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03c-SelectedPlots.R
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03c-SelectedPlots.R
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## -- Setup paths ----------------
ROOT <- Sys.getenv("ROOT")
INC <- Sys.getenv("DataInc")
INC_ROOT <- file.path(ROOT, "01-incidence")
PDATA <- file.path(INC_ROOT, "02-Results", "PlotData")
OUT <- file.path(INC_ROOT, "03-Outputs", "Plots")
if (!dir.exists(OUT)) dir.create(OUT)
## -- Source script files ----
source(file.path(ROOT, "00-common", "00-CodeMap.R"))
source(file.path(ROOT, "00-common", "00-Functions.R"))
## -- Load packages --------
Fn$quietly_load(c(
"tidytable", "data.table", "stringr",
"purrr", "forcats",
"ggplot2", "ggh4x"
))
## -- Export function --------------------
export_plots <- function(plot_obj, base_filename, width, height, path = OUT, unit = "in", formats = c("pdf", "jpeg", "svg", "png"), ...) {
walk(formats, function(frmt){
exp_path <- here::here(path, toupper(frmt))
if (!dir.exists(exp_path)) dir.create(exp_path, recursive = TRUE)
fname = here::here(exp_path, glue::glue(base_filename, frmt, .sep = "."))
cat(glue::glue("Saving: {fname}."), "\n")
ggplot2::ggsave(
plot_obj,
filename = fname,
device = if (frmt == "pdf") cairo_pdf else if (frmt == "svg") svglite::svglite else NULL,
width = width,
height = height,
units = unit,
dpi = 300,
...
)
})
}
## -- Plots ----
SelectedPlots <- new.env()
evalq(
{
Spline <- new.env()
AgeAdjusted <- new.env()
AgeSpecific <- new.env()
APC <- new.env()
PropPlot <- new.env()
},
SelectedPlots
)
## -- Theme adjustment ------------------------------
ggplot2::theme_set(ggthemes::theme_few())
theme_update(panel.grid = element_line(color = "#f0f0f0"))
## -- Spline Plots ----
evalq({
## -- By: Sex, Tstage --------
evalq({
BySexTstage <- function(filter = NULL, log = FALSE, na.rm = TRUE, ...) {
PlotData <- readRDS(here::here(PDATA, "Spline-BySexTstage.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# PlotData <- PlotData$Spline$BySexTstage(filter = filter, logY = logY)
plot <- Pn$adj_rate_plot(
data = PlotData,
type = "spline",
group_var = "Tstage",
col_var = "Sex",
log = log,
scales = "free_y",
independent = "y",
se = FALSE,
...
)
suppressMessages({
plot <- plot + scale_y_continuous(
breaks = scales::breaks_width(5),
limits = c(0, 25)
)
})
return(plot)
}
}, Spline)
## -- By: Sex, Thinner Cases ----
evalq({
BySexThinStage <- function(filter = NULL, log = FALSE, na.rm = TRUE, ...) {
PlotData <- readRDS(here::here(PDATA, "Spline-BySexThinStage.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# PlotData <- PlotData$Spline$BySexThinStage(filter = filter, logY = logY)
Pn$adj_rate_plot(
data = PlotData,
type = "spline",
group_var = "Tstage",
col_var = "Sex",
row_var = "Case",
lrow = 1,
scales = "free_y",
independent = "y",
se = FALSE,
log = log,
...
)
}
}, Spline)
## -- By: Sex, TSubStage ----
evalq({
BySexTSubStage <- function(filter = NULL, log = FALSE, na.rm = TRUE, ...) {
PlotData <- readRDS(here::here(PDATA, "Spline-BySexTSubStage.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# PlotData <- PlotData$Spline$BySexTSubStage(filter = filter, logY = logY)
Pn$adj_rate_plot(
data = PlotData,
type = "spline",
group_var = "Tstage",
col_var = "Sex",
log = log,
se = FALSE,
...
