Making Tables

A quick review of the gt package for making tables

I’ve wanted to dive into the gt package for making tables for awhile now. Here is a quick example of a table using data from a tidy tuesday a few weeks back. It seems my hugo theme is over-writing some of the table css, I’ll have to check that out.

library(tidyverse)

library(gt)

Let’s read in the cocktails recipie

Show code

boston_cocktails <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-26/boston_cocktails.csv') %>% 
  janitor::clean_names()

Let’s make the data reasonable!

boston_tidy <- boston_cocktails %>% 
  # fix the measurements
  separate(measure, into = c('value', 'measurement'), sep = " ", remove = FALSE) %>% 
  # remove values that don't have properly formed measurements crudely
  filter(measurement == "oz") %>% 
  # convert fractions to decimals
  mutate(value = case_when(
    value == "1/2" ~ ".5",
    value == "1/3" ~ ".33",
    value == "1/4" ~ ".25",
    value == "3/4" ~ ".74",
    TRUE ~ value
  )) %>% 
  # magic
  type_convert() %>% 
  # only keep realistic values
  filter(value <= 5)

boston_table <- boston_tidy%>% 
  filter(!category %in% c('Non-alcoholic Drinks', 'Rum', 'Shooters')) %>% 
  group_by(category, name) %>% 
  summarize(total_ingredients = which.max(ingredient_number),
            total_ounces = sum(value)) %>% 
  arrange(category, desc(total_ingredients)) %>% 
  slice_head(n = 5) %>% 
  arrange(category) %>% 
  ungroup()

make the table

boston_gt_table <- gt(boston_table, 
                      rowname_col = "name",
                      groupname_col = "category")


boston_gt_table <- boston_gt_table %>% 
  tab_header(
    title = "Top cocktails by Category",
    subtitle = "Top 5 cocktails based on number of ingredients"
  ) %>% 
  tab_spanner(label = "Drink Information",
              columns = vars("total_ingredients", "total_ounces")) %>% 
  cols_label(
    total_ingredients = "Total Ingredients",
    total_ounces = "Total Ounces",
    name = "Name"
  ) %>% 
  summary_rows(
    groups = TRUE,
    columns = vars(total_ingredients, total_ounces),
    fns = list(Total = "sum"),
    formatter = fmt_number,
    decimals = 2,
    use_seps = TRUE
  ) 

boston_gt_table %>% 
  tab_options(table.width = "100%",
              table.font.size = "smaller",
              data_row.padding = "3px") %>% 
  tab_source_note(
    source_note = md("Data comes from the [Tidy Tuesday project](https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-05-26/readme.md)")
  ) %>% 
  opt_align_table_header(align = "center")

Top cocktails by Category
Top 5 cocktails based on number of ingredients
	Drink Information
	Total Ingredients	Total Ounces
Brandy
Champs Elysees Cocktail	5	4.00
Cherry Blossom	5	2.50
D'artagnan	5	6.50
Deauville Cocktail	5	3.00
Gilroy Cocktail	5	3.48
Total	25.00	19.48
Cocktail Classics
Eye-Opener	6	6.50
Gloom Lifter	6	4.00
Hyatt's Jamaican Banana	6	5.50
New Orleans Gin Fizz	6	6.00
Prairie Oyster Cocktail	6	6.00
Total	30.00	28.00
Cordials and Liqueurs
Praire Oyster Cocktail	5	5.00
Absinthe Special Cocktail	2	1.25
Amaretto Rose	2	1.00
Amaretto Sour	2	1.24
Amber Amour	2	0.50
Total	13.00	8.99
Gin
The Winkle	6	11.00
Vow Of Silence	5	2.24
The Wink	4	4.50
Water Lily	4	2.96
Western Rose	4	3.00
Total	23.00	23.70
Rum - Daiquiris
Hai Karate	6	7.00
Ko Adang	6	5.00
Zombie	6	7.00
Floridita No. 3	5	4.24
Hush and Wonder	5	7.48
Total	28.00	30.72
Tequila
Amante Picante	6	10.00
Chupa Cabra	6	5.24
Flower Power	6	9.50
Oldest Temptation	6	5.25
The Nomad South	6	5.50
Total	30.00	35.49
Vodka
Ibiza	6	5.50
Long Island Iced Tea	6	4.20
Purple Passion Iced Tea	6	3.24
Sonic Blaster	6	4.50
Thyme Collins	6	10.00
Total	30.00	27.44
Whiskies
Blarney Stone Cocktail	6	5.50
California Lemonade	6	6.25
Carre Reprise	6	5.50
Chi-Town Flip	6	5.47
Double Standard Sour	6	3.73
Total	30.00	26.45
Data comes from the Tidy Tuesday project

More to come!