ABC vs. CBS: Comparing Fictional Presidential Poll Data in R

 # Assignment 3 - Fictional 2016 Presidential Poll Data

# Note: This data is made up and does NOT reflect real election results


# Candidate names

Name <- c("Jeb", "Donald", "Ted", "Marco", "Carly", "Hillary", "Bernie")


# Poll results from ABC and CBS

ABC_poll <- c(4, 62, 51, 21, 2, 14, 15)

CBS_poll <- c(12, 75, 43, 19, 1, 21, 19)


# Combine data into a data frame

poll_data <- data.frame(Name, ABC_poll, CBS_poll)


# Add a column to compare differences between polls

poll_data$Difference <- CBS_poll - ABC_poll


# View the data

print(poll_data)


# Optional: Simple bar plot comparison

barplot(

  t(as.matrix(poll_data[, c("ABC_poll", "CBS_poll")])),

  beside = TRUE,

  names.arg = poll_data$Name,

  col = c("gray70", "gray40"),

  legend.text = c("ABC Poll", "CBS Poll"),

  main = "Fictional 2016 Presidential Poll Results",

  ylab = "Poll Percentage"

)

For this assignment, I created a fictional dataset based on the 2016 presidential election to compare polling results from two different sources: ABC and CBS. The dataset includes several candidates and their corresponding poll percentages from each source. Even though the data is made up, it still shows how different polling organizations can report noticeably different results for the same candidates. For example, CBS consistently reports higher support for some candidates like Donald and Hillary, while ABC shows stronger results for others such as Ted. This highlights how polling outcomes can vary depending on methodology, sample size, or question wording. Using vectors in R made it easy to store and compare these values, and combining them into a data frame helped organize the data clearly. This exercise reinforced how R is designed to handle structured data efficiently, which aligns with the ideas discussed in The Art of R Programming, Chapters 3–5, where Matloff emphasizes R’s strength in vector-based operations and clean data organization.

Comments

Post a Comment

Popular posts from this blog

Assignment #10: Building Your Own R Package

 # Proposal for My R Package: Friedman For this project, I am proposing an R package called **Friedman**. The purpose of this package is to provide beginner-friendly tools for simple data analysis and visualization in R. Many students and new R users struggle with repetitive tasks such as cleaning datasets, calculating summary statistics, and creating clear visualizations. This package will help make those tasks easier by grouping useful functions into one simple package. The main audience for this package is students, beginner analysts, and anyone who wants a more straightforward way to explore data in R. Instead of writing long code repeatedly, users will be able to call functions from Friedman to quickly summarize and visualize their data. This makes the package especially useful for class assignments, small research projects, and practice with R programming. Some of the key functions I plan to implement include: - `clean_data()` – removes missing values or standardizes column n...
Read more

Module # 4 Programming structure assignment

 # ------------------------------------------------------------ # hospital_analysis.R # Hospital patient intake dataset: plots + brief summaries # ------------------------------------------------------------ # ---- 1) Create the dataset ---- Freq <- c(0.6, 0.3, 0.4, 0.4, 0.2, 0.6, 0.3, 0.4, 0.9, 0.2) BP   <- c(103, 87, 32, 42, 59, 109, 78, 205, 135, 176) # first: bad = 1, good = 0, NA = missing First <- c(1, 1, 1, 1, 0, 0, 0, 0, NA, 1) # second: low = 0, high = 1 Second <- c(0, 0, 1, 1, 0, 0, 1, 1, 1, 1) # finaldecision: low = 0, high = 1 FinalDecision <- c(0, 1, 0, 1, 0, 1, 0, 1, 1, 1) hospital_data <- data.frame(Freq, BP, First, Second, FinalDecision) # Print the dataset print("Hospital dataset:") print(hospital_data) # ---- 2) Boxplot: Blood Pressure by Final Decision ---- # Side-by-side comparison of BP for Low vs High final decision boxplot(   BP ~ FinalDecision,   data = hospital_data,   names = c("Low Priority (0)", "High Priority (...
Read more