Table of Contents （🔎 Click to expand／collapse）
- GSP787 —— Insights from Data with BigQuery
- Overview
- Challenge Scenario
- Query 1: Total Confirmed Cases
- Query 2: Worst Affected Areas
- Query 3: Identifying Hotspots
- Query 4: Fatality Ratio
- Query 5: Identifying specific day
- Query 6: Finding days with zero net new cases
- Query 7: Doubling rate
- Query 8: Recovery rate
- Query 9: CDGR - Cumulative Daily Growth Rate
- Task: Create a Datastudio report
Quest Outline （🔎 Click to expand／collapse）
| Level | Code | Name | Note |
|---|---|---|---|
| Introductory | GSP281 |
Introduction to SQL for BigQuery and Cloud SQL | EN |
| Introductory | GSP072 |
BigQuery: Qwik Start - Console | EN |
| Introductory | GSP071 |
BigQuery: Qwik Start - Command Line | EN |
| Fundamental | GSP407 |
Exploring Your Ecommerce Dataset with SQL in Google BigQuery | EN |
| Fundamental | GSP408 |
Troubleshooting Common SQL Errors with BigQuery | |
| Introductory | GSP409 |
Explore and Create Reports with Data Studio | |
| Expert | GSP787 |
Insights from Data with BigQuery: Challenge Lab |
This lab is recommended for students who have enrolled in the Insights from Data with BigQuery quest. Are you ready for the challenge?
You're part of a public health organization which is tasked with identifying answers to queries related to the Covid-19 pandemic. Obtaining the right answers will help the organization in planning and focusing healthcare efforts and awareness programs appropriately.
The dataset and table that will be used for this analysis will be: bigquery-public-data.covid19_open_data.covid19_open_data. This repository contains country-level datasets of daily time-series data related to COVID-19 globally. It includes data relating to demographics, economy, epidemiology, geography, health, hospitalizations, mobility, government response, and weather.
Build a query that will answer "What was the total count of confirmed cases on Apr 15, 2020?" The query needs to return a single row containing the sum of confirmed cases across all countries. The name of the column should be total_cases_worldwide.
SELECT
SUM(cumulative_confirmed) AS total_cases_worldwide
FROM
`bigquery-public-data.covid19_open_data.covid19_open_data`
WHERE
date = "2020-04-15"Build a query for answering "How many states in the US had more than 100 deaths on Apr 10, 2020?" The query needs to list the output in the field count_of_states.
Hint: Don't include NULL values.
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 > 100Build a query that will answer "List all the states in the United States of America that had more than 1000 confirmed cases on Apr 10, 2020?" The query needs to return the State Name and the corresponding confirmed cases arranged in descending order. Name of the fields to return state and total_confirmed_cases.
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 > 1000Build a query that will answer "What was the case-fatality ratio in Italy for the month of April 2020?" Case-fatality ratio here is defined as (total deaths / total confirmed cases) * 100. Write a query to return the ratio for the month of April 2020 and containing the following fields in the output: total_confirmed_cases, total_deaths, case_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"Build a query that will answer: "On what day did the total number of deaths cross 10000 in Italy?" The query should return the date in the format yyyy-mm-dd.
SELECT
date
FROM
`bigquery-public-data.covid19_open_data.covid19_open_data`
WHERE
country_name = 'Italy'
AND cumulative_deceased > 10000
ORDER BY
date
LIMIT
1The following query is written to identify the number of days in India between 21 Feb 2020 and 15 March 2020 when there were zero increases in the number of confirmed cases. However it is not executing properly. You need to update the query to complete it and obtain the result:
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
)Using the previous query as a template, write a query to find out the dates on which the confirmed cases increased by more than 10% compared to the previous day (indicating doubling rate of ~ 7 days) in the US between the dates March 22, 2020 and April 20, 2020. The query needs to return the list of dates, the confirmed cases on that day, the confirmed cases the previous day, and the percentage increase in cases between the days. Use the following names for the returned fields: Date, Confirmed_Cases_On_Day, Confirmed_Cases_Previous_Day and Percentage_Increase_In_Cases.
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 > 10Build a query to list the recovery rates of countries arranged in descending order (limit to 10) on the date May 10, 2020. Restrict the query to only those countries having more than 50K confirmed cases. The query needs to return the following fields: country, recovered_cases, confirmed_cases, 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
10The following query is trying to calculate the CDGR on May 10, 2020(Cumulative Daily Growth Rate) for France since the day the first case was reported. The first case was reported on Jan 24, 2020. The CDGR is calculated as:
((last_day_cases/first_day_cases)^1/days_diff)-1)
Where :
last_day_casesis the number of confirmed cases on May 10, 2020first_day_casesis the number of confirmed cases on Feb 02, 2020days_diffis the number of days between Feb 02 - May 10, 2020
The query isn’t executing properly. Can you fix the error to make the query execute successfully?
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) 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, SQRT((last_day_cases/first_day_cases),(1/days_diff))-1 as cdgr
from summaryNote: Refer to the following page to learn more about the SQL function referenced LEAD().
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,
POWER(last_day_cases/first_day_cases,1/days_diff)-1 AS cdgr
FROM
summaryCreate a Datastudio report that plots the following for the United States:
- Number of Confirmed Cases
- Number of Deaths
- Date range : 2020-03-15 to 2020-04-30
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- Click EXPLORE DATA > Explore with Data Studio.
- Click AUTHORIZE on the Welcome to Google Data Studio dialog.
- In the Data Studio report, select Add a chart > Time-series Chart.
- Add
country_casesandcountry_deathsto the Metric field. - Click Save.