From 7242a81d502f5b9bf22a2c4593b9a772396ffef6 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 27 Aug 2018 12:00:12 +0200
Subject: [PATCH] Add project description

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+# Tidy Data
+
+The purpose of this repository is to re-do the work described in the paper
+[Tidy Data](tidy-data.pdf) by Hadley Wickham (member of the RStudio team) in
+Python.
+
+The paper was published in 2014 in the Journal of
+[Statistical Software](https://www.jstatsoft.org/article/view/v059i10). The
+author offers it for free download
+[here](http://vita.had.co.nz/papers/tidy-data.html). Furthermore, the original
+R code is available in a Github
+[repository](https://github.com/hadley/tidy-data)
+
+After installing this project, it is recommended to first read the paper to get
+the big picture and then work through the six Jupyter notebooks (listed further
+below).
+
+See installation notes at the bottom.
+
+## Summary
+
+
+### Definition
+
+**Tidy** data is defined as data that comes in a table form adhering to the
+following requirements:
+
+1. Each variable forms a column.
+2. Each observation forms a row.
+3. Each type of observational unit forms a table.
+
+This is equivalent to Codd's 3rd normal form (in the context of relational
+databases). A dataset that does not satisfy these properties is called
+**messy**.
+
+
+### Tidying messy Data
+
+The five most common problems with messy data are as follows:
+
+- Column headers are values, not variable names
+[[notebook](1_column_headers_are_values.ipynb)]
+- Multiple variables are stored in one column
+[[notebook](2_multiple_variables_stored_in_one_column.ipynb)]
+- Variables are stored in both rows and columns
+[[notebook](3_variables_are_stored_in_both_rows_and_columns.ipynb)]
+- Multiple types of observational units are stored in the same table
+[[notebook](4_multiple_types_in_one_table.ipynb)]
+- A single observational unit is stored in multiple tables
+[[notebook](5_one_type_in_multiple_tables.ipynb)]
+
+Further, a [case study](6_case_study.ipynb) shows the advantages of tidy data
+(as standardized input/output to statistical functions).
+
+## Download & Installation
+
+Create a local copy of this repository with:
+
+`git clone https://github.com/webartifex/tidy-data.git`
+
+This project uses [pipenv](https://docs.pipenv.org/) to manage its
+dependencies.
+
+To install all third-party Python packages in the most recent version into a
+project-local virtual environment, run:
+
+`pipenv install`
+
+To install all packages with the same version as of the time of creating this
+project (for exact reproducability), run:
+
+`pipenv install --ignore-pipfile`