Project-4 Recreating Our World in Data
Obesity, Its Causes and Risks - Soniya Adhi
OBESITY AND MORTALITY, According to the National Institutes of Health, obesity and overweight together are the second leading cause of preventable death in the United States, close behind tobacco use. An estimated 300,000 deaths per year are due to the obesity epidemic.
Citation : https://www.wvdhhr.org/bph/oehp/obesity/mortality.htm#
Introduction
In this project we are recreating the graphs from Our World and the theme is Obesity. Also we took the datasets from the site itself mentioned in the graph.
Loading Libraries
library(tidyverse)
library(forcats)
library(ggplot2)
library(plotly)
library(scales)
options(scipen = 999)
theme_set(theme_minimal())Soniya Adhi :
I have loaded all the needed libraries in this chunk of code. Scales is used to convert the number into millions and scipen is used to convert scientific numbers to a normal number.Also have set the background for the graphs to minimal using theme_minimal().
Loading Data
death <-read_csv("data/number-of-deaths-by-risk-factor.csv",col_types = cols())
obD <-read_csv("data/share-of-deaths-obesity.csv",col_types = cols())
edis <-read_csv("data/number-with-an-eating-disorder.csv",col_types = cols())
persm <-read_csv("data/percent-of-men-and-women-using-social-media-platforms-in-the-us.csv",
col_types = cols())Description :
Using read_csv function, I read the data into the respective variables. I used col_types=cols() in order to specify the column types. The death dataset contains the Death categories as columns and the number is given as the values for the columns. obD contains the Entity i.e; the country name its code and the year and the Obesity values. edis contains the country names in Entity and the year and the values for Female and Male respectively. persm contains the country names in Entity code year and the social media usage among men and women in the US and the total Monthly active users
Data Explorations
When the deaths of people are categorized into various categories and the number of deaths are taken and plotted in a graph from 1990-2017 years. We can observe that the risk of Obesity to people has been graded in the top 5 risks. So we can say the Obesity has a high potential in giving risk to people and being a major cause of their death. So we are seeing one major cause and what it can lead to. Firstly I am showing the count of deaths based on different Risk Categories,
Deaths based on different Risk Factors
Description :
I used different colors for different categories, I saved the colors in mycolors. I scaled the numbers and converted it to million. I renamed the column names and filter the dataset to make a neater subset.I subsetted the dataset and used pivot longer to make the number of rows longer and reduce the number of columns.I grouped by Risk Factor and plotted the graph using geom bar and flipped the coordinate axis and did few adjustments to display the above graph.
Results :
From the above graph, we see the graphical display of number of deaths by different risk factors in the world till 2017.Out of which the highest number of deaths is highest for the people with high blood pressure and the next highest risk is for people who Smoke.In this graph the risk categories are displayed on y axis and the count of deaths is shown on xaxis.
Citation:
https://www.r-graph-gallery.com/267-reorder-a-variable-in-ggplot2.html
https://r-graphics.org/recipe-appearance-hide-gridlines
From the above graph, we see Obesity stands in the top 5 potential risks to human health and which causes death to a huge number of people. Now we will be going a bit deep into the percent of people who died due to Obesity. We will be plotting this based on different countries and also the average percent of people who are dead with Obesity in the World.
Deaths due to Obesity
Description :
I used different colors for different countries, saved the colors in colors2.I renamed the column names and filter the dataset to make a neater subset.I plotted a connected scatter plot in R using geom_line and geom_point.I colored the graph manually and gave break values on x axis and labelled them appropriately. I used coord_cartesian in order to specify the x and y axis of the plot and gave the limits of x axis and y axis. I used geom_text in order to display the entity of each line. I used ggplotly in order to make this interactive. In ggplotly, we cannot draw dotted or dashed line in the background , So was not able to get the background. Also the tooltip is based on individual entity and not combined one, nudge_x and nudge_y shows us at what position the text has to be displayed from the graph.
