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styling #38

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Feb 14, 2025
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styling #38

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215 changes: 133 additions & 82 deletions R/descriptive.R
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Original file line number Diff line number Diff line change
Expand Up @@ -20,34 +20,41 @@ table_confint <- function(scores, group_var = NULL) {
total_forecasts <- nrow(scores)
total_models <- n_distinct(scores$Model)
if (!is.null(group_var)) {
scores <- scores |>
group_by(.data[[group_var]])
scores <- scores |>
group_by(.data[[group_var]])
}
table <- scores |>
summarise(n_forecasts = n(),
p_forecasts = round(n() / total_forecasts * 100, 1),
n_models = n_distinct(Model),
p_models = round(n_models / total_models * 100, 1),
mean = mean(wis, na.rm = TRUE),
lower = t.test(wis)$conf.int[1],
upper = t.test(wis)$conf.int[2],
median = median(wis, na.rm = TRUE),
lq = quantile(wis, 0.25, na.rm = TRUE),
uq = quantile(wis, 0.75, na.rm = TRUE),
se = sd(wis, na.rm = TRUE) / sqrt(sum(!is.na(wis)))
) |>
mutate(across(c(mean, lower, upper,
median, lq, uq), ~ round(., 2)),
Models = paste0(n_models, " (", p_models, "%)"),
Forecasts = paste0(n_forecasts, " (", p_forecasts, "%)"),
"Mean WIS (95% CI)" = paste0(mean, " (",
lower, "-", upper, ")"),
"Median WIS (IQR)" = paste0(median, " (", lq, "-", uq, ")"))
summarise(
n_forecasts = n(),
p_forecasts = round(n() / total_forecasts * 100, 1),
n_models = n_distinct(Model),
p_models = round(n_models / total_models * 100, 1),
mean = mean(wis, na.rm = TRUE),
lower = t.test(wis)$conf.int[1],
upper = t.test(wis)$conf.int[2],
median = median(wis, na.rm = TRUE),
lq = quantile(wis, 0.25, na.rm = TRUE),
uq = quantile(wis, 0.75, na.rm = TRUE),
se = sd(wis, na.rm = TRUE) / sqrt(sum(!is.na(wis)))
) |>
mutate(
across(c(
mean, lower, upper,
median, lq, uq
), ~ round(., 2)),
Models = paste0(n_models, " (", p_models, "%)"),
Forecasts = paste0(n_forecasts, " (", p_forecasts, "%)"),
"Mean WIS (95% CI)" = paste0(
mean, " (",
lower, "-", upper, ")"
),
"Median WIS (IQR)" = paste0(median, " (", lq, "-", uq, ")")
)

if (!is.null(group_var)) {
table <- table |>
rename("Variable" = all_of(group_var)) |>
mutate(group = group_var)
table <- table |>
rename("Variable" = all_of(group_var)) |>
mutate(group = group_var)
}
return(table)
}
Expand All @@ -60,8 +67,10 @@ create_raw_table1 <- function(scores, targets) {
horizon <- table_confint(scores, "Horizon") |>
filter(!is.na(Variable))
trend <- table_confint(scores, "Trend")
bind_rows(overall, method, targets,
horizon, trend)
bind_rows(
overall, method, targets,
horizon, trend
)
}

print_table1 <- function(scores) {
Expand All @@ -75,7 +84,8 @@ print_table1 <- function(scores) {
colnames(table)[!(colnames(table) %in% c("Variable", "group"))] <-
paste(
colnames(table)[!(colnames(table) %in% c("Variable", "group"))],
outcome, sep = "_"
outcome,
sep = "_"
)
return(table)
})
Expand Down Expand Up @@ -118,16 +128,18 @@ print_table1 <- function(scores) {
col.names = str_remove(colnames(table1), "_.*$"),
align = c("l", rep("r", ncol(table1) - 1))
) |>
pack_rows(index = c(" " = 1,
"Method" = 5,
"Number of country targets" = 2,
"Week ahead horizon" = 4,
"3-week trend in incidence" = 3)) |>
pack_rows(index = c(
" " = 1,
"Method" = 5,
"Number of country targets" = 2,
"Week ahead horizon" = 4,
"3-week trend in incidence" = 3
)) |>
add_header_above(headers_to_add)
}

