knitr::opts_chunk$set(echo = TRUE)fbg <- 112
if (fbg < 100) {
print("Normal")
} else if (fbg >= 100 & fbg <= 125) {
print("Prediabetes")
} else {
print("Diabetes")
}## [1] "Prediabetes"body_temp <- 38.2
if (body_temp <= 37.5) {
print("Normal Temperature")
} else {
print("Fever Detected")
}## [1] "Fever Detected"bmi <- 29.5
# Because no BMI classification range was given. I Googled it and used the following range
# BMI <18.5 Underweight, 18.5 to 24.9 Normal weight, 25.0 to 29.9 Overweight, and 30.0 and above Obesity
if (bmi <= 18.5) {
print("Underweight")
} else if (bmi >= 18.5 & bmi <= 24.9) {
print("Normal weight")
} else if (bmi >= 25 & bmi <=29.9) {
print("Overweight")
} else {
print("Obese")
}## [1] "Overweight"hb <- 10.5
if (hb <= 10.5) {
print("Anemia")
} else {
print("Normal")
}## [1] "Anemia"ldl <- 165
if (ldl < 130) {
print("Optimal")
} else if (ldl >=130 & ldl <= 159) {
print ("Borderline")
} else {
print("High")
}## [1] "High"# Creating my dataframe
clinic_df <- data.frame(
Name = c("Aisha", "Rahman", "Rima", "Hossain"),
Age = c(45, 52, 37, 60),
BMI = c(31.2, 28.5, 24.1, 33.4),
BP_Systolic = c(150, 165, 120, 175)
)
print (clinic_df)## Name Age BMI BP_Systolic
## 1 Aisha 45 31.2 150
## 2 Rahman 52 28.5 165
## 3 Rima 37 24.1 120
## 4 Hossain 60 33.4 175clinic_df$hypertensive <- ifelse(clinic_df$BP_Systolic >= 140, "Yes", "No")
print(clinic_df)## Name Age BMI BP_Systolic hypertensive
## 1 Aisha 45 31.2 150 Yes
## 2 Rahman 52 28.5 165 Yes
## 3 Rima 37 24.1 120 No
## 4 Hossain 60 33.4 175 YesBackground: • “Obese” if BMI ≥30 • “Overweight” if BMI 25–29.9 • “Normal” if BMI <25 Task: Use nested ifelse() to classify patients and store in a new column “BMI_Status”.
Create a Gene Expression Matrix ypes
clinic_df$bmi_status <- ifelse(clinic_df$BMI >= 30, "Obese", ifelse(clinic_df$BMI >=25 & clinic_df$BMI <= 29.9, "Overweight", "Normal"))
print(clinic_df)## Name Age BMI BP_Systolic hypertensive bmi_status
## 1 Aisha 45 31.2 150 Yes Obese
## 2 Rahman 52 28.5 165 Yes Overweight
## 3 Rima 37 24.1 120 No Normal
## 4 Hossain 60 33.4 175 Yes Obeseelderly <-clinic_df[clinic_df$Age >= 50, ]
print(elderly)## Name Age BMI BP_Systolic hypertensive bmi_status
## 2 Rahman 52 28.5 165 Yes Overweight
## 4 Hossain 60 33.4 175 Yes Obeseclinic_df$risk_level <- ifelse(clinic_df$Age > 50 & clinic_df$hypertensive =="Yes", "High Risk", ifelse(clinic_df$Age > 50 | clinic_df$hypertensive =="Yes", "Moderate Risk", "Low Risk"))
print(clinic_df)## Name Age BMI BP_Systolic hypertensive bmi_status risk_level
## 1 Aisha 45 31.2 150 Yes Obese Moderate Risk
## 2 Rahman 52 28.5 165 Yes Overweight High Risk
## 3 Rima 37 24.1 120 No Normal Low Risk
## 4 Hossain 60 33.4 175 Yes Obese High Riskx <- c("Saturday", "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
for (i in 1: length(x)) {
print(paste("Please upload lab data for", x[i], sep = " "))
}## [1] "Please upload lab data for Saturday"
## [1] "Please upload lab data for Sunday"
## [1] "Please upload lab data for Monday"
## [1] "Please upload lab data for Tuesday"
## [1] "Please upload lab data for Wednesday"
## [1] "Please upload lab data for Thursday"
## [1] "Please upload lab data for Friday"Background: The lab wants a yearly log for 2015 to 2025. Task: Use a
for loop to print “Processing year:
for (i in 2015:2025) {
print(paste("Processing year", i, sep = " "))
}## [1] "Processing year 2015"
## [1] "Processing year 2016"
## [1] "Processing year 2017"
## [1] "Processing year 2018"
## [1] "Processing year 2019"
## [1] "Processing year 2020"
## [1] "Processing year 2021"
## [1] "Processing year 2022"
## [1] "Processing year 2023"
## [1] "Processing year 2024"
## [1] "Processing year 2025"#Creating my dataframe
hiv <- data.frame(
Name = c("Rahim", "Sumaiya", "Babul", "Joya"),
CD4_Count = c(120, 480, 230, 700)
)
print(hiv)## Name CD4_Count
## 1 Rahim 120
## 2 Sumaiya 480
## 3 Babul 230
## 4 Joya 700# Adding immunity_status
hiv$immunity_status <- ifelse(hiv$CD4_Count < 200, "Severe Immunodeficiency", ifelse(hiv$CD4_Count >= 200 & hiv$CD4_Count <= 500, "Moderate", "Normal"))
print(hiv)## Name CD4_Count immunity_status
## 1 Rahim 120 Severe Immunodeficiency
## 2 Sumaiya 480 Moderate
## 3 Babul 230 Moderate
## 4 Joya 700 Normal#Extracting severe patient
severe <- hiv[hiv$immunity_status == "Severe Immunodeficiency", ]
print(severe)## Name CD4_Count immunity_status
## 1 Rahim 120 Severe Immunodeficiency# Creating my data as vector
infection_score <- c(10, 35, 50, 80, 95)
# To generate a loop for new condition we need to generate an empty vector where new data generated by fulfiling the condition will be stored.
severity_label <- c()
# Now the "For-loop"
for (i in 1:length(infection_score)) {
if (infection_score[i] < 30) {
severity_label[i] <- "Mild"
} else if (infection_score[i] < 70) {
severity_label[i] <- "Moderate"
} else {
severity_label[i] <- "Severe"
}
}
# Finally combine infection_score and severity_label data
infection_df <- data.frame(infection_score, severity_label)
print(infection_df)## infection_score severity_label
## 1 10 Mild
## 2 35 Moderate
## 3 50 Moderate
## 4 80 Severe
## 5 95 Severe# Creating my dataframe
ward <- data.frame(
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)
)
print(ward)## Name Age Temp SpO2
## 1 Mina 25 36.8 99
## 2 Rafi 50 38.5 95
## 3 Sima 61 39.2 91
## 4 Rony 45 37.0 98
## 5 Asha 70 38.0 89
## 6 Nayeem 58 36.5 97# Creating an empty column in the loop and setup my conditions
ward$Condition <- c()
for (i in 1:length(ward)) {
if (ward$Temp[i] > 38 & ward$SpO2[i] < 94) {
ward$Condition[i] <- "Critical"
} else if (ward$Temp[i] > 37 & ward$SpO2[i] < 96) {
ward$Condition[i] <- "At Risk"
} else {
ward$Condition[i] <- "Stable"
}
}
print(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 Stable
## 6 Nayeem 58 36.5 97 Stable#Summary table according to condition
summary <- table(ward$Condition)
print(summary)##
## At Risk Critical Stable
## 1 1 4