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Introduction CCExtractor is an open-source project, led by Carlos Fernandez, that collaborates closely with Red Hen. CCExtractor extracts closed captioning, teletext, and other metadata from television transport streams. See their project page. Instructions To use the new OCR capabilities, see.
Installation We should use the latest version, which is on github: and often not yet on Sourceforge. It typically has new features we want. To download it, issue this command in Linux (or Mac): wget This command will download the software in a zipped (compressed) format in a file called master.zip. To unzip (decompress) the file, issue unzip master.zip The files will be unzipped into a directory (folder) called ccextractor-master. Rename it to the current version number (which keeps incrementing): mv ccextractor-master ccextractor0.84 Walk into the directory: cd ccextractor0.84 You'll see the file raspberrypi.md - read it for the simple instructions to build ccextractor for these devices.
Typically, you'll need these. Apt-get install libleptonica-dev libtesseract-dev libcurl4-gnutls-dev tesseract-ocr You'll also see several subdirectories, including one called 'linux' and one called 'mac'. Walk into the appropriate subdirectory: cd linux You'll see a file called 'build'. Run it like this:./build This compiles (builds) the CCExtractor program; it can take anywhere from a few seconds to a couple of minutes, depending on how fast your computer is.
The build command creates a file that's always called 'ccextractor'. Rename it to track which version you just built: mv ccextractor ccextractor-0.78 Copy that file into your program directory: sudo cp ccextractor-0.78 /usr/local/bin Now test the new ccextractor version (e.g. Ccextractor-0.78) for both previous and new functionality. When you are satisfied, then. Walk into your program directory and create a symbolic link to the new version: cd /usr/local/bin sudo ln -sf ccextractor-0.78 ccextractor In the list of files (ls -l), you should see something like this: lrwxrwxrwx 1 root staff 16 Oct 2 05:48 ccextractor - ccextractor-0.78 -rwxr-xr-x 1 root staff 1687840 Oct 2 05:47 ccextractor-0.78 The program is now fully installed. Locate the teletext.
Whether it is forecasting your quarterly sales or comparing historical data, working with time series data is big part of business analytics. Sometimes patterns are easy to see, while in others they might be elusive especially when comparing different time series with short/frequent cyclical patterns.
A common question could be understanding the impact of a marketing campaign. Did it make a difference at all? What’s the return?
A true randomized treatment-control experiment would be the ideal case for running tests but it is not always practical, if at all possible. So let’s see how we can try to answer this question using Bayesian Structural Time Series models. For this post, I generated a dataset with 3 columns.
In the image below, Sales is at the top, with 2 economic indicators below it. Let’s assume these two economic indicators are “Consumer Sentiment” and “Spending on Durable Goods” and that they have been good predictors of my sales and there are no other unaccounted for variables that impact my sales.
I know the date I started my marketing campaign. Since time flows in one direction, if it has any impact, it would be after this date. If I can use the data from days prior to the campaign to train my model, I can forecast what would have happened if I had not run the campaign. Then by comparing it with the actual sales data, I can understand what difference it made. While it was initially hard to visually detect this change, now that I added the “expected” forecast (red line), I can clearly see the bump marketing campaign gave me by looking at the gap between green and red lines. In the workbook, I also added to the tooltip a textual summary describing the statistical significance of the results which the package conveniently generates.
Let’s also look at the cumulative effect of the marketing campaign. It looks like, we made an extra 748 thousand dollars. By comparing this with the amount we spent on the marketing campaign, we can decide whether this was a good investment.
You can download the sample workbook from. The workbook uses the which can be installed from GitHub. You can find more info on the installation steps in the notes section at the end of the blog post.
One thing to keep in mind, CausalImpact package doesn’t work well with lags e.g. If Consumer Sentiment in January is linked to Sales in July, you’ll have to shift your dates first. Another common type of time series analysis involves understanding dependency relationships between time series with different lags e.g. Could Consumer Sentiment from 3 months ago help me make a better prediction of my Sales next month?
A frequently used method for this type of analysis is Granger causality. Granger causality is not necessarily true causality hence the term is often used as “X Granger-causes Y”. It means the patterns in X are approximately repeated in Y after some lag e.g. 4 months in the example I gave above.
