Calculating Average Cost Per Day for Patients in R: A Step-by-Step Guide

Calculating Average Cost Per Day for Patients with Different Diagnosis Codes and Filtering by Age and Stay Duration

Introduction

In this article, we will explore how to calculate the average cost per day for patients with different diagnosis codes and filter the results based on age and stay duration. We will also discuss how to identify if a patient stayed at least one day in the hospital.

We will be using R as our programming language of choice and will leverage the dplyr library for data manipulation and analysis.

Reading Data from a Text File

The first step is to read the data from a text file. The heartatk4R dataset is provided as a comma-separated values (CSV) file, which we can easily read into R using the read.csv() function.

heartatk4R <- read.table("http://statland.org/AP/R/heartatk4R.txt", 
                         header = TRUE, sep = "\t", 
                         colClasses = c("character", "factor", "factor", "factor","factor", "numeric", "numeric", "numeric"), 
                         na.strings = "*")

In this code snippet, we use the read.table() function to read the CSV file into R. We specify the header = TRUE argument to indicate that the first row of the file contains column names. The sep = "\t" argument specifies that the values are separated by tabs, and the colClasses argument assigns data types to each column.

Filtering Data by Sex, Age, and Diagnosis Code

We want to filter the data to include only female patients aged between 20 and 70 years who stayed at least one day in the hospital. We can use the dplyr library’s pipe operator (%>%) to chain together multiple operations.

library(dplyr)

# Filter data by sex, age, and diagnosis code
tt <- heartatk4R %>%
  filter(SEX == "F" & AGE > 20 & AGE < 70)

In this code snippet, we use the filter() function to select only the rows where the SEX column is equal to “F”, and the AGE column falls within the range of 20 to 70 years.

Calculating Average Cost Per Day

To calculate the average cost per day for patients with different diagnosis codes, we can use the aggregate() function from the base R library. However, this approach has a limitation: it only calculates the mean value for each group, without considering individual patient data.

A better approach is to use the dplyr library’s group_by() and summarise() functions to calculate the average cost per day for each diagnosis code.

# Group by diagnosis code and calculate average cost per day
tt <- tt %>%
  group_by(DIAGNOSIS) %>%
  summarise(AvgCostPerDay = mean(CHARGES, na.rm = TRUE))

In this code snippet, we use the group_by() function to group the data by the DIAGNOSIS column. We then use the summarise() function to calculate the average cost per day for each diagnosis code.

Sorting Results in Descending Order

To sort the results in descending order based on the average cost per day, we can use the arrange() function from the dplyr library.

# Sort results in descending order by average cost per day
tt <- tt %>%
  arrange(AvgCostPerDay = -mean(CHARGES, na.rm = TRUE))

In this code snippet, we use the arrange() function to sort the data in descending order based on the average cost per day.

Identifying Patients Who Stayed at Least One Day

To identify patients who stayed at least one day in the hospital, we can use a simple approach: calculate the number of days each patient was hospitalized and check if it’s greater than 0.

# Calculate number of days each patient was hospitalized
tt <- tt %>%
  mutate(DaysHospitalized = CHARGES / AVG_RATE)

# Filter patients who stayed at least one day
tt <- tt %>%
  filter(DaysHospitalized > 0)

In this code snippet, we use the mutate() function to calculate the number of days each patient was hospitalized by dividing the CHARGES column (which represents the total cost) by the AVG_RATE column (which represents the average daily rate).

We then use the filter() function to select only the patients who stayed at least one day.

Conclusion

In this article, we explored how to calculate the average cost per day for patients with different diagnosis codes and filter the results based on age and stay duration. We used R as our programming language of choice and leveraged the dplyr library for data manipulation and analysis.

Last modified on 2024-03-29