# Different Chart types in tableau with real-time examples

**Introduction :**

In this modern era, there are lots and lots of data recorded around us. Some of the data are recorded and analyzed to improve the results. This analysis of data can be done easily by visualizing it. This process is known as "Data visualization".It is a way of converting our data and information into graphs and charts by visual tools. By this, we can identify and improve the areas which need improvement and we can also predict the sales. There are many visuals tool used, one of the familiar tools is "tableau".Tableau is a tool that generates data into "reports" and "dashboards" in the form of "charts".The dashboard includes all the information in a single view. There are many types of charts available in tableau, that can be seen one by one in this blog.

Here in this blog, we are going to see how the superstore uses tableau as a data visualization tool to analyze and improve business growth. Below we can see different types of charts and their uses. In the superstore data, we have information on orders, people, and products.

Source of data: superstore data. In super store sales data we have three tables such as Order table, People table, and return table. Under the order table, we have the Order ID, Order date, Ship date, Ship mode, Customer ID, Customer name, Segment, Region, City, State, Postal code, Country, Category, Subcategory, Product name, Sales, Profit, Order(count), Quantity, Discounts. In the people table, we have Region(people), Manager, and People(count). In the return table, we have Order ID(returns), Returned, and Return(count). We will be forming tables with these parameters.

**Charts :**

** **Charts are very interactive and cover all the important information required to view. For making charts in tableau we need either dimension or measure or a combination of both (i.e., 1 dimension 2 measure,1 dimension 1 measure, etc). Dimensions are qualitative values such as names, months, and geographical data. Measure are quantitative values such as numeric data, which can be measured. There are many different types of charts used in tableau according to the data used. Let us see the most used types of charts. Note: Always have “entire view” for the graphs than “standard view”.

Area chart :

Area charts are used for quantitative information that is happening periodically i.e, it happens daily, weekly, monthly, or __yearly.__ To create an area chart we need one date, zero or more dimensions, and one or more dimensions. Now the superstore wants their month-wise sales using shipment __modes.__ so we take order date month-wise in columns, and sales in rows now how to add another dimension i.e, "ship mode". This third dimension can be given in the color of the marks. By this, the various ship mode can be colored differently according to their sales. For this area chart visualization will be great.

steps to create an area chart :

Order date(Dimension) is given in column:

Sales(measure) is given in row:

when we give the dimension and measures in the row and column the automated graph will be created according to the data given.

The automated graph is created by the given data:

we can change this automated graph into an area by selecting "area graph" from the marks on the left:

An area chart is created successfully. The above graph represents the month-wise sales area chart we need to add the "shipment" method. For calculating it we just drag and drop ship mode in the color of marks.

steps to create month-wise sales with ship mode area chart :

We used 2 measures (ship date and sales) and included the ship mode(1st class, same day, second class, and standard class).In area charts, the area under the line in the graph will be covered. Maximum coverage in the graph has maximum records or values. In__ __this graph we can see the standard class has the highest sales then comes the second class then the first class. Same-day ship mode class has a minimum area covered so it has minimum sales. Now the super stores can find the standard mode of shipping gives more profit than same-day in-store sales.__ __superstore__ __tries to__ __improve the in-store sales in need to gain more profit.

Line chart :

Line charts as the name say it displays the data in the form of lines which is the connection of a series of points. The creation of a line chart requirement is the same as an area chart , it needs one date ,zero or more dimensions, and one or more measures.

steps to create a line chart :

when we give the dimension and measure tableau creates the automated __chart.__ To get line chart just select the "line" in the marks .

We can see phones and chairs has the largest sales than others . Superstore wants the subcategory-wise sales using ship mode.After adding the colors to ship mode a colorful line chart giving about the sales details will be created.

superstore has fewer sales on art,book cover, envelops, fasteners, labels and supplies than others can be observed from this line graph and also same day sales are also low than other ship modes. It will try to improve the sales of these items to yield more profit.

Bar charts :

Bar charts visualize the chart by using measures against dimensions in the form of bars between two axis. we have three types of bar charts in tableau. They are :

horizontal bar chart

stacked bar chart

side by side bar chart

steps to create a simple bar chart :

When we drag and drop the order date and sales in columns and rows an automated chart according to the dimension and measure will be created. To convert this chart into bar charts select the option "bar" charts from the marks on the left.

