Assignment – Loading Data into a Data Frame

We are often tasked with taking data in one form and transforming it for easier downstream analysis. We will spend several weeks in this course on tidying and transformation operations. Some of this work could be done in SQL or R (or Python or…). Here, you are asked to use R—you may use any base functions or packages as you like. Your task is to first choose one of the provided datasets on fivethirtyeight.com that you find interesting:

https://data.fivethirtyeight.com/

You should first study the data and any other information on the GitHub site, and read the associated fivethirtyeight.com article.

To receive full credit, you should:

  1. Take the data, and create one or more code blocks. You should finish with a data frame that contains a subset of the columns in your selected dataset. If there is an obvious target (aka predictor or independent) variable, you should include this in your set of columns. You should include (or add if necessary) meaningful column names and replace (if necessary) any non-intuitive abbreviations used in the data that you selected. For example, if you had instead been tasked with working with the UCI mushroom dataset, you would include the target column for edible or poisonous, and transform “e” values to “edible.” Your deliverable is the R code to perform these transformation tasks.

  2. Make sure that the original data file is accessible through your code—for example, stored in a GitHub repository or AWS S3 bucket and referenced in your code. If the code references data on your local machine, then your work is not reproducible!

  3. Start your R Markdown document with a two to three sentence “Overview” or “Introduction” description of what the article that you chose is about, and include a link to the article.

  4. Finish with a “Conclusions” or “Findings and Recommendations” text block that includes what you might do to extend, verify, or update the work from the selected article.

  5. Each of your text blocks should minimally include at least one header, and additional non-header text.

  6. You’re of course welcome—but not required–to include additional information, such as exploratory data analysis graphics (which we will cover later in the course).

  7. Place your solution into a single R Markdown (.Rmd) file and publish your solution out to rpubs.com.

  8. Post the .Rmd file in your GitHub repository, and provide the appropriate URLs to your GitHub repository and your rpubs.com file in your assignment link.

Overview

The article, “Why Many Americans Don’t Vote”, goes through anecdotes of different citizens and their reasoning for their choices of why to vote/not vote. The data itself contains the political views and demographics of 6,000+ people who were polled about their voting history. As the article was created in Fall 2020, the topic of the 2020 presidential election was tied heavily with the findings from the data.

Article Link: https://projects.fivethirtyeight.com/non-voters-poll-2020-election/

Datasource: https://data.fivethirtyeight.com/

Data Preparation

#load the data
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.2.3
## Warning: package 'ggplot2' was built under R version 4.2.3
## Warning: package 'tibble' was built under R version 4.2.3
## Warning: package 'tidyr' was built under R version 4.2.3
## Warning: package 'readr' was built under R version 4.2.3
## Warning: package 'dplyr' was built under R version 4.2.3
## Warning: package 'lubridate' was built under R version 4.2.3
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.1     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(openintro)
## Loading required package: airports
## Loading required package: cherryblossom
## Loading required package: usdata
#load csv into a dataframe
voters_data <- read.csv('https://raw.githubusercontent.com/nk014914/Data-607/main/nonvoters_data.csv')

#cleaning column names
colnames(voters_data)[colnames(voters_data) == "RespId"] = "unique_respondent_ID"
colnames(voters_data)[colnames(voters_data) == "income_cat"] = "income_category"
colnames(voters_data)[colnames(voters_data) == "ppage"] = "age"
#subset non-voters and remove columns

non_voters <- subset(voters_data, voter_category != "always", select = c('unique_respondent_ID','weight','age','educ','race','gender','income_category','voter_category'))

Glimpse into the data

#summarize the data
summary(non_voters)
##  unique_respondent_ID     weight            age            educ          
##  Min.   :470003       Min.   :0.2298   Min.   :22.00   Length:4025       
##  1st Qu.:472381       1st Qu.:0.8039   1st Qu.:36.00   Class :character  
##  Median :474434       Median :0.9936   Median :50.00   Mode  :character  
##  Mean   :474946       Mean   :1.0197   Mean   :49.45                     
##  3rd Qu.:476447       3rd Qu.:1.2138   3rd Qu.:62.00                     
##  Max.   :488322       Max.   :3.0386   Max.   :94.00                     
##      race              gender          income_category    voter_category    
##  Length:4025        Length:4025        Length:4025        Length:4025       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##

I created some basic bar charts to visualize our demographics.

#exploratory graphics

#demographic barchart
ggplot(data = non_voters, aes(x = voter_category)) + 
  geom_bar()

ggplot(data = non_voters, aes(x = educ)) + 
  geom_bar()

ggplot(data = non_voters, aes(x = income_category)) + 
  geom_bar()

#

Findings and Recommendations

Based on the initial exploration of the data it was interested to see how of the people who do not regularly vote, a major of the sample size has gone to college. Generally, we assume that non-voters are usually those who do not have a strong educational background. With these findings, I would like to delve more into what the correlation between education and voting behavior is.

The article goes into the different voter demographics (Race, Income, Age, Education, Party ID) to see each category’s voting behavior. I believe that they can extend their finding by creating a correlation matrix, which might be helpful in determining which predictors have the strongest impact on weight.