autocruller

Auto ˈɔːtəʊ - short for automobile, a car
Cruller ˈkrʌl ər a pastry roll or bun

autocruller - car bun

{autocruller} allows one to download, manipulate and visualize CO² readings from the indoorco2map.com dataset.

Please support that project by contributing CO² readings, reporting bugs, telling your friends and financially supporting it (if you feel inclined).

Carbon Dioxide is important, Y’all

There is a well documented relationship between indoor levels of CO² and the amount of ventilation in indoor environments. Buildings with high indoor levels of CO² have poor ventilation and are therefore more likely to be vectors of airborne diseases (like COVID-19, Measles, and Flu) and to trap indoor pollutants.

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Installation

You can install the development version of autocruller from GitHub with:

# install.packages("pak")
pak::pak("samherniman/autocruller")

Get started

Download the latest data with ac_get_co2()

library(autocruller)

ac_df <- ac_get_co2()

That will give you a dataframe with all the current CO² measurements.

dplyr::glimpse(ac_df)
#> Rows: 10,660
#> Columns: 18
#> $ .id            <int> 1, 2, 3, 4, 4, 4, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 12,…
#> $ combined_id    <chr> "n_10000066104", "n_10000660271", "n_10013427033", "n_1…
#> $ osmtag         <chr> "supermarket", "fast_food", "supermarket", "greengrocer…
#> $ osmkey         <chr> "shop", "amenity", "shop", "shop", "shop", "shop", "sho…
#> $ countryid      <chr> "DEU", "USA", "DEU", "DEU", "DEU", "DEU", "DEU", "AUT",…
#> $ countryname    <chr> "Germany", "United States of America", "Germany", "Germ…
#> $ nuts3id        <chr> "DEA1B", "", "DE712", "DEA2C", "DEA2C", "DEA2C", "DEA2C…
#> $ nwrname        <chr> "ALDI Süd", "Naya", "EDEKA Langanki", "Hofladen Schmitz…
#> $ startTime      <dbl> 1.728744e+12, 1.746378e+12, 1.730192e+12, 1.730131e+12,…
#> $ ppmavg         <dbl> 1795.4000, 613.8235, 579.3333, 608.6000, 484.6000, 832.…
#> $ co2array       <list> <1623, 1699, 1745, 1805, 1836, 1831, 1854, 1860, 1842,…
#> $ ventilation    <chr> "False", "False", "False", "False", "False", "False", "…
#> $ openwindows    <chr> "False", "False", "False", "False", "False", "False", "…
#> $ occupancylevel <chr> "undefined", "undefined", "undefined", "undefined", "un…
#> $ customnotes    <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "Ha…
#> $ co2atlaskey    <chr> "shop", "dining_drinking", "shop", "other", "other", "o…
#> $ date           <dttm> 2024-10-12 15:32:07, 2025-05-04 17:55:28, 2024-10-29 0…
#> $ geometry       <POINT [°]> POINT (6.241554 51.5718), POINT (-73.99597 40.751…

If you want the co2arrays to be in long format, you can do this:

ac_df_long <- tidyr::unnest_longer(ac_df, co2array)

Make a graph

pak::pak("tidyplots")

You can compare averages with the wide format data

library(tidyplots)

types_c <- c(
  "arts_centre", "events_centre", "cinema",
  "social_facility", "theatre", "music_venue",
  "music", "nightclub", "social_centre", "concert_hall"
)
ac_df |>
  dplyr::filter(osmtag %in% types_c) |>
  tidyplot(
    x = ventilation,
    y = ppmavg,
    color = ppmavg
  ) |>
  add_data_points_beeswarm(size = 3) |>
  adjust_y_axis(transform = "log2") |>
  adjust_size(width = NA, height = NA, unit = "cm") |>
  adjust_font(fontsize = 16) |>
  adjust_x_axis_title("Ventilation system on?") |>
  adjust_y_axis_title("CO2 ppm average") |>
  remove_legend() |>
  adjust_title("CO2 Averages at music venues") |>
  adjust_caption("Data from indoorCO2map.com")

And track things over time with the long format data

ac_df_long |>
  dplyr::filter(.id == 127) |>
  dplyr::mutate(
    measurement_sequence = dplyr::row_number()
  ) |>
  tidyplot(
    x = measurement_sequence,
    y = co2array,
    color = co2array
  ) |>
  add_data_points() |>
  adjust_size(width = NA, height = NA, unit = "cm") |>
  adjust_font(fontsize = 16) |>
  adjust_x_axis_title("Time") |>
  adjust_y_axis_title("CO2 ppm") |>
  remove_legend() |>
  adjust_title("CO2 measurements at recording 127") |>
  adjust_caption("Data from indoorCO2map.com")