P a g e 1 | 2 INFS 5116 – DATA VISUALISATION Visualisation Project Report (SP5 2019) Due 1 December by 11pm General instructions: • This assignment is worth 50% of your final grade and it is due no...

Visualization Results
Visualization Results
Untitled
29 November 2019
Introduction:
The main objective of this entire project is to demonstrate how simple visualization could be extensively used to find some informative insights and how it can provide a wide range of data-driven depths that could answer some important questions regarding real-life issues. Facts from Economics and World health-related topics are the most trending across any time frame or any typical socio-economic situation. So, this project will try to stride towards answering some questions with constant effort and with the help of visualization only to show the strength of a proper plot that could have some serious impact on these real-life issues. Different kinds of attributes and a wide range of curves and visualization techniques have been covered in this project and some of them are scatterplots, time series related plots, ridge plots, histograms, etc. The key question of this context is thus the following:
The world is assumed to be divided into two halves namely the developed or in other words, rich countries like western countries and the other half as developing countries like Africa, Asia, etc. So, the question arises here is that specification correct to assume?
Another critical and alarming question is whether the income distribution or the skewness of the income distribution has become worse.
Data Source:
The data has been extracted from one of the libraries called “dslabs” in R itself. The “gapminder” dataset is very popular from the socio-economic perspective and the data is mainly regarding the income and health-related outcomes and attributes comprised of the information from the year 1960 to 2016. Some of the attributes in the year of 2016 have missing values and hence mainly the data contains information up to the year 2015. The data set is stored as a data frame format and it is comprised of 10545 observations across 9 different variables or attributes. The variables are country, year, infant mortality or more specifically infant deaths per thousand, life expectancy measured in years, fertility or an average number of children possessed by a woman, the population of the country, the corresponding GDP according to the data of World Bankdev, the continent, region from the geographic perspective.
Visualization Results:
Basically, the data was dedicated to the public fora better understanding about the truth of different socio-economic facts. The coverage of different issues and news are generally falsified to a greater extent by the media and hence the cofounder of an organization named Hans Rosling decided to come forward with the truth with the proof of these facts through visualization technique on the public data. A glimpse of a random 10 observations of the data is shown below.
## country year infant_mortality life_expectancy fertility
## 100 Luxembourg 1960 33.0 68.99 2.35
## 101 Macao, China 1960 NA 64.93 4.95
## 102 Macedonia, FYR 1960 120.0 60.85 3.72
## 103 Madagascar 1960 112.0 41.96 7.30
## 104 Malawi 1960 218.2 38.51 6.91
## 105 Malaysia 1960 67.4 59.89 6.19
## 106 Maldives 1960 NA 38.07 7.02
## 107 Mali 1960 237.4 29.61 6.70
## 108 Malta 1960 36.4 68.32 3.53
## 109 Mauritania 1960 135.0 43.91 6.78
## 110 Mauritius 1960 67.8 58.74 6.17
## population gdp continent region
## 100 314586 4302303281 Europe Western Europe
## 101 171456 NA Asia Eastern Asia
## 102 1488664 NA Europe Southern Europe
## 103 5099371 2087989940 Africa Eastern Africa
## 104 3618604 347712093 Africa Eastern Africa
## 105 8160975 6631037036 Asia South-Eastern Asia
## 106 89875 NA Asia Southern Asia
## 107 5263730 NA Africa Western Africa
## 108 312788 NA Europe Southern Europe
## 109 858170 319353267 Africa Western Africa
## 110 660023 NA Africa Eastern Africa
The first attempt to visualize the fact of life expectancy rate with the increasing fertility rate. We start our analysis for the corresponding variables 30 years ago for the year 1985.
So, to compare this previous result with that of 2015, we can use the facet method in ggplot. The plot is thus given below:
The decreasing nature of life expectancy against the fertility rate can be seen to be stable a little bit over the years and the decreasing trend has been improved further. The fact that is evident from here is the shift of the clusters of the developing world to the western world. The matter is vivid in the case of...