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This app written in R makes a map from Strava data with all the stops along the way. It can be used to find an optimal path for commuting as it highlights the stops that you make at traffic signals, for example. 

The project can be forked on GitHub: https://github.com/i7ed/read-Strava-GPX 

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Many books that are published on R are also freely available online at Bookdown.org. If authoring a book in R, then use the bookdown package.

A longer description is available on https://www.r-bloggers.com/how-to-self-publish-a-book-customizing-bookdown/

 

A Python alternative would be Sphinx. Here is an example on statistical physics.

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Sometimes it is convenient to have the data in the same file as the computation in R. Further complications arise that text files often have irregular number of columns or empty columns. Reading such data with read.table() into R is accomplished in the following way:

r = read.table(sep='\n', stringsAsFactors = FALSE,
text = "ORDER DATE
STATUS
SOURCE
DELIVERY MODE
ORDER NUMBER
AMOUNT
06/22/2017")
# get all the dates
grep('^\\d{2}',r$V1)
The text can then be parsed separately and saved in a data.frame() object. 

 

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With abundant data, graphing has become an essential to represent the data. Besides ggplot2, there are tools from BBC. Some good examples are given on the "You can replicate almost any plot with R"

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Documentation is essential for any R package to be used over time. The followin R-packages, I developed, still need a website documentation,which can be easily developed with teh pkgdown R script.

Currently, my packages are described using a markdown README file, see:

- QuantumPPMS package

- X-ray Rigaku package

- Atomic force microscopy image analysis package

  1. R Markdown
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