Class to encapsulate filtered datasets
FilteredData.RdClass to encapsulate filtered datasets
Class to encapsulate filtered datasets
Details
The main purpose of this class is to provide a collection of reactive datasets, each dataset having a filter state that determines how it is filtered.
For each dataset, get_filter_expr returns the call to filter the dataset according
to the filter state. The data itself can be obtained through get_data.
The datasets are filtered lazily, i.e. only when requested / needed in a Shiny app.
By design, any dataname set through set_dataset cannot be removed because
other code may already depend on it. As a workaround, the underlying
data can be set to NULL.
The class currently supports variables of the following types within datasets:
choices: variable of typefactor, e.g.ADSL$COUNTRY,iris$Specieszero or more options can be selected, when the variable is a factorlogical: variable of typelogical, e.g.ADSL$TRT_FLAGexactly one option must be selected,TRUEorFALSEranges: variable of typenumeric, e.g.ADSL$AGE,iris$Sepal.Lengthnumerical range, a range within this range can be selecteddates: variable of typeDate,POSIXltOther variables cannot be used for filtering the data in this class.
Common arguments are:
filtered: whether to return a filtered result or notdataname: the name of one of the datasets in thisFilteredDatavarname: one of the columns in a dataset
Methods
Method new()
Initialize a FilteredData object
Usage
FilteredData$new(data_objects, join_keys = NULL, code = NULL, check = FALSE)Arguments
data_objects-
(
list) should contain.datasetdata object object supported byFilteredDataset.metatada(optional) additional metadata attached to thedataset.keys(optional) primary keys.datalabel(optional) label describing thedataset.parent(optional) whichNULLis a parent of this one.
join_keys(
JoinKeysor NULL) seeteal.data::join_keys().code(
CodeClassorNULL) seeteal.data::CodeClass.check(
logical(1)) whether data has been check against reproducibility.
Method datanames()
Gets datanames
The datanames are returned in the order in which they must be evaluated (in case of dependencies).
Method get_filterable_varnames()
Gets variable names of a given dataname for the filtering.
Method get_call()
Gets a call to filter the dataset according to the filter state
It returns a call to filter the dataset only, assuming the
other (filtered) datasets it depends on are available.
Together with self$datanames() which returns the datasets in the correct
evaluation order, this generates the whole filter code, see the function
FilteredData$get_filter_code.
For the return type, note that rlang::is_expression returns TRUE on the
return type, both for base R expressions and calls (single expression,
capturing a function call).
The filtered dataset has the name given by self$filtered_dataname(dataname)
This can be used for the Show R Code generation.
Method get_code()
Gets the R preprocessing code string that generates the unfiltered datasets
Method get_filtered_dataset()
Gets FilteredDataset object which contains all informations
related to specific dataset.
Usage
FilteredData$get_filtered_dataset(dataname = character(0))Method get_data()
Gets filtered or unfiltered dataset
For filtered = FALSE, the original data set with
set_data is returned including all attributes.
Method get_filter_overview()
Get filter overview table in form of X (filtered) / Y (non-filtered)
This is intended to be presented in the application.
The content for each of the data names is defined in get_filter_overview_info method.
Method get_keys()
Method get_varlabels()
Gets labels of variables in the data
Variables are the column names of the data.
Either, all labels must have been provided for all variables
in set_data or NULL.
Method set_dataset()
Adds a dataset to this FilteredData
Method set_code()
Sets the R preprocessing code for single dataset
Method get_filter_state()
Gets the reactive values from the active FilterState objects.
Gets all active filters in the form of a nested list.
The output list is a compatible input to self$set_filter_state.
Method get_formatted_filter_state()
Returns the filter state formatted for printing to an IO device.
Examples
utils::data(miniACC, package = "MultiAssayExperiment")
datasets <- teal.slice:::FilteredData$new(
list(iris = list(dataset = iris),
mae = list(dataset = miniACC)
),
join_keys = NULL
)
fs <- list(
iris = list(
Sepal.Length = list(selected = c(5.1, 6.4), keep_na = TRUE, keep_inf = FALSE),
Species = list(selected = c("setosa", "versicolor"), keep_na = FALSE)
),
mae = list(
subjects = list(
years_to_birth = list(selected = c(30, 50), keep_na = TRUE, keep_inf = FALSE),
vital_status = list(selected = "1", keep_na = FALSE),
gender = list(selected = "female", keep_na = TRUE)
),
RPPAArray = list(
subset = list(ARRAY_TYPE = list(selected = "", keep_na = TRUE))
)
)
)
datasets$set_filter_state(state = fs)
cat(shiny::isolate(datasets$get_formatted_filter_state()))Method set_filter_state()
Sets active filter states.
