title: “AgendaSettingAnalysis” author: “Julia Rutledge” date: “2025-09-19” output: html_document editor_options: markdown: wrap: 72
Rationale
Agenda Setting Theory determines which topics are important and deserve media coverage. It is likely that certain media outlets would determine the topic of immigration to be a top issue than others. Therefore, covering the topic more frequently. More specifically between media outlets Fox and CNN.
Hypothesis
The proportion of stories about immigration will differ from being a top issue or not a top issue depending on if it is covered by Fox or CNN.
Variables and Method
Both the dependent and independent variables are categorical. The dependent variable is the topic of immigration and the independent variable is preferred network. The information is taken from a data set of 600 frequent Fox and CNN viewers. The categorical independent variable determines whether the subject is a frequent viewer of Fox or CNN. The categorical dependent variable determines whether the subject considers immigration as a top issue or not a top issue.
A crosstabulation and Chi Square Test was used to determine which media outlet devoted to covering the topic of immigration more.
Results and Discussion
The graph below shows the percentages of top issue versus not top issue from the two media outlets as a result of the Chi Square Test.
## function (..., exclude = if (useNA == "no") c(NA, NaN), useNA = c("no",
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## if (!miss.use && !miss.exc && doNA && match(NA, exclude,
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## <bytecode: 0x6553cc809da8>
## <environment: namespace:base>The average of immigration as a top issue was higher from Fox than CNN. But higher across both platforms as not a top issue overall.
Code
# ------------------------------
# Setup: Install and load packages
# ------------------------------
if (!require("tidyverse")) install.packages("tidyverse") # Data wrangling & plotting
if (!require("gmodels")) install.packages("gmodels") # Crosstabs
if (!require("gt")) install.packages("gt") # Table formatting
library(tidyverse)
library(gmodels)
library(gt)
# ------------------------------
# Load the data
# ------------------------------
# Replace "YOURFILENAME.csv" with your dataset name
mydata <- read.csv("TopIssue.csv") #Edit
# ------------------------------
# Define Dependent (DV) and Independent (IV) variables
# ------------------------------
# Replace YOURDVNAME and YOURIVNAME with actual column names in your data
mydata$DV <- mydata$Immigration #Edit
mydata$IV <- mydata$PreferredNetwork #Edit
# ------------------------------
# Visualization: Stacked bar chart of IV by DV
# ------------------------------
graph <- ggplot(mydata, aes(x = IV, fill = DV)) +
geom_bar(colour = "black") +
scale_fill_brewer(palette = "Paired") +
labs(
title = "Distribution of DV by IV",
x = "Independent Variable",
y = "Count",
fill = "Dependent Variable"
)
#Show the graph
graph
# ------------------------------
# Crosstabulation of DV by IV (DV = rows, IV = columns)
# ------------------------------
crosstab <- mydata %>%
count(DV, IV) %>%
group_by(IV) %>%
mutate(RowPct = 100 * n / sum(n)) %>%
ungroup() %>%
mutate(Cell = paste0(n, "\n(", round(RowPct, 1), "%)")) %>%
select(DV, IV, Cell) %>%
pivot_wider(names_from = IV, values_from = Cell)
# Format into gt table
crosstab_table <- crosstab %>%
gt(rowname_col = "DV") %>%
tab_header(
title = "Crosstabulation of DV by IV",
subtitle = "Counts and (Column Percentages)"
) %>%
cols_label(
DV = "Dependent Variable"
)
# Show the polished crosstab table
crosstab_table
# ------------------------------
# Chi-squared test of independence
# ------------------------------
options(scipen = 999) # Prevents scientific notation
chitestresults <- chisq.test(mydata$DV, mydata$IV)
# ------------------------------
# Format Chi-squared test results into a table
# ------------------------------
chitest_summary <- tibble(
Test = "Chi-squared Test of Independence",
Chi_sq = chitestresults$statistic,
df = chitestresults$parameter,
p = chitestresults$p.value
)
chitest_table <- chitest_summary %>%
gt() %>%
# Round χ² and p-value to 3 decimals, df to integer
fmt_number(columns = c(Chi_sq, p), decimals = 3) %>%
fmt_number(columns = df, decimals = 0) %>%
tab_header(
title = "Chi-squared Test Results",
subtitle = "Test of Independence between DV and IV"
) %>%
cols_label(
Test = "Test",
Chi_sq = "Chi-squared Statistic",
df = "Degrees of Freedom",
p = "p-value"
)
# Show the formatted results table
chitest_table