by Joseph Rickert The world may indeed be awash with data, however, it is not always easy to find a suitable data set when you need one. As the number of people becoming involved with R and data science increases so does the need for interesting data sets for creating examples, showcasing machine learning algorithms and developing statistical analyses. The most difficult data sets to find are those that would provide the foundation for impressive big data examples: data sets with a 100 million rows and hundreds of variables.The problem with big data, however, is that most of it is...
A popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA stands for AutoRegressive Integrated Moving Average and represents a cornerstone in time series forecasting. It is a statistical method that has gained immense popularity due to its efficacy in handling various standard temporal structures present in time series data. […]
Statistical tools for data analysis and visualization
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more. To keep receiving these articles, sign up on DSC.… Read More »19 Interesting Articles About Excel
Last quarter was a bit too busy to write some new blog post because of a new job. And changing the job often come along with changing the tools you work with. That was my way to Tableau. Tableau is one of the new stars in the BI/Analytics world and definitely worth a look. The people at Tableau describe their tool as an instrument that combines interactive visual analytics, sophisticated visualization methods, an easy to use graphical user-interface and a lot of connectors for dozens of different data sources ranging from standard Excel to more “Big Data” like things such as different Hadoop distributions and NoSQL databases. What is really impressing is that you can create interactive visualizations in just no time. I’m currently using Tableau to carry out explorative analysis and reporting up to management level (creating high polish PDFs). I’m quite satisfied with Tableaus capabilities for this even that Tableaus focuses more on Self-BI. What I missed are sophisticated analytical methods like different algorithms for classification, regression, clustering et cetera. So I was really happy that Tableau announced the integration of R in their upcoming release and quickly enrolled in their beta tester program a few weeks ago. Time now to share some insights from my first experiments with the beta version of the upcoming Tableau release 8.1. First step is to get the connection between R and Tableau based on Rserve up and running. Following the approach given here it should be really easy even for Windows. It is good to know that you can launch Rserve with “R CMD Rserve_d” instead of “R CMD Rserve” to grab some information from the communication between Tableau and R for debugging (here you can find another short comment about some more advanced debugging techniques). Alternatively you can launch Rserve from within R with: library(Rserve) Rserve() If everything works fine you can test you connection from inside Tableau by selecting “Help -> Manage R Connections …”. Be sure to replace “localhost” with “127.0.0.1” because of a bug in the current beta version (tested with 8.1 Beta 2 - may be fixed in newer versions) which will slow down performance otherwise. The integration uses so called table calculations to retrieve results from calculations within R. A table calculation could be used to state a calculation like “Partition the sales data by region and calculate the overall revenue per product category”. This means that the arguments from Tableau to R have to be specified as aggregates. The result of the R calculation then should be one value or the same number of values as in the partition (vector). This sounds a little bit difficult and to be honest in the beginning it was difficult for me to understand the logic behind. But let’s try some examples to demonstrate what that means. The first one is derived from the official Tableau blog and demonstrates the application of k-Means using the iris dataset. Our aim is visualize the resulting clustering which should give us some understanding about how it matches the true classification. We use a calculated field to pass the data to R and get the resulting clustering ids from the k-Means function. The code for the table calculation function inside the calculated field is very simple: SCRIPT_INT('result
The primary mission of the F-102 was to intercept and destroy enemy aircraft. It was the world's first supersonic all-weather jet interceptor and the USAF's first operational delta-wing aircraft. The F-102 made its initial flight on Oct. 24, 1953, and,
I'm using the "tips" data set in ggplot2. If I do sp = ggplot(tips,aes(x=total_bill, y = tip/total_bill)) + geom_point(shape=1) + facet_grid(sex ~ day) The plot comes out fine. ...
Making sense of otherwise meaningless numbers through storytelling is a must-have skill for the future.
Images of Oddball, Waddlesworth, Rolly, Wizzer, Lucky, Domino and other puppies from Disney's 101 Dalmatians.
Pongo, Perdita and their super-adorable puppies are in for thrills, hilarious spills and an epic action-packed adventure.
Images of Oddball, Waddlesworth, Rolly, Wizzer, Lucky, Domino and other puppies from Disney's 101 Dalmatians.
Kristin Herrera
When are people doing activities like playing baseball or working out? This visualization shows how Americans spend free time, and when each activity peaks.