)
}
}, Spline)
## -- By: Sex, Tstage, Anatomic Site ------------------------------
evalq({
BySexTstageSite <- function(filter = NULL, log = FALSE, na.rm = TRUE, ...) {
PlotData <- readRDS(here::here(PDATA, "Spline-BySexTstageSite.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# Change Head and neck to Head/neck
levels(PlotData$AnatomicSite)[1] <- "Head/neck"
# PlotData <- PlotData$Spline$BySexTstageSite(filter = filter, logY = logY)
plot <- Pn$adj_rate_plot(
data = PlotData[Sex != "Sum"],
type = "spline",
group_var = "Tstage",
col_var = "AnatomicSite",
row_var = "Sex",
lrow = 1,
scales = "free_y",
independent = "y",
log = log,
subset = "AnatomicSite != 'Other'",
se = FALSE,
...
)
suppressMessages({
plot <- plot +
ggh4x::facetted_pos_scales(
y = list(
AnatomicSite == "Trunk" ~ scale_y_continuous(
breaks = scales::breaks_width(5),
limits = c(0, 15)
),
AnatomicSite == "Head and neck" ~ scale_y_continuous(
breaks = scales::breaks_width(1),
limits = c(0, 3)
),
AnatomicSite == "Upper limbs" ~ scale_y_continuous(
breaks = scales::breaks_width(1),
limits = c(0, 4)
),
AnatomicSite == "Lower limbs" ~ scale_y_continuous(
breaks = scales::breaks_width(2),
limits = c(0, 8)
)
)
)
})
return(plot)
}
}, Spline)
## -- By: Sex, Tstage, Melanoma Type ------------------------------
evalq({
BySexTstageType <- function(filter = NULL, log = FALSE, na.rm = TRUE, ...) {
PlotData <- readRDS(here::here(PDATA, "Spline-BySexTstageType.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# PlotData <- PlotData$Spline$BySexTstageType(filter = filter, logY = logY)
plot <- Pn$adj_rate_plot(
data = PlotData[Sex != "Sum"],
type = "spline",
group_var = "Tstage",
col_var = "MelanomaType",
row_var = "Sex",
lrow = 1,
scales = "free_y",
independent = "y",
log = log,
subset = "MelanomaType != 'Other'",
se = FALSE,
...
)
suppressMessages({
plot <- plot +
ggh4x::facetted_pos_scales(
y = list(
MelanomaType == "Superficial spreading" ~ scale_y_continuous(
breaks = scales::breaks_width(5),
limits = c(0, 25)
),
MelanomaType == "Nodular" ~ scale_y_continuous(
breaks = scales::breaks_width(1),
limits = c(0, 4)
),
MelanomaType == "Lentigo maligna" ~ scale_y_continuous(
breaks = scales::breaks_width(0.5),
limits = c(0, 1.5)
)
)
)
})
return(plot)
}
}, Spline)
## -- By: Sex, Tstage, Health Region ------------------------------
evalq({
BySexTstageRegion <- function(filter = NULL, log = FALSE, na.rm = TRUE, ...) {
PlotData <- readRDS(here::here(PDATA, "Spline-BySexTstageRegion.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# PlotData <- PlotData$Spline$BySexTstageRegion(filter = filter, logY = logY)
PlotData[, HealthRegion := CodeMap$factor_map(HealthRegion, "HealthRegion")]
plot <- Pn$adj_rate_plot(
data = PlotData[Sex != "Sum"],
type = "spline",
group_var = "Tstage",
col_var = "HealthRegion",
row_var = "Sex",
lrow = 1,
scales = "free_y",
independent = "y",
log = log,
se = FALSE,
...
)
suppressMessages({
plot <- plot +
ggh4x::facetted_pos_scales(
y = list(
HealthRegion == "Northern" ~ scale_y_continuous(
breaks = scales::breaks_width(1),
limits = c(0, 3)
)
)
)
})
return(plot)
}
}, Spline)
## -- By: Sex, Tstage, Season ------------------------------
evalq({
BySexTstageSeason <- function(filter = NULL, log = FALSE, na.rm = TRUE, ...) {
PlotData <- readRDS(here::here(PDATA, "Spline-BySexTstageSeason.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# PlotData <- PlotData$Spline$BySexTstageSeason(filter = filter, logY = logY)
PlotData[, Season := CodeMap$factor_map(Season, "Season")]
plot <- Pn$adj_rate_plot(
data = PlotData[Sex != "Sum"],
type = "spline",
group_var = "Tstage",
col_var = "Season",
row_var = "Sex",
lrow = 1,
log = log,
se = FALSE,
...
