Plot trends in longitudinal variables — line

line_plot allows for flexible visualization of repeated measures variables from panel_data frames.

Usage

line_plot(
  data,
  var,
  id = NULL,
  wave = NULL,
  overlay = TRUE,
  show.points = TRUE,
  subset.ids = FALSE,
  n.random.subset = 9,
  add.mean = FALSE,
  mean.function = "lm",
  line.size = 1,
  alpha = if (overlay) 0.5 else 1
)

Arguments

data: Either a panel_data frame or another data frame.
var: The unquoted name of the variable of interest.
id: If data is not a panel_data object, then the id variable.
wave: If data is not a panel_data object, then the wave variable.
overlay: Should the lines be plotted in the same panel or each in their own facet/panel? Default is TRUE, meaning they are plotted in the same panel.
show.points: Plot a point at each wave? Default is TRUE.
subset.ids: Plot only a subset of the entities' lines? Default is NULL, meaning plot all ids. If TRUE, a random subset (the number defined by n.random.subset) are plotted. You may also supply a vector of ids to choose them yourself.
n.random.subset: How many entities to randomly sample when subset.ids is TRUE.
add.mean: Add a line representing the mean trend? Default is FALSE. Cannot be combined with overlay.
mean.function: The mean function to supply to geom_smooth when add.mean is TRUE. Default is "lm", but another option of interest is "loess".
line.size: The thickness of the plotted lines. Default: 0.5
alpha: The transparency for the lines and points. When overlay = TRUE, it is set to 0.5, otherwise 1, which means non-transparent.

Value

The ggplot object.

Examples


data("WageData")
wages <- panel_data(WageData, id = id, wave = t)
line_plot(wages, lwage, add.mean = TRUE, subset.ids = TRUE, overlay = FALSE)
#> `geom_smooth()` using formula = 'y ~ x'