splnr_apply_cutoffs() transforms numeric feature data in an sf dataframe
into binary (0 or 1) presence/absence values based on specified cutoffs.
It provides flexibility to either keep values above a cutoff as 1 (default)
or invert this logic to keep values below a cutoff as 1.
Arguments
- features
An
sfdataframe. It must contain ageometrycolumn and at least one numeric column to which cutoffs will be applied.- Cutoffs
A numeric value or a named numeric vector of cutoffs.
If a single unnamed numeric value, it's applied to all numeric columns.
If a named numeric vector, names must correspond to numeric column names in
features.
All cutoff values must be between
0and1.- inverse
A logical value (
TRUEorFALSE). IfTRUE, values below theCutoffsare converted to1(and others to0). IfFALSE(default), values at or above theCutoffsare converted to1.
Value
A modified sf dataframe with the same structure and geometry as
features, but with all targeted numeric columns transformed into binary
(0 or 1) values based on the specified cutoffs and inverse setting.
Details
This function is crucial for standardizing feature data, such as species
probability distributions or habitat suitability scores, into a binary format
often required for conservation planning and spatial analysis (e.g., in
prioritizr).
The function operates in two primary modes based on the Cutoffs parameter:
Single Cutoff: If
Cutoffsis a single numeric value (e.g.,0.5), this value is applied uniformly to all numeric columns in thefeaturesdataframe, excluding thegeometrycolumn. For each numeric cell: - Ifvalue >= Cutoffs, it becomes1. - Ifvalue < Cutoffs, it becomes0. -NAvalues are always converted to0.Named Vector of Cutoffs: If
Cutoffsis a named numeric vector (e.g.,c("feature1" = 0.5, "feature2" = 0.3)), each specified cutoff is applied individually to its corresponding named column infeatures. This allows for different thresholds for different features. The same transformation rules as above apply to each specified column.
The inverse parameter provides additional control over the binarization:
inverse = FALSE(default): Values at or above the cutoff become1.inverse = TRUE: Values below the cutoff become1. After initial binarization (where values >= cutoff are 1), the binary results are flipped (0s become 1s, and 1s become 0s) to achieve the inverse effect.
All NA values in the numeric columns are consistently converted to 0 during
the binarization process, regardless of the inverse setting.
Examples
# Example 1: Single cutoff (0.5) applied to all numeric feature columns
# (Spp1_Prob, Spp2_Prob, and Cost will be binarized based on 0.5)
df_single_cutoff <- splnr_apply_cutoffs(dat_species_prob, Cutoffs = 0.5)
#> Applying single cutoff of 0.5 to all numeric feature columns.
print(df_single_cutoff)
#> Simple feature collection with 780 features and 5 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 100 ymin: -50 xmax: 160 ymax: 2
#> Geodetic CRS: WGS 84
#> # A tibble: 780 × 6
#> geometry Spp1 Spp2 Spp3 Spp4 Spp5
#> <POLYGON [°]> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ((100 -50, 102 -50, 102 -48, 100 -48, 100 -50)) 1 0 0 0 0
#> 2 ((102 -50, 104 -50, 104 -48, 102 -48, 102 -50)) 1 0 1 1 0
#> 3 ((104 -50, 106 -50, 106 -48, 104 -48, 104 -50)) 0 1 0 1 1
#> 4 ((106 -50, 108 -50, 108 -48, 106 -48, 106 -50)) 1 1 1 1 1
#> 5 ((108 -50, 110 -50, 110 -48, 108 -48, 108 -50)) 0 1 0 1 1
#> 6 ((110 -50, 112 -50, 112 -48, 110 -48, 110 -50)) 1 0 1 0 0
#> 7 ((112 -50, 114 -50, 114 -48, 112 -48, 112 -50)) 0 1 0 0 0
#> 8 ((114 -50, 116 -50, 116 -48, 114 -48, 114 -50)) 0 0 0 0 1
#> 9 ((116 -50, 118 -50, 118 -48, 116 -48, 116 -50)) 0 1 1 0 1
#> 10 ((118 -50, 120 -50, 120 -48, 118 -48, 118 -50)) 1 0 1 0 1
#> # ℹ 770 more rows
# Example 2: Named cutoffs for specific columns
# Spp1_Prob >= 0.6 becomes 1, Spp2_Prob >= 0.4 becomes 1
df_named_cutoffs <- splnr_apply_cutoffs(
dat_species_prob,
Cutoffs = c("Spp1" = 0.6, "Spp2" = 0.4)
)
#> Applying named cutoffs to specific feature columns.
#> Applying cutoff 0.6 to column 'Spp1'.
