INSS615 Group 3 Project Implementation
Morgan State University
Department of Information Science & Systems
Fall 2024
INSS 615: Data Wrangling for Visualization
Name: Frenandez Lawrence, Mohammed Naveed Afroz Mulla, Onyebuchi Obiefuna
Due: November 24, 2024 (Sunday)
Project Implementation Phases: Phases 2, 3, 4, and 5 (as outlined on pages 4 & 5 of the syllabus) of the project should be completed using R Notebook. After completing phase 5, a knitted version of the document (containing descriptions/explanations of steps, code, and output of phases 2 to 5) should be uploaded by the specified due date. In addition to the final knitted version of the R Notebook, each group is expected to upload a written project report of 2 to 3 pages by the same due date. The report should summarize the project, including challenges faced, lessons learned, and the contributions of each group member.
Phase 2: Data Acquisition and Exploration Loading Dataset and Viewing Structure of the data
# Loading necessary libraries
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Loading the dataset
Obesitydata <- read.csv("/Users/frenandezlawrence/Downloads/Dirty_Obesity_Dataset_.csv")
# Viewing the structure and summary of the data
str(Obesitydata)## 'data.frame': 1630 obs. of 15 variables:
## $ Sex : chr " Female " " Female " " Female " " Female " ...
## $ Age : num 18 18 18 18 18 18 19 19 19 19 ...
## $ Height : num 155 158 159 162 165 176 152 158 159 162 ...
## $ Overweight_Obese_Family : int 2 2 2 2 2 1 2 2 2 2 ...
## $ Consumption_of_Fast_Food : int 2 2 2 2 1 1 2 2 2 2 ...
## $ Frequency_of_Consuming_Vegetables: int 3 3 2 2 2 1 3 3 2 2 ...
## $ Number_of_Main_Meals_Daily : int 1 1 1 2 1 1 1 2 2 2 ...
## $ Food_Intake_Between_Meals : int 3 1 3 2 3 4 1 4 2 2 ...
## $ Smoking : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Liquid_Intake_Daily : num 1 1 3 2 1 2 3 1 1 2 ...
## $ Calculation_of_Calorie_Intake : int 2 2 2 2 2 2 2 1 2 1 ...
## $ Physical_Excercise : int 3 1 2 1 3 4 2 1 2 2 ...
## $ Schedule_Dedicated_to_Technology : int 3 3 3 3 3 3 3 3 3 3 ...
## $ Type_of_Transportation_Used : int 4 3 4 4 2 4 2 3 4 4 ...
## $ Class : int 2 2 2 2 2 2 2 2 2 3 ...summary(Obesitydata)## Sex Age Height Overweight_Obese_Family
## Length:1630 Min. : -5.10 Min. :-10.3 Min. :1.000
## Class :character 1st Qu.: 25.00 1st Qu.:161.0 1st Qu.:2.000
## Mode :character Median : 32.00 Median :168.0 Median :2.000
## Mean : 33.18 Mean :168.2 Mean :1.833
## 3rd Qu.: 41.00 3rd Qu.:174.0 3rd Qu.:2.000
## Max. :200.20 Max. :800.7 Max. :2.000
##
## Consumption_of_Fast_Food Frequency_of_Consuming_Vegetables
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :2.000
## Mean :1.728 Mean :2.064
## 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :2.000 Max. :3.000
## NA's :10
## Number_of_Main_Meals_Daily Food_Intake_Between_Meals Smoking
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000
## Mean :1.872 Mean :2.394 Mean :1.695
## 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.000
## Max. :3.000 Max. :4.000 Max. :2.000
## NA's :10
## Liquid_Intake_Daily Calculation_of_Calorie_Intake Physical_Excercise
## Min. :-10.700 Min. :1.000 Min. :1.000
## 1st Qu.: 1.000 1st Qu.:2.000 1st Qu.:2.000
## Median : 2.000 Median :2.000 Median :3.000
## Mean : 2.189 Mean :1.822 Mean :3.262
## 3rd Qu.: 3.000 3rd Qu.:2.000 3rd Qu.:4.000
## Max. :100.300 Max. :2.000 Max. :5.000
## NA's :5
## Schedule_Dedicated_to_Technology Type_of_Transportation_Used Class
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :3.000
## Mean :2.015 Mean :2.666 Mean :2.677
## 3rd Qu.:2.750 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :3.000 Max. :5.000 Max. :4.000
## Phase 2: Data Acqusition and Exploration Checking Missing Values
# Counting missing values in each column
missing_values <- colSums(is.na(Obesitydata))
