Lack of personal interaction, Internet and Social Networks are generating increased isolation and mental health issues becoming the new epidemic of this century. International organizations are bringing more awareness on the effects that these phenomena is causing such as suicide. The dataset I choose compiles suicide rates by differeent factors. My objective show awareness with an impactful , easy to use dashboard that will enable the user to explore the data set in an interactive way showing how suicide behaves by country, gender,age, and different ways to group this data and make the user think about this important social issue.
This dataset was compliled by Kaggle from other sources including the UN Developmental Program, World Bank, and World Health OrganiZation
This is an observational study
data<- read.csv('https://raw.githubusercontent.com/sortega7878/DATA608/master/FINAL/master.csv')
summary (data)## ï..country year sex age
## Austria : 382 Min. :1985 female:13910 15-24 years:4642
## Iceland : 382 1st Qu.:1995 male :13910 25-34 years:4642
## Mauritius : 382 Median :2002 35-54 years:4642
## Netherlands: 382 Mean :2001 5-14 years :4610
## Argentina : 372 3rd Qu.:2008 55-74 years:4642
## Belgium : 372 Max. :2016 75+ years :4642
## (Other) :25548
## suicides_no population suicides.100k.pop
## Min. : 0.0 Min. : 278 Min. : 0.00
## 1st Qu.: 3.0 1st Qu.: 97498 1st Qu.: 0.92
## Median : 25.0 Median : 430150 Median : 5.99
## Mean : 242.6 Mean : 1844794 Mean : 12.82
## 3rd Qu.: 131.0 3rd Qu.: 1486143 3rd Qu.: 16.62
## Max. :22338.0 Max. :43805214 Max. :224.97
##
## country.year HDI.for.year gdp_for_year....
## Albania1987: 12 Min. :0.483 1,002,219,052,968: 12
## Albania1988: 12 1st Qu.:0.713 1,011,797,457,139: 12
## Albania1989: 12 Median :0.779 1,016,418,229 : 12
## Albania1992: 12 Mean :0.777 1,018,847,043,277: 12
## Albania1993: 12 3rd Qu.:0.855 1,022,191,296 : 12
## Albania1994: 12 Max. :0.944 1,023,196,003,075: 12
## (Other) :27748 NA's :19456 (Other) :27748
## gdp_per_capita.... generation
## Min. : 251 Boomers :4990
## 1st Qu.: 3447 G.I. Generation:2744
## Median : 9372 Generation X :6408
## Mean : 16866 Generation Z :1470
## 3rd Qu.: 24874 Millenials :5844
## Max. :126352 Silent :6364
## I’ll be focusing the visualizations on comparing suicides rates among multiple variables such as country , age, sex, year and countries variables such as HDI for year has almost 2/3 of data missing so no relevance and sampling of generation variable present spikes that may take us away from what I want to show in the visualization.
The format will be an interactive app/dashboard with multiple interactive screens that will allow the user to dissect the information in different ways (e.g. global statistics, gender statistics, by country statistics, search). app will be interactive and deployed.
This compiled dataset pulled from four other datasets linked by time and place, and was built to find signals correlated to increased suicide rates among different cohorts globally, across the socio-economic spectrum. References
United Nations Development Program. (2018). Human development index (HDI). Retrieved from http://hdr.undp.org/en/indicators/137506
World Bank. (2018). World development indicators: GDP (current US$) by country:1985 to 2016. Retrieved from http://databank.worldbank.org/data/source/world-development-indicators#
[Szamil]. (2017). Suicide in the Twenty-First Century [dataset]. Retrieved from https://www.kaggle.com/szamil/suicide-in-the-twenty-first-century/notebook
World Health Organization. (2018). Suicide prevention. Retrieved from http://www.who.int/mental_health/suicide-prevention/en/