Check if Dataset Has Minimum Required Columns — has_min

Helper function to determine if a specific dataset has sufficient columns of a given type for analysis.

Usage

has_min_cols(col_list, dataset_idx, min_count = 1)

has_min_cols(col_list, dataset_idx, min_count = 1)

Arguments

col_list: A named list of column vectors (output from check_col_types())
dataset_idx: Integer index of the dataset to check
min_count: Minimum number of columns required (default: 1)

Value

Logical indicating whether the dataset has sufficient columns

Examples

ttd <- load_tt_data("Moore's Law")
#> Error in load_tt_data("Moore's Law"): No entries found for title: 'Moore's Law'
num_cols <- check_col_types(ttd, "num")
#> Error: object 'ttd' not found
has_min_cols(num_cols, 1, min_count = 2)
#> Error: object 'num_cols' not found
ttd <- load_tt_data("Moore’s Law")
#> INFO [2026-04-10 16:29:33] Starting import for cpu.csv from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-09-03/cpu.csv
#> SUCCESS [2026-04-10 16:29:33] Successfully loaded cpu.csv
#> INFO [2026-04-10 16:29:33] Starting import for gpu.csv from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-09-03/gpu.csv
#> SUCCESS [2026-04-10 16:29:33] Successfully loaded gpu.csv
#> INFO [2026-04-10 16:29:33] Starting import for ram.csv from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-09-03/ram.csv
#> SUCCESS [2026-04-10 16:29:33] Successfully loaded ram.csv
num_cols <- check_col_types(ttd, "num")
has_min_cols(num_cols, 1, min_count = 2)
#> [1] TRUE