers of New York City.
This is the second post to using the Urban Ranger Response Call from NYC Open Data first post. The data did not have specific location information, such as XY coordinates, but there was a field Property in the database.
This is my contribution to TidyTuesday
In this, I’m attempting to use the gganimate package for the first time to create an animation showing volcanoe eruptions over the past century and their location.
Examples of other projects using Python for a mapping project and iNaturalist data.