Asthma Presence

Author

Crows: Artemas Souder, Danielle Clark, Kensley House

Introduction

Knowing the factors that influence Asthma can be integrally in determining what factors might influence asthma severity

Research Questions

What factors are associated with the presence of Asthma?
How does BMI interact with Asthma Risk and Asthma Severity?

Data

Data was collected from Kaggle: Asthma Dataset

setwd("C:\\Users\\star-\\Data Science\\R stuff")
library(readxl)
library(ggplot2)
library(tidyverse)
library(scales)
library(ggrepel)
library(patchwork)
library(gridExtra)
library(gganimate)
library(plotly)


asthma <- read.csv("C:\\Users\\star-\\Data Science\\R stuff\\synthetic_asthma_dataset.csv")
glimpse(asthma)

Rows: 10,000
Columns: 17
$ Patient_ID              <chr> "ASTH100000", "ASTH100001", "ASTH100002", "AST…
$ Age                     <int> 52, 15, 72, 61, 21, 83, 87, 75, 75, 88, 24, 3,…
$ Gender                  <chr> "Female", "Male", "Female", "Male", "Male", "O…
$ BMI                     <dbl> 27.6, 24.6, 17.6, 16.8, 30.2, 27.8, 32.3, 29.7…
$ Smoking_Status          <chr> "Former", "Former", "Never", "Never", "Never",…
$ Family_History          <int> 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0…
$ Allergies               <chr> "None", "Dust", "None", "Multiple", "None", "P…
$ Air_Pollution_Level     <chr> "Moderate", "Low", "Moderate", "High", "Modera…
$ Physical_Activity_Level <chr> "Sedentary", "Moderate", "Moderate", "Sedentar…
$ Occupation_Type         <chr> "Outdoor", "Indoor", "Indoor", "Outdoor", "Ind…
$ Comorbidities           <chr> "Diabetes", "Both", "None", "Both", "None", "N…
$ Medication_Adherence    <dbl> 0.38, 0.60, 0.38, 0.60, 0.82, 0.18, 0.18, 0.53…
$ Number_of_ER_Visits     <int> 0, 2, 0, 1, 3, 2, 0, 0, 2, 3, 1, 0, 0, 0, 0, 0…
$ Peak_Expiratory_Flow    <dbl> 421.0, 297.6, 303.3, 438.0, 535.0, 232.9, 370.…
$ FeNO_Level              <dbl> 46.0, 22.9, 15.3, 40.1, 27.7, 45.1, 14.1, 17.6…
$ Has_Asthma              <int> 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0…
$ Asthma_Control_Level    <chr> "N/A", "N/A", "N/A", "Poorly Controlled", "N/A…

asthma <- asthma|>
  mutate( Age_groups = case_when(
          Age < 26 ~ " 25 & Under",
          Age >= 26 & Age < 51 ~ "26 - 50",
          Age >= 51 & Age < 76 ~ "51 - 75",
          Age >= 76 ~ "76+"), 
          BMI_groups = case_when(
            BMI < 21.6 ~ "First Quantile",
            BMI >= 21.6 & BMI < 25 ~ "Second Quantile",
            BMI >= 25 & BMI < 28.4 ~ "Third Quantile",
            BMI >= 28.4 ~ "Fourth Quantile")
          )
Asthma <- asthma

Age and BMI are transformed into categories.

Results

asthma|>
  ggplot(aes(x=BMI_groups, fill = Age_groups))+
  geom_bar()+facet_wrap(~Has_Asthma)+
  labs(x= "Asthma BMI Levels",
       title = "Barcharts of BMI and Age Groups",
       subtitle = "For differnt occupation locations")

Age_distribution_by_group <- ggplot(asthma, aes(x = Age_groups, color = Gender))+
   geom_bar()+
   labs(title = "Bar chart of Age Distribution by Group",
        ) +
    theme_minimal()
 
 Age_distribution <- ggplot(asthma, aes(x = Age,color = Gender))+
   geom_histogram()+
   labs(title = "Bar chart of Age Distribution",
   ) +
   theme_minimal()
 