Examples
utils::data(miniACC, package = "MultiAssayExperiment")
datasets <- teal.slice:::FilteredData$new(
list(iris = list(dataset = iris),
mae = list(dataset = miniACC)
),
join_keys = NULL
)
fs <- list(
iris = list(
Sepal.Length = list(selected = c(5.1, 6.4), keep_na = TRUE, keep_inf = FALSE),
Species = list(selected = c("setosa", "versicolor"), keep_na = FALSE)
),
mae = list(
subjects = list(
years_to_birth = list(selected = c(30, 50), keep_na = TRUE, keep_inf = FALSE),
vital_status = list(selected = "1", keep_na = FALSE),
gender = list(selected = "female", keep_na = TRUE)
),
RPPAArray = list(
subset = list(ARRAY_TYPE = list(selected = "", keep_na = TRUE))
)
)
)
datasets$set_filter_state(state = fs)
shiny::isolate(datasets$get_filter_state())Method remove_filter_state()
Remove one or more FilterState of a FilteredDataset in a FilteredData object
Method remove_all_filter_states()
Remove all FilterStates of a FilteredDataset or all FilterStates of a FilteredData object
Method restore_state_from_bookmark()
Sets this object from a bookmarked state
Only sets the filter state, does not set the data
and the preprocessing code. The data should already have been set.
Also checks the preprocessing code is identical if provided in the state.
Since this function is used from the end-user part, its error messages are more verbose. We don't call the Shiny modals from here because this class may be used outside of a Shiny app.
Arguments
state(
named list)
containing fieldsdata_hash,filter_statesandpreproc_code.check_data_hash-
(
logical) whether to check thatmd5sumsagree for the data; may not make sense with randomly generated data per session Module for the right filter panel in the teal app with a filter overview panel and a filter variable panel.This panel contains info about the number of observations left in the (active) datasets and allows to filter the datasets.
Method srv_filter_panel()
Arguments
id(
character(1))
an ID string that corresponds with the ID used to call the module's UI function.active_datanamesfunction / reactivereturning datanames that should be shown on the filter panel, must be a subset of thedatanamesargument provided toui_filter_panel; if the function returnsNULL(as opposed tocharacter(0)), the filter panel will be hidden
Method srv_filter_overview()
Arguments
id(
character(1))
an ID string that corresponds with the ID used to call the module's UI function.active_datanames(
function,reactive)
returning datanames that should be shown on the filter panel, must be a subset of thedatanamesargument provided toui_filter_panel; if the function returnsNULL(as opposed tocharacter(0)), the filter panel will be hidden.
Examples
library(shiny)
datasets <- teal.slice:::FilteredData$new(
list(
iris = list(dataset = iris),
mtcars = list(dataset = mtcars, metadata = list(type = "training"))
)
)
# get datanames
datasets$datanames()
#> [1] "iris" "mtcars"
df <- datasets$get_data("iris", filtered = FALSE)
print(df)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5.0 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> 11 5.4 3.7 1.5 0.2 setosa
#> 12 4.8 3.4 1.6 0.2 setosa
#> 13 4.8 3.0 1.4 0.1 setosa
#> 14 4.3 3.0 1.1 0.1 setosa
#> 15 5.8 4.0 1.2 0.2 setosa
#> 16 5.7 4.4 1.5 0.4 setosa
