Simplifies Exploratory Data Analysis


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Documentation for package ‘explore’ version 1.3.1

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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_not_numeric Drop all not numeric variables
drop_var_no_variance Drop all variables with no variance
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_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
weight_target Weight target variable