corriebar / women-in-parliament-hex-sticker

Women in Parliament Hex Sticker

Women in Parliament Hex Sticker & Data

Are you bored with using the R iris, mtcars, ... datasets?

Then use the "Women in Parliament" data from the World Bank instead!. Great for teaching, learning, presentations or reprex. It has a hex sticker too!

Women in Parliament Hex Sticker

Download: PNG (754x873) or SVG.

Bonus Image

Download: PNG (3508x2480) or SVG.

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World Bank "Women in Parliament" Data

The raw data for "Proportion of seats held by women in national parliaments" ("single or lower parliamentary chambers only") by can be directly downloaded from:

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

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

Source Data

The data originates from the "Inter-Parliamentary Union" (IPU) 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). The data has to be scraped from the IPU website (please check the robots.txt file first).

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Importing the Data into R

The data can be imported into R using the wbstats package or by reading in the CSV file available from the World Bank's website.

R wbstat package

The R package wbstats provides access to World Bank's indicator data. Use the following code to get the women in parliament data:

library(wbstats)
wip <- wb(indicator = "SG.GEN.PARL.ZS") 

Importing the Raw Data into R

First download the latest CSV file from:

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

Below I will refer to this file as "WiP-Data.csv" but please use the actual file name that you save it as.

Using data.table

library(data.table)
wipdt <- fread("WiP-Data.csv",
               skip = 4, header = TRUE, check.names=TRUE)
WP <- melt(wipdt,
           id.vars = grep("Name|Code", names(wipdt), value = TRUE),
           measure = patterns("^X"),
           variable.name = "YearC",
           value.name = c("pctWiP"),
           na.rm = TRUE)
WP[, Year:=as.numeric(gsub("[^[:digit:].]", "",  YearC))][
   , YearC:=NULL]

Using tidyverse

library(readr)
library(dplyr)
library(tidyr)
wiptv <- read_csv("WiP-Data.csv", skip = 4) 
names(wiptv) <- make.names(names(wiptv))
wipTidy <- wiptv %>% 
    gather(key=YearC, value=pctWiP, starts_with("X"), na.rm=TRUE) %>% 
    mutate(Year = parse_number(YearC)) %>% 
    select(-YearC)

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R Guides Using Women in Parliament Data

Use the following R guides to get ideas on how to teach using the women in parliament data:

• Women in Parliament -- data.table Edition (PDF)
  • GitHub Repo: https://github.com/saghirb/WiP-rdatatable

• Women in Parliament – Tidyverse Edition (PDF)
  • GitHub Repo: https://github.com/saghirb/WiP-tidyverse

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Acknowledgements

The images were create by Marina Costa guided by Andreia Carlos and myself.

You can view Marina's great portfolio at:

• https://cargocollective.com/marinacostaportfolio

Thank you Marina and Andreia as it was really nice to work with you both.