A simple bar graph was created between the order date and sales. From this chart, super store can able to see that the year 2021 has maximum sales than all other years. By this superstore can view last four year sales and can compare each other. This is also known as a vertical bar chart.

Types of Bar chart :

Horizontal Bar chart :

As the name says the bar in the bar chart will be horizontally placed. These horizontal graphs will be often used in butterfly charts .where we combine two bar graphs to attain the butterfly __charts.__ __It__ needs zero or more dimensions and one or more measures.

Requirement: sub-category wise sales.

steps to create a Horizontal bar chart :

Let's create a sub-category wise sales horizontal bar chart. After creating a bar graph of sub-category wise sales, right side of the tableau we can find the show me icon.

Show me icon suggest some of the graphs which will suitable for the dimension and measure given. From there we have to select "horizontal bars".after selecting it our graph will turn from vertical bars to horizontal bars.

This graph is very useful when we used to determine profit:

Let us have subcategory(dimension) and profit(measure). When we give the dimension and measure the automated graph created then select "horizontal" bars.

The horizontal bar graph of sub-category wise profit will appear but the super store data has some negative values.

To highlight the positive values drag and drop the profit values into colors of marks.

Now we can able to observe positive and negative values clearly from the horizontal graph.

Stacked Bar chart:

A stacked bar in Tableau is a type of bar chart that represents values in the form of** "**segmented bars". Here, each bar is divided into different segments, which provide further details about the field and regions. With this, not only can you compare the main data variables, but also have the distribution of smaller variables in every bar.

Requirements: month-wise profit representation according to category-wise segmented.

Steps to create stacked bar chart :

Here stacked chart is created by placing the order date in the column and the Profit in rows and adding the category to colors in the mark to find the category-wise profit for the past four years. Then when we select the stacked bar graph option we can obtain the graph easily. From this graph, the superstore can see that all the year technology sales are higher than all others and in the years 2019 and 2021 furniture sales are less than other two years. Office supply sales are more or less same in the all four years. It can clearly see the sales portion according to category.

Side by Side bar graph :

Side by side bar graph is very useful we want to compare two measures with one dimension. For example, if we want to compare category-wise sales and profit at the same time we can use side by side bar graph for a better visual experience.

Requirement: compare profit and sales.

when we place our dimension in column and measures in rows these bar chart will be available. For obtaining side by side bar graph we just select that option from show me . After selecting side by side bar chart our graph will turn into :

From this super store easily compare sales and profit with category accordingly.

Dual axis chart :

A dual axis is useful for analyzing two measures with different __scales.__ __It__ is created between two measures and one dimension.

Requirement: compare profit and sales according to every month and wants better visualization than stacked bar charts.

steps to create dual axis chart :

After giving the superstore order date ( dimension ) in a column and sales( measure), and profit( measure) in a row we obtain two graphs in the tableau as seen above. Change the order date from year to month and we can find separate profit graph marks and sales graph marks. In marks, we can change our graphs separately for profit and sales.

when we click the drop-down button of the second measure in row(profit) we can find the dual axis option. When we select the dual-axis option both graphs will be merged to get the dual-axis.

Now we can see that our both graphs merged but when we see the axis variation profit and sales are not the same.

When we right on any one of the axis we can find the " synchronize axis" option. Select it . After selecting the "synchronize axis" option superstore graph will turn into :

synchronized dual axis chart is obtained now both profit and sales are in line graph representation. for a better view, we can choose different graphs for profit and sales separately.

Here we have a Bar chart for sales and a line chart for profit. we can select colors and size and we can label them separately in their respective marks given. As a result, we can provide better visualization of profit, sales, and order date for superstore to look at their growth.

Here we have given a bar chart to both the measure and we added label to both of the measures to see their exact number and sales and profit for a better visualization experience. Like this, we can any kind of graph for the measures to visualize them differently.

Pie chart :

Pie charts are useful when we visualize the proportion of contribution. It needs one or more dimensions and one or two __measure.__ Pie charts require at least one or more dimensions and one or two measures.

we can use pie on aggregated fields such as percentage ratio, and sales ratio. There are two ways to create a pie chart. For creating a pie we need one or more dimension and one or two measure.

Requirement: want to see sub category wise sales.

steps to create a pie chart :

When we give the category(dimension) in columns and sales( measure) the below automated graph is created. The pie chart is selected from the show me icon.