)
suppressMessages({
plot <- plot + scale_y_continuous(breaks = scales::breaks_width(2), limits = c(0, 8))
})
return(plot)
}
}, Spline)
## -- By: Sex, TSubStage for recent periods ----------------
evalq({
BySexTSubStageRecent <- function(filter = NULL, log = FALSE, na.rm = TRUE, ...) {
PlotData <- readRDS(here::here(PDATA, "Spline-BySexTSubStageRecent.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
browser()
tstage_lbl <- PlotData %>%
filter(Imp == "Pooled") %>%
summarize(
N = round(sum(N) / max(imp)),
.by = "Tstage"
) %>% mutate(Tstage = CodeMap$get_Tstage_label(Tstage)) %>%
xtabs(N ~ Tstage, data = .) %>%
Fn$label_vec()
sex_lbl <- PlotData %>%
filter(Imp == "Pooled") %>%
summarize(
N = round(sum(N) / max(imp)),
.by = "Sex"
) %>%
xtabs(N ~ Sex, data = .) %>%
Fn$label_vec()
# PlotData <- PlotData$Spline$BySexTSubStage(filter = filter, logY = logY)
PlotData[!is.na(Tstage)] %>%
mutate(TSubStageCode = case_when(
grepl("a", TSubStage) ~ "a",
grepl("b", TSubStage) ~ "b",
TRUE ~ "–"
)) %>%
ggplot(aes(DiagYear)) +
facet_grid(
cols = vars(Tstage), rows = vars(Sex),
labeller = labeller(
Tstage = \(x) tstage_lbl[CodeMap$get_Tstage_label(x)],
Sex = sex_lbl
)
) +
geom_ribbon(
data = ~subset(.x, Imp == "Pooled"),
aes(ymin = lci_spl, ymax = uci_spl, fill = TSubStage, group = TSubStage),
alpha = 0.15
) +
geom_line(
data = ~subset(.x, Imp == "Complete" & !is.na(TSubStage)),
alpha = 0.05,
aes(y = adj.rate, color = TSubStage, group = TSubStage, linetype = TSubStage),
show.legend = FALSE
) +
geom_point(
data = ~subset(.x, Imp == "Complete" & !is.na(TSubStage)),
shape = 21,
fill = "whitesmoke",
alpha = 0.05,
aes(y = adj.rate, color = TSubStage),
show.legend = FALSE
) +
geom_line(
data = ~subset(.x, Imp == "Pooled"),
aes(y = adj.rate, color = TSubStage, group = TSubStage, linetype = TSubStage),
alpha = 0.15
) +
geom_line(
data = ~subset(.x, Imp == "Pooled"),
aes(y = adj.rate_spl, color = TSubStage, group = TSubStage, linetype = TSubStage)
) +
geom_point(
data = ~subset(.x, Imp == "Pooled"),
shape = 21,
fill = "whitesmoke",
aes(y = adj.rate, color = TSubStage),
) +
scale_x_continuous(
breaks = seq(2008, 2020, 2)
) +
scale_y_continuous(
breaks = scales::breaks_extended(10)
) +
scale_color_manual(
aesthetics = c("color", "fill"),
values = rep(ggthemes::stata_pal()(4), each = 3),
breaks = apply(CJ(paste0("T", 1:4), c("", "a", "b")), 1, paste, collapse = "")
) +
scale_linetype_manual(
values = rep(c(3, 1, 2), 4),
breaks = apply(CJ(paste0("T", 1:4), c("", "a", "b")), 1, paste, collapse = "")
) +
ggthemes::theme_few() +
theme(
legend.position = "bottom",
legend.justification = "left",
legend.key.width = unit(1, "cm"),
panel.grid = element_line(color = "#f0f0f0")
) +
guides(
color = guide_legend(nrow = 1, title.position = "top"),
fill = guide_legend(nrow = 1, title.position = "top")
) +
labs(
x = "Year of diagnosis",
y = "Age-standardized incidence rate\n per 100,000 person-years",
color = "Thickness sub-category",
fill = "Thickness sub-category",
linetype = "Thickness sub-category"
# caption = "NOTE: Ulceration missing were coded as no ulceration"
)
