Add summaries

This lesson is called Add summaries, part of the Making Beautiful Tables with R course. This lesson is called Add summaries, part of the Making Beautiful Tables with R course.

Transcript

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It’s always good to not just show the data, but to also to show a summary of the data. In this case, it could be something as simple as the maximum number of penguins of a specific species living on an island over the years.

maximum_summary <- penguin_counts_wider |> 
  group_by(island) |> 
  summarise(across(
    .cols = -year,
    .fns = max
  )) |> 
  mutate(year = 'Maximum')

total_summary <- penguin_counts_wider |> 
  group_by(island) |> 
  summarise(across(
    .cols = -year,
    .fns = sum
  )) |> 
  mutate(year = 'Total')
  

bind_rows(penguin_counts_wider, maximum_summary, total_summary) |> 
  mutate(island = paste('Island: ', island)) |> 
  arrange(island, year) |> 
  as_grouped_data(groups = 'island') |> 
  as_flextable(hide_grouplabel = TRUE) |> 
  set_header_labels(
    island = 'Island',
    year = '',
    Adelie_female = 'Female',
    Adelie_male = 'Male',
    Chinstrap_female = 'Female',
    Chinstrap_male = 'Male',
    Gentoo_female = 'Female',
    Gentoo_male = 'Male'
  ) |> 
  add_header_row(
    values = c('', 'Adelie', 'Chinstrap', 'Gentoo'),
    colwidths = c(1, 2, 2, 2)
  ) |> 
  add_header_lines(
    values = c('Penguins in the Palmer Archipelago', 'Data is courtesy of the {palmerpenguins} R package')
  ) |> 
  align(i = 3, align = 'center', part = 'header') |> 
  colformat_num(i = ~ (is.na(island)), na_str = '-') |> 
  align(
    i = ~ (year %in% 2007:2009),
    j = 'year',
    align = 'right'
  ) |> 
  autofit()

Your Turn

Solution

average_life_exps <- gapminder_data |> 
  filter(country %in% selected_countries$country, str_ends(year, '7')) |> 
  group_by(continent, year) |> 
  summarise(Average = mean(life_exp), .groups = 'drop') |> 
  pivot_wider(names_from = year, values_from = Average, names_prefix = 'year') |> 
  mutate(country = 'Average', .before = 2)

max_life_exps <- gapminder_data |> 
  filter(country %in% selected_countries$country, str_ends(year, '7')) |> 
  group_by(continent, year) |> 
  summarise(Maximum = max(life_exp), .groups = 'drop') |> 
  pivot_wider(names_from = year, values_from = Maximum, names_prefix = 'year') |> 
  mutate(country = 'Maximum', .before = 2)

selected_countries |> 
  bind_rows(average_life_exps, max_life_exps) |> 
  arrange(continent, country %in% c('Average', 'Maximum')) |> 
  as_grouped_data(
    groups = 'continent'
  ) |> 
  as_flextable(hide_grouplabel = TRUE) |> 
  set_header_labels(values = new_names) |> 
  add_header_row(
    values = c('', '50s-70s', '80s-20s'),
    colwidths = c(1, 3, 3)
  ) |> 
  add_header_lines(
    values = c('Life expectancties over time', 'Data is courtesy of the {gapminder} R package')
  ) |> 
  align(i = 3, align = 'center', part = 'header') |> 
  colformat_double(digits = 2) |> 
  autofit()