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while read p; do git clone https://github.com/$p ${p%/*}; done <files.txt

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for FILE in *.zip; do unzip -j -d ${FILE%.*} $FILE; done

# for rar, first install it via `brew install rar` & provide permissions for app to launch (e.g., on Mac)
for FILE in *.rar; do unrar X $FILE -op${FILE%.*} -ep; done

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# From a variance-covariance matrix to a cholesky decomposition (and back)

## Let's first define our var-covar matrix
varcovar = diag(3)
varcovar[1,2] <- .5
varcovar[2,1] <- .5

## Let's get our (upper) cholesky decomposition
decomp = chol(varcovar)

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dir.create('../gen/test', recursive = T)

sink('../gen/test/test.txt')
cat('This is test 1')
sink()

dir.create('../gen/test2', recursive = T)

sink('../gen/test2/test2.txt')
cat('This is test 2')

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# Cleanup, wiping the (sub)directories output/temp/audit

wipe: 
	R -e "unlink('../../gen/data-preparation/output/*.*')"
	R -e "unlink('../../gen/data-preparation/temp/*.*')"
	R -e "unlink('../../gen/data-preparation/audit/*.*')"

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# Looping
for (i in 1:10) {
  print(i)
}

# Using looping with return values (i.e., to "save" stuff to carry on working)
results = lapply(1:10, function(x) x*2)

# Demo to download all of Europe's listing data to R
library(googledrive)

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# HOW TO MANAGE MEMORY ISSUES IN R?

# A common problem of data- and computation-intensive projects
# in R is memory management.

# Suppose you would like to estimate a series of models,
# but estimating all of them would exceed your available
# memory.
#
# One solution could be to have individual R scripts