1.

age<-dat[dat$PatientAge >= 65,]
q1<-quantile(age$Ordered.to.Complete...Mins, .25)
q3<-quantile(age$Ordered.to.Complete...Mins, .75)
IQR<-dplyr::filter(age, Ordered.to.Complete...Mins>=q1 & Ordered.to.Complete...Mins<=q3)
hist(IQR$Ordered.to.Complete...Mins, main = "time required for X-ray orders for Medicare patients", xlab = "x")

The histogram has a right-skewed distribution.

2.

radiotech62<-dat[dat$Radiology.Technician == 62,]
radiotech65<-dat[dat$Radiology.Technician == 65,]
median(radiotech62$Ordered.to.Complete...Mins)
## [1] 80
median(radiotech65$Ordered.to.Complete...Mins)
## [1] 27

Radiology Technician 62 takes longer than Radiology Technician 65.

3.

stat<-dat[dat$Priority == "STAT",]
routine<-dat[dat$Priority == "Routine",]
boxplot(stat$PatientAge,routine$PatientAge, main = "STAT versus Routine",names=c("STAT","Routine"),ylab = "Age")

These boxplots tell you that STAT orders for an X-ray are more common in a more variety of ages while Routine are common in middle age to older patients.

4.

floor3w <- dat[dat$Loc.At.Exam.Complete == "3W", ]
floor4w <- dat[dat$Loc.At.Exam.Complete == "4W", ]
mean(floor3w$Ordered.to.Complete...Mins) 
## [1] 1463.051
mean(floor4w$Ordered.to.Complete...Mins)
## [1] 1675.451
sd(floor3w$Ordered.to.Complete...Mins)
## [1] 3894.639
sd(floor4w$Ordered.to.Complete...Mins)
## [1] 4387.644

Floor 4w has a higher mean and standard deviation.