HW5

HW5

muffins<-read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/refs/heads/main/RadDat_IMSE.csv")
dat<-muffins[,c("PatientAge","Radiology.Technician","Priority","Loc.At.Exam.Complete","Ordered.to.Complete...Mins")]
head(dat)

##   PatientAge Radiology.Technician Priority Loc.At.Exam.Complete
## 1         75                   65  Routine                  GTU
## 2         87                   65  Routine                  GTU
## 3         35                   16  Routine                   3W
## 4         51                   24  Routine                   4W
## 5         67                   37     STAT        Emergency Ctr
## 6         54                    7  Routine                   3E
##   Ordered.to.Complete...Mins
## 1                         47
## 2                         48
## 3                         41
## 4                       1401
## 5                         42
## 6                        129

dat <- dat[dat$Ordered.to.Complete...Mins >= 0, ]

#1

qualmed <- dat[dat$PatientAge > 64, ]
q1 <- quantile(qualmed$Ordered.to.Complete...Mins,.25)
median <- quantile(qualmed$Ordered.to.Complete...Mins,.5)
q3 <- quantile(qualmed$Ordered.to.Complete...Mins,.75)
iqr <- qualmed[qualmed$Ordered.to.Complete...Mins > q1 & qualmed$Ordered.to.Complete...Mins < q3, ]
hist(iqr$Ordered.to.Complete...Mins,
     main = "Histogram of Completion Time and Frequency",
     xlab = "Time")

The histogram starts high with the highest frequncy being at around 25. Then it trends downward.

#2

rad <- dat[dat$Radiology.Technician %in% c(62, 63, 64, 65), ]
med62 <- median(rad[rad$Radiology.Technician == 62, "Ordered.to.Complete...Mins"])
med63 <- median(rad[rad$Radiology.Technician == 63, "Ordered.to.Complete...Mins"])
med64 <- median(rad[rad$Radiology.Technician == 64, "Ordered.to.Complete...Mins"])
med65 <- median(rad[rad$Radiology.Technician == 65, "Ordered.to.Complete...Mins"])
#cat works and print doesnt idk
cat("62 median:", med62)

## 62 median: 80

cat("63 median:", med63)

## 63 median: 18

cat("64 median:", med64)

## 64 median: 82

cat("65 median:", med65)

## 65 median: 27

64 has the highest median and 63 has the lowest so 63 is the quickest and 64 is the slowest

#3

boxplot(PatientAge ~ Priority, data = dat,
        main = "Boxplot",
        xlab = "Order Priority", ylab = "Patient Age")

The boxplots tell me that routines median has a higher age than stats so stats is slightly younger when it comes to pateint age and order proirity.

#4

f3W <- dat[dat$Loc.At.Exam.Complete == "3W", ]
f4W <- dat[dat$Loc.At.Exam.Complete == "4W", ]
m3W <- mean(f3W$Ordered.to.Complete...Mins)
sd3W <- sd(f3W$Ordered.to.Complete...Mins)
m4W <- mean(f4W$Ordered.to.Complete...Mins)
sd4W <- sd(f4W$Ordered.to.Complete...Mins)
cat("3W Mean:", round(m3W, 1), "SD:", round(sd3W, 1))

## 3W Mean: 1463.1 SD: 3894.6

cat("4W Mean:", round(m4W, 1), "SD:", round(sd4W, 1))

## 4W Mean: 1675.5 SD: 4387.6

3W mean and sd is lower than 4W so 3W has quicker completion times.