As a result of this, past values of X can be used to predict future values of Y. You might be thinking if X happened 4 months before Y and there is a correlation, how is that not a causal relationship? If both X and Y are driven by a 3rd, unknown process (Z) with different lag, it might look like Granger-causality exists between X and Y while in reality manipulation of X would not change Y, only changes to Z would. The roosters crow before dawn, but they don’t cause the sun to rise. That being said, lack of true causality does not mean one thing can’t be useful in predicting another. Humans relied on for hundreds of years. Let’s explore Granger causality with an example.
Below you can see two time series: The University of Michigan “Consumer Sentiment Index” and “New One Family Houses Sold” from. Can consumer sentiment in previous months help better predict future home sales? There are several packages in R to do this test of course.
For this post, I used package and a few methods from package to estimate the number of differences required to make the sample time series data stationary. You can download the sample workbook from. Based on this analysis, with 4 lags, it appears that Consumer Sentiment unidirectionally Granger-causes Home Sales. Thurman and Fisher’s 1988 “Chickens, Eggs and Causality, or Which Came First?” applies Granger causality to this age-old question concluding that the egg came first. It is a fun, short read with tongue-in-cheek future research directions like potential applications of Granger causality to “He who laughs last, laughs best” and “Pride goeth before destruction and an haughty spirit before a fall.” and proof that journal papers and statistics can be fun. As for more serious reading, Judea Pearl’s “Causality: Models, Reasoning and Inference” is a great resource on the topic of causality.
NOTES Installing CausalImpact ( is a dependency but at the time of this post the default version you’d get from CRAN still had problems): install.packages('= NULL, type = 'local') library(devtools) devtools::installgithub('google/CausalImpact'). Clustering is a very common data mining task and has a wide variety of applications from customer segmentation to grouping of text documents.
K-means clustering was one of the examples I used on back in Tableau 8.1. Many others in Tableau community wrote similar articles explaining how different clustering techniques can be used in Tableau via R integration. One thing I didn’t see getting much attention was time series clustering and using algorithms.
So I thought it might be good to cover both in single post. Let’s say you have multiple time series curves (stock prices, social media activity, temperature readings) and want to group similar curves together. Series don’t necessarily align perfectly, in fact they could be even about events that are happening at different pace. There are of course many algorithms to achieve this task but R conveniently offers a package for. Below is what my calculated field in Tableau looks like. I started by loading the package, then converted my data from long table format (all time series are in the same column) to wide table format (each series is a separate column).
I then computed distance matrix using dtw as my method and applied with average linkage. This gives a tree which I then prune to get the desired number of clusters. And finally, data is converted back into a long table before being pulled back into Tableau. Screenshot is from Tableau 10, but don’t worry if you’re not on the Beta program.
You can download Tableau 8.1 version of the workbook from. Two of my favorite new Tableau blogs are “” and “”. Former is a great resource if you want to learn how to leverage R inside Tableau by walking through sample use cases. Examples are interesting and very thoroughly explained.
This was a topic I heard from several of our customers and had been planning to blog about but haven’t had the chance. “Data + Science” is another awesome blog. Jeff has been doing some great work with various types of undirected graphs. Yesterday he published a blog post about as a follow up to his beautiful post from a few months earlier. What I love about Tableau is the expressive power and flexibility of the product which combined with such a great user community leads to something new and beautiful almost everyday not to mention countless learning opportunities and cross-pollination. Take the graph below for example. Olivier Catherin took Jeff’s Sankey Diagram idea as an inspiration and further expanded upon it to create the following graph.
In 2010, Lustig et. Al from IBM classified business analytics workloads into three major categories in their paper “The Analytics Journey”: Descriptive Analytics: A set of technologies and processes that use data to understand and analyze business performance Predictive Analytics: The extensive use of data and mathematical techniques to uncover explanatory and predictive models of business performance representing the inherit relationship between data inputs and outputs/outcomes. Prescriptive Analytics: A set of mathematical techniques that computationally determine a set of high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the goal of improving business performance. You can find the full paper. For those who don’t have time to read the whole paper, in short the journey starts with the question “what is going on?” moves on to “what is likely to happen in the future?” and finishes with “what can we do to make a certain outcome more likely?”.
When you start talking about constraints and improving performance, it starts sounding awfully similar to mathematical/computational optimization, don’t you think? Because optimization is a major part of it. Imagine, in your business, you discovered a relationship between profit, frequency of promotions and promotion amount. Given your customer base growth, you can forecast your profit for the next year, and even run what if scenarios by changing these parameters manually and see how they impact the forecast. The more promotions, the more people buy, so you see increased profit because of the volume but probably after a certain point, you get diminishing returns.
