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Tableau Essentials: Tips for New users and Choosing the Right Charts

In this blog, I am going to explain some of the tips and tricks in Tableau. 

While there are multiple articles that shares tips & tricks in Tableau that targets Tableau Users with wide range of experiences from beginners to experts, this blog post focuses on the tips & tricks that would boost the productivity for a newbie on Tableau. 


This blog also covers the topic of 'Choosing the Right charts' which helps You to choose the right chart based on the requirements.

All the images are by the author. 


First we will see some tips and tricks useful while using Tableau.

  • #1: How to get the summary of a worksheet? 

To get the summary of a worksheet, right click anywhere in the blank space and choose the option ‘Summary’ 

 

This option is also available in the menu ‘Worksheet’: 

 

 

  • #2: If you get a worksheet done by someone else, how to understand the viz? 

Tableau gives an option to describe the worksheet. Option is available under the menu ‘Worksheet’ 

 

This will give explanation on the Marks, Measures, Dimensions and will describe the calculated fields also if any. We will be able to see the data source details as well. 

 

  • #3: How to rename a pill?  For example, there is a calculated field in the Rows and it needs to be renamed.   

 

Double click the pill you want to rename, then add two slashes in the beginning of the name. Press Shift and do Enter. Then release Shift and hit one more Enter. Then the pill will be renamed.

 

 

 

 

  • #4: Re-showing hidden labels for Rows. 

If you choose to hide the label and later you want to show that, then the following options to re-show the labels can be used.  

First option is you can simply right click and choose ‘Show Field Labels for Rows’ in the label area. Sometimes this option may not be available. In that case you can follow the below option. 

Option is in the menu ‘Analysis’. 

 

  • #5: How to show column labels at the top. 

By default, column labels appear at the bottom of the chart. This tip explains how we can show the column labels at the top of the chart instead of showing at the bottom of the chart. In some cases, this helps in improving the readability of the labels. The option is available in the menu ‘Analysis’. 

This gives a dialog box as below. Uncheck the option 'Show innermost level at bottom of view when there is a vertical axis' to place the label on the top. 

 

 

  • #6: How to remove “Abc” from tableau table with dimensions? 

In the following example, I have added the dimensions- Category and Sub-Category and there will be a third column with the text “Abc”. Will see how we can remove those. 

Method 1: Under marks change the ‘Automatic’ to ‘Polygon’  

 This will result in the following and you can resize the last column. 

 

 

Method 2: Create a calculated field with just a blank space as below. 

 

“Abc” in the table will be removed once you drag this calculation to the Text field in the marks. 


  • #7: How to add field header for single measure in text table? 

In the below example, it shows the Sales for each Sub-category in different categories. But the sales column doesn’t have any header shown. Will see how we can have that header. 

 

 

Select one more measure say ‘Profit’ . 

Then in the Filters->Measure Names, uncheck ‘Profit’ which we actually don’t want to display and that will result in below table with field header added for sales. 

 

 

  • #8: How to create separate color legend per measure? 

The below example has Sub-category and the measure values-Profit, Quantity and Sales. We will see how we can apply different colors for each measure. This can be done in two ways. 

Method 1: Hold ctrl (Command in Mac) and drag the ‘Measure Names’ from the columns to the color in the Marks. 

 

In the above, measures are colored individually with different colors but there is no color gradient to describe lower/higher values in each measure. This can be achieved by the following method.  

Method 2:  Use ctrl key (Command in Mac) and bring the ‘Measure Values’ from the Marks to the color shelf. Then from the drop-down in the Color marks choose ‘Use Separate Legends’ as below. 

 

 

 

Then from the color legend that appears towards the right side of the sheet, choose different colors for each measure. I have chosen the below colors by editing the legends and the final table will look like as below. 

 

And if you choose ‘Square’ instead of ‘Automatic’ from the Marks, it will result in below representation. 

 

 

  • #9: How to create color legend per dimensions. 

If we add one dimension to the Color marks and try to add one more dimension in Color, the first dimension added to color will get removed. In the below example, I added Category to the Color first. Then I needed Sub-Category as well in the Color, but when I try to add Sub-Category to Color, the color on Category disappears. 

 

The solution for this is: Hold ctrl key (Command in Mac) and select the dimensions you need and drag it together to the Color. This will give you the below chart. 

The colors can be edited in the color legend which appears in the right side of the sheet. You will get the option once you click the dropdown arrow. You can choose whatever color you wish to have in the viz. 

 

 Next, we can get some idea on the chart selection for beginners. Here I will be giving details on some of the chart suggestions as well.

Representations can be mainly grouped into below categories:

  • Change over time

    • To show trends or patterns over a continuous period.

    • Mainly to represent:

      • How does the data change evolve over time?

      • Are there any trends or patterns in data over time?

    • Charts suggestions:

      • Line chart: A line chart is a type of graph that displays information as a series of data points connected by straight line segments. It is particularly useful for showing trends over time or for illustrating continuous data relationships. Each data point represents a value at a specific point in time or along a continuous scale, making it easy to visualize patterns and changes in the data over the plotted dimension.

