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.
explore 
Simplifies Exploratory Data Analysis:
- Interactive data exploration:
explore() - Use AI to unveil hidden patterns in your data (xgboost, RF, logreg, DT):
explain_*() - Generate an automated report of your data (or patterns in your data):
report() - Manual exploration:
explore(),describe(),explain_*(),abtest(), ... - 18 ready to use datasets for teaching & testing:
use_data_*(),create_data_*()
# install from CRAN
install.packages("explore")
Examples
# interactive data exploration
library(explore)
beer <- use_data_beer()
beer |> explore()
# describe data
beer |> describe()
# A tibble: 11 × 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 name chr 0 0 161 NA NA NA
2 brand chr 0 0 29 NA NA NA
3 country chr 0 0 3 NA NA NA
4 year dbl 0 0 1 2023 2023 2023
5 type chr 0 0 3 NA NA NA
6 color_dark dbl 0 0 2 0 0.09 1
7 alcohol_vol_pct dbl 2 1.2 35 0 4.32 8.4
8 original_wort dbl 5 3.1 54 5.1 11.3 18.3
9 energy_kcal_100ml dbl 11 6.8 34 20 39.9 62
10 carb_g_100ml dbl 16 9.9 44 1.5 3.53 6.7
11 sugar_g_100ml dbl 16 9.9 26 0 0.72 4.6
# explore data manually
beer |> explore(type)
beer |> explore(energy_kcal_100ml)
beer |> explore(energy_kcal_100ml, target = type)
beer |> explore(alcohol_vol_pct, energy_kcal_100ml, target = type)
# explore manually with color and interactive
beer |>
explore(sugar_g_100ml, color = "gold") |>
interact()
