New {explore} release!
{explore} 1.3.3 introduces use_data_wordle() and yyyymm_calc()
{explore} is an R package that simplifies exploratory data analysis. You can do interactive data exploration, generate an automated report or use AI to reveal hidden patterns in your data.
use_data_wordle()
With this release of {explore}
you can put to the test
the power of small data
of my daily WORDLE quest
WORDLE is a popular word-puzzle game. A friend and I started playing it daily and comparing our results. I collected the data and made it available in {explore}
library(explore)
wordle <- use_data_wordle()
wordle |> describe()
# A tibble: 10 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 round int 0 0 60 1 30.5 60
2 word chr 0 0 60 NA NA NA
3 language chr 0 0 1 NA NA NA
4 noun int 0 0 2 0 0.9 1
5 player chr 0 0 2 NA NA NA
6 try int 0 0 6 2 3.96 7
7 aeiou int 0 0 3 1 2.15 3
8 unique int 0 0 3 3 4.55 5
9 common int 0 0 4 2 3.85 5
10 rare int 0 0 3 0 0.58 2
Explore it by yourself or check my blog-post about my WORDLE journey https://rolkra.github.io/wordle/
yyyymm_calc()
{explore} has a function new
yyyymm_calc(), a vision true?
Year and month, a swift command,
the right answer, close at hand.
In some datasets dates are stored in the format yyyymm(e.g. 202411 for November 2024).
Adding 6 month to it causes a lot of work - converting it into a real date, adding months, reconvert back to yyyymm. Now there is an easy way to that:
Let’s say we have a database with a collection of Austrian beers. The year and month of production is stored.
library(tidyverse)
library(explore)
# use beer data with random year/month produced
beer <- use_data_beer() |>
add_var_random_cat(
name = "produced",
cat = c(202210, 202211, 202212, 202301))
beer |> select(name, produced) |> head(12)
# A tibble: 12 x 2
name produced
<chr> <dbl>
1 Puntigamer Maerzen 202212
2 Puntigamer PR0,0ST 202210
3 Puntigamer Panther 202211
4 Puntigamer Winterbier 202301
5 Puntigamer Zwickl 202212
6 Goesser Maerzen 202212
7 Goesser Helles 202301
8 Goesser Naturgold 202210
9 Goesser Spezial 202211
10 Goesser Gold 202212
11 Goesser Bock 202210
12 Goesser Stiftsbraeu 202211
Now we can add 6 month and 1 year easily:
# calculate produced + 6 month & produced + 1 year
beer |>
select(name, produced) |>
mutate(
best = yyyymm_calc(produced, add_month = 6),
check = yyyymm_calc(produced, add_year = 1)
)
# A tibble: 12 x 4
name produced best check
<chr> <dbl> <dbl> <dbl>
1 Puntigamer Maerzen 202212 202306 202312
2 Puntigamer PR0,0ST 202210 202304 202310
3 Puntigamer Panther 202211 202305 202311
4 Puntigamer Winterbier 202301 202307 202401
5 Puntigamer Zwickl 202212 202306 202312
6 Goesser Maerzen 202212 202306 202312
7 Goesser Helles 202301 202307 202401
8 Goesser Naturgold 202210 202304 202310
9 Goesser Spezial 202211 202305 202311
10 Goesser Gold 202212 202306 202312
11 Goesser Bock 202210 202304 202310
12 Goesser Stiftsbraeu 202211 202305 202311
Cheers!