}
}, Spline)
}, SelectedPlots)
## -- Export Spline Plots ----------------
spl_by_sex_tstage <- SelectedPlots$Spline$BySexTstage()
# export_plots(spl_by_sex_tstage, "Spline-BySexTstage", 11, 4.5)
export_plots(spl_by_sex_tstage, "Spline-BySexTstage", 7, 3, scale = 1.5)
spl_by_sex_tstage_site <- SelectedPlots$Spline$BySexTstageSite()
export_plots(spl_by_sex_tstage_site, "Spline-BySexTstageSite", 7, 4, scale = 1.5)
spl_by_sex_tstage_type <- SelectedPlots$Spline$BySexTstageType()
export_plots(spl_by_sex_tstage_type, "Spline-BySexTstageType", 6, 4, scale = 1.5)
spl_by_sex_tstage_region <- SelectedPlots$Spline$BySexTstageRegion()
export_plots(spl_by_sex_tstage_region, "Spline-BySexTstageRegion", 7, 4, scale = 1.5)
spl_by_sex_tstage_season <- SelectedPlots$Spline$BySexTstageSeason()
export_plots(spl_by_sex_tstage_season, "Spline-BySexTstageSeason", 7, 4, scale = 1.5)
spl_by_sex_tsubstage <- SelectedPlots$Spline$BySexTSubStageRecent()
export_plots(spl_by_sex_tsubstage, "Spline-BySexTsubStage", 7, 4, scale = 1.5)
## -- AgeAdjusted Plots ------------------------------
evalq({
## -- By: Sex, Tstage ------------------------------
evalq({
BySexTstage <- function(segmented = FALSE, filter = NULL, log = FALSE, na.rm = TRUE, ...) {
PlotData <- readRDS(here::here(PDATA, "AgeAdj-BySexTstage.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
type <- if (segmented) "segmented" else "age-adjusted"
# PlotData <- PlotData$AgeAdjusted$BySexTstage(
# segmented = segmented,
# filter = filter,
# logY = logY
# )
PlotData[, Size := uci - lci]
PlotData %>%
Pn$adj_rate_plot(
type = type,
col_var = "Sex",
# size_var = "Size",
log = log,
...
)
}
}, AgeAdjusted)
## -- By: Sex, Thinner Cases ----
evalq({
BySexThinStage <- function(segmented = FALSE, filter = NULL, log = FALSE, na.rm = TRUE, ...) {
type <- if (segmented) "segmented" else "age-adjusted"
PlotData <- readRDS(here::here(PDATA, "AgeAdj-BySexThinStage.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# PlotData <- PlotData$AgeAdjusted$BySexThinStage(
# segmented = segmented,
# filter = filter,
# logY = logY
# )
PlotData[, Size := uci - lci]
PlotData %>% Pn$adj_rate_plot(
type = type,
col_var = "Sex",
row_var = "Case",
# size_var = "Size",
log = log,
scales = "free_y",
independent = "y",
...
)
}
}, AgeAdjusted)
## -- By: Sex, Tstage, Anatomic Site ------------------------------
evalq({
BySexTstageSite <- function(segmented = FALSE, filter = NULL, log = FALSE, na.rm = TRUE, ...) {
type <- if (segmented) "segmented" else "age-adjusted"
PlotData <- readRDS(here::here(PDATA, "AgeAdj-BySexTstageSite.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# Change Head and neck to Head/neck
levels(PlotData$AnatomicSite)[1] <- "Head/neck"
# PlotData <- PlotData$AgeAdjusted$BySexTstageSite(
# segmented = segmented,
# filter = filter,
# logY = logY
# )
PlotData[, Size := uci - lci]
PlotData[!is.na(Tstage) & Sex != "Sum"] %>%
Pn$adj_rate_plot(
type = type,
col_var = "AnatomicSite",
row_var = "Sex",
# size_var = "Size",
log = log,
scales = "free_y",
independent = "y",
...