#> 17 5.4 3.9 1.3 0.4 setosa
#> 18 5.1 3.5 1.4 0.3 setosa
#> 19 5.7 3.8 1.7 0.3 setosa
#> 20 5.1 3.8 1.5 0.3 setosa
#> 21 5.4 3.4 1.7 0.2 setosa
#> 22 5.1 3.7 1.5 0.4 setosa
#> 23 4.6 3.6 1.0 0.2 setosa
#> 24 5.1 3.3 1.7 0.5 setosa
#> 25 4.8 3.4 1.9 0.2 setosa
#> 26 5.0 3.0 1.6 0.2 setosa
#> 27 5.0 3.4 1.6 0.4 setosa
#> 28 5.2 3.5 1.5 0.2 setosa
#> 29 5.2 3.4 1.4 0.2 setosa
#> 30 4.7 3.2 1.6 0.2 setosa
#> 31 4.8 3.1 1.6 0.2 setosa
#> 32 5.4 3.4 1.5 0.4 setosa
#> 33 5.2 4.1 1.5 0.1 setosa
#> 34 5.5 4.2 1.4 0.2 setosa
#> 35 4.9 3.1 1.5 0.2 setosa
#> 36 5.0 3.2 1.2 0.2 setosa
#> 37 5.5 3.5 1.3 0.2 setosa
#> 38 4.9 3.6 1.4 0.1 setosa
#> 39 4.4 3.0 1.3 0.2 setosa
#> 40 5.1 3.4 1.5 0.2 setosa
#> 41 5.0 3.5 1.3 0.3 setosa
#> 42 4.5 2.3 1.3 0.3 setosa
#> 43 4.4 3.2 1.3 0.2 setosa
#> 44 5.0 3.5 1.6 0.6 setosa
#> 45 5.1 3.8 1.9 0.4 setosa
#> 46 4.8 3.0 1.4 0.3 setosa
#> 47 5.1 3.8 1.6 0.2 setosa
#> 48 4.6 3.2 1.4 0.2 setosa
#> 49 5.3 3.7 1.5 0.2 setosa
#> 50 5.0 3.3 1.4 0.2 setosa
#> 51 7.0 3.2 4.7 1.4 versicolor
#> 52 6.4 3.2 4.5 1.5 versicolor
#> 53 6.9 3.1 4.9 1.5 versicolor
#> 54 5.5 2.3 4.0 1.3 versicolor
#> 55 6.5 2.8 4.6 1.5 versicolor
#> 56 5.7 2.8 4.5 1.3 versicolor
#> 57 6.3 3.3 4.7 1.6 versicolor
#> 58 4.9 2.4 3.3 1.0 versicolor
#> 59 6.6 2.9 4.6 1.3 versicolor
#> 60 5.2 2.7 3.9 1.4 versicolor
#> 61 5.0 2.0 3.5 1.0 versicolor
#> 62 5.9 3.0 4.2 1.5 versicolor
#> 63 6.0 2.2 4.0 1.0 versicolor
#> 64 6.1 2.9 4.7 1.4 versicolor
#> 65 5.6 2.9 3.6 1.3 versicolor
#> 66 6.7 3.1 4.4 1.4 versicolor
#> 67 5.6 3.0 4.5 1.5 versicolor
#> 68 5.8 2.7 4.1 1.0 versicolor
#> 69 6.2 2.2 4.5 1.5 versicolor
#> 70 5.6 2.5 3.9 1.1 versicolor
#> 71 5.9 3.2 4.8 1.8 versicolor
#> 72 6.1 2.8 4.0 1.3 versicolor
#> 73 6.3 2.5 4.9 1.5 versicolor
#> 74 6.1 2.8 4.7 1.2 versicolor
#> 75 6.4 2.9 4.3 1.3 versicolor
#> 76 6.6 3.0 4.4 1.4 versicolor
#> 77 6.8 2.8 4.8 1.4 versicolor
#> 78 6.7 3.0 5.0 1.7 versicolor
#> 79 6.0 2.9 4.5 1.5 versicolor
#> 80 5.7 2.6 3.5 1.0 versicolor
#> 81 5.5 2.4 3.8 1.1 versicolor
#> 82 5.5 2.4 3.7 1.0 versicolor
#> 83 5.8 2.7 3.9 1.2 versicolor
#> 84 6.0 2.7 5.1 1.6 versicolor
#> 85 5.4 3.0 4.5 1.5 versicolor
#> 86 6.0 3.4 4.5 1.6 versicolor
#> 87 6.7 3.1 4.7 1.5 versicolor
#> 88 6.3 2.3 4.4 1.3 versicolor
#> 89 5.6 3.0 4.1 1.3 versicolor
#> 90 5.5 2.5 4.0 1.3 versicolor
#> 91 5.5 2.6 4.4 1.2 versicolor
#> 92 6.1 3.0 4.6 1.4 versicolor
#> 93 5.8 2.6 4.0 1.2 versicolor
#> 94 5.0 2.3 3.3 1.0 versicolor
#> 95 5.6 2.7 4.2 1.3 versicolor
#> 96 5.7 3.0 4.2 1.2 versicolor
#> 97 5.7 2.9 4.2 1.3 versicolor
#> 98 6.2 2.9 4.3 1.3 versicolor
#> 99 5.1 2.5 3.0 1.1 versicolor
#> 100 5.7 2.8 4.1 1.3 versicolor
#> 101 6.3 3.3 6.0 2.5 virginica
#> 102 5.8 2.7 5.1 1.9 virginica
#> 103 7.1 3.0 5.9 2.1 virginica
#> 104 6.3 2.9 5.6 1.8 virginica
#> 105 6.5 3.0 5.8 2.2 virginica
#> 106 7.6 3.0 6.6 2.1 virginica
#> 107 4.9 2.5 4.5 1.7 virginica
#> 108 7.3 2.9 6.3 1.8 virginica
#> 109 6.7 2.5 5.8 1.8 virginica
#> 110 7.2 3.6 6.1 2.5 virginica
#> 111 6.5 3.2 5.1 2.0 virginica
#> 112 6.4 2.7 5.3 1.9 virginica
#> 113 6.8 3.0 5.5 2.1 virginica
#> 114 5.7 2.5 5.0 2.0 virginica
#> 115 5.8 2.8 5.1 2.4 virginica
#> 116 6.4 3.2 5.3 2.3 virginica
#> 117 6.5 3.0 5.5 1.8 virginica
#> 118 7.7 3.8 6.7 2.2 virginica
