Description

This dataset contains the county-wise vote share of the United States presidential election of 2020, and in the future 2024, the main advantage of the dataset is that it contains various important county statistics such as the counties racial composition, median and mean income, income inequality, population density, education level, population and the counties occupational distribution.

_Imp: this dataset will be updated as the 2024 results come in, I will also be adding more county demographic data, if you have any queries or suggestions please feel free to comment _

Motivation

The reasons for constructing this dataset are many, however the prime reason was to aggregate all the data on counties along with the election result data for easy analysis in one place. I noticed that Kaggle contains no datasets with detailed county information, and that using the US census bureau site is pretty difficult and time consuming to extract data so it would be better to have a pre-prepared table of data

Columns

• The first columns contain information on the county and state
• The next columns contain the 2020 vote both raw and %
• The next columns contain the education level of the county population
• Following that we have information about the income and income inequality in the county
• Then we have the county racial composition
• The counties population and population density
• The final columns contain information about the distribution of occupations in the county

U.S. President 1976–2020

This data file contains constituency (state-level) returns for elections to the U.S. presidency from 1976 to 2020.

For more information please refer : https://guides.lib.jjay.cuny.edu/c.php?g=992251&p=7179356

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Analysis of ‘US Election 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/unanimad/us-election-2020 on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Please, If you enjoyed this dataset, don't forget to upvote it.

US Election 2020

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3244747%2F5173f21bc8eaea8335539cc942338b4d%2Fheader_win.png?generation=1605608056355359&alt=media" alt="">

Context

For this year, was the 59th quadrennial presidential election held on Tuesday, November 3, 2020. To win the election, the candidate needs 270 out of 538 electoral votes. A good sign, that show if a candidate is doing well, is if they win states that aren't expcted to go their way.

Content

This dataset contains county-level data from 2020 US Election.

Acknowledgements

• US election 2020: A really simple guide

--- Original source retains full ownership of the source dataset ---

This data file contains constituency (state-level) returns for elections to the U.S. Senate from 1976 to 2020.

This Dataset is collected by: MIT Election Data and Science Lab (Massachusetts Institute of Technology) (MEDSL) http://electionlab.mit.edu

U.S. Election Tweets 2020

Data includes tweets leading up to the U.S. 2020 election with hashtags as follows: #trump,. #biden, #2020election, #presidentialelection, #electionissues.

Over 600k tweets from Oct. 14 - Nov. 4 2020.

Please, If you enjoyed this dataset, don't forget to upvote it.

US Election 2020

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3244747%2F5173f21bc8eaea8335539cc942338b4d%2Fheader_win.png?generation=1605608056355359&alt=media" alt="">

Context

For this year, was the 59th quadrennial presidential election held on Tuesday, November 3, 2020. To win the election, the candidate needs 270 out of 538 electoral votes. A good sign, that show if a candidate is doing well, is if they win states that aren't expcted to go their way.

Content

This dataset contains county-level data from 2020 US Election.

Acknowledgements

• US election 2020: A really simple guide

[READ THIS FIRST! DATASETS FOR Academic/Learning/Non-commercial purpose]

Context

US Election 2020 is very interesting to look into as it is an election in the middle of a pandemic. Me and my teammate created a twitter crawler using Twitter API and Tweepy for my Artificial Intelligence coursework. We chose Donald Trump as a subject of interest as President Trump was known for his twitter interaction.

I decided to deploy my crawler on post-voting day to conduct a sentiment analysis.

Tweet text in this datasets is suitable for Sentiment Analysis usage.

Content

This raw datasets is crawled using Tweepy library and Twitter API. 2500 tweets were gathered per 15 minutes. There are total of 247,500 row of entries and 13 columns, with the total of 3,217,500 cells of data. Data cleaning is needed to perform before doing any analysis.

Datasets date range: 4th November 2020 - 11th November 2020 Tweets with "Trump", "DonalTrump", "realDonalTrump" were capture.

(The User = user of the particular row) username: Twitter User handle accDesc: Description of the user on profile location: Location of the tweet following: Total number of account the user is following followers: Total number of followers of the user totaltweets: Total tweets created of the user usercreated: Date of the user registered his/her Twitter account tweetcreated: Date of the tweet created favouritecount: tweet <3 count (equivalent to like on Facebook) retweetcount: Total tweet's retweet (equivalent to share on Facebook) text: Text body of the tweet tweetsource: Device used to create this tweet hashtags: hashtag of the tweet in JSON format

Acknowledgements and Disclaimers

Banner and thumbnail courtesy of > visuals < from unsplash.com

Much thanks to my teammate Jiacheng Loh and ChenZhen Li for the efforts.

Please do not use this datasets for any malicious attempts, any damage done is not under the responsible of me.

This datasets were gathered for the purpose of learning and not for commercial purposes.