)
}
}, AgeAdjusted)
## -- By: Sex, Tstage, Melanoma Type ------------------------------
evalq({
BySexTstageType <- function(segmented = FALSE, filter = NULL, log = FALSE, na.rm = TRUE, ...) {
type <- if (segmented) "segmented" else "age-adjusted"
PlotData <- readRDS(here::here(PDATA, "AgeAdj-BySexTstageType.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# PlotData <- PlotData$AgeAdjusted$BySexTstageType(
# segmented = segmented,
# filter = filter,
# logY = logY
# )
PlotData[, Size := uci - lci]
PlotData[!is.na(Tstage) & Sex != "Sum "] %>%
Pn$adj_rate_plot(
type = type,
col_var = "MelanomaType",
row_var = "Sex",
# size_var = "Size",
log = log,
scales = "free_y",
independent = "y",
...
)
}
}, AgeAdjusted)
## -- By: Sex, Tstage, Health Region ------------------------------
evalq({
BySexTstageRegion <- function(segmented = FALSE, filter = NULL, na.rm = TRUE, log = FALSE, ...) {
type <- if (segmented) "segmented" else "age-adjusted"
PlotData <- readRDS(here::here(PDATA, "AgeAdj-BySexTstageRegion.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# PlotData <- PlotData$AgeAdjusted$BySexTstageRegion(
# segmented = segmented,
# filter = filter,
# logY = logY
# )
PlotData[, Size := uci - lci]
PlotData[, HealthRegion := CodeMap$factor_map(HealthRegion, "HealthRegion")]
plot <- Pn$adj_rate_plot(
data = PlotData[Sex != "Sum" & !is.na(Tstage)],
type = type,
group_var = "Tstage",
col_var = "HealthRegion",
row_var = "Sex",
# size_var = "Size",
scales = "free_y",
independent = "y",
log = log,
...
)
suppressMessages({
plot <- plot +
ggh4x::facetted_pos_scales(
y = list(
HealthRegion == "Northern" ~ scale_y_continuous(
breaks = scales::breaks_width(1),
limits = c(0, 3)
)
)
)
})
return(plot)
}
}, AgeAdjusted)
## -- By: Sex, Tstage, Season ------------------------------
evalq({
BySexTstageSeason <- function(segmented = FALSE, filter = NULL, na.rm = TRUE, log = FALSE, ...) {
type <- if (segmented) "segmented" else "age-adjusted"
PlotData <- readRDS(here::here(PDATA, "AgeAdj-BySexTstageSeason.rds"))
if (!is.null(filter)) PlotData <- PlotData[eval(parse(text = filter))]
if (na.rm) PlotData <- na.omit(PlotData)
# PlotData <- PlotData$AgeAdjusted$BySexTstageSeason(
# segmented = segmented,
# filter = filter,
# logY = logY
# )
PlotData[, Size := uci - lci]
PlotData[, Season := CodeMap$factor_map(Season, "Season")]
plot <- Pn$adj_rate_plot(
data = PlotData[Sex != "Sum" & !is.na(Tstage)],
type = type,
group_var = "Tstage",
col_var = "Season",
row_var = "Sex",
# size_var = "Size",
log = log,
...
