Assignment 1 – Data Analysis using R Programming
2026-03-28
Group 7: Esther Agyemang, Omar Alamodi, Aliha Bashir, Hetal Bulsara, Andy Huang, James Huynh, Nasteha Mohamed, and Tina Shen
George Brown Polytechnic
COMP 4033 – Health Informatics Data Analytics
1. Dataset Structure
This section provides an overview of the dataset structure, including the number of variables and their data types.
str(oral_data)## 'data.frame': 84922 obs. of 25 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Country : chr "Italy" "Japan" "UK" "Sri Lanka" ...
## $ Age : int 36 64 37 55 68 70 41 53 62 50 ...
## $ Gender : chr "Female" "Male" "Female" "Male" ...
## $ Tobacco.Use : chr "Yes" "Yes" "No" "Yes" ...
## $ Alcohol.Consumption : chr "Yes" "Yes" "Yes" "Yes" ...
## $ HPV.Infection : chr "Yes" "Yes" "No" "No" ...
## $ Betel.Quid.Use : chr "No" "No" "No" "Yes" ...
## $ Chronic.Sun.Exposure : chr "No" "Yes" "Yes" "No" ...
## $ Poor.Oral.Hygiene : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Diet..Fruits...Vegetables.Intake. : chr "Low" "High" "Moderate" "Moderate" ...
## $ Family.History.of.Cancer : chr "No" "No" "No" "No" ...
## $ Compromised.Immune.System : chr "No" "No" "No" "No" ...
## $ Oral.Lesions : chr "No" "No" "No" "Yes" ...
## $ Unexplained.Bleeding : chr "No" "Yes" "No" "No" ...
## $ Difficulty.Swallowing : chr "No" "No" "No" "No" ...
## $ White.or.Red.Patches.in.Mouth : chr "No" "No" "Yes" "No" ...
## $ Tumor.Size..cm. : num 0 1.78 3.52 0 2.83 ...
## $ Cancer.Stage : int 0 1 2 0 3 2 1 0 3 3 ...
## $ Treatment.Type : chr "No Treatment" "No Treatment" "Surgery" "No Treatment" ...
## $ Survival.Rate..5.Year.... : num 100 83.3 63.2 100 44.3 ...
## $ Cost.of.Treatment..USD. : num 0 77773 101165 0 45355 ...
## $ Economic.Burden..Lost.Workdays.per.Year.: int 0 177 130 0 52 91 105 0 136 82 ...
## $ Early.Diagnosis : chr "No" "No" "Yes" "Yes" ...
## $ Oral.Cancer..Diagnosis. : chr "No" "Yes" "Yes" "No" ...2. Variables in Dataset
The dataset contains the following variables:
names(oral_data)## [1] "ID"
