Package: explore 1.4.0

explore: Simplifies Exploratory Data Analysis

Interactive data exploration with one line of code, automated reporting or use an easy to remember set of tidy functions for low code exploratory data analysis.

Authors:Roland Krasser [aut, cre]

explore_1.4.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
explore/json (API)

# Install 'explore' in R:
install.packages('explore', repos = c('https://rolkra.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rolkra/explore/issues

Pkgdown/docs site:https://rolkra.github.io

On CRAN:

Conda:

data-explorationdata-visualisationdecision-treesedarmarkdownshinytidy

10.76 score 243 stars 1 packages 312 scripts 929 downloads 3 mentions 83 exports 75 dependencies

Last updated from:de1b7a26cd. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK225
source / vignettesOK259
linux-release-x86_64OK170
macos-release-arm64OK137
macos-oldrel-arm64OK102
windows-develOK136
windows-releaseOK132
windows-oldrelOK128
wasm-releaseOK133

Exports:abtestadd_var_idadd_var_random_01add_var_random_catadd_var_random_dbladd_var_random_intadd_var_random_moonadd_var_random_starsignbalance_targetclean_varcount_pctcreate_data_abtestcreate_data_appcreate_data_buycreate_data_churncreate_data_emptycreate_data_esotericcreate_data_newslettercreate_data_personcreate_data_randomcreate_data_unfaircreate_notebook_exploredata_dict_mddecryptdescribedescribe_alldescribe_catdescribe_numdescribe_tbldrop_obs_ifdrop_obs_with_nadrop_var_by_namesdrop_var_low_variancedrop_var_no_variancedrop_var_not_numericdrop_var_with_naencryptexplain_forestexplain_logregexplain_treeexplain_xgboostexploreexplore_allexplore_barexplore_colexplore_corexplore_countexplore_densityexplore_shinyexplore_targetpctexplore_tblformat_num_autoformat_num_kMBformat_num_spaceformat_targetformat_typeget_colorget_nrowget_typeget_var_bucketsguess_cat_numinteractmix_colorplot_legend_targetpctplot_textpredict_targetreplace_na_withreportrescale01show_colorsimplify_texttotal_fig_heightuse_data_beeruse_data_diamondsuse_data_irisuse_data_mpguse_data_mtcarsuse_data_penguinsuse_data_starwarsuse_data_titanicuse_data_wordleweight_targetyyyymm_calc

Dependencies:askpassbase64encbslibcachemclicommonmarkcpp11crosstalkcurldata.tabledigestdplyrDTevaluatefarverfastmapfontawesomeforcatsfsgenericsggplot2gluegridExtragtablehighrhtmltoolshtmlwidgetshttpuvhttrisobandjquerylibjsonliteknitrlabelinglaterlazyevallifecyclemagrittrmemoisemimeopensslotelpalmerpenguinspillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownrpartrpart.plotS7sassscalesshinysourcetoolsstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxtableyaml

Data
Use data | Penguins | Starwars | Diamonds | Iris | mpg | mtcars | Titanic | Beer | Create data | A/B testing | App | Buy | Churn | Esoteric | Newsletter | Person | Random | Unfair | Empty
Rendered fromdata.Rmdusingknitr::rmarkdown

Last update: 2025-06-24
Started: 2023-10-04

Tips & tricks
Count with percent | Add id | User defined report | Data Dictionary | Color | Period yyyymm
Rendered fromtips-tricks.Rmdusingknitr::rmarkdown

Last update: 2025-06-24
Started: 2023-10-07

explore
Interactive data exploration | Report variables | Grow a decision tree | Explore dataset | Explore variables | Explore variables with a target | Explore multiple variables | Explore correlation between two variables | Explore options | Describing data | Use data | Create data | Basic data cleaning | Create notebook
Rendered fromexplore.Rmdusingknitr::rmarkdown

Last update: 2024-11-10
Started: 2019-05-13

A/B test
Interactive A/B testing | A/B testing with tidy data | Describe dataset | Hypothesis age > 50 | Hypothesis Mobile Voice vs Broadband Usage | A/B testing with count data | Hypothesis Female | A/B testing manual
Rendered fromabtest.Rmdusingknitr::rmarkdown

Last update: 2024-10-11
Started: 2023-10-04

Explore mtcars
How to explore the mtcars dataset using the explore package. | Explore dataset | Explore variables | Number of gears? | High miles per gallon? | Horsepower?
Rendered fromexplore-mtcars.Rmdusingknitr::rmarkdown

Last update: 2024-04-15
Started: 2024-04-15

Explore penguins
How to explore the penguins dataset using the explore package. | Describe variables | Data cleaning | Explore variables | Which species?
Rendered fromexplore-penguins.Rmdusingknitr::rmarkdown

Last update: 2024-04-15
Started: 2024-04-15

Explore titanic
How to explore the Titanic dataset using the explore package. | Explore dataset | Explore variables | Survived? | Other correlations
Rendered fromexplore-titanic.Rmdusingknitr::rmarkdown

Last update: 2024-04-15
Started: 2024-04-15

Clean / Drop
Rename variable | Replace NA values | Set min max values | Rescale 0 to 1 | Cleaning text | Drop variables | Drop observations
Rendered fromclean-drop.Rmdusingknitr::rmarkdown