)
return(plot)
}
}, AgeAdjusted)
}, SelectedPlots)
## -- AgeSpecific Plots ----------------
evalq({
## -- By: Age, Sex and Tstage ----------------
evalq({
ByAgeSexTstage <- function() {
PlotData <- readRDS(here::here(PDATA, "ASP-Data-Sex-Tstage.rds"))
plot_data <- PlotData %>%
tidytable::select(-7) %>%
mutate(imp = as.numeric(imp)) %>%
filter(imp != 0)
sex_lbl <- plot_data %>%
summarize(N = round(sum(N) / max(imp)), .by = "Sex") %>%
xtabs(N ~ Sex, data = .) %>%
Fn$label_vec()
plot_data <- plot_data %>%
summarize(
N = round(sum(N) / max(imp)),
est = mean(est),
lower = mean(lower),
upper = mean(upper),
.by = c(AgeGroup5, Sex, Tstage)
)
ggplot(plot_data, aes(AgeGroup5, est, ymin = lower, ymax = upper)) +
geom_ribbon(
aes(group = Tstage, fill = Tstage), alpha = 0.15
) +
geom_line(
aes(group = Tstage, color = Tstage)
) +
geom_point(
shape = 21, fill = "whitesmoke", aes(color = Tstage)
) +
facet_grid(cols = vars(Sex), labeller = labeller(Sex = sex_lbl)) +
scale_x_discrete(
labels = \(x) gsub("-", "–", x)
) +
scale_y_continuous(
breaks = scales::breaks_width(5)
) +
ggthemes::scale_color_stata(
name = "T category",
label = CodeMap$get_Tstage_label
) +
ggthemes::scale_fill_stata(
name = "T category",
label = CodeMap$get_Tstage_label
) +
ggthemes::theme_few() +
theme(
legend.position = "bottom",
legend.justification = "left",
panel.grid = element_line(color = "#f0f0f0"),
axis.text.x = element_text(angle = 45, hjust = 1)
) +
labs(
x = "Age at diagnosis",
y = "Age-specific incidence rate\n per 100,000 person-year"
)
}
}, AgeSpecific)
}, SelectedPlots)
## -- Export AgeSpecific Plots ----------------
asp_by_sex_tstage <- SelectedPlots$AgeSpecific$ByAgeSexTstage()
export_plots(asp_by_sex_tstage, "ASP-BySexTstage", 7, 4, scale = 1.5)
## -- APC Plots------------------------------
evalq({
## -- APC: Cohort by Age ----
evalq({
CohortByAge <- function(BySex = FALSE, Group = NULL, filter = "Age > 20", logY = TRUE, age_var = "Age", show_label = FALSE, na.rm = TRUE, ...) {
fname <- glue::glue("APC")
if (BySex) fname <- glue::glue(fname, "-Sex")
if (!is.null(Group)) fname <- glue::glue(fname, "-{Group}")
apc_data <- readRDS(here::here(PDATA, glue::glue("{fname}.rds")))
if (!is.null(filter)) apc_data <- apc_data[eval(parse(text = filter))]
if (na.rm) apc_data <- na.omit(apc_data)
# apc_data <- PlotData$APC(
# BySex = BySex,
# Group = Group,
# filter = filter
# )
if (!missing(Group)) {
if (Group == "Tstage") {
apc_data[, Tstage := forcats::fct_relabel(Tstage, CodeMap$get_Tstage_label)]
}
}
apc_data <- apc_data[est > 0 & Imp == "Pooled"]
apc_data[, c(age_var) := paste(
get(age_var), get(age_var) + 4,
sep = "-"
)]
Plot <- apc_data %>%
ggplot(aes(
x = Cohort,
y = est,
group = get(age_var),
color = as.factor(get(age_var))
)) +
geom_line(na.rm = TRUE) +
geom_point(
size = rel(0.8),
shape = 21,
fill = "whitesmoke",
na.rm = TRUE
)
if (show_label) {
Plot <- Plot +
ggh4x::geom_text_aimed(
aes(label = AgeCat),
data = ~subset(.x, Period == max(Period)),
hjust = -0.2,
check_overlap = TRUE,
color = "grey40",
size = if (!is.null(Group)) rel(3) else rel(4)
)
}
Plot <- Plot +
scale_size_continuous(range = c(0.01, 2), guide = "none") +
scale_x_continuous(
breaks = scales::breaks_extended(ifelse(!is.null(Group), 5, 10))