Data were public in the public domain, therefore i assume these data is open for all.

Limitations

Datasets are gathered with at least 15 minutes interval, therefore datecreated distribution is not equal and may not include all tweets created within the date range.

Analysis of ‘US Senate(state level)- election 1976-2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aryakrishnanar/us-senatestate-level-election-19762020 on 30 September 2021.

--- Dataset description provided by original source is as follows ---

This data file contains constituency (state-level) returns for elections to the U.S. Senate from 1976 to 2020.

it contains the year of election, details of constituency, electoral stage ,i.e, general/runoff/primary name of winning candidate and party, total no of votes, votes obtained by the winning parties, whether candidates are write-in or not and the official/unofficial result.

--- Original source retains full ownership of the source dataset ---

This dataset is collected from 1824 to 2020: 1. Year: Description: The year in which the U.S. election took place. Type: Numeric (Integer) Example: 1824, 1860, 1920, 2020

1. Candidate: Description: The name of the candidate participating in the election. Type: String (Candidate's name) Example: John Adams, Abraham Lincoln, Franklin D. Roosevelt, Joe Biden

2. Party: Description: The political party affiliation of the candidate. Type: String (Party name or abbreviation) Example: Democratic, Republican, Whig, Libertarian

3. Popular Vote: Description: The total number of votes that the candidate received in the popular vote. Type: Numeric (Integer) Example: 500,000, 5,000,000, 70,000,000

4. Result: Description: The outcome of the election for the specified candidate. Type: String (e.g., "Winner," "Runner-up," "Withdrew") Example: Winner, Runner-up, Withdrew, Conceded

5. Percentage: Description: The percentage of the total popular vote received by the candidate. Type: Numeric (Float) Example: 25.3%, 49.8%, 60.5%

This dataset appears to capture essential information about U.S. elections over time, including details about the candidates, their political party affiliations, the number of popular votes they received, the outcome of the election, and the percentage of the total popular vote they secured. This comprehensive dataset allows for the analysis of historical U.S. election trends and outcomes.

Dataset

This dataset was created by Blake Feiza

Released under CC0: Public Domain

Contents

US presidential election 1976 to 2020

Context

This data comes from the Associated Press - the AP has been tracking vote counts in US elections since 1848 and their data is widely considered to be accurate.

Content

The variables in this dataset are: - state: State to which the vote count corresponds - state_abr: Two-letter abbreviation of state name - trump_pct: Percentage of the vote won by Donald Trump - biden_pct: Percentage of the vote won by Joe Biden - trump_win: Binary variable denoting whether Donald Trump won the vote in a state - biden_win: Binary variable denoting whether Joe Biden won the vote in a state

The County Presidential Election Returns 2000-2020 dataset describes the results of the various United States Presidential elections, broken out on a per county basis. It provides the details on the votes cast per candidate, the political party to whom the candidate belongs, and the mode by which the votes were cast. The data was collected by the MIT Election Data and Science Lab, and is housed on the Harvard Dataverse.

References: 1. https://electionlab.mit.edu/ 2. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VOQCHQ

Context

As you all know, America's Presidential election 2020 will going to happen in November. The result of which will going to be a big thing not just only for American citizens but other countries people too and this will ultimately effect the relations with other countries. So, I have scraped tweet replies of Twitter handle of both the final runners i.e., Donald Trump and Joe Biden. So, the idea is to predict result of this upcoming election and get some insights on how to make predictions with better accuracy.

Content

• This dataset is collected through scraping Twitter handles of Donald Trump and Joe Biden separately for a week using python script and retrieving replies on tweets done by both of them. The idea is to look for the sentiments of people towards their representative and how they are reacting to the tweets done by both the leaders. So that one can predict results in advance by analysing people sentiments using data science skills. Below I have attached two csv files, one is for Donald Trump and another is for Joe Biden. Both of the csv files are having two common columns: 1st column user contains user name who have replied to the tweets. 2nd column text contains text of replies on each tweet.

• To get more insights on it and how to start with the project, you can refer to my article below. This was a small fun project to get insights thorugh this dataset where I am not training any model as such. I am simply analysing people's sentiment using NLP library TextBlob. So, you can refer to this article to get started with the dataset on how to clean it, analyze it and visualize it. Feel free to use some other algorithms to achieve greater accuracy. Predicting US Presidential Election Result Using Twitter Sentiment Analysis with Python

Inspiration

This dataset actually gives immense opportunities to revise your data science skills from fundamentals. - Data Cleaning as this dataset includes lots of noise which will be going to be a barrier in making good model. - Exploratory Data Analysis - Data Analysis - Data Visualization

• It will be great if you can perform some analysis on this text and get insights or pre-poll survey like thing for the upcoming elections and predict result in advance.

