Advanced Tableau Analytics – Multi Condition Triggers and Alerts in Bollinger Bands

We have explored Bollinger Bands implementation in my previous post. In this article, we will explore deep on implementing advanced features such as multi-conditions and alerts on Bollinger Bands.

As we visualize the trend of data (e.g. stock price movement),  don’t you think it is beneficial to have alerts in visualization if the price violates upper or lower control limits?  Using Tableau, that can be straightforward implementation with a calculated field for condition and using a Mark Label on the chart as shown belowTableau_Bollinger_Multi_Chart_Conditions



But what if the requirement is to show this alert based on one more condition that was to meet/trigger in another sheet or  based on two or three such conditions from different sheets?

Multi-Chart conditions

Going a step further, imagine that you want to have a dashboard that shows alerts based on combination of conditions from different sheets. Here the implementation gets tricky as Tableau by default is not designed for inter-chart interaction as each sheet generates visualizations based on its underlying data and data source.

One way of implementing is to use multiple data sources and link them in your chart. This is possible if you can link (join) data source of ChartA with that of ChartB with a common dimension. The resulting implementation can have scenarios like :

  • Trigger condition in ChartA has been met
  • Trigger condition in ChartB has been met
  • In Chart C, check for its own Trigger condition, check for trigger in ChartA and ChartB display an Alert, which can be a strategy or aid a decision ( for example, in Stock Trading it can be a BUY/SELL/HOLD decision)

In real-lime, this can be extended further for advanced features to generate Dynamic Dashboards and email alerts. With analytics evolving smarter, you don’t want to view all data visualizations everyday and will be interested only in those visualizations where the triggers have been met.

The final implementation is below


I will update this post with source and implementation steps shortly.


Bollinger Bands analysis using Tableau

Download source files used in this article

Volatility analysis charts using Bollinger Bands are often used for trading decisions. Those who track stock price movements must be familiar with charts types shown below


Bollinger Bands are intervals drawn on price chart at standard deviation levels above and below the corresponding moving average.

Bollinger Bands consist of :

  • an N-period moving average (MA)
  • an upper band at K times an N-period standard deviation above the moving average (MA + Kσ)
  • a lower band at K times an N-period standard deviation below the moving average (MA − Kσ)

Let us see how can we develop this chart using Tableau.

In this example, I connect to a database table which has daily price information of a stock. Simple data source – dimension as Date and measure as price.

Step one is to create a line chart, as shown below


Next we need to add 4 calculated files

  • Moving Average
  • Standard Deviation
  • Upper Band
  • Lower Band


Note: The above formulas have used 20 as N-period (look back period) and 2 as K-times to multiply since these are  typical values used in real world. If we need to change these dynamically, we can consider using them as parameter fields.

Add ‘Measure Names‘ as filter to your sheet (select MA, LB and UB in the filter selection). Then drag and drop ‘Measure Values’ to Rows in the chart.


Choose ‘Dual Axis’ for Measure Values and synchronize the axis as next step.


We have the Bollinger Bands generated; however the color schemes are not intuitive. So lets modify that a little.

Here is the final chart


Download source files used in this article

Cluster Analysis using Tableau and R – Part-2

Related article : Cluster Analysis using Tableau and R – Part-1

We have performed clustering analysis from both Tableau and R in my previous post. Tableau uses k-means algorithm for cluster analysis which partitions the data into k clusters with a center or mean value of all the points in each. Clustering is based on the distance each measure lies from the center.

Let’s look into that in detail.

First generate a cluster scatter plot in Tableau as we did in part -1 using Iris data set.


Right click on the cluster that we added and choose Describe Clusters option


This provides you the clustering details given below


Now lets perform the k-means clustering from R and print the cluster

#copying iris to myiris variable
#Remove Species column
myiris$Species <- NULL
kmeans.result <- kmeans(myiris,3)

#print the cluster data


Check the cluster means against that of Tableau cluster centers. Aren’t these comparable? However Tableau clustering analysis is limited and the default one is k-means as compared to the number of packages and functions available in R to perform various types of clustering. I will dedicate a future article to cover cluster and fpc packages in R.

In this case, what options that a Tableau user has for extended/advanced clustering? The answer is R integration by calling R packages from Tableau using similar steps that I explained in this article.

Cluster Analysis using Tableau and R – Part-1

This article introduces you to similar clustering analysis on your data using Tableau and R. Data files and source used in this post can be downloaded using the link below.

Download source files used in this article

Clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. We will perform the analysis in both Tableau and R using the same data.

Clustering Analysis using Tableau

To start with, we connect Tableau to Iris data set.  You can download this from UCI Machine Learning Repository

First connect the Tableau work book to this csv data source and launch a new sheet. Drag the measures petal length, petal width to columns and sepal length, width to rows.


Next, disable aggregation of measures using Analysis->Aggregate Measures


Alternately, to keep it simple, you can choose to analyze only 2 measures as shown below. But in this article, we go with all 4 measures as above


If you observe, these scatter-plots does not identify or differentiate any groups. However in our case, the data set already has a column specifying flower species of these measures. So let us view it by dragging ‘Species’ to color which shows the distinct species groups as below:


Well, imagine what if we didn’t had the ‘species’ data handy and we wanted to identify the clusters based on the measures. Lets see how it can be accomplished using Tableau Cluster Analysis.

