Adeel Abdullah-Khi

R_assg_2.R

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+df <-read.csv("E://DIH//Assignment2//hospitaldata.csv", strip.white = T, na.strings = c("-",""," ","\t","\n",NA), stringsAsFactors = F)
+library("dplyr")
+library("lubridate")
+library("ggplot2")
+View(df)
+head(df)
+#renaming columns
+df<-rename(df, 
+       Consulting_Doctor=`Consulting..Doctor`,
+       Total_Charges=`Total..Charges`,
+       Amount_Received=`Amount..Received.`,
+       Amount_Balance=`Amount..Balance`,
+       Amount_Received_By=`Amount.Received.By`,
+       Amount_in_Hospital=`Amount.in.Hospital`,
+       Receptionist_Name=`Receptionist..Name`,
+       Next_Apt=`Next.Apt`)
+
+df$Date <- as.Date(strptime(df$Date, "%a, %B %d, %Y"))
+
+df$Time <- format(strptime(df$Time, format='%I:%M %p'), '%I:%M %p')
+
+health_data<-tbl_df(df)
+
+health_data<-mutate(health_data,
+                    Age= as.integer(Age),
+                    Total_Charges=as.integer(Total_Charges),
+                    Amount_in_Hospital=as.integer(Amount_in_Hospital),
+                    Time=as.character(Time),
+                    Date=as.character(Date))
+
+health_data%>%
+select(Date)%>%
+transmute(Date=substring(Date, regexpr(' ', Date)+1, nchar(Date)))-> Dates
+Dates
+
+health_data%>%
+select(Date)%>%
+transmute(day= substring(Date,1,regexpr(',', Date)-1))-> Days
+Days
+
+
+
+#Q2
+#Dates<-select(health_data, Date)
+#Dates<-mutate(Dates, substring(Date,",")[[1]][1])
+df['Date']<-lapply(df['Date'], as.character)
+
+#Q3
+summarize(health_data, average=mean(Age))
+
+#Q4
+health_data%>%
+filter(Age<12)%>%
+select(Age)%>%
+summarize(total.count=n())
+
+#Q5
+health_data%>%
+filter(Sex=='M')%>%
+group_by(Procedure)%>%
+tally(sort=T)
+
+health_data%>%
+  filter(Sex=='F' | Sex=='f')%>%
+  group_by(Procedure)%>%
+  tally(sort=T)
+
+#Q6
+health_data%>%
+  group_by(Consulting_Doctor)%>%
+  summarize(sum=sum(Total_Charges))%>%
+  arrange(desc(sum))
+
+#Q7
+health_data%>%
+  group_by(Procedure)%>%
+  summarize(sum=sum(Total_Charges))%>%
+  arrange(desc(sum))
+
+#Q8
+time<-hour(strptime(health_data$Time, "%I:%M %p"))
+which.max(table(time))
+
+#Q9
+health_data%>%
+  mutate(time_bracket = ifelse(time >= 6 & time <= 12, "Morning",
+                        ifelse(time > 12 & time <= 16, "Afternoon",
+                        ifelse(time > 16 & time <= 19, "Evening",
+                        ifelse(time > 19, "Night", NA)))))
+
+
+
+#Q10
+health_data%>%
+  group_by(id)%>%
+  summarize(length=n())%>%
+  filter(length>1)%>%
+  nrow()
+
+#Q11
+health_data%>%
+  group_by(id,Procedure)%>%
+  summarize(length=n())%>%
+  filter(length>1)%>%
+  print()
+
+#Q12
+health_data%>%
+  group_by(id)%>%
+  summarize(length=n())%>%
+  filter(length>1)%>%
+  print()
+
+#Q13
+health_data%>%
+  filter(Sex=='M')%>%
+  select(Age)%>%
+  summarize(median=median(Age,na.rm=TRUE))%>%
+  print()
+
+health_data%>%
+  filter(Sex=='F'| Sex=='f')%>%
+  select(Age)%>%
+  summarize(median=median(Age,na.rm=TRUE))%>%
+  print()
+
+#Q14
+health_data$Amount_Balance<-gsub(",", "" ,health_data$Amount_Balance, fixed=TRUE)
+health_data$Amount_Balance<-gsub(".00", "" ,health_data$Amount_Balance, fixed=TRUE)
+
+health_data%>%
+  mutate(Amount_Balance=as.integer(Amount_Balance))%>%
+  select(Amount_Balance)%>%
+  summarize(sum=sum(Amount_Balance,na.rm=TRUE))%>%
+  print()
+
+#Q15
+health_data%>%
+  filter(Procedure=="Consultation")%>%
+  select(Total_Charges)%>%
+  summarize(sum=sum(Total_Charges,na.rm=TRUE))%>%
+  print()
+
+#Q16
+health_data%>%
+  select(Age,Total_Charges) %>%
+  filter(!is.na(Age),!is.na(Total_Charges)) ->corr
+
+cor(corr$Age,corr$Total_Charges)
+
+#Q17
+qplot(health_data$Age, xlab="Age")
+
+#Q18
+health_data%>%
+  filter(Procedure== 'X Ray' | Procedure== 'Scalling' ) %>%
+  select(Total_Charges)%>%
+  summarize(sum=sum(Total_Charges,na.rm=TRUE))%>%
+  print()
+
+write.csv(health_data, "E://DIH//Assignment2//clean_hospital_data_R.csv")
+
+