ct_selection_untitled-5CA6C21E.R

library(rio)
library(janitor)

st <- import("E://data/springtide.sav") %>% 
  clean_names()


# Select and rename relevant variables
flourish_long <- st %>%
  select(id, starts_with("flourish_")) %>% 
  pivot_longer(cols = -id, 
               names_to = "Question", 
               values_to = "Response") %>%
  filter(!is.na(Response)) %>%  # Remove missing values
  mutate(
    Response = factor(Response, levels = c(1, 2, 3, 4, 5), 
                      labels = c("Strongly Disagree", "Disagree", "Neither", "Agree", "Strongly Agree")),
    Question = factor(Question, 
                      levels = c("flourish_purpose", "flourish_social", "flourish_engage",
                                 "flourish_others", "flourish_capable", "flourish_good",
                                 "flourish_future", "flourish_respect"),
                      labels = c("I Live a Purposeful & Meaningful Life", "My social relationships are supportive and rewarding", "I am engaged and interested in my daily activities",
                                 "I actively contribute to the happiness and well-being of others", "I am competent and capable in the activities that are important to me", "I am a good person and live a good life",
                                 "I am optimistic about my future", "People respect me"))
  )

flourish_summary <- flourish_long %>%
  group_by(Question, Response) %>%
  summarise(n = n(), .groups = "drop") %>%
  group_by(Question) %>%
  mutate(pct = n / sum(n))

flourish_summary %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(Response))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ Question, ncol =1, strip.position = "top") +
  theme_rb() +
  scale_fill_manual(values = c("#d73027", "#fc8d59", "#fee08b", "#91bfdb", "#4575b4")) +  # Custom color palette
  theme(legend.position = "bottom") +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle=0)) +
  guides(fill = guide_legend(reverse=T, nrow = 1)) +
  theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
  theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank()) +
  geom_text(aes(label = ifelse(pct >.05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "black") +
  # geom_text(aes(label = ifelse(age2 == "18-35", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "white") +
  # geom_text(aes(label = ifelse(age2 == "36-44", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "white") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  labs(x = "", y = "", title = "Human Flourishing Questions Among 13-25 Year Olds", caption = "@ryanburge + @religiondata\nData: Springtide Survey of Young People and Civic Life, 2024") 
save("springtide_flourishing_questions.png", ht = 8)



# Process the data: Count flourishing statements agreed to
flourish_counts <- st %>%
  select(id, r_religious, starts_with("flourish_")) %>% 
  pivot_longer(cols = starts_with("flourish_"), names_to = "Flourish_Question", values_to = "Response") %>%
  filter(!is.na(Response)) %>%  # Remove missing responses
  mutate(Agreed = ifelse(Response >= 4, 1, 0)) %>%  # 1 if Agree (4) or Strongly Agree (5), else 0
  group_by(id, r_religious) %>%
  summarise(Num_Agreed = sum(Agreed), .groups = "drop") %>%
  filter(!is.na(r_religious))  # Remove missing religiosity values

# Compute mean agreement count per religiosity level
rel_summary <- flourish_counts %>%
  group_by(r_religious) %>%
  mean_ci(Num_Agreed, ci = .84)  %>%
  mutate(Religiosity = factor(r_religious, levels = 1:4,
                              labels = c("Not", "Slightly", 
                                         "Moderately", "Very"))) %>% 
  mutate(type = "How Religious Are You?")



# Process the data: Count flourishing statements agreed to
flourish_counts <- st %>%
  select(id, r_spiritual, starts_with("flourish_")) %>% 
  pivot_longer(cols = starts_with("flourish_"), names_to = "Flourish_Question", values_to = "Response") %>%
  filter(!is.na(Response)) %>%  # Remove missing responses
  mutate(Agreed = ifelse(Response >= 4, 1, 0)) %>%  # 1 if Agree (4) or Strongly Agree (5), else 0
  group_by(id, r_spiritual) %>%
  summarise(Num_Agreed = sum(Agreed), .groups = "drop") %>%
  filter(!is.na(r_spiritual))  # Remove missing religiosity values

# Compute mean agreement count per religiosity level
sp_summary <- flourish_counts %>%
  group_by(r_spiritual) %>%
  mean_ci(Num_Agreed, ci = .84)  %>%
  mutate(Religiosity = factor(r_spiritual, levels = 1:4,
                              labels = c("Not", "Slightly", 
                                         "Moderately", "Very"))) %>% 
  mutate(type = "How Spiritual Are You?")
  
