events-et.Rmdh

An event table in rxode2 is a specialized data frame that acts as a
container for all of rxode2's events and observation times.

To create an rxode2 event table you may use the code `eventTable()`,
`et()`, or even create your own data frame with the right event
information contained in it.  This is closely related to the [types of
events that rxode2 supports](rxode2-event-types.html).

```{r}
library(rxode2)
library(units)
(ev <- et())
```
or

```{r}
(ev <- et())
```

With this event table you can add sampling/observations or doses by
piping or direct access.

This is a short table of the two main functions to create dosing

| add.dosing()    | et() | Description                         |
|-----------------|------|-------------------------------------|
| dose            | amt  | Dose/Rate/Duration amount           |
| nbr.doses       | addl | Additional doses or number of doses |
| dosing.interval | ii   | Dosing Interval                     |
| dosing.to       | cmt  | Dosing Compartment                  |
| rate            | rate | Infusion rate                       |
| start.time      | time | Dosing start time                   |
|                 | dur  | Infusion Duration                   |

Sampling times can be added with `add.sampling(` sampling times `)` or
`et(` sampling times `)`.  [Dosing intervals and sampling
windows](#add-doses-and-samples-within-a-sampling-window) are also
supported.

With most of the documentation we will be using the `et()` syntax,
though both are supported.

For these models, we can illustrate by using the model shared in the
rxode2 tutorial:

```{r}
## Model from rxode2 tutorial
m1 <-function() {
  ini({
    KA   <- 2.94E-01
    CL   <- 1.86E+01
    V2   <- 4.02E+01
    Q    <- 1.05E+01
    V3   <- 2.97E+02
    Kin  <- 1
    Kout <- 1
    EC50 <- 200
    ## Added modeled bioavaiblity, duration and rate
    fdepot <- 1
    durDepot <- 8
    rateDepot <- 1250
  })
  model({
    C2          <- centr/V2
    C3          <- peri/V3
    d/dt(depot) <- -KA*depot
    f(depot)    <- fdepot
    dur(depot)  <- durDepot
    rate(depot) <- rateDepot
    d/dt(centr) <- KA*depot - CL*C2 - Q*C2 + Q*C3
    d/dt(peri)  <-                    Q*C2 - Q*C3
    d/dt(eff)   <- Kin - Kout*(1-C2/(EC50+C2))*eff;
    eff(0)      <- 1
  })
}
```

## Adding doses to the event table

Once created you can add dosing to the event table by the
`add.dosing()`, and `et()` functions.

Using the `add.dosing()` function you have:

| argument        | meaning                                 |
|-----------------|-----------------------------------------|
| dose            | dose amount                             |
| nbr.doses       | Number of doses;  Should be at least 1. |
| dosing.interval | Dosing interval; By default this is 24. |
| dosing.to       | Compartment where dose is administered. |
| rate            | Infusion rate                           |
| start.time      | The start time of the dose              |

```{r}
ev <- eventTable(amount.units="mg", time.units="hr")

## The methods ar attached to the event table, so you can use 
## them directly
ev$add.dosing(dose=10000, nbr.doses = 3)# loading doses
## Starts at time 0; Default dosing interval is 24

## You can also pipe the event tables to these methods.
ev <- ev %>%
  add.dosing(dose=5000, nbr.doses=14,
             dosing.interval=12)# maintenance

ev
```

Notice that the units were specified in the table. When specified, the
units use the `units` package to keep track of the units and convert
them if needed.  Additionally, `ggforce` uses them to label the
`ggplot` axes.  The `set_units` and `drop_units` are useful to set and
drop the rxode2 event table units.

In this example, you can see the time axes is labeled:

```{r}
rxSolve(m1, ev) %>% plot(C2)
```


If you are more familiar with the NONMEM/rxode2 event records, you can
also specify dosing using `et` with the dose elements directly:

```{r}
ev <- et(timeUnits="hr") %>%
  et(amt=10000, until = set_units(3, days),
     ii=12) # loading doses

ev
```

Which gives:

```{r}
rxSolve(m1, ev) %>% plot(C2)
```

This shows how easy creating event tables can be.