#> Applying cutoff 0.4 to column 'Spp2'.
print(df_named_cutoffs)
#> Simple feature collection with 780 features and 5 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 100 ymin: -50 xmax: 160 ymax: 2
#> Geodetic CRS: WGS 84
#> # A tibble: 780 × 6
#> geometry Spp1 Spp2 Spp3 Spp4 Spp5
#> <POLYGON [°]> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ((100 -50, 102 -50, 102 -48, 100 -48, 100… 1 0 0.0969 0.435 0.0418
#> 2 ((102 -50, 104 -50, 104 -48, 102 -48, 102… 0 1 0.504 0.503 0.360
#> 3 ((104 -50, 106 -50, 106 -48, 104 -48, 104… 0 1 0.285 0.755 0.653
#> 4 ((106 -50, 108 -50, 108 -48, 106 -48, 106… 0 1 0.564 0.503 0.529
#> 5 ((108 -50, 110 -50, 110 -48, 108 -48, 108… 0 1 0.150 0.863 0.753
#> 6 ((110 -50, 112 -50, 112 -48, 110 -48, 110… 1 1 0.807 0.458 0.374
#> 7 ((112 -50, 114 -50, 114 -48, 112 -48, 112… 0 1 0.00963 0.102 0.114
#> 8 ((114 -50, 116 -50, 116 -48, 114 -48, 114… 0 0 0.481 0.231 0.764
#> 9 ((116 -50, 118 -50, 118 -48, 116 -48, 116… 0 1 0.552 0.00978 0.552
#> 10 ((118 -50, 120 -50, 120 -48, 118 -48, 118… 1 1 0.695 0.00687 0.815
#> # ℹ 770 more rows
# Example 3: Single cutoff (0.5) with inverse logic
# Values BELOW 0.5 become 1.
df_inverse_cutoff <- splnr_apply_cutoffs(dat_species_prob, Cutoffs = 0.5, inverse = TRUE)
#> Applying single cutoff of 0.5 to all numeric feature columns.
#> Inverse logic applied: values below cutoff will be 1.
print(df_inverse_cutoff)
#> Simple feature collection with 780 features and 5 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 100 ymin: -50 xmax: 160 ymax: 2
#> Geodetic CRS: WGS 84
#> # A tibble: 780 × 6
#> geometry Spp1 Spp2 Spp3 Spp4 Spp5
#> <POLYGON [°]> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ((100 -50, 102 -50, 102 -48, 100 -48, 100 -50)) 0 1 1 1 1
#> 2 ((102 -50, 104 -50, 104 -48, 102 -48, 102 -50)) 0 1 0 0 1
#> 3 ((104 -50, 106 -50, 106 -48, 104 -48, 104 -50)) 1 0 1 0 0
#> 4 ((106 -50, 108 -50, 108 -48, 106 -48, 106 -50)) 0 0 0 0 0
#> 5 ((108 -50, 110 -50, 110 -48, 108 -48, 108 -50)) 1 0 1 0 0
#> 6 ((110 -50, 112 -50, 112 -48, 110 -48, 110 -50)) 0 1 0 1 1
#> 7 ((112 -50, 114 -50, 114 -48, 112 -48, 112 -50)) 1 0 1 1 1
#> 8 ((114 -50, 116 -50, 116 -48, 114 -48, 114 -50)) 1 1 1 1 0
#> 9 ((116 -50, 118 -50, 118 -48, 116 -48, 116 -50)) 1 0 0 1 0
#> 10 ((118 -50, 120 -50, 120 -48, 118 -48, 118 -50)) 0 1 0 1 0
#> # ℹ 770 more rows
# Example 4: Named cutoffs with inverse logic
df_named_inverse <- splnr_apply_cutoffs(
dat_species_prob,
Cutoffs = c("Spp1" = 0.7, "Spp2" = 0.3),
inverse = TRUE
)
#> Applying named cutoffs to specific feature columns.
#> Applying cutoff 0.7 to column 'Spp1'.
#> Inverse logic applied for column 'Spp1': values below cutoff will be 1.
#> Applying cutoff 0.3 to column 'Spp2'.
#> Inverse logic applied for column 'Spp2': values below cutoff will be 1.
print(df_named_inverse)
#> Simple feature collection with 780 features and 5 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 100 ymin: -50 xmax: 160 ymax: 2
#> Geodetic CRS: WGS 84
#> # A tibble: 780 × 6
#> geometry Spp1 Spp2 Spp3 Spp4 Spp5
#> <POLYGON [°]> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ((100 -50, 102 -50, 102 -48, 100 -48, 100… 1 1 0.0969 0.435 0.0418
#> 2 ((102 -50, 104 -50, 104 -48, 102 -48, 102… 1 0 0.504 0.503 0.360
#> 3 ((104 -50, 106 -50, 106 -48, 104 -48, 104… 1 0 0.285 0.755 0.653
#> 4 ((106 -50, 108 -50, 108 -48, 106 -48, 106… 1 0 0.564 0.503 0.529
#> 5 ((108 -50, 110 -50, 110 -48, 108 -48, 108… 1 0 0.150 0.863 0.753
#> 6 ((110 -50, 112 -50, 112 -48, 110 -48, 110… 0 0 0.807 0.458 0.374
#> 7 ((112 -50, 114 -50, 114 -48, 112 -48, 112… 1 0 0.00963 0.102 0.114
#> 8 ((114 -50, 116 -50, 116 -48, 114 -48, 114… 1 1 0.481 0.231 0.764
#> 9 ((116 -50, 118 -50, 118 -48, 116 -48, 116… 1 0 0.552 0.00978 0.552
#> 10 ((118 -50, 120 -50, 120 -48, 118 -48, 118… 1 0 0.695 0.00687 0.815
#> # ℹ 770 more rows