Author: maheema268

Mahima Singh : Independent learning

For the independent learning portion of the course, I didn’t want to only use lynda.com.  While lynda.com does provide exercise files and walk along I wanted to get an eclectic understanding of HTML and JavaScript for my final project.

I plan to use a one page HTML template to present my findings (Data) then host it on GitHub or someplace free.

Understanding how to Host sites on GitHub.

For this, I went through a GitHub Tutorial for GitHub

The hosting part of it was pretty straight forward. What was a little confusing to me was playing around with the HTML template that I download off the internet. Luckily in class, we have been tinkering with already exciting code. So that is exactly what it did. I played around with the HTML index file of the template and occasionally found myself on StackOverflow trying to figure out how to “Hyperlink an enter div.” Once I got this figured out, I was pretty confident that I will be able to at least Host my project on GitHub. But I would only be certain once I tried.

Integrating JS into HTML

This part took a lot of trial and error. The first place I looked for help was W3schools. It was simple to understand and I could use the snippets to play around with some of the stuff I had on my computer. I used some of the JS charts we had used in class as examples to integrate into a sample HTML template I was working with.

Lynda Requirement

I watched Bootstrap Layouts: responsive single-page design.

The HTML template I was working with was from Bootstrap. While it is known to be easy to work with, one thing I couldn’t get round was the column and row makeup of the Bootstrap page. The video was exactly what I needed. I was stuck trying to get my charts into multiple columns on the bootstrap template and watching this three-hour long video not only helped me solve that problem but it taught me many new things on bootstrap.

In the end, it all came together. The things I learned in class, especially the tinkering with Javascript along the extra learning I did made this project possible.

The final project can be viewed here: Trump and the media

 

 

 

Assignment 6 : Mahima Singh

With Rupert SandersGhost in the shell getting mixed reviews because fans kept comparing it to the original anime, I decided to do a comparison of anime with their live action counterparts.

I took the ratings from IMDB  and plotted it in this mixed bar chart.

The data entry into the JS code was easy enough, but formatting the original chart after that was a nice challenge.

I experimented with D3’s categorical colors and the charts’ axis formatting by writing a function of my own to make sure the ‘Rating” axis didn’t read negative values.

 

If you are familiar with pop culture and know a little bit about good cinema then you will know that James Wong’s white-washed adaptation of Dragon ball Z, Dragon ball Evolution was one of the worst live action remakes of any anime ever. So it is no surprise that Dragon Ball Evolution has the worst rating.

What is surprising to me is that Sailor Moon’s live adaptation (tv series) got the closest rating to its anime version. The Sailor Moon anime is a tough act to follow. But according to IMDB ratings, it looks like ‘Pretty Guardian Sailor Moon’ comes close enough.

Another obvious trend is the high ratings of the phenomenally successful anime, Death Note. While the anime did a little better, the film came pretty close and I say this from experience. Shûsuke Kaneko’s 2006 Death Note, had its pitfalls but it was a good movie. I doubt Netflix’s adaptation, set to release in August this year, will be able to live up to the hype. But we can only wait.

Here is a list of live action films (based on popular anime) in the making. Some names like Naruto, Bleach, Fullmetal Alchemist, Tokyo Ghoul and Attack on Titan have a lot riding on them because of their incredible fan base.

 

Assignment 5 : Mahima Singh

Morbid warning.

After doing research for a project for my Natural Language Processing class, I came across this database of the Last Statement of inmates on death row in Texas.

I decided to scrape the website, pull out the last statements and figure out what the most used words were. You can find the code on my GitHub here.

As we can guess, by the end of their time, an inmate does think of their family.
The word ‘Love’ is used the most, followed by ‘Family’. Other words relating to retribution such as ‘God’ and ‘Sorry’ also make the top-15 list.

Here is the full data that is available on the site.  The “Info” column will give you information on the inmate and the crimes the committed and the “Link” column will take you to the text of their last statement.

 

FactTexas was the first state in the world to carry out an execution by lethal injection in 1982 

Assignment 4 : Mahima Singh

Disney just announced that they plan to continue telling Star Wars stories for the next 15 years. As an ‘EU’ (extended universe) fan of the franchise, I am excited that the Star Wars cannon will continue to grow in my lifetime. In my nostalgic look back at episode I-VII, I realized that characters hardly ever say the word ‘lightsaber’ in the films.  After a quick fan-forum search I learned that the word Lightsaber is used only eight times in all the films till episode VII.   I decided to do a histogram of some of the more popular Star Wars phrases.

May the Force be with you

I have a bad feeling about this

The Force

The Dark Side

I scraped through the scripts of episodes I-VII ( The official script of Rogue one hasn’t been released yet) and counted the number of times each phrase was said and by whom.

This two-level drill down of a treemap represents that dataset.

Design wise I wanted to do a lot more. But as we studied in class, High charts, like any other third-party application, has its drawbacks when it comes to customization.

Assignment 3: Mahima Singh

I was recently re-watching ‘Samurai Jack‘ because the new season is on its way and I noticed the the ‘Cartoon Network’ logo has taken a beating since ‘Samurai Jack’. So I did a little Googling and found out that the logo has gone through a lot, even before the network launched in 1992.
Here is a timeline of those changes:

Assignment 2 : Mahima Singh

All data is interesting. Choosing one dataset to visualize took a little longer than I thought it would. Ultimately I decided on the “Hate Crime” data because it has so many levels. The data dictionary is adequality explains the fields and I didn’t have to clean any of the data myself. Some other datasets that involved age groups required me to convert 0ct-10-2019 to 10-19 because the file had that column set to date type.

There are two kinds of crimes in the dataset: “Crimes Against Persons” and “Property Crimes”. While these two are choices under the “Crime Type” column, the type of bias isn’t classified into groups. This data was found in the “Hate Crime Overview” document in the “about” tab on the site.

To find the total number of crimes per type for each bias, I filtered on that particular crime type and summed all the columns. Then I just took these numbers and pasted them into different sheets of the inforg.am chart making sure that the correct columns came under the correct bias type.

I have worked with inforg.am before and my personal favorite theme for a chart has always been “Tokyo.” It fit well with the nature of the data. Even though the area charts are giving us an idea of the number of crimes across the different biases, having information on the total number of incidents and especially the number of offenders and the number of victims adds value to the visualization.

Hate Crimes by Bias Type: 2010 -2015
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