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Introduction to Data Analysis and Visualization with R

Solution: Help Patients

Learn how to perform essential data transformation tasks in R with patient datasets. Understand data type conversion, handling missing values, calculating averages, creating new variables, and filtering data. This lesson guides you through multiple approaches to reach meaningful analytical outcomes, enhancing your skills in managing real-world health data.

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In this lesson, we’ll go through the challenge and explain the tasks required from us one by one. Check out the solution provided in the container below. Please note that there is not just one correct answer. We can still reach the same result by taking different paths.

R
# We use the 'patients' dataset in this exercise
library(dplyr)
print(head(patients))
print('----------The details of the dataset: -----')
print(str(patients))
print('--------- The result: ----------')
# First we define the average BMI for females and males by ignoring the null values
avg_f =  mean(patients[patients$gender=='Female',]$BMI,na.rm = TRUE) # Average BMI for female patients
avg_m =  mean(patients[patients$gender=='Male',]$BMI,na.rm = TRUE)   # Average BMI for male patients
result <- patients %>% mutate(BMI = ifelse(is.na(BMI)&gender=='Female',avg_f,BMI))%>% #  Impute null values with female average
  mutate(BMI = ifelse(is.na(BMI)&gender=='Male',avg_m,BMI))%>%  #  Impute null values with male average
  mutate(fasting_glucose=as.double(fasting_glucose))%>%     # Convert the character data type into double data type
  mutate(glucose_diff = abs((fasting_glucose - mean(fasting_glucose))/sd(fasting_glucose)))%>%  #  Calculate the Z-score for each fasting glucose value and create a new column
  mutate(avg_bp = (high_bp + low_bp)/2)%>%  # Create a new column for the average blood pressure
  mutate(in_danger= ifelse(avg_bp>=100,TRUE,FALSE))%>%  # Create a binary column stating that the patient is in danger if the average blood pressure is over 110
  select(c(id,gender,BMI,fasting_glucose,glucose_diff,avg_bp,in_danger))%>% # Select the columns desired by the managers.
  filter(glucose_diff >= 2 & BMI >= 25 & in_danger == TRUE)  # Filter the patients that carry danger in the given measurements.
print(result)   # Check the patients who are in danger
print('-------- The number of rows: -------')
result<- result%>%nrow()  # Find the number of patients who needs to be care.
print(result)

  • Line 5: We check the data types of the columns. Notice that the fasting_glucose column is in character data type even though the characters are numeric. ...