tests/testthat/test-demo.R

In duckdblabs/duckplyr: A 'DuckDB'-Backed Version of 'dplyr'

test_that("demo queries work", {
  skip_if_not_installed("lubridate")

  withr::local_envvar(DUCKPLYR_FORCE = TRUE)

  wday <- lubridate::wday
  hour <- lubridate::hour

  expect_snapshot({
    tips_by_day_hour <- taxi_data_2019 %>%
      filter(total_amount > 0) %>%
      mutate(tip_pct = 100 * tip_amount / total_amount, dn = wday(pickup_datetime), hr = hour(pickup_datetime)) %>%
      summarise(
        avg_tip_pct = mean(tip_pct),
        n = n(),
        .by = c(dn, hr)
      ) %>%
      arrange(desc(avg_tip_pct))

    tips_by_day_hour
  })

  expect_snapshot({
    tips_by_passenger <- taxi_data_2019 %>%
      filter(total_amount > 0) %>%
      mutate(tip_pct = 100 * tip_amount / total_amount) %>%
      summarise(
        avg_tip_pct = median(tip_pct),
        n = n(),
        .by = passenger_count
      ) %>%
      arrange(desc(passenger_count))

    tips_by_passenger
  })

  expect_snapshot({
    popular_manhattan_cab_rides <- taxi_data_2019 %>%
      filter(total_amount > 0) %>%
      inner_join(zone_map, by = join_by(pickup_location_id == LocationID)) %>%
      inner_join(zone_map, by = join_by(dropoff_location_id == LocationID)) %>%
      filter(Borough.x == "Manhattan", Borough.y == "Manhattan") %>%
      select(start_neighborhood = Zone.x, end_neighborhood = Zone.y) %>%
      summarise(
        num_trips = n(),
        .by = c(start_neighborhood, end_neighborhood),
      ) %>%
      arrange(desc(num_trips))

    popular_manhattan_cab_rides
  })

  expect_snapshot({
    num_trips_per_borough <- taxi_data_2019 %>%
      filter(total_amount > 0) %>%
      inner_join(zone_map, by = join_by(pickup_location_id == LocationID)) %>%
      inner_join(zone_map, by = join_by(dropoff_location_id == LocationID)) %>%
      mutate(pickup_borough = Borough.x, dropoff_borough = Borough.y) %>%
      select(pickup_borough, dropoff_borough, tip_amount) %>%
      summarise(
        num_trips = n(),
        .by = c(pickup_borough, dropoff_borough)
      )

    num_trips_per_borough_no_tip <- taxi_data_2019 %>%
      filter(total_amount > 0, tip_amount == 0) %>%
      inner_join(zone_map, by = join_by(pickup_location_id == LocationID)) %>%
      inner_join(zone_map, by = join_by(dropoff_location_id == LocationID)) %>%
      mutate(pickup_borough = Borough.x, dropoff_borough = Borough.y, tip_amount) %>%
      summarise(
        num_zero_tip_trips = n(),
        .by = c(pickup_borough, dropoff_borough)
      )

    num_zero_percent_trips <- num_trips_per_borough %>%
      inner_join(num_trips_per_borough_no_tip, by = join_by(pickup_borough, dropoff_borough)) %>%
      mutate(num_trips = num_trips, percent_zero_tips_trips = 100 * num_zero_tip_trips / num_trips) %>%
      select(pickup_borough, dropoff_borough, num_trips, percent_zero_tips_trips) %>%
      arrange(desc(percent_zero_tips_trips))

    num_zero_percent_trips
  })
})