Tableau Charts
Introduction:
Tableau is a visual analytic tool that helps people see and understand their data. It is designed to transform and display data in a way that can be easily understood .
You can start with Tableau public free edition to create interactive dashboards . You can also share them on web with your friends and colleagues.
Most common way to display data is in the form of charts and graphs. There are different kind of charts where you can display your data . Selecting the right visualization ensures that data is not just seen but truly understood , acting as a bridge between complex data sets and actionable decisions.
Different kind of charts to visualise data:
The below charts are explained based on Hospital Data Set.
Line Chart :
Use line chart to show changes over time . The primary goal of this chart is to show BP control percentages fluctuate from Week1 to Week 18 .This line chart makes it easier to the viewer to follow the general flow of the data and helps him to figure out the drops and peaks in a glance.
Column Chart :
Use Column chart(Vertical bar chart) to show comparison between several data points . The below chart shows the Controlled BP among patients based on their visit type.
Bar Chart :
Bar charts are similar to column chart, except the data is displayed horizontally . It is also used to show comparison between several data points . The below chart shows the distribution of providers across different medical departments.
Pie Chart :
Pie charts are best used to display "part - to- whole" relationships . Pie charts are most effective when they are limited to 4-5 categories .Here the below pie chart displays how much each speciality contributes to the total 100% of providers.
Funnel Chart :
Funnel charts are best used to visualize " top -to bottom level hierarchy" . A funnel chart is used to represent values that decrease at each subsequent level. The below chart visualises ranks the count of provider speciality from the largest group at the top to the smallest at the bottom.
Bubble Chart :
Bubble charts are used to display data in cluster of circles, mainly comparing the multiple data and the relationship between them without using axes. The below chart shows the patient discharge disposition at a glance showing the majority of them were discharged to home from the hospital.
Side by Side Chart :
Side by Side charts allows to compare multiple sub- categories within a single category . It is effectively a "un-stacked version of a stacked bar chart, making it easier to compare the relative sizes of individual sub-categories. It breaks down where patients went after their hospital stay based on their health condition.
Heat Map :
Heat map uses color intensity and size to represent data density and trends across two or more data points, allowing for quick identification of hot spots . The below map displays the patient outcomes across various hospital services. By using color and size , it says larger squares indicate a higher volume of cases, while smaller dots shows rarer occurrences .
Tree Map :
Tree map uses rectangles to represent hierarchical data and part-to-whole data relationships. They could display even thousands of data simultaneously. The below chart shows the area of each rectangle is proportional to a specific value (in this case "Expected Mortality") . This makes it easy to see that Pneumonia has highest expected mortality rate compared to any other diagnosis.
Dual Axis Chart :
This chart is an extension to line chart, which is used to compare two dimensions that share common time period but might have different scales or units . The below chart is comparing expected and observed mortality over a 10-week period .
Scatter Plot :
Scatter plot is used to compare two measures ,uses symbols to visualize data. The below chart is powerful way to visualize clinical performance by comparing expected mortality against observed mortality . The diagonal(trend) line is the average relationship between two variables. On the trend lines means outcomes are as expected.
Above the trend lines means higher than expected mortality . These set of data requires clinical review to perform better quality care. Below the trend line means lower than expected mortality displaying that this set of data has performed better than expected.
Bullet Chart :
Bullet Chart which is variation of Bar chart, two measures ,used to compare the primary measure against target data . The below chart compares the actual counts of deaths (Observed mortality) against the expected mortality for each diagnosis . The below chart shows that hospital is performing well and reducing the number of deaths by providing good clinical care.
Lets wrap-up what we learned in this blog:
Line Chart : Best for showing trends and fluctuations.
Column Chart : Best for comparing values across different categories.
Bar Chart: Best for comparing values across different categories and ranking.
Pie Chart : Best used to display "Part-to-Whole " relationships.986
Funnel Chart : Best used to visualize "top-to bottom" level hierarchy.
Bubble Chart: Best used to compare relative sizes .
Side by Side Chart: Best used to compare sub-categories across main groups.
Heat map: Best used to compare multi-variable correlations.
Tree map: Best for showing hierarchical part -to -whole using area size.
Dual Axis Line Chart : Best for comparing two related metrics side-by-side over time.
Scatter Plot: Best for showing the relationship between two different numerical variables.
Bullet Chart: Best used to track performance against a goal or a benchmark.
There are few other charts like Gantt chart , Box and Whisker plot, Histogram and many more to learn and display our data effectively in Tableau. We will most likely learn in our next blogs with different data sets.
Thanks for reading my first blog, I hope you enjoyed and learned about Tableau charts.