# Plot over time by explanatory variable ----------------------------------
plot_over_time <- function(scores, ensemble, add_plot, show_uncertainty = TRUE){
plot_over_time <- function(scores, ensemble, add_plot, show_uncertainty = TRUE) {
quantiles <- c(0.25, 0.5, 0.75)

plot_over_time_target <- scores |>
Expand All @@ -136,26 +148,36 @@ plot_over_time <- function(scores, ensemble, add_plot, show_uncertainty = TRUE){
reframe(
n = n(),
value = quantile(wis, quantiles, na.rm = TRUE),
quantile = paste0("q", quantiles)) |>
quantile = paste0("q", quantiles)
) |>
pivot_wider(names_from = quantile) |>
# Plot
ggplot(aes(x = target_end_date,
col = CountryTargets,
fill = CountryTargets)) +
ggplot(aes(
x = target_end_date,
col = CountryTargets,
fill = CountryTargets
)) +
geom_line(aes(y = q0.5), alpha = 0.5)
if (show_uncertainty) {
plot_over_time_target <- plot_over_time_target +
geom_ribbon(aes(ymin = q0.25, ymax = q0.75),
alpha = 0.1, col = NA)
alpha = 0.1, col = NA
)
}
plot_over_time_target <- plot_over_time_target +
facet_wrap(~outcome_target, scales = "free_y") +
scale_x_date(date_labels = "%b %Y") +
scale_fill_manual(values = c("Single-country" = "#e7298a",
"Multi-country" = "#e6ab02"),
aesthetics = c("col", "fill")) +
labs(x = NULL, y = "median WIS (log scale)",
fill = NULL, col = NULL) +
scale_fill_manual(
values = c(
"Single-country" = "#e7298a",
"Multi-country" = "#e6ab02"
),
aesthetics = c("col", "fill")
) +
labs(
x = NULL, y = "median WIS (log scale)",
fill = NULL, col = NULL
) +
theme(
legend.position = "bottom",
strip.background = element_blank()
Expand All @@ -167,23 +189,29 @@ plot_over_time <- function(scores, ensemble, add_plot, show_uncertainty = TRUE){
reframe(
n = n(),
value = quantile(wis, quantiles, na.rm = TRUE),
quantile = paste0("q", quantiles)) |>
quantile = paste0("q", quantiles)
) |>
pivot_wider(names_from = quantile) |>
# Plot
ggplot(aes(x = target_end_date, col = Method, fill = Method)) +
geom_line(aes(y = q0.5), alpha = 0.5)
if (show_uncertainty) {
plot_over_time_method <- plot_over_time_method +
geom_ribbon(aes(ymin = q0.25, ymax = q0.75),
alpha = 0.1, col = NA)
alpha = 0.1, col = NA
)
}
plot_over_time_method <- plot_over_time_method +
facet_wrap(~outcome_target, scales = "free_y") +
scale_x_date(date_labels = "%b %Y") +
scale_fill_brewer(aesthetics = c("col", "fill"),
type = "qual", palette = 2) +
labs(x = NULL, y = "median WIS (log scale)",
fill = NULL, col = NULL) +
scale_fill_brewer(
aesthetics = c("col", "fill"),
type = "qual", palette = 2
) +
labs(
x = NULL, y = "median WIS (log scale)",
fill = NULL, col = NULL
) +
theme(
legend.position = "bottom",
strip.background = element_blank()
Expand All @@ -208,8 +236,10 @@ plot_over_time <- function(scores, ensemble, add_plot, show_uncertainty = TRUE){
plot_density <- function(scores) {
plot_conditional_density <- function(scores, group_var) {
scores |>
ggplot(aes(x = log_wis,
col = .data[[group_var]])) +
ggplot(aes(
x = log_wis,
col = .data[[group_var]]
)) +
geom_density() +
facet_wrap(~outcome_target, scales = "free") +
labs(x = "Log of the weighted interval score") +
Expand All @@ -222,33 +252,39 @@ plot_density <- function(scores) {

method <- plot_conditional_density(scores, "Method")
targets <- plot_conditional_density(scores, "CountryTargets")
#affiliated <- plot_conditional_density(scores, "CountryTargetAffiliated")
# affiliated <- plot_conditional_density(scores, "CountryTargetAffiliated")

plot_density_patchwork <- method +
targets +
#affiliated +
# affiliated +
plot_layout(ncol = 1) +
plot_annotation(tag_levels = "A")
return(plot_density_patchwork)
}

