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Create charts using appropriate variables of interest. Then publish your work and share your link in the forum
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Hi everyone,
Here is my dashboard for this week’s assignment.
Allow me to explain the graphs and illustrations which are separated by tabs (kindly skip to Page 2).
Page 2
These graphs illustrate the bar chart, map graph, and multi-row card visualizations, respectively. The bar chart us group by continent. The map graph visualizes the “spread” of the confirmed cases across continents. Lastly, the multi-row card provides the instant look for each country case count. Overall, these three charts/graphs provide an overview of the cumulative count of COVID-19 cases worldwide.Page 3
The graph illustrates the ratio of daily deaths and confirmed cases of COVID-19 per country. This will give an idea on how many deaths were reported per day, in relation to the number of cases detected in that day. Usually, peaks in the graph indicate a surge. Since COVID-19 case reporting a sometimes delayed, the effects of death can only be observed a few days or weeks after the surge has passed. This can be an indicator of how deadly the surge was.Page 4
Funnel plots are usually best when there are two variables being compared in a same measure. For example, for an age group 10-20, it would be good to see the ratio of males:females in terms of mortality rate. However, the source data has only limited information. Hence, for this visualization, the GDP grouped by continent was presented. Same goes for the pie chart since there are no other parameters that can be presented using a pie chart.Page 5
The line chart shows the cumulative confirmed COVID-19 cases as compared to deaths through time, categorized by country. This is a standard visualization across known dashboards for COVID-19 as it provides a good overview of how fast a country tallies confirmed cases and deaths through time. An added feature, forecasting, was used to project the course in terms of case counts and tallies for a period of 6 months. This will help in preparing the amount of financial and healthcare resources in the coming months.Page 6
Blank page, ignore.Page 7
This line and clustered column chart visualizes the 7-day moving average by country. 7-day moving average is a usual indicator in epidemiologic trends particularly on diseases that have incubation time such as COVID-19. Also, 7-day moving average somehow balances the trend by protecting it from random influxes, eg. testing laboratories are closed on Sunday. This was calculated using “Measure” feature of PowerBI.Page 8
Sparklines indicate trends across time. For this visualization, I used a Matrix with continent as a main grouping variable, followed by countries. Sparklines within matrices provide quick comparison across region (or continents) in terms of confirmed, deaths, or recovered COVID-19 case.Page 9
Treemap was used to visualize confirmed COVID-19 case of a country as a ratio of the whole confirmed case counts. This provides an overview of the COVID-19 situation as well.Page 10
A good comparison for a scatter plot would be the plot of total number of tests conducted and total confirmed cases. However, due to data limitation, deaths vs confirmed cases was plotted instead. This plot gives an idea on the spread of datapoints across axis – whether the give off the same ratio as other countries or as an extreme outlier. This graph could be perceived as an extension of stacked column chart where it can give information relative to other countries. -
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Hello everyone,
My dashboard this week is : http://tinyurl.com/mrmcsvjvMy idea is to illustrate overall Covid situations and reducing overcrowded figures for the general reader. Understanding Covid situations without having to spend much time is the idea.
Changes of cases (Confirmed, Deaths) by time could be seen in Stack column chart, Line chart with forecast and Stack column with line. These charts show number that changes by time. Reader could easily spot highest numbers.
Comparison cased (Confirmed, Deaths, Recovered) by continents are illustrated in Bar chart, Tree map and Donut chart. These area or length comparison might be convenient for reader to roughly compare number between continents and notice the order of cases number by regions.
Each country’s figures are quite detailed. I managed to display each country’s cases number in Card and Map which reader can select preference country by clicking in the map or choose from slicer at the side. Others chart use Top N filter to reduced overcrowded numbers. Line, Funnel and Scatter plot show selected Top N countries by confirmed cases. Sparkline shows trend changed by time comparison between selected Top N countries by population. numbers.
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Dear All,
Please see my report in link: https://bit.ly/3AkPnRk
There are 12 charts to be displayed. In order to visualize the data and get some insights from them,
I tried to combine charts and other elements in 9 pagesBar and Tree chart ::
This visualization focus on daily confirmed case segmented by continents.
The stacked bar chart shows trends of COVID-19 infection across region over time period.
Tree map would show the quantity in comparative view to other continents to emphasize more on “number” of cases.