Results:
This graph mainly shows us the Entities which are displayed as lines. The entities are displayed in different colors based on the entities. The Obesity value is displayed on y axis and the number of years are displayed on the xaxis The dots are displayed based on the year on xaxis. All these dots are connected based on the years on x axis. The highest rate of increase in Obesity is found in United States and the least rate of interest was found in Chad.
Citation : https://github.com/plotly/plotly.js/issues/4045
From the above graph, we could see that USA stands high in the percent of obese people due to which the World’s % of population suffering with Obesity has also increased. It is said that Obesity can contribute to eating disorder behaviors,as these are each associated with severe physical and mental health consequences.
About one third (34%) are obese, meaning that they are 20% or more above normal, healthy weight. Many of these people have binge eating disorder. In addition, about 31 percent of American teenage girls and 28 percent of boys are somewhat overweight.
Citation : https://www.edreferral.com/statistics
Lets see the number of people suffering with eating disorder in the World. We will show a clear display of the difference between the count of male and female gender on this graph. As mentioned above the girls are more in compared to the boys.
No: of people with eating disorder
Description :
Here in this graph we filtered the data to World data, and selected necessary columns and renamed them. I them performed pivot longer and got a new column Prevalance which tells us the value. I converted Gender column into factors and plotted an area graph and filled the colors manually. Also gave the break values for tick marks on x axis and y axis. Also I used geom_text to display the values and made the graph interactive.
Results :
The area plot shows two different areas, male, female on the graph. The number of people who are suffering with eating disorders are mostly woman when compared to men. The Women gender is more susceptible to eating disorders which could be caused due to stress or any other reason.
Citation: https://github.com/plotly/plotly.js/issues/4045
Also US stands top in people suffering from Obesity. Inactivity is the new normal.Lack of exercise is also a major culprit in the obesity epidemic. It’s been decades since most Americans worked in fields and on factory floors, a far greater majority of us are sitting throughout our workday. Also It is said, that the children spending more than three hours a day on Social Media are more likely to be Obese.
Lets see how the Percent of people using Social Media is increasing day by day. Below I plotted the usage of major Social Media platforms among men and women in USA.
Percentage of men/women using Social Media
Description :
In this graph, I filtered the dataset and selected certain columns. I filtered the entity column to get the social media platforms and renamed them accordingly.Also I added a new column which is the total number of users for a single platform. I plotted a bubble plot which is interactive and shows the values on hovering. I also used coord cartesian and drew a line as a diagonal to the coordinate plot.
Results :
The number of social media platforms has increased every year. Along with the ease and increase of Social Media, the number of users has also increased. The people irrespective of age and gender are addicted to social media. The percentage of users among men and women are increasing based on the years. The highest number of users or subscribers are for Youtube and Facebook and the next highest is for Instagram. The % of users who are women are displayed based on x axis and the % who are men are displayed on y axis.
Citation: https://github.com/plotly/plotly.js/issues/4045
From the graph, It is said that the usage of Youtube is high compared to other platforms. The impact of media on children health has been strongly debated for years, with a lot of attention being given to the increased time children are spending on platforms such as YouTube.Also current evidence suggests that screen media exposure leads to obesity in children and adolescents through increased eating while viewing; exposure to high-calorie, low-nutrient food and beverage marketing that influences children’s preferences, purchase requests, consumption habits; and reduced sleep duration.
Conclusion
We observed the risk factors which causes death in people. We saw the leading country which is USA in the percentage of Obese people. Also we saw Female Gender is more prone to Eating disorders more when compared to the men. Also the usage of Social media platforms by men and women are shown in the graph. Thus we can make this and use the Social Media platforms to make a positive impact rather than a negative impact on children as the social media usage is more. We can create an influence using this and making it an Obese free place and reduce the risk of deaths.
Note : I consent to Drs. Dykes and Shreiner using my recreated visualizations in their K-12 Social Studies instructional materials.