The avatar for @tashapiro
Tanya Shapiro
Independent Data Analytics Consultant, passionate about data visualization and storytelling · she/her 🏳️‍🌈
Public
Edited
Aug 25, 2023
2 forks
12 stars
2
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tornados.csv
Type Table, then Shift-Enter. Ctrl-space for more options.

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tornados
SELECT t. *
,CAST(FLOOR(yr / 10) * 10 AS INTEGER) AS decade
,CASE WHEN CAST(FLOOR(yr / 10) * 10 AS INTEGER)<=1970 THEN 1
WHEN CAST(FLOOR(yr / 10) * 10 AS INTEGER)<=2000 THEN 2
ELSE 3 END AS row_index
,CASE
WHEN CAST(FLOOR(yr / 10) * 10 AS INTEGER) IN (1950,1980, 2010) THEN 1
WHEN CAST(FLOOR(yr / 10) * 10 AS INTEGER) IN (1960,1990, 2020) THEN 2
WHEN CAST(FLOOR(yr / 10) * 10 AS INTEGER) IN (1970,2000) THEN 3
ELSE 4 end as col_index
FROM tornados t
WHERE yr>=${form.minYear} and yr<=${form.maxYear}

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filtered
SELECT distinct SUBSTRING(cast(decade as char),1,4) || 's' as decade, col_index, row_index from filtered
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tornados
SELECT
year as year
, month as month
, COALESCE(tornados,0) as tornados
FROM
(SELECT distinct mo as month from tornados) m
CROSS JOIN
(SELECT distinct yr as year from tornados where yr>=1992) y
LEFT JOIN
(SELECT yr, mo, count(*) as tornados from tornados group by 1,2) t on t.yr=y.year and m.month = t.mo
ORDER BY year, month
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usGeo = FileAttachment("us-geo.json").json()
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cities = [
{ name: "Houston", lat: 29.7604, lon: -95.3698, color:'black'},
{ name: "Tampa", lat: 27.9506, lon: -82.4572, color:'black'},
{name: "Denver", lat: 39.7392,lon: -104.9903, color:'black'},
{name: "Oklahoma City", lat:35.4676, lon:-97.5164, color:'white'},
{name: "Jackson", lat:32.2988, lon:-90.1848, color:'black'},
{name:"Dallas", lat:32.7767, lon:-96.7970, color:'black'},
{name:"Tulia", lat:34.5359, lon:-101.7585, color:'black'},
{name:"Great Bend", lat:38.3645, lon:-98.7648, color:'black'}
];
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