Moreover, the three map could be drill down to country level to see the estimated number of confirmed case in particular countries in each continents.
This would help reader understand the overview of number of COVID-19 spreading pattern and may help in prevention action decision.Column Chart ::
This visualization shows facet plot of daily confirmed COVID-19 infection cases comparing across region (top 6)
This would emphasize on trends and patterns of infection over time period.
The change infection patterns maybe correlated with the sub-type of virus in each continents.
This would help epidemiologist understand more on the pattern including peak, through and the tail of peak, which could be used for forecastingDonut chart and line chart ::
This visualization would focus on the cumulative confirmed case. The line chart show the accumulation of case over time. While the donut chart would show the number of cases contributed by each continents in %
This would help us understand the source of patient pools over time.
This would help the high level policy maker understand the burden of COVID-19 throughout the 2 years pandemic situation, which could be use in addition to any other charts to understand to burden of pandemic in this regions.Line chart with forecast and map ::
This visualization purpose is to show the trend over time of daily confirmed case and the forecast in the next period segmented by continents. The map was added to bring the sense of pandemic spreading globally.
This charts could be used for forecasting the upcoming situation, which could be use for preventive action planningLine with column chart ::
This visualization showing the line chart and stacked column chart.
The stacked column charts showing the number of daily confirmed case. The line chart represents the daily death case.
The crossing of both parameter would represent the contagious level of COVID-19 infection.Sparkline chart and card ::
This visualization key element is Sparkline which is line chart showing the number of daily confirmed case over time
The red and green dots show the minimum and maximum number respectively.
I also add the card showing the aggregated statistic parameter as a reference value, including min, max, median.
This chart would help us understand the trend over time as well as the key statistical value.
By reading this chart, policy maker would understand more on the trend over time and the current situation compared with the central tendency data point.Map ::
This visualization shows the daily confirmed cases among continents.
The size of the bubble is number of daily confirmed cases.
I also put the population and case per 1 M in the right table as a benchmark for reader’s reference.Funnel plot ::
This visualization main element is funnel plot of cumulative confirmed case across regions
I also add the card showing the aggregated statistic parameter as a reference value, including min, max, median.
This would bring the sense of “top rank” across the globe and also show its own reference values.Scatter plot ::
This scatter plot show the correlation between daily confirmed cases and daily death cases.
Seeing the shifting through right and/or up plane would indicate the rising of daily confirmed cases and daily death cases over time. -
Hi all,
kindly see my updated Power BI dashboard for COVID-19 cases visualization. The first page of the dashboard gives us summary of total cases and total reporting country by using “Cards” tool. The rest of the pages show the different approach of visualisation, for example using Line Chart to visualise the growth of daily COVID-19 cases and deaths. There is also a Map visualisation to show the distribution of COVID-19 cases accross the world.
Thank you.
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Dear all,
Please follow the link below to my report for this week.
https://bit.ly/3R3TmaJ
The first summary page presents a map and table with relevant information including the number of cases confirmed, recovered, and the death, both overall and by country. Subsequent pages show the relevant variables in different types of charts and graphics. -
Dear all,
Here is my updated dashboard https://bit.ly/3IcqCsn
The dashboard consists of:Page 2:
Cards and multi-row cards contain confirmation data for all Covid-19 cases in the world, as well as recovery and death data. Barchart contains data on confirmed cases of Covid-19 per continent and a distribution map per country, plus a slicer to view data per time range. This data visualization can describe the distribution of caseloads per continent and country.Page 3:
A line chart that contains data on confirmed cases, cures, and deaths per day, this data can be used to evaluate daily data and can be selected per country with a slicer.
line chart containing daily cases and forecasting
line and clustered column chart containing daily case confirmation data and the cumulative number of casesPage 4:
a stacked column chart containing data on confirmed cases per continent per country with the highest filtering of 10 cases and a treemap containing GDP data per continent per country. Based on the visualization, it can be seen that the top 10 countries have GDPs above 2T.Page 5:
Funnel chart containing the top 10 mortality data and a sunburst pie containing mortality data per continent and per country. Death data is important to evaluate the control of a pandemic in a country.Page 6:
Sparkline chart containing daily death data that can be filtered by country with a slicerpAGE 7:
Scatter plot chart that contains a comparison of GDP and cases per 1 billion population, based on the chart, many countries with cases over 60 billion have GDP less than 1 billion -
Here is my link https://tinyurl.com/5hcxyzfu
The first page is a summary dashboard composed of a Map, Card, Funnel, and a stacked bar chart which can adjust with the date slicer. This dashboard illustrates the overall pictured of total confirmed case of COVID-19 all over the world.
