540 search results for "knitr"

Processing an XL grading sheet with R

June 13, 2015
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Processing an XL grading sheet with R

This tutorial is dedicated to Vincent and to Erwan, the former for proposing me an interesting challenge to solve with R and the latter for his suspicious remarks about the language not being suited for this kind of task. Disclaimer: of course, this script can not be used on a computer with Windows OS... Problem

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15 Easy Solutions To Your Data Frame Problems In R

June 11, 2015
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15 Easy Solutions To Your Data Frame Problems In R

R’s data frames regularly create somewhat of a furor on public forums like Stack Overflow and Reddit. Starting R users often experience problems with the data frame in R and it doesn’t always seem to be straightforward. But does it really need to be so? Well, not necessarily. With today’s post, DataCamp wants to show The post

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Running Go language chunks in R Markdown (Rmd) files

June 8, 2015
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After seeing IPython Markdown Opportunities in IPython Notebooks and Rstudio in the feeds today I wondered how easy/hard it would be to write a handler for something like go code. After taking a look at Yihui Xie‘s knitr source it seemed like it would be fairly easy to mimic a combination of Rcpp and “shell”...

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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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“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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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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