)
Plot <- Plot + scale_color_viridis_d(
"Age group",
na.value = "grey40",
direction = -1,
end = 0.9,
labels = \(x) gsub("^85.*", "85+", x), # c(first(x, -1), gsub("-\\d+", "+", last(x))),
guide = guide_legend(
reverse = TRUE,
title.position = "top",
title.hjust = 0.5,
ncol = if (BySex) 1 else NULL,
nrow = if (BySex) NULL else 2
)
)
Plot <- Plot +
theme(
legend.position = ifelse(BySex, "right", "bottom"),
legend.justification = if (is.null(Group) & !BySex) NULL else c(1, 0),
plot.caption = element_text(hjust = 0, size = rel(1)),
plot.caption.position = "plot"
) +
labs(
x = "Birth year",
y = "Age-specific incidence rate\nper 100,000 person-years"
)
if (logY) {
Plot <- Plot + scale_y_log10(breaks = scales::breaks_log(10))
} else {
Plot <- Plot + scale_y_continuous(breaks = scales::breaks_extended(10))
}
if (BySex | !is.null(Group)) {
row_label <- col_label <- NULL
if (BySex) {
row_label <- apc_data[, Fn$label_vec(round(xtabs(N ~ Sex)/unique(imp)))]
}
if (!is.null(Group)) {
col_label <- apc_data[, Fn$label_vec(round(xtabs(N ~ get(Group))/unique(imp)))]
} else {
col_label <- apc_data[, Fn$label_vec(round(xtabs(N ~ Sex)/unique(imp)))]
}
facet_label <- labeller(
.rows = row_label,
.cols = col_label
)
}
if (!is.null(Group) & BySex) {
Plot <- Plot + facet_grid(cols = vars(get(Group)), rows = vars(Sex), labeller = facet_label, ...)
} else if (is.null(Group) & BySex) {
Plot <- Plot + facet_grid(cols = vars(Sex), labeller = facet_label, ...)
} else if (!is.null(Group) & !BySex) {
Plot <- Plot + facet_grid(cols = vars(get(Group)), labeller = facet_label, ...)
}
return(Plot)
}
}, APC)
## -- APC: Period by Age ------------------------------
evalq({
PeriodByAge <- function(BySex = FALSE, Group = NULL, filter = "Age > 20", logY = TRUE, age_var = "Age", show_label = FALSE, na.rm = TRUE, ...) {
fname <- glue::glue("APC")
if (BySex) fname <- glue::glue(fname, "-Sex")
if (!is.null(Group)) fname <- glue::glue(fname, "-{Group}")
apc_data <- readRDS(here::here(PDATA, glue::glue("{fname}.rds")))
if (!is.null(filter)) apc_data <- apc_data[eval(parse(text = filter))]
if (na.rm) apc_data <- na.omit(apc_data)
# apc_data <- PlotData$APC(
# BySex = BySex,
# Group = Group,
# filter = filter
# )
if (!missing(Group)) {
if (Group == "Tstage") {
apc_data[, Tstage := forcats::fct_relabel(Tstage, CodeMap$get_Tstage_label)]
}
}
apc_data <- apc_data[est > 0 & Imp == "Pooled"]
apc_data[, c(age_var) := paste(
get(age_var), get(age_var) + 4,
sep = "-"
)]
Plot <- apc_data %>%
ggplot(aes(Period, est, group = get(age_var), color = get(age_var))) +
geom_line(na.rm = TRUE) +
geom_point(
shape = 21,
fill = "whitesmoke",
na.rm = TRUE,
size = rel(0.8)
)
if (show_label) {
Plot <- Plot +
ggh4x::geom_text_aimed(
aes(label = AgeCat),
data = ~subset(.x, Period == max(Period)),
hjust = -0.2,
check_overlap = TRUE,
color = "grey40",
size = if (!is.null(Group)) rel(3) else rel(4)
)
}
Plot <- Plot +
scale_x_continuous(
breaks = scales::breaks_extended(ifelse(!is.null(Group), 5, 10)),
limits = c(1983, 2020)
)
Plot <- Plot + scale_color_viridis_d(
"Age group",
na.value = "grey40",
direction = -1,
labels = \(x) gsub("^85.*", "85+", x), # c(first(x, -1), gsub("-\\d+", "+", last(x))),
end = 0.9,
guide = guide_legend(
reverse = TRUE,
