Key Findings

Our analysis reveals a clear relationship between education and happiness levels. Higher education is generally associated with greater reported happiness. Political trust varies significantly across party lines and ideological positions, with interesting regional and demographic patterns.

Next Steps

Future research should explore the causal mechanisms behind these relationships.

Demographics & Education

Happiness Distribution Across Education Levels
Happiness Distribution by Gender

How happy are you with your life right now?

Political Attitudes

This shows how party identification aligns with political ideology.

Do you think you can usually trust strangers?

Gender distribution across different social class categories.

--- title: "{{< iconify ph lightbulb >}} Key Findings" format: html --- Our analysis reveals a clear relationship between education and happiness levels. Higher education is generally associated with greater reported happiness. Political trust varies significantly across party lines and ideological positions, with interesting regional and demographic patterns. ## Next Steps Future research should explore the causal mechanisms behind these relationships. ```{r setup} #| echo: false #| warning: false #| message: false #| error: false #| results: 'hide' # Load required libraries library(dashboardr) library(dplyr) library(highcharter) # Global chunk options knitr::opts_chunk$set( echo = FALSE, warning = FALSE, message = FALSE, error = FALSE, fig.width = 12, fig.height = 8, dpi = 300 ) # Load data from dataset_2867obs.rds data <- readRDS('dataset_2867obs.rds') # Data summary cat('Dataset loaded:', nrow(data), 'rows,', ncol(data), 'columns\n') ``` ## Demographics & Education ::: {.panel-tabset} ### {{< iconify ph chart-bar >}} Happiness Distribution Across Education Levels How happy are you with your life right now? ```{r demographics-3} # Happiness Distribution Across Education Levels result <- create_stackedbar( data = data, title = "Happiness Distribution Across Education Levels", x_var = "degree_1a", stack_var = "happy_1a", subtitle = "Percentage breakdown within each education category", x_label = "Education Level", y_label = "Percentage of Respondents", stack_label = "Happiness Level", stacked_type = "percent", x_order = c("Lt High School", "High School", "Junior College", "Bachelor", "Graduate"), stack_order = c("Very Happy", "Pretty Happy", "Not Too Happy"), tooltip_suffix = "%", color_palette = c("#2E86AB", "#A23B72", "#F18F01") ) # Apply height to highcharter object if (inherits(result, 'highchart')) { result <- highcharter::hc_chart(result, height = 500) } result ``` ### {{< iconify ph gender-intersex >}} Happiness Distribution by Gender ```{r demographics-4} # Happiness Distribution by Gender result <- create_stackedbar( data = data, title = "Happiness Distribution by Gender", x_var = "sex_1a", stack_var = "happy_1a", subtitle = "Gender differences in reported happiness levels", x_label = "Gender", y_label = "Percentage of Respondents", stack_label = "Happiness Level", stacked_type = "percent", stack_order = c("Very Happy", "Pretty Happy", "Not Too Happy"), tooltip_suffix = "%", color_palette = c("#2E86AB", "#A23B72", "#F18F01") ) # Apply height to highcharter object if (inherits(result, 'highchart')) { result <- highcharter::hc_chart(result, height = 450) } result ``` How happy are you with your life right now? ::: ## Political Attitudes ::: {.panel-tabset} ### {{< iconify ph users-three >}} Party ID by Political Views This shows how party identification aligns with political ideology. ```{r politics-4} # Party ID by Political Views result <- create_stackedbar( data = data, title = "Party ID by Political Views", x_var = "polviews_1a", stack_var = "partyid_1a", subtitle = "Distribution of party identification across political ideology", x_label = "Political Views", y_label = "Percentage of Respondents", stack_label = "Party ID", stacked_type = "percent", tooltip_suffix = "%", color_palette = c("#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2") ) # Apply height to highcharter object if (inherits(result, 'highchart')) { result <- highcharter::hc_chart(result, height = 550) } result ``` ### {{< iconify ph map-pin >}} Trust Levels by US Region ```{r politics-5} # Trust Levels by US Region result <- create_stackedbar( data = data, title = "Trust Levels by US Region", x_var = "region_1a", stack_var = "trust_1a", subtitle = "Regional variation in interpersonal trust", x_label = "US Region", y_label = "Percentage of Respondents", stack_label = "Trust Level", stack_order = c("Can Trust", "Can't Be Too Careful", "Depends"), stacked_type = "percent", tooltip_suffix = "%", color_palette = c("#2E8B57", "#DAA520", "#CD5C5C") ) # Apply height to highcharter object if (inherits(result, 'highchart')) { result <- highcharter::hc_chart(result, height = 500) } result ``` Do you think you can usually trust strangers? ### {{< iconify ph chart-pie >}} Gender Distribution Across Social Classes Gender distribution across different social class categories. ```{r politics-6} # Gender Distribution Across Social Classes result <- create_stackedbar( data = data, title = "Gender Distribution Across Social Classes", x_var = "class_1a", stack_var = "sex_1a", subtitle = "With custom labels and ordering", x_label = "Self-Reported Social Class", y_label = "Number of Respondents", stack_label = "Gender", x_order = c("Lower Class", "Working Class", "Middle Class", "Upper Class"), stack_order = c("Female", "Male"), stacked_type = "counts", tooltip_prefix = "Count: ", color_palette = c("#E07A5F", "#3D5A80") ) # Apply height to highcharter object if (inherits(result, 'highchart')) { result <- highcharter::hc_chart(result, height = 450) } result ``` ::: ## Social Issues ::: {.panel-tabset} ### {{< iconify ph heatmap >}} Trust by Education and Age ```{r social-3} # Trust by Education and Age result <- create_heatmap( data = data, title = "Trust by Education and Age", x_var = "degree_1a", y_var = "age_1a", value_var = "trust_1a", subtitle = "Average trust levels across education and age groups", x_label = "Education Level", y_label = "Age Group", value_label = "Trust Level", x_order = c("Lt High School", "High School", "Junior College", "Bachelor", "Graduate"), color_palette = c("#d7191c", "#fdae61", "#ffffbf", "#abdda4", "#2b83ba"), tooltip_prefix = "Trust: ", tooltip_suffix = "/3", tooltip_labels_format = "{point.value:.2f}" ) # Apply height to highcharter object if (inherits(result, 'highchart')) { result <- highcharter::hc_chart(result, height = 600) } result ``` This heatmap reveals trust patterns across education and age groups. ### {{< iconify ph chart-pie >}} Trust by Region and Education Educational and regional patterns in trust distribution. ```{r social-4} # Trust by Region and Education result <- create_heatmap( data = data, title = "Trust by Region and Education", x_var = "region_1a", y_var = "degree_1a", value_var = "trust_1a", subtitle = "Educational and regional patterns in trust levels", x_label = "Region", y_label = "Education Level", value_label = "Trust Level", y_order = c("Lt High School", "High School", "Junior College", "Bachelor", "Graduate"), color_palette = c("#d7191c", "#fdae61", "#ffffbf", "#abdda4", "#2b83ba"), tooltip_prefix = "Trust: ", tooltip_suffix = "/3", tooltip_labels_format = "{point.value:.2f}" ) # Apply height to highcharter object if (inherits(result, 'highchart')) { result <- highcharter::hc_chart(result, height = 550) } result ``` :::