#data management and visualization

[first-of-al]

Model 4 - Creating graphs for my data

Creating graphs for my data

Data have been managed well, and it is time to graph the variable tables. This is vital as it will provide readers with visual representations of data and help to better display results.

[first-of-al]

Model 3

Making Data Management Decisions

The below each variable is sub categorized and frequency is shown for each category.

[first-of-al]

Model 2

Running MY First Program

My analyze will show the results of which countries has the lowest breast cancer as on the table variable below. In this table below will shows the five countries has the lowest new female breast cancer world wide cases which are less than 10 cases per 100,000 of each country population.

[first-of-al]

Research Project on Gapminder Codebook

My selected dataset is Gapminder for this research.

Questions on this research and the the hypothesis:

1- What is the relationship between breast cancer per 100TH and co2 emissions?

2- Is there any relation between urban rate and suicide per 100th?

My hypothesis of how both breastcancerper100TH is negatively affected from co2emissions and urbanrate from suicideper100TH.

The literature review about this research:

Air pollution and Breast Cancer: A Review. Curr Epidemiol Rep. Author manuscript; available in PMC: 2019 Jun 1. Published in final edited form as: Curr Epidemiol Rep. 2018 Mar 27;5(2):92–100. doi: 10.1007/s40471-018-0143-2

Opportunities to improve the environmental sustainability of breast cancer care: A scoping review. The Breast Volume 84, December 2025, 104593

The relationship between population density and suicide: a systematic review and meta-analysis. Journal of Psychiatric Research Volume 189, September 2025, Pages 116-124

A Systematic Review of the Effects of Urban Living on Suicidality and Self-Harm in the UK and Ireland. J Urban Health. 2022 Apr 4;99(3):385–408. doi: 10.1007/s11524-022-00611-z

Variables selected:

breastcancerper100TH: breast cancer new cases per 100,000 female.

co2emissions: cumulative CO2 emission (metric tons), Total amount of CO2 emission in metric tons since 1751.

suicideper100TH: suicide, age adjusted, per 100 000 and Mortality due to self-inflicted injury.

urbanrate: Urban population refers to people living in urban areas.

[anudataenthus]

Getting the Research Project started

Dataset selection - Gapminder Dataset

A rich source of information that allows to explore various socio-economic factors and relationships. In this blog ,we will delve into two intriguing research questions .

I have chosen the Gapminder dataset for this assignment.

2. Topic of Interest

I am trying to examine the association between Income per person and Life expectancy across different countries?

:

What is the relationship between Life expectancy and Income per person in different countries?

2. Is there a relationship between Life expectancy, urbanization and alcohol consumption?

3. My Codebook

4. Income per person may depend on urbanization and this may led to different lifestyles that may affect the life expectancy. Alternatively Income will decrease when there is a recession or unemployment rate becomes higher.

Secondly I would like to examine Urbanization and alcohol consumption has any relationship with the life expectancy. Increase of income and Urbanization may increase the consumption of alcohol and may affect Life expectancy.

5. Research Questions

So, basically here I would like to explore the relationship between Life Expectancy and two other variables:

What is the relationship between Life expectancy and Income per person in different countries?

2. Is there a relationship between Life expectancy, urbanization and alcohol consumption?

Variable selection

We will focus on the following variables

incomeperperson- represents the average income of individuals

lifeexpectancy -average no.of years a person expected to live

urbanrate - Urban population living in urban area

alcconsumption- Alcohol consumption per adult

6. Literature review

“The Urbanization Process and Economic Growth: The So-What Question”. Vernon Henderson, Journal of Economic Growth, Volume 8, March 2003, Issue 1, pp 47-71

"The Intimate Link Between Income Levels and Life Expectancy: Global Evidence from 213Years*,Michael Jetter, University of Western Australia and CESifo Sabine Laudage, Deutsches Institut für Entwicklungspolitik (DIE),David Stadelmann, University of Bayreuth

3. "Determinants of inequalities in life expectancy: an international comparative study of eight risk factors",Published by Elsevier Ltd.

4. "Economic development, alcohol consumption and life expectancy in low- and lower-middle-income countries in the Western Pacific Region: a structural equation modelling study", Authors:Jürgen Rehm,Huan Jiang,Ahmed S Hassan,Pol Rovira,Kevin D Shield.

7.Hypothesis:

My hypothesis that both "incomeperperson" and “urbanrate” variables are positively associated with "life expectancy”.

[drt-coursera]

Data Management and VisualizationModule 4 - Python My1stProgram

Basic data manipulation for converting ' ' to Nan:

Bar graph for categorical variables:

histogram for quantitative variable:

standard deviation and other descriptive statistics for quantitative variables:

Scatteerplot is created to show data desribed below:

The basic hypothesis is disproved here as it is clearly visible the more the income the most the female employ rate is.

[drt-coursera]

Data Management and Visualization - Module 2

My first program:

Input CSV: Gapminder

My variables:

- urbanrate

- employrate

- internetuserate

My program:

read database:

Print 'urbanrate' quantity, percentage and missing data:

Print 'employrate' quantity, percentage and missing data:

Print 'interentuserate' quantity, percentage and missing data:

Output:

Creating subsets:

Output Subsets:

Copy of subset:

Summary:

Here I created frequency distributions displayed as count & percentage, also displays the missing data.

[youngtigercheesecake]

Week 4

Creating graphs for your data code

import pandas import numpy import pandas as pd import seaborn import matplotlib.pyplot as plt

data = pd.read_csv('gapminder_pds.csv', low_memory=False)

bindata = data.copy()

convert variables to numeric format using convert_objects function

data['internetuserate'] = pd.to_numeric(data['internetuserate'], errors='coerce') bindata['internetuserate'] = pd.cut(data.internetuserate, 10)

data['incomeperperson'] = pd.to_numeric(data['incomeperperson'], errors='coerce') bindata['incomeperperson'] = pd.cut(data.incomeperperson, 10)

data['employrate'] = pd.to_numeric(data['employrate'], errors='coerce') bindata['employrate'] = pd.cut(data.employrate, 10)

data['femaleemployrate'] = pd.to_numeric(data['femaleemployrate'], errors='coerce') bindata['femaleemployrate'] = pd.cut(data.femaleemployrate, 10)

data['polityscore'] = pd.to_numeric(data['polityscore'], errors='coerce') bindata['polityscore'] = data['polityscore'] sub2 = bindata.copy()

Scatterplot for the Association Between Employment rate and lifeexpectancy

scat1 = seaborn.regplot(x="internetuserate", y="incomeperperson", fit_reg=False, data=data) plt.xlabel('Internet use rate') plt.ylabel('Income per person') plt.title('Scatterplot for the Association Between Internet use rate and Income per person')

This scatterplot show the relationship and seems to be exponential.

Univariate histogram for quantitative variable:

seaborn.distplot(data["incomeperperson"].dropna(), kde=False); plt.xlabel('Income per person')

The graph is highly right skewed. Incomes are small for most of the world and the wealthy tail is quite long.

Univariate histogram for quantitative variable:

seaborn.distplot(data["employrate"].dropna(), kde=False); plt.xlabel('Employ rate')

Summary

It looks like there are associations between Internet use rate and income per person going up with internet use rate and going up an an accelerating rate.