## Adding sampling to an event table

If you notice in the above examples, rxode2 generated some default
sampling times since there was not any sampling times.  If you wish
more control over the sampling time, you should add the samples to the
rxode2 event table by `add.sampling` or `et`

```{r}
ev <- eventTable(amount.units="mg", time.units="hr")

## The methods ar attached to the event table, so you can use them
## directly
ev$add.dosing(dose=10000, nbr.doses = 3)# loading doses

ev$add.sampling(seq(0,24,by=4))

ev
```
Which gives:

```{r}
solve(m1, ev) %>% plot(C2)
```

Or if you use `et` you can simply add them in a similar way to `add.sampling`:

```{r}
ev <- et(timeUnits="hr") %>%
  et(amt=10000, until = set_units(3, days),
     ii=12) %>% # loading doses
  et(seq(0,24,by=4))

ev
```

which gives the following rxode2 solve:

```{r}
solve(m1, ev) %>% plot(C2)
```

Note the jagged nature of these plots since there was only a few
sample times.

## Expand the event table to a multi-subject event table.

The only thing that is needed to expand an event table is a list of
IDs that you want to expand;

```{r}
ev <- et(timeUnits="hr") %>%
  et(amt=10000, until = set_units(3, days),
     ii=12) %>% # loading doses
  et(seq(0,48,length.out=200)) %>%
  et(id=1:4)

ev
```

You can see in the following simulation there are 4 individuals that are solved for:

```{r}
set.seed(42)
rxSetSeed(42)
solve(m1, ev,
      params=data.frame(KA=0.294*exp(rnorm(4)),
                        CL=18.6*exp(rnorm(4)))) %>%
    plot(C2)
```

## Add doses and samples within a sampling window

In addition to adding fixed doses and fixed sampling times, you can
have windows where you sample and draw doses from.  For dosing windows
you specify the time as an ordered numerical vector with the lowest
dosing time and the highest dosing time inside a list.

In this example, you start with a dosing time with a 6 hour dosing window:

```{r}
set.seed(42)
rxSetSeed(42)
ev <- et(timeUnits="hr") %>%
  et(time=list(c(0,6)), amt=10000, until = set_units(2, days),
     ii=12) %>% # loading doses
  et(id=1:4)

ev
```

You can clearly see different dosing times in the following simulation:
```{r}
ev <- ev %>% et(seq(0,48,length.out=200))

solve(m1, ev,
      params=data.frame(KA=0.294*exp(rnorm(4)),
                        CL=18.6*exp(rnorm(4)))) %>%
  plot(C2)
```

Of course in reality the dosing interval may only be 2 hours:

```{r}
set.seed(42)
rxSetSeed(42)
ev <- et(timeUnits="hr") %>%
  et(time=list(c(0,2)), amt=10000, until = set_units(2, days),
     ii=12) %>% # loading doses
  et(id=1:4) %>%
  et(seq(0,48,length.out=200))

solve(m1, ev,
      params=data.frame(KA=0.294*exp(rnorm(4)),
                        CL=18.6*exp(rnorm(4)))) %>%
  plot(C2)
```

The same sort of thing can be specified with sampling times.  To
specify the sampling times in terms of a sampling window, you can
create a list of the sampling times.  Each sampling time will be a two
element ordered numeric vector.

```{r}
rxSetSeed(42)
set.seed(42)
ev <- et(timeUnits="hr") %>%
  et(time=list(c(0,2)), amt=10000, until = set_units(2, days),
     ii=12) %>% # loading doses
  et(id=1:4)

## Create 20 samples in the first 24 hours and 20 samples in the
## second 24 hours
samples <- c(lapply(1:20, function(...){c(0,24)}),
             lapply(1:20, function(...){c(20,48)}))

## Add the random collection to the event table
ev <- ev %>% et(samples)

library(ggplot2)
solve(m1, ev, params=data.frame(KA=0.294*exp(rnorm(4)),
                                CL=18.6*exp(rnorm(4)))) %>%
  plot(C2) + geom_point()
```

This shows the flexibility in dosing and sampling that the rxode2 event tables allow.

## Combining event tables

Since you can create dosing records and sampling records, you can
create any complex dosing regimen you wish.  In addition, rxode2 allows
you to combine event tables by `c`, `seq`, `rep`, and `rbind`.

