1632 search results for "Excel"

24 Days of R: Day 11

December 11, 2013
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24 Days of R: Day 11

I don't know how often Michael Caine appeared in a Shakespearean work, but I'm sure that he has and I'm sure that he was excellent. A bit pressed for time today, so just a simple word cloud featuring the full text of King Lear. I found the text at a website that I presume is

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Part 3: Random Forests and Model Selection Considerations

December 10, 2013
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Part 3:  Random Forests and Model Selection Considerations

I want to wrap this series up on the breast cancer data set and move on to other topics.  Here I will include the random forest technique and evaluate all three modeling techniques together, including the conditional inference tree and bootstrap a...

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Adobe Analytics Implementation Documentation in 60 seconds

December 9, 2013
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When I was working as a digital analytics consultant, no question quite had the ability to cause belly laughs AND angst as, “Can you send me an updated copy of your implementation documentation?” I saw companies that were spending six-or-seven-figures annually on their analytics infrastructure, multi-millions in salary for employees and yet the only way Adobe Analytics Implementation...

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Storing package data in custom environments

Storing package data in custom environments If you do R package development, sometimes you want to be able to store variables specific to your package, without cluttering up the users workspace. One way to do this is by modifying the global options. This is done by packages grDevices and parallel. Sometimes this doesn't seem to work quite right (see this...

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Tim Hortons Density

Tim Hortons Density

Tim Hortons Density Inspired by this post, I wanted to examine the locations and density of Tim Hortons restaurants in Canada. Using Stats Canada data, each census tract is queried on Foursquare for Tims locations. Setup options(stringsAsFactors = F) require(timmysDensity) require(plyr) require(maps) require(ggplot2) require(geosphere) Statistics Canada Census Data The actual Statistics Canada data at the dissemination block level can be downloaded from here. You will want to...

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On the growth of R and Python for data science

December 6, 2013
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On the growth of R and Python for data science

A recent article by Matt Asay claims that "Python is displacing R as the language for data science". Python has certainly made some great strides in recent years, evolving beyond a data processing tool (an area where Python excels) to a data analysis tool. The Pandas project, in particular, has greatly expanded Python's ability to handle statistical data sets...

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Averaged Input Assumptions and Momentum

December 4, 2013
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Averaged Input Assumptions and Momentum

Today I want to share another interesting idea contributed by Pierre Chretien. Pierre suggested using Averaged Input Assumptions and Momentum to create reasonably quiet strategy. The averaging techniques are used to avoid over-fitting any particular frequency. To create Averaged Input Assumptions we combine returns over different look-back periods, giving more weight to the recent returns,

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R – Analyze any data frame in Saiku

December 4, 2013
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R – Analyze any data frame in Saiku

In my previous article I have shown how R can be used to analyze PostgreSQL tables in Saiku using dynamically generated OLAP cubes. Today I will show you how you can analyze any R data frame in Saiku. WIth Saiku you can easily create excel-like pivot t...

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Maximizing Return from Every Item in the Marketing Research Questionnaire

December 3, 2013
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Maximizing Return from Every Item in the Marketing Research Questionnaire

Consumers will not complete long questionnaires, so marketing research must get the most it can from every item.  In this post, we look into the toolbox of R packages and search for statistical models that enable us to learn a great deal about eac...

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24 Days of R: Day 1

December 1, 2013
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24 Days of R: Day 1

Last year, the good people at is.R() spent December publishing an R advent calendar. This meant that for 24 days, every day, there was an interesting post featuring analysis and some excellent visualizations in R. I think it's an interesting (if very challenging) exercise and I'm going to try to do it myself this year.

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