Man pages for explore
Simplifies Exploratory Data Analysis

abtestA/B testing
abtest_shinyA/B testing interactive
abtest_targetnumA/B testing comparing two mean
abtest_targetpctA/B testing comparing percent per group
add_var_idAdd a variable id at first column in dataset
add_var_random_01Add a random 0/1 variable to dataset
add_var_random_catAdd a random categorical variable to dataset
add_var_random_dblAdd a random double variable to dataset
add_var_random_intAdd a random integer variable to dataset
add_var_random_moonAdd a random moon variable to dataset
add_var_random_starsignAdd a random starsign variable to dataset
balance_targetBalance target variable
check_vec_low_varianceCheck vector for low variance
clean_varClean variable
count_pctAdds percentage to dplyr::count()
create_data_abtestCreate data of A/B testing
create_data_appCreate data app
create_data_buyCreate data buy
create_data_churnCreate data churn
create_data_emptyCreate an empty dataset
create_data_esotericCreate data esoteric
create_data_newsletterCreate data newsletter
create_data_personCreate data person
create_data_randomCreate data random
create_data_unfairCreate data unfair
create_notebook_exploreGenerate a notebook
cut_vec_num_avgCut a variable
data_dict_mdCreate a data dictionary Markdown file
decryptdecrypt text
describeDescribe a dataset or variable
describe_allDescribe all variables of a dataset
describe_catDescribe categorical variable
describe_numDescribe numerical variable
describe_tblDescribe table
drop_obs_ifDrop all observations where expression is true
drop_obs_with_naDrop all observations with NA-values
drop_var_by_namesDrop variables by name
drop_var_low_varianceDrop all variables with low variance
drop_var_not_numericDrop all not numeric variables
drop_var_no_varianceDrop all variables with no variance
drop_var_with_naDrop all variables with NA-values
encryptencrypt text
explain_forestExplain a target using Random Forest.
explain_logregExplain a binary target using a logistic regression (glm)....
explain_treeExplain a target using a simple decision tree (classification...
explain_xgboostExplain a binary target using xgboost
exploreExplore a dataset or variable
explore_allExplore all variables
explore_barExplore categorical variable using bar charts
explore_corExplore the correlation between two variables
explore_countExplore count data (categories + frequency)
explore_densityExplore density of variable
explore-packageexplore: Simplifies Exploratory Data Analysis
explore_shinyExplore dataset interactive
explore_targetpctExplore variable + binary target (values 0/1)
explore_tblExplore table
format_num_autoFormat number as character string (auto)
format_num_kMBFormat number as character string (kMB)
format_num_spaceFormat number as character string (space as big.mark)
format_targetFormat target
format_typeFormat type description
get_colorGet predefined colors
get_nrowGet number of rows for a grid plot
get_typeReturn type of variable
get_var_bucketsPut variables into "buckets" to create a set of plots instead...
guess_cat_numReturn if variable is categorical or numerical
interactMake a explore-plot interactive
log_info_ifLog conditional
mix_colorMix colors
plot_legend_targetpctPlots a legend that can be used for explore_all with a binary...
plot_textPlot a text
plot_var_infoPlot a variable info
predict_targetPredict target using a trained model.
replace_na_withReplace NA
reportGenerate a report of all variables
rescale01Rescales a numeric variable into values between 0 and 1
show_colorShow color vector as ggplot
simplify_textSimplifies a text string
target_explore_catExplore categorical variable + target
target_explore_numExplore Nuberical variable + target
total_fig_heightGet fig.height for RMarkdown-junk using explore_all()
use_data_beerUse the beer data set
use_data_diamondsUse the diamonds data set
use_data_irisUse the iris flower data set
use_data_mpgUse the mpg data set
use_data_mtcarsUse the mtcars data set
use_data_penguinsUse the penguins data set
use_data_starwarsUse the starwars data set
use_data_titanicUse the titanic data set
weight_targetWeight target variable
explore documentation built on Sept. 11, 2024, 7:40 p.m.