Snowman submission

data/curated/new_submission/burn_duration.csv

@@ -0,0 +1,68 @@
+year,duration
+1923,60
+1952,6
+1953,8
+1956,4
+1958,8
+1959,8
+1961,7
+1965,20
+1966,16
+1967,6
+1968,5
+1969,10
+1970,40
+1971,5
+1972,8
+1973,26
+1974,5.12
+1975,22
+1976,11
+1977,27
+1978,12
+1979,19
+1980,17
+1981,14.17
+1982,13
+1983,24.33
+1984,22
+1985,24
+1986,14
+1987,17
+1988,40
+1989,24
+1990,10.5
+1991,12
+1992,10.22
+1993,23.5
+1994,21.92
+1995,5.85
+1996,8
+1997,7.5
+1998,10.22
+1999,23.87
+2000,16.75
+2001,26.38
+2002,12.4
+2003,5.7
+2004,11.7
+2005,17.87
+2006,10.47
+2007,12.15
+2008,26.02
+2009,12.92
+2010,12.9
+2011,10.93
+2012,12.12
+2013,35.18
+2015,20.65
+2016,43.57
+2017,9.93
+2018,20.52
+2019,17.73
+2020,NA
+2021,12.95
+2022,37.98
+2023,57
+2024,NA
+2025,26.5

data/curated/new_submission/burn_duration.md

@@ -0,0 +1,4 @@
+|variable |class  |description                           |
+|:--------|:------|:-------------------------------------|
+|year     |double |Year of Sechselaeuten festival. |
+|duration |double |Time elapsed from ignition of Boeoeg effigy until explosion, in minutes. |

data/curated/new_submission/cleaning.R

@@ -0,0 +1,42 @@
+# Paste code used to create the curated dataset here. Include comments as
+# necessary. If you did not need to clean the data, use a comment like the one
+# below, but also load the data with readr::read_csv() to ensure the data can be
+# loaded, and to use with `saving.R`. Delete this block of comments.
+
+## burn duration ----
+# https://github.com/philshem/Sechselaeuten-data
+burn_duration <- readr::read_csv(file = "https://raw.githubusercontent.com/philshem/Sechselaeuten-data/refs/heads/master/boeoegg_burn_duration.csv") |> 
+  dplyr::mutate(duration = round(burn_duration_seconds / 60, digits = 2)) |> 
+  dplyr::select(year, duration)
+
+## variable selection ----
+variable_selection <- c("tre200m0", "tre200mn", "tre200mx", "sre000m0", "sremaxmv", "rre150m0")
+
+## climate metadaata ----
+climate_metadata <- readr::read_delim(
+  file = "https://data.geo.admin.ch/ch.meteoschweiz.ogd-smn/ogd-smn_meta_parameters.csv", 
+  delim = ";"
+  ) |> 
+  dplyr::select(c(1, ends_with("_en"), 10:13)) |> 
+  dplyr::filter(parameter_granularity == "M") |> 
+  dplyr::filter(parameter_shortname %in% variable_selection) |> 
+  dplyr::select(variable = parameter_shortname, description = parameter_description_en)
+
+## climate data ----
+climate_data <- readr::read_delim(
+  file = "https://data.geo.admin.ch/ch.meteoschweiz.ogd-smn/sma/ogd-smn_sma_m.csv", 
+  delim = ";"
+  ) |>
+  dplyr::select(date = reference_timestamp, dplyr::any_of(variable_selection)) |>
+  dplyr::mutate(date = lubridate::dmy_hm(date), year = lubridate::year(date), month = lubridate::month(date)) |>
+  dplyr::filter(month %in% 6:8) |>
+  dplyr::group_by(year) |> 
+  dplyr::summarise(dplyr::across(.cols = -c(date, month), .fns = \(x) mean(x, na.rm = TRUE))) |>
+  dplyr::ungroup() |> 
+  dplyr::mutate(sre000m0 = sre000m0 / 60) |> 
+  dplyr::mutate(dplyr::across(.cols = -c(year), .fns = \(x) round(x, digits = 2))) |> 
+  dplyr::mutate(dplyr::across(.cols = -c(year), .fns = \(x) ifelse(is.nan(x), NA, x)))
+
+## combine datasets ----
+sechselaeuten <- dplyr::left_join(x = burn_duration, y = climate_data, by = dplyr::join_by(year)) |> 
+  dplyr::mutate(record = ifelse(tre200m0 >= 19, TRUE, FALSE))