# Calculating the percentage of missing values
missing_percentage <- (missing_values / nrow(Obesitydata)) * 100
# Combining results into a data frame
missing_summary <- data.frame(
Column = names(Obesitydata),
Missing_Values = missing_values,
Percentage_Missing = missing_percentage
)
# Print missing summary
print(missing_summary)## Column
## Sex Sex
## Age Age
## Height Height
## Overweight_Obese_Family Overweight_Obese_Family
## Consumption_of_Fast_Food Consumption_of_Fast_Food
## Frequency_of_Consuming_Vegetables Frequency_of_Consuming_Vegetables
## Number_of_Main_Meals_Daily Number_of_Main_Meals_Daily
## Food_Intake_Between_Meals Food_Intake_Between_Meals
## Smoking Smoking
## Liquid_Intake_Daily Liquid_Intake_Daily
## Calculation_of_Calorie_Intake Calculation_of_Calorie_Intake
## Physical_Excercise Physical_Excercise
## Schedule_Dedicated_to_Technology Schedule_Dedicated_to_Technology
## Type_of_Transportation_Used Type_of_Transportation_Used
## Class Class
## Missing_Values Percentage_Missing
## Sex 0 0.0000000
## Age 0 0.0000000
## Height 0 0.0000000
## Overweight_Obese_Family 0 0.0000000
## Consumption_of_Fast_Food 10 0.6134969
## Frequency_of_Consuming_Vegetables 0 0.0000000
## Number_of_Main_Meals_Daily 10 0.6134969
## Food_Intake_Between_Meals 0 0.0000000
## Smoking 0 0.0000000
## Liquid_Intake_Daily 0 0.0000000
## Calculation_of_Calorie_Intake 0 0.0000000
## Physical_Excercise 5 0.3067485
## Schedule_Dedicated_to_Technology 0 0.0000000
## Type_of_Transportation_Used 0 0.0000000
## Class 0 0.0000000Phase 2: Data Acqusition and Exploration Visualizing Missing Data
# Visualize missing data using VIM package
library(VIM)## Loading required package: colorspace## Loading required package: grid## VIM is ready to use.## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleepaggr_plot <- aggr(Obesitydata, col=c('navyblue', 'red'),
numbers=TRUE, sortVars=TRUE,
labels=names(Obesitydata),
cex.axis=0.7, gap=3,
ylab=c("Histogram of Obesity Missing Data", "Pattern"))
##
## Variables sorted by number of missings:
## Variable Count
## Consumption_of_Fast_Food 0.006134969
## Number_of_Main_Meals_Daily 0.006134969
## Physical_Excercise 0.003067485
## Sex 0.000000000
## Age 0.000000000
## Height 0.000000000
## Overweight_Obese_Family 0.000000000
## Frequency_of_Consuming_Vegetables 0.000000000
## Food_Intake_Between_Meals 0.000000000
## Smoking 0.000000000
## Liquid_Intake_Daily 0.000000000
## Calculation_of_Calorie_Intake 0.000000000
## Schedule_Dedicated_to_Technology 0.000000000
## Type_of_Transportation_Used 0.000000000
## Class 0.000000000Phase 2: Data Acqusition and Exploration Summary Statistic for Numeric Fields
# Select numeric columns
numeric_data <- Obesitydata %>%
select_if(is.numeric)
# Summary statistics
summary(numeric_data)## Age Height Overweight_Obese_Family
## Min. : -5.10 Min. :-10.3 Min. :1.000
## 1st Qu.: 25.00 1st Qu.:161.0 1st Qu.:2.000
## Median : 32.00 Median :168.0 Median :2.000
## Mean : 33.18 Mean :168.2 Mean :1.833
## 3rd Qu.: 41.00 3rd Qu.:174.0 3rd Qu.:2.000
## Max. :200.20 Max. :800.7 Max. :2.000
##
## Consumption_of_Fast_Food Frequency_of_Consuming_Vegetables
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :2.000
## Mean :1.728 Mean :2.064
## 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :2.000 Max. :3.000
## NA's :10
## Number_of_Main_Meals_Daily Food_Intake_Between_Meals Smoking
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000
