Analysis ; A Chronic Clinic Dataset II

Previously in Part I of this Analysis; A Chronic Clinic Dataset I developed some dashboards and ran some Sql queries on the data set that did not have any null values. In Part II of this series I will be looking at the data that had null values and see what kind of conclusions I can draw. I will also do some joins to see if that will help with gaining helpful insights. So sit back and let's get to work.

Data Analysis Tools and Data Preparation

In this analysis I will not use Power BI alot but will just ran queries using Sql. After converting the data into a Csv file and renamed it flucvsNull. I uploaded it into https://sqliteonline.com/ and Power BI to ran queries and draw conclusions. Here are my findings. I then started running Sql commands on both datasets the flucvsNull and the flu_csv.

Analyzing the Data ; Power BI & Sql

Total Number of Both Non Chronic and Chronic Patients

I ran sql commands to get the same results with the following codes below

SELECT count ( patient_id_) FROM flucvsNull
 
 SELECT count ( patient_id_) FROM flu_csv

select avg (age) as average_age from flu_csv

select avg (age) as average_age from flucvsNull

Gender Count of Chronic Patients

Gender Count of Non Chronic Patients

SELECT Count (gender) as women from flu_csv 
 where gender = 'F'

SELECT Count (gender) as men from flu_csv
 where gender = 'M'

SELECT Count (gender) as men from flucvsNull
 where gender = 'M'

SELECT Count (gender) as women from flucvsNull
 where gender = 'F'

I also did a join using Union All to combine the two datasets into one;

SELECT patient_id_,age,gender,chronic_conditions FROM flucvsNull
UNION all
 SELECT patient_id_,age,gender,chronic_conditions FROM flu_csv
 order by patient_id_ desc

SELECT count (patient_id_) patient_id_ FROM flucvsNull
UNION all
SELECT count (patient_id_) patient_id_ FROM flu_csv
 where chronic_conditions = 'NULL'

SELECT count (patient_id_) patient_id_ FROM flucvsNull
UNION all
 SELECT count (patient_id_) patient_id_ FROM flu_csv
 where chronic_conditions = 'Pregnant'

SELECT AVG (age) AS AVERAGE_AGE FROM flucvsNull
UNION all
SELECT AVG (age) AS AVERAGE_AGE FROM flu_csv

Conclusion

The analysis above was carried out using Power BI and running Sql queries thank you very much for reading my page; should have any suggestions, comments or have a better way of running some of these queries or do them totally differently please let me know. Once again thank you very much for reading my post.