After selecting the pie chart option our automated bar chart will be converted into a pie chart :

we can see that our columns and rows disappeared and we can find the category in colors and sales in size and angles in marks.

while creating pie charts we put our measures in angles and dimensions in colors to differentiate. In the above pie chart are looking more or less the same in its portion.

In this, we gave the labels to category and sales from marks so that we can view the pie portions exactly.

Another method of creating a pie chart :

Another method of creating a pie chart is just directly drag and drop the category (dimension) into colors and sales (measure) in size and angle into the marks .so by directly dropping our dimensions and measures we can able to create a pie chart.

Donut chart :

A Donut chart is a combination of a pie chart and a circle chart. It is a good representation of the pie chart.

Let's create a subcategory-wise sales and profit donut chart.

Steps for creating a donut chart :

For creating a donut chart the first step will be creating a dummy measure. The dummy measure given is avg(0). After giving dummy measures we can find two graphs appeared separately and we can also find separate marks for both.

In the first dummy marks, we create a pie chart by selecting the circle option from 1st marks.

Then drag and drop the subcategory into the colors of the pie chart.

After giving the subcategory into colors we can notice that the pie is equally divided. Now let's give the sales into angles.

Now after giving the sales to angles the pie will be divided into subcategories according to sales. we can also add labels for subcategory and sales for better understanding.

After giving labels the perfect pie chart of the donut chart is shown below.

For the second dummy measure marks we create a circle chart where the color of the chart should be white and the size of this chart should be less than the pie chart created for the first dummy measure.

The next step is to give the label for profit then the profit will be appeared under the circle chart which we should place in the middle of the circle chart.

To create a donut chart just merge this pie and circle chart using the dual axis option.

Donut chart final __output.__ It is a colorful representation by combining both pie and circle charts which results in a donut __chart.__ As this looks like a donut it is named a "donut chart".

Regional wise chart :

when our data is geographic data, such as state and city names there we use this regional-wise chart. For this, we need one geographical field and zero or one measure.

Requirement: superstore wants state-wise sales.

Steps to create a regional wise chart :

Give the state in columns and sales ( measure) in rows. Then the automated graph as shown below will appear.

After giving state and sales we can notice that the map graph is highlighted in the "show me" icon.

After selecting one of the graphs suggested by show me our chart will be converted into a regional chart and the state and sales will be moved to marks, columns, and rows to become longitude and latitude. Here dark blue represents the highest sales and light blue represents the lowest sales and in between, we can't able to differentiate the sales.

,

For better and exact differentiation of sales between states, we just add the labels to sales and __state.__ __As__ we see the sales they look non-realistic in the real world. Because sales can be in billion and trillion not in thousands.

So let's format the sales number. For Formatting the sales number just right on the sales label there we can find the format option. click on it.

In the format, the window clicks the down arrow of the number and selects the "current(standard)" option, and selects united states. Automatically our sales numbers will be converted into dollars and values will be changed accordingly.

If we don't want to display it as it is because some have many numbers, we can display them in thousands, millions, etc. For that select "currency (custom)" and select the display units as "Thousand".

After applying all, the perfect regional chart is obtained as shown below.

Scatter plot :

A scatter plot is used to visualize relationships between numerical variables. For making a scatter plot we 2 to 4 measures, zero or more dimensions__.__ __It__ is used to compare measures mostly.

Requirement: Need to check the relationship between profit and sales growth according to categories and subcategories.

Steps to create a scatter plot :

Once we gave our measure tableau itself creates a scatter plot automatically. Otherwise, we can also get a scatter plot by selecting it from the "show me" icon. Here we can see only one dot since it is the sum of profit and sales.

In need to see the scatter plot of profit and sales let's remove the sum. It can be removed by deselecting "aggregate measures" from the analysis in the header.

After removing our scatterplot of profit and sales will be like the given below

Let's have aggregated measures and now give colors to the category. After placing categories in colors we get three dots which is the sum of the profit and sales of each category.

To see deeply the relation between profit and sales let's add a subcategory into detail of the marks. After giving details to subcategories, colors to the category. A scatter plot of profit and sales will be like given below. Here each subcategory belongs to each category so they are represented by the same __colors.__ So this is our scatter plot for profit and sales as per category and subcategory.

In order to see how these are correlated we need to add trend lines to this scatter plot.

After selecting trend lines our scatter plot will appear as shown below.

Before we see the relationship between profit and sales of the superstore, let us see about filter and correlation. so that we understand the above scatter plot very well.

