Tag: community

# David Robinson’s R Programming Screencasts

David Robinson (aka drob) is one of the best known R programmers.

Since a couple of years David has been sharing his knowledge through streaming screencasts of him programming. It’s basically part of R’s #tidytuesday movement.

Alex Cookson decided to do us all a favor and annotate all these screencasts into a nice overview.

Here you can search for video material of David using a specific function or method. There are already over a thousand linked fragments!

Very useful if you want to learn how to visualize data using `ggplot2` or `plotly`, how to work with factors in `forcats`, or how to tidy data using `tidyr` and `dplyr`.

For instance, you could search for specific R functions and packages you want to learn about:

Thanks David for sharing your knowledge, and thanks Alex for maintaining this overview!

# Sentiment Analysis of Stranger Things Seasons 1 and 2

Jordan Dworkin, a Biostatistics PhD student at the University of Pennsylvania, is one of the few million fans of Stranger Things, a 80s-themed Netflix series combining drama, fantasy, mystery, and horror. Awaiting the third season, Jordan was curious as to the emotional voyage viewers went through during the series, and he decided to examine this using a statistical approach. Like I did for the seven Harry Plotter books, Jordan downloaded the scripts of all the Stranger Things episodes and conducted a sentiment analysis in R, of course using the `tidyverse` and `tidytext`. Jordan measured the positive or negative sentiment of the words in them using the AFINN dictionary and a first exploration led Jordan to visualize these average sentiment scores per episode:

Jordan jokingly explains that you might expect such overly negative sentiment in show about missing children and inter-dimensional monsters. The less-than-well-received episode 15 stands out, Jordan feels this may be due to a combination of its dark plot and the lack of any comedic relief from the main characters.

Reflecting on the visual above, Jordan felt that a lot of the granularity of the actual sentiment was missing. For a next analysis, he thus calculated a rolling average sentiment during the course of the separate episodes, which he animated using the `animation` package:

Jordan has two new takeaways: (1) only 3 of the 17 episodes have a positive ending – the Season 1 finale, the Season 2 premiere, and the Season 2 finale – (2) the episodes do not follow a clear emotional pattern. Based on this second finding, Jordan subsequently compared the average emotional trajectories of the two seasons, but the difference was not significant:

Potentially, it’s better to classify the episodes based on their emotional trajectory than on the season they below too, Jordan thought next. Hence, he constructed a network based on the similarity (temporal correlation) between episodes’ temporal sentiment scores. In this network, the episodes are the nodes whereas the edges are weighted for the similarity of their emotional trajectories. In that sense, more distant episodes are less similar in terms of their emotional trajectory. The network below, made using `igraph` (see also here), demonstrates that consecutive episodes (1 → 2, 2 → 3, 3 → 4) are not that much alike:

A community detection algorithm Jordan ran in MATLAB identified three main trajectories among the episodes:

Looking at the average patterns, we can see that group 1 contains episodes that begin and end with neutral emotion and have slow fluctuations in the middle, group 2 contains episodes that begin with negative emotion and gradually climb towards a positive ending, and group 3 contains episodes that begin on a positive note and oscillate downwards towards a darker ending.

– Jordan on Medium.com

Jordan final suggestion is that producers and scriptwriters may consciously introduce these variations in emotional trajectories among consecutive episodes in order to get viewers hooked. If you want to redo the analysis or reuse some of the code used to create the visuals above, you can access Jordan’s R scripts here. I, for one, look forward to his analysis of Season 3!