Replace missing values in a scan

comparison_replaceMissing(heightValues, replacement = 0)

Arguments

heightValues

list/tibble column of x3p objects

replacement

value to replace NAs

Value

A list of the same length as the input containing x3p objects for which NA values have been replaced.

Examples


data(fadul1.1_processed,fadul1.2_processed)

cellTibble <- fadul1.1_processed %>%
comparison_cellDivision(numCells = c(8,8)) %>%
dplyr::mutate(regionHeightValues =
             comparison_getTargetRegions(cellHeightValues = cellHeightValues,
                                         target = fadul1.2_processed)) %>%
dplyr::mutate(cellPropMissing =
                 comparison_calcPropMissing(heightValues = cellHeightValues),
              regionPropMissing =
           comparison_calcPropMissing(heightValues = regionHeightValues)) %>%
dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) %>%
dplyr::mutate(cellHeightValues =
              comparison_standardizeHeights(heightValues = cellHeightValues),
             regionHeightValues =
        comparison_standardizeHeights(heightValues = regionHeightValues)) %>%
dplyr::mutate(cellHeightValues =
                  comparison_replaceMissing(heightValues = cellHeightValues),
             regionHeightValues =
                comparison_replaceMissing(heightValues = regionHeightValues))

head(cellTibble)
#> # A tibble: 6 x 5
#>   cellIndex cellHeightValues regionHeightValues cellPropMissing regionPropMissi~
#>   <chr>     <named list>     <named list>                 <dbl>            <dbl>
#> 1 1, 6      <x3p>            <x3p>                        0.833            0.811
#> 2 2, 7      <x3p>            <x3p>                        0.657            0.700
#> 3 2, 8      <x3p>            <x3p>                        0.834            0.757
#> 4 3, 8      <x3p>            <x3p>                        0.353            0.638
#> 5 4, 8      <x3p>            <x3p>                        0.153            0.576
#> 6 5, 1      <x3p>            <x3p>                        0.117            0.771