data/curated/new_submission/climate_data.csv

@@ -0,0 +1,163 @@
+year,tre200m0,tre200mn,tre200mx,sre000m0,sremaxmv,rre150m0
+1864,16.73,NA,NA,NA,NA,99.33
+1865,18.03,NA,NA,NA,NA,113.77
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+1867,17.63,NA,NA,NA,NA,155.47
+1868,18.73,NA,NA,NA,NA,96.57
+1869,17.27,NA,NA,NA,NA,175.93
+1870,18.33,NA,NA,NA,NA,95.1
+1871,17.17,NA,NA,NA,NA,127.87
+1872,17.47,NA,NA,NA,NA,187.37
+1873,19.23,NA,NA,NA,NA,121.83
+1874,18.27,NA,NA,NA,NA,145.27
+1875,18.03,NA,NA,NA,NA,129.83
+1876,18.3,NA,NA,NA,NA,231.9
+1877,18.77,NA,NA,NA,NA,117.5
+1878,17.1,NA,NA,NA,NA,185.73
+1879,17.23,NA,NA,NA,NA,126.43
+1880,17.33,NA,NA,NA,NA,128.9
+1881,18.37,NA,NA,NA,NA,145.63
+1882,16,7.13,30.83,NA,NA,152.13
+1883,16.4,6.57,30.7,NA,NA,123.37
+1884,16.77,5.67,30.93,215.03,NA,148.13
+1885,18.13,7.43,30.17,277.37,NA,88.4
+1886,16.73,7.23,31,205.67,NA,197.9
+1887,18.27,5.93,31.37,280.46,NA,106.83
+1888,16.1,7.47,32.27,200.43,NA,209.4
+1889,17.1,8.5,31.67,215.1,NA,147.77
+1890,16.03,5.67,NA,208.17,NA,152.57
+1891,16.37,7.33,29.83,199.1,NA,177.5
+1892,17.77,7.33,30.5,245.19,NA,108
+1893,17.77,7.17,29.5,261.97,NA,82.03
+1894,17,8.17,31.17,228.3,NA,119.93
+1895,17.4,6.83,32,255.89,NA,79.97
+1896,16.47,6.83,27.83,193.73,NA,123.13
+1897,18.03,7.67,31.5,221.1,NA,123.6
+1898,17,5.67,32,237.47,NA,100.1
+1899,17.93,7,31.83,273.6,NA,52.7
+1900,17.67,8.67,30.33,240.77,NA,114.67
+1901,17.3,7,30.5,232.27,53,118.9
+1902,16.7,6.67,31.67,228.5,52,148.97
+1903,16.2,7,31.17,194.47,44.67,141.37
+1904,18.73,8.67,32.67,264.37,60,77.9
+1905,18.3,8.33,33.17,256.77,58.33,147.23
+1906,17.03,5.5,32.5,251.2,57.67,111.43
+1907,16.5,6.1,30.33,238.77,54.33,115.23
+1908,17.27,7.53,31.77,255.03,58.33,113.33
+1909,15.57,6.33,30.7,198.8,45.33,163.3
+1910,16.33,7.87,31.07,208.1,47.67,177.23
+1911,19.1,8.23,32.8,301.63,68.67,70.83
+1912,15.77,7.53,29.1,173.87,39.67,155.17
+1913,15.37,6.83,29.03,184.57,42.67,130.37
+1914,16.07,6.4,30.43,207.37,47.33,129.17
+1915,16.87,6.7,29.83,230.8,52.67,117.83
+1916,15.63,6.9,29.43,186.53,42.33,150.7
+1917,17.47,8.13,30.9,227.8,52,165.6
+1918,15.97,5.6,32.17,233.47,53.33,109.33
+1919,16.7,5.7,30.93,239.17,55,90.6
+1920,16.23,6.27,30.9,210.63,48,94.2
+1921,17.83,7.6,34.1,254.8,57.67,81.77
+1922,16.63,6.9,31.17,219.57,50,143.07
+1923,16.67,7.03,32.47,247.43,56.33,73.97
+1924,15.63,6.23,30.63,187.27,42.33,156.2
+1925,16.87,7.4,32.67,228.63,52,116.23
+1926,15.93,7.37,30.13,200.8,46.33,115.07
+1927,16.73,6.8,32.07,230.57,52.67,155.63
+1928,18.6,8.5,33.8,269.77,61.33,83.27
+1929,17.73,7.83,31.8,241.07,55,113.33
+1930,17.7,8.57,31.6,223.13,50.67,153.5
+1931,17.1,7.37,32.87,216.37,49.33,163.67
+1932,17.37,8.77,31.1,209.87,48.33,142.77
+1933,17.33,7.27,31.77,249.63,57,115.33
+1934,17.43,8.8,32.2,229.8,52,146.87
+1935,18.37,8.17,32.97,264.8,60,86.4
+1936,16.63,7.97,30.83,198.23,45.67,158.1
+1937,17.9,8.93,33.2,205.57,46.67,89.57
+1938,17.37,7.2,32.33,222.5,50.33,165.8
+1939,17.17,8.27,29.73,215.7,49.33,121.13
+1940,16.27,6.63,29.27,222.87,50.67,140.43
+1941,17.57,7.93,31,231.53,52.67,132.3
+1942,17.5,7.7,32.07,244.67,56,125.53
+1943,17.87,9.1,33.03,237.97,54.67,86.47
+1944,18.13,9.9,31.8,209.86,48.67,112.27
+1945,18.6,8.5,33.13,268.8,61.33,106.3
+1946,17.17,8,33.57,218.13,49.67,179.3
+1947,20,8.97,36.6,281.99,64.67,64.37
+1948,16.23,7.67,31.1,178.5,41,139.97
+1949,18.8,8.2,33.53,278.6,63,44.23
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+1962,16.93,5.77,32.47,274.6,63.67,84.8
+1963,16.73,8.13,31.17,206.27,46.67,115.87
+1964,17.73,7.57,32.07,264.57,60.67,90.2
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+1969,16.13,6.1,29.5,215.77,49.33,144.57
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+1975,16.53,7.33,29.1,197.2,45,186.1
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+1980,15.6,6.8,29.03,143.7,33.33,120
+1981,16.53,7.07,29.03,176.77,42,109.83
+1982,17.5,9.27,29.07,173.44,40.67,186.9
+1983,19.07,9.1,31.37,207.14,48.67,52.83
+1984,16.8,8.4,30.53,198.34,47,75.13
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+2010,18.03,8.5,30.5,197.45,46,126.37
+2011,17.5,8.5,31.9,203.32,48,130.37
+2012,18.3,9.07,31.9,216.92,51.33,173.37
+2013,18.5,8.23,33.93,249.57,58.67,82.17
+2014,17.33,9.13,30.7,198.89,46.33,149.8
+2015,20.17,9.63,33.17,259.3,61,80.6
+2016,18.27,8.73,31.73,210.42,49.33,166.33
+2017,19.53,10.13,32.37,226.42,53.33,119.87
+2018,20.17,9.4,32.63,267.33,62.67,80.33
+2019,19.73,9.83,33.5,251.68,59,109.37
+2020,18.67,9.23,31.33,231.04,54.33,112.93
+2021,17.97,9.83,30.13,190.13,44.67,169.37
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+2025,19.6,10.5,32.8,250.81,58.67,126.8

