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Input validation for
extract_data (#147)
* Checks for missingness #139 * Checks if table name exists #139 * Checks for whether col exists in table #139 * Print generic updated to handle tibble and full databses #139, extract_data only assigns class if full database supplied #139 * Added checks for column values if traits table supplied #139 * Added checks on returned output for non-matches and will not throw error, updated tests accordingly #139 * Added namespace in helper function to pass R CMD CHECK * Moved helper test file to testthat dir and bumped version number
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,105 @@ | ||
| # Check missingness | ||
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| check_arg_missingness <- function(database, col, col_value){ | ||
| if(rlang::is_missing(database) | rlang::is_missing(col) | rlang::is_missing(col_value)) | ||
| cli::cli_abort(c( | ||
| "x" = "`database`, `col` or `col_value` must be supplied!" | ||
| ) | ||
| ) | ||
| } | ||
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| # Check if table name exists | ||
| # Note that users can supply the $traits table to database, for that reason table can be NA | ||
| # First check if table is NA | ||
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| check_table_name_exists <- function(database, table){ | ||
| if(!is.na(table) & !tibble::is_tibble(database)){ # database is NOT $traits and `table` is supplied | ||
| if(! names(database) %in% table |> any()){ # Does any names of the tables in database contain `table` | ||
| cli::cli_abort( | ||
| c( | ||
| "x" = "`{table}` is not a valid table name", | ||
| "i" = "Check `names(database)` and try again!" | ||
| ) | ||
| ) | ||
| } | ||
| } | ||
| } | ||
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| # Check if col exists in specified table when database is traits.build object | ||
| check_col_exists_in_table <- function(database, table, col){ | ||
| # If traits table supplied and no table is specified | ||
| if(tibble::is_tibble(database)){ | ||
| if(! names(database) %in% col |> any()){ # Does any names in table contain `col` | ||
| cli::cli_abort(c( | ||
| "x" = "`{col}` is not a valid column name in the `traits` table", | ||
| "i" = "Check `names(database$traits)` and try again!" | ||
| ) | ||
| ) | ||
| } | ||
| } else( | ||
| if(! names(database[[table]]) %in% col |> any()){ # Does any names in table contain `col` | ||
| cli::cli_abort(c( | ||
| "x" = "`{col}` is not a valid column name in the `{table}` table", | ||
| "i" = "Check `names(database${table})` and try again!" | ||
| ) | ||
| ) | ||
| } | ||
| ) | ||
| } | ||
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| # Check if col_value exists in the col after attempted extraction | ||
| # Accommodating for multiple values supplied AND partial matching | ||
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| check_col_value_exists <- function(ret, table, col, col_value){ | ||
| if(tibble::is_tibble(ret)){ | ||
| if(nrow(ret) == 0) | ||
| cli::cli_abort(c( | ||
| "x" = "`{col_value}` is not a valid value in `{col}` column of the `traits` table", | ||
| "i" = "Check spelling of `{col_value}` and try again!" | ||
| ) | ||
| ) | ||
| } else( | ||
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| if(nrow(ret$traits) == 0) | ||
| cli::cli_abort(c( | ||
| "x" = "`{col_value}` is not a valid value in `{col}` column of the `{table}` table", | ||
| "i" = "Check spelling of `{col_value}` and try again!" | ||
| ) | ||
| ) | ||
| ) | ||
| } | ||
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| # | ||
| # # Get possible col values | ||
| # available_values <- database[[table]][col] |> dplyr::pull() |> unique() | ||
| # | ||
| # # Check if there are non-matches | ||
| # if(length(col_value) > 1) | ||
| # concat_col_value <- paste(col_value, collapse = "|") | ||
| # | ||
| # partial_matches <- stringr::str_detect(available_values, concat_col_value) | ||
| # | ||
| # if(length(partial_matches) > 1) | ||
| # | ||
| # # Prompt user which one is non-match | ||
| # if(length(no_match) >= 1){ | ||
| # cli::cli_warn(c("x" = "`{no_match}` is not a valid value in `{col}` of the `{table}` table")) | ||
| # } | ||
|
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| # # Check if col_value exists in the col | ||
| # # Accommodating for multiple values supplied | ||
| # | ||
| # # Get possible col values | ||
| # available_values <- database[[table]][col] |> dplyr::pull() |> unique() | ||
| # | ||
| # # Check if there are non-matches | ||
| # no_match <- col_value[which(! col_value %in% available_values)] | ||
| # | ||
| # # Identify matches | ||
| # matches <- col_value[which(col_value %in% available_values)] | ||
| # | ||
| # # Prompt user which one is non-match | ||
| # if(length(no_match) > 0){ | ||
| # cli::cli_warn("`{no_match}` is not a valid value in `{col}` of the `{table}` table") | ||
| # cli::cli_alert_success("Continuing data extraction for {.val {matches}}") | ||
| # } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,28 @@ | ||
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| test_database_structure <- function(database, taxa = NA, dataset_id = NA, n_row = NA) { | ||
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| table_names <- c("traits", "locations", "contexts", "methods", "excluded_data", "taxonomic_updates", "taxa", "contributors", | ||
| "sources", "definitions", "schema", "metadata", "build_info") | ||
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| testthat::expect_type(database, "list") | ||
| testthat::expect_equal(class(database), "traits.build") | ||
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| testthat::expect_equal(names(database), table_names) | ||
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| testthat::expect_contains(database$traits$taxon_name |> unique(), database$taxa$taxon_name |> unique()) | ||
| testthat::expect_contains(database$traits$dataset_id |> unique(), database$methods$dataset_id |> unique()) | ||
| testthat::expect_contains(paste(database$traits$dataset_id, database$traits$trait_name) |> unique(), paste(database$methods$dataset_id, database$methods$trait_name) |> unique()) | ||
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| if(!is.na(taxa)) { | ||
| testthat::expect_contains(database$traits$taxon_name |> unique(), taxa |> unique()) | ||
| } | ||
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| if(!is.na(dataset_id)) { | ||
| testthat::expect_contains(database$traits$dataset_id |> unique(), dataset_id |> unique()) | ||
| } | ||
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| if(!is.na(n_row)) { | ||
| testthat::expect_equal(database$traits |> nrow(), n_row) | ||
| } | ||
| } |
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