What is the sweet spot? Prescriptive analytics tools can tell you what combination of these parameters can maximize your profit.
Let’s look at an example of how to do this in Tableau., I have annual stock returns for a number of companies over 20 years. We will be using a metric called which characterizes how well the return of an asset compensates the investor for the risk taken. Our goal is to find the portfolio that maximizes Sharpe ratio. We will use R’s optim function which comes with the base R package so there is no need to install or load any libraries. Our data is in long format, that is; we have one column for all returns, a column that associates each row with a particular stock and a column with year. Our calculation needs a matrix so we will first splitthe long table into chunks and then add each chunk as a new column ( cbind) next to each other.
SharpeRatio function contains the logic for calculating Sharpe Ratio and it takes the percentages of each stock in your portfolio as input. You may have notice the constant 0.05, which is my assumption as the risk-free interest rate, the theoretical rate of return of an investment that an investor would expect from an absolutely risk-free investment. For example US treasury bonds are often considered risk-free. Also some of the optimization constraints are captured in the function.
Considering the fact that each stock is associated with a percentage of total portfolio value, these have to be non-negative and add up to 1. We enforce the range of each percentage being between 1 and 0 using an if condition and when condition is not met we introduce a high penalty ( 1e7) so our optimizer avoids those. Adding up to 1 is enforced by the line p. When the data has a lot of features that interact in complicated non-linear ways, it is hard to find a global regression model i.e.
A single predictive formula that holds over the entire dataset. An alternative approach is to partition the space into smaller regions, then into sub-partitions (recursive partitioning) until each chunk can be explained with a simple model. There are two main types of decision trees: Classification trees: Predicted outcome is the class the data belongs.
Regression trees: Predicted outcome is continuous variable e.g. A real number such as the price of a commodity. There are many ensemble machine learning methods that take advantage of decision trees. Perhaps the best known is the Random Forest classifier that constructs multiple decision trees and outputs the class which corresponds to the mode of the classes output by individual trees. Let’s start with a classification tree. For decision trees I will use the package but, and are some other packages that can be used to same effect.
All the data used in the examples below are retrieved from. In the example workbook you can find links to documentation and credits for each dataset. First dataset contains composition (Sodium, Calcium, Magnesium content) and physical properties (RI – Refractive Index) of samples for different types of glass. If you were to find broken glass in a crime scene, would you be able to tell what it is Here is how you can create a classification tree to answer this question: This calculation will just work as long as you have Rserve running and rpart package installed since it is self-contained as it loads the library, trains the model and then uses the model to make predictions within the same calculation.
Model fit happens in the part underlined in orange, the tree is pruned to the level where minimum error occurs in the part underlined in blue and finally the prediction happens in the part underlined in green. Alternatively you could use the control setting in rpart function to impose some rules on splits. In the example below control=rpart.control(minsplit=10, cp=0.001) requires at least 10 items in a node before attempting a split and that a split must decrease the overall cost complexity factor by 0.001. You may have noticed that in this example we are using the same dataset for training and prediction, not to mention we are not looking at any diagnostic measures to gage the quality of fit.
I did this firstly because I wanted to provide a simple, self-contained example that just works and secondly, the reason for the blog post is to show how a particular technique can be used from within Tableau as opposed to providing guidelines for model fitting and selection. In real life scenarios, you would train the model using one “tagged” dataset, verify the quality, save the trained model and use it to predict with different datasets serving as input.
Save and reuse workflow has been explained in detail with an example in my. But I haven’t talked about how you can use the same approach to iterate on the model by going back and forth between your favorite R development environment and Tableau as needed before you deploy the final model for everyone to use via a Tableau dashboard.
