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Data Wrangling & GAN
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## R Dependencies
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When using R I like using [RStudio](https://www.rstudio.com). I think it's the best IDE for R, and makes iterating on code very easy and quick. Within RStudio there is a package manager that can help you install the packages I have listed here:
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* dyplr
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* ggplot2
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The following two packages are installed a little differently.
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First install [Bioconductor](http://bioconductor.org/)
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Then you may install [GEOQuery](http://genomicsclass.github.io/book/pages/GEOquery.html)
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Those two packages are used in [get_gse_data.r](./mkdataset/get_gse_data.r), which can get any GSE, given the GSE ID and gene symbol column name.
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## Python Dependencies
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I used a virtual environment by [virtualenv](https://pypi.python.org/pypi/virtualenv). If you want to use it as well, I recommend [this installation tutorial](http://docs.python-guide.org/en/latest/dev/virtualenvs/).
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> **NOTE**: Using a virtual environment doesn't allows you to use [matplotlib](http://matplotlib.org/faq/virtualenv_faq.html) directly. You need to map it to your system's copy of **matplotlib** because the graphics libraries to create window frames is closely tied to the operating system.
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The prominent packages are:
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* numpy
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* Pandas
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* Scikit-Learn
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* TensorFlow
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* Keras
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* keras_adversarial
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To install all the dependencies quickly and easily you should use [`pip`](https://pypi.python.org/pypi/pip/)
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```bash
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pip install -r requirements.txt
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```
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## How to Run any Script
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Just navigate to the folder containing the script, and run it directly.
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### R
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If you're using RStudio, then all you need to do is **source** the script. There is a button for that in the top right corner of the editor window. Else from the command line:
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```r
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R CMD BATCH <name_of_script>
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```
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### Python
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Just run it directly from the command line. Assuming that you environment is prepared, and you have all the dependencies, all you have to do is
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```
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python gan.py
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```
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