gg <- bind_rows(rel_summary, sp_summary) %>% 
  select(relig = Religiosity, mean, lower, upper, type)


gg %>% 
  ggplot(., aes(x = relig, y = mean, fill = relig)) +
  geom_col(color = 'black') +
  facet_wrap(~ type) +
  theme_rb() +
  error_bar() +
  scale_fill_manual(values = c(
    "Not" = "#d73027",       # Dark Red
    "Slightly" = "#fc8d59",  # Orange
    "Moderately" = "#fee08b", # Yellow
    "Very" = "#91bfdb"       # Light Blue
  )) +
  geom_text(aes(y = .5, label = round(mean, 2)), position = position_dodge(width = .9), size = 11, family = "font") +   
  labs(x = "", y = "", title = "Does Religion and/or Spirituality Lead to More Human Flourishing?", 
       subtitle = "Number of Flourishing Items Agreed To", 
       caption = "@ryanburge + @religiondata\nData: Springtide Survey of Young People and Civic Life, 2024")
save("flourishing_spirit_rel.png")



# Compute the mean flourishing agreement for each religiosity-spirituality combination
flourish_heatmap <- st %>%
  select(id, r_religious, r_spiritual, starts_with("flourish_")) %>%
  pivot_longer(cols = starts_with("flourish_"), names_to = "Flourish_Question", values_to = "Response") %>%
  filter(!is.na(Response)) %>%  # Remove missing responses
  mutate(Agreed = ifelse(Response >= 4, 1, 0)) %>%  # 1 if Agree (4 or 5), else 0
  group_by(id, r_religious, r_spiritual) %>%
  summarise(Num_Agreed = sum(Agreed), .groups = "drop") %>%
  filter(!is.na(r_religious), !is.na(r_spiritual)) %>%  # Remove missing values
  group_by(r_religious, r_spiritual) %>%
  summarise(Avg_Agreed = mean(Num_Agreed), .groups = "drop") %>%
  mutate(
    Religiosity = factor(r_religious, levels = 1:4, labels = c("Not", "Slightly", "Moderately", "Very")),
    Spirituality = factor(r_spiritual, levels = 1:4, labels = c("Not", "Slightly", "Moderately", "Very"))
  )

# Create the heatmap
ggplot(flourish_heatmap, aes(x = Religiosity, y = Spirituality, fill = Avg_Agreed)) +
  geom_tile(color = "black") +  # Creates the heatmap squares
  geom_text(aes(label = round(Avg_Agreed, 2), 
                color = ifelse(Avg_Agreed > 6.25, "white", "black")), 
            size = 9, family = "font") +  # Adjusts text color conditionally
  scale_fill_gradient(low = "#fee08b", high = "#d73027") +  # Gradient from yellow to red
  scale_color_identity() +  # Ensures color mapping is applied
  labs(x = "How Religious Are You?", y = "How Spiritual Are You?",
       title = "Flourishing by Religious and Spiritual Identification",
       subtitle = "Average Number of Flourishing Statements Agreed To",
       fill = "Avg. Agreement",
       caption = "@ryanburge + @religiondata\nData: Springtide Survey of Young People and Civic Life, 2024") +
  theme_rb() +
  theme(panel.grid = element_blank())

# Save the plot
save("flourishing_heatmap.png", wd = 6)



# Load necessary libraries
library(tidyverse)

# Function to calculate flourishing agreement by a given grouping variable
process_flourish_growth <- function(data, group_var, group_labels) {
  data %>%
    select(id, !!sym(group_var), starts_with("flourish_")) %>% 
    pivot_longer(cols = starts_with("flourish_"), names_to = "Flourish_Question", values_to = "Response") %>%
    filter(!is.na(Response)) %>%  # Remove missing responses
    mutate(Agreed = ifelse(Response >= 4, 1, 0)) %>%  # 1 if Agree (4 or 5), else 0
    group_by(id, !!sym(group_var)) %>%
    summarise(Num_Agreed = sum(Agreed), .groups = "drop") %>%
    filter(!is.na(!!sym(group_var))) %>%  # Remove missing values
    group_by(!!sym(group_var)) %>%
    summarise(Avg_Agreed = mean(Num_Agreed), .groups = "drop") %>%
    mutate(
      Category = factor(!!sym(group_var), levels = c(-1, 0, 1), labels = group_labels),
      Type = ifelse(group_var == "relig_growth", "Change in Religiousness Over Prior Year", "Change in Spirituality Over Prior Year")
    )
}