This is the dataset I used to figure out which sociodemographic factor including the current pandemic status of each state has the most significan impace on the result of the US Presidential election last year. I also included sentiment scores of tweets created from 2020-10-15 to 2020-11-02 as well, in order to figure out the effect of positive/negative emotion for each candidate - Donald Trump and Joe Biden - on the result of the election.

Details for each variable are as below: - state: name of each state in the United States, including District of Columbia - elec16, elec20: dummy variable indicating whether Trump gained the electoral votes of each state or not. If the electors casted their votes for Trump, the value is 1; otherwise the value is 0 - elecchange: dummy variable indicating whether each party flipped the result in 2020 compared to that of the 2016 - demvote16: the rate of votes that the Democrats, i.e. Hillary Clinton earned in the 2016 Presidential election - repvote16: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2016 Presidential election - demvote20: the rate of votes that the Democrats, i.e. Joe Biden earned in the 2020 Presidential election - repvote20: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2020 Presidential election - demvotedif: the difference between demvote20 and demvote16 - repvotedif: the difference between repvote20 and repvote16 - pop: the population of each state - cumulcases: the cumulative COVID-19 cases on the Election day - caseMar ~ caseOct: the cumulative COVID-19 cases during each month - Marper10k ~ Octper10k: the cumulative COVID-19 cases during each month per 10 thousands - unemp20: the unemployment rate of each state this year before the election - unempdif: the difference between the unemployment rate of the last year and that of this year - jan20unemp ~ oct20unemp: the unemployment rate of each month - cumulper10k: the cumulative COVID-19 cases on the Election day per 10 thousands - b_str_poscount_total: the total number of positive tweets on Biden measured by the SentiStrength - b_str_negcount_total: the total number of negative tweets on Biden measured by the SentiStrength - t_str_poscount_total: the total number of positive tweets on Trump measured by the SentiStrength - t_str_poscount_total: the total number of negative tweets on Trump measured by the SentiStrength - b_str_posprop_total: the proportion of positive tweets on Biden measured by the SentiStrength - b_str_negprop_total: the proportion of negative tweets on Biden measured by the SentiStrength - t_str_posprop_total: the proportion of positive tweets on Trump measured by the SentiStrength - t_str_negprop_total: the proportion of negative tweets on Trump measured by the SentiStrength - white: the proportion of white people - colored: the proportion of colored people - secondary: the proportion of people who has attained the secondary education - tertiary: the proportion of people who has attained the tertiary education - q3gdp20: GDP of the 3rd quarter 2020 - q3gdprate: the growth rate of the 3rd quarter 2020, compared to that of the same quarter last year - 3qsgdp20: GDP of 3 quarters 2020 - 3qsrate20: the growth rate of GDP compared to that of the 3 quarters last year - q3gdpdif: the difference in the level of GDP of the 3rd quarter compared to the last quarter - q3rate: the growth rate of the 3rd quarter compared to the last quarter - access: the proportion of households having the Internet access

Dataset

This dataset was created by Mohamad

Released under U.S. Government Works

Contents

Dataset

This dataset was created by Hue Dinh

Contents

Dataset

This dataset was created by Pravin Tomar

Contents

Comprehensive and standardized election results of winning and losing candidates for the offices of President, U.S. House, U.S. Senate, and Governor. Between 2006 and 2020 alone, there were over 4,000 races for Congress and Governor in the general election, in which 7,710 Democratic and Republican candidates ran for office. This is a candidate-level dataset that is comprehensive for Presidential, Congressional and gubernatorial contests between 2006 and 2020 with standardized names for each unique candidate, party, incumbency, vote totals, and election results.

Context

The goal of this dataset is to provide a tidy way to access to the transcripts of speeches given by various US politicians in the context of the 2020 US Presidential Election. Transcripts have been scraped from rev.com. Some other information, such as location and type of speech, have been manually added to the dataset.

Content

The dataset has the following columns:

speaker: Who gave the speech

title: a title or a description of speech

text: the transcript of the speech

location: the location or the platform where the speech was give

type: type of speech (e.g., campaign speech, interview or debate)

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Context

I am quoting this dataset from a twitter feed that I found really interesting for analysis

Content

The dataset is scraped from vote counting of the 2020 USA elections across all 50 states and how the ratio and total count per state according to the timestamp of the vote counting

Acknowledgements

The dataset is quoted from this twitter feed # https://twitter.com/APhilosophae/status/1325592112428163072

Inspiration

The anonymous data scientist who created this dataset for analysis, in non disputed states like California, we can see the ratio between Rep:Dem votes being quite stable at the end of the timeline and a clear winner can be observed. But in the disputed swing states there is indeed a jump in ratio between the data in the very end of the vote counting that turned the results around. We can see the abnormal change in WI MI another key swing states.