Lifestyle Patterns and Their Impact on Obesity Across Genders

Avinash Juttiga (s4033296)

Last Updated on 30th October 2024

Introduction

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.3

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)

## Warning: package 'tidyr' was built under R version 4.3.3

library(readxl)



obesity_data <- read_excel("D:/RMIT CLASSES/3 semester/Data Visualization and Communications/ASSIGNMNET 3/Obesity_Dataset.xlsx")

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obesity_data <- obesity_data %>%
  mutate(across(everything(), as.numeric))



comparison_summary <- obesity_data %>%
  group_by(Sex) %>%
  summarise(across(everything(), list(mean = ~mean(. , na.rm = TRUE), sd = ~sd(. , na.rm = TRUE))))


# Box plot of Age by Sex
plot1<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), y = Age, fill = factor(Sex))) +
       geom_boxplot() +
       labs(x = "Sex", y = "Age", title = "Comparison of Age between Males and Females") +
       scale_fill_discrete(name = "Sex", labels = c("Male", "Female")) +
       theme_minimal()

# Box plot of Height by Sex
plot2<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), y = Height, fill = factor(Sex))) +
       geom_boxplot() +
       labs(x = "Sex", y = "Height", title = "Comparison of Height between Males and Females") +
       scale_fill_discrete(name = "Sex", labels = c("Male", "Female")) +
       theme_minimal()

# Bar plot for Overweight_Obese_Family by Sex
plot3<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Overweight_Obese_Family))) +
       geom_bar(position = "dodge") +
       labs(x = "Sex", fill = "Overweight/Obese Family", title = "Comparison of Overweight/Obese         Family History by Sex") +
       scale_fill_discrete(labels = c("Yes", "No")) +
       theme_minimal()

# Bar plot for Consumption_of_Fast_Food by Sex
plot4<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Consumption_of_Fast_Food))) +
       geom_bar(position = "dodge") +
       labs(x = "Sex", fill = "Consumption of Fast Food", title = "Comparison of Fast Food    Consumption by Sex") +
       scale_fill_discrete(labels = c("Yes", "No")) +
       theme_minimal()


# Bar plot for Frequency_of_Consuming_Vegetables by Sex
plot5<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Frequency_of_Consuming_Vegetables))) +
  geom_bar(position = "dodge") +
  labs(x = "Sex", fill = "Frequency of Consuming Vegetables", title = "Comparison of Vegetable Consumption Frequency by Sex") +
  scale_fill_discrete(labels = c("Rarely", "Sometimes", "Always")) +
  theme_minimal()


plot6<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Number_of_Main_Meals_Daily))) +
       geom_bar(position = "dodge") +
       labs(x = "Sex", fill = "Number of Main Meals Daily", title = "Comparison of Main Meals     Frequency by Sex") +
       scale_fill_discrete(labels = c("1-2", "3", "3+")) +
       theme_minimal()



# Bar plot for Food_Intake_Between_Meals by Sex
plot7<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Food_Intake_Between_Meals))) +
       geom_bar(position = "dodge") +
       labs(x = "Sex", fill = "Food Intake Between Meals", title = "Comparison of Food Intake    Between Meals by Sex") +
       scale_fill_discrete(labels = c("Rarely", "Sometimes", "Usually", "Always")) +
       theme_minimal()


# Bar plot for Smoking by Sex
plot8<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Smoking))) +
  geom_bar(position = "dodge") +
  labs(x = "Sex", fill = "Smoking", title = "Comparison of Smoking Habits by Sex") +
  scale_fill_discrete(labels = c("Yes", "No")) +
  theme_minimal()


# Bar plot for Liquid Intake Daily by Sex
plot9<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Liquid_Intake_Daily))) +
  geom_bar(position = "dodge") +
  labs(x = "Sex", fill = "Liquid Intake Daily", title = "Comparison of Liquid Intake by Sex") +
  scale_fill_discrete(labels = c("Less than 1 liter", "1-2 liters", "More than 2 liters")) +
  theme_minimal()

# Bar plot for Calculation of Calorie Intake by Sex
plot10<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Calculation_of_Calorie_Intake))) +
  geom_bar(position = "dodge") +
  labs(x = "Sex", fill = "Calorie Intake Calculation", title = "Comparison of Calorie Intake Calculation by Sex") +
  scale_fill_discrete(labels = c("Yes", "No")) +
  theme_minimal()


# Bar plot for Physical Exercise by Sex
plot11<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Physical_Excercise))) +
  geom_bar(position = "dodge") +
  labs(x = "Sex", fill = "Physical Exercise", title = "Comparison of Physical Exercise by Sex") +
  scale_fill_discrete(labels = c("No activity", "1-2 days", "3-4 days", "5-6 days", "6+ days")) +
  theme_minimal()


# Bar plot for Schedule Dedicated to Technology by Sex
plot12<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Schedule_Dedicated_to_Technology))) +
  geom_bar(position = "dodge") +
  labs(x = "Sex", fill = "Technology Use Schedule", title = "Comparison of Technology Use Schedule by Sex") +
  scale_fill_discrete(labels = c("0-2 hours", "3-5 hours", "More than 5 hours")) +
  theme_minimal()


# Bar plot for Type of Transportation Used by Sex
plot13<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Type_of_Transportation_Used))) +
  geom_bar(position = "dodge") +
  labs(x = "Sex", fill = "Type of Transportation", title = "Comparison of Transportation Type by Sex") +
  scale_fill_discrete(labels = c("Automobile", "Motorbike", "Bike", "Public transport", "Walking")) +
  theme_minimal()


