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")# interactive data exploration
library(explore)
beer<- use_data_beer()
beer|> explore()
# describe databeer|> 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 manuallybeer|> 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 interactivebeer|> explore(sugar_g_100ml, color="gold") |> interact()