Prepare data for use with the `$set_data()`

method. This is not
required for use but tries to simplify the most common use case
where you have a data.frame with some column indicating "dust
time step" (`name_time`

), and other columns that might be use in
your `data_compare`

function. Each row will be turned into a named
R list, which your `dust_data`

function can then work with to get
this time-steps values. See Details for use with multi-pars
objects.

`dust_data(object, name_time = "time", multi = NULL)`

- object
An object, at this point must be a data.frame

- name_time
The name of the data column within

`object`

; this column must be integer-like and every integer must be nonnegative and unique- multi
Control how to interpret data for multi-parameter dust object; see Details

A list of dust time/data pairs that will be used for the compare function in a compiled model. Each element is a list of length two or more where the first element is the time step and the subsequent elements are data for that time step.

Note that here "dust time step" (`name_time`

) refers to the *dust*
time step (which will be a non-negative integer) and not the
rescaled value of time that you probably use within the model. See
dust_generator for more information.

The data object as accepted by `data_set`

must be a list and
each element must itself be a list with two elements; the dust
`time`

at which the data applies and any R object that corresponds
to data at that point. We expect that most of the time this second
element will be a key-value list with scalar keys, but more
flexibility may be required.

For multi-data objects, the final format is a bit more awkward;
each time step we have a list with elements `time`

, `data_1`

,
`data_2`

, ..., `data_n`

for `n`

parameters. There are two ways of
creating this that might be useful: *sharing* the data across all
parameters and using some column as a grouping value.

The behaviour here is driven by the `multi`

argument;

`NULL`

: (the default) do nothing; this creates an object that is suitable for use with a`pars_multi = FALSE`

dust object.`<integer>`

(e.g., multi = 3); share the data across 3 sets of parameters. This number must match the number of parameter sets that your dust object is created with`<column_name>`

(e.g., multi = "country"); the name of a column within your data to split the data at. This column must be a factor, and that factor must have levels that map to integers 1, 2, ..., n (e.g.,`unique(as.integer(object[[multi]]))`

returns the integers`1:n`

).

```
d <- data.frame(time = seq(0, 50, by = 10), a = runif(6), b = runif(6))
dust::dust_data(d)
#> [[1]]
#> [[1]][[1]]
#> [1] 0
#>
#> [[1]][[2]]
#> [[1]][[2]]$time
#> [1] 0
#>
#> [[1]][[2]]$a
#> [1] 0.9970691
#>
#> [[1]][[2]]$b
#> [1] 0.5470434
#>
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [1] 10
#>
#> [[2]][[2]]
#> [[2]][[2]]$time
#> [1] 10
#>
#> [[2]][[2]]$a
#> [1] 0.1490355
#>
#> [[2]][[2]]$b
#> [1] 0.8348018
#>
#>
#>
#> [[3]]
#> [[3]][[1]]
#> [1] 20
#>
#> [[3]][[2]]
#> [[3]][[2]]$time
#> [1] 20
#>
#> [[3]][[2]]$a
#> [1] 0.5185566
#>
#> [[3]][[2]]$b
#> [1] 0.02795603
#>
#>
#>
#> [[4]]
#> [[4]][[1]]
#> [1] 30
#>
#> [[4]][[2]]
#> [[4]][[2]]$time
#> [1] 30
#>
#> [[4]][[2]]$a
#> [1] 0.8461201
#>
#> [[4]][[2]]$b
#> [1] 0.4693843
#>
#>
#>
#> [[5]]
#> [[5]][[1]]
#> [1] 40
#>
#> [[5]][[2]]
#> [[5]][[2]]$time
#> [1] 40
#>
#> [[5]][[2]]$a
#> [1] 0.7182697
#>
#> [[5]][[2]]$b
#> [1] 0.80568
#>
#>
#>
#> [[6]]
#> [[6]][[1]]
#> [1] 50
#>
#> [[6]][[2]]
#> [[6]][[2]]$time
#> [1] 50
#>
#> [[6]][[2]]$a
#> [1] 0.241314
#>
#> [[6]][[2]]$b
#> [1] 0.8140513
#>
#>
#>
```