# Bar plot for Class by Sex
plot14<-ggplot(obesity_data, aes(x = factor(Sex, labels = c("Male", "Female")), fill = factor(Class))) +
  geom_bar(position = "dodge") +
  labs(x = "Sex", fill = "Class", title = "Comparison of Weight Class by Sex") +
  scale_fill_discrete(labels = c("Underweight", "Normal", "Overweight", "Obesity")) +
  theme_minimal()

Obesity is a serious and chronic disease with genetic interactions. It is defined as an excessive amount of fat tissue in the body that is harmful to health. The main risk factors for obesity include social, psychological, and eating habits. Obesity is a significant health problem for all age groups in the world. This data-set includes lifestyle factors , boi-metric data, dietary habits and transportation choices of nearly 1610 individuals.Some of the factors used in this data are as follows: Overweight/Obese Families

-Consumption of Fast Food -Frequency of Consuming Vegetables -Number of Main Meals Daily -Food Intake Between Meals -Smoking -Liquid Intake Daily -Calculation Of Calorie Intake -Physical Exercise -Schedule Dedicated to Technology -Type of Transportation Used -Class

1.PLots of Age,Height by Sex

library(ggplot2)

plot1

plot2

The above two box plots gives us a comparison analysis of age and height between the males and females among the 1610 individuals.Both males and females have similar age distribution with a median age of mid 30’s. whereas the height distribution shows a notable difference , with males taller than females.

2.Family History of Obesity by Gender

plot3

The above plot illustrates that for both sexes, individuals with no family history of overweight or obesity are more dominant than those with such a family history, with a larger number of females in each category than males.

3.Fast Food Consumption by Gender

# Bar plot for Consumption_of_Fast_Food by Sex
plot4

The visualization provides that a significant portion of both sexes do not consume fast food, with females in particular having a higher count in the “No” category.

4.Vegetable Consumption Frequency by Gender

# Bar plot for Frequency_of_Consuming_Vegetables by Sex
plot5

In the above visualization highlights that while both genders have similar patterns, females tend to report a slightly higher frequency of always consuming vegetables.

5.Meal and Snacking Habits by Gender

# Bar plot for Number_of_Main_Meals_Daily by Sex
plot6

plot7

Both sexes generally have three main meals a day. Females show a marginally higher tendency towards frequent snacking. This combined pattern shows us the gender-based differences in eating habits, with a consistent eating meals across genders, but snacking behavior is slightly higher .

6.Smoking Prevalence by Gender

# Bar plot for Smoking by Sex
plot8

The above plot implies that smoking is more uniformly distributed among males, while females are mostly non-smokers.

7.Hydration and Calorie Tracking Habits by Gender

# Bar plot for Liquid Intake Daily by Sex
plot9

# Bar plot for Calculation of Calorie Intake by Sex
plot10

Both genders tend to take more than 2 liters of liquids daily, calorie intake tracking is less used, with most individuals from both sexes not engaging in this practice.The above plot shows us sufficient hydration and low engagement with calorie monitoring across genders.

8.Gender Patterns in Exercise and Technology Use

# Bar plot for Physical Exercise by Sex
plot11

# Bar plot for Schedule Dedicated to Technology by Sex
plot12

Both genders tend to exercise regularly and use technology moderately, with slight variations. Females have a slightly higher interest in both frequent physical activity and moderate use of technology.

9.Preferred Modes of Transportation by Gender

# Bar plot for Type of Transportation Used by Sex
plot13

Both genders mostly depend on automobiles and public transport,while males show preference for motorbikes and females are more favorable for biking and walking.

Strategies for Obesity Prevention and Management

-Increasing Physical Activity also integrating various types like aerobic and strength training.

-Improve Dietary Choices by limiting fast food and more intake of balanced meals rich in vegetables and proteins.

-Staying hydrated by drinking at-least 2 liters of water daily to support metabolism.

-Reducing screen time by taking regular breaks from screens and integrating a short physical activities.

-Educate on portion sizes and caloric content to help manage intake with strict calorie counting.

-BY quitting smoking to improve overall health and energy levels. -Promote walking and add physical activity into daily routines by setting Health Goals

Conclusion

The visualizations provide a comprehensive overview of lifestyle factors contributing to obesity across genders. Key observations include differences in exercise frequency, dietary habits, hydration, smoking, technology use, and transportation preferences. Both males and females show similar patterns in primary habits like meal frequency and hydration, though females tend to engage slightly more in regular physical activity and consume vegetables more frequently. However, both genders show high screen time, limited calorie tracking, and a reliance on automobiles, which are potential risk factors for sedentary lifestyles.

These insights suggest that while both genders have areas where they are taking positive steps, there are several lifestyle modifications that could help prevent and manage obesity more effectively. By focusing on increasing physical activity, improving dietary awareness, reducing sedentary behaviors, and promoting community support, tailored interventions can support healthier lifestyles and reduce obesity risk across both genders.

References: Koklu, N., & Sulak, S. A. (2024). Using artificial intelligence techniques for the analysis of obesity status according to the individuals’ social and physical activities. Sinop Üniversitesi Fen Bilimleri Dergisi, 9(1), 217–239. https://doi.org/10.33484/sinopfbd.1445215