cg_plot — cg_plot • chronogram

Create a ggplot2 object, plotting a user-defined y axis over time.

ggplot2 objects retain the entirety of the provided dataset. This allows later adjustments, such as adding extra geom_layers with new information, or applying facets. To find this data examine obj$data. If you save ggplot2 objects, all source data is ALSO saved. cg_plot() removes any un-used data by default (drop_vars=TRUE). In writing a study specific ggplot2, it is best practice to select minimal columns before calling ggplot().

Usage

cg_plot(
  cg,
  x = NULL,
  y_values,
  drop_vars = TRUE,
  point_alpha = 0.4,
  point_shape = 20,
  link_obs = TRUE,
  link_colour = "grey",
  link_alpha = 0.4,
  ...
)

Arguments

cg: chronogram
x: a column of time to use as x axis. If NULL, will default to the chronogram's calendar date attribute. A user may want to derive and use alternatives eg 'daysSinceDose2'.
y_values: column within chronogram containing the data you wish to plot.
drop_vars: Default TRUE. See description.
point_alpha: alpha for geom_point().
point_shape: shape for geom_point().
link_obs: Default TRUE. Draw a line to link results from same individual?
link_colour: colour for geom_line()
link_alpha: alpha for geom_line()
...: passed to aes()

Examples


library(ggplot2)
library(patchwork)

data("built_smallstudy")
cg <- built_smallstudy$chronogram

p1 <- cg_plot_meta(cg,
  visit = serum_Ab_S
)
#> Function provided to illustrate chronogram ->
#>           ggplot2 interface.
#> Function assumes the
#>           presence of {dose_1, date_dose_1, dose_2, date_dose_2}
#>           columns.
#>           Users are likely to want to write their own,
#>           study-specific applications

p2 <- cg_plot(cg,
  y_values = serum_Ab_S
)
#> Function provided to illustrate chronogram ->
#>           ggplot2 interface.
#>           Users are likely to want to write their own,
#>           study-specific applications

p2 / p1


(p2 + scale_y_log10()) / p1
#> Warning: log-10 transformation introduced infinite values.
#> Warning: log-10 transformation introduced infinite values.