Filter :

As the name suggests, it filters and highlights the required data. Tableau uses it to see the precise

details of the data. Let's take a region-wise profit and sales chart where profit and sales will be given in rows and columns and region is given in colors.

we can find the filter above the marks in tableau.

Which data you want to apply filter can be dragged and dropped in the filter box. Now we want to filter the regions and see the profit and sales separately. As soon as we dropped, a checkbox will be opened as shown below.

Select the required field which wanted to be included while applying the filter. Here we want all the regions so that we can separately view all four __regions__ and so we selected all the regions.

After applying we can notice the region in the filter. In order to get the required results to let's have the filter check box.

To get the filter check box click the drop-down button of the filter and select show filter.

The filter check box is visible in right.

To extract each region separately by selecting the checkboxes. Here, we can see only the “central” and “south” region results since it has been selected in the filter option.

Now let's see about correlation.

Correlation :

Correlation can be defined as the relation between two factors. There are three types of correlation. These are calculated by “trend line”(A linear trend model is computed for the two given factors.).

Types of Correlation:

1. Positive correlation

2. zero correlation

3. Negative correlation

Positive Correlation :

When we find an inclined trend line between the axis we can say a “positive correlation” exists between those factors.

Below scatter plot is an example of a positive correlation. Here we can see the inclined trendlines between sales and profit i.e when the sales “increase” and profit “increase” simultaneously. If two-factor increases simultaneously it results in inclined trend lines in the graph then the factors are “positively correlated”.

Zero Correlation :

Here we can find “parallel” trend lines that mean there is “no increase” in both factors. Both will be equal for a while.

In the below graph, we can see a kind of zero relation where sales and profit are the same approximately.

Negative Correlation:

In this type, we can find the inverted inclined trend lines. Here one factor increases the other factor decreases.

Here we can see the trendlines coming down i.e when the sales “increase” and profit “decreases”.Negative correlation examples can be clearance sales and garage sales. when there is a loss we can find a Negative correlation.

coming back to our scatter plot.

In the above scatter plot, we can observe a positive correlation for furniture and office supplies whereas technology has a zero correlation __relation.No__ negative correlation is observed in superstore data.

Funnel chart :

A funnel chart is a specialized chart used to demonstrate a business or stage __process.__ __It__ is used to differentiate certain processes or track some related stages or some conversion stages. It is just a different representation of our data to make our graphs more attractive and more understanding. Let's see how to make region-wise sales chart.

Steps to create a funnel chart :

Let's give our sales( measure ) into rows. The following graph will appear.

Let us give colors to the region in marks. As soon as we gave colors our graph will turn into a stacked bar graph as shown below.

Let's add the sales to size so that our stacked bar graph will be changed into different sizes according to the sales as shown below.

The next step is to change from "Standard view" to "Entire view"

when changing the view our graph will be turned as shown below.

To obtain the funnel shape we have to choose ascending order or descending order sort option from the header.

Here I gave sort regions descending by sales so my graph is arranged from higher sales to lower sales which gives us the funnel-shaped chart.

If a super store wants the sales details which are above sales of 7000. when we have requirements like this we can add sales to the filter and change the range and let's see how the graph changes.

After changing our sales rate starting from 7000 our graph changes as shown below where we can notice the change in the region. Now the central region has the biggest sale, the south, and east has the same sales approximately and the west has the lowest sales. We can see changes between the two graphs after changing the sales range.

Forecast chart :

A forecast chart is used to predict the future upcoming result, it generates the future result by the data recorded. For creating a forecast chart we need one date entry in a column or row.

Requirement: superstore wants the region-wise sales prediction for upcoming years.

steps to create a forecast chart :

Lets us have the order date in a column, segment, and sales in a __row.__ __As__ soon as the date is given segment wise sales chart according to order date will be created as shown below.

Lets us add region into colors in order to view the sales data deeper. The below chart will be obtained when we add region into the colors of the marks.

After selecting the forecast option we can see the forecast prediction chart for the next three years will appear.

If a superstore wants predictions for the next five years. Right-click on the forecast graph and select the forecast option.

After selecting the forecast option a pop window will appear. There select the option "until" and change the no of years according to the requirements. Here we changed up to 5 years.

The final region-wise sales according to order date forecast chart for the next 5 years is shown below.