title.position = "top",
title.hjust = 0.5,
ncol = if (BySex) 1 else NULL,
nrow = if (BySex) NULL else 2
)
)
Plot <- Plot +
theme(
legend.position = ifelse(BySex, "right", "bottom"),
legend.justification = c(1, 0),
plot.caption = element_text(hjust = 0, size = rel(1)),
plot.caption.position = "plot"
) +
labs(
x = "Calendar year at diagnosis",
y = "Age-specific incidence rate\nper 100,000 person-years"
)
if (logY) {
Plot <- Plot + scale_y_log10(breaks = scales::breaks_log(10))
} else {
Plot <- Plot + scale_y_continuous(breaks = scales::breaks_extended(10))
}
if (BySex | !is.null(Group)) {
row_label <- col_label <- NULL
if (BySex) {
row_label <- apc_data[, Fn$label_vec(
round(xtabs(N ~ Sex)/unique(imp))
)]
}
if (!is.null(Group)) {
col_label <- apc_data[, Fn$label_vec(
round(xtabs(N ~ get(Group))/unique(imp))
)]
} else {
col_label <- apc_data[, Fn$label_vec(
round(xtabs(N ~ Sex)/unique(imp))
)]
}
facet_label <- labeller(
.rows = row_label,
.cols = col_label
)
}
if (!is.null(Group) & BySex) {
Plot <- Plot + facet_grid(
cols = vars(get(Group)),
rows = vars(Sex),
labeller = facet_label,
...
)
} else if (is.null(Group) & BySex) {
Plot <- Plot + facet_grid(
cols = vars(Sex),
labeller = facet_label,
...
)
} else if (!is.null(Group) & !BySex) {
Plot <- Plot + facet_grid(
cols = vars(get(Group)),
labeller = facet_label,
...
)
}
return(Plot)
}
}, APC)
## -- APC: Age by Cohort ------------------------------
evalq({
AgeByCohort <- function(BySex = FALSE, Group = NULL, filter = "Age > 20", logY = TRUE, cohort_var = "Cohort", show_label = FALSE, na.rm = TRUE, ...) {
fname <- glue::glue("APC")
if (BySex) fname <- glue::glue(fname, "-Sex")
if (!is.null(Group)) fname <- glue::glue(fname, "-{Group}")
apc_data <- readRDS(here::here(PDATA, glue::glue("{fname}.rds")))
if (!is.null(filter)) apc_data <- apc_data[eval(parse(text = filter))]
if (na.rm) apc_data <- na.omit(apc_data)
# apc_data <- PlotData$APC(
# BySex = BySex,
# Group = Group,
# filter = filter
# )
if (!missing(Group)) {
if (Group == "Tstage" & !is.null(Group)) {
apc_data[, Tstage := forcats::fct_relabel(Tstage, CodeMap$get_Tstage_label)]
}
}
apc_data <- apc_data[est > 0 & Imp == "Pooled"]
Plot <- apc_data %>%
ggplot(aes(Age, est, group = get(cohort_var), color = get(cohort_var))) +
geom_line(na.rm = TRUE) +
geom_point(
shape = 21,
fill = "whitesmoke",
size = rel(0.8),
na.rm = TRUE
)
if (show_label) {
Plot <- Plot +
ggh4x::geom_text_aimed(
aes(label = CohortCat),
data = ~subset(.x, Age == max(Age)),
hjust = -0.2,
check_overlap = TRUE,
color = "grey40",
size = if (!is.null(Group)) rel(3) else rel(4)
)
}
Plot <- Plot +
scale_size_continuous(range = c(0.2, 2), guide = "none") +
scale_x_continuous(
breaks = scales::breaks_extended(ifelse(!is.null(Group), 5, 10)),
limits = c(apc_data[, min(Age)], apc_data[, max(Age) + if (show_label) 10 else 0])
)
if (is.numeric(apc_data[[cohort_var]])) {
Plot <- Plot + scale_color_viridis_b(
"Birth year",
aesthetics = c("color", "fill"),
na.value = "grey40",
breaks = scales::breaks_extended(n = 10),
direction = 1,
end = 0.9,
guide = guide_colorbar(
barwidth = if (BySex) unit(0.02, "npc") else unit(0.7, "npc"),
barheight = if (BySex) unit(0.7, "npc") else unit(0.02, "npc"),
title.position = "top",