explore

Simplifies Exploratory Data Analysis:

• Interactive data exploration using explore()
• Generate an automated report of your data (or patterns in your data) using report()
• Manual exploration using explore(), describe(), explain_*(), abtest(), ...

# 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()

Get started

• Get started with {explore}
• Explore penguins (tidy-data)
• Explore titanic (count-data)
• Explain a target using machine learning
• Generate a report of all variables in the data
• Generate a report of all patterns in the data
• Datasets in {explore}
• A/B testing
• and more ...