Visualizing survey data
Lecture 16
March 19, 2026
Top-line survey reports
Stacked bar charts
Show the code
stack_bar_p <- ggplot(
data = iran_war_long,
mapping = aes(x = pct, y = pid3, fill = response)
) +
geom_col() +
scale_x_continuous(labels = label_percent(), position = "top") +
scale_fill_discrete_diverging(
palette = "Blue-Red",
labels = label_wrap(width = 8),
guide = guide_legend(
reverse = TRUE,
theme = theme(legend.key.width = unit(1, "cm"))
)
) +
labs(
x = NULL,
y = NULL,
fill = NULL,
title = "Do you support or oppose the war with Iran?",
caption = "Source: YouGov (March 13-16, 2026)"
) +
theme(
legend.position = "bottom",
panel.grid = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.length.x = unit(0.25, "cm")
)
stack_bar_p
With a double axis
Diverging, with extra neutrals
Show the code
# main plot
div_bar_no_neutral_p <- iran_war_long |>
# remove Not sure responses
filter(response != "Not sure") |>
# shift negative responses to the left of the origin
mutate(
pct = if_else(
response %in% c("Strongly oppose", "Somewhat oppose"),
-pct,
pct
)
) |>
ggplot(mapping = aes(x = pct, y = pid3, fill = response)) +
geom_col() +
scale_x_continuous(
breaks = seq(from = -.8, to = .6, by = .2),
labels = label_percent(),
position = "top"
) +
scale_fill_discrete_diverging(
palette = "Blue-Red",
labels = label_wrap(width = 8),
guide = guide_legend(
reverse = TRUE,
theme = theme(legend.key.width = unit(1, "cm"))
)
) +
labs(
x = NULL,
y = NULL,
fill = NULL
) +
theme(
legend.position = "bottom",
panel.grid = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.length.x = unit(0.25, "cm")
)
# separate plot for neutrals
div_bar_neutral_p <- iran_war_long |>
filter(response == "Not sure") |>
ggplot(mapping = aes(x = pct, y = pid3, fill = response)) +
geom_col() +
scale_x_continuous(
breaks = c(0, .2),
limits = c(NA, .2),
labels = label_percent(),
position = "top"
) +
scale_fill_discrete_diverging(
palette = "Blue-Red",
labels = label_wrap(width = 8),
guide = guide_legend(
reverse = TRUE,
theme = theme(legend.key.width = unit(1, "cm"),
legend.justification = "left")
)
) +
labs(
x = NULL,
y = NULL,
fill = NULL
) +
theme(
legend.position = "bottom",
panel.grid = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.length.x = unit(0.25, "cm")
)
# combine together with patchwork
div_bar_no_neutral_p +
div_bar_neutral_p +
# add shared title and caption
plot_annotation(
title = "Do you support or oppose the war with Iran?",
caption = "Source: YouGov (March 13-16, 2026)"
) +
# combine shared axes
plot_layout(
widths = c(4, 1),
axes = "collect"
)
Diverging, integrated neutrals
Show the code
# split the neutral responses in half and plot them on either side of the origin
div_int_neutral_p <- iran_war_long |>
# neutral will be split into two equal halves
mutate(pct_plot = if_else(response == "Not sure", pct / 2, pct)) |>
# duplicate neutral rows so one half can go left and one right
uncount(
weights = if_else(response == "Not sure", 2L, 1L),
.id = "neutral_half"
) |>
# invert oppose responses and one half of neutrals to negative values
mutate(
pct_plot = case_when(
response %in% c("Strongly oppose", "Somewhat oppose") ~ -pct_plot,
response == "Not sure" & neutral_half == 1L ~ -pct_plot,
.default = pct_plot
),
# convert response to character vector
# factors with duplicated levels can cause issues with plotting
response = as.character(response)
) |>
ggplot(mapping = aes(x = pct_plot, y = pid3, fill = response)) +
# reverse the stacking order since response is no longer a factor
geom_col(position = position_stack(reverse = TRUE)) +
geom_vline(xintercept = 0, linewidth = 0.4, color = "gray40") +
scale_x_continuous(
breaks = seq(from = -.8, to = .6, by = .2),
labels = label_percent(),
position = "top"
) +
# manually generate the diverging color scale since response is no longer a factor
scale_fill_manual(
labels = label_wrap(width = 8),
# fix the order to match the other plots
breaks = c(
"Strongly support",
"Somewhat support",
"Not sure",
"Somewhat oppose",
"Strongly oppose"
),
# generate palette manually as a character vector
values = diverging_hcl(palette = "Blue-Red", n = 5),
guide = guide_legend(
reverse = TRUE,
theme = theme(legend.key.width = unit(1, "cm"))
)
) +
labs(
x = NULL,
y = NULL,
fill = NULL,
title = "Do you support or oppose the war with Iran?",
caption = "Source: YouGov (March 13-16, 2026)"
) +
theme(
legend.position = "bottom",
panel.grid = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.length.x = unit(0.25, "cm")
)
div_int_neutral_p
Split bars
Show the code
split_bars_p <- ggplot(
data = iran_war_long,
mapping = aes(x = pct, y = pid3, fill = response)
) +
geom_col() +
scale_x_continuous(
breaks = seq(from = 0, to = 1, by = 0.2),
labels = label_percent(),
position = "top"
) +
scale_fill_discrete_diverging(
palette = "Blue-Red",
guide = "none"
) +
facet_wrap(
facets = vars(response |> fct_rev()),
nrow = 1,
space = "free_x",
scales = "free_x",
labeller = label_wrap_gen(width = 15)
) +
labs(
x = NULL,
y = NULL,
fill = NULL,
title = "Do you support or oppose the war with Iran?",
caption = "Source: YouGov (March 13-16, 2026)"
) +
theme(
panel.grid = element_blank(),
axis.ticks.y = element_blank(),
axis.ticks.length.x = unit(0.25, "cm"),
# shrink the text size for the facet labels to fit
strip.text = element_text(size = rel(0.7),
margin = margin(t = 1, r = 0, b = 1, l = 0, unit = "mm"))
)
split_bars_p
ae-15
Compare the advantages of different bar chart designs for reporting Likert scale data
