Function reference • gentleman

gentleman Package

gentleman-package gentleman: gentleman: Helpers for Data Preparation, Descriptives, Models, & Publication

Datasets

df: Simulated cross-lagged dataset

Data Preparation

add_composites(): Add composites (item means)

add_vars_from_one_df_to_another(): Add variables from one data.frame to another

cast(): Cast variables of one type to another

contr.dummy_or_effect(): Contrasts for factors using effect coding (when k > 2) or dummy coding (when k = 2)

dichotomize(): Dichotomize by picking value returned by a function

get_fuzzy_match(): Fuzzy-match strings

group_some_factor_levels(): Combine some factor levels using a map

make_factors_into_effect_codes(): Generates effect codes for factors

make_numeric_if_possible(): Make numeric if possible

recode_using_excel_map(): Recode values using Excel map (lookup table)

recode_values_to_NA(): Recode values to NA

remove_blank_factor_levels(): Remove blank factor levels

remove_empty_rows() remove_empty_cols(): Remove empty rows or columns

remove_factors_with_too_many_levels(): Remove factors with too many levels

remove_non_ascii_from_df(): Remove non-ASCII characters from data.frame

remove_outliers(): Flag and remove outliers

remove_vars_with_too_many_missing(): Remove variables with too many missing

rescale_min_max(): Rescale using min-max normalization

reverse_code(): Reverse-code numeric variables

scale_and_combine(): Combine numeric variables into one

standardize(): Standardize all numeric variables in a data.frame

to_long(): Pivot data.frame from wide to long

transpose_df(): Transpose data.frame

winsorize(): Winsorize variables

Descriptive Statistics

ana_fn_aov(): Returns p-values from ANOVA for specified vars and grouping variable

ana_fn_chisq(): Returns p-values from Chi-square tests for specified vars and grouping variable

ana_fn_rm_aov(): Returns p-values from repeated-measures (RM) ANOVA for specified vars and grouping (time) variable

compare_pairs_of_vars(): Compares pairs of variables using matched-pairs t-tests

format_p(): Format p-values with 3 decimals, no leading 0s, and < .001

get_desc_table(): Get a table of descriptive statistics for numeric or factor variables

get_desc_time(): Get a table of descriptive statistics using RM-ANOVA for numeric variables

get_sig_differences_between_groups(): Get variables for which a group difference exists

plot_density_by_groups(): Plot density by groups

tbl_fn_fac(): Internal, generic function to get summary table for factor variables

tbl_fn_num(): Internal, generic function to get summary table for numeric variables

Models

compare_sig_effects_in_two_pub_tables(): Compare significant effects from two publication tables

decompose_interaction(): Decompose 2-way (x1*x2) interaction

get_crosslagged_model(): Generate lavaan syntax for cross-lagged model

get_fmodel(): Get model object(s) using formula interface

get_lavaan_interaction_plot(): Get interaction plot for lavaan model

get_measurement_model(): Generate lavaan syntax for measurement model

get_mediation_model(): Generate lavaan syntax for mediation model

get_mediation_model_2level(): Generate lavaan syntax for 2-level mediation model

get_mediation_model_3wave(): Generate lavaan syntax for 3-wave longitudinal mediation model

get_moderated_mediation_model(): Generate lavaan syntax for moderated mediation

get_qca(): Run qualitative comparative analysis (QCA)

get_sig_effects_from_pub_table(): Extract significant effects from a publication table

make_pub_table_from_broom_tidy(): Generate publication table from broom::tidy summaries

make_pub_table_from_lavaan_models(): Generate publication table from lavaan models

Clusters

add_cluster_assignment(): Add cluster with iterative variable selection

calc_cluster_importances(): Calculate variable importances in clustering

get_cluster(): Get cluster assignment using Gower distance matrix

Machine Learning

add_predictions_from_automl(): Add predictions from an AutoML object to a test set

add_target_back_to_test_set_from_ref_table(): Add target values back in the test set

get_automl_config(): Create configuration object for running AutoML

get_automl_model(): Run an H2O AutoML model

init_h2o(): Initialize a local H2O cluster

keep_only_vars_in_both_train_and_test(): Remove variables from train and test sets when they are unique

plot_and_print_variable_importances(): Plot and print variable importances from an AutoML model

remove_target_from_test_and_add_ref_to_env(): Remove target from test set and back up values in reference table

run_automl_pipeline(): Run a full AutoML pipeline

scale_numeric_features_in_train_and_test(): Scale numeric features in train and test sets

ship_train_and_test_to_h2o(): Send train-test data.frames to H2O cluster

ttsplit(): Split data.frame into training and test sets

Logger

get_git_commit(): Retrieve current git commit information

get_logger(): Create a logger

logtext(): Log output to text file

Helpers

copy() copy2() copy3(): Wrappers for writing a data.frame to clipboard or to disk

empty_df(): Create empty data.frame with column names

get_all_pairs(): Get all possible pairs from vector

get_all_pairs_with_op(): Helper function to generate pairs with an operator separator

make_df_from_named_list(): Combine rows of named list of data.frames

`%P%` `%p%` `%N%` `%C%` `%c%`: Pasting operators

replace_in_nested_list(): Replace values in (possibly nested) list of vectors

replace_in_vector(): Replace all occurrences of a value in a vector and replace

replace_in_vector_at_position(): Replace vector element at given position with value

substr_right(): Substring character from the right side

view(): Alias for utils::View()