WiP-Tidy-Data-dt-Exercise.Rmd

---
title: "Women in Parliament - Tidy Data"
author: "Your Name Here"
date: "`r format(Sys.time(), '%a %d %b %Y (%H:%M:%S)')`"
output:
  html_document:
    highlight: tango
    theme: united
    toc: yes
    toc_depth: 3
    toc_float:
      collapsed: no
      smooth_scroll: no
  pdf_document:
    toc: yes
    toc_depth: '3'
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(here)
```

```{r imageStart, echo=FALSE, out.width="20%"}
knitr::include_graphics("images/Women_in_Parliament_hex.svg")
```


# Objectives

*Explore the geographical and time trends for the percentage of women in 
national parliaments.*

# Understanding the Data

## The World Bank Data 

The raw data for *"Proportion of seats held by women in national parliaments"* 
includes the percentage of women in parliament (_"single or lower parliamentary chambers 
only"_) by country (region) and year. It can be downloaded from:

- https://data.worldbank.org/indicator/SG.GEN.PARL.ZS 

As part of its "open data" mission the World Bank offers _"free and open access 
to global development data"_ kindly licensed under the "Creative Commons Attribution 
4.0 (CC-BY 4.0)".

## Source Data

The data originates from the "Inter-Parliamentary Union" (IPU)[^ipuwebsite] which
provides an *_"Archive of statistical data on the percentage of women in 
national parliaments"_* going back to 1997 on a monthly basis:

- http://archive.ipu.org/wmn-e/classif-arc.htm

The World Bank data is for “single or lower parliamentary chambers only”, while 
the IPU also presents data for “Upper Houses or Senates”. Moreover, the IPU provides 
the actual numbers used to calculate the percentages (which the World Bank does not).

## Data limitations

Take caution when interpreting these data, as parliamentary systems vary from country 
to country, and in some cases over time. Some of the issues to consider include:

- Who has, and who does not have, the right to become a Member of Parliament (MP)?
- How does someone become an MP? Through democratic elections? How is "democratic
election" defined? 
- What is the real power of MPs and their parliament? Can MPs make a difference?

## Data definitions & assumptions

### "Women"

The definition for “women” is not given, so we will assume that it refers to a 
binary classification for gender (sex).

### "Country (Region)"

The definition of countries and regions can change over time.  (e.g. formation of new
countries after conflicts, new member states joining a pre-existing collective). 
How are these changes reflected in the data? How do they affect the interpretation? 

## Pro tip

Understand the limitations of your data before anybody else points them out to you. 

# About the data file

The data is stored in a file called: ``r list.files(path = here("data"), pattern = "^API")``

To simplify things we have copied it to `WB-WiP.csv` (which also allows us to 
maintain the original file in case something goes wrong).

## Pro tip

Always keep a backup copy of the data. Alternatively, set the data file(s) to 
“read-only” to protect it from being overwritten or modified.

# Importing the data

Based on our findings above, we can "skip" the first four lines and treat the
fifth line as column (variable) names. Also note the use of the `check.names`
argument to ensure that the column names are compliant in R.

```{r readData, collapse=TRUE}
library(data.table)
library(here)
wip <- fread(here("data", "WB-WiP.csv"), 
             skip = 4, header = TRUE,
             check.names = TRUE)
```

# Continue from here...







```{r imageEnd, echo=FALSE, out.width="60%"}
knitr::include_graphics("images/Women_in_Parliament_rect.svg")
```