Start with our initial plot, i.e.


Go to Analytics tab, and drag ‘Clusters’ as shown in the screen capture below. Tableau automatically identifies the number of clusters.


Leave the defaults



Note that we have got exactly same cluster grouping as we got using ‘Species’ dimensi.on data.

Tableau uses k-means algorithm for cluster analysis which partitions the data into k clusters with a center or mean value of all the points in each. Clustering is based on the distance each measure lies from the center.

Cluster Analysis using R

To start with, let us revisit Tableau plot for iris data between petal.length and petal.width with cluster analysis.


Let’s go to RStudio, and plot this using ggplot (note that iris is available as part of the datasets installed with R)

ggplot(iris, aes(Petal.Width, Petal.Length, color = Species)) + 


Note that we get identical grouping in R plot, but we used species column data to group (color) the data.

Let us look at how to perform the cluster analysis to identify clusters in R.

First take a copy of iris dataset to another variable

#cluster analysis - Biju Paulose
#copying iris to myiris variable
#printing data 

For our analysis, we do not want to use species column/data. So lets remove that from the new dataset.

#Remove Species column
myiris$Species <- NULL
#printing data to verify

Lets use k-means function for generating 3 clusters and plot the data

kmeans.result <- kmeans(myiris,3)
# plot the clusters
plot(myiris[c("Petal.Width", "Petal.Length")],col=kmeans.result$cluster)

The result is given below.  As you can compare with the analysis performed in Tableau above, we could generate the same clustering of data from R. We will examine these more closely in my next article.


Download source files used in this article

Using R forecast from Tableau


  • RStudio (with forecast package installed)
  • Tableau Desktop (with connectivity established to RServe service. For details of R integration with Tableau, please refer my previous post here)
  • R Programming knowledge
Download source files used for this article

Forecasting allows business to arrive at more realistic estimates and targets for future. Tableau analytics provide the option of generating forecasts which many of you must be familiar with. In this article, we will look at how to make use of the R forecast package from Tableau. This will be a reference for the capabilities that you can bring in from R.

For a simple demonstration of R forecast, we can use the R air passenger time series data set for 2 year forecasting as below

myts1 <- ets(AirPassengers)
plot(forecast.ets(myts1, h=24))


Tableau’s native forecasting has similar capability – an example shown below using the superstore dataset


For the R integration, start a new sheet connecting to globalstore dataset and generate timeseries graph for sales (orderdate by months and Sum[Sales])


To generate a forecast using R package, create a calculated field with the script as shown below


myts <- ts(.arg1,start=c(2011,1), frequency=12);
myforecast <- forecast(myts, h=.arg2[1]);
append(.arg1[(.arg2[1]+1):monthsts],myforecast$mean,after= monthsts
SUM([Sales]),[Forecast Months])

The scripts creates timeseries for sales starting Jan 2011, generates forecast and appends starting x months (specified by parameter ‘Forecast months’) before the last month in the series.

You can view the forecast series by adding calculated field (SalesForecast) to the row. To make it intuitive, create the formula isForecast as below and drag to color.



Forecast vs Actual

To view forecast vs actual side by side, you can add sales to row


But this does not give you a clear understanding or limits your ability to compare. The solution is to bring them together (dual-axis) and then synchronize both axis. The result is shown below


Download source files used for this article


How to integrate R with Tableau

We have seen how R can be integrated to your data science project using Power BI or Visual Studio(RTVS). Now its time to look at R integration with Tableau.

Before we get started with the steps, let us discuss how this is beneficial.

Tableau is a great visualization tool which helps you to understand your data, provide interactivity and assist in making business decisions. R integration is going to bring the capabilities of to your Tableau visualizations – such as statistical functions predictive analysis. The advantage of interactive visualizations in Tableau powered by the complex statistical analysis behind the scenes using R presents a strong case for data scientists to go for this integration.

I have added a high level representation below of this implementation. You can call R functions from Tableau and it passes the result back to Tableau which can be used to generate visualizations. You can utilize all packages (difficult to accomplish using Tableau scripting alone) that are running in R Server and generate visualizations using the resultant data (complex to accomplish using R alone).



As first step, make sure that RServe is running as a service that you can connect to. The screenshots below shows how to install RServe from RStudio.


Start the service


Now RServe is ready for connections. Go to Tableau and choose the option Help–> Settings and Performance–>Manage External Service Connection


In this case, my RServe is running on the same PC. So I selected localhost as server. Default RServe port is 6311. Leave that as is and test your connection as below


Above message confirms that you have established the connectivity with R service.

Next, we will look at an example calling R scripts from Tableau.

R rattle launch Error – libatk-1.0-0.dll is missing from your computer

If you encounter the below error while trying to launch rattle package in R Studio, that indicate that GTK+ is missing.

The program can’t start because libatk-1.0-0.dll is missing from your computer. Try reinstalling the program to fix this problem.

Error in inDL(x, as.logical(local), as.logical(now), …) :
unable to load shared object ‘C:/Users/GEEK2/Documents/R/win-library/3.3/RGtk2/libs/x64/RGtk2.dll’:
LoadLibrary failure: The specified module could not be found.

Solution :  Re-Install package > install.packages(“rattle”)

If the popup asks for installing GTK+, select OK