# Ridge plot by model --------------------
plot_ridges <- function(scores){
plot_ridges <- function(scores) {
scores |>
group_by(Model) |>
mutate(median_score = median(wis, na.rm = TRUE),
mutate(
median_score = median(wis, na.rm = TRUE),
lq = quantile(wis, 0.25, na.rm = TRUE),
uq = quantile(wis, 0.75, na.rm = TRUE)) |>
uq = quantile(wis, 0.75, na.rm = TRUE)
) |>
ungroup() |>
mutate(Model = fct_reorder(Model, median_score)) |>
filter(wis >= lq & wis <= uq) |>
# Plot
ggplot(aes(x = wis, y = Model, fill = stat(x))) +
geom_density_ridges_gradient(scale = 1.5,
rel_min_height = 0.01,
quantile_lines = TRUE, quantiles = 2) +
scale_fill_viridis_c(name = "Interval score",
option = "C", direction = -1) +
geom_density_ridges_gradient(
scale = 1.5,
rel_min_height = 0.01,
quantile_lines = TRUE, quantiles = 2
) +
scale_fill_viridis_c(
name = "Interval score",
option = "C", direction = -1
) +
theme_ridges() +
labs(x = "Interval score IQR", y = "Model") +
theme(legend.position = "none")
Expand All @@ -263,16 +299,21 @@ table_targets <- function(scores) {
summarise(target_count = n(), .groups = "drop") |>
ungroup() |>
group_by(Model, outcome_target) |>
summarise(CountryTargets = all(target_count <= 2),
min_targets = min(target_count),
max_targets = max(target_count),
mean = mean(target_count),
median = median(target_count),
consistent = min_targets==max_targets) |>
summarise(
CountryTargets = all(target_count <= 2),
min_targets = min(target_count),
max_targets = max(target_count),
mean = mean(target_count),
median = median(target_count),
consistent = min_targets == max_targets
) |>
mutate(CountryTargets = factor(CountryTargets,
levels = c(TRUE, FALSE),
labels = c("Single-country",
"Multi-country")))
levels = c(TRUE, FALSE),
labels = c(
"Single-country",
"Multi-country"
)
))
return(table_targets)
}

Expand All @@ -297,7 +338,8 @@ table_metadata <- function(scores) {
pivot_wider(
names_from = "outcome_target",
values_from = "Forecasts",
values_fill = "") |>
values_fill = ""
) |>
rename("Country Targets" = CountryTargets) |>
arrange(Model)
return(metadata_table)
Expand All @@ -311,19 +353,28 @@ plot_linerange <- function(group_var) {
reframe(
n = n(),
value = quantile(wis, quantiles),
quantile = paste0("q", quantiles)) |>
quantile = paste0("q", quantiles)
) |>
pivot_wider(names_from = quantile) |>
# Plot
ggplot(aes(y = .data[[group_var]], col = Horizon, fill = Horizon)) +
geom_point(aes(x = q0.5), alpha = 0.8,
position = position_dodge(width = 1)) +
geom_linerange(aes(xmin = q0.25, xmax = q0.75), linewidth = 4,
alpha = 0.5, position = position_dodge(width = 1)) +
geom_linerange(aes(xmin = q0.01, xmax = q0.99), linewidth = 4,
alpha = 0.2, position = position_dodge(width = 1)) +
labs(y = NULL, x = NULL,
col = "Week ahead forecast Horizon",
fill = "Week ahead forecast Horizon") +
geom_point(aes(x = q0.5),
alpha = 0.8,
position = position_dodge(width = 1)
) +
geom_linerange(aes(xmin = q0.25, xmax = q0.75),
linewidth = 4,
alpha = 0.5, position = position_dodge(width = 1)
) +
geom_linerange(aes(xmin = q0.01, xmax = q0.99),
linewidth = 4,
alpha = 0.2, position = position_dodge(width = 1)
) +
labs(
y = NULL, x = NULL,
col = "Week ahead forecast Horizon",
fill = "Week ahead forecast Horizon"
) +
scale_color_viridis_d(direction = 1) +
theme(legend.position = "bottom")
return(plot)
Expand Down Expand Up @@ -386,7 +437,7 @@ trends_plot <- function(scores) {
geom_line() +
scale_colour_brewer(palette = "Set2", na.value = "grey") +
theme(legend.position = "bottom") +
facet_wrap(~ location, scales = "free_y") +
facet_wrap(~location, scales = "free_y") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
xlab("")
return(p)
Expand Down
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