# Statistical Analysis Functions in R

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Lately, I’ve been using statistical tests on a daily basis. I’ve noticed that I have to format my data the same way in order to get it into R (tab-delimited flat file essentially). Every other change in order to prep that data structure for any sort of statistical analysis function require minimal modification to the data structure.**Optinalysis**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Why not kill several birds with the same stone? I’ve just written a wrapper function around a few smaller, independent R scripts which perform statistical analysis tests.

Get the code here:

https://github.com/ngopal/Statistical-Analysis-Functions

The input to the wrapper is a single, tab-delimited file (with the first row being the header). The output is a few PDF files, each complete with plots for different statistical analysis tests.

The statistical analysis tests which are currently performed are:

- Principal Component Analysis (PCA)
- K-means Clustering
- Hierarchical Clustering

There are a few R scripts in the currently released package which require the user to download external libraries. These R scripts are turned off by default.

I am considering adding more statistical tests to the package–perhaps a t-test with a few box-and-whisker plots.

Now I can run one script and output several plots in one slick shot.

To

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