## [2] "Country"
## [3] "Age"
## [4] "Gender"
## [5] "Tobacco.Use"
## [6] "Alcohol.Consumption"
## [7] "HPV.Infection"
## [8] "Betel.Quid.Use"
## [9] "Chronic.Sun.Exposure"
## [10] "Poor.Oral.Hygiene"
## [11] "Diet..Fruits...Vegetables.Intake."
## [12] "Family.History.of.Cancer"
## [13] "Compromised.Immune.System"
## [14] "Oral.Lesions"
## [15] "Unexplained.Bleeding"
## [16] "Difficulty.Swallowing"
## [17] "White.or.Red.Patches.in.Mouth"
## [18] "Tumor.Size..cm."
## [19] "Cancer.Stage"
## [20] "Treatment.Type"
## [21] "Survival.Rate..5.Year...."
## [22] "Cost.of.Treatment..USD."
## [23] "Economic.Burden..Lost.Workdays.per.Year."
## [24] "Early.Diagnosis"
## [25] "Oral.Cancer..Diagnosis."3. Preview of Data
Below are the first 15 rows of the dataset:
head(oral_data, 15)## ID Country Age Gender Tobacco.Use Alcohol.Consumption HPV.Infection
## 1 1 Italy 36 Female Yes Yes Yes
## 2 2 Japan 64 Male Yes Yes Yes
## 3 3 UK 37 Female No Yes No
## 4 4 Sri Lanka 55 Male Yes Yes No
## 5 5 South Africa 68 Male No No No
## 6 6 Taiwan 70 Male Yes No Yes
## 7 7 USA 41 Female Yes Yes No
## 8 8 Italy 53 Male Yes Yes Yes
## 9 9 Sri Lanka 62 Female Yes No Yes
## 10 10 Germany 50 Male Yes No No
## 11 11 Sri Lanka 65 Male No Yes No
## 12 12 Sri Lanka 34 Male Yes No No
## 13 13 France 56 Male Yes Yes Yes
## 14 14 Australia 59 Female Yes Yes No
## 15 15 UK 43 Male Yes No No
## Betel.Quid.Use Chronic.Sun.Exposure Poor.Oral.Hygiene
## 1 No No Yes
## 2 No Yes Yes
## 3 No Yes Yes
## 4 Yes No Yes
## 5 No No Yes
## 6 Yes No Yes
## 7 No No No
## 8 No No Yes
## 9 Yes No Yes
## 10 No No Yes
## 11 Yes No No
## 12 No No Yes
## 13 Yes No Yes
## 14 No Yes Yes
## 15 No No No
## Diet..Fruits...Vegetables.Intake. Family.History.of.Cancer
## 1 Low No
## 2 High No
## 3 Moderate No
## 4 Moderate No
## 5 High No
## 6 Moderate Yes
## 7 Moderate No
## 8 Moderate No
## 9 Low No
## 10 High No
## 11 Moderate Yes
## 12 Low No
## 13 Moderate No
## 14 Low No
## 15 Low No
## Compromised.Immune.System Oral.Lesions Unexplained.Bleeding
## 1 No No No
## 2 No No Yes
## 3 No No No
## 4 No Yes No
## 5 No No No
## 6 No Yes Yes
## 7 No No No
## 8 No Yes No
## 9 No No No
## 10 No No No
## 11 No Yes No
## 12 No Yes Yes
## 13 No No No
## 14 No Yes No
## 15 Yes No No
## Difficulty.Swallowing White.or.Red.Patches.in.Mouth Tumor.Size..cm.
## 1 No No 0.000000
## 2 No No 1.782186
## 3 No Yes 3.523895
## 4 No No 0.000000
## 5 No No 2.834789
## 6 No No 1.692675
## 7 Yes Yes 5.794843
## 8 No No 0.000000
## 9 Yes No 5.999476
## 10 Yes No 5.282246
## 11 No Yes 4.724627
## 12 No No 0.000000
## 13 Yes Yes 0.000000
## 14 No Yes 0.000000
## 15 No Yes 0.000000
## Cancer.Stage Treatment.Type Survival.Rate..5.Year....
## 1 0 No Treatment 100.00000
## 2 1 No Treatment 83.34010
## 3 2 Surgery 63.22287
## 4 0 No Treatment 100.00000
## 5 3 No Treatment 44.29320
## 6 2 Surgery 67.40727
## 7 1 No Treatment 80.79813
## 8 0 No Treatment 100.00000
## 9 3 Surgery 41.45993
## 10 3 Radiation 33.75381
## 11 3 Targeted Therapy 42.81490
## 12 0 No Treatment 100.00000
## 13 0 No Treatment 100.00000
## 14 0 No Treatment 100.00000
## 15 0 No Treatment 100.00000
## Cost.of.Treatment..USD. Economic.Burden..Lost.Workdays.per.Year.
## 1 0.00 0
## 2 77772.50 177
## 3 101164.50 130
## 4 0.00 0
## 5 45354.75 52
## 6 96504.00 91
## 7 86131.25 105
## 8 0.00 0
## 9 42630.00 136
## 10 75150.25 82
## 11 63721.00 84
## 12 0.00 0
## 13 0.00 0
## 14 0.00 0
## 15 0.00 0
## Early.Diagnosis Oral.Cancer..Diagnosis.
## 1 No No
## 2 No Yes
## 3 Yes Yes
## 4 Yes No
## 5 No Yes
## 6 Yes Yes
## 7 Yes Yes
## 8 Yes No
## 9 No Yes
## 10 No Yes
## 11 Yes Yes
## 12 Yes No
## 13 Yes No
## 14 Yes No
## 15 Yes No4. User-Defined Function
Calculate a risk score based on the person’s data with tobacco, alcohol, HPV, family history, and compromised immune system.
