Problem 1: Patient Registry

Task: Create a character vector with their names and print the second patient.

#  character vector
patients <- c("Rahim", "Karim", "Sultana")

# second patient
patients[2]
## [1] "Karim"

Problem 2: Blood Pressure Data

Task: Store these in a numeric vector and extract the 3rd and 4th patients’ SBP.

# Store SBP values
sbp <- c(145, 160, 138, 152)

# Extract 3rd and 4th values
sbp[c(3, 4)]
## [1] 138 152

Problem 3: Gene Expression Counts

Task: Create a vector and calculate the mean expression.

# Create vector
tp53_counts <- c(500, 800, 450, 600)

# Calculate mean
mean(tp53_counts)
## [1] 587.5

Problem 4: Small Cohort Data Frame

Task: Create a data frame from the dataset (Name, Age, BMI).

# Create vectors
name <- c("Ali", "Sonia", "Kabir")
age <- c(35, 29, 41)
bmi <- c(24.5, 28.2, 31.0)

# Create data frame
cohort_df <- data.frame(Name = name, Age = age, BMI = bmi)
cohort_df
##    Name Age  BMI
## 1   Ali  35 24.5
## 2 Sonia  29 28.2
## 3 Kabir  41 31.0

Problem 5: Add BMI Classification

Task: Add a new column “BMI_Class” based on WHO guidelines.

# Add BMI_Class column using nested ifelse
cohort_df$BMI_Class <- ifelse(cohort_df$BMI < 25, "Normal",
                       ifelse(cohort_df$BMI >= 25 & cohort_df$BMI <= 29.9, "Overweight", "Obese"))

cohort_df
##    Name Age  BMI  BMI_Class
## 1   Ali  35 24.5     Normal
## 2 Sonia  29 28.2 Overweight
## 3 Kabir  41 31.0      Obese

Problem 6: Subset Patients by Age

Task: Use conditional indexing to filter patients older than 30 years.

cohort_df[cohort_df$Age > 30, ]
##    Name Age  BMI BMI_Class
## 1   Ali  35 24.5    Normal
## 3 Kabir  41 31.0     Obese

Problem 7: Subset Patients by BMI

Task: Filter the data frame to select only those classified as “Obese”.

cohort_df[cohort_df$BMI_Class == "Obese", ]
##    Name Age BMI BMI_Class
## 3 Kabir  41  31     Obese

Problem 8: Create a Gene Expression Matrix

Task: Create a matrix with proper row and column names for BRCA1, EGFR, and MYC.

# Create matrix data
gene_data <- c(120, 150, 130,  # BRCA1
               300, 350, 400,  # EGFR
               800, 900, 950)  # MYC

# Build matrix
gene_matrix <- matrix(gene_data, nrow = 3, ncol = 3, byrow = TRUE)

# Assign row and column names
rownames(gene_matrix) <- c("BRCA1", "EGFR", "MYC")
colnames(gene_matrix) <- c("P1", "P2", "P3")

gene_matrix
##        P1  P2  P3
## BRCA1 120 150 130
## EGFR  300 350 400
## MYC   800 900 950

Problem 9: Extract Specific Expression Value

Task: Extract MYC’s expression for Patient 2.

gene_matrix["MYC", "P2"]
## [1] 900

Problem 10: Create a Multi-Object List

Task: Create a list with ages, the gene expression matrix, and the nutrition data frame.

ages_vec <- c(35, 29, 41)

multi_list <- list(
  PatientAges = ages_vec,
  GeneExpression = gene_matrix,
  NutritionData = cohort_df
)

# Print the list structure
multi_list
## $PatientAges
## [1] 35 29 41
## 
## $GeneExpression
##        P1  P2  P3
## BRCA1 120 150 130
## EGFR  300 350 400
## MYC   800 900 950
## 
## $NutritionData
##    Name Age  BMI  BMI_Class
## 1   Ali  35 24.5     Normal
## 2 Sonia  29 28.2 Overweight
## 3 Kabir  41 31.0      Obese

Problem 11: Extract from a List

Task: Extract the second row of the matrix from the list (EGFR expression).

multi_list$GeneExpression[2, ]
##  P1  P2  P3 
## 300 350 400

Problem 12: Conditional Statement for Glucose Check

Task: Write an if-else statement to classify FBG stored as x.

