Whee, more Black Mirror!

Black Mirror is back and the internet is collectively splurging all over the place like some sort of weird masturbatory analogy I can’t seem to find my way out of. Anyway, Netflix once again manages to give us more of what we already love and now there’s quite literally 100% more of Black Mirror than last week. Well, technically not quite 100% because of that christmal special bumping the previous episode count from 6 to 7 but whatever, you get it and “roughly 185.

Remember the X-Files?

With the return of the X-Files in form of a miniseries, I was tempted to catch up on the original run of the show, since I had only seen the occasional episode in the late 90’s or early 00’s (my mom was a big fan). Being me, I already looked up the X-Files episodes ratings on trakt.tv to see if there’s something interesting about them, but I didn’t think there was. However, when I listened to the Incomparable talking about the show, I learned that apparently X-Files can be divided into the “myth arc” and regular, more stand-alone episodes. That’s when I realized I need to get my tv show analysis boots on and try to see what I could do. To my delight, I noticed that the appropriate Wikipedia article neatly marks the myth arc episodes, ready for plucking.

And then I started plucking.

2015 TV Recap

I’m late to the game, I know, but I’ve been keeping a list of notable shows I watched in 2015 since spring, and I think I owe it to past-me to put that in blog-form.

Jessica Jones recap

So I’ve been watching Marvel’s Jessica Jones over the past couple days, as one does, and I have opinions and stuff about it. However, since I believe that a plot is worth more than word stuff, I present to you my viewing expierence in data.

Quickly Compare your TV show ratings to trakt.tv

library(tRakt) # install via devtools::install_github("jemus42/tRakt") library(dplyr) library(tidyr) library(ggplot2) get_trakt_credentials(username = "Your Username") slug <- "dig" # Slug from trakt.tv show url trakt.user.ratings(type = "episodes") %>% filter(show.slug == slug) %>% arrange(season, episode) %>% select(rating, season, episode, title) %>% mutate(season = factor(season, ordered = T)) %>% rename(user.rating = rating) %>% left_join((trakt.get_all_episodes(slug) %>% select(rating, title, epnum))) %>% gather("type", value = "rating", user.rating, rating) %>% ggplot(data = ., aes(x = epnum, y = rating, colour = type)) + geom_point(size = 6, colour = "black") + geom_point(size = 5) + ylim(c(5, 10)) + scale_colour_discrete(labels = c("My Rating", "Trakt.

So Cougar Town ended

So Cougar Town ended, and I don’t know how I feel about that. Six seasons can be a very long time, and not every shows handles the aging process well. Cougar Town was not one of those shows in my opinion, which is largely due to the show’s self awareness and continued jokes about exactly that. The last season was a rough decline, with Bobby leaving and all, but at least we got a few more bromance jokes out of that, soo… yay?

Shows going down the drain lately

I don’t know if you’ve noticed, but lately I’ve done a lot of stuff with tv shows. Along the way, I noticed some trends with a few shows which seemed quite interesting to me, namely some shows were going straight down the drain, at least as far as their recent ratings are concerned. The projects I’m referring to are these two: 100 Popular Shows on trakt.tv 100 Trending Shows on trakt.

So I threw R at a thousand(ish) TV shows

Analyzing TV shows seems to be what I do these days. So I wanted to keep my newfound calling going and sucked the data for about a thousand shows out of the trakt.tv API, which was nice enough to only fail on me, like, twice. So, after some time of intense data pulling, I found myself with the more or less complete data (show info, season info, episode data) for 988 shows (and that’s why I keep referring to 1000(ish)).

Overanalyzing TV Shows

Overanalyzing tv shows has kind of become my jam. So why not totally overdo it. Note that everything I describe in this blogpost is purely for the lulz, and I don’t pretend there’s any scientific merit to it. I just like throwing maths at data. After I more or less succesfully plotted all the things, I wanted to go full blown statisticy on the subject. While my knowledge of statistics isn’t nearly as extensive as I’d like it to, I at least know a little about comparing groups.

Something something tvshowstwentyfourteen

Yet another year has oh screw this bullshit you know what’s up. TV shows, I watched them this year as well, here are some of the ones I liked. A Young Doctor’s Notebook Based on russian stories, Harry Potter finds himself as a young doctor, deployed to a lonely hospital in the midst of the russian nowhere. Some revolution stuff going on around him and lots of heroin to keep him company.

I just wanted to rewatch Stargate

Stargate SG-1, while probably a mediocre show in the grand scheme of sci-fi shows, it’s the sci-fi show I grew up with, so I tend to enjoy rewatching parts of it occasionally. Well, at least I rewatched it twice so far. The full thing. 10 seasons. Yep. Even those last two. So this time, I wanted to cherry-pick the good™ episodes, and of course efficient cherry-picking in 2014 involves R, the trakt.