## Mean :1.872 Mean :2.394 Mean :1.695
## 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.000
## Max. :3.000 Max. :4.000 Max. :2.000
## NA's :10
## Liquid_Intake_Daily Calculation_of_Calorie_Intake Physical_Excercise
## Min. :-10.700 Min. :1.000 Min. :1.000
## 1st Qu.: 1.000 1st Qu.:2.000 1st Qu.:2.000
## Median : 2.000 Median :2.000 Median :3.000
## Mean : 2.189 Mean :1.822 Mean :3.262
## 3rd Qu.: 3.000 3rd Qu.:2.000 3rd Qu.:4.000
## Max. :100.300 Max. :2.000 Max. :5.000
## NA's :5
## Schedule_Dedicated_to_Technology Type_of_Transportation_Used Class
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :3.000
## Mean :2.015 Mean :2.666 Mean :2.677
## 3rd Qu.:2.750 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :3.000 Max. :5.000 Max. :4.000
## Phase 2: Data Acqusition and Exploration Exploratory Visualizations: Histogram of Age
ggplot(Obesitydata, aes(x = Age)) +
geom_histogram(binwidth = 5, fill = "blue", color = "white") +
labs(title = "Distribution of Age", x = "Age", y = "Frequency")
Exploratory Visualizations: Boxplot for Height
ggplot(Obesitydata, aes(y = Height)) +
geom_boxplot(fill = "orange") +
labs(title = "Boxplot of Height", y = "Height (cm)")
Exploratory Visualizations: Scatterplot of Age vs Height
ggplot(Obesitydata, aes(x = Age, y = Height)) +
geom_point(alpha = 0.6) +
labs(title = "Age vs. Height", x = "Age (In Years)", y = "Height (cm)")
Phase 3: Data Cleaning and Transformation Handling Missing Values
#As we have learnt throughout the course for handling missing data if it is less than 5% of data we can simply omit. Therefore we omitted missing values from our dataset since it is less than 5%.
Obesitydata_cleaned <- na.omit(Obesitydata)Phase 3: Data Cleaning and Transformation Outlier Detection
# Calculating IQR for a Height
Q1 <- quantile(Obesitydata_cleaned$Height, 0.25, na.rm = TRUE)
Q3 <- quantile(Obesitydata_cleaned$Height, 0.75, na.rm = TRUE)
IQR <- Q3 - Q1
# Determining Height outliers
outliers <- Obesitydata_cleaned$Height[Obesitydata_cleaned$Height < (Q1 - 1.5 * IQR) | Obesitydata_cleaned$Height > (Q3 + 1.5 * IQR)]
print(outliers)## [1] 800.7 500.5 -10.3# Calculating IQR for Age
AgeQ1 <- quantile(Obesitydata_cleaned$Age, 0.25, na.rm = TRUE)
AgeQ3 <- quantile(Obesitydata_cleaned$Age, 0.75, na.rm = TRUE)
AgeIQR <- AgeQ3 - AgeQ1
# Determining Age outliers
Ageoutliers <- Obesitydata_cleaned$Age[Obesitydata_cleaned$Age < (AgeQ1 - 1.5 * AgeIQR) | Obesitydata_cleaned$Age > (AgeQ3 + 1.5 * AgeIQR)]
# Printing the Age outliers
print(Ageoutliers)## [1] -5.1 200.2# Calculating IQR for Liquid Intake
LiquidQ1 <- quantile(Obesitydata_cleaned$Liquid_Intake_Daily, 0.25, na.rm = TRUE)
LiquidQ3 <- quantile(Obesitydata_cleaned$Liquid_Intake_Daily, 0.75, na.rm = TRUE)
LiquidIQR <- LiquidQ3 - LiquidQ1
# Determining Liquid Intake Outliers
Liquidoutliers <- Obesitydata_cleaned$Liquid_Intake_Daily[Obesitydata_cleaned$Liquid_Intake_Daily < (LiquidQ1 - 1.5 * LiquidIQR) | Obesitydata_cleaned$Liquid_Intake_Daily > (LiquidQ3 + 1.5 * LiquidIQR)]
# Printing the Liquid Intake outliers
print(Liquidoutliers)## [1] 50.5 -10.7 100.3Phase 3: Data Cleaning and Transformation Outlier Removal
#Obesitydata_no_outliers$Age <- as.numeric(Obesitydata_no_outliers$Age)
Obesitydata_no_outliers <- Obesitydata_cleaned %>%
filter(Obesitydata_cleaned$Height >= (Q1 - 1.5 * IQR) & Obesitydata_cleaned$Height<= (Q3 + 1.5 * IQR) &
Obesitydata_cleaned$Age >= (AgeQ1 - 1.5 * AgeIQR) & Obesitydata_cleaned$Age <= (AgeQ3 + 1.5 * AgeIQR) &
Obesitydata_cleaned$Liquid_Intake_Daily >= (LiquidQ1 - 1.5 * LiquidIQR) & Obesitydata_cleaned$Liquid_Intake_Daily <= (LiquidQ3 + 1.5 * LiquidIQR))Phase 3: Data Cleaning and Transformation Data Conversion of Liquid Intake and Age From Dbl to Int
view(Obesitydata_no_outliers)
Obesitydata_no_outliers$Age <- as.numeric(Obesitydata_no_outliers$Age)
Obesitydata_no_outliers$Liquid_Intake_Daily <- as.numeric(Obesitydata_no_outliers$Liquid_Intake_Daily)Advance Visualizations: Correlation Heatmap
