#Problem 1: Evaluate Glucose Level
patient_fbg<- 112
glucose_value<-if(patient_fbg<100){
print("normal")
}else if(patient_fbg<=125){
print("Prediabetes")
}else{
print("diabetes")
} ## [1] "Prediabetes"#Problem 2: Check for Fever
patient_temp<-38.2
status<-if(patient_temp>37.5){print("fever detected")
}else{print("Normal")}## [1] "fever detected"#problem3:Problem 3: BMI Classification
participant_BMI<-29.5
BMI_status<-if(participant_BMI<29.5){print("Normal")
}else{print("overweight")}## [1] "overweight"#Problem 4: Lab Result Flagging
patient_hb_level<-10.5
lab_result<-if(patient_hb_level<12){print("anemia")
}else{print("normal")}## [1] "anemia"#Problem 5: Evaluate Cholesterol Panel
patient_LDL<-165
cholesterol_panel<-if(patient_LDL<130){print("optimal")
}else if(patient_LDL>130 & patient_LDL<160){print("Borderline")
}else{print("high")}## [1] "high"#Problem 6: Create a Clinical Dataset
names<-c("Aisha","Rahman","Rima","Hossain")
age<-c(45,52,37,60)
BMI<-c(31.2,28.5,24.1,33.4)
Bp_systolic<-c(150,165,120,175)
patients<-data.frame(names,age,BMI,Bp_systolic)
patients## names age BMI Bp_systolic
## 1 Aisha 45 31.2 150
## 2 Rahman 52 28.5 165
## 3 Rima 37 24.1 120
## 4 Hossain 60 33.4 175#7Problem 7: Add a Hypertension Column
names<-c("Aisha","Rahman","Rima","Hossain")
age<-c(45,52,37,60)
BMI<-c(31.2,28.5,24.1,33.4)
Bp_systolic<-c(150,165,120,175)
patients<-data.frame(names,age,BMI,Bp_systolic)
patients## names age BMI Bp_systolic
## 1 Aisha 45 31.2 150
## 2 Rahman 52 28.5 165
## 3 Rima 37 24.1 120
## 4 Hossain 60 33.4 175patients$hypertensive<-ifelse((Bp_systolic>=140),"yes","no")
patients## names age BMI Bp_systolic hypertensive
## 1 Aisha 45 31.2 150 yes
## 2 Rahman 52 28.5 165 yes
## 3 Rima 37 24.1 120 no
## 4 Hossain 60 33.4 175 yes#Problem 8: Add an Obesity Status Column
names<-c("Aisha","Rahman","Rima","Hossain")
age<-c(45,52,37,60)
BMI<-c(31.2,28.5,24.1,33.4)
Bp_systolic<-c(150,165,120,175)
patients<-data.frame(names,age,BMI,Bp_systolic)
patients## names age BMI Bp_systolic
## 1 Aisha 45 31.2 150
## 2 Rahman 52 28.5 165
## 3 Rima 37 24.1 120
## 4 Hossain 60 33.4 175patients$BMI_Status<-ifelse(BMI<25,"normal",
ifelse(BMI>=30,
"obese",
"overwight")
)
patients## names age BMI Bp_systolic BMI_Status
## 1 Aisha 45 31.2 150 obese
## 2 Rahman 52 28.5 165 overwight
## 3 Rima 37 24.1 120 normal
## 4 Hossain 60 33.4 175 obese#Problem 9: Identify Elderly Patients
names<-c("Aisha","Rahman","Rima","Hossain")
age<-c(45,52,37,60)
BMI<-c(31.2,28.5,24.1,33.4)
Bp_systolic<-c(150,165,120,175)
patients<-data.frame(names,age,BMI,Bp_systolic)
patients## names age BMI Bp_systolic
## 1 Aisha 45 31.2 150
## 2 Rahman 52 28.5 165
## 3 Rima 37 24.1 120
## 4 Hossain 60 33.4 175eldery_geriatric_trial<-patients[patients$age>=50,]
eldery_geriatric_trial## names age BMI Bp_systolic
## 2 Rahman 52 28.5 165
## 4 Hossain 60 33.4 175#Problem 10: Assign Risk Level
names<-c("Aisha","Rahman","Rima","Hossain")
age<-c(45,52,37,60)
BMI<-c(31.2,28.5,24.1,33.4)
Bp_systolic<-c(150,165,120,175)
patients<-data.frame(names,age,BMI,Bp_systolic)
patients## names age BMI Bp_systolic
## 1 Aisha 45 31.2 150
## 2 Rahman 52 28.5 165
## 3 Rima 37 24.1 120
## 4 Hossain 60 33.4 175patients$hypertensive<-ifelse((Bp_systolic>=140),"yes","no")
ifelse(BMI>=30,
"obese",
"overwight")## [1] "obese" "overwight" "overwight" "obese"eldery_geriatric_trial<-patients[patients$age>=50,]
patients## names age BMI Bp_systolic hypertensive
