Project
2024-04-23
For years Americans have been struggling to control their weight due to a variety of factors like sugary foods and lack of proper diet management. This problem has expanded to a global scale. As processed, unhealthy foods have become cheaper than healthy alternatives and technology has played a greater part in everyone’s lives, people have started to gain weight. As more and more people become overweight and obese there are a litany of health issues that can appear including diabetes, high blood pressure, heart disease, etc. In my project, which uses a dataset collected from respondents in Central and South America, I hope to find trends within my visualizations that can show factors that both play and do not play a role in the obesity epedemic. Factors like activity level, amount of vegetables consumed, family history, calories consumed, unhealthy habits, primary mode of transportation, and amount of meals consumed.
How prevalent is obesity in Central and South America? As you can see with the visualization below obesity has been growing in Mexico, Peru, and Colombia at a steady rate since the 1970’s. To analyze my dataset I decided to group my data into 4 different categories, those who were listed as insufficient weight, normal weight, overweight, and obese. If you look at the visualization below you can see the disparity between the 4 categories with insufficient and normal weight people making up only 559 of the 2111 (~26%) respondents. Overweight people made up around 27% of the obervations and obese people made up the vast majority of observations with more than 46% of observations being classified as obese. Obese people making up nearly half of the population clearly shows that obesity has become a prevalent issue in these Central and South American countries.
## Age Gender Height Weight CALC FAVC FCVC NCP SCC SMOKE CH2O
## 1 21 Female 1.62 64.0 no no 2 3 no no 2
## 2 21 Female 1.52 56.0 Sometimes no 3 3 yes yes 3
## 3 23 Male 1.80 77.0 Frequently no 2 3 no no 2
## 4 27 Male 1.80 87.0 Frequently no 3 3 no no 2
## 5 22 Male 1.78 89.8 Sometimes no 2 1 no no 2
## 6 29 Male 1.62 53.0 Sometimes yes 2 3 no no 2
## family_history_with_overweight FAF TUE CAEC MTRANS
## 1 yes 0 1 Sometimes Public_Transportation
## 2 yes 3 0 Sometimes Public_Transportation
## 3 yes 2 1 Sometimes Public_Transportation
## 4 no 2 0 Sometimes Walking
## 5 no 0 0 Sometimes Public_Transportation
## 6 no 0 0 Sometimes Automobile
## NObeyesdad
## 1 Normal_Weight
## 2 Normal_Weight
## 3 Normal_Weight
## 4 Overweight_Level_I
## 5 Overweight_Level_II
## 6 Normal_Weight
## PhantomJS not found. You can install it with webshot::install_phantomjs(). If it is installed, please make sure the phantomjs executable can be found via the PATH variable.While the commonly held beleif is that increased vegetable consumption and steady, regular physical exercise should lead to healthier outcomes with individuals who eat vegetables regularly being in the “normal” weight range, the data from the respondents actually challenged that belief. If you look at the alluvial chart below, it is analyzing the flow of respondents from their vegetable consumption (Sometimes, Often, Very Often) to their level of physical activity (Low, Medium, High). Firstly you would think that there would be a clear trend of those eating vegetables very often being more normal weight than the other groups. However, there isn’t really a discernable trend as there are plenty of obese people coming from each of the three groups, this also holds true for physical activity where you would expect those who have higher physical activity to have much lower rates of being overweight/obese but there was no clear trend indicating thaht physical exercise had a discernable impact on what weight group the person matched into.
This finding not only challenges popularly held beliefs about
obesity, it also hints to the fact that we need to look at other facets
of the respondents lives that could be leading to their weight issues
rather than just looking at the two most common things. There are a
variety of other factors that I would like to look at that give us a
better understanding of the underlying factors that are related to
living a healthier lifestyle.
The stacked bar graph pictured below shows interesting evidence of what may be a potential genetic/environmental influence of family history on obesity. When a respondent said that they had no family history of obesity only 8 of those respondents fell into the obese weight group. In contrast, when the respondent reported that they did have an obese family member, 964 of those respondents fell into the obese weight group. This huge difference could highlight either a genetic predisposition towards obesity or show that being in an environment where families share certain eating and activity behaviors could be contributing to their condition.
When looking at the effect of diet on obesity we see a very striking correlation between those who focus on eating high caloric foods vs. those who focus on eating low caloric foods. Those who regularly consumed high caloric foods fell into the obese category more than 50% of the time. In contrast, those who wanted lower calorie options had a much better weight distribution, only 7.76% were considered obese while 32% fell into the normal weight category. This visualization really helps to highlight how important focusing on eating lower calorie options is for maintaining a better body weight. Adding another factor that is key in understanding what factors play a part into obesity.
In this visualization we are exploring the effects of “unhealthy” habits, like smoking, drinking and snacking, on obesity. We often assume that these habits will lead to a more unhealthy lifestyle but our visualization seems to contradict that earlier assumption. Infact, a lot of the people who partake in the unhealthy habits are considered to be insufficient weight while the distribution for those not involved in those activities is more evenly split between the 4 different weight groups. This shows that healthy and unhealthy habits may not be as strong a component of obesity as we previously thought.
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatterThis visualization is an interesting insight into the relationship between the amount of water drank and the weight of various weight groups. The trend line for normal weight, overweight, and obese show that in each weight group as the amount of water drank decreases the weight of the respondent generally increases. The inverse relationship with water consumption and weight within each weight group shows that hydration plays an important role in weight management.
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatterWalking is generally a mode of transportation taken up by those who are healthier weightwise with the majority of the participants falling into normal weight and overweight when walking.
Conclusion
At the end of my project I have come to the conclusion that the factors that have significant trends in terms of weight managment are whether you regularly eat high or low calorie meals, your family history with obesity, as well as your water consumption. This was far different from what I expected when I started the project because I largely assumed that factors like physical activity, primary means of transport, unhealthy habits like drinking, smoking, snacking, etc. and amount of vegetables eaten would have clear and discernable trends towards respondents falling into heavier weight categories. If I had the chance to expand upon this project I would like to explore other factors like stress levels, income, and health education play a role into the rate of obesity in different communities.