Project 1
Victoria
2025-10-18
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ purrr 1.1.0
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsimmigration <- read.csv("immigration.csv")Research Question
How do political beliefs affect opinions on what should happen to workers who have illegally entered the U.S.?
Introduction
In recent years, immigration policy has become a divisive topic in American politics. This project investigates how registered voters with different political ideologies (conservative, moderate, and liberal) differ in their views on how the U.S. should handle workers who have entered the country illegally.
The dataset used in this project, titled immigration, contains responses from 910 randomly selected registered voters in Tampa, Florida. The participants were asked about their political ideology and their opinion on what should happen to workers who have illegally entered the U.S. The dataset contains two columns:
• response: This variable records each respondent’s opinion with four possible answers — Apply for citizenship, Guest worker, Leave the country, and Not sure.
• political: This variable records each respondent’s political ideology — conservative, moderate, or liberal.
The goal of this analysis is to explore whether political ideology is associated with more supportive or stricter views toward illegal workers.
Data Analysis
To address the research question, we will perform basic data cleaning and exploratory data analysis (EDA). This includes checking for missing values, summarizing counts by political affiliation, and comparing how each group responded to the immigration question. We will use functions such as filter(), mutate(), and summary(), and create a bar plot to visualize how opinions differ across political ideologies.
head(immigration)## response political
## 1 Apply for citizenship conservative
## 2 Apply for citizenship conservative
## 3 Apply for citizenship conservative
## 4 Apply for citizenship conservative
## 5 Apply for citizenship conservative
## 6 Apply for citizenship conservativesummary(immigration)## response political
## Length:910 Length:910
## Class :character Class :character
## Mode :character Mode :characterClean Data - Remove Missing or unknown responses
library(dplyr)
clean_data <- immigration %>%
filter(response != "Not sure")
print(clean_data)## response political
## 1 Apply for citizenship conservative
## 2 Apply for citizenship conservative
## 3 Apply for citizenship conservative
## 4 Apply for citizenship conservative
## 5 Apply for citizenship conservative
## 6 Apply for citizenship conservative
## 7 Apply for citizenship conservative
## 8 Apply for citizenship conservative
## 9 Apply for citizenship conservative
## 10 Apply for citizenship conservative
## 11 Apply for citizenship conservative
## 12 Apply for citizenship conservative
## 13 Apply for citizenship conservative
## 14 Apply for citizenship conservative
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## 16 Apply for citizenship conservative
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## 18 Apply for citizenship conservative
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## 58 Apply for citizenship moderate
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## 178 Apply for citizenship liberal
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## 274 Apply for citizenship liberal
## 275 Apply for citizenship liberal
## 276 Apply for citizenship liberal
## 277 Apply for citizenship liberal
## 278 Apply for citizenship liberal
## 279 Guest worker conservative
## 280 Guest worker conservative
## 281 Guest worker conservative
## 282 Guest worker conservative
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## 400 Guest worker moderate
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## 513 Guest worker liberal
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## 538 Guest worker liberal
## 539 Guest worker liberal
## 540 Guest worker liberal
## 541 Leave the country conservative
## 542 Leave the country conservative
## 543 Leave the country conservative
## 544 Leave the country conservative
## 545 Leave the country conservative
## 546 Leave the country conservative
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## 890 Leave the country liberalCount responses by political ideology
response_counts <- clean_data %>%
group_by(political, response) %>%
summarise(count = n())## `summarise()` has grouped output by 'political'. You can override using the
## `.groups` argument.print(response_counts)## # A tibble: 9 × 3
## # Groups: political [3]
## political response count
## <chr> <chr> <int>
## 1 conservative Apply for citizenship 57
## 2 conservative Guest worker 121
## 3 conservative Leave the country 179
## 4 liberal Apply for citizenship 101
## 5 liberal Guest worker 28
## 6 liberal Leave the country 45
## 7 moderate Apply for citizenship 120
## 8 moderate Guest worker 113
## 9 moderate Leave the country 126Bar plot to visualize the results
library(ggplot2)
ggplot(response_counts, aes(x = political, y = count, fill = response)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Opinions on Illegal Workers by Political Ideology",
x = "Political Ideology", y = "Number of Respondents",
fill = "Response") +
theme_minimal()
Overall political ideology distribution
table(immigration$political)##
## conservative liberal moderate
## 372 175 363Conclusion
The analysis shows that political ideology is strongly associated with opinions on how to handle illegal workers. Liberal respondents were much more likely to support allowing illegal workers to apply for citizenship. In contrast, conservatives were more likely to believe these workers should leave the country. Moderates tended to fall between the two, often favoring the guest worker option.
Future Directions
Although this dataset offers valuable insight into the views of Tampa voters, it would be beneficial to compare this data across other cities or states to see if these patterns hold nationally. Future studies could also include additional variables such as age, race, education, or income to better understand the influences behind people’s views on immigration policy.
References
- SurveyUSA. (2012, January 27–29). News Poll #18927: Immigration (OpenIntro data). In OpenIntro. Retrieved from https://www.openintro.org/data/index.php?data=immigration