## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo=TRUE,
                      fig.width=6,
                      fig.height=4)
library(cowplot)
library(PKNCA)
library(knitr)
library(ggplot2)
source("../tests/testthat/generate.data.R")
scale_colour_discrete <- scale_colour_hue
scale_fill_discrete <- scale_fill_hue

## ----check-ggplot, include=!requireNamespace("ggplot2"), results="asis"-------
cat("ggplot2 is required for this vignette to work correctly.  Please install the ggplot2 library and retry building the vignette.")

## ----intro_interval_plot, echo=FALSE------------------------------------------
# Simulate concentration-time data and setup the PKNCAconc object
d_conc <-
  PKNCAconc(
    generate.conc(nsub=1,
                  ntreat=1,
                  time.points=c(0, 1, 2, 4, 6, 8, 12, 24, 36, 48),
                  nstudies=1,
                  nanalytes=1,
                  resid=0),
    conc~time|treatment+ID)
print(d_conc)
# Use superposition to simulate multiple doses
d_conc_multi <-
  superposition(d_conc,
                tau=168,
                dose.times=seq(0, 168-24, by=24),
                n.tau=1)

## ----intro_interval_plot-visualization, eval=requireNamespace("ggplot2")------
# Plot the concentration-time data and the interval
ggplot(d_conc_multi, aes(x=time, y=conc)) +
  geom_ribbon(data=d_conc_multi[d_conc_multi$time >= 144,],
              aes(ymax=conc, ymin=0),
              fill="skyblue") +
  geom_point() + geom_line() +
  scale_x_continuous(breaks=seq(0, 168, by=12)) +
  scale_y_continuous(limits=c(0, NA)) +
  labs(x="Time Since First Dose (hr)",
       y="Concentration\n(arbitrary units)")

## ----intro_interval_spec------------------------------------------------------
intervals_manual <- data.frame(start=144, end=168, auclast=TRUE)
knitr::kable(intervals_manual)

PKNCAdata(d_conc, intervals=intervals_manual)

## ----select_group-------------------------------------------------------------
intervals_manual <-
  data.frame(
    treatment=c("Drug 1 Single", "Drug 1 Multiple", "Drug 1 Multiple"),
    start=c(0, 0, 216),
    end=c(Inf, 24, 240),
    aucinf.obs=c(TRUE, FALSE, FALSE),
    auclast=c(FALSE, TRUE, TRUE)
  )
knitr::kable(intervals_manual)

## ----infinity_interval_plot, echo=FALSE---------------------------------------
# Simulate concentration-time data and setup the PKNCAconc object
d_conc <-
  PKNCAconc(
    generate.conc(nsub=1,
                  ntreat=1,
                  time.points=c(0, 1, 2, 4, 6, 8, 12, 24, 36, 48),
                  nstudies=1,
                  nanalytes=1,
                  resid=0),
    conc~time|treatment+ID)
print(d_conc)

## ----infinity_interval_plot-visualization, eval=requireNamespace("ggplot2")----
# Use superposition to simulate multiple doses
ggplot(as.data.frame(d_conc)[as.data.frame(d_conc)$time <= 48,], aes(x=time, y=conc)) +
  geom_ribbon(data=as.data.frame(d_conc),
              aes(ymax=conc, ymin=0),
              fill="skyblue") +
  geom_point() + geom_line() +
  scale_x_continuous(breaks=seq(0, 72, by=12)) +
  scale_y_continuous(limits=c(0, NA)) +
  labs(x="Time Since First Dose (hr)",
       y="Concentration\n(arbitrary units)")

## ----infinity_interval_spec---------------------------------------------------
intervals_manual <-
  data.frame(
    start=0,
    end=Inf,
    auclast=TRUE,
    aucinf.obs=TRUE
  )
print(intervals_manual)

my.data <- PKNCAdata(d_conc, intervals=intervals_manual)

## ----multiple_intervals_plot, echo=FALSE--------------------------------------
# Simulate concentration-time data and setup the PKNCAconc object
d_conc <-
  PKNCAconc(
    generate.conc(nsub=1,
                  ntreat=1,
                  time.points=c(0, 1, 2, 4, 6, 8, 12, 24, 36, 48),
                  nstudies=1,
                  nanalytes=1,
                  resid=0),
    conc~time|treatment+ID)
print(d_conc)
# Use superposition to simulate multiple doses
d_conc_multi <-
  superposition(d_conc,
                tau=168,
                dose.times=seq(0, 168-24, by=24),
                n.tau=1)

## ----multiple_intervals_plot-visualization, eval=requireNamespace("ggplot2")----
# Plot the concentration-time data and the interval
ggplot(d_conc_multi, aes(x=time, y=conc)) +
  geom_ribbon(data=d_conc_multi[d_conc_multi$time <= 24,],
              aes(ymax=conc, ymin=0),
              fill="skyblue") +
  geom_ribbon(data=d_conc_multi[d_conc_multi$time >= 144,],
              aes(ymax=conc, ymin=0),
              fill="lightgreen") +
  geom_point() + geom_line() +
  scale_x_continuous(breaks=seq(0, 168, by=12)) +
  scale_y_continuous(limits=c(0, NA)) +
  labs(x="Time Since First Dose (hr)",
       y="Concentration\n(arbitrary units)")