Text chart :

The text chart is similar to a table. We typically create text tables by placing one dimension on the Rows and another dimension on the Columns. Then we complete the view by dragging one or more measures to Text on the Marks. A text table uses the text mark type. Text tables are also called cross-tabs or pivot tables. Let's see Category wise sales in the form of a text chart.

Steps to create a Text chart :

Let's have order date in columns, segments, and categories in __rows.__ As soon as we gave the__ __data a table will appear as shown below.

Now we have our category, segment, and order date in the chart but we can find some "ABC" inside the table. Now let's fill this "ABC" with sales. For this, we can two methods either we can just drag and drop sales in the text of marks otherwise we can just double-click on sales. By doing this our table will be filled with sales details as shown below.

Here we can make the grand totals of this table. For finding the total we need to click analytics.

After selecting "Total" the text chart will be converted as shown below

Now we see the total for each segment-wise, category-wise, and yearly too (i.e,) total row-wise and column-wise.

For better understanding, we can change these whole numbers into percentages. For changing the totals click the drop-down button of sales then select "Quick table calculation" and then select "Percent of total".

After converting it we can find the percentage total, and we can change this percentage total across columns also.

For changing the total column wise click the drop-down button of sales, then select "compute using" and then select "Table(down)".

After selecting the "Table down" option our total will be along column-wise.

By this Text chart super store can easily verify and check whether their sales are running 100 percent.

Word cloud chart :

A word cloud chart is a visualization method that displays how frequently words appear in a given text data
and makes the size of each word proportional to its frequency. All the words are then arranged in a cluster or cloud of words. Alternatively, the words can also be arranged in any format like horizontal lines, columns, or within a __shape.__ __It__ Provides a colorful representation. Mostly used in text analytics cases.

Requirement: city-wise sales. we can present these city-wise sales in a colorful creative way using this word cloud chart.

Steps to create a word cloud chart :

The first step is to drag the city into text and colors in marks. Below one shows the city in text.

Dropping city in colors.

After dropping the city into colors and text, the final result is shown above.

To change the city word size according to sales let's drop the sales into size.

After dropping the sales into size the automated chart will appear.

To change the automated graph into a word cloud select "text" chart from the marks.

After changing into a text graph it turns into a word cloud graph.

Here larger the city names larger the sales. For example, The city like New York, Los Angeles, and Seattle have more sales than Chicago, Newark, and Columbus. New York has the largest sales of any other city.

If a superstore wants to filter the city which has sales of more than 7000 we can just add the city to the filter and set the range. When we add city to the filter a pop window as shown below will appear.

In that window choose "Condition" then select "By field" there set the range ">7000" and select "Ok"

After setting the range our word cloud has the city names where sales are more than 7000.

Bubble chart:

A bubble chart as the name suggests visualizes the measures and dimensions in the bubbles form. The sizes of bubbles can be determined by measure size for effective visualization. The color of bubbles can be set so that we can differentiate the members present in a __dimension.__ __To__ create a bubble chart we need zero or more dimensions and one or more measures.

Requirement: Give different creative visualization for subcategory-wise sales and profit.

Steps to create a bubble chart :

Let's give Subcategory (dimension) to column and Sales (Measure) to row.

After giving dimensions and measure the automated graph as shown above.

To create a bubble chart click on the "Show me" icon and select the "bubble" chart option.

After selecting the " bubble" option our bubbles chart will appear. Here bubble chart color is formed according to subcategory-wise color and the size of that subcategory will be decided by sales.

Once we selected the bubble chart our dimension and measure will be shifted from row and column to marks. we can also create a bubble chart by directly giving sales to size and subcategory to colors and selecting the "circle" chart.

superstore wants to have subcategory-wise sales and profit in this situation we can include profit for colors. After giving profit to colors, our final bubble chart will appear as shown below:

Here we can notice that text is given to subcategories, size to sales, and colors to profit. we can create a bubble chart directly by giving these measures and dimensions to marks and selecting the "circle" chart. And we can notice the profit scale to refer to. Here phones and chairs are the large bubbles among others so it has more sales and more profit where table bubble is also large but its color is orange which indicates a loss. By these variations, we can easily separate profit and loss and sales growth using a bubble chart.

Tree map chart :

A treemap chart is a data visualization technique that is used to display hierarchical data using nested __rectangles.__ __It__ is similar to a stacked bar chart but here the given data will be arranged like stacked boxes from higher to __lower.__ __In__ the circle, it will difficult to identify which subcategory sold more which comes first, and which comes next, in this case, we can use a treemap chart to differentiate__.__ __To__ create a tree map we need one or more dimensions,1 or 2 measures.