## Sequencing event tables

One way to combine event table is to sequence them by `c`, `seq` or
`etSeq`. This takes the two dosing groups and adds at least one
inter-dose interval between them:

```{r}
## bid for 5 days
bid <- et(timeUnits="hr") %>%
       et(amt=10000,ii=12,until=set_units(5, "days"))

## qd for 5 days
qd <- et(timeUnits="hr") %>%
      et(amt=20000,ii=24,until=set_units(5, "days"))

## bid for 5 days followed by qd for 5 days
et <- seq(bid,qd) %>% et(seq(0,11*24,length.out=100));

rxSolve(m1, et) %>% plot(C2)
```

When sequencing events, you can also separate this sequence by a
period of time; For example if you wanted to separate this by a week,
you could easily do that with the following sequence of event tables:

```{r}
## bid for 5 days followed by qd for 5 days
et <- seq(bid,set_units(1, "week"), qd) %>%
    et(seq(0,18*24,length.out=100));

rxSolve(m1, et) %>% plot(C2)

```

Note that in this example the time between the bid and the qd event
tables is exactly one week, not 1 week plus 24 hours because of the
inter-dose interval.  If you want that behavior, you can sequence it
using the `wait="+ii"`.

```{r}
## bid for 5 days followed by qd for 5 days
et <- seq(bid,set_units(1, "week"), qd,wait="+ii") %>%
    et(seq(0,18*24,length.out=100));

rxSolve(m1, et) %>% plot(C2)
```

Also note, that rxode2 assumes that the dosing is what you want to
space the event tables by, and clears out any sampling records when
you combine the event tables.  If that is not true, you can also use
the option `samples="use"`

## Repeating event tables

You can have an event table that you can repeat with `etRep` or `rep`.
For example 4 rounds of 2 weeks on QD therapy and 1 week off of
therapy can be simply specified:

```{r}
qd <-et(timeUnits = "hr") %>%
  et(amt=10000, ii=24, until=set_units(2, "weeks"), cmt="depot")

et <- rep(qd, times=4, wait=set_units(1,"weeks")) %>%
      add.sampling(set_units(seq(0, 12.5,by=0.005),weeks))

rxSolve(m1, et)  %>% plot(C2)
```

This is a simplified way to use a sequence of event tables.
Therefore, many of the same options still apply; That is `samples` are
cleared unless you use `samples="use"`, and the time between event
tables is at least the inter-dose interval.  You can adjust the timing
by the `wait` option.

## Combining event tables with rbind

You may combine event tables with `rbind`.  This does not consider the
event times when combining the event tables, but keeps them the same
times. If you space the event tables by a waiting period, it also does
not consider the inter-dose interval.

Using the previous `seq` you can clearly see the difference.  Here was the sequence:
```{r}
## bid for 5 days
bid <- et(timeUnits="hr") %>%
       et(amt=10000,ii=12,until=set_units(5, "days"))

## qd for 5 days
qd <- et(timeUnits="hr") %>%
      et(amt=20000,ii=24,until=set_units(5, "days"))

et <- seq(bid,qd) %>%
    et(seq(0,18*24,length.out=500));

rxSolve(m1, et) %>% plot(C2)
```

But if you bind them together with `rbind`
```{r}
## bid for 5 days
et <- rbind(bid,qd) %>%
    et(seq(0,18*24,length.out=500));

rxSolve(m1, et) %>% plot(C2)
```

Still the waiting period applies (but does not consider the inter-dose interval)

```{r}
et <- rbind(bid,wait=set_units(10,days),qd) %>%
    et(seq(0,18*24,length.out=500));

rxSolve(m1, et) %>% plot(C2)
```

You can also bind the tables together and make each ID in the event
table unique; This can be good to combine cohorts with different
expected dosing and sampling times.  This requires the `id="unique"`
option;  Using the first example shows how this is different in this case:

```{r}
## bid for 5 days
et <- etRbind(bid,qd, id="unique") %>%
    et(seq(0,150,length.out=500));

library(ggplot2)
rxSolve(m1, et) %>% plot(C2) + facet_wrap( ~ id)
```

## Event tables in Rstudio Notebooks

In addition to the output in the console which has been shown in the above examples, Rstudio notebook output is different and can be seen in the following screenshots;

The first screenshot shows how the event table looks after evaluating
it in the Rstduio notebook

![](eventTable-output-notebook-02.png)

This is a simple dataframe that allows you to page through the
contents.  If you click on the first box in the Rstudio notebook
output, it will have the notes about the event table:

![](eventTable-output-notebook-01.png)
## Expanding events

Event tables can be expanded so they contain an `addl` data item, like
the following example:

```{r}
ev <- et() %>%
  et(dose=50, ii=8, until=48)

ev
```

You can expand the events so they do not have the `addl` items by
`$expand()` or `etExpand(ev)`:

The first, `etExpand(ev)` expands the event table without modifying the original data frame:

```{r}
etExpand(ev)
```

You can see the `addl` events were expanded, however the original data
frame remained intact:

```{r}
print(ev)
```

If you use `ev$expand()` it will modify the `ev` object.  This is
similar to an object-oriented method:


```{r}
ev$expand()
ev
```