Published 11月 13, 2021 by with 0 comment

GSP787 - Insights from Data with BigQuery: Challenge Lab


Overview
You must complete a series of tasks within the allocated time period. Instead of following step-by-step instructions, you'll be given a scenario and a set of tasks - you figure out how to complete it on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.


To score 100% you must complete all tasks within the time period!

When you take a Challenge Lab, you will not be taught Google Cloud concepts. To build the solution to the challenge presented, use skills learned from the labs in the quest this challenge lab is part of. You will be expected to extend your learned skills; you will be expected to change broken queries.


Scenario
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.


Query 1: Total Confirmed Cases
Build a query that will answer "What was the total count of confirmed cases on April 25, 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.
Columns to reference:
cumulative_confirmed
date

Copy the following code to the Query editor and then click Run.

SELECT sum(cumulative_confirmed) as total_cases_worldwide FROM `bigquery-public-data.covid19_open_data.covid19_open_data` where date='2020-06-25'



Query 2: Worst Affected Areas
Build a query for answering "How many states in the US had more than 100 deaths on April 25, 2020 ?" The query needs to list the output in the field count_of_states.

Hint: Don't include NULL values.
Columns to reference:
country_name
subregion1_name (for state information)
cumulative_deceased

Copy the following code to the Query editor and then click Run.

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='2020-04-25' and subregion1_name is NOT NULL
group by subregion1_name
)

select count(*) as count_of_states
from deaths_by_states
where death_count > 100



Query 3: Identifying Hotspots
Build a query that will answer "List all the states in the United States of America that had more than 1500 confirmed cases on April 25, 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.
Columns to reference:
country_code
subregion1_name (for state information)
cumulative_confirmed

Copy the following code to the Query editor and then click Run.

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='2020-04-25' AND subregion1_name is NOT NULL
GROUP BY subregion1_name
ORDER BY total_confirmed_cases DESC ) WHERE total_confirmed_cases > 1500



Query 4: Fatality Ratio
Build 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.
Columns to reference:
country_name
cumulative_confirmed
cumulative_deceased

Copy the following code to the Query editor and then click Run.

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'



Query 5: Identifying specific day
Build a query that will answer: "On what day did the total number of deaths cross 8000 in Italy?" The query should return the date in the format yyyy-mm-dd.
Columns to reference:
country_name
cumulative_deceased

Copy the following code to the Query editor and then click Run.

SELECT date
FROM `bigquery-public-data.covid19_open_data.covid19_open_data` 
where country_name="Italy" and cumulative_deceased>8000
order by date asc
limit 1



Query 6: Finding days with zero net new cases
The following query is written to identify the number of days in India between 21, Feb 2020 and 13, 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
)

Copy the following code to the Query editor and then click Run.

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 '2020-03-13'
  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



Query 7: Doubling rate
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.

Copy the following code to the Query editor and then click Run.

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



Query 8: Recovery rate
Build a query to list the recovery rates of countries arranged in descending order (limit to 10 ) upto 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.
Columns to reference:
country_name
cumulative_confirmed
cumulative_recovered

Copy the following code to the Query editor and then click Run.

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



Query 9: CDGR - Cumulative Daily Growth Rate
The following query is trying to calculate the CDGR on April 25, 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_cases is the number of confirmed cases on May 10, 2020
first_day_cases is the number of confirmed cases on Jan 24, 2020
days_diff is the number of days between Jan 24 - 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 summary


Copy the following code to the Query editor and then click Run.

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',
      '2020-04-25')
  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 summary



Create a Datastudio report
Create a Google Data Studio report that plots the following for the United States:

Number of Confirmed Cases
Number of Deaths
Date range : 2020-03-18 to 2020-04-28

Hint: Use the below image as a reference while building the report and make sure your report is similar to it.

Hint: Do not use Explore with Data Studio option from BigQuery.

Copy the following code to the Query editor and then click Run.

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 '2020-03-18'
  AND '2020-04-28'
  AND country_name ="United States of America"
GROUP BY date

Click on EXPLORE DATA > Explore with Data Studio.


Authorize Data Studio to access BigQuery.
You may fail to create a report for the first-time login of Data Studio. Click + Blank Report and accept the Terms of Service. Go back to the BigQuery page and click Explore with Data Studio again.
In the new Data Studio report, select Add a chart > Time series Chart.

Add country_cases and country_deaths to the Metric field.
Click Save to commit the change.



Congratulations!
In this lab you used BigQuery to query the public Covid dataset (bigquery-public-data.covid19_open_data.covid19_open_data) to answer important questions related to the Covid-19 pandemic. You also created a Datastudio report for one of the query outputs to visualize the cases and deaths trend.


Reference:
1. Qwiklabs

2. Google Cloud Certification - Associate Cloud Engineer

3. Google Data Studio


最初發表 / 最後更新: 2021.11.13 / 2021.11.13

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