643 search results for "Knitr"

IPython Markdown Opportunities in IPython Notebooks and Rstudio

June 6, 2015
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IPython Markdown Opportunities in IPython Notebooks and Rstudio

One of the reasons I started working on the Wrangling F1 Data With R book was to see what the Rmd (RMarkdown) workflow was like. Rmd allows you to combine markdown and R code in the same document, as well as executing the code blocks and then displaying the results of that code execution inline

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A Practical Example of Calculating Padé Approximant Coefficients Using R

June 4, 2015
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A Practical Example of Calculating Padé Approximant Coefficients Using R

Introduction I recently had the opportunity to use Padé approximants. There is a lot of good information available on line on the theory and applications of using Padé approximants, but I had trouble finding a good example explaining just how to calculate the co-efficients. Basic Background Hearken back to undergraduate calculus for a moment. For Read the full...

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The challenge of combining 176 x #otherpeoplesdata to create the Biomass And Allometry Database

June 3, 2015
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The challenge of combining 176 x #otherpeoplesdata to create the Biomass And Allometry Database

Despite the hype around "big data", a more immediate problem facing many scientific analyses is that large-scale databases must be assembled from a collection of small independent and heterogeneous fragments -- the outputs of many and isolated scientific studies conducted around the globe. Collecting and compiling these fragments is challenging at both political and technical levels. The political challenge is...

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Auditing data transformation

June 2, 2015
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Auditing data transformation can be simply described as gathering metadata about the transformation process. The most basics metadata would be a timestamp, atomic transformation description, data volume on input, data volume on output, time elapsed. If you work with R only interactively you may find it more like a fancy tool. On the other hand for automated scheduled...

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Auditing data transformation

June 2, 2015
By

Auditing data transformation can be simply described as gathering metadata about the transformation process. The most basics metadata would be a timestamp, atomic transformation description, data volume on input, data volume on output, time elapsed. If you work with R only interactively you may find it more like a fancy tool. On the other hand for automated scheduled...

Read more »

“Mail merge” with RMarkdown

June 1, 2015
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“Mail merge” with RMarkdown

The term “mail merge” might not be familiar to those who have not worked in an office setting, but here is the Wikipedia definition: Mail merge is a software operation describing the production of multiple (and potentially large numbers of) documents from … Continue reading →

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Climate projections by cities: R + Shiny + rCharts + leaflet

May 27, 2015
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Climate projections by cities: R + Shiny + rCharts + leaflet

I have approached a final draft of my Community Charts version 4 Lite, now with leaflet map integration. This R Shiny web application shows projected climate trends for various Alaska and western Canada communities. Note that if no other users are already connected to the app, it will take a moment (maybe ten seconds?) to

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My favorite R bug

May 23, 2015
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My favorite R bug

In this note am going to recount “my favorite R bug.” It isn’t a bug in R. It is a bug in some code I wrote in R. I call it my favorite bug, as it is easy to commit and (thanks to R’s overly helpful nature) takes longer than it should to find. The … Continue reading...

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Stata’s Academic Growth Nearly as Fast as R’s

May 11, 2015
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Stata’s Academic Growth Nearly as Fast as R’s

by Bob Muenchen Analytics tools take significant effort to master, so once learned people tend to stick with them for much of their careers. This makes the tools used in academia of particular interest in the study of future trends … Continue reading →

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devtools 1.8.0

May 11, 2015
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devtools 1.8.0

Devtools 1.8 is now available on CRAN. Devtools makes it so easy to build a package that it becomes your default way to organise code, data and documentation. You can learn more about developing packages at http://r-pkgs.had.co.nz/. Get the latest version of devtools with: install.packages("devtools") There are three main improvements: More helpers to get you

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