Gender inequality is one of the most concerning areas which led the United Nations (UN) to set Sustainable Development Goal 5 — achieve gender equality and empower all women and girls. To assess the worldwide present day scenario, one of the best evaluation metrics is to rank the nation on the basis of the contribution of women in leadership and decision-making. To generate insights on the same ground, the _proportion of seats held by women in national parliaments (%) _can be considered as a foundational stone (data). The reason behind it is that in the present world, there are many regions where discriminatory laws and institutional practices are dominant to limit women’s capacity to run for political office. Systemic inequality, lack of access to education, limited options for workforce participation, and many other constraints on personal freedoms limit women from accessing the resources and opportunities to pursue a political career.


About Data

The data source is a part of the Makeover Monday challenge (2020W30) and has been collated by The World Bank as part of their World Development Indicators database. The data reveals the country-wise proportion of seats held by women in national parliaments (%) at YoY Level (1997–2019). This data is part of the Visualize Gender Equality — Viz5 program.


Exploratory Data Analysis (EDA)

The first step to generate insight out of data is to explore it. For this scenario, Tidyverse was used which is a key ingredient to do EDA in R. Following is the glimpse of data:

head(Female_Political_Representation)

Country.Name  Country.Code Year Proportion of Seats
1      Albania          ALB 1997                  NA
2      Albania          ALB 1998                  NA
3      Albania          ALB 1999          0.05161290
4      Albania          ALB 2000          0.05161290
5      Albania          ALB 2001          0.05714286
6      Albania          ALB 2002          0.05714286

#data-science #exploration #gender-equality #tidyverse #storytelling #data analysis

Key insights obtained by mining Gender Equality data using Tidyverse
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