#> 119 7.7 2.6 6.9 2.3 virginica
#> 120 6.0 2.2 5.0 1.5 virginica
#> 121 6.9 3.2 5.7 2.3 virginica
#> 122 5.6 2.8 4.9 2.0 virginica
#> 123 7.7 2.8 6.7 2.0 virginica
#> 124 6.3 2.7 4.9 1.8 virginica
#> 125 6.7 3.3 5.7 2.1 virginica
#> 126 7.2 3.2 6.0 1.8 virginica
#> 127 6.2 2.8 4.8 1.8 virginica
#> 128 6.1 3.0 4.9 1.8 virginica
#> 129 6.4 2.8 5.6 2.1 virginica
#> 130 7.2 3.0 5.8 1.6 virginica
#> 131 7.4 2.8 6.1 1.9 virginica
#> 132 7.9 3.8 6.4 2.0 virginica
#> 133 6.4 2.8 5.6 2.2 virginica
#> 134 6.3 2.8 5.1 1.5 virginica
#> 135 6.1 2.6 5.6 1.4 virginica
#> 136 7.7 3.0 6.1 2.3 virginica
#> 137 6.3 3.4 5.6 2.4 virginica
#> 138 6.4 3.1 5.5 1.8 virginica
#> 139 6.0 3.0 4.8 1.8 virginica
#> 140 6.9 3.1 5.4 2.1 virginica
#> 141 6.7 3.1 5.6 2.4 virginica
#> 142 6.9 3.1 5.1 2.3 virginica
#> 143 5.8 2.7 5.1 1.9 virginica
#> 144 6.8 3.2 5.9 2.3 virginica
#> 145 6.7 3.3 5.7 2.5 virginica
#> 146 6.7 3.0 5.2 2.3 virginica
#> 147 6.3 2.5 5.0 1.9 virginica
#> 148 6.5 3.0 5.2 2.0 virginica
#> 149 6.2 3.4 5.4 2.3 virginica
#> 150 5.9 3.0 5.1 1.8 virginica
datasets$get_metadata("mtcars")
#> $type
#> [1] "training"
#>
datasets$set_filter_state(
list(iris = list(Species = list(selected = "virginica")))
)
isolate(datasets$get_call("iris"))
#> $filter
#> iris_FILTERED <- dplyr::filter(iris, Species == "virginica")
#>
datasets$set_filter_state(
list(mtcars = list(mpg = list(selected = c(15, 20))))
)
isolate(datasets$get_filter_state())
#> $iris
#> $iris$Species
#> $iris$Species$selected
#> [1] "virginica"
#>
#> $iris$Species$keep_na
#> [1] FALSE
#>
#>
#>
#> $mtcars
#> $mtcars$mpg
#> $mtcars$mpg$selected
#> [1] 15 20
#>
#> $mtcars$mpg$keep_na
#> [1] FALSE
#>
#> $mtcars$mpg$keep_inf
#> [1] FALSE
#>
#>
#>
isolate(datasets$get_filter_overview("iris"))
#> Obs Subjects
#> iris "50/150" ""
isolate(datasets$get_filter_overview("mtcars"))
#> Obs Subjects
#> mtcars "13/32" ""
isolate(datasets$get_call("iris"))
#> $filter
#> iris_FILTERED <- dplyr::filter(iris, Species == "virginica")
#>
isolate(datasets$get_call("mtcars"))
#> $filter
#> mtcars_FILTERED <- dplyr::filter(mtcars, mpg >= 15 & mpg <= 20)
#>
## ------------------------------------------------
## Method `FilteredData$get_formatted_filter_state`
## ------------------------------------------------
utils::data(miniACC, package = "MultiAssayExperiment")
datasets <- teal.slice:::FilteredData$new(
list(iris = list(dataset = iris),
mae = list(dataset = miniACC)
),
join_keys = NULL
)
#> Loading required package: MultiAssayExperiment
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#>
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#>
#> Filter, Find, Map, Position, Reduce, anyDuplicated, append,
#> as.data.frame, basename, cbind, colnames, dirname, do.call,
#> duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#> lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#> pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
#> tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#>
#> Attaching package: ‘S4Vectors’
#> The following objects are masked from ‘package:base’:
#>
#> I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
#>
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#>
#> rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#>
#> anyMissing, rowMedians
