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Insights from Data with BigQuery: Challenge Lab [GSP787]
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GSP787 : Insights from Data with BigQuery: Challenge Lab :-
----------------------------------------------------------------------------------------------------------------------------------------------
// Navigation Menu -> BigQuery.
----------------------------------------------------------------------------------------------------------------------------------------------
Task - 1 : Total Confirmed Cases :-
SELECT sum(cumulative_confirmed) as total_cases_worldwide FROM `bigquery-public-data.covid19_open_data.covid19_open_data` where date='2020-04-15'
----------------------------------------------------------------------------------------------------------------------------------------------
Task - 2 : Worst Affected Areas :-
with deaths_by_states as (
SELECT subregion1_name as state, sum(cumulative_deceased) as death_count
FROM `bigquery-public-data.covid19_open_data.covid19_open_data`
where country_name="United States of America" and date='2020-04-10' and subregion1_name is NOT NULL
group by subregion1_name
)
select count(*) as count_of_states
from deaths_by_states
where death_count > 100
----------------------------------------------------------------------------------------------------------------------------------------------
Task - 3 : Identifying Hotspots :-
SELECT * FROM (
SELECT subregion1_name as state, sum(cumulative_confirmed) as total_confirmed_cases
FROM `bigquery-public-data.covid19_open_data.covid19_open_data`
WHERE country_code="US" AND date='2020-04-10' AND subregion1_name is NOT NULL
GROUP BY subregion1_name
ORDER BY total_confirmed_cases DESC ) WHERE total_confirmed_cases > 1000
----------------------------------------------------------------------------------------------------------------------------------------------
Task - 4 : Fatality Ratio :-
SELECT sum(cumulative_confirmed) as total_confirmed_cases, sum(cumulative_deceased) as total_deaths, (sum(cumulative_deceased)/sum(cumulative_confirmed))*100 as case_fatality_ratio
FROM `bigquery-public-data.covid19_open_data.covid19_open_data`
where country_name="Italy" AND date BETWEEN '2020-04-01'and '2020-04-30'
----------------------------------------------------------------------------------------------------------------------------------------------
Task - 5 : Identifying specific day :-
SELECT date
FROM `bigquery-public-data.covid19_open_data.covid19_open_data`
where country_name="Italy" and cumulative_deceased>10000
order by date asc
limit 1
----------------------------------------------------------------------------------------------------------------------------------------------
Task - 6 : Finding days with zero net new cases :-
WITH india_cases_by_date AS (
SELECT
date,
SUM( cumulative_confirmed ) AS cases
FROM
`bigquery-public-data.covid19_open_data.covid19_open_data`
WHERE
country_name ="India"
AND date between '2020-02-21' and '2020-03-15'
GROUP BY
date
ORDER BY
date ASC
)
, india_previous_day_comparison AS
(SELECT
date,
cases,
LAG(cases) OVER(ORDER BY date) AS previous_day,
cases - LAG(cases) OVER(ORDER BY date) AS net_new_cases
FROM india_cases_by_date
)
select count(*)
from india_previous_day_comparison
where net_new_cases=0
----------------------------------------------------------------------------------------------------------------------------------------------
Task - 7 : Doubling rate :-
WITH us_cases_by_date AS (
SELECT
date,
SUM(cumulative_confirmed) AS cases
FROM
`bigquery-public-data.covid19_open_data.covid19_open_data`
WHERE
country_name="United States of America"
AND date between '2020-03-22' and '2020-04-20'
GROUP BY
date
ORDER BY
date ASC
)
, us_previous_day_comparison AS
(SELECT
date,
cases,
LAG(cases) OVER(ORDER BY date) AS previous_day,
cases - LAG(cases) OVER(ORDER BY date) AS net_new_cases,
(cases - LAG(cases) OVER(ORDER BY date))*100/LAG(cases) OVER(ORDER BY date) AS percentage_increase
FROM us_cases_by_date
)
select Date, cases as Confirmed_Cases_On_Day, previous_day as Confirmed_Cases_Previous_Day, percentage_increase as Percentage_Increase_In_Cases
from us_previous_day_comparison
where percentage_increase > 10
----------------------------------------------------------------------------------------------------------------------------------------------
Task - 8 : Recovery rate :-
WITH cases_by_country AS (
SELECT
country_name AS country,
sum(cumulative_confirmed) AS cases,
sum(cumulative_recovered) AS recovered_cases
FROM
bigquery-public-data.covid19_open_data.covid19_open_data
WHERE
date = '2020-05-10'
GROUP BY
country_name
)
, recovered_rate AS
(SELECT
country, cases, recovered_cases,
(recovered_cases * 100)/cases AS recovery_rate
FROM cases_by_country
)
SELECT country, cases AS confirmed_cases, recovered_cases, recovery_rate
FROM recovered_rate
WHERE cases > 50000
ORDER BY recovery_rate desc
LIMIT 10
----------------------------------------------------------------------------------------------------------------------------------------------
Task - 9 : CDGR - Cumulative Daily Growth Rate :-
WITH
france_cases AS (
SELECT
date,
SUM(cumulative_confirmed) AS total_cases
FROM
`bigquery-public-data.covid19_open_data.covid19_open_data`
WHERE
country_name="France"
AND date IN ('2020-01-24',
'2020-05-10')
GROUP BY
date
ORDER BY
date)
, summary as (
SELECT
total_cases AS first_day_cases,
LEAD(total_cases) OVER(ORDER BY date) AS last_day_cases,
DATE_DIFF(LEAD(date) OVER(ORDER BY date),date, day) AS days_diff
FROM
france_cases
LIMIT 1
)
select first_day_cases, last_day_cases, days_diff, POW((last_day_cases/first_day_cases),(1/days_diff))-1 as cdgr
from summary
----------------------------------------------------------------------------------------------------------------------------------------------
Task - 10 : Create a Datastudio report :-
SELECT
date, SUM(cumulative_confirmed) AS country_cases,
SUM(cumulative_deceased) AS country_deaths
FROM
`bigquery-public-data.covid19_open_data.covid19_open_data`
WHERE
date BETWEEN '2020-03-15'
AND '2020-04-30'
AND country_name ="United States of America"
GROUP BY date