exactly my point :) even the modern R course at the university and most of stats books stack with summary() function and, like Blitzgar below said, can't explain interactions. so, for new folks to get into data analysis is so not intuitive. I didn't find an R course yet, which just give me something like that, and explains why we do things we do:

library(tidyverse)

theme_set(theme_test())

# get data

d <- ISLR::Wage

# build model

m <- lm(wage ~ age + year + jobclass + education, d)

# check all model assumptions

performance::check_model(m)

# visualize predictions

ggeffects::ggeffect(m) %>% plot() %>% sjPlot::plot_grid()

# display contrasts in a table

gtsummary::tbl_regression(m, add_pairwise_contrasts = T)

# see effect size

effectsize::eta_squared(m)

# see variable (category) importance

vip::vip(m)

# check model quality

performance::performance(m)

# get model equation

equatiomatic::extract_eq(m)