library(tidyverse)

library(gt)

gapminder_data <- gapminder::gapminder |> 
  janitor::clean_names() |> 
  select(continent, country, year, life_exp) |> 
  mutate(
    year = as.character(year),
    # Year is really categorical with numeric labels
    country = as.character(country) 
  ) 
gapminder_data

library(gtExtras)

gt_plt_summary(gapminder_data) 

selected_countries <- gapminder_data  |> 
  # Filter to use only six years (those that end in 7)
  filter(str_ends(year, "7")) |>
  # sample two countries per continent
  group_by(continent, country) |> 
  nest() |> 
  group_by(continent) |> 
  slice_sample(n = 2) |> 
  ungroup() |> 
  unnest(data) |> 
  # Rearrange the data into table format
  pivot_wider(names_from = year, names_prefix = 'year', values_from = life_exp)

selected_countries


# New column names
new_colnames <- colnames(selected_countries) |> str_remove('(country|year)')

names(new_colnames) <- colnames(selected_countries)


selected_countries |>
  gt(groupname_col = 'continent') |>
  tab_header(title = 'Life Expectancies over time',
             subtitle = 'Data is courtesy of the Gapminder foundation') |>
  cols_label(.list = new_colnames) |>
  fmt_number(columns = where(is.numeric), decimals = 2) |>
  gt_theme_nytimes()




# Two colors from the Okabe Ito color palette
color_palette <- c("#CC79A7", "#009E73")

selected_countries |> 
  gt(groupname_col = 'continent') |> 
  tab_header(
    title = 'Life Expectancies over time',
    subtitle = 'Data is courtesy of the Gapminder foundation'
  ) |> 
  cols_label(.list = new_colnames) |> 
  fmt_number(columns = where(is.numeric), decimals = 2) |> 
  gt_theme_538() |> 
  gt_color_rows(
    columns = year2007, 
    domain = c(30, 85),
    palette = color_palette
  )

# Two colors from the Okabe Ito color palette
color_palette <- c("#CC79A7", "#009E73")

selected_countries |> 
  gt(groupname_col = 'continent') |> 
  tab_header(
    title = 'Life Expectancies over time',
    subtitle = 'Data is courtesy of the Gapminder foundation'
  ) |> 
  cols_label(.list = new_colnames) |> 
  fmt_number(columns = where(is.numeric), decimals = 2) |> 
  gt_theme_538() |> 
  gt_color_rows(
    columns = c(year1957, year2007), 
    domain = c(30, 85),
    palette = color_palette
  )



















data(CO2)
head(CO2)
CO2 |> glimpse()
# basic summary table
CO2 %>% select(!c(Plant,conc)) %>% tbl_summary()

# summary split by a categorical variable
CO2 %>% select(!c(Plant,conc)) %>% tbl_summary(by = Type)

# summary split by a categorical variable with p-value
CO2 %>% select(!c(Plant,conc)) %>% 
  tbl_summary(by = Type) %>% add_p()

# include overall, extra heading, custom stats
CO2 %>% select(!c(Plant,conc)) %>%
  tbl_summary(
    by = Type,
    statistic = list(
      all_continuous() ~ "{mean} ({sd})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), digits = all_continuous() ~ 2) %>% 
  add_p() %>% add_overall() %>%
  modify_spanning_header(c("stat_1", "stat_2") ~ "**Location**")

# crosstabs
CO2 %>%
  tbl_cross(
    row = Type,
    col = Treatment,
    percent = "cell"
  ) %>%
  add_p()