library(corrplot)## corrplot 0.94 loaded# Select numeric columns
numeric_data <- Obesitydata_no_outliers %>%
select_if(is.numeric)
# Calculate correlations
cor_matrix <- cor(numeric_data, use = "complete.obs")
# Plot correlation heatmap
corrplot(cor_matrix, method = "color", addCoef.col = "black", number.cex = 0.7)
Advance Visualizations: Bar Plot For Categorical Variables
ggplot(Obesitydata_no_outliers, aes(x = Sex)) +
geom_bar(fill = "blue", alpha = 0.7) +
labs(title = "Distribution of Sex", x = "Sex", y = "Count")
Advance Visualizations: Pair Plot (Multivariate Analysis)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2# Pair plot for selected numeric variables
ggpairs(Obesitydata_no_outliers, columns = c("Age", "Height", "Liquid_Intake_Daily"))
Phase 4: Data Shaping and Structuring Categorizing Age
#rm(Structured_obesity_data)
#View(Obesitydata_no_outliers)
Structured_obesity_data <- Obesitydata_no_outliers
Structured_obesity_data$Age_Group <- cut(Structured_obesity_data$Age, breaks = c(0, 17, 35, 60, 100),
labels = c("Child", "Young Adult", "Adult", "Senior"))
view(Structured_obesity_data)
summary(Structured_obesity_data)## Sex Age Height Overweight_Obese_Family
## Length:1602 Min. :18.00 Min. :150.0 Min. :1.00
## Class :character 1st Qu.:25.00 1st Qu.:161.0 1st Qu.:2.00
## Mode :character Median :32.00 Median :168.0 Median :2.00
## Mean :33.08 Mean :167.7 Mean :1.83
## 3rd Qu.:41.00 3rd Qu.:174.0 3rd Qu.:2.00
## Max. :54.00 Max. :193.0 Max. :2.00
## Consumption_of_Fast_Food Frequency_of_Consuming_Vegetables
## Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :1.727 Mean :2.064
## 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :2.000 Max. :3.000
## Number_of_Main_Meals_Daily Food_Intake_Between_Meals Smoking
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000
## Mean :1.875 Mean :2.397 Mean :1.693
## 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.000
## Max. :3.000 Max. :4.000 Max. :2.000
## Liquid_Intake_Daily Calculation_of_Calorie_Intake Physical_Excercise
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :3.000
## Mean :2.108 Mean :1.822 Mean :3.258
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:4.000
## Max. :3.000 Max. :2.000 Max. :5.000
## Schedule_Dedicated_to_Technology Type_of_Transportation_Used Class
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :3.000
## Mean :2.016 Mean :2.665 Mean :2.677
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :3.000 Max. :5.000 Max. :4.000
## Age_Group
## Child : 0
## Young Adult:969
## Adult :633
## Senior : 0
##
## Categorizing Overweight Obese Family
Structured_obesity_data$Overweight_Family <- Structured_obesity_data$Overweight_Obese_Family
Structured_obesity_data <- mutate(Structured_obesity_data, Overweight_Family = recode(Structured_obesity_data$Overweight_Obese_Family, '1' = "Yes",'2' = "No"))Categorizing Consumption of Fast Food
Structured_obesity_data <- mutate(Structured_obesity_data,FastFood_Consumption = recode(Structured_obesity_data$Consumption_of_Fast_Food, '1' = "Yes",'2' = "No"))Categorizing Frequency of Vegetables
Structured_obesity_data <- mutate(Structured_obesity_data,Vegetable_Intake = recode(Structured_obesity_data$Frequency_of_Consuming_Vegetables, '1' = "Rarely", '2' = 'Sometimes', '3' = "Always"))Categorizing of Number of Food Intake Between Meals
Structured_obesity_data <- mutate(Structured_obesity_data,Food_Intake = recode(Structured_obesity_data$Food_Intake_Between_Meals, '1' = "Rarely", '2' = "Sometimes", '3' = "Usually", '4' = "Always"))Categorizing Smokers
Structured_obesity_data <- mutate(Structured_obesity_data,Smoke = recode(Structured_obesity_data$Smoking, '1' = "Yes", '2' = "No"))Categorizing Liquid Intake