risk_score <- function(tobacco, alcohol, hpv, family_history, immune_system) {
(tobacco == "Yes") * 2 +
(alcohol == "Yes") +
(hpv == "Yes") * 2 +
(family_history == "Yes") +
(immune_system == "Yes")
}
risk_score(oral_data$Tobacco.Use[1],
oral_data$Alcohol.Consumption[1],
oral_data$HPV.Infection[1],
oral_data$Family.History.of.Cancer[1],
oral_data$Compromised.Immune.System[1])## [1] 55. Filter Rows Based on Criteria
Filter rows based on the following criteria:
- Stage 1 cancer
- Tumor sizes less than 5 cm
- From Sri Lanka
- Over the age of 60
- Female
- No tobacco use
- No betel quid use
oral_data %>% filter(
oral_data$Cancer.Stage == "1",
oral_data$Tumor.Size..cm. < "5",
oral_data$Country == "Sri Lanka",
oral_data$Age > 60,
oral_data$Gender == "Female",
oral_data$Tobacco.Use == "No",
oral_data$Betel.Quid.Use == "No")## ID Country Age Gender Tobacco.Use Alcohol.Consumption HPV.Infection
## 1 5547 Sri Lanka 66 Female No No No
## 2 8641 Sri Lanka 63 Female No Yes No
## 3 11363 Sri Lanka 68 Female No Yes No
## 4 21549 Sri Lanka 70 Female No No No
## 5 41070 Sri Lanka 61 Female No Yes No
## 6 42048 Sri Lanka 69 Female No Yes No
## 7 51622 Sri Lanka 73 Female No No No
## 8 52435 Sri Lanka 70 Female No Yes Yes
## 9 60156 Sri Lanka 65 Female No No No
## 10 68135 Sri Lanka 70 Female No No No
## 11 79435 Sri Lanka 80 Female No Yes Yes
## Betel.Quid.Use Chronic.Sun.Exposure Poor.Oral.Hygiene
## 1 No No No
## 2 No No No
## 3 No No Yes
## 4 No No Yes
## 5 No No No
## 6 No No Yes
## 7 No No No
## 8 No Yes No
## 9 No No Yes
## 10 No No No
## 11 No No Yes
## Diet..Fruits...Vegetables.Intake. Family.History.of.Cancer
## 1 Moderate No
## 2 Moderate No
## 3 High Yes
## 4 Moderate No
## 5 Moderate No
## 6 Moderate No
## 7 Moderate No
## 8 Low No
## 9 Moderate No
## 10 Low No
## 11 Moderate No
## Compromised.Immune.System Oral.Lesions Unexplained.Bleeding
## 1 No No No
## 2 No No Yes
## 3 No No Yes
## 4 No Yes Yes
## 5 No Yes No
## 6 No Yes No
## 7 No Yes No
## 8 No Yes Yes
## 9 No Yes No
## 10 No No No
## 11 No Yes No
## Difficulty.Swallowing White.or.Red.Patches.in.Mouth Tumor.Size..cm.
## 1 No Yes 3.845308
## 2 Yes No 4.825246
## 3 Yes No 2.383717
## 4 No No 4.198427
## 5 No Yes 3.756328
## 6 No No 4.519694
## 7 Yes No 4.578338
## 8 Yes Yes 4.084550
## 9 No Yes 3.249317
## 10 Yes No 3.942891
## 11 Yes No 2.621289
## Cancer.Stage Treatment.Type Survival.Rate..5.Year....
## 1 1 Radiation 89.98152
## 2 1 Radiation 89.68025
## 3 1 Chemotherapy 83.32314
## 4 1 Chemotherapy 83.88464
## 5 1 Surgery 81.95862
## 6 1 No Treatment 83.57633
## 7 1 Radiation 84.75359
## 8 1 Radiation 85.83111
## 9 1 Chemotherapy 83.84482
## 10 1 Chemotherapy 88.86750
## 11 1 Radiation 80.87980
## Cost.of.Treatment..USD. Economic.Burden..Lost.Workdays.per.Year.
## 1 98652.50 155
## 2 53808.75 140
## 3 25171.25 135
## 4 80856.25 48
## 5 97940.00 65
## 6 94122.50 109
## 7 81845.00 79
## 8 41413.75 41
## 9 76490.00 41
## 10 60420.00 173
## 11 89812.50 36
## Early.Diagnosis Oral.Cancer..Diagnosis.
## 1 Yes Yes
## 2 No Yes
## 3 No Yes
## 4 Yes Yes
## 5 Yes Yes
## 6 Yes Yes
## 7 Yes Yes
## 8 Yes Yes
## 9 Yes Yes
## 10 No Yes
## 11 Yes Yes6. Identifying Dependent & Independent Variables
# Independent var: Tumor Size -- Dependent var: Cost of Treatment
head(data.frame(oral_data$Tumor.Size..cm., oral_data$Cost.of.Treatment..USD.), 15)## oral_data.Tumor.Size..cm. oral_data.Cost.of.Treatment..USD.
## 1 0.000000 0.00
## 2 1.782186 77772.50
## 3 3.523895 101164.50
## 4 0.000000 0.00
## 5 2.834789 45354.75
## 6 1.692675 96504.00
## 7 5.794843 86131.25
## 8 0.000000 0.00
## 9 5.999476 42630.00
## 10 5.282246 75150.25
## 11 4.724627 63721.00
## 12 0.000000 0.00
## 13 0.000000 0.00
## 14 0.000000 0.00
## 15 0.000000 0.00# Independent var: Diet (fruits, veg intake) -- Dependent var: Cancer Stage
head(data.frame(oral_data$Diet..Fruits...Vegetables.Intake., oral_data$Cancer.Stage), 15)## oral_data.Diet..Fruits...Vegetables.Intake. oral_data.Cancer.Stage
## 1 Low 0
## 2 High 1
## 3 Moderate 2
## 4 Moderate 0
## 5 High 3
## 6 Moderate 2
## 7 Moderate 1
## 8 Moderate 0
## 9 Low 3
## 10 High 3
## 11 Moderate 3
## 12 Low 0
## 13 Moderate 0
## 14 Low 0
## 15 Low 07. Data Cleaning and Adjustments
Remove missing values in your dataset. Identify and remove duplicated data in your dataset.
