#Anime Statistics by Christopher McMahan

Introduction

This document was created for my intro to analytics programming course final project. Here I decided to perform an analysis on popular Anime. Such as determining if the genre of anime can relate to rating people give it or if their is any correlation between the different genres and total number of views. I chose this topic because this is a popular form of entertainment which I personally partake in.

I will be using a data set found on Kaggle called Anime Recommendations Database. This data set is composed of user prefence data from over 73,516 users on 12,294 anime s. With each user being able to add anime to their completed list and give it a rating and is compilation of those ratings.

Here is a link to the Anime Recommendations Database

The purpose of this article is display my data analysis of the Anime Recommendations Database. Along side that I will also show my sentimental analysis on r/anime on Reddit. This social media platform is one build around cheerful communities with sometimes overly enthusiastic users in each comuminty about there fan bases and opinions. This is to see how the anime watching communitees envioment is like and to see if they are that of possitive indivules with honest opions. I am proposing this because it is indivules like those on reddit who love sharing their interest in the subject who could have been apart of the Anime Recomdations Database.

This subredit can be found by using the link here to r/anime

The results of this data analysis will show any relations and connections to genres, viewers and the animes alongside how the anime watching communittees honesty.

Data Dictionary

## # A tibble: 7 × 2
##   Varable  Description                                                       
##   <chr>    <chr>                                                             
## 1 anime_id "myanimelist.net's unique id identifying an anime."               
## 2 name     "full name of anime."                                             
## 3 genre    "comma separated list of genres for this anime."                  
## 4 type     "movie, TV, OVA, etc."                                            
## 5 episodes "how many episodes in this show. (1 if movie)."                   
## 6 rating   "average rating out of 10 for this anime."                        
## 7 members  "number of community members that are in this anime's\n\"group\"."

Summary Statistics

Grene Statistics

When looking at the different genres in which these animes come in, we can compare them to each other based on the average rating of each genre.

First lets take a look at the hiest rated anime genres, and see if their are any connections to toher data types.

This graphic can help show that top five genres which the anime community would rate the highest are Thriller being the highest rated then, Josei, Mystery, Police, and Shounen. Along with this we can also see that these top five genres have another visible trend of as they gain more viewers and members there rating can increase.

Lets take a look at the lowest rated anime genres now and see how they comapare.

When now looking at the lowest rated anime genres we fine these being Hentai, Kids, Yuri, Music and the lowest rating going to Dementia. In this graphic we do see a differnt trend then in the top rated anime genres and that is the amount of views does not correspond to the ratings in which these genres are ordered.

Now lets look at how all the anime genres can be scales on a graphic based on thier average rating and the number of members those animes and genres have.

This graphic can show us something very interesting and that similarly to the top five genres there does appear to be a connection between the rating and anime genre is given and the number of members who have watched an anime of that genre. So the more popular and anime becomes it is more likely to gain a higher rating which does actually make sense and that this can be related to the genres of anime and not just the shows themselves is interesting in its own.

Content Type Statistics

When looking at all the different animes, we find that they come in several forms such as TV shows, Movies, OVA’s, Specials and more. The Graphics below show the relation of these individual content types to episodes and ratings.

One prompt I had was that I wondered if their was any distribution between the differences in ratings across the different Content Types. I used a histogram to show this.

This box plot does show that their may be some distribution between the different content and the average ratings the animes in these types fit. One think that can be noticed is that TV does have the smaller quartile as while their are a lot of different ratings, a majority of them are in the same quartile. This could be linked to the next graphic.

Comparing the different content Types to how many of each their are in this data set.

We can see that items that involve the caparison to TV types may have a more accurate or helpful depiction due to the larger sample available of these content types.

One last graphrac is of the connection between the type of content and the number of members that type has.

This grapgic can play alongside the Number of Anime’s by Type as TV also has the most members with the exception of OVA’s even though they have a large sum of Anime’s doesn’t have the largest member count.

This data can be understood alongside the two other bar charts which show how many animes are in each type and how many members in total watch each type. We can see that the TV type has the smallest Quartile as this could be due to the large sum of Animes which fit under this Type and due to the large sum of members who watch this type of anime. Alongside this we can see that there are very few outliers in the Music and ONA types most likely due to the smaller number of these animes present in the data set and the lower numbers of viewers. Overall It shows that together these three tables have a correlation between each other and that their is some distribution a musts the different types of anime.

Sentiment Analysis

When performing this analysis I collected data from a social media and content sharing platform called Reddit. The data I grabbed was the most recent 100 postings to the r/Anime sub-reddit page. I did this in order to help determine they type of community in which those who watch anime and about the type of indivules who may have helped create the initial Anime Database that was used in this project.

Positivity vs Negativity

With a community as broad as this I looked into the top 100 posts and performed a sentimental analysis of tge words used to determine if this community is one of positiveity and honesty.

It seems that overall this sub-reddits sentiments are mostly positive.Which can help lead me to my idea that this is an honest community when it comes to their opinions and ratings of things such as anime.

Conclusion

It seems that overall this sub-reddits sentiments are mostly positive. In order to improve analysis I would recommend getting more reviews along with possibly comparing r/anime to other similar sub-reddits and lastly looking at the data a different times of the year such as when the release reason comes around.