# Process flourishing agreement for religious and spiritual growth
flourish_relig_growth <- process_flourish_growth(st, "relig_growth", 
                                                 c("Less", "No Change", "More"))

flourish_spirit_growth <- process_flourish_growth(st, "spirit_growth", 
                                                  c("Less", "No Change", "More"))

# Combine both datasets
flourish_combined_growth <- bind_rows(flourish_relig_growth, flourish_spirit_growth) %>% select(Avg_Agreed, Category, Type)

# Load necessary libraries
library(tidyverse)

# Load necessary libraries
library(tidyverse)

# Create the visualization
ggplot(flourish_combined_growth, aes(x = Category, y = Avg_Agreed, fill = Category)) +
  geom_col(color = "black") +
  geom_text(aes(label = round(Avg_Agreed, 2)), hjust = -0.3, size = 8, family = "font") +  # Add labels inside bars
  scale_fill_manual(values = c(
    "Less" = "#d73027",      # Red-Orange
    "No Change" = "#fdae61", # Warm Yellow-Orange
    "More" = "#4575b4"       # Deep Blue
  )) +
  labs(x = "", y = "Avg. # of Flourishing Statements Agreed To",
       title = "Human Flourishing by Change in Religiousness & Spirituality",
       subtitle = "Number of Flourishing Items Agreed To by Self-Reported Change",
       caption = "@ryanburge + @religiondata\nData: Springtide Survey of Young People and Civic Life, 2024") +
  facet_wrap(Type ~ ., ncol = 1) +  # Facet vertically instead of horizontally
  coord_flip() + 
  scale_y_continuous(limits = c(0, 7.25)) +
  theme_rb(legend = FALSE)

# Save the plot
save("flourishing_growth_vertical.png")


# Load necessary libraries
library(tidyverse)

# Compute the mean flourishing agreement for each religious-spiritual growth combination
flourish_heatmap <- st %>%
  select(id, relig_growth, spirit_growth, starts_with("flourish_")) %>%
  pivot_longer(cols = starts_with("flourish_"), names_to = "Flourish_Question", values_to = "Response") %>%
  filter(!is.na(Response)) %>%  # Remove missing responses
  mutate(Agreed = ifelse(Response >= 4, 1, 0)) %>%  # 1 if Agree (4 or 5), else 0
  group_by(id, relig_growth, spirit_growth) %>%
  summarise(Num_Agreed = sum(Agreed), .groups = "drop") %>%
  filter(!is.na(relig_growth), !is.na(spirit_growth)) %>%  # Remove missing values
  group_by(relig_growth, spirit_growth) %>%
  summarise(Avg_Agreed = mean(Num_Agreed), .groups = "drop") %>%
  mutate(
    Relig_Growth = factor(relig_growth, levels = c(-1, 0, 1), 
                          labels = c("Less Religious", "No Change", "More Religious")),
    Spirit_Growth = factor(spirit_growth, levels = c(-1, 0, 1), 
                           labels = c("Less Spiritual", "No Change", "More Spiritual"))
  )

# Create the heatmap
ggplot(flourish_heatmap, aes(x = Relig_Growth, y = Spirit_Growth, fill = Avg_Agreed)) +
  geom_tile(color = "black") +  # Creates the heatmap squares
  geom_text(aes(label = round(Avg_Agreed, 2), 
                color = ifelse(Avg_Agreed > 6.25, "white", "black")), 
            size = 13, family = "font") +  # Adjusts text color based on value
  scale_fill_gradient(low = "#fee08b", high = "#d73027") +  # Gradient from yellow to red
  scale_color_identity() +  # Ensures color mapping is applied
  labs(x = "Change in Religiousness", y = "Change in Spirituality",
       title = "Flourishing by Change in Religiousness & Spirituality",
       subtitle = "Average Number of Flourishing Statements Agreed To",
       fill = "Avg. Agreement",
       caption = "@ryanburge + @religiondata\nData: Springtide Survey of Young People and Civic Life, 2024") +
  theme_rb() +
  theme(panel.grid = element_blank())

