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library(readr)
+hpddata <- read_csv("E:/DIH/R Assignment/Assignment - 22-03-2017/hospitaldata.csv")
+
## Parsed with column specification:
+## cols(
+## Date = col_character(),
+## id = col_integer(),
+## Time = col_character(),
+## Age = col_character(),
+## Sex = col_character(),
+## Consulting..Doctor = col_character(),
+## Specialty = col_character(),
+## Procedure = col_character(),
+## Total..Charges = col_character(),
+## Amount..Received. = col_integer(),
+## Amount..Balance = col_character(),
+## Amount.Received.By = col_character(),
+## Amount.in.Hospital = col_integer(),
+## Receptionist..Name = col_character(),
+## Next.Apt = col_character()
+## )
+
# for rename column first should be install deplyr package
+library("dplyr")
+
##
+## Attaching package: 'dplyr'
+
## The following objects are masked from 'package:stats':
+##
+## filter, lag
+
## The following objects are masked from 'package:base':
+##
+## intersect, setdiff, setequal, union
+
library(tidyr)
+#Please remove the dots in the names, so it may become easier for you to work through it.
+#Q.1
+names(hpddata) <- gsub("\\.", "", names(hpddata))
+View(hpddata)
+# Data Cleaning
+hpddata<- hpddata%>%
+ mutate(Age=ifelse(Age=="-", NA,Age))
+
+hpddata<-hpddata%>%
+ mutate(Age = ifelse(grepl("M", Age), parse_number(Age)/12, ifelse(grepl("-", Age), "",Age)))
+
+hpddata<- hpddata%>%
+ mutate(Sex=replace(Sex,Sex=='f','F'))
+hpddata<- hpddata%>%
+ mutate(Sex=replace(Sex,Sex=="-", NA))
+hpddata<- hpddata%>%
+ mutate(AmountBalance=replace(AmountBalance,AmountBalance=="-", NA))
+hpddata<-mutate(hpddata, Time=format(strptime(hpddata$Time, "%I:%M %p"), format="%H:%M"))
+#Q.2
+
+Maximum_days<-
+ hpddata%>%
+ separate(Date,c("Day"))%>%
+ group_by(Day)%>%
+ summarize(count_no =n())%>%
+ filter(count_no==max(count_no))
+
## Warning: Too many values at 222 locations: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
+## 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...
+
View(Maximum_days)
+
+ #Q.3
+
+ Avg_age_Patients<-hpddata%>%
+ filter(!is.na(Age))%>%
+ summarize(cal_avg = mean(parse_number(Age)))
+ View(Avg_age_Patients)
+
+ # Q.4
+
+ children_visits<- hpddata%>%
+ filter(parse_number(Age)>0 , parse_number(Age)<=12)%>%
+ summarize(total_children= n())
+ View(children_visits)
+
+ # Q.5
+
+ gender_type<-hpddata%>%
+ filter(!is.na(Sex))%>%
+ group_by(Sex,Procedure)%>%
+ summarize(total_gender= n())%>%
+ filter(total_gender==max(total_gender))
+ View(gender_type)
+
+ # Q.6
+
+ high_paid_con_Var<- hpddata%>%
+ filter(ConsultingDoctor!='Nursing Staff')%>%
+ filter(!is.na(AmountReceived))%>%
+ group_by(ConsultingDoctor)%>%
+ summarize(high_paid_con = sum(AmountReceived))%>%
+ filter(high_paid_con == max(high_paid_con))
+ View(high_paid_con_Var)
+
+ # Q.7
+
+ high_paid_prc_type_Var <-hpddata%>%
+ filter(!is.na(AmountReceived))%>%
+ group_by(Procedure)%>%
+ summarize(high_paid_prc_type = sum(AmountReceived))%>%
+ filter(high_paid_prc_type==max(high_paid_prc_type))
+
+ View(high_paid_prc_type_Var)
+
+
+ #Q.8
+
+ max_freq_time<-hpddata%>%
+ filter(!is.na(Time),Time!='-')%>%
+ group_by(Time)%>%
+ summarize(Total_row_count=n())%>%
+ filter(Total_row_count==max(Total_row_count))
+ View(max_freq_time)
+
+ #Q.9
+ slot_hour<-hpddata%>%
+ filter(!is.na(Time),Time!='-')%>%
+ separate(Time,c("slot_hour"))%>%
+ mutate(time_bracket = ifelse(parse_number(slot_hour)>=6&parse_number(slot_hour)<12,'Morning',
+ ifelse(parse_number(slot_hour)>=12&parse_number(slot_hour)<16,'Afternoon',
+ ifelse(parse_number(slot_hour)>=16&parse_number(slot_hour)<20,'Evening',
+ ifelse(parse_number(slot_hour)>=20&parse_number(slot_hour)<=24,'Night','Night')))))
+
## Warning: Too many values at 193 locations: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
+## 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...
+
View(slot_hour)
+
+ #Q.10
+
+ repeated_patients<-hpddata%>%
+ group_by(id)%>%
+ summarize(repeated_count =n())%>%
+ filter(repeated_count>1)%>%
+ mutate(total_repeated=1)%>%
+ group_by(total_repeated)%>%
+ summarize(sum(total_repeated))
+ View(repeated_patients)
+ #Q.11
+ repeated_patients<-hpddata%>%
+ group_by(id)%>%
+ summarize(repeated_count =n())%>%
+ filter(repeated_count>1)%>%
+ select(id)
+ #Q.12
+ repeated_patients<-hpddata%>%
+ group_by(id,Procedure)%>%
+ summarize(repeated_count =n())%>%
+ filter(repeated_count>1)%>%
+ group_by(id,Procedure)
+ View(repeated_patients)
+ #Q.13
+ gender_median_age <-hpddata%>%
+ filter(!is.na(Age),Age!='-')%>%
+ group_by(Sex)%>%
+ mutate(Age=parse_number(Age))%>%
+ summarize(median_age=median(parse_number(Age)))
+ View(gender_median_age)
+
+
+ # Q.14
+ total_amount_balance<-hpddata%>%
+ filter(!is.na(AmountBalance))%>%
+ summarize(Total_balance_amount=sum(parse_number(AmountBalance)))
+ View(total_amount_balance)
+ #Q.15
+ total_cost_Consultation<-hpddata%>%
+ filter(Procedure=="Consultation",!is.na(AmountReceived))%>%
+ summarize(Total_cost=sum(parse_number(AmountReceived)))
+ View(total_cost_Consultation)
+ #Q.17
+ age_group_data<-hpddata%>%
+ filter(!is.na(Age))%>%
+ mutate(Age_group = ifelse(parse_number(Age)<=12,'Children','Elder') )%>%
+ group_by(Age_group)%>%
+ summarize(no_visits = n())%>%
+ filter(no_visits==max(no_visits))
+ View(age_group_data)
+ #Q.18
+ total_xray_scalling <-hpddata%>%
+ filter(Procedure=='X Ray'|Procedure=='Scalling')%>%
+ summarize(Total_amount= sum(AmountReceived))
+ View(total_xray_scalling)
+
+
+
+
+