Plot an hv_trends object

Draws a LOESS smooth overlaid with per-x-value summary-statistic points (mean or median computed at construction time).

Usage

# S3 method for class 'hv_trends'
plot(
  x,
  smoother = "loess",
  span = 0.75,
  se = FALSE,
  point_size = 2.5,
  point_shape = 19L,
  alpha = 0.2,
  ...
)

Arguments

x: An hv_trends object.
smoother: Smoothing method passed to geom_smooth. Default "loess".
span: Span for LOESS smoother. Default 0.75.
se: Logical; show confidence ribbon around smooth? Default FALSE.
point_size: Size of the annual summary points. Default 2.5.
point_shape: Integer shape code for the summary points (single-group only; ignored when group_col is set — use scale_shape_manual() instead). Default 19L.
alpha: Transparency of the smooth ribbon when se = TRUE. Default 0.2.
...: Ignored; present for S3 consistency.

Value

A bare ggplot object; compose with + to add scales, axis limits, labels, and hv_theme.

Examples

# --- tp.rp.trends.sas: single continuous outcome, 1968-2000 ---------------
one <- sample_trends_data(n = 600, year_range = c(1968L, 2000L),
                          groups = NULL)
plot(hv_trends(one, group_col = NULL)) +
  ggplot2::scale_x_continuous(limits = c(1968, 2000),
                              breaks = seq(1968, 2000, 4)) +
  ggplot2::scale_y_continuous(limits = c(0, 10),
                              breaks = seq(0, 10, 2)) +
  ggplot2::labs(x = "Year", y = "Cases/year") +
  hv_theme("poster")
#> Warning: Removed 600 rows containing non-finite outside the scale range
#> (`stat_smooth()`).
#> Warning: Removed 33 rows containing missing values or values outside the scale range
#> (`geom_point()`).


# --- tp.lp.trends.sas: binary % outcomes, 1970-2000 by 10 ----------------
dta_lp <- sample_trends_data(
  n = 800, year_range = c(1970L, 2000L),
  groups = c("Shock %", "Pre-op IABP %", "Inotropes %"))
plot(hv_trends(dta_lp)) +
  ggplot2::scale_colour_manual(
    values = c("Shock %" = "steelblue", "Pre-op IABP %" = "firebrick",
               "Inotropes %" = "forestgreen"), name = NULL) +
  ggplot2::scale_x_continuous(limits = c(1970, 2000),
                              breaks = seq(1970, 2000, 10)) +
  ggplot2::scale_y_continuous(limits = c(0, 100),
                              breaks = seq(0, 100, 10)) +
  ggplot2::coord_cartesian(xlim = c(1970, 2000), ylim = c(0, 100)) +
  ggplot2::labs(x = "Year", y = "Percent (%)") +
  hv_theme("poster")


# --- tp.lp.trends.age.sas: age on x-axis, 25-85 by 10 -------------------
dta_age <- sample_trends_data(
  n = 600, year_range = c(25L, 85L),
  groups = c("Repair %", "Bioprosthesis %"), seed = 7L)
plot(hv_trends(dta_age)) +
  ggplot2::scale_x_continuous(limits = c(25, 85),
                              breaks = seq(25, 85, 10)) +
  ggplot2::scale_y_continuous(limits = c(0, 100),
                              breaks = seq(0, 100, 20)) +
  ggplot2::coord_cartesian(xlim = c(25, 85), ylim = c(0, 100)) +
  ggplot2::labs(x = "Age (years)", y = "Percent (%)") +
  hv_theme("poster")
#> Warning: Removed 1 row containing non-finite outside the scale range (`stat_smooth()`).


# --- tp.lp.trends.polytomous.sas: repair types, 1990-1999 by 1 ----------
dta_poly <- sample_trends_data(
  n = 800, year_range = c(1990L, 1999L),
  groups = c("CE", "Cosgrove", "Periguard", "DeVega"), seed = 5L)
plot(hv_trends(dta_poly)) +
  ggplot2::scale_colour_manual(
    values = c(CE = "steelblue", Cosgrove = "firebrick",
               Periguard = "forestgreen", DeVega = "goldenrod3"),
    name = "Repair type") +
  ggplot2::scale_x_continuous(limits = c(1990, 1999),
                              breaks = seq(1990, 1999, 1)) +
  ggplot2::scale_y_continuous(limits = c(0, 100),
                              breaks = seq(0, 100, 10)) +
  ggplot2::coord_cartesian(xlim = c(1990, 1999), ylim = c(0, 100)) +
  ggplot2::labs(x = "Year", y = "Percent (%)") +
  hv_theme("poster")


# --- Save ----------------------------------------------------------------
if (FALSE) { # \dontrun{
tr <- hv_trends(
  sample_trends_data(n = 800, year_range = c(1985L, 2015L),
                     groups = c("I", "II", "III", "IV")),
  summary_fn = "median"
)
p <- plot(tr) +
  ggplot2::scale_colour_brewer(palette = "Set1", name = "NYHA Class") +
  ggplot2::scale_x_continuous(limits = c(1985, 2015),
                              breaks = seq(1985, 2015, 5)) +
  ggplot2::labs(x = "Years", y = "%") +
  hv_theme("poster")
ggplot2::ggsave("trends.pdf", p, width = 11.5, height = 8)
} # }

# --- Global theme (set once per session) ----------------------------------
if (FALSE) { # \dontrun{
old <- ggplot2::theme_set(hv_theme_manuscript())
plot(hv_trends(dta_poly)) +
  ggplot2::scale_colour_brewer(palette = "Dark2", name = "Repair type")
ggplot2::theme_set(old)
} # }

# See vignette("plot-decorators", package = "hvtiPlotR") for theming,
# colour scales, annotation labels, and saving plots.

Usage

Arguments

Value

See also

Examples