#How to use for loop in r code
Parallelized code refers to code that is optimized to use the cores available to it on a machine. This means your computer course process things faster. However, by default, most functions in R only take advantage of one core on your machine. They make it easier to parallelize your code: Most computers these days have more than one core that can be used to process your data. The apply function has a for loop in its definition – Source: Patrick Burns - the Inferno.Ģ. This is not vectorization, it is loop-hiding. Source: Hadley Wikham, stackoverflow commentĪ common reflex is to use a function in the apply family. In this case you can have the best of both worlds: an elegant answer using vectorisation that is also very fast, (million > 0) * 2 - 1. Your code will never get more elegant or easier to understand on its own. Your code will get faster on its own as computers get faster and R-core optimises the internals of R. Lately, answers on stackoverflow have over-emphasised speed. They also tend to prevent bugs because they eliminate the book keeping code needed with loops. The point of the apply (and plyr) family of functions is not speed, but expressiveness. Here’s what the master, Hadley Wikham has to say about expressive code and the apply family: They make your code more expressive and in turn easier to read: There are several good reasons to use the apply family of functions.ġ. So, for example you can use the lapply function (list apply) on the list of file names that you generate when using list.files(). However if you want to scale this automation to process more and / or larger files, the R apply family of functions are useful to know about.Īpply functions perform a task over and over - on a list, vector, etc. For loops are a good start to automating your code.
#How to use for loop in r how to
In the previous lessons, you learned how to use for loops to perform tasks that you want to implement over and over - for example on a set of files. You will need a computer with internet access to complete this lesson.
8.1 Fire / spectral remote sensing data - in R.SECTION 8 QUANTIFY FIRE IMPACTS - REMOTE SENSING.SECTION 7 MULTISPECTRAL IMAGERY R - NAIP, LANDSAT, FIRE & REMOTE SENSING.
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