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Find valid numeric, categorical, and logical variables in a dataframe

Source: R/identify_valid_variables.R
identify_valid_variables.Rd

Returns a list with the names of the valid numeric, categorical, and logical variables in a modelling dataframe.

Usage

identify_valid_variables(
  df = NULL,
  responses = NULL,
  predictors = NULL,
  decimals = 4,
  quiet = FALSE,
  ...
)

Arguments

df

(required; dataframe, tibble, or sf) A dataframe with responses (optional) and predictors. Must have at least 10 rows for pairwise correlation analysis, and 10 * (length(predictors) - 1) for VIF. Default: NULL.

responses

(optional; character, character vector, or NULL) Name of one or several response variables in df. Default: NULL.

predictors

(required, character vector) Names of the predictors to identify. Default: NULL

decimals

(required, integer) Number of decimal places for the zero variance test. Smaller numbers will increase the number of variables detected as near-zero variance. Recommended values will depend on the range of the numeric variables in 'df'. Default: 4

quiet

(optional; logical) If FALSE, messages are printed. Default: FALSE.

...

(optional) Internal args (e.g. function_name for validate_arg_function_name, a precomputed correlation matrix m, or cross-validation args for preference_order).

Value

list

  • numeric: character vector of numeric predictors.

  • categorical: character vector of categorical (character and factor) predictors.

  • logical: character vector of logical predictors.

See also

Other data_types: identify_categorical_variables(), identify_logical_variables(), identify_numeric_variables(), identify_response_type(), identify_zero_variance_variables()

Author

Blas M. Benito, PhD

Examples


data(vi_smol, vi_predictors)

x <- identify_valid_variables(
  df = vi_smol,
  predictors = vi_predictors
)

x
#> $numeric
#>  [1] "topo_slope"                 "topo_diversity"            
#>  [3] "topo_elevation"             "swi_mean"                  
#>  [5] "swi_max"                    "swi_min"                   
#>  [7] "swi_range"                  "soil_temperature_mean"     
#>  [9] "soil_temperature_max"       "soil_temperature_min"      
#> [11] "soil_temperature_range"     "soil_sand"                 
#> [13] "soil_clay"                  "soil_silt"                 
#> [15] "soil_ph"                    "soil_soc"                  
#> [17] "soil_nitrogen"              "solar_rad_mean"            
#> [19] "solar_rad_max"              "solar_rad_min"             
#> [21] "solar_rad_range"            "growing_season_length"     
#> [23] "growing_season_temperature" "growing_season_rainfall"   
#> [25] "growing_degree_days"        "temperature_mean"          
#> [27] "temperature_max"            "temperature_min"           
#> [29] "temperature_range"          "temperature_seasonality"   
#> [31] "rainfall_mean"              "rainfall_min"              
#> [33] "rainfall_max"               "rainfall_range"            
#> [35] "evapotranspiration_mean"    "evapotranspiration_max"    
#> [37] "evapotranspiration_min"     "evapotranspiration_range"  
#> [39] "cloud_cover_mean"           "cloud_cover_max"           
#> [41] "cloud_cover_min"            "cloud_cover_range"         
#> [43] "aridity_index"              "humidity_mean"             
#> [45] "humidity_max"               "humidity_min"              
#> [47] "humidity_range"            
#> 
#> $categorical
#>  [1] "koppen_zone"        "koppen_group"       "koppen_description"
#>  [4] "soil_type"          "biogeo_ecoregion"   "biogeo_biome"      
#>  [7] "biogeo_realm"       "country_name"       "continent"         
#> [10] "region"             "subregion"         
#>