gt Datasets

Source: ../../../data/GHE/mpn/deployment/deployments/2020-08-17/vignettes/gt-datasets.Rmd

The gt package comes with six built-in datasets for experimenting with the gt API: countrypops, sza, gtcars, sp500, pizzaplace, and exibble. While each dataset has different subject matter, all of them will be used to develop gt examples with consistent syntax.

Each dataset is stored as a tibble, ranging from very small (like exibble, an example tibble of 8 rows) to quite large in size (e.g., at nearly 50,000 rows: pizzaplace). Larger datasets are typically impractical as gt output tables but they provide opportunities for demonstrating preprocessing using tidyverse tools like dplyr and tidyr (upstream of gt’s gt() entry point).

In many gt workflows, there will often be prep work like this where the creation of the input table and any salient information (e.g., metadata for footnotes) will be done alongside the preparation of the display table.

In the next few examples, we’ll show how tables can be created with each of these datasets. Each example will be preceded with a set of requirements that serve as a design brief. This planning stage can be very useful in your own work for the purpose of organization. The hope is that this collection of simple examples will inspire the creation of much more interesting display tables with gt.

countrypops

This dataset provides the total populations of 215 countries on a yearly basis, from 1960 to 2017. The source data comes from the World Bank, where it has been cleaned and tidied up for inclusion into this package. Each row contains a population value for a country in a given year, where NA values for population indicate that the country did not exist in that particular year.

The countrypops dataset is a tibble with 12,470 rows and 5 variables. Here are explanations for each of the variables:

Column Type Description
country_name chr Name of the country
country_code_2 chr The 2-letter ISO 3166-1 country code
country_code_3 chr The 3-letter ISO 3166-1 country code
year int The year for the population estimate
population int The population estimate, midway through the year

A countrypops Example

The table that we’ll create from countrypops will meet these requirements:

  1. use countries from Oceania
  2. countries in different regions of Oceania will be grouped together
  3. provide populations for the 1995, 2005, and 2015 years only; they should appear as separate columns with a spanner group column stating that these columns refer to population values
  4. format population figures to contain commas
  5. provide a descriptive title
# Get vectors of 2-letter country codes for
# each region of Oceania
Australasia <- c("AU", "NZ")
Melanesia <- c("NC", "PG", "SB", "VU")
Micronesia <- c("FM", "GU", "KI", "MH", "MP", "NR", "PW")
Polynesia <- c("PF", "WS", "TO", "TV")

# Create a gt table based on a preprocessed `countrypops`
countrypops %>%
  dplyr::filter(country_code_2 %in% c(
    Australasia, Melanesia, Micronesia, Polynesia)
  ) %>%
  dplyr::filter(year %in% c(1995, 2005, 2015)) %>%
  dplyr::mutate(region = case_when(
    country_code_2 %in% Australasia ~ "Australasia",
    country_code_2 %in% Melanesia ~ "Melanesia",
    country_code_2 %in% Micronesia ~ "Micronesia",
    country_code_2 %in% Polynesia ~ "Polynesia",
  )) %>%
  tidyr::spread(key = year, value = population) %>%
  dplyr::arrange(region, desc(`2015`)) %>%
  dplyr::select(-starts_with("country_code")) %>%
  gt(
    rowname_col = "country_name",
    groupname_col = "region"
  ) %>%
  tab_header(title = "Populations of Oceania's Countries in 1995, 2005, and 2015") %>%
  tab_spanner(
    label = "Total Population",
    columns = vars(`1995`, `2005`, `2015`)
  ) %>%
  fmt_number(
    columns = vars(`1995`, `2005`, `2015`),
    decimals = 0,
    use_seps = TRUE
  )
Populations of Oceania's Countries in 1995, 2005, and 2015
Total Population
1995 2005 2015
Australasia
Australia 18,072,000 20,394,800 23,850,784
New Zealand 3,673,400 4,133,900 4,595,700
Melanesia
Papua New Guinea 4,894,276 6,314,709 7,919,825
Solomon Islands 359,225 469,885 587,482
New Caledonia 193,816 232,250 272,400
Vanuatu 168,235 209,370 264,603
Micronesia
Guam 145,561 158,402 161,797
Kiribati 77,730 92,325 112,407
Micronesia (Federated States) 107,556 106,196 104,433
Northern Mariana Islands 56,278 63,744 54,816
Marshall Islands 51,015 52,055 52,994
Palau 17,253 19,906 21,288
Nauru 9,969 10,114 12,475
Polynesia
French Polynesia 215,196 254,886 277,690
Samoa 170,157 179,929 193,759
Tonga 96,076 101,041 106,364
Tuvalu 9,230 10,027 11,001