# Assign a sample value to x for demonstration
x <- 115 

if (x < 100) {
  status <- "Normal"
} else if (x >= 100 & x <= 125) {
  status <- "Prediabetes"
} else {
  status <- "Diabetes"
}

cat("Glucose Status for x =", x, "is:", status, "\n")
## Glucose Status for x = 115 is: Prediabetes

Problem 13: Identify High-Risk Patients

Task: Create the data frame and extract “high risk” patients (Age > 40 AND BMI >= 30).

# 1. Create data frame
cardio_name <- c("Tania", "Mahir", "Jui", "Imran")
cardio_age <- c(42, 37, 45, 50)
cardio_bmi <- c(31.5, 28.0, 29.5, 32.0)

cardio_df <- data.frame(Name = cardio_name, Age = cardio_age, BMI = cardio_bmi)
cat("Original Cardio Data Frame:\n")
## Original Cardio Data Frame:
print(cardio_df)
##    Name Age  BMI
## 1 Tania  42 31.5
## 2 Mahir  37 28.0
## 3   Jui  45 29.5
## 4 Imran  50 32.0
# 2. Extract high-risk patients
high_risk <- cardio_df[cardio_df$Age > 40 & cardio_df$BMI >= 30, ]
cat("\nHigh-Risk Patients:\n")
## 
## High-Risk Patients:
print(high_risk)
##    Name Age  BMI
## 1 Tania  42 31.5
## 4 Imran  50 32.0

Problem 14: Integrate Different Biomedical Data in a List

Task: Create the list and extract specific data points.

# 1. Create Objects
# Demographic data frame
demo_df <- data.frame(Name = c("Pt_A", "Pt_B", "Pt_C"),
                      Age = c(55, 62, 48),
                      Sex = c("F", "M", "M"))

# Matrix of tumor markers (CA125, CEA, PSA)
tumor_vals <- c(35.2, 12.5, 4.1,  # CA125
                5.0, 8.2, 2.1,     # CEA
                1.2, 4.5, 0.8)     # PSA
tumor_matrix <- matrix(tumor_vals, nrow = 3, byrow = TRUE)
rownames(tumor_matrix) <- c("CA125", "CEA", "PSA")
colnames(tumor_matrix) <- c("P1", "P2", "P3")

# Vector of survival times
survival_vec <- c(24, 18, 36) # months

# Create List
onco_list <- list(Demographics = demo_df, 
                  TumorMarkers = tumor_matrix, 
                  SurvivalTimes = survival_vec)

# 2. Extractions
cat("CEA expression for the 2nd patient:\n")
## CEA expression for the 2nd patient:
print(onco_list$TumorMarkers["CEA", "P2"])
## [1] 8.2
cat("\nSurvival time for the 3rd patient:\n")
## 
## Survival time for the 3rd patient:
print(onco_list$SurvivalTimes[3])
## [1] 36

Problem 15: Automated Classification of Anemia Status

Task: Classify Hb values and add to a data frame.

# 1. Store Hb values
hb_vals <- c(11.2, 13.5, 9.8, 14.1, 12.0, 8.7)

# 2. Use ifelse() to classify
anemia_class <- ifelse(hb_vals < 12, "Anemia", "Normal")

# 3. Add to a data frame
patient_ids <- paste0("Patient_", 1:6)
final_anemia_df <- data.frame(ID = patient_ids, 
                              Hemoglobin = hb_vals, 
                              Status = anemia_class)

final_anemia_df
##          ID Hemoglobin Status
## 1 Patient_1       11.2 Anemia
## 2 Patient_2       13.5 Normal
## 3 Patient_3        9.8 Anemia
## 4 Patient_4       14.1 Normal
## 5 Patient_5       12.0 Normal
## 6 Patient_6        8.7 Anemia