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Transform PSCIS Assessment Worksheet Data by Calculating Embedment Score

Source: R/fpr_xfm_paw_score_embed.R
fpr_xfm_paw_score_embed.Rd

This function calculates the embedment score for BC Provincial Stream Crossing Inventory System (PSCIS) data based on conditions involving continuous embedment, pipe diameter, and average depth of embedment.

Usage

fpr_xfm_paw_score_embed(
  dat,
  col_continuous_embeddedment_yes_no = continuous_embeddedment_yes_no,
  col_diameter_or_span_meters = diameter_or_span_meters,
  col_average_depth_embededdment_meters = average_depth_embededdment_meters,
  col_embed_score = embed_score,
  risk_high = 10,
  risk_mod = 5,
  risk_low = 0
)

Arguments

dat

[dataframe] A dataframe containing the PSCIS data.

col_continuous_embeddedment_yes_no

[character] A column name specifying continuous embedment ("Yes" or "No"), as a string or tidy-select syntax. Default is `continuous_embeddedment_yes_no`.

col_diameter_or_span_meters

[character] A column name specifying the diameter or span, as a string or tidy-select syntax. Default is `diameter_or_span_meters`.

col_average_depth_embededdment_meters

[character] A column name specifying the average depth of embedment, as a string or tidy-select syntax. Default is `average_depth_embededdment_meters`.

col_embed_score

[character] A column name for the output embedment score, as a string or tidy-select syntax. Default is `embed_score`.

risk_high

[numeric] A numeric value representing the risk score for non-continuous embedment. Default is `10`, aligning with the Pisces assessment worksheet.

risk_mod

[numeric] A numeric value representing the risk score for moderate embedment conditions. Default is `5`, aligning with the Pisces assessment worksheet.

risk_low

[numeric] A numeric value representing the risk score for low-risk embedment conditions. Default is `0`, aligning with the Pisces assessment worksheet.

Value

[dataframe] A dataframe with the specified column for embedment score added or updated.

See also

Other xfm paw: fpr_xfm_paw_all_scores_result(), fpr_xfm_paw_barrier_result(), fpr_xfm_paw_score_cv_len(), fpr_xfm_paw_score_cv_slope(), fpr_xfm_paw_score_final(), fpr_xfm_paw_score_outlet_drop(), fpr_xfm_paw_score_swr(), fpr_xfm_paw_swr()

Examples

dat <- data.frame(
  continuous_embeddedment_yes_no = c("No", "Yes", "Yes"),
  diameter_or_span_meters = c(2, NA, 3),
  average_depth_embededdment_meters = c(0.4, 0.1, NA)
)
fpr_xfm_paw_score_embed(
  dat
)
#>   continuous_embeddedment_yes_no diameter_or_span_meters
#> 1                             No                       2
#> 2                            Yes                      NA
#> 3                            Yes                       3
#>   average_depth_embededdment_meters embed_score
#> 1                               0.4          10
#> 2                               0.1           0
#> 3                                NA           0