Last update: 2024-02-24
Started: 2024-02-24

Explain
Explain / Model | Decision Tree | Random Forest | XGBoost | Logistic Regression | Balance Target
Rendered fromexplain.Rmdusingknitr::rmarkdown

Last update: 2024-02-24
Started: 2023-10-04

Predict
Train model
Rendered frompredict.Rmdusingknitr::rmarkdown

Last update: 2024-02-22
Started: 2023-10-06

Report variables
Explore | Describe Data | Explore Variables
Rendered fromreport.Rmdusingknitr::rmarkdown

Last update: 2023-10-07
Started: 2023-10-06

Report target
Explore | Describe Data | Describe Target | Explore Variables
Rendered fromreport-target.Rmdusingknitr::rmarkdown

Last update: 2023-10-06
Started: 2023-10-06

Report targetpct
Explore | Describe Data | Describe Target | Explore Variables
Rendered fromreport-targetpct.Rmdusingknitr::rmarkdown

Last update: 2023-10-06
Started: 2023-10-06

Describe
Describe table | Describe all variables | Describe one variable
Rendered fromdescribe.Rmdusingknitr::rmarkdown

Last update: 2023-10-04
Started: 2023-10-04

Readme and manuals

Help Manual

Help pageTopics
A/B testingabtest
A/B testing interactiveabtest_shiny
A/B testing comparing two meanabtest_targetnum
A/B testing comparing percent per groupabtest_targetpct
Add a variable id at first column in datasetadd_var_id
Add a random 0/1 variable to datasetadd_var_random_01
Add a random categorical variable to datasetadd_var_random_cat
Add a random double variable to datasetadd_var_random_dbl
Add a random integer variable to datasetadd_var_random_int
Add a random moon variable to datasetadd_var_random_moon
Add a random starsign variable to datasetadd_var_random_starsign
Balance target variablebalance_target
Check vector for low variancecheck_vec_low_variance
Clean variableclean_var
Adds percentage to dplyr::count()count_pct
Create data of A/B testingcreate_data_abtest
Create data appcreate_data_app
Create data buycreate_data_buy
Create data churncreate_data_churn
Create an empty datasetcreate_data_empty
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
Generate a notebookcreate_notebook_explore
Cut a variablecut_vec_num_avg
Create a data dictionary Markdown filedata_dict_md
decrypt textdecrypt
Describe a dataset or variabledescribe
Describe all variables of a datasetdescribe_all
Describe categorical variabledescribe_cat
Describe numerical variabledescribe_num
Describe tabledescribe_tbl
Drop all observations where expression is truedrop_obs_if
Drop all observations with NA-valuesdrop_obs_with_na
Drop variables by namedrop_var_by_names
Drop all variables with low variancedrop_var_low_variance
Drop all variables with no variancedrop_var_no_variance
Drop all not numeric variablesdrop_var_not_numeric
Drop all variables with NA-valuesdrop_var_with_na
encrypt textencrypt
Explain a target using Random Forest.explain_forest
Explain a binary target using a logistic regression (glm). Model chosen by AIC in a Stepwise Algorithm ('MASS::stepAIC()').explain_logreg
Explain a target using a simple decision tree (classification or regression)explain_tree
Explain a binary target using xgboostexplain_xgboost
Explore a dataset or variableexplore
Explore all variablesexplore_all
Explore categorical variable using bar chartsexplore_bar
Explore data without aggregation (label + value)explore_col
Explore the correlation between two variablesexplore_cor
Explore count data (categories + frequency)explore_count
Explore density of variableexplore_density
Explore dataset interactiveexplore_shiny
Explore variable + binary target (values 0/1)explore_targetpct
Explore tableexplore_tbl
Format number as character string (auto)format_num_auto
Format number as character string (kMB)format_num_kMB
Format number as character string (space as big.mark)format_num_space
Format targetformat_target
Format type descriptionformat_type
Get predefined colorsget_color
Return type of variableget_type
Put variables into "buckets" to create a set of plots instead one large plotget_var_buckets
Return if variable is categorical or numericalguess_cat_num
Make a explore-plot interactiveinteract
Log conditionallog_info_if
Mix colorsmix_color
Plots a legend that can be used for explore_all with a binary targetplot_legend_targetpct
Plot a textplot_text
Plot a variable infoplot_var_info
Predict target using a trained model.predict_target
Replace NAreplace_na_with
Generate a report of all variablesreport
Rescales a numeric variable into values between 0 and 1rescale01
Show color vector as ggplotshow_color
Simplifies a text stringsimplify_text
Explore categorical variable + targettarget_explore_cat
Explore Nuberical variable + targettarget_explore_num
Get fig.height for RMarkdown-junk using explore_all()total_fig_height
Use the beer data setuse_data_beer
Use the diamonds data setuse_data_diamonds
Use the iris flower data setuse_data_iris
Use the mpg data setuse_data_mpg
Use the mtcars data setuse_data_mtcars
Use the penguins data setuse_data_penguins
Use the starwars data setuse_data_starwars
Use the titanic data setuse_data_titanic
Use the wordle data setuse_data_wordle
Weight target variableweight_target
Calculate with periods (format yyyymm)yyyymm_calc