sza

The solar zenith angle is one measure of the solar position. It can be thought of as ‘height’ of the sun in relation to an observer. A solar zenith angle of 0° indicates that the sun is directly overhead (a different solar angle, the solar altitude angle, is 90° in this instance). With the sun at the horizontal (e.g., during sunrise/sunset) we observe the solar zenith angle to be around 90° (there is the matter of atmospheric refraction). During nighttime, solar zenith angles in the range of 90–180 are possible (again, depending on the location of the observer).

The sza dataset has calculated values for the solar zenith angles every 30 minutes from 04:00 to 12:00 (true solar time). Temporally, these calculations are for the 1st of every month of the year. Spatially, the observer is located at somewhere along latitudes 20°N, 30°N, 40°N, and 50°N (because we are using true solar time, the longitude is unimportant). This is quite an extensive topic, and more information can be found by using ?sza in the R console or by visiting this page.

The sza dataset is a tibble with 816 rows and 4 variables. Here are explanations for each of the variables:

Column Type Description
latitude dbl The latitude in decimal degrees for the observations
month fct The measurement month; all calculations where conducted for the first day of each month
tst chr The true solar time at the given latitude and date (first of month) for which the solar zenith angle is calculated
sza dbl The solar zenith angle in degrees, where NAs indicate that sunrise hadn't yet occurred by the tst value

An sza Example

The table that we’ll create from sza will meet these requirements:

  1. filter the data to just use the 20°N data and remove the latitude column
  2. NA values from sza column are to be removed
  3. reshape the table so that columns of tst (true solar time) contain angles in degrees (from the sza column)
  4. the gt output table will have the months as row labels in the stub
  5. missing values will be replaced with an empty string (so that those cells are blank)
  6. a stubhead label will state what’s inside the stubs (months, at 20°N)
  7. the table will have a heading decorated with the HTML Black Sun with Rays (&#x2600;) symbol
  8. to fit the large amount of data in a small area, use some table options to reduce text size and row padding
# Create a gt table based on a preprocessed `sza`
sza %>%
  dplyr::filter(latitude == 20) %>%
  dplyr::select(-latitude) %>%
  dplyr::filter(!is.na(sza)) %>%
  tidyr::spread(key = "tst", value = sza) %>%
  gt(rowname_col = "month") %>%
  fmt_missing(
    columns = TRUE,
    missing_text = ""
  ) %>%
  tab_stubhead(label = html("month<br>(20&deg;N)")) %>%
  tab_header(title = html("&#x2600; Solar Zenith Angles &#x2600;")) %>%
  tab_options(
    column_labels.font.size = "smaller",
    table.font.size = "smaller",
    data_row.padding = px(3)
  )
☀ Solar Zenith Angles ☀
month
(20°N)		 0530 0600 0630 0700 0730 0800 0830 0900 0930 1000 1030 1100 1130 1200
jan 84.9 78.7 72.7 66.1 61.5 56.5 52.1 48.3 45.5 43.6 43.0
feb 88.9 82.5 75.8 69.6 63.3 57.7 52.2 47.4 43.1 40.0 37.8 37.2
mar 85.7 78.8 72.0 65.2 58.6 52.3 46.2 40.5 35.5 31.4 28.6 27.7
apr 88.5 81.5 74.4 67.4 60.3 53.4 46.5 39.7 33.2 26.9 21.3 17.2 15.5
may 85.0 78.2 71.2 64.3 57.2 50.2 43.2 36.1 29.1 26.1 15.2 8.8 5.0
jun 89.2 82.7 76.0 69.3 62.5 55.7 48.8 41.9 35.0 28.1 21.1 14.2 7.3 2.0
jul 88.8 82.3 75.7 69.1 62.3 55.5 48.7 41.8 35.0 28.1 21.2 14.3 7.7 3.1
aug 83.8 77.1 70.2 63.3 56.4 49.4 42.4 35.4 28.3 21.3 14.3 7.3 1.9
sep 87.2 80.2 73.2 66.1 59.1 52.1 45.1 38.1 31.3 24.7 18.6 13.7 11.6
oct 84.1 77.1 70.2 63.3 56.5 49.9 43.5 37.5 32.0 27.4 24.3 23.1
nov 87.8 81.3 74.5 68.3 61.8 56.0 50.2 45.3 40.7 37.4 35.1 34.4
dec 84.3 78.0 71.8 66.1 60.5 55.6 50.9 47.2 44.2 42.4 41.8