      • Sparkline chart: A sparkline chart is a compact, simple, and condensed line chart that displays trends and variations in data without axes or labels. Typically integrated within text or tables, sparklines provide a quick visual summary of data trends over time or across categories. They are designed to be small enough to fit within a line of text or alongside data cells, offering immediate insights at a glance.

      • Area chart: An area chart is a type of graph that displays quantitative data over time, using filled-in areas below the lines connecting data points. It visually emphasizes the magnitude of change over time and is effective for showing trends and comparing the magnitude of different groups. Area charts are similar to line charts but provide additional emphasis on the cumulative value of the data series.

  • Magnitude comparison

    • This is for size comparison. These can be relative or absolute.

    • For example:

      • Which categories have the highest or lowest sales?

      • Show comparison of sales across different categories

    • Chart suggestions:

      • Bar chart: A bar chart is a graphical representation of data where rectangular bars of equal width are used to show the frequency, count, or other measures within categories. The length of each bar corresponds to the value it represents, making it easy to compare values across different categories. Bar charts are effective for visualizing categorical data and identifying patterns or trends among groups.

      • Lollipop chart: A lollipop chart is a hybrid visualization that combines elements of both a bar chart and a line chart. It features markers (often circles) at the ends of vertical lines, where the circles represent data points and the lines connect them. Lollipop charts are useful for highlighting individual data points while still showing the overall trend or distribution across categories.

      • Bubble chart: In the context of magnitude comparison, a bubble chart visualizes data points using circles (bubbles), where the size of each bubble represents the magnitude or value of a specific variable. Larger bubbles indicate higher values, while smaller bubbles correspond to lower values. This allows for a quick visual comparison of magnitudes across categories or groups, showing not only the relative sizes but also any patterns or disparities in the data.

  • Correlation

    • To show the relationship between two or more measures.

    • Mainly to understand:

      • Is there a correlation between two measures?

      • How strongly related are the two variables?

    • Chart suggestions:

      • Scatterplot: A scatterplot is a type of graph that displays the relationship between two variables by plotting data points on a Cartesian plane, where each axis represents one variable. In the context of correlation, a scatterplot helps visualize the strength and direction of the relationship between the variables.

      • Dual-line chart: A dual line chart, also known as a dual-axis line chart, is used to display two different sets of data on the same chart with dual y-axes, allowing for the comparison of their trends and correlation over time or another continuous variable.

  • Ranking

    • To show the position in an ordered list (Relative Ranking)

    • For example:

      • To show top 10 customers by sales

      • To show lowest 10 products by sales

    • Chart suggestions:

      • Lollipop chart: In the context of ranking, a lollipop chart is used to visually represent the ranking or order of categories or data points. It typically consists of circles (or points) representing the data points and lines extending from these circles to a baseline, indicating their position in the ranking

      • Funnel chart: In the context of ranking, a funnel chart is a specialized type of chart that represents stages in a process or levels of a hierarchy, typically with a decreasing size or quantity at each successive stage or level. It visualizes how data progressively narrows down as it moves through different stages, often from a broader category to a narrower subset.

  • Distribution

    • Show values in a dataset and how often they occur

    • For example:

      • What is the distribution of customer's ages?

      • What are the busiest time in our workday?

    • Chart suggestions:

      • Histogram: In the context of distribution, a histogram is a graphical representation of data that shows the frequency or count of data points falling within specified intervals, called bins. It displays the shape and spread of data distribution across a continuous range.

      • Box-plot: In the context of distribution, a box plot (also known as a box-and-whisker plot) is a graphical representation that summarizes the distribution of a dataset, particularly focusing on its central tendency, variability, and skewness.

      • Scatter plot: In the context of distribution, a scatterplot is a graphical representation that displays the relationship between two variables by plotting data points on a Cartesian plane. Unlike histograms or box plots, which summarize distributions for single variables, scatterplots visualize the joint distribution of two variables simultaneously.

  • Part-to-whole

    • Show how a whole breakdown into its components

    • For example:

      • How much each category contributes to overall sales?

    • Chart suggestions:

      • Pie chart: A pie chart is a circular graph divided into slices to represent numerical proportions. Each slice corresponds to a category and its size is proportional to the quantity it represents relative to the whole. Pie charts are effective for showing parts of a whole, such as distribution of different categories within a dataset, providing a quick visualization of relative proportions.

      • Donut chart: A donut chart is similar to a pie chart but with a hole in the center, giving it a ring-like appearance. Like a pie chart, it represents numerical proportions where each segment's size corresponds to its proportion of the whole. Donut charts are useful for displaying categorical data and comparing the contributions of different categories or groups while allowing for easy comparison of relative sizes due to their circular and segmented format.

  • Flow

    • Visualize the movement or flow of the data over time

    • Mainly to understand:

      • How do things move from one point to another?

    • Chart suggestion:

      • Waterfall chart: A waterfall chart shows how numbers change step by step. It begins with a starting point and uses bars that go up or down to show how each addition or subtraction affects the total. It's great for seeing how different factors add up or subtract from a total amount.  


Thank You for reading! I appreciate taking time to explore my blog!

 

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