Navigation gets you from where you are to where you want to be. Speaking of navigation, you can jump to selected sections of this post: Navigation; R-bloggers; Task views; Rdocumentation.org; sos package; ??; apropos; ls; methods; getAnywhere; :::; find; args; grep; %in%; str; getwd; file.choose; Spyglass summary; browser; See also. Overview Figure 1: A map
In pipeR 0.4 version, one of the new features is Pipe() function. The function basically creates a Pipe object that allows command chaining with $, and thus makes it easier to perform operations in pipeline without any external operator.
In this post, I will introduce how to use this function and some basic knowledge about how it works. But before...
… or Inferring Identity from Observations Let’s assume the following application: A conservation organisation starts a project to geographically catalogue the remaining representatives of an endangered plant species. For that purpose hikers are encouraged to communicate the location of the plant … Continue reading →
Imagine you just started a job at a new company. You watched World War Z recently, so you're in a skeptical mood, and given that your last two startups failed from what you believe to be a lack of data, you're giving everything an extra critical eye.
You start by thinking about the impact of the sales team. How...
The universe is full of magical things patiently waiting for our wits to grow sharper (Eden Phillpots) I launched this blog 7 months ago and published 30 posts during this time. These are some of my figures until now: more than 15.000 views from 125 countries (below you can find my map of the empire)
The latest in a series by Daniel Hanson Introduction Correlations between holdings in a portfolio are of course a key component in financial risk management. Borrowing a tool common in fields such as bioinformatics and genetics, we will look at how to use heat maps in R for visualizing correlations among financial returns, and examine behavior in both a...
We begin with a data matrix, a set of numbers arrayed so that each row contains information from a different consumer. Marketing research focuses on the consumer, but the columns are permitted more freedom, although they ought to tell us something abou...
When you apply machine learning algorithms on a regular basis, on a wide variety of data sets, you find that certain data issues come up again and again: Missing values (NA or blanks) Problematic numerical values (Inf, NaN, sentinel values like 999999999 or -1) Valid categorical levels that don’t appear in the training data (especially
Recently, there are some comments said that sometimes clusterProfiler failed in KEGG enrichment analysis.
kaji331 compared cluserProfiler with GeneAnswers and found that clusterProfiler gives larger p values. The result forces me to test it.
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