Reorder multiple rows in descending order. Rename some of the column names in your dataset.
clean_data = oral_data %>% filter(!is.na(Cost.of.Treatment..USD.))
clean_data = clean_data %>% distinct()
clean_data = clean_data %>% arrange(desc(Cost.of.Treatment..USD.))
clean_data = clean_data %>% rename(
Drinker = Alcohol.Consumption,
Treatment.Cost.USD = Cost.of.Treatment..USD.
)8. Adding New Variables to the Data Frame
Add a “Cost per cm” column based on Treatment Cost divided by Tumor Size.
clean_data = clean_data %>% mutate(cost.per.cm = Treatment.Cost.USD / Tumor.Size..cm.)9. Creating a Training Set
Create a training set using random number generator engine with seed
1234 with 5% the size of the main data set.
set.seed(1234)
training_data <- clean_data %>% sample_frac(0.05, replace = FALSE)10. Summary of the Main Data Set
summary(clean_data)## ID Country Age Gender
## Min. : 1 Length:84922 Min. : 15.00 Length:84922
## 1st Qu.:21231 Class :character 1st Qu.: 48.00 Class :character
## Median :42462 Mode :character Median : 55.00 Mode :character
## Mean :42462 Mean : 54.51
## 3rd Qu.:63692 3rd Qu.: 61.00
## Max. :84922 Max. :101.00
##
## Tobacco.Use Drinker HPV.Infection Betel.Quid.Use
## Length:84922 Length:84922 Length:84922 Length:84922
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Chronic.Sun.Exposure Poor.Oral.Hygiene Diet..Fruits...Vegetables.Intake.
## Length:84922 Length:84922 Length:84922
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Family.History.of.Cancer Compromised.Immune.System Oral.Lesions
## Length:84922 Length:84922 Length:84922
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Unexplained.Bleeding Difficulty.Swallowing White.or.Red.Patches.in.Mouth
## Length:84922 Length:84922 Length:84922
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Tumor.Size..cm. Cancer.Stage Treatment.Type Survival.Rate..5.Year....
## Min. :0.000 Min. :0.000 Length:84922 Min. : 10.00
## 1st Qu.:0.000 1st Qu.:0.000 Class :character 1st Qu.: 65.23
## Median :0.000 Median :0.000 Mode :character Median :100.00
## Mean :1.747 Mean :1.119 Mean : 79.50
## 3rd Qu.:3.480 3rd Qu.:2.000 3rd Qu.:100.00
## Max. :6.000 Max. :4.000 Max. :100.00
##
## Treatment.Cost.USD Economic.Burden..Lost.Workdays.per.Year. Early.Diagnosis
## Min. : 0 Min. : 0.00 Length:84922
## 1st Qu.: 0 1st Qu.: 0.00 Class :character
## Median : 0 Median : 0.00 Mode :character
## Mean : 39110 Mean : 52.03
## 3rd Qu.: 76468 3rd Qu.:104.00
## Max. :159988 Max. :179.00
##
## Oral.Cancer..Diagnosis. cost.per.cm
## Length:84922 Min. : 4176
## Class :character 1st Qu.: 14770
## Mode :character Median : 22254
## Mean : 28090
## 3rd Qu.: 34968
## Max. :156336
## NA's :4257311. Mean and Range of Treatment Costs
mean(clean_data$Treatment.Cost.USD, na.rm = TRUE)## [1] 39109.88range(clean_data$Treatment.Cost.USD, na.rm = TRUE)## [1] 0 15998812. Scatter Plot
Scatter plot of Survival Rate (5 years) and Treatment Cost.
ggplot(data = training_data, aes(x = Survival.Rate..5.Year...., y = Treatment.Cost.USD)) +
geom_point(color = "steelblue", size = 1.2)
13. Bar Plot
Bar plot of displaying number of individuals at each cancer stage.
ggplot(data = clean_data, aes(x = Cancer.Stage, fill = Gender)) + geom_bar() +
ylab("Number of Individuals")
14. Pearson Correlation
A correlation near 1 implies high correlation; the dataset has about -0.8 correlation, so there is a strong negative linear relationship between Treatment Cost and Survival Rate.
cor(clean_data$Treatment.Cost.USD, clean_data$Survival.Rate..5.Year...., method="pearson")## [1] -0.8066187