You will notice a sheet in the example workbook named SaveTree. This sheet contains a calculated field that has the following line: save(fit, file = 'C:/Users/bberan/R/myclassificationtree.rda') which saves the fitted model to a file on my computer. Once you update the file path to a valid path on your computer and it successfully saves, you can easily open the model from your R environment and examine the tree structure and relevant statistics, prune the tree etc. Once you’re done you can run the same line from your R console and overwrite the file, then read it from Tableau to use for predictions. SCRIPTSTR('library(rpart); load('C:/Users/bberan/R/myclassificationtree.rda'); t(data.frame(predict(fit, newdata= data.frame(Type =.arg1, Al =.arg2, Ba=.arg3, Ca =.arg4, Fe =.arg5, K=.arg6, Mg =.arg7, Na =.arg8, RI =.arg9, Si=.arg10), type = 'class')))1,;', ATTR(Type),AVG(Al),AVG(Ba),AVG(Ca),AVG(Fe),AVG(K), AVG(Mg),AVG(Na),AVG(RI),AVG(Si)) There is a dedicated sheet in the example workbook that uses random forest approach on the same dataset. It uses the which you will need to install. However the syntax is almost identical to the classification tree example above so I will skip over it to move on to regression trees right away.
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For regression tree example, I will use a separate dataset which contains make, model year, displacement, # cylinders etc. For different cars based on which we will try to predict MPG. Once again using the rpart package, the calculation can be written as follows. It is similar to the classification tree script. The minor differences are highlighted. Note that the discrete variables such as make, origin and # cylinders are used as factors. After some pruning, the regression tree looks like the following when you draw it in R where leaf nodes show the MPG values for cars classified under that group.
You can find the Tableau workbook with the examples. Forecast packages normally return just the forecast results which is fewer rows than the input data and at the same time, doesn’t make sense to join back in. Here is a relevant thread from Tableau forums. Before we move on any further, I would like to emphasize that Tableau has a built in forecast functionality which makes most of what we will cover here possible with the click of a button and comes with many more convenience features.
The intent of the blog post is to show an alternative approach while very complicated can be useful for some. The example in the forums uses the fpp package. For variety in this blog post I will be using the forecast package. It is worthwhile noting that, we worked very closely with Prof. Hyndman, the author of R’s forecast package when developing the forecasting feature in Tableau 8.
Let’s start with the visualizing the fit and residuals for your in-sample forecast. You will find this in the first sheet of the workbook I provided a link for at the end of the post. We have two time series in this chart. To reduce the network traffic, I retrieved both columns in a single call to Rserve.
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Let’s take a look at the calculation. First, we are loading the library. For this to work, please make sure the library and its dependencies are installed.
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You can find more about how to preload libraries for better performance in my posts and. Then we are creating a time series object ( ts) using Earnings column (.arg1), since it is quarterly our deltat is 1/4 and our time series starts at the beginning of 1960. You will notice the paste function is taking two columns from the resulting forecast ( fit and residuals) and concatenating them using as the separator which allows us to retrieve both columns at once. Then in the calculated fields named Fit and Residuals, we tokenize the results. Only one of the calculated fields is shown below for brevity. So far we’ve been dealing with the in-sample forecast, so we haven’t yet touched the core question about how to retrieve the future predictions into Tableau.
Without further ado, let’s jump to the more exciting part. I will demonstrate two approaches. The first one is domain shifting which is shown in the second sheet. Our calculation as shown above is very similar to the one we looked at earlier.
But this time we are asking for a forecast ( mean) of length ( h=) set by the Tableau parameter named PeriodsToForecast and using append function to add the predicted rows to the end of the Earnings column (which answers the question 2 above) while dropping just as many rows from the top. The result is shown below. Since we forecast 10 quarters, our time series now extend 2.5 year beyond 1980 but our chart doesn’t start at 1960 anymore.
We achieve this also by shifting the date axis by the same amount as shown below. But what if you don’t want to trim rows from the beginning of your data?
That’s where densification comes to our aid. The second approach relies on the fact that Tableau can fill in the missing values given a range. What we will do is to shift the last data point to the date we want our forecast to end. This will create a gap between the last value and the previous and Tableau’s “Show Missing Values” option will automatically add rows to fill that gap. The last date is moved out to provide the full date extent for forecasting using the following calculated field. Here MaxDate is created at the data source level by joining the main table with a sub-query that returns the MAX(Quarter) in the input data. Our calculated field looks very similar to the previous calculation.
Important changes are highlighted which involve removing the blank rows densification added from the data passed on to R’s forecast function as input and pulling back the shifted last data point to the correct date for the forecasting function. Then the results get merged back in using the same append function but this time without anything getting trimmed. The result is shown below. For comparison we are forecasting 10 quarters (same as above). Now our time series extend 2.5 year beyond 1980 while chart still starts at 1960.
You can download the example workbook from.