abtest                  A/B testing
abtest_shiny            A/B testing interactive
abtest_targetnum        A/B testing comparing two mean
abtest_targetpct        A/B testing comparing percent per group
add_var_id              Add a variable id at first column in dataset
add_var_random_01       Add a random 0/1 variable to dataset
add_var_random_cat      Add a random categorical variable to dataset
add_var_random_dbl      Add a random double variable to dataset
add_var_random_int      Add a random integer variable to dataset
add_var_random_moon     Add a random moon variable to dataset
add_var_random_starsign
                        Add a random starsign variable to dataset
balance_target          Balance target variable
check_vec_low_variance
                        Check vector for low variance
clean_var               Clean variable
count_pct               Adds percentage to dplyr::count()
create_data_abtest      Create data of A/B testing
create_data_app         Create data app
create_data_buy         Create data buy
create_data_churn       Create data churn
create_data_empty       Create an empty dataset
create_data_esoteric    Create data esoteric
create_data_newsletter
                        Create data newsletter
create_data_person      Create data person
create_data_random      Create data random
create_data_unfair      Create data unfair
create_notebook_explore
                        Generate a notebook
cut_vec_num_avg         Cut a variable
data_dict_md            Create a data dictionary Markdown file
decrypt                 decrypt text
describe                Describe a dataset or variable
describe_all            Describe all variables of a dataset
describe_cat            Describe categorical variable
describe_num            Describe numerical variable
describe_tbl            Describe table
drop_obs_if             Drop all observations where expression is true
drop_obs_with_na        Drop all observations with NA-values
drop_var_by_names       Drop variables by name
drop_var_low_variance   Drop all variables with low variance
drop_var_no_variance    Drop all variables with no variance
drop_var_not_numeric    Drop all not numeric variables
drop_var_with_na        Drop all variables with NA-values
encrypt                 encrypt text
explain_forest          Explain a target using Random Forest.
explain_logreg          Explain a binary target using a logistic
                        regression (glm). Model chosen by AIC in a
                        Stepwise Algorithm ('MASS::stepAIC()').
explain_tree            Explain a target using a simple decision tree
                        (classification or regression)
explain_xgboost         Explain a binary target using xgboost
explore                 Explore a dataset or variable
explore_all             Explore all variables
explore_bar             Explore categorical variable using bar charts
explore_col             Explore data without aggregation (label +
                        value)
explore_cor             Explore the correlation between two variables
explore_count           Explore count data (categories + frequency)
explore_density         Explore density of variable
explore_shiny           Explore dataset interactive
explore_targetpct       Explore variable + binary target (values 0/1)
explore_tbl             Explore table
format_num_auto         Format number as character string (auto)
format_num_kMB          Format number as character string (kMB)
format_num_space        Format number as character string (space as
                        big.mark)
format_target           Format target
format_type             Format type description
get_color               Get predefined colors
get_type                Return type of variable
get_var_buckets         Put variables into "buckets" to create a set of
                        plots instead one large plot
guess_cat_num           Return if variable is categorical or numerical
interact                Make a explore-plot interactive
log_info_if             Log conditional
mix_color               Mix colors
plot_legend_targetpct   Plots a legend that can be used for explore_all
                        with a binary target
plot_text               Plot a text
plot_var_info           Plot a variable info
predict_target          Predict target using a trained model.
replace_na_with         Replace NA
report                  Generate a report of all variables
rescale01               Rescales a numeric variable into values between
                        0 and 1
show_color              Show color vector as ggplot
simplify_text           Simplifies a text string
target_explore_cat      Explore categorical variable + target
target_explore_num      Explore Nuberical variable + target
total_fig_height        Get fig.height for RMarkdown-junk using
                        explore_all()
use_data_beer           Use the beer data set
use_data_diamonds       Use the diamonds data set
use_data_iris           Use the iris flower data set
use_data_mpg            Use the mpg data set
use_data_mtcars         Use the mtcars data set
use_data_penguins       Use the penguins data set
use_data_starwars       Use the starwars data set
use_data_titanic        Use the titanic data set
use_data_wordle         Use the wordle data set
weight_target           Weight target variable
yyyymm_calc             Calculate with periods (format yyyymm)