data/curated/new_submission/climate_data.md

@@ -0,0 +1,9 @@
+|variable |class  |description                           |
+|:--------|:------|:-------------------------------------|
+|year     |double |Year of summer climate observation in Zurich, Switzerland. |
+|tre200m0 |double |Average air temperature 2 m above ground in degrees Celsius. |
+|tre200mn |double |Minimum air temperature 2 m above ground in degrees Celsius. |
+|tre200mx |double |Maximum air temperature 2 m above ground in degrees Celsius. |
+|sre000m0 |double |Total sunshine duration in hours. |
+|sremaxmv |double |Total sunshine duration as a percentage of the possible maximum. |
+|rre150m0 |double |Total precipitation in mm. |

data/curated/new_submission/climate_metadata.csv

@@ -0,0 +1,7 @@
+variable,description
+rre150m0,Precipitation; monthly total
+sre000m0,Sunshine duration; monthly total
+sremaxmv,Sunshine duration; relation of the monthly total to the possible maximum
+tre200m0,Air temperature 2 m above ground; monthly mean
+tre200mn,Air temperature 2 m above ground; absolute monthly minimum
+tre200mx,Air temperature 2 m above ground; absolute monthly maximum

data/curated/new_submission/climate_metadata.md

@@ -0,0 +1,4 @@
+|variable    |class     |description                           |
+|:-----------|:---------|:-------------------------------------|
+|variable    |character |Climate variable observed by MeteoSwiss in Zurich. |
+|description |character |English language description of climate variable. |

data/curated/new_submission/intro.md

@@ -0,0 +1,9 @@
+
+This week we're exploring the weather prediction of Zurich's infamous exploding snowman!
+
+> The Boeoegg is a snowman effigy made of cotton wool and stuffed with fireworks, created every year for Zurich's "Sechselaeuten" spring festival. The saying goes that the quicker the Boeoeg's head explodes, the finer the summer will be. 
+
+- Check the burn duration of our snowman against the average summer temperature. Does folk science stand its ground against hard science?
+- Can you find a number of successive years so that our snowman's predictions seem more accurate? 
+- Does our snowman's forecasting ability improve if you choose climate variables other than temperature?
+- What happened in the years for which there was no duration recorded? You can check the Wikipedia entry for "Sechselaeuten" for some funny anectodes!

data/curated/new_submission/meta.yaml

@@ -0,0 +1,16 @@
+title: "Can an exploding snowman predict the summer season?"
+article:
+  title: "Boeoegg prediction"
+  url: "https://www.meteoswiss.admin.ch/weather/weather-and-climate-from-a-to-z/boeoegg-prediction.html"
+data_source:
+  title: "OpenData for Zurich's Sechselaeuten"
+  url: "https://github.com/philshem/Sechselaeuten-data"
+images:
+# Please include at least one image, and up to three images
+- file: "sechselaeuten.png"
+  alt: >
+    A dot plot of the average summer temperature in Zurich versus the burn duration of the Böög in minutes, with a line of best fit and record summers annotated.
+credit:
+  post: "Thanks to Matt for submitting this dataset!"
+  github: "@econmaett"
+  bluesky: "@econmaett.github.io"