Calculate the "missingness" (NAs) of clinical meta data of an ADAT.
For the S3 plotting method, see plot.Map().
Usage
calcMissingnessMap(data, include.pattern = ".", exclude.pattern = NULL)
# S3 method for missingness_map
print(x, ...)Arguments
- data
A data frame (or
soma_adat) of only the meta data columns (i.e. no analytes).- include.pattern
Character (optional). A regular expression string used in a
grep()call to include matching column names. Defaults to include all column names in the meta data (".").- exclude.pattern
Character (optional). A regular expression string used in a
grep()call to exclude matching column names.- x
An object of class
"missingness_map".- ...
Arguments for S3 print methods.
Value
A list of class c("missingness_map", "Map") containing:
- matrix
A boolean matrix of
TRUE/FALSEwhether each sample is in missingness according the the stated criteria.- names
A character vector containing the names of the meta data columns.
- rows.by.freq
A logical indicating if the samples are ordered by missingness frequency. Currently always FALSE.
- legend.sub
A character string containing the plot legend subtitle.
- title
A character string containing the plot title.
- x.lab
A character string containing the plot x-axis label.
See also
Other Calc Map:
calcFoldchangeMatrix(),
calcOutlierMap(),
getFlaggedIds(),
plot.Map()
Examples
sample.adat <- SomaDataIO::example_data
meta <- sample.adat[, SomaDataIO::getMeta(sample.adat)]
# random assign NAs
cols <- rep(1:ncol(meta), each = 3)
rows <- as.integer(replicate(ncol(meta), sample(1:nrow(meta), 3)))
meta[cbind(rows, cols)] <- NA
mm <- calcMissingnessMap(meta)
class(mm)
#> [1] "missingness_map" "Map" "list"
# S3 print method
mm
#> ══ SomaLogic Missingness Map ═════════════════════════════════════════════
#> Missingness Map dim '192 x 34'
#> Title 'Meta Data Missingness Map'
#> Class Table NA
#> Legend Sub-title 'MetaData'
#> ══════════════════════════════════════════════════════════════════════════