LEdecomp (version 1.0.4)

plot.LEdecomp: Plot Life-Expectancy Decomposition Results (ggplot2)

Description

Plot contributions (or sensitivities) to a life expectancy difference by age or by age and cause using ggplot2. This is just for a quick default plot method.

Usage

# S3 method for LEdecomp
plot(
  x,
  what = c("LEdecomp", "sens"),
  geom = c("auto", "line", "bar"),
  col = NULL,
  lwd = 1.2,
  xlab = "Age",
  ylab = NULL,
  main = NULL,
  legend = TRUE,
  legend_pos = "right",
  abridged_midpoints = FALSE,
  layout = c("overlay", "facet"),
  ncol = NULL,
  ...
)

Value

Invisibly returns the ggplot object (after printing).

Arguments

x

An object of class "LEdecomp".

what

Which series to plot: "LEdecomp" (default) or "sens".

geom

Plot geometry: "auto", "line", or "bar". If "auto", "bar" is used for what = "LEdecomp" and "line" for what = "sens".

col

Optional vector of colors for causes. If NULL, a fixed palette is used and recycled as needed.

lwd

Line width for cause lines (default 1.2).

xlab, ylab, main

Axis labels and main title. If ylab is NULL, the default is "Difference explained (years)" for what = "LEdecomp" and "Sensitivity d(e0)/d(mx)" for what = "sens".

legend

Logical. Show legend (primarily relevant for layout = "overlay").

legend_pos

Legend position passed to theme(legend.position = ...). Accepts "none", "left", "right", "bottom", "top", or numeric coordinates c(x, y) in [0,1] inside the panel.

abridged_midpoints

Logical. If TRUE and ages are abridged (0, 1, 5, 10, ...), plot against bin midpoints instead of lower bounds.

layout

Plot layout for cause-of-death results: "overlay" (all causes in one panel) or "facet" (one panel per cause).

ncol

Number of columns to use when layout = "facet". If NULL, a default is chosen based on the number of causes.

...

Reserved for future use.

Details

By default, if what = "LEdecomp" we plot using bars (geom = "bar"), but you can override this. For bar plots, recall it's the area, not the height that the eye reads; for this reason, if your data is in non-single ages, we divide out the interval width, so that the implied uniform graduation to single ages still sums to the gap. If what = "sens" note we plot on a single-age scale even if the data are in abridged ages.

Examples

data("US_data_CoD", package = "LEdecomp")
allc <- subset(US_data_CoD, Period == 2010 & cause == "All-causes") |>
  as.data.frame()

# Make Female vs Male all-cause schedules, Age 0:100
ac_w <- reshape(allc[, c("Gender","Age","mxt")],
                timevar = "Gender", idvar = "Age", direction = "wide")
names(ac_w) <- sub("^mxt\\.", "", names(ac_w))
ac_w <- ac_w[order(ac_w$Age), ]

dec_ac <- LEdecomp(
  mx1 = ac_w$Male,
  mx2 = ac_w$Female,
  age = 0:100,
  method = "sen_arriaga"
)

# Simple single-line plot
# \donttest{
plot(dec_ac, main = "All-cause Arriaga, 2010 Female vs Male")
# }
## End(Not run)
## Example 2: Cause of death, one year, Female vs Male
cod <- subset(US_data_CoD, Period == 2010 & cause != "All-causes")
cod_w <- reshape(cod[, c("Gender","Age","cause","mxt")],
                 timevar = "Gender", idvar = c("cause","Age"),
                 direction = "wide")|>
  as.data.frame()
names(cod_w) <- sub("^mxt\\.", "", names(cod_w))
cod_w <- cod_w[order(cod_w$cause, cod_w$Age), ]

dec_cod <- LEdecomp(
  mx1 = cod_w$Male,
  mx2 = cod_w$Female,
  age = 0:100,
  n_causes = length(unique(cod_w$cause)),
  cause_names = unique(cod$cause_id),
  method = "sen_arriaga"
)

# Overlay of all causes
# \donttest{
plot(dec_cod, layout = "overlay", main = "Arriaga CoD, 2010 Female vs Male", legend.pos = "top")

# Facet by cause (3 columns)
plot(dec_cod, layout = "facet", ncol = 3, main = "Arriaga by cause (faceted)")
# }

## Example 3: How to add an all-cause total line yourself (overlay)
# \donttest{
p <- plot(dec_cod, layout = "overlay", main = "Overlay with manual Total")
y_mat <- if (is.matrix(dec_cod$LEdecomp)) dec_cod$LEdecomp else
  matrix(dec_cod$LEdecomp, nrow = length(dec_cod$age))
total <- rowSums(y_mat)
p + ggplot2::geom_line(
  data = data.frame(age = dec_cod$age, total = total),
  mapping = ggplot2::aes(x = .data$age, y = .data$total),
  inherit.aes = FALSE, color = "black", linewidth = 1.1)
# }