Ihtsham_r_assignment2

Ihtsham_lhr.R

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+library("ggplot2")
+library("dplyr")
+library("tidyr")
+library("stringr")
+library("lubridate")
+
+getwd()
+setwd("C:/Users/muhammad.ihtsham/Documents")
+mydata <- read.csv("hospitaldata.csv")
+
+# Converting to table format
+mydata <- tbl_df(mydata)
+
+# Formatting date & time columns
+mydata$Date <- as.Date(strptime(mydata$Date, "%a, %B %d, %Y"))
+mydata$Time <- format(strptime(mydata$Time, format='%I:%M %p'), '%I:%M %p')
+
+# Removing characters from age
+mydata$Age <- as.numeric(gsub("[^0-9]",'',mydata$Age))
+
+# Changing sex characters to uppercase
+mydata$Sex <- toupper(mydata$Sex)
+
+# Remove Cancelled to NA in total charges
+mydata$TotalCharges <- as.numeric(gsub("cancelled", NA, ignore.case = TRUE, mydata$TotalCharges))
+
+# Remove Cancelled to NA in procedure
+mydata$Procedure <- (gsub("cancelled", NA, ignore.case = TRUE, mydata$Procedure))
+
+
+# Coverting AmountBalance to numeric
+mydata$AmountBalance <- as.numeric(gsub(",",'',mydata$AmountBalance))
+class(mydata$AmountBalance)
+
+
+# 1: Removing dots from column names
+names(mydata) <- gsub("\\.", "", names(mydata))
+
+
+# 2: Most visits in a day
+print(paste("Most visited day of the week is",
+            weekdays(mydata$Date[which(table(mydata$Date) == max(table(mydata$Date)))])))
+
+# 3: Average age of patients
+mean(mydata$Age, na.rm = TRUE)
+
+# 4: How many children were entertained (Age: 1-12)
+child <- which(mydata$Age >= 1 & mydata$Age <= 12)
+length(child)
+
+# 5: Visit in abundance by gender wise
+mydata %>%
+  group_by(Sex, Procedure) %>%
+  tally(sort = TRUE) %>%
+  filter(!is.na(Sex)) %>%
+  View()
+
+# 6: Which doctor is earning highest
+highest_earner <- mydata %>%
+  group_by(ConsultingDoctor) %>%
+  summarise(total=sum(TotalCharges), na.rm = TRUE) %>%
+  arrange(TotalCharges)
+
+View(highest_earner)
+
+# 7: Which procedure type earns highest
+
+highest_procedure <-  mydata %>%
+  group_by(Procedure) %>%
+  summarise_each(funs(sum(TotalCharges, na.rm = TRUE))) %>%
+  select(Procedure, TotalCharges) %>%
+  filter(!is.na(Procedure)) %>%
+  arrange(desc(TotalCharges))
+
+View(highest_procedure)
+
+# 8: Time with highest frequency
+highest_freq_time <-  mydata %>%
+  filter(!is.na(Date), !is.na(Time)) %>%
+  group_by(Date,Time) %>%
+  tally() %>%
+  arrange(desc(n))
+View(highest_freq_time)
+
+# 10: Repeated visitors
+visitor <-  mydata %>%
+  group_by(id) %>%
+  tally() %>%
+  filter(n > 1) %>%
+  arrange(desc(n))
+
+View(visitor)
+
+# 11: ID of repeated visitors
+print(visitor$id)
+
+# 12: Patients visited again for same problem
+patients_repeated <-  mydata %>%
+  group_by(id, Procedure) %>%
+  tally() %>%
+  filter(!is.na(Procedure), n > 1) %>%
+  arrange(desc(n))
+
+View(patients_repeated)
+
+# 13: Median age
+mydata %>% 
+  group_by(Sex) %>%
+  summarise(median(Age, na.rm = TRUE))
+
+# 14: Total amount
+paste("Total amount is",sum(mydata$AmountBalance, na.rm = TRUE))
+
+# 15: Money made by consultation
+mydata %>%
+  group_by(Procedure) %>%
+  summarise(Total_Amount = sum(TotalCharges, na.rm = TRUE)) %>%
+  filter(Procedure %in% c("Consultation"))
+
+# 16: Relation b/w age & total charges
+cor.test(mydata$Age, mydata$TotalCharges)
+
+# 17: Highest age group visits
+highest_age_visit <- mydata %>%
+  group_by(Age) %>%
+  tally() %>%
+  filter(!is.na(Age)) %>%
+  arrange(desc(n))
+View(highest_age_visit)
+
+# 18: Total cost by x-ray and scalling
+Total_cost <- mydata %>%
+  filter(Procedure %in% c("X Ray", "Scalling")) %>%
+  summarise(Total = sum(TotalCharges, na.rm = TRUE)) 
+View(Total_cost)
+
+
+
+
+
+
+
+