## ----multiple_intervals_spec--------------------------------------------------
intervals_manual <-
  data.frame(
    start=c(0, 144),
    end=c(24, 168),
    auclast=TRUE
  )
knitr::kable(intervals_manual)

my.data <- PKNCAdata(d_conc, intervals=intervals_manual)

## ----overlapping_intervals_plot, echo=FALSE-----------------------------------
# Simulate concentration-time data and setup the PKNCAconc object
d_conc <-
  PKNCAconc(
    generate.conc(nsub=1,
                  ntreat=1,
                  time.points=c(0, 1, 2, 4, 6, 8, 12, 24, 36, 48),
                  nstudies=1,
                  nanalytes=1,
                  resid=0),
    conc~time|treatment+ID)
print(d_conc)

## ----overlapping_intervals_plot-visualization, eval=requireNamespace("ggplot2")----
# Use superposition to simulate multiple doses
ggplot(as.data.frame(d_conc), aes(x=time, y=conc)) +
  geom_ribbon(data=as.data.frame(d_conc),
              aes(ymax=conc, ymin=0),
              fill="lightgreen",
              alpha=0.5) +
  geom_ribbon(data=as.data.frame(d_conc)[as.data.frame(d_conc)$time <= 24,],
              aes(ymax=conc, ymin=0),
              fill="skyblue",
              alpha=0.5) +
  geom_point() + geom_line() +
  scale_x_continuous(breaks=seq(0, 168, by=12)) +
  scale_y_continuous(limits=c(0, NA)) +
  labs(x="Time Since First Dose (hr)",
       y="Concentration\n(arbitrary units)")

## ----overlapping_intervals_spec-----------------------------------------------
intervals_manual <-
  data.frame(
    start=0,
    end=c(24, Inf),
    auclast=TRUE,
    aucinf.obs=c(FALSE, TRUE)
  )
knitr::kable(intervals_manual)

my.data <- PKNCAdata(d_conc, intervals=intervals_manual)

## ----interval_yes_no, echo=FALSE, eval=requireNamespace("ggplot2")------------
ggplot_intervals <- function(definition, intervals) {
  intervals$within <-
    c("No", "Yes")[intervals$interval.start >= min(definition$start.x) &
                     intervals$interval.end >= max(definition$start.x)]
  intervals$within <-
    factor(intervals$within, levels=c("Yes", "No"))
  ggplot(intervals,
         aes(x=interval.start,
             xend=interval.end,
             y=y,
             yend=y,
             linetype=within)) +
    geom_segment() +
    geom_segment(data=definition,
                 mapping=aes(x=start.x, xend=start.x, y=start.y, yend=end.y),
                 arrow=arrow(),
                 inherit.aes=FALSE) +
    geom_point(aes(x=interval.start, y=1),
               shape="|",
               size=4) +
    geom_point(aes(x=max(interval.end), y=1),
               shape="|",
               size=4) +
    scale_y_continuous(breaks=NULL) +
    labs(x="Time",
         y=NULL)
}

interval_definition <-
  data.frame(start.x=c(0, 24),
             start.y=0.5,
             end.y=0.9)
show_intervals <-
  data.frame(interval.start=c(0, 4, 12, 24),
             interval.end=c(4, 12, 24, 48),
             y=1)
ggplot_intervals(interval_definition,
                 show_intervals)

interval_definition <-
  data.frame(start.x=c(0, 16),
             start.y=0.5,
             end.y=0.9)
show_intervals <-
  data.frame(interval.start=c(0, 4, 12, 24),
             interval.end=c(4, 12, 24, 48),
             y=1)
ggplot_intervals(interval_definition,
                 show_intervals)


## ----parameters-available, echo=FALSE-----------------------------------------
interval_spec <- get.interval.cols()
interval_spec <- interval_spec[sort(names(interval_spec))]

get_function_for_calc <- function(x) {
  if (is.na(x$FUN)) {
    if (is.null(x$depends)) {
      "(none)"
    } else {
      paste("See the parameter name", x$depends)
    }
  } else {
    x$FUN
  }
}

kable(
  data.frame(
    `Parameter Name`=names(interval_spec),
    `Unit Type`=vapply(X = interval_spec, FUN = "[[", "unit_type", FUN.VALUE = ""),
    `Parameter Description`=vapply(X = interval_spec, FUN = "[[", "desc", FUN.VALUE = ""),
    `Function for Calculation`=vapply(X = interval_spec, FUN = get_function_for_calc, FUN.VALUE = ""),
    check.names=FALSE,
    stringsAsFactors=FALSE
  ),
  row.names=FALSE
)