Batch Process Multiple Snowflake Queries for Consonance Functions

Source: R/curve_snowflake.R

Executes multiple queries against a Snowflake database and constructs consonance distributions for each result set.

curve_snowflake_batch(conn, queries, estimate_col = "estimate",
  lower_col = "lower", upper_col = "upper", conf.level = 0.95,
  steps = 1000, cores = getOption("mc.cores", 1L), table = TRUE)

Arguments

conn

A DBI connection object to Snowflake database.

queries

A named list of SQL query strings.

estimate_col

Name of the column containing point estimates.

lower_col

Name of the column containing lower bounds.

upper_col

Name of the column containing upper bounds.

conf.level

Confidence level of the input intervals. Default is 0.95.

steps

Number of consonance levels to compute. Default is 1000.

cores

Number of cores for parallel computation.

table

Logical. If TRUE (default), includes summary tables.

Value

A named list of concurve objects, one for each query.

See also

curve_snowflake() for single query processing

plot_compare() for comparing results

Examples

if (FALSE) { # \dontrun{
queries <- list(
  "Model A" = "SELECT estimate, lower_ci, upper_ci FROM results WHERE model = 'A'",
  "Model B" = "SELECT estimate, lower_ci, upper_ci FROM results WHERE model = 'B'"
)

results <- curve_snowflake_batch(conn, queries,
  estimate_col = "estimate",
  lower_col = "lower_ci",
  upper_col = "upper_ci"
)

# Plot comparison
plot_compare(results[["Model A"]][[1]], results[["Model B"]][[1]])
} # }