Visualizing Survey Data with R

table(pew$FACEREC2_W99)

pew_clean <- pew %>%
  filter(!is.na(FACEREC2_W99), FACEREC2_W99 != "Refused") %>%
  droplevels()

prop.table(table(pew_clean$FACEREC2_W99))

ggplot(pew_clean, aes(x = FACEREC2_W99)) +
  geom_bar(fill = "grey60") +
  labs(
    title = "Public Opinion on Police Use of Facial Recognition Technology",
    subtitle = "Pew American Trends Panel, Wave 99 (Nov. 2021)",
    x = "",
    y = "Number of Respondents"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5))

pew_clean_prop <- pew_clean %>%
  count(FACEREC2_W99) %>%
  mutate(prop = n / sum(n))

ggplot(pew_clean_prop, aes(x = FACEREC2_W99, y = prop)) +
  geom_col(fill = "grey60") +
  scale_y_continuous(labels = scales::percent) +
  geom_text(aes(label = scales::percent(prop, accuracy = 1)),
            vjust = -0.3, size = 3) +
  labs(
    title = "Public Opinion on Police Use of Facial Recognition Technology",
    subtitle = "Pew American Trends Panel, Wave 99 (Nov. 2021)",
    x = "",
    y = "Proportion of Respondents"
  ) +
  theme_minimal()

table(pew$F_PARTY_FINAL)

pew_clean <- pew_clean %>%
  filter(!F_PARTY_FINAL %in% c("Something else", "Refused")) %>%
  droplevels()

party_colors <- c(
  "Democrat" = "#4F81BD",     
  "Republican" = "#C0504D",   
  "Independent" = "#9BBB59" 
)

ggplot(pew_clean, aes(x = FACEREC2_W99, fill = F_PARTY_FINAL)) +
  geom_bar(position = "dodge") +
  scale_fill_manual(values = party_colors) +
  labs(
    title = "Public Opinion on Police Use of Facial Recognition Technology by Partisanship",
    x = "",
    y = "Number of Respondents",
    fill = "Partisanship"
  ) +
  theme_minimal()

table(pew$F_GENDER)

pew_clean <- pew_clean %>%
  filter(!F_GENDER %in% c("In some other way", "Refused")) %>%
  droplevels() %>%
  mutate(F_GENDER = forcats::fct_recode(F_GENDER,
    "Female" = "A woman",
    "Male" = "A man"
  ))

ggplot(pew_clean, aes(x = FACEREC2_W99)) +
  geom_bar(fill = "grey60", width = 0.5) +
  facet_wrap(~ F_GENDER, ncol = 1) +  
  labs(
    title = "Public Opinion on Police Use of Facial Recognition Technology by Gender",
    x = "",
    y = "Number of Respondents"
  ) +
  theme_minimal()

# estimate proportions for three AI-related questions using survey design

pew_design <- svydesign(ids = ~0, # ~0 or ~1 indicates no clusters
                        weights = ~WEIGHT_W99, # calibration weights
                        data = pew)

q1 <- svymean(~FACEREC2_W99, # facial recognition
              design = pew_design,
              na.rm = T)
q2 <- svymean(~SMALG2_W99, # algorithms
              design = pew_design,
              na.rm = T)
q3 <- svymean(~DCARS2_W99, # driverless passenger vehicles
              design = pew_design,
              na.rm = T)

# combine and label proportion estimates for plotting

ai_society <- tibble(
  prop = round(c(q1[-4], q2[-4], q3[-4]) * 100, 0),
  type = rep(c("Facial recognition", "Algorithms", "Driverless vehicles"), each = 3),
  response = rep(c("Good idea for society", "Bad idea for society", "Not sure"), times = 3)
) %>%
  mutate(
    type = factor(type, levels = c("Driverless vehicles", "Algorithms", "Facial recognition")),
    response = factor(response, levels = c("Not sure", "Bad idea for society", "Good idea for society"))
  )

ai_society %>%
  ggplot(aes(x = type, y = prop, fill = response)) +
  geom_col(width = 0.4) +

  # add percentage labels inside bars
  geom_text(
    aes(label = prop, color = response),
    position = position_stack(vjust = 0.5),
    size = 3.5
  ) +

  # flip to horizontal bars
  coord_flip() +

  # custom fill and text color
  scale_fill_manual(
    values = c(
      "Good idea for society" = "#2f6ead",
      "Bad idea for society" = "#95a644",
      "Not sure" = "#d6d6d6"
    ),
    name = ""
  ) +
  scale_color_manual(
    values = c(
      "Good idea for society" = "white",
      "Bad idea for society" = "white",
      "Not sure" = "black"
    )
  ) +

  # titles and labels
  labs(
    title = str_wrap("The public is more open to applications of human enhancement and AI", 80),
    subtitle = str_wrap("% of U.S. adults who say the widespread use of each of the following artificial intelligence and human enhancement applications has been/would be a...", 100),
    x = NULL,
    y = NULL
  ) +

  # reverse legend order to match stacking order
  guides(fill = guide_legend(reverse = TRUE), color = "none") +

  # clean minimal theme
  theme_minimal(base_size = 11) +
  theme(
    legend.position = "top",
    panel.background = element_blank(),
    axis.ticks = element_blank(),
    axis.text.x = element_blank(),
    plot.title = element_text(size = 13, face = "bold", hjust = 0),
    plot.subtitle = element_text(size = 10, color = "grey40", hjust = 0, face = "italic")
  )

ggplot(ai_society, aes(x = type, y = prop, fill = response)) +
  geom_col(width = 0.4) +
  coord_flip() +

  # dashed lines below each category label
  geom_vline(xintercept = c(1.5, 2.5), linetype = "dashed", color = "grey60", linewidth = 0.4) +

  # label inside bars, right aligned
  geom_text(
    aes(label = prop, color = response),
    position = position_stack(vjust = 0.5),
    hjust = 1.05,
    size = 4
  ) +

  # facet by response to mimic Pew's column layout
  facet_wrap(~ fct_rev(response), nrow = 1) +

  # custom fill and label colors (matching Pew palette)
  scale_fill_manual(
    values = c(
      "Good idea for society" = "#2f6ead",     
      "Bad idea for society"  = "#95a644",     
      "Not sure"              = "#d6d6d6"      
    ),
    name = NULL
  ) +
  scale_color_manual(
    values = c(
      "Good idea for society" = "white",
      "Bad idea for society"  = "white",
      "Not sure"              = "black"
    )
  ) +

  # axis and title formatting
  labs(
    title = str_wrap("Majority says brain chip implants for improved cognitive abilities would be bad idea for society; public more open to other applications of human enhancement and AI", 100), 
    subtitle = str_wrap("% of U.S. adults who say the widespread use of each of the following artificial intelligence and human enhancement applications has been/would be a ...", 120),
    x = NULL,
    y = NULL
  ) +

  theme_minimal(base_size = 12) +
  theme(
    panel.grid = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks = element_blank(),
    legend.position = "none",
    strip.background = element_blank(),
    strip.text = element_text(face = "bold", size = 11, hjust = 0.5),
    plot.title = element_text(size = 13, face = "bold", hjust = 0),
    plot.subtitle = element_text(size = 10, color = "grey30", hjust = 0, face = "italic")
  )