gtcars

The gtcars dataset takes off where mtcars left off. It contains 47 cars from the 2014-2017 model years. Many of the gtcars vehicles are grand tourers. Indeed, many of these provide the ability to cross an entire continent at speed and in comfort yet, when it’s called for, they will allow you to experience driving thrills. The chassis and suspension are in most cases top-notch and supply superb handling and roadholding on all routes one would conceivably encounter during the grand touring experience. The two plus two (2 + 2) seating configuration is smartly designed to deliver comfort for a driver and passenger, adequate space for luggage, and have room to spare.

The gtcars dataset is a tibble with 47 rows and 15 variables. Here are explanations for each of the variables:

Column Type Description
mfr chr The name of the car manufacturer
model chr The car's model name
year int The car's model year
trim chr A short description of the car model's trim
bdy_style chr An identifier of the car's body style, which is either coupe, convertible, sedan, or hatchback
hp, hp_rpm int The car's horsepower and the associated RPM level
trq, trq_rpm int The car's torque and the associated RPM level
mpg_c, mpg_h int The miles per gallon fuel efficiency rating for city and highway driving
drivetrain chr The car's drivetrain which, for this dataset is either rwd (Rear Wheel Drive) or awd (All Wheel Drive)
trsmn chr The codified transmission type, where the number part is the number of gears; the car could have automatic transmission (a), manual transmission (m), an option to switch between both types (am), or, direct drive (dd)
ctry_origin chr The country name for where the vehicle manufacturer is headquartered

A gtcars Example

The table that we’ll create from gtcars will meet these requirements:

  1. only include German cars
  2. limit the dataset to the top two most expensive offerings from each German manufacturer
  3. the information included will be the manufacturer (mfr), the car model (model), the drivetrain, and the price (msrp)
  4. add a table title
  5. combine the car make and model into a single column
  6. capitalize the drivetrain text
  7. format the prices as USD currency with commas and no decimal places shown
  8. relabel the column headings to provide nicer labels
  9. add two footnotes that explain the drivetrain abbreviations and that state the currency of the msrp prices; ensure that the footnote marks are lowercase letters
# Create a gt table based on a preprocessed `gtcars`
gtcars %>%
  dplyr::filter(ctry_origin == "Germany") %>%
  dplyr::group_by(mfr) %>%
  dplyr::top_n(2, msrp) %>%
  dplyr::ungroup() %>%
  dplyr::select(mfr, model, drivetrain, msrp) %>%
  gt() %>%
  tab_header(title = "Select German Automobiles") %>%
  cols_merge(
    columns = vars(mfr, model),
    hide_columns = vars(model)
  ) %>%
  text_transform(
    locations = cells_body(columns = vars(drivetrain)),
    fn = function(x) toupper(x)
  ) %>%
  fmt_currency(
    columns = vars(msrp),
    currency = "USD",
    decimals = 0
  ) %>%
  cols_label(
    mfr = "Car",
    drivetrain = "Drivetrain",
    msrp = "MSRP"
  ) %>%
  tab_footnote(
    footnote = "Prices in USD.",
    locations = cells_column_labels(columns = vars(msrp))
  ) %>%
  tab_footnote(
    footnote = "AWD = All Wheel Drive, RWD = Rear Wheel Drive.",
    locations = cells_column_labels(columns = vars(drivetrain))
  ) %>%
  opt_footnote_marks(marks = "letters")
Select German Automobiles
Car Drivetraina MSRPb
BMW i8 AWD $140,700
BMW M6 RWD $113,400
Audi R8 AWD $115,900
Audi S8 AWD $114,900
Mercedes-Benz AMG GT RWD $129,900
Mercedes-Benz SL-Class RWD $85,050
Porsche 911 RWD $84,300
Porsche Panamera RWD $78,100

