Sandbox Document

This example is taken from DevOps for Data Science.

Penguin Size and Mass by Sex and Species

library(palmerpenguins)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(ggplot2)
log <- log4r::logger()

df <- palmerpenguins::penguins
log4r::info(log, "data loaded")

INFO  [2025-01-20 18:55:09] data loaded

df %>%
  group_by(species, sex) %>%
  summarise(
    across(
      where(is.numeric), 
      \(x) mean(x, na.rm = TRUE)
      )
    ) %>%
  knitr::kable()

`summarise()` has grouped output by 'species'. You can override using the
`.groups` argument.

species	sex	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	year
Adelie	female	37.25753	17.62192	187.7945	3368.836	2008.055
Adelie	male	40.39041	19.07260	192.4110	4043.493	2008.055
Adelie	NA	37.84000	18.32000	185.6000	3540.000	2007.000
Chinstrap	female	46.57353	17.58824	191.7353	3527.206	2007.971
Chinstrap	male	51.09412	19.25294	199.9118	3938.971	2007.971
Gentoo	female	45.56379	14.23793	212.7069	4679.741	2008.069
Gentoo	male	49.47377	15.71803	221.5410	5484.836	2008.066
Gentoo	NA	45.62500	14.55000	215.7500	4587.500	2008.400

log4r::info(log, "Table produced")

INFO  [2025-01-20 18:55:09] Table produced

Penguin Size vs Mass by Species

df %>%
  ggplot(aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() + 
  geom_smooth(method = "lm")

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

log4r::info(log, "plot produced")

INFO  [2025-01-20 18:55:11] plot produced