Requirement: Same requirement as the bubble chart is taken i.e, subcategory-wise sales and profit.

Steps to create a treemap chart :

Let's give the subcategory (dimension) and sales (measure) into rows and columns. After giving dimensions and measurements an automated graph will appear.

To convert select the "treemap" option from the "show me" icon.

A treemap arranged from highest value to lowest value will appear as shown below.

Let's add another measure "Profit " into colors. After adding profit to colors and sales will change in size. And profit ranges are also indicated on the right side.

By this tree map, we can notice that Phones have more sales and profit than chairs. Labels have the lowest sales and tables have more sales than binders but has no profit.

Histogram Chart :

A histogram is very useful to visualize measures and their frequency. It shows the distribution of numeric data. It shows both frequency and measure value by default, which will be useful in many cases. For example, if we want to analyze the discount frequency given by the superstore, in that case, creating a histogram chart will be very __useful.__ __It__ can be created by one measure.

Requirement: Determine the frequency of quantity ordered from the superstore.

Steps to create a histogram.

Drag and drop the Quantity into rows. After applying we can see a single quantity graph.

To obtain a histogram chart select the "histogram" option from the show me icon.

The histogram chart will appear but our bin size ranges from 0 to 1.77. we won't order anything like 1.77.

After creating the histogram chart we can find the new option "Quantity(bin)" click on it and select the "edit " option.

As soon as we selected the edit a pop-up window will appear there change the size of the bin from "1.77" to 1.

Now we can see the frequency of order clearly. we can observe that the quantity of 1 is ordered "899" times and the quantity of 2 is ordered "2402 " times and so on from this histogram graph.

Bump Chart :

A bump chart is a visual representation of the special form of the line __plot.__ __It__ has a relatively simple purpose—they are used to visualize changes in rank over time. A Bump Chart is used to compare two dimensions against each other using one of the Measure values. The Bump chart can be used to show the ranking order of the required data from the given data.

Requirement: Observe region-wise sales and arrange them in ranking order.

Steps to create a Bump chart :

Let's give the order date(dimension) in column and sales(measure) in rows and the region is dropped into colors of marks. After applying all the parameters the automated graph will appear.

Here we can see the data is arranged according to sale. we want to see the overall ranking.

To Rank them select the drop-down button of the sum in the column. Then choose quick table calculation and then select rank as shown below.

As the result, we get a graph like this and it is not arranged according to region wise it is arranged according to the table.

Now select the drop-down button of sales and select "compute using" and then choose "region".

After applying it our graph gets arranged in rank order according to region wise.

This is the final representation of the bump chart.

To make it more attractive we can put again sales in rows and repeat all process and merge it using a dual axis.

Here we made another sales graph using shapes.

And these two graphs are merged using a dual axis.

This is our final representation of the bump this is done to present our data more attractive. From this bump chart super store can observe The region "west " places first rank in the year "2018,2020,2021" and in the year "2019" it secures second place. Through this, we can observe the ranking of each and every region from the given data by bump chart.

Pareto chart :

A Pareto chart is a bar graph, where the lengths of the bars represent frequency or cost i.e, time or money. In this, the data are arranged with the longest bars on the left and the shortest to the right. In this way, the chart will visually depict which situations are more __significant.__ __It__ has an 80-20 rule which means 80% of world happenings will have 20% of the cause, and 80% of work will be done by 20% of people.

Requirement: The superstore wants sub-category-wise sales and wants to find 80% of sales according to sub-category.

Steps to create a Pareto chart :

First, enter the sub-category into a column and enter two sales in rows. Here we are picking two sales in order to compare.

After giving dimension and measures we get an automated graph as shown above.

sort them descendingly by selecting the sort option from the header.

The next step is to click the drop-down button of the second sale and click "Add table calculation".

After selecting "add table calculation" a new window will pop up there select the drop-down button of difference from there select "Running Total".

After selecting "running total" in our second graph, select "Add secondary calculation" and in the secondary calculation, type give "percent of the total".Now the second graph changes from lowest to highest.

Merge two charts by the "dual axis" option.

The merge chart result will be shown below.

Here for the first sale, we choose a bar chart and for the second one, we selected a line chart for a better view.