a AWD = All Wheel Drive, RWD = Rear Wheel Drive.

b Prices in USD.

sp500

The S&P 500 is a capitalization-weighted index of about 500 leading companies (where bigger companies have more influence within the index) that have common stock listed in either the NYSE or NASDAQ markets. The companies chosen are intended to provide representation of the U.S. economy. This index is a managed list (managed by S&P Dow Jones Indices LLC) with occasional changes of the constituent companies based on their performance and changes in the economy.

There is daily S&P 500 data available in the sp500 dataset, with daily indicators (price statistics, volume, etc.) from 1950 to 2015, inclusive. There are 16,607 rows in the dataset, and 7 variables:

Column Type Description
date date The date expressed as `Date` values
open, high, low, close dbl The day's opening, high, low, and closing prices in USD; the close price is adjusted for splits
volume dbl The number of trades for the given `date`
adj_close dbl The close price adjusted for both dividends and splits

An sp500 Example

The table that we’ll create from sp500 will meet these requirements:

  1. use only data from the period 2010-06-02 to 2010-06-15
  2. the adjusted close adj_close column won’t be included
  3. a title and subtitle will be added to describe the contents of the table
  4. put the column labels in title case
  5. format the date column to appear as ‘2 Jun 2010’
  6. have the price columns (open, high, low, close) appear in USD
  7. the large numbers in volume will be shown as billions (with the B suffix)
  8. up- and down-pointing triangles (in green and red) will be added alongside the close price as appropriate
# Define the start and end dates for the data range
start_date <- "2010-06-02"
end_date <- "2010-06-15"

# The HTML decimal references for the black
# up- and down-pointing triangles are: #9650 and #9660;
# use an in-line style to apply color
up_arrow <- "<span style=\"color:green\">&#9650;</span>"
down_arrow <- "<span style=\"color:red\">&#9660;</span>"

# Create a gt table based on a preprocessed `sp500`
sp500 %>%
  dplyr::filter(date >= start_date & date <= end_date) %>%
  dplyr::select(-adj_close) %>%
  gt() %>%
  tab_header(
    title = "S&P 500",
    subtitle = glue::glue("{start_date} to {end_date}")
  ) %>%
  fmt_date(
    columns = vars(date),
    date_style = 7
  ) %>%
  fmt_currency(
    columns = vars(open, high, low, close),
    currency = "USD"
  ) %>%
  fmt_number(
    columns = vars(volume),
    scale_by = 1 / 1E9,
    pattern = "{x}B"
  ) %>%
  text_transform(
    locations = cells_body(
      columns = "close",
      rows = close > open),
    fn = function(x) paste(x, up_arrow)
  ) %>%
  text_transform(
    locations = cells_body(
      columns = "close",
      rows = close < open),
    fn = function(x) paste(x, down_arrow)
  ) %>%
  cols_label(
    date = "Date", open = "Open", high = "High",
    low = "Low", close = "Close", volume = "Volume"
  )
S&P 500
2010-06-02 to 2010-06-15
Date Open High Low Close Volume
15 Jun 2010 $1,091.21 $1,115.59 $1,091.21 $1,115.23 ▲ 4.64B
14 Jun 2010 $1,095.00 $1,105.91 $1,089.03 $1,089.63 ▼ 4.43B
11 Jun 2010 $1,082.65 $1,092.25 $1,077.12 $1,091.60 ▲ 4.06B
10 Jun 2010 $1,058.77 $1,087.85 $1,058.77 $1,086.84 ▲ 5.14B
9 Jun 2010 $1,062.75 $1,077.74 $1,052.25 $1,055.69 ▼ 5.98B
8 Jun 2010 $1,050.81 $1,063.15 $1,042.17 $1,062.00 ▲ 6.19B
7 Jun 2010 $1,065.84 $1,071.36 $1,049.86 $1,050.47 ▼ 5.47B
4 Jun 2010 $1,098.43 $1,098.43 $1,060.50 $1,064.88 ▼ 6.18B
3 Jun 2010 $1,098.82 $1,105.67 $1,091.81 $1,102.83 ▲ 5.00B
2 Jun 2010 $1,073.01 $1,098.56 $1,072.03 $1,098.38 ▲ 5.03B

