1241 search results for "how to import image file to R"

Getting going – importing data and plotting a simple graphic

July 20, 2010
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Getting going – importing data and plotting a simple graphic

The most difficult part of the learning curve in R is often getting going – many datasets are pre-installed in the packages and organised, so it is difficult to see how you to import your own data into R.  This post takes you step by step through the process of making a table from a

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Speed-reading files, revisited

December 29, 2009
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Speed-reading files, revisited

In a post earlier this month, it seemed as though compressing a data file before reading it into R could save you some time. With some feedback from readers and further experimentation, we might need to revisit that conclusion To recap, in our previous experiment it took 170 seconds to read a 182Mb text file into R. But if...

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Image Compression with the SVD in R

December 17, 2009
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Image Compression with the SVD in R

Over the next few posts, I’m going to be reviewing the use of R to implement the most commonly used matrix techniques for image manipulation. The code will be surprisingly simple to understand, because the real magic behind these techniques lies in the mathematics that R provides an abstract interface to. To start, I’m going

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Export Data Frames To Multi-worksheet Excel File

October 6, 2009
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Export Data Frames To Multi-worksheet Excel File

A few weeks ago I needed to export a number of data frames to separate worksheets in an Excel file. Although one could output csv-files from R and then import them manually or with the help of VBA into Excel, I was after a more streamlined solution, as I would need to repeat this process

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How to Import MS Excel Data into R

September 26, 2009
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How to Import MS Excel Data into R

s Sir Francis Bacon said, “Histories make men wise; poets witty; the mathematics subtile; natural philosophy deep; moral grave; logic and rhetoric able to contend.” And Windows stupid. He should have added the last sentence if he were a Windows user in this age. 1. Avoid Using M$ Excel A lot of R users often ask this question:

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Hillary Clinton’s Biggest 2016 Rival: Herself

January 30, 2016
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Hillary Clinton’s Biggest 2016 Rival: Herself

In a recent post I noted that despite Bernie Sanders doing better in many important indicators, Obama 2008 received 3x more media coverage than Sanders 2016.Reasonably, a reader of my blog noted that not all coverage was equal, that a presidential hope...

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Mapping US Religion Adherence by County in R

January 25, 2016
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Mapping US Religion Adherence by County in R

Today’s guest post is by Julia Silge. After reading Julia’s analysis of religions in America (“This is the Place, Apparently“) I invited her to teach my readers how to map information about US Religious Adherence by County in R. Julia can be found blogging here or on Twitter. I took Ari’s free email course for The post

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Intro to Text Analysis with R

January 23, 2016
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Guest post by Christopher Johnson from www.codeitmagazine.com One of the most powerful aspects of using R is that you can download free packages for so many tools and types of analysis.  Text analysis is still somewhat in its infancy, but is very promising.  It is estimated that as much as 80% of the world’s data is unstructured, while most types...

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Filling in the gaps – highly granular estimates of income and population for New Zealand from survey data

January 22, 2016
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Filling in the gaps – highly granular estimates of income and population for New Zealand from survey data

Individual-level estimates from survey data I was motivated by web apps like the British Office of National Statistics’ How well do you know your area? and How well does your job pay? to see if I could turn the New Zealand Income Survey into an individual-oriented estimate of income given age group, qualification, occupation, ethnicity, region and hours...

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R & GGMap Visualization Case Study

January 21, 2016
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Guest post by Krishna Prasad As a business analyst using R, one has often stumbled across situations for visually representing the data on a map. Here are some common scenarios: ·         Represent the most densely populated cities on a Map ·         Showing the cheapest places to live in ·         Where can one buy a home with the cheapest home Insurance...

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