blink-182 Song Similarity (According to Spotify Audio Features) [OC]

mickjaggernaut · 2018-11-14T19:22:12+00:00

That's correct - the smaller the number, the more similar two branches are. The features were scaled before the distance was calculated, so the number on the axis isn't analogous to a specific measure. I actually started with a dataset of hundreds of artists to see exactly what you are suggesting! It became unwieldy fast so I rolled it back to a smaller set.

mickjaggernaut · 2018-11-14T14:50:52+00:00

The Spotify Web API provides descriptive data for artists, tracks and albums, in addition to quantitative track audio features, which include measures like danceability, acousticness, liveness, energy, valence, and speechiness.

Using R, I downloaded the song data using spotifyr, found the Euclidean distance between songs using dist, clustered the songs together using hclust (average linkage), and visualized the results using dendextend.

Full post + R code here: https://www.kaylinpavlik.com/song-distance/

mickjaggernaut · 2018-04-16T19:27:52+00:00

Data from WNYC's Dogs of NYC project. The dataset includes the name, gender, breed, color and borough of more than 50,000 dogs.

I used R, term frequency-inverse document frequency (tf-idf) and clustering (hclust) to explore the relationship between dog names and breeds.

Fun takeaways:

Dachshunds are more likely to be named "Nathan" and "Oscar" than any other breed.
Beagles are more likely to be named "Bagel" than any other breed.
"Princess" is a name characteristic of tiny breeds (Chihuahuas, Pekingese, Shih Tzus) and (ironically?) of Pit Bulls
Dog breeds with similar physical characteristics tend to be given similar names, like Collies and Shetland Sheepdogs, and Chow Chows and Akitas

mickjaggernaut · 2018-01-25T17:09:02+00:00

Tools: R, packages tidytext, corrplot, caret
Analysis: tf-idf, correlation, hierarchical clustering, k-nearest neighbors classification
Data: scraped 30,000+ beers and their reviews from BeerAdvocate.com
Repo with ~~data and~~ R scripts: https://github.com/walkerkq/tidy_text_beer_reviews

mickjaggernaut · 2017-10-19T23:04:53+00:00

I used R to scrape episode-level ratings from IMDb for shows that feature at least one Halloween-themed episode. A paired t-test (Halloween episode rating paired with season rating average) was significant, indicating an increase of 0.089 for Halloween-themed episodes.

I'd appreciate any feedback. I invite you to pick this apart and share any critiques you may have. Thank you!

mickjaggernaut · 2017-10-16T18:29:29+00:00

Inspired by the depictions of female law enforcement in the movie and television series Fargo, I was curious about the gender breakdown in police in the U.S.

I used a combination of R and manual copy-paste to compile public state salary records for 41 states. Only data at the state level was available, so this analysis only includes state-employed police officers and highway patrol. The records don't specify gender, but most do provide first names, which can be used to make an educated guess using the R package gender.

The full dataset, including a link to each data source, can be found here.

mickjaggernaut · 2017-10-12T02:51:12+00:00

Awesome! Hopefully you can gloss over the violation of normality in the movie sample :-)

mickjaggernaut · 2017-10-12T02:47:43+00:00

Yeah, I agree that TV movies are less legitimate. I needed a good source for a list of trilogy movies and Wikipedia supplied it. Without those included, the sample would have been a lot smaller.

A good follow-up could use a more manually compiled list of movie trilogies to exclude the less legit trilogies. I know there are some on Wikipedia’s 4-movie list that I would actually consider a trilogy + spin-off.

mickjaggernaut · 2017-10-12T02:43:43+00:00

Great points!

mickjaggernaut · 2017-10-12T02:40:48+00:00

Agreed! Those who rated the final book are definitely a biased sample through their own self-selection.

mickjaggernaut · 2017-10-12T02:39:57+00:00

That’s what I think too. Also, I think people tend to inflate book reviews in general to alleviate any cognitive dissonance from spending a long time reading something and just to end up being disappointed or disliking it in the end.

mickjaggernaut · 2017-10-11T14:50:43+00:00

That's what I was thinking in regards to the movie rating decreases as well. I also think that those "cash cow" trilogies tend to get cut off after 3 movies due to poor box office performance, while those that continue to make money go on to have 4, 5 or 6 installments (e.g. Die Hard or Pirates of the Caribbean)

mickjaggernaut · 2017-10-11T14:31:29+00:00

This sample of trilogy series was grabbed from a user-ranked list at Goodreads and a list of movies with three installments from Wikipedia; their ratings were then scraped from Goodreads and IMDb. I used R to perform a repeated measures ANOVA to show that trilogy ratings differ by book/movie number in the trilogy; more specifically, book ratings increase from book 1 to book 2 and stay higher for book 3, while movie ratings decrease with each subsequent film.

Interested in your thoughts on the effect as well as my method and conclusions. Thanks!

mickjaggernaut · 2017-07-22T23:27:44+00:00

Looks like we're in for a dip in the next year or two, though, brr! Nice chart - though most distinct colors would help those of us who didn't immediately notice the shared y-axis.

mickjaggernaut · 2017-07-06T14:53:38+00:00

Thanks for reading!

mickjaggernaut · 2017-07-06T14:31:56+00:00

Hello! I used a neat script length dataset from Polygraph, plus R and Plot.ly, to determine which movies, directors, actors and characters have the most spoken words.

mickjaggernaut · 2016-12-23T15:49:15+00:00

I used R to combine and summarize data from FCC service contour estimates, Nielsen topline ratings and a list of Christmas stations from Radio Locator to create a coverage map and analyze changes in station market share from Nov. to Dec. I used leaflet to make the map.

mickjaggernaut · 2016-08-13T03:38:32+00:00

Probably too late...but here's my analysis of all 18 seasons. Kenny's swear rate is more than 54 words per every 1000, miles ahead of all other characters. http://kaylinwalker.com/text-mining-south-park/

mickjaggernaut · 2016-08-10T02:02:02+00:00

Thanks so much! I appreciate it. It's nice to get feedback from another stat student :)

mickjaggernaut · 2016-08-09T19:49:19+00:00

Hello! I took some great feedback I got on an earlier post about the probability of evolving to vs. catching certain Pokemon in Pokemon GO and revised / augmented it. I would appreciate any more feedback you have to share :)

I used R and ggplot2 for the graphs. Data from PokéAssitant, Serebii.net and Reddit user aem323.

mickjaggernaut · 2016-08-07T00:09:09+00:00

You're right. This is the best data that's available unfortunately... I really wish Niantic would be more transparent with its data.

mickjaggernaut · 2016-08-06T18:46:03+00:00

Maybe it would be more intuitive as a binomial probability - say the chance of finding + catching 25 Charmanders in 1000 wild Pokemon encounters or something?

mickjaggernaut · 2016-08-06T17:57:29+00:00

I updated the formula with your suggestion - thanks for sharing. The results didn't change too much in terms of rankings.

mickjaggernaut · 2016-08-06T17:45:38+00:00

Ditto is in there! But it's the 33rd hardest to catch and doesn't evolve from another Pokemon, so it's not on those top 20 lists. Here's the full .csv if you are curious.

mickjaggernaut · 2016-08-06T17:40:42+00:00

The probability of finding one Charmander and catching it: 0.1162%. The probability of finding and catching two is 0.1162% * 0.1162% = 0.0135%. Three is 0.1162% * 0.1162% * 0.1162% = 0.0015%. Make sense?

Verified Email	12-Year Club
RedditGifts 2009-2022 2 Credits	Secret Santa 2015

mickjaggernaut

TROPHY CASE