pizzaplace

The pizzaplace dataset is unusual to say the least. It brings up more questions than answers. Why is it that the ‘The Greek’ pizza (the_greek) comes in XL and XXL sizes whilst (almost) all the other pizzas adhere to the S-M-L paradigm? Why is the ‘Brie Carre’ pizza (brie_carre) only small? Also, is any of this real (?), and, what is the nature of reality? (All of these questions are quite complicated… however, while I can doubt the existence of the material world, I cannot doubt the existence of myself as someone thinking about all the delicious pizzas on offer at pizzaplace.)

We have somehow obtained the 2015 sales from the pizzaplace, where each row is a pizza sold. There are 32 different types of pizza in 4 different categories: classic, chicken, supreme, and veggie. It was a great year of sales, personal problems notwithstanding. A kitchen fire in late September did not help with the morale situation. Nevertheless, $817,860 in sales for the year! That was indeed something to be cheerful about.

Let’s learn more about how this fascinating dataset is structured:

Column Type Description
id chr The ID for the order, which consists of one or more pizzas at a given `date` and `time`
date chr A character representation of the order `date`, expressed in the ISO 8601 date format (YYYY-MM-DD)
time chr A character representation of the order time, expressed as a 24-hour time the ISO 8601 extended time format (hh:mm:ss)
name chr The short name for the pizza
size chr The size of the pizza, which can either be S, M, L, XL (rare!), or XXL (even rarer!); most pizzas are available in the S, M, and L sizes but exceptions apply
type chr The category or type of pizza, which can either be `classic`, `chicken`, `supreme`, or `veggie`
price dbl The price of the pizza and the amount that it sold for (in USD)

A pizzaplace Example

Let’s make a reporting table from the pizzaplace dataset with these requirements:

  1. obtain the total sale numbers and revenue from each size of pizza from each category (type)
  2. create a gt table where each row represents a combination of size-type (size provides the row labels and type forms the row groups)
  3. add a title to explain the contents of the table
  4. format the numeric sold column to use commas and no decimal places
  5. format the currency values (income) to be in USD currency
  6. add a summary for each grouping that provides total sell counts and revenue amounts
  7. color the row groups and summary cells to add a little pizzazz
# Create a gt table based on a preprocessed `pizzaplace`
pizzaplace %>%
  dplyr::group_by(type, size) %>%
  dplyr::summarize(
    sold = n(),
    income = sum(price)
  ) %>%
  gt(rowname_col = "size") %>%
  tab_header(title = "Pizzas Sold in 2015") %>%
  fmt_number(
    columns = vars(sold),
    decimals = 0,
    use_seps = TRUE
  ) %>%
  fmt_currency(
    columns = vars(income),
    currency = "USD"
  ) %>%
  summary_rows(
    groups = TRUE,
    columns = vars(sold),
    fns = list(TOTAL = "sum"),
    formatter = fmt_number,
    decimals = 0,
    use_seps = TRUE
  ) %>%
  summary_rows(
    groups = TRUE,
    columns = "income",
    fns = list(TOTAL = "sum"),
    formatter = fmt_currency,
    currency = "USD"
  ) %>%
  tab_options(
    summary_row.background.color = "#ACEACE",
    row_group.background.color = "#FFEFDB"
  )
#> `summarise()` regrouping output by 'type' (override with `.groups` argument)
Pizzas Sold in 2015
sold income
chicken
L 4,932 $102,339.00
M 3,894 $65,224.50
S 2,224 $28,356.00
TOTAL 11,050 $195,919.50
classic
L 4,057 $74,518.50
M 4,112 $60,581.75
S 6,139 $69,870.25
XL 552 $14,076.00
XXL 28 $1,006.60
TOTAL 14,888 $220,053.10
supreme
L 4,564 $94,258.50
M 4,046 $66,475.00
S 3,377 $47,463.50
TOTAL 11,987 $208,197.00
veggie
L 5,403 $104,202.70
M 3,583 $57,101.00
S 2,663 $32,386.75
TOTAL 11,649 $193,690.45