If we want to display the running total in the second graph just drag and drop the sales into the label of marks. There select the drop-down button of sales and repeat the "table calculation" process.

This is the final look of the Pareto chart here we can notice phone contributes 14.37% of sales and goes on and 80% of sales are by phones, chairs, storage, tables, binders, machines, accessories, copiers, and bookcases.

Box plot chart :

The box plot is a graphical representation of statistical data based on minimum, maximum, and median(average). As the name suggests boxplot looks like a rectangle with lines extending to the top and __bottom.__ __It__ is mainly used for viewing outliers in the graph. For the box plot, we need zero or more dimensions and one or more measures.

Requirement: show the outliers discount in the subcategory-wise discount data.

Steps to create Box plot :

Drag and drop sub-categories into columns and discount into rows. Here we remove the sum of (discount) because some of the discounts will be nothing. In order to remove it, deselect the "aggregate measures".

Then select "box plot " from the show me icon.

Box plot for the subcategory-wise discount will appear as shown below.

From this box plot we can observe that appliance has one 80% discount outlier and furnishing has one 60%discount outlier.

control chart :

The control chart is used to study how a process changes over time. Here the given data are plotted in time order. It always has a central line for the average, an upper line for the upper control limit, and a lower line for the lower control limit. These lines are determined from historical data. It can be used to see growth and in the medical field, it can be used to detect a person's blood pressure range.

Steps to create a control chart :

Let's have the order date in the column and the profit in the row. The automated chart as given below will appear.

Select the day option from the drop-down button of the year.

After selecting it our graph will be shown below.

Before creating a control chart let's see about CALCULATED FIELDS and their usage in tableau.

Calculated field :

These are found by selecting the drop-down button near the search bar as shown in the picture.

These are used to create formulas, set the conditions, and used to create average value, upper limit value, and lower limit value. They are also used to extract strings, and numbers from the given data.

Let's take the same date-wise profit chart here the superstore arises a requirement like the profit above 2000 should be highlighted in one color and below zero should be highlighted in a different color. Let's see how to make this requirement using a calculated field.

After selecting the calculated field a pop-up window will appear, where we can enter our formula as shown below and select OK.

Label created using calculated field will be visible. Just drag and drop labels to colors and sizes to see the profit variations.

After applying all our profit variation will appear as shown below. Therefore these calculated fields are used to set the formulas.

Coming back to our control chart if we want to create an average, upper limit, and lower limit lines we can do that using calculated fields.

In this chart, we can create the average by using the calculated field as shown below.

After creating the average we can see under "measure names" to include that in our graph drag and drop in detail of marks.

To see the average line in our graph right click on the profit axis and select "add reference line".

After selecting the reference line a window will pop there in the valid field we can find the "average" create and select it.

After adding it we can find the average line in our graph as shown below.

Repeat the process and create Upper control limit (UCL) and Lower control limit(LCL)

The formula for UCL.

Formula for LCL.

We also included UCL and LCL in the marks.

In the reference line selects "band" and choose UCL and LCL.

After applying our final graph with the average, the upper control limit and lower control limit will appear as shown below.

Bullet chart :

A bullet chart can be used as a gauge or indicator to show the performance of measures. Two measures can be compared to each other using this. For example, if we have estimated profits and actual profits, we can compare both to check whether we acquired the required __profit.__ __It__ requires zero or more dimensions and 2 measures.

Requirements: Check whether the subcategory achieved the required profit.

Steps to create a Bullet chart :

After giving Sub-category(dimension) in columns, sales, and profit ( measures) the automated graph will appear as shown below.

Then select the "bullet" graph option from the "show me" icon.

The bullet chart for the given data is obtained but it has to be arranged in a proper way. Because here the black line represents "sales " and the blue one represents "profit".We want the black line to be profit and another one to be sales to compare.

To arrange this right click on the below axis and select "Swap Reference Line Fields".

After swapping our bullet graph will appear as shown below. Now the black line refers to " profit " and the blue represents "sales" when we hover over the grey lines we can see the graph will indicate "60% of profit" "80% of profit"

Therefore by this bullet chart, we can able to track our profit and check its growth.

**Conclusion** :

In this blog, we saw how the tableau was used to create various types of data visualization charts of superstores__.__ __It__ helps us to understand its growth in a simple easier way. Hope this blog helps you to understand data visualization and its chart types. Please follow for similar content.