Posts Tagged ‘ Data Summary ’

Programming with R – Processing Football League Data Part II

December 3, 2010
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Following on from the previous post about creating a football result processing function for data from the football-data.co.uk website we will add code to the function to generate a league table based on the results to date. To create the league table we need to count various things such as the number of games played, number

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Programming with R – Processing Football League Data Part I

November 23, 2010
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In this post we will make use of football results data from the football-data.co.uk website to demonstrate creating functions in R to automate a series of standard operations that would be required for results data from various leagues and divisions. The first step is to consider what control options should be available as part of the

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Charting the performance of cricket all-rounders – IT Botham

August 16, 2010
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Charting the performance of cricket all-rounders – IT Botham

Cricket is a sport that generates a large volume of performance data and corresponding debate about the relative qualities of various players over their careers and in relation to their contemporaries. The cricinfo website has an extensive database of statistics for professional cricketers that can be searched to access the information in various formats. As an

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R Commander – data manipulation and summaries

June 13, 2010
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R Commander – data manipulation and summaries

Previously we considered the R Commander interface as a simple GUI for the R statistical software system. Here we will look at how to undertake data manipulation and creating basic statistical summaries of data sets. Fast Tube by Casper The R Commander GUI has two menus “Data” and “Statistics” that are used for manipulating data sets and

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R and Tolerance Intervals

April 19, 2010
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Confidence intervals and prediction intervals are used by statisticians on a regular basis. Another useful interval is the tolerance interval that describes the range of values for a distribution with confidence limits calculated to a particular percentile of the distribution. The R package tolerance can be used to create a variety of tolerance intervals of

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