 ## combined chart to show how things differ
 Age_distribution + Age_distribution_by_group

ggplot(asthma, aes(x = Has_Asthma, fill = Gender))+
  geom_bar()+
  scale_x_discrete(name = "Asthma Presence",breaks = c(0, 1),
                     labels = c('No Asthma', "Has Asthma"))+
  annotate(geom = "label", x=2.2, y=4100,
           label = '24.33% Have Asthma', hjust = "center",
           vjust = "bottom",
           color = "red")+
  annotate(
    geom = "segment", x= 2.2, y=4100,
    xend = 2, yend = 2500,
    color = "blue",
    arrow = arrow(type = "closed"))+
  
  annotate(geom = "label", x=1.6, y=5500,
           label = '75.67% Do Not Have Asthma', hjust = "left",
           color = "red")+
  annotate(
    geom = "segment", x= 1.6, y=5500,
    xend = 1.45, yend = 4100,
    color = "blue",
    arrow = arrow(type = "closed"))+
  labs(subtitle = 'For both groups there are an approximately equal numbers of males and females at 48%', 
       title = "Barchart For Presence of Asthma by Gender")

#ggplot(data = asthma,aes(x=Age, y = BMI, color = Has_Asthma))+
#  geom_point()+geom_smooth(data = asthma|>
 #                  filter(Has_Asthma ==0), color = "red3")+
  #geom_smooth(data = asthma|>
   #             filter(Has_Asthma ==1), color = "blue3")+
  #scale_color_discrete(labels = c('Does Not Have Asthma', "Has Asthma"))+
 # labs(title = "Scatterplot for BMI by Age when Looking at Presence of Asthma",
  #     subtitle = "Students with Asthma", color = "Has Asthma")

#histogram for occ type in BMI
ggplot(data=asthma,aes(x=BMI, fill = Has_Asthma))+
  facet_wrap(~Occupation_Type)+geom_histogram()+
  scale_fill_discrete(labels = c('Does Not Have Asthma', "Has Asthma"))+
  labs(title = "Faceted Histogram for BMI by Asthma Presence and Occupation Location")

Interactive Elements

plot_ly(
  data= asthma,
  x=~BMI,
  type = "box"
)%>%
  layout(
    xaxis = list(title = "BMI")
  )

plot_ly(
  data= asthma,
  x=~BMI,
  type = "histogram"
)%>%
  layout(
    xaxis = list(title = "BMI"),
    yaxis = list(title = "Number of People")
  )

Analysis

#density ridges plot
#library(ggridges)
#ggplot(asthma, aes(x=BMI, y=Has_Asthma,
 #                  fill = Occupation_Type,
  #                 color = Has_Asthma))+
  #geom_density_ridges(alpha = .4,show.legend = FALSE)

#boxplots for  Asthma Control Levels and BMI
asthma|>filter(Asthma_Control_Level!="N/A")|>
  ggplot(aes(x=Asthma_Control_Level,y=BMI, fill = Gender))+
  geom_boxplot()+facet_wrap(~Occupation_Type)+
  labs(x= "Asthma Control Level",
       title = "Boxplots for Asthma Control Levels and BMI",
       subtitle = "For differnt occupation locations")

ggplot(Asthma, aes(x = BMI, y = Medication_Adherence)) +
   geom_point(
     data = Asthma |>
       filter(Has_Asthma == 1)) +
   geom_smooth()+
   labs(title = "Scatterd graph of Medication Adherance by BMI",
        subtitle = "Individuals with Asthma")

 ## medication adherance per activity level
 ggplot(Asthma, aes(x = Medication_Adherence, color = Gender)) +
   geom_histogram() +
   facet_wrap( ~ Physical_Activity_Level) +
   labs(title = "Stacked bar chart of Medication Adherance
                  by Activity Level and Gender",
        subtitle = "Individuals with Asthma"
        )

 ##look into prop chart or side/side bar vs stacked
 
 
 ## medication adherance of men who work outdoor 
 ggplot(Asthma, aes(x = Medication_Adherence)) +
  geom_boxplot(data = Asthma |>
              filter(Gender == "Male" & Occupation_Type == "Outdoor")) +
  facet_wrap( ~ Physical_Activity_Level)+
  labs(title = "Box plot of Medication Adherance
                by Activity Level"
       )

  # all graphs are the same 
  
 
 ##Is there a difference in medication adherence between genders
 ggplot(Asthma, aes(x = Medication_Adherence)) +
   geom_bar()+
   facet_grid( ~ Gender)

 #maybe?
 ## change to scatter plot
 
 ggplot(Asthma, aes(x = BMI)) +
   geom_bar()+
   facet_grid( ~ Gender)

Contact Information

xsouder@students.kennesaw.edu

dclar175@students.kennesaw.edu

khouse15@students.kennesaw.edu