Create a teal_slice object that holds complete information on filtering one variable.
Check out teal_slice-utilities functions for working with teal_slice object.
Usage
teal_slice(
dataname,
varname,
id,
expr,
choices = NULL,
selected = NULL,
keep_na = NULL,
keep_inf = NULL,
fixed = FALSE,
anchored = FALSE,
multiple = TRUE,
title = NULL,
...
)Arguments
- dataname
(
character(1)) name of data set- varname
(
character(1)) name of variable- id
(
character(1)) identifier of the filter. Must be specified whenexpris set. Whenvarnameis specified thenidis set to"{dataname} {varname}"by default.- expr
(
character(1)) string providing a logical expression. Must be a validRexpression which can be evaluated in the context of the data set. For adata.framevar == "x"is sufficient, butMultiAssayExperiment::subsetByColDatarequiresdatanameprefix, e.g.data$var == "x".- choices
(
vector) optional, specifies allowed choices; When specified it should be a subset of values in variable denoted byvarname; Type and size depends on variable type. Factors are coerced to character.- selected
(
vector) optional, specifies selected values fromchoices; Type and size depends on variable type. Factors are coerced to character.- keep_na
(
logical(1)) optional flag specifying whether to keep missing values- keep_inf
(
logical(1)) optional flag specifying whether to keep infinite values- fixed
(
logical(1)) flag specifying whether to fix this filter state (forbid setting state)- anchored
(
logical(1)) flag specifying whether to lock this filter state (forbid removing and inactivating)- multiple
(
logical(1)) optional flag specifying whether more than one value can be selected; only applicable toChoicesFilterStateandLogicalFilterState- title
(
character(1)) optional title of the filter. Ignored whenvarnameis set.- ...
additional arguments which can be handled by extensions of
teal.sliceclasses.
Value
A teal.slice object. Depending on whether varname or expr was specified, the resulting
teal_slice also receives class teal_slice_var or teal_slice_expr, respectively.
Details
teal_slice object fully describes filter state and can be used to create,
modify, and delete a filter state. A teal_slice contains a number of common fields
(all named arguments of teal_slice), some of which are mandatory, but only
dataname and either varname or expr must be specified, while the others have default
values.
Setting any of the other values to NULL means that those properties will not be modified
(when setting an existing state) or that they will be determined by data (when creating new a new one).
Entire object is FilterState class member and can be accessed with FilterState$get_state().
A teal_slice can come in two flavors:
teal_slice_var- this describes a typical interactive filter that refers to a single variable, managed by theFilterStateclass. This class is created whenvarnameis specified. The object retains all fields specified in the call.idcan be created by default and need not be specified.teal_slice_expr- this describes a filter state that refers to an expression, which can potentially include multiple variables, managed by theFilterStateExprclass. This class is created whenexpris specified.datanameandanchoredare retained,fixedis set toTRUE,idbecomes mandatory,titleremains optional, while other arguments are disregarded.
A teal_slice can be passed FilterState/FilterStateExpr constructors to instantiate an object.
It can also be passed to FilterState$set_state to modify the state.
However, once a FilterState is created, only the mutable features can be set with a teal_slice:
selected, keep_na and keep_inf.
Special consideration is given to two fields: fixed and anchored.
These are always immutable logical flags that default to FALSE.
In a FilterState instantiated with fixed = TRUE the features
selected, keep_na, keep_inf cannot be changed.
Note that a FilterStateExpr is always considered to have fixed = TRUE.
A FilterState instantiated with anchored = TRUE cannot be removed.
Filters in SumarizedExperiment and MultiAssayExperiment objects
To establish a filter on a column in a data.frame, dataname and varname are sufficient.
MultiAssayExperiment objects can be filtered either on their colData slot (which contains subject information)
or on their experiments, which are stored in the experimentList slot.
For filters referring to colData no extra arguments are needed.
If a filter state is created for an experiment, that experiment name must be specified in the experiment argument.
Furthermore, to specify filter for an SummarizedExperiment one must also set arg
("subset" or "select", arguments in the subset() function for SummarizedExperiment)
in order to determine whether the filter refers to the SE's rowData or colData.
Examples
x1 <- teal_slice(
dataname = "data",
id = "Female adults",
expr = "SEX == 'F' & AGE >= 18",
title = "Female adults"
)
x2 <- teal_slice(
dataname = "data",
varname = "var",
choices = c("F", "M", "U"),
selected = "F",
keep_na = TRUE,
keep_inf = TRUE,
fixed = FALSE,
anchored = FALSE,
multiple = TRUE,
id = "Gender",
extra_arg = "extra"
)
is.teal_slice(x1)
#> [1] TRUE
as.list(x1)
#> $dataname
#> [1] "data"
#>
#> $id
#> [1] "Female adults"
#>
#> $expr
#> [1] "SEX == 'F' & AGE >= 18"
#>
#> $fixed
#> [1] TRUE
#>
#> $anchored
#> [1] FALSE
#>
#> $title
#> [1] "Female adults"
#>
as.teal_slice(list(dataname = "a", varname = "var"))
#> {
#> "dataname" : "a",
#> "varname" : "var",
#> "id" : "a var",
#> "fixed" : false,
#> "anchored" : false,
#> "multiple" : true
#> }
format(x1)
#> [1] "{\n \"dataname\" : \"data\",\n \"id\" : \"Female adults\",\n \"expr\" : \"SEX == 'F' & AGE >= 18\",\n \"fixed\" : true,\n \"anchored\" : false,\n \"title\" : \"Female adults\"\n}"
format(x1, show_all = TRUE, trim_lines = FALSE)
#> [1] "{\n \"dataname\" : \"data\",\n \"id\" : \"Female adults\",\n \"expr\" : \"SEX == 'F' & AGE >= 18\",\n \"fixed\" : true,\n \"anchored\" : false,\n \"title\" : \"Female adults\"\n}"
print(x1)
#> {
#> "dataname" : "data",
#> "id" : "Female adults",
#> "expr" : "SEX == 'F' & AGE >= 18",
#> "fixed" : true,
#> "anchored" : false,
#> "title" : "Female adults"
#> }
print(x1, show_all = TRUE, trim_lines = FALSE)
#> {
#> "dataname" : "data",
#> "id" : "Female adults",
#> "expr" : "SEX == 'F' & AGE >= 18",
#> "fixed" : true,
#> "anchored" : false,
#> "title" : "Female adults"
#> }