exibble

The example tibble that’s useful for gt is called exibble. It’s 8 rows, has clear ordering of data, and the columns contain data that can be tested with the various gt formatter functions (fmt*()). Here is a table describing the columns of exibble:

Column Type Description
num dbl A numeric column ordered with increasingly larger values
char chr A character column composed of names of fruits from a to h
fctr fct A factor column with numbers from 1 to 8, written out
date, time, datetime chr Character columns with dates, times, and datetimes
currency dbl A numeric column that is useful for testing currency-based formatting
row chr A character column in the format `row_X` which can be useful for testing with row label in a table stub
group chr A character column with four `grp_a` values and four `grp_b` values which can be useful for testing tables that contain row groups

An exibble Example

Let’s test as many formatter functions as possible with exibble while also using row labels and row groups (furnished by the row and group columns). We’ll format num to display numbers with 2 decimal places. The dates in date will be formatted with date_style 6 (the m_day_year style, use info_date_style() to learn about all of them). The 24-h time values in time will use time_style 4 (hm_p, more info at info_time_style()). Datetimes as in datetime column can be formatted with the fmt_datetime() function (which uses the date_style and time_style arguments). The column currency will be formatted as a currency with fmt_currency and we’ll consider these values to be euros (currency = "EUR").

# Create a gt table based on `exibble`
exibble %>%
  gt(
    rowname_col = "row",
    groupname_col = "group"
  ) %>%
  fmt_number(
    columns = vars(num),
    decimals = 2) %>%
  fmt_date(
    columns = vars(date),
    date_style = 6
  ) %>%
  fmt_time(
    columns = vars(time),
    time_style = 4
  ) %>%
  fmt_datetime(
    columns = vars(datetime),
    date_style = 6,
    time_style = 4
  ) %>%
  fmt_currency(
    columns = vars(currency),
    currency = "EUR"
  ) %>%
  tab_options(
    column_labels.font.size = "small",
    table.font.size = "small",
    row_group.font.size = "small",
    data_row.padding = px(3)
  )
num char fctr date time datetime currency
grp_a
row_1 0.11 apricot one Jan 15, 2015 1:35 PM Jan 1, 2018 2:22 AM €49.95
row_2 2.22 banana two Feb 15, 2015 2:40 PM Feb 2, 2018 2:33 PM €17.95
row_3 33.33 coconut three Mar 15, 2015 3:45 PM Mar 3, 2018 3:44 AM €1.39
row_4 444.40 durian four Apr 15, 2015 4:50 PM Apr 4, 2018 3:55 PM €65,100.00
grp_b
row_5 5,550.00 NA five May 15, 2015 5:55 PM May 5, 2018 4:00 AM €1,325.81
row_6 NA fig six Jun 15, 2015 NA Jun 6, 2018 4:11 PM €13.26
row_7 777,000.00 grapefruit seven NA 7:10 PM Jul 7, 2018 5:22 AM NA
row_8 8,880,000.00 honeydew eight Aug 15, 2015 8:20 PM NA €0.44