Reddit's /r/Gamestop

Merge this dataset with gamestop price data to study how the chat impacted

By SocialGrep [source]

About this dataset

The stonks movement spawned by this is a very interesting one. It's rare to see an Internet meme have such an effect on real-world economy - yet here we are.

This dataset contains a collection of posts and comments mentioning GME in their title and body text respectively. The data is procured using SocialGrep. The posts and the comments are labelled with their score.

It'll be interesting to see how this effects the stock market prices in the aftermath with this new dataset

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How to use the dataset

The file contains posts from Reddit mentioning GME and their score. This can be used to analyze how the sentiment on GME affected its stock prices in the aftermath

Research Ideas

• To study how social media affects stock prices
• To study how Reddit affects stock prices
• To study how the sentiment of a subreddit affects stock prices

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: six-months-of-gme-on-reddit-comments.csv | Column name | Description | |:-------------------|:------------------------------------------------------| | type | The type of post or comment. (String) | | subreddit.name | The name of the subreddit. (String) | | subreddit.nsfw | Whether the subreddit is NSFW. (Boolean) | | created_utc | The time the post or comment was created. (Timestamp) | | permalink | The permalink of the post or comment. (String) | | body | The body of the post or comment. (String) | | sentiment | The sentiment of the post or comment. (String) | | score | The score of the post or comment. (Integer) |

File: six-months-of-gme-on-reddit-posts.csv | Column name | Description | |:-------------------|:------------------------------------------------------| | type | The type of post or comment. (String) | | subreddit.name | The name of the subreddit. (String) | | subreddit.nsfw | Whether the subreddit is NSFW. (Boolean) | | created_utc | The time the post or comment was created. (Timestamp) | | permalink | The permalink of the post or comment. (String) | | score | The score of the post or comment. (Integer) | | domain | The domain of the post or comment. (String) | | url | The URL of the post or comment. (String) | | selftext | The selftext of the post or comment. (String) | | title | The title of the post or comment. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit SocialGrep.

Version 5 release notes:
Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.
Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.
Version 4 release notes:
Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics.
Version 3 release notes:
Add data for 2016.Order rows by year (descending) and ORI.Version 2 release notes:
Fix bug where Philadelphia Police Department had incorrect FIPS county code.
The Arrests by Age, Sex, and Race data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1980-2015 into a single file. These files are quite large and may take some time to load.
All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

I did not make any changes to the data other than the following. When an arrest column has a value of "None/not reported", I change that value to zero. This makes the (possible incorrect) assumption that these values represent zero crimes reported. The original data does not have a value when the agency reports zero arrests other than "None/not reported." In other words, this data does not differentiate between real zeros and missing values. Some agencies also incorrectly report the following numbers of arrests which I change to NA: 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99999, 99998.

To reduce file size and make the data more manageable, all of the data is aggregated yearly. All of the data is in agency-year units such that every row indicates an agency in a given year. Columns are crime-arrest category units. For example, If you choose the data set that includes murder, you would have rows for each agency-year and columns with the number of people arrests for murder. The ASR data breaks down arrests by age and gender (e.g. Male aged 15, Male aged 18). They also provide the number of adults or juveniles arrested by race. Because most agencies and years do not report the arrestee's ethnicity (Hispanic or not Hispanic) or juvenile outcomes (e.g. referred to adult court, referred to welfare agency), I do not include these columns.

To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. Please note that some of the FIPS codes have leading zeros and if you open it in Excel it will automatically delete those leading zeros.

I created 9 arrest categories myself. The categories are:
Total Male JuvenileTotal Female JuvenileTotal Male AdultTotal Female AdultTotal MaleTotal FemaleTotal JuvenileTotal AdultTotal ArrestsAll of these categories are based on the sums of the sex-age categories (e.g. Male under 10, Female aged 22) rather than using the provided age-race categories (e.g. adult Black, juvenile Asian). As not all agencies report the race data, my method is more accurate. These categories also make up the data in the "simple" version of the data. The "simple" file only includes the above 9 columns as the arrest data (all other columns in the data are just agency identifier columns). Because this "simple" data set need fewer columns, I include all offenses.

As the arrest data is very granular, and each category of arrest is its own column, there are dozens of columns per crime. To keep the data somewhat manageable, there are nine different files, eight which contain different crimes and the "simple" file. Each file contains the data for all years. The eight categories each have crimes belonging to a major crime category and do not overlap in crimes other than with the index offenses. Please note that the crime names provided below are not the same as the column names in the data. Due to Stata limiting column names to 32 characters maximum, I have abbreviated the crime names in the data. The files and their included crimes are:

Index Crimes
MurderRapeRobberyAggravated AssaultBurglaryTheftMotor Vehicle TheftArsonAlcohol CrimesDUIDrunkenness
LiquorDrug CrimesTotal DrugTotal Drug SalesTotal Drug PossessionCannabis PossessionCannabis SalesHeroin or Cocaine PossessionHeroin or Cocaine SalesOther Drug PossessionOther Drug SalesSynthetic Narcotic PossessionSynthetic Narcotic SalesGrey Collar and Property CrimesForgeryFraudStolen PropertyFinancial CrimesEmbezzlementTotal GamblingOther GamblingBookmakingNumbers LotterySex or Family CrimesOffenses Against the Family and Children
Other Sex Offenses
ProstitutionRapeViolent CrimesAggravated AssaultMurderNegligent ManslaughterRobberyWeapon Offenses
Other CrimesCurfewDisorderly ConductOther Non-trafficSuspicion
VandalismVagrancy
Simple
This data set has every crime and only the arrest categories that I created (see above).
If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

The review dataset for 3 video games - Call of Duty : Black Ops 3, Persona 5 Royal and Counter Strike: Global Offensive was taken through a web scrape of SteamDB [https://steamdb.info/] which is a large repository for game related data such as release dates, reviews, prices, and more. In the initial scrape, each individual game has two files - customer reviews (Count: 100 reviews) and price time series data.

To obtain data on the reviews of the selected video games, we performed web scraping using R software. The customer reviews dataset contains the date that the review was posted and the review text, while the price dataset contains the date that the price was changed and the price on that date. In order to clean and prepare the data we first start by sectioning the data in excel. After scraping, our csv file fits each review in one row with the date. We split the data, separating date and review, allowing them to have separate columns. Luckily scraping the price separated price and date, so after the separating we just made sure that every file had similar column names.

After, we use R to finish the cleaning. Each game has a separate file for prices and review, so each of the prices is converted into a continuous time series by extending the previously available price for each date. Then the price dataset is combined with its respective in R on the common date column using left join. The resulting dataset for each game contains four columns - game name, date, reviews and price. From there, we allow the user to select the game they would like to view.