Generating Insights from Covid-19 Data using Power BI

This article will be focused mainly on a step by step approach on how to visualize data using Power BI. In case you’re new or a beginner in Power BI, here is my former article which constituted the Power BI features, installation process, and basic understanding of the concept.

Hidden within your data lies important insights that may drive things forward. It can be very hard connecting the figures by looking at the numbers alone. The world of Power BI brings your data into life. It can be so much fascinating to see how much useful information can be extracted and displayed in a simple readable way through Power BI.

About the Data

The Covid-19 response data is gotten from She Code Africa Cohort 2 Mentoring Program. However, there may be some discrepancies between this data and the former data as this is a recent and updated one. Attached here is a link to the data used for this visualization.

The Step by Step Approach

Power BI doesn’t require much technical expertise to use. This means anyone can use it without worrying about a learning curve or any complicated problem. The first thing you do is clicking on the already installed Power BI app on your PC. Right in front of you displays the Power BI environment.

In other to load in your data, Power BI can connect with many databases and deal with files in different formats but we will be working on a CSV file. So you click on Get Data>> Test/CSV>> Connect.

Since we have the data already downloaded on our PC with the name response(1).csv. Loading in the data straight up may look like the best thing to do but we should also think of doing a bit of transformation on our data because it may contain duplicates, missing values, and all. So we, therefore, clicked on Open to load the data and then Transform data.

From the look of things on our Power Query Editor, the column New Deaths appears to have tons of missing values that may hamper the correctness of our visualization. We can remove the whole New Deaths column from our data by left-clicking on the column which can be done differently with different PCs, to clicking on the Remove Column bar at the top ribbon of our Power Query Editor.

The topmost row named World contains the sum of each of the cases in each country. This row seems to be irrelevant. We can do away with it by clicking on the Remove Rows bar at the top with us specifying the row to be removed by inputting 1 and it automatically discards it.

We just finished transforming our data. Simultaneously, we clicked on the Close & Apply bar at the top left side of the ribbon. Then, the report page comes up.

The Data page shows an enlarged view of our response data.

The Model page views the relationship between our response data. We have the Report page as a playground for viewing and displaying our visualizations.

Since we have everything set up, let’s start working on generating insights.

Working on the Proposed Data

The Field Pane displays the table, folders, and fields in our data that are available for us to use. We can just click or drag a field into the page to start a new visualization. Charts can be added by clicking on the preferred chart on the pane. We made this by clicking on the Total_Cases, Total_Deaths, Total_Tests columns, and the Line and Clustered Column Chart afterward.

Making Tables

The table shown in the above image at the lower part was made by clicking on the table chart and the Field Pane columns respectively. The Field Pane enables us to know the column we are working with, owing to the fact that it shows a ticked yellow mark. The image below displays the table chart marked as T and the slicer as S.

The Slicer

Slicer in Power BI is a type of on canvas visual filters. It enables us to sort and filter a packed report and view only the information we want. Unlike filters, the slicers are present as a visual on the report and let us select values as we are analyzing reports. The Slicer was made by clicking on the Slicer icon on the visualization pane thereafter, the continent on the field pane.

The Card Visual

The above visuals ain’t so elaborate enough. So we contemplated and decided to use The Card Visual. It shows us a single value, text, number, sales amount e.t.c. It can be done by creating a column chart with one number and select the card icon on the visualization pane. Note: The continent visuals below were made using The Card and The Slicer.

So far, we’ve been talking about the ways of making interesting visuals. How about checking what we’ve generated from our data?

The Africa Continent

As of recent, we see Africa did about 7,000,000 tests on its citizens, had 728,000 cases of Covid-19, 1014 recovered citizens, 1047 critical conditions, and about 15,000 total number of deaths.

The North America Continent

This shows North America to have done about 55,000,000 tests on its citizens, had 5,000,000 cases of Covid-19, 1 recovered citizen, 20,000 critical conditions, and about 197,000 total number of deaths. Covid-19 in North America is way more crucial than that of Africa.

The South America Continent

We have the South America continent to have done about 12,000,000 tests on its citizens, had 3,000,000 cases of Covid-19, 13 recovered citizen, 14,000 critical conditions, and about 118,000 total number of deaths. This is kinda lower compared to that of North America but higher than that of Africa.

The Asia Continent

This shows Asia did up to something about 140,000,000 tests on its citizens, had 3,000,000 cases of Covid-19, 1028 recovered citizen, 20,000 critical conditions, and about 81,000 total number of deaths.

The Europe Continent

About 79,000,000 tests appeared to have been done, Europe had 3,000,000 cases of Covid-19, 1022 recovered citizen, 5309 critical conditions, and about 199,000 total number of deaths.

The Australia and Oceania Continent

Australia did about 4,000,000 tests on its citizens, had 14,000,000 cases of Covid-19, 1506 recovered citizens, 33 critical conditions, and about 145 total number of deaths. Australia seems lucky compared to the other continents.

Deductions

• Europe had the highest total number of death while Australia had the lowest death.
• Asia had the highest total tests taken on its citizens while Australia had the lowest test.
• Asia, North America had a very high serious and critical condition while Australia had the lowest
• North America had the highest total cases while Australia had the lowest.
• The most less affected continent by covid-19 is seen to be the Australia/Oceania continent.

Getting closer to the end of this article, a title can be displayed in each visualized data for easy accessibility.

You can get this done by clicking on the Text box icon at the top and assigning whatever title we desire for our report.

If you see this report to be informative enough, do well to drop some claps. Happy Reading!