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 |