Assignment3.
Quintin Cortez
Jimmy
Last compiled on June 04, 2025 at 9:58 AM - CDT
1 Load Packages
library(readxl)2 Introduction
Care One Hospital is a mid-size (approximately 350 bed) teaching hospital that is connected with a medical school at a major university. It has been a recognized leader in providing quality care and recently achieved magnet status. Care One Hospital is a not for profit county hospital, providing care to anyone in need regardless of their ability to pay. They have an emergency room connected with their hospital, several floors for general inpatient care, and several departments of specialty care.
The administrators at Care One hospital have recently become concerned about the uptick in the length of stay of patients at their hospital. After doing a bit of investigation into this problem, several providers at Care One Hospital have noted that the time taken to receive an ordered X-Ray is excessively long. Care One Hospital has pulled 5 months of data from the years 2016-2017. The data has been de-identified, that is patients, providers, and radiology technicians have been assigned a unique identification number.
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2.1 Link to Case Study
Data Preparation
First, we will read in the raw data
df<-read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/refs/heads/main/RadDat_CareOne.csv")
head(df)## Unique.Identifier PatientAge Radiology.Technician
## 1 1 75 65
## 2 2 87 65
## 3 3 35 16
## 4 4 51 24
## 5 5 67 37
## 6 6 54 7
## CatalogCode In.Rad.Room Ordering.Physician
## 1 DX Abdomen 2 vw w/single chest 1 4
## 2 DX Abdomen 2 vw w/single chest 1 4
## 3 DX Abdomen 2 vw w/single chest 1 150
## 4 DX Abdomen 2 vw w/single chest 1 130
## 5 DX Abdomen 2 vw w/single chest 1 173
## 6 DX Abdomen 2 vw w/single chest 1 349
## PatientTypeMnemonic Encounter.Type Priority OrderDateTime
## 1 IP Inpatient Routine 12/27/16 10:32
## 2 IP Inpatient Routine 1/13/17 11:44
## 3 IP Inpatient Routine 1/2/17 17:19
## 4 IP Inpatient Routine 11/13/16 10:13
## 5 IP Inpatient STAT 12/13/16 3:22
## 6 IP Inpatient Routine 1/17/17 5:38
## ExamCompleteDateTime FinalDateTime Loc.At.Exam.Complete
## 1 12/27/16 11:19 12/28/16 14:32 GTU
## 2 1/13/17 12:32 1/14/17 16:00 GTU
## 3 1/2/17 18:00 1/3/17 7:44 3W
## 4 11/14/16 9:34 11/14/16 16:40 4W
## 5 12/13/16 4:04 12/13/16 3:19 Emergency Ctr
## 6 1/17/17 7:47 1/17/17 10:55 3E
## Exam.Completed.Bucket Section Exam.Room
## 1 8a-8p DX DX Rm 1
## 2 8a-8p DX DX Rm 1
## 3 8a-8p EC DX DX Rm 5 (EC)
## 4 8a-8p DX DX Rm 1
## 5 12a-8a EC DX DX Rm 5 (EC)
## 6 12a-8a DX DX PortableWe would like to keep only the columns with the patient age and priority
df2<-data.frame(df$PatientAge,df$Priority)
colnames(df2)<-c("PatientAge","Priority")
head(df2)## PatientAge Priority
## 1 75 Routine
## 2 87 Routine
## 3 35 Routine
## 4 51 Routine
## 5 67 STAT
## 6 54 RoutineNow, we will change the variable types to be a number and a factor.
df2$Priority<-as.factor(df2$Priority)
str(df2)## 'data.frame': 43631 obs. of 2 variables:
## $ PatientAge: int 75 87 35 51 67 54 34 65 67 40 ...
## $ Priority : Factor w/ 2 levels "Routine","STAT": 1 1 1 1 2 1 1 1 1 1 ...Basic Descriptive Statistics
Let’s look at some basic descriptive statistics for each variable.
Patient Age
Here are some summary statistics for Patient Age
summary(df2$PatientAge)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 33.0 53.0 50.5 67.0 102.0Here is a boxplot of Patient Age
boxplot(df2$PatientAge)
3 Complete R Code
It is a good idea to include this at the end of every RMarkdown document
df<-read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/refs/heads/main/RadDat_CareOne.csv")
head(df)
df2<-data.frame(df$PatientAge,df$Priority)
colname(df2)<-c("PatientAge","Priority")
head(df2)
df2$PatientAge<-as.numeric(df2$PatientAge)
df2$Priority<-as.factor(df2$Priority)
str(df2)
summary(df2$PatientAge)
boxplot(df2$PatientAge)