Structured_obesity_data <- mutate(Structured_obesity_data,Liquid_Intake = recode(Structured_obesity_data$Liquid_Intake_Daily, '1' = "Less Than 1 Liter", '2' = "Between 1 and 2 Liters", '3' = "Greater Than 2 Liters"))Categorizing Calorie Intake
Structured_obesity_data <- mutate(Structured_obesity_data,Calculate_Calorie_Intake = recode(Structured_obesity_data$Calculation_of_Calorie_Intake, '1' = "Yes", '2' = 'No'))Categorizing of Number of Food Intake Between Meals
Structured_obesity_data <- mutate(Structured_obesity_data,Exercise = recode(Structured_obesity_data$Physical_Excercise, '1' = "No Physical Activity", '2' = '1 to 2 days', '3' = "3 to 4 days", '4' = "5 to 6 days", '5' = "6+ days"))Categorizing Schedule Dedicated To Technology
Structured_obesity_data <- mutate(Structured_obesity_data,Screen_Time = recode(Structured_obesity_data$Schedule_Dedicated_to_Technology, '1' = "0 to 2 Hours", '2' = "3 to 5 Hours", '3' = "5+ Hours"))Categorizing Types Of Transportation
Structured_obesity_data <- mutate(Structured_obesity_data,Transportation_Mode = recode(Structured_obesity_data$Type_of_Transportation_Used, '1' = "Automobile", '2' = "MotorBike", '3' = "Bike", '4' = "Public Transportation", '5' = "Walking"))Categorizing Class
Structured_obesity_data <- mutate(Structured_obesity_data,Obesity_Class = recode(Structured_obesity_data$Class, '1' = "Underweight", '2' = "Normal", '3' = "Overweight", '4' = "Obesity"))Viewing Dataset Converted With Categorical Variables
#View(Structured_obesity_data)
visualizationData <- Structured_obesity_data[, c( "Age", "Sex", "Height", "Age_Group","Overweight_Family", "FastFood_Consumption", "Vegetable_Intake", "Food_Intake", "Smoke", "Liquid_Intake", "Calculate_Calorie_Intake", "Exercise", "Screen_Time", "Transportation_Mode", "Obesity_Class" )]
#write.csv(visualizationData,"Group3visualizationdata.csv", row.names = FALSE)Phase 5: Data Visualization After Cleaning and Transforming Data Histogram of Age
# After cleaning by removing outliers and missing data we can see that our age distribution histogram looks different.
ggplot(visualizationData, aes(x = Age)) +
geom_histogram(binwidth = 5, fill = "blue", color = "white") +
labs(title = "Distribution of Age", x = "Age", y = "Frequency")
Boxplot for Height
#Our Box plot looks normal and we can better interpret the distribution of height in our dataset after cleaning and transformation.
ggplot(visualizationData, aes(y = Height)) +
geom_boxplot(fill = "orange") +
labs(title = "Boxplot of Height", y = "Height (cm)")
Scatterplot of Age vs Height
ggplot(visualizationData, aes(x = Age, y = Height)) +
geom_point(alpha = 0.6) +
labs(title = "Age vs. Height", x = "Age (Years)", y = "Height (cm)")
Histogram of Obesity Categories
# After cleaning and transformation of the data we are able to see the distribution of obesity categories in the dataset. We can conclude that a most of the individuals in the data set is either over weight or obese.
ggplot(visualizationData, aes(x = Obesity_Class)) +
geom_bar(fill = "blue", alpha = 0.7) +
labs(title = "Distribution of Obesity Category", x = "Obesity Category", y = "Count")
Correlation of Height and Age Using a Heatmap
library(corrplot)
# Select numeric columns
viz_numeric_data <- visualizationData %>%
select_if(is.numeric)
# Calculate correlations
cor_matrix <- cor(viz_numeric_data, use = "complete.obs")
# Plot correlation heatmap using corrplot
corrplot(cor_matrix, method = "color",
col = colorRampPalette(c("red", "white", "green"))(200),
type = "lower",
addCoef.col = "black", # Add correlation coefficients to the heatmap
tl.col = "black", # Text label color
title = "Correlation Heatmap of Height and Age")