The second page is 100% stage column chart and line with stacked column chart which can adjust by the date and countries slicer. So you can see the proportion of daily confirmed case by continents with a stacked column and total confirmed and daily confirmed cases by countires with a line graph.
The third page consist of Sunburst and tree map with a date slicer. So you can see the proportion of daily confirm cases.
The fourth page is Line with forecast and Sparkline with countries slicer, so you can see the trend of daily confirmed cases and predict the amount of infected patients in the future.
The last page is Scatter Plot with date slicer which demonstrates an association between total confirmed cases and recovered cases by countries.
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Please find my dashboard here https://tinyurl.com/y3ua7hee
The first page shows the distribution of cases across the location using maps, cards/rows, and slicers.
The second page displays the stacked bar, column, and donut chart for confirmed, recovered, and deaths cases.
The third page shows the line graphs demonstrating the confirmed case over time, forecast deaths, and the combined confirmed case (column) with the line of deaths to see the trend.
The fourth page shows a sparkline chart of daily confirmed cases in the top 10 countries.
The fifth page displays a funnel chart and TreeMap of the number of deaths/recovered by country.
The last page shows the scatter plots between each country’s daily confirmed cases (x-axis and size) and sum deaths (y-axis) over time (1-month bins). -
Hi everyone,
You can access my report via link: https://tinyurl.com/yv3j8nk6The report shows the data by time, country, continent in the daily confirmed basis by using different types of chart.
In the sparkline page, it shows the top 5 of country that have the most daily confirmed cases.
In the last page, scatter plot chart shows the cumulative death cases and recovered cases to represent about how severe of the disease and how well each country cope with Covid-19 cases in their country. -
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Hello everyone,
This is my Dashboard
Page 1: A table for all reported cases per date with a slicer to filter by country name.
Page 2 and 3: Bar charts and Column charts show daily confirmed cases over time using continent legend.
Page 4: Pie, Donut, and Sunburst charts for Daily confirmed cases per continent and country name.
Page 5, 6: line and Area charts for daily confirmed cases comparing cases development for each country.
Page 7: Line chart for confirmed cases with the forecast.
Page 8, 9: Column and Line Clustered charts with stucked line of total confirmed cases.
Page 10: Line clustered showing the number of the daily confirmed cases over time
Page 11: Map chart shows the daily confirmed cases among continents with cards and multi-row cards.
Page 12: Funnel chart and sunburst pie with the top 10 mortality data per continent and country
Page 13: Scatter plot shows the correlation between daily confirmed cases and daily death cases over time. -
Here is my My Dashboard
1. The First Page is overall page
– Use Card and Multi-Row Card to display big-sized number of Total Cases, Total deaths, and Total Recovered.
– I decided to use donut chart to demonstrate Proportion of Total case by Continent to let you see big picture of covid19
– I used line chart with trends line and prediction to represent Total confirmed cases by time.
– Slicer to filter by location to be able to see in detail.2. The Second page provide more dynamic detail.
– First, I used map to give geographical information about total cases in each area. Bubbles’ size represent number of cases.
– I put 100% stack column to represent proportion of cases by time and put color for each continent. This will help you to see changes of Covid19 situation dynamically during the last 3 year.
– I also put bar chart for daily cases that use legend to identify continent that help you to see more detail other than just proportion in each continent.3. The third page
– Stack column and line chart demonstrate Total cases and daily cases along with Sparkline chart for top 5 cumulative cases countries. This chart can help to understand in-depth detail about relationship between Daily cases, Total cases, and severity in each area.4. This page can help you understand more about total cases and time.
– I used stack column to demonstrate total cases over time in each continents and used clustered column to separately demonstrate cases in each year.5.Treemap and Funnel chart.
– Treemap help us to understand proportion of cases in each continent and countries. and the funnel chart helped us to understand changes of 2-weeks cases (I use 14days as bins size)
– I also provide you matrix containing exact number of cases in each countries6. The last page is Scatter chart to understand relationship of confirmed cases and deaths
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