This function ranks rows in a data frame based on the weighted scores of selected columns. The function normalizes the specified columns (using Min-Max normalization), computes a weighted score for each row, and assigns a rank based on these scores. If no weights are provided, the function defaults to equal weighting for the columns.
rank_results(df, columns_to_maximize, weights = NULL)A data frame containing the columns to be ranked.
A character vector of column names that should be maximized and used for ranking.
A numeric vector of weights for the columns. The length of this vector must match the number of columns in columns_to_maximize. Defaults to NULL, in which case equal weights are applied.
A data frame with the original columns, the normalized columns, the weighted score for each row, and the rank based on the weighted score.
# Create a sample data frame
df <- data.frame(
A = c(1, 2, 3, 4),
B = c(2, 3, 4, 5),
C = c(3, 4, 5, 6)
)
# Specify columns to maximize
columns_to_maximize <- c("A", "B")
# Rank the rows based on the specified columns
ranked_df <- rank_results(df, columns_to_maximize, weights = c(0.7, 0.3))
# View the ranked data frame
print(ranked_df)
#> # A tibble: 4 × 7
#> A B C norm_A norm_B weighted_score rank
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 4 5 6 1 1 1 1
#> 2 3 4 5 0.667 0.667 0.667 2
#> 3 2 3 4 0.333 0.333 0.333 3
#> 4 1 2 3 0 0 0 4