# Simplifies Exploratory Data Analysis

## Documentation for package ‘explore’ version 1.1.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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
clean_var | Clean variable
count_pct | Adds percentage to dplyr::count()
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_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
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
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)
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_nrow | Get number of rows for a grid plot (deprecated, use total_fig_height() instead)
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
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
simplify_text | Simplifies a text string
target_explore_cat | Explore categorical variable + target
target_explore_num | Explore categorical 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