homework 5
JP Adewoye
2025-06-18
dat2<-read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/refs/heads/main/RadDat_IMSE.csv")head(dat2)## 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 Ordering.Physician PatientTypeMnemonic
## 1 DX Abdomen 2 vw w/single chest 4 IP
## 2 DX Abdomen 2 vw w/single chest 4 IP
## 3 DX Abdomen 2 vw w/single chest 150 IP
## 4 DX Abdomen 2 vw w/single chest 130 IP
## 5 DX Abdomen 2 vw w/single chest 173 IP
## 6 DX Abdomen 2 vw w/single chest 349 IP
## Priority OrderDateTime ExamCompleteDateTime FinalDateTime
## 1 Routine 12/27/16 10:32 12/27/16 11:19 12/28/16 14:32
## 2 Routine 1/13/17 11:44 1/13/17 12:32 1/14/17 16:00
## 3 Routine 1/2/17 17:19 1/2/17 18:00 1/3/17 7:44
## 4 Routine 11/13/16 10:13 11/14/16 9:34 11/14/16 16:40
## 5 STAT 12/13/16 3:22 12/13/16 4:04 12/13/16 3:19
## 6 Routine 1/17/17 5:38 1/17/17 7:47 1/17/17 10:55
## Ordered.to.Complete...Mins Ordered.to.Complete...Hours Loc.At.Exam.Complete
## 1 47 0.7833333 GTU
## 2 48 0.8000000 GTU
## 3 41 0.6833333 3W
## 4 1401 23.3500000 4W
## 5 42 0.7000000 Emergency Ctr
## 6 129 2.1500000 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 Portablesummary(dat2$Ordered.to.Complete...Mins)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -196 18 33 1644 67 112168?hist## starting httpd help server ... donehist(dat2$Ordered.to.Complete...Mins)
hist(dat2$Ordered.to.Complete...Mins,breaks = 10)
hist(dat2$Ordered.to.Complete...Mins,main = "histogram of Ordered.to.Complete...Mins",xlab ="Ordered.to.Complete...Mins",ylab = "frequency",col = "aquamarine")
q1 <- quantile(dat2$Ordered.to.Complete...Mins, 0.25, na.rm = TRUE)
q3 <- quantile(dat2$Ordered.to.Complete...Mins, 0.75, na.rm = TRUE)
iqr_data <- subset(dat2, Ordered.to.Complete...Mins >= q1 & Ordered.to.Complete...Mins <= q3)
hist(iqr_data$Ordered.to.Complete...Mins,
breaks = 10,
main = "Histogram of X-Ray Completion Time (IQR, Age 65+)",
xlab = "Completion Time (Minutes)",
ylab = "Frequency",
col = "aquamarine")
#The histogram is right-skewed, indicating that while most X-ray orders
for Medicare patients are completed in a shorter amount of time
library(dplyr)## Warning: package 'dplyr' was built under R version 4.4.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionmedian_times <- dat2 %>%
filter(Radiology.Technician %in% c(62, 63, 64, 65)) %>%
group_by(Radiology.Technician) %>%
summarise(median_time = median(Ordered.to.Complete...Mins, na.rm = TRUE))
print(median_times)## # A tibble: 4 × 2
## Radiology.Technician median_time
## <int> <int>
## 1 62 80
## 2 63 18
## 3 64 82
## 4 65 27#Overall, technician 64 shows the best median speed, while technician 65 might need to improve efficiency.
boxplot(PatientAge ~ Priority,
data = dat2,
main = "Patient Age by Order Priority (STAT vs Routine)",
xlab = "Order Priority",
ylab = "Patient Age",
col = c("pink", "skyblue"))
#Each boxplot represents the distribution of patient ages for each
priority group (STAT and Routine).The box shows the interquartile range
(IQR), the middle 50% of patient ages. The line inside the box is the
median age. The whiskers show the approximate range of ages
library(dplyr)
stats_by_floor <- dat2 %>%
filter(Loc.At.Exam.Complete %in% c("3W", "4W")) %>%
group_by(Loc.At.Exam.Complete) %>%
summarise(
mean_time = mean(Ordered.to.Complete...Mins, na.rm = TRUE),
sd_time = sd(Ordered.to.Complete...Mins, na.rm = TRUE),
count = n()
)
print(stats_by_floor)## # A tibble: 2 × 4
## Loc.At.Exam.Complete mean_time sd_time count
## <chr> <dbl> <dbl> <int>
## 1 3W 1463. 3895. 952
## 2 4W 1675. 4388. 668#X-Ray orders on 3W are completed faster and with more consistency compared to 4W, indicating better efficiency on 3W.