LightLogR (version 0.9.2)

gg_state: Add states to gg_day() or gg_days() plots

Description

gg_state() is a helper function to add state information to plots generated with gg_day(), gg_days(), or gg_doubleplot(). The function can draw on any column in the dataset, but factor-like or logical columns make the most sense. The time series must be based on a column called Datetime.

Usage

gg_state(
  ggplot_obj,
  State.colname,
  aes_fill = NULL,
  aes_col = NULL,
  alpha = 0.2,
  on.top = FALSE,
  ignore.FALSE = TRUE,
  ...
)

Value

a modified ggplot object with the states added.

Arguments

ggplot_obj

A ggplot object generated with gg_day() or gg_days() (or gg_doubleplot(). The dataset used to create these must have a Datetime column.

State.colname

The colname of the state to add to the plot. Must be part of the dataset. Expects a symbol.

aes_fill, aes_col

conditional aesthetics for ggplot2::geom_rect(). The default (NULL) will be ignored, so that col and fill arguments can be set through the ... arguments. As the states work from a summarized dataset, only a few columns are available for filling/coloring: The State.colname, Grouping variables, and variables created by using extract_states().

alpha

A numerical value between 0 and 1 representing the transparency of the states. Default is 0.2.

on.top

Logical scalar. If TRUE, the states will be plotted on top of the existing plot. If FALSE, the states will be plotted underneath the existing plot. Default is FALSE.

ignore.FALSE

Logical that drops FALSE values of a logical state column, so that only TRUE values are recognized as a state. Is only relevant for logical state columns and will be ignored otherwise. Default is TRUE.

...

Additional arguments given to the ggplot2::geom_rect() used to construct the state shading. Can be used to change the fill color or other aesthetic properties.

Examples

#creating a simple TRUE/FALSE state in the sample data: Light above 250 lx mel EDI
#and a second state that cuts data into chunks relating to the Brown et al. 2022 thresholds
#(+aggregating Data to 5 minute intervals & reducing it to three days)
state_data <-
  sample.data.environment |>
   dplyr::mutate(state = MEDI > 250) |>
   Brown_cut(MEDI, state2) |> 
   aggregate_Datetime(unit = "5 mins") |>
   filter_Datetime(length = "3 days")

state_data |>
 gg_days() |>
 gg_state(state)

#state 2 has more than one valid state, thus we need to assign a fill aesthetic
state_data |>
 gg_days() |>
 gg_state(state2, aes_fill = state2) +
 ggplot2::scale_fill_manual(values=c("#868686FF", "#EFC000FF", "#0073C2FF"))
 #this line is simply for sensible colors

#same, but with gg_day()
state_data |>
 dplyr::filter(Id == "Participant") |>
 gg_day(geom = "line") |>
 gg_state(state, fill = "red")

 #more complex state
 state_data |>
 dplyr::filter(Id == "Participant") |>
 gg_day(geom = "line") |>
 gg_state(state2, aes_fill = state2)

 #with gg_doubleplot
 state_data |>
 dplyr::filter(Id == "Participant") |>
 gg_doubleplot() |>
 gg_state(state2, aes_fill = state2)