# Save the plot
save("flourishing_9square_heatmap.png", wd = 6)



# Load necessary libraries
library(tidyverse)

# Prepare the dataset
flourish_reg_data <- st %>%
  select(id, starts_with("flourish_"), r_religious, r_spiritual, h_income, r_community, i_race, i_gender, r_age) %>%
  pivot_longer(cols = starts_with("flourish_"), names_to = "Flourish_Question", values_to = "Response") %>%
  filter(!is.na(Response)) %>%  # Remove missing values
  mutate(Agreed = ifelse(Response >= 4, 1, 0)) %>%  # Convert to agreement (1 = Agree/Strongly Agree, 0 = Otherwise)
  group_by(id, r_religious, r_spiritual, h_income, r_community, i_race, i_gender, r_age) %>%
  summarise(Num_Agreed = sum(Agreed), .groups = "drop") %>%  # Sum of agreements per respondent
  mutate(age = r_age,
    male = ifelse(i_gender == 2, 1, 0),  # Dichotomous variable for Male
    white = ifelse(i_race == 1, 1, 0),   # Dichotomous variable for White
    h_income = ifelse(h_income == 999, NA, h_income)) %>%
  drop_na()  # Remove missing values

# Run the regression model
flourish_model <- lm(Num_Agreed ~ r_religious + r_spiritual + h_income + age + white + male, data = flourish_reg_data)

# Display model summary
summary(flourish_model)

# Load necessary libraries
library(tidyverse)
library(broom)

# Extract regression coefficients and confidence intervals
coefs <- tidy(flourish_model, conf.int = TRUE) %>%
  filter(term != "(Intercept)") %>%  # Remove intercept for cleaner plot
  mutate(term = dplyr::recode(term,
                       "r_religious" = "Religiosity",
                       "r_spiritual" = "Spirituality",
                       "h_income" = "Household Income",
                       "age" = "Age",
                       "white" = "White",
                       "male" = "Male"
  ))

# Create coefficient plot
ggplot(coefs, aes(x = reorder(term, estimate), y = estimate, ymin = conf.low, ymax = conf.high)) +
  geom_pointrange(color = "#1f78b4", stroke = 1, shape = 21, fill = "white") +  # Blue points and confidence intervals
  geom_hline(yintercept = 0, linetype = "dashed", color = "red") +  # Reference line at 0
  coord_flip() +  # Flip for readability
  labs(
    x = "",
    y = "Coefficient Estimate",
    title = "Regression Coefficients for Flourishing Model",
    subtitle = "95% Confidence Intervals Shown",
    caption = "@ryanburge + @religiondata\nData: Springtide Survey of Young People and Civic Life, 2024"
  ) +
  theme_rb()

# Save the plot
save("flourishing_coefficients.png", ht = 3.5)


# Load necessary libraries
library(tidyverse)

# Compute average flourishing score by age
flourish_by_age <- st %>%
  select(id, r_age, starts_with("flourish_")) %>%
  pivot_longer(cols = starts_with("flourish_"), names_to = "Flourish_Question", values_to = "Response") %>%
  filter(!is.na(Response)) %>%  # Remove missing responses
  mutate(Agreed = ifelse(Response >= 4, 1, 0)) %>%  # 1 if Agree (4 or 5), else 0
  group_by(id, r_age) %>%
  summarise(Num_Agreed = sum(Agreed), .groups = "drop") %>%
  group_by(r_age) %>%
  mean_ci(Num_Agreed, ci = .84)

flourish_by_age %>% 
  ggplot(aes(x = r_age, y = mean, fill = mean)) +
  geom_col(color = 'black') +
  theme_rb() +
  error_bar() +
  scale_fill_gradientn(colors = c("#d73027", "#fee08b", "#1a9641")) +  # Green → Yellow → Red
  geom_text(aes(y = .5, label = round(mean, 2)), position = position_dodge(width = .9), size = 6, family = "font") +   
  geom_vline(xintercept = 17.5, linetype = "dashed", color = "black", linewidth = .65) +  # Vertical dashed line between 17 and 18
  labs(x = "Age of Respondent", y = "", title = "Human Flourishing Score by Age of Respondent", 
       subtitle = "Number of Flourishing Items Agreed To", 
       caption = "@ryanburge + @religiondata\nData: Springtide Survey of Young People and Civic Life, 2024")

# Save the plot
save("flourishing_age_compared.png")