#>
#> Attaching package: ‘MultiAssayExperiment’
#> The following object is masked _by_ ‘.GlobalEnv’:
#>
#> miniACC
fs <- list(
iris = list(
Sepal.Length = list(selected = c(5.1, 6.4), keep_na = TRUE, keep_inf = FALSE),
Species = list(selected = c("setosa", "versicolor"), keep_na = FALSE)
),
mae = list(
subjects = list(
years_to_birth = list(selected = c(30, 50), keep_na = TRUE, keep_inf = FALSE),
vital_status = list(selected = "1", keep_na = FALSE),
gender = list(selected = "female", keep_na = TRUE)
),
RPPAArray = list(
subset = list(ARRAY_TYPE = list(selected = "", keep_na = TRUE))
)
)
)
datasets$set_filter_state(state = fs)
cat(shiny::isolate(datasets$get_formatted_filter_state()))
#> Filters for dataset: iris
#> Filtering on: Sepal.Length
#> Selected range: 5.100 - 6.400
#> Include missing values: TRUE
#> Filtering on: Species
#> Selected values: setosa versicolor
#> Include missing values: FALSE
#> Filters for dataset: mae
#> Subject filters:
#> Filtering on: years_to_birth
#> Selected range: 30.000 - 50.000
#> Include missing values: TRUE
#> Filtering on: vital_status
#> Selected values: 1
#> Include missing values: FALSE
#> Filtering on: gender
#> Selected values: female
#> Include missing values: TRUE
#> Assay RPPAArray filters:
#> Subsetting:
#> Filtering on: ARRAY_TYPE
#> Selected values:
#> Include missing values: TRUE
## ------------------------------------------------
## Method `FilteredData$set_filter_state`
## ------------------------------------------------
utils::data(miniACC, package = "MultiAssayExperiment")
datasets <- teal.slice:::FilteredData$new(
list(iris = list(dataset = iris),
mae = list(dataset = miniACC)
),
join_keys = NULL
)
fs <- list(
iris = list(
Sepal.Length = list(selected = c(5.1, 6.4), keep_na = TRUE, keep_inf = FALSE),
Species = list(selected = c("setosa", "versicolor"), keep_na = FALSE)
),
mae = list(
subjects = list(
years_to_birth = list(selected = c(30, 50), keep_na = TRUE, keep_inf = FALSE),
vital_status = list(selected = "1", keep_na = FALSE),
gender = list(selected = "female", keep_na = TRUE)
),
RPPAArray = list(
subset = list(ARRAY_TYPE = list(selected = "", keep_na = TRUE))
)
)
)
datasets$set_filter_state(state = fs)
shiny::isolate(datasets$get_filter_state())
#> $iris
#> $iris$Sepal.Length
#> $iris$Sepal.Length$selected
#> [1] 5.1 6.4
#>
#> $iris$Sepal.Length$keep_na
#> [1] TRUE
#>
#> $iris$Sepal.Length$keep_inf
#> [1] FALSE
#>
#>
#> $iris$Species
#> $iris$Species$selected
#> [1] "setosa" "versicolor"
#>
#> $iris$Species$keep_na
#> [1] FALSE
#>
#>
#>
#> $mae
#> $mae$subjects
#> $mae$subjects$years_to_birth
#> $mae$subjects$years_to_birth$selected
#> [1] 30 50
#>
#> $mae$subjects$years_to_birth$keep_na
#> [1] TRUE
#>
#> $mae$subjects$years_to_birth$keep_inf
#> [1] FALSE
#>
#>
#> $mae$subjects$vital_status
#> $mae$subjects$vital_status$selected
#> [1] "1"
#>
#> $mae$subjects$vital_status$keep_na
#> [1] FALSE
#>
#>
#> $mae$subjects$gender
#> $mae$subjects$gender$selected
#> [1] "female"
#>
#> $mae$subjects$gender$keep_na
#> [1] TRUE
#>
#>
#>
#> $mae$RPPAArray
#> $mae$RPPAArray$subset
#> $mae$RPPAArray$subset$ARRAY_TYPE
#> $mae$RPPAArray$subset$ARRAY_TYPE$selected
#> [1] ""
#>
#> $mae$RPPAArray$subset$ARRAY_TYPE$keep_na
#> [1] TRUE
#>
#>
#>
#>
#>