## 1 Aisha 45 31.2 150 yes
## 2 Rahman 52 28.5 165 yes
## 3 Rima 37 24.1 120 no
## 4 Hossain 60 33.4 175 yespatients$risk_level<-ifelse(age>50 & patients$hypertensive=="yes","high risk",
ifelse((age>50 & patients$hypertensive=="no")|(age<=50 & patients$hypertensive=="yes"),"moderate risk","low risk"))
patients## names age BMI Bp_systolic hypertensive risk_level
## 1 Aisha 45 31.2 150 yes moderate risk
## 2 Rahman 52 28.5 165 yes high risk
## 3 Rima 37 24.1 120 no low risk
## 4 Hossain 60 33.4 175 yes high risk#Section C – Loops and Iteration
x<-c(1,2,3,4,5,6,7)
for(x in 1:7){
print(paste("please upload the lab data for day",x))
}## [1] "please upload the lab data for day 1"
## [1] "please upload the lab data for day 2"
## [1] "please upload the lab data for day 3"
## [1] "please upload the lab data for day 4"
## [1] "please upload the lab data for day 5"
## [1] "please upload the lab data for day 6"
## [1] "please upload the lab data for day 7"#Problem 12: Sequential Year Tracker
years<-(2015:2025)
for(years in 2015:2025){
print(paste("processing year:",years))
}## [1] "processing year: 2015"
## [1] "processing year: 2016"
## [1] "processing year: 2017"
## [1] "processing year: 2018"
## [1] "processing year: 2019"
## [1] "processing year: 2020"
## [1] "processing year: 2021"
## [1] "processing year: 2022"
## [1] "processing year: 2023"
## [1] "processing year: 2024"
## [1] "processing year: 2025"#Problem 13: Viral Load Tracking
viral_loads<-c(100, 550, 1200, 20000, 850)
for (viral_load in viral_loads)
{if(viral_load<1000){print("normal")
}else if (viral_load<10000){
print("Elevated")
}else{
print("critical")
}
}## [1] "normal"
## [1] "normal"
## [1] "Elevated"
## [1] "critical"
## [1] "normal"#Problem 14: Automated Clinical Categorization
name<-c("Rahim","Sumaiya","Babul","Joya")
CD4_Count<-c(120,480,230,700)
HIV_patients<-data.frame(name,CD4_Count)
HIV_patients## name CD4_Count
## 1 Rahim 120
## 2 Sumaiya 480
## 3 Babul 230
## 4 Joya 700HIV_patients$Immunity_Status<-ifelse(HIV_patients$CD4_Count<200,"severe",ifelse(HIV_patients$CD4_Count>500,"Normal","Moderate"))
HIV_patients## name CD4_Count Immunity_Status
## 1 Rahim 120 severe
## 2 Sumaiya 480 Moderate
## 3 Babul 230 Moderate
## 4 Joya 700 Normal#Problem 15: Integrating Loops and Conditionals for Clinical Scoring
Infection_score<-c(10,35,50,80,95)
severity<-c()
for (score in Infection_score){
if (score<30){
severity<-c(severity,"Mild")
} else if (score<70){
severity<-c(severity,"Moderate")
}else{severity<-c(severity,"Severe")
}
}
severity## [1] "Mild" "Moderate" "Moderate" "Severe" "Severe"Severity_label<-c("Mild","Moderate","Moderate","Severe","Severe")
clinical_score<-data.frame(Infection_score,Severity_label)
clinical_score## Infection_score Severity_label
## 1 10 Mild
## 2 35 Moderate
## 3 50 Moderate
## 4 80 Severe
## 5 95 Severe#Problem 16: Combine All Skills – Hospital Ward Analysis
Name <- c("Mina", "Rafi", "Sima", "Rony", "Asha", "Nayeem")
Age <- c(25, 50, 61, 45, 70, 58)
Temp <- c(36.8, 38.5, 39.2, 37.0, 38.0, 36.5)
SpO2 <- c(99, 95, 91, 98, 89, 97)
hospital_ward <- data.frame(Name, Age, Temp, SpO2)
Condition <- c()
for (i in 1:nrow(hospital_ward)) {
if (hospital_ward$Temp[i] > 38 & hospital_ward$SpO2[i] < 94) {
Condition <- c(Condition, "Critical")
} else if (hospital_ward$Temp[i] > 37 & hospital_ward$SpO2[i] < 96) {
Condition <- c(Condition, "At Risk")
} else {
Condition <- c(Condition, "Stable")
}
}
hospital_ward$Condition <- Condition
hospital_ward## Name Age Temp SpO2 Condition
## 1 Mina 25 36.8 99 Stable
## 2 Rafi 50 38.5 95 At Risk
## 3 Sima 61 39.2 91 Critical
## 4 Rony 45 37.0 98 Stable
## 5 Asha 70 38.0 89 At Risk
## 6 Nayeem 58 36.5 97 Stable