Shayan haider isb r assignment2

Shayan.R

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+library(readr)
+library(dplyr)
+library(ggplot2)
+library(tidyr)
+library(mosaic)
+library(data.table)
+library(lubridate)
+library(stringi)
+
+hosp_data <- read_csv("~/DIH/Assignment/Assignment 2/hospitaldata.csv")
+View(hosp_data)
+
+###Q1, remove the dots in the names?
+names(hosp_data) <- gsub(".", "", names(hosp_data), fixed = TRUE)
+
+###Q2, Which day of the week is expected to have most visits? Ans = Monday 51
+hosp_data <- separate(hosp_data , Date , c("day" , "date") , sep="," , extra = "merge") ##making aseparate column Day which contains the weekday
+day_occurances <- table(unlist(hosp_data$day))
+max(day_occurances)
+
+
+###Q3, What is the average age of patients? Ans==32.40
+
+hosp_data$Age <- gsub("-", NA , hosp_data$Age , fixed = T)
+hosp_data$Age <- gsub("", NA , hosp_data$Age , fixed = T)
+hosp_data$Age <- gsub(" ", NA , hosp_data$Age, fixed = T)
+
+month2year <- function(x){
+  print(x)
+  if(is.na(x) == FALSE & grepl("M" , x)){
+    age_year <- gsub("M", "",  x , fixed = T)
+    age_year <- as.integer(age_year)
+    age_year<-age_year/12
+    print(age_year)
+    return(age_year)
+    }
+  else{
+    x <- as.integer(x)
+    return(x)
+    }
+}
+
+hosp_data$Age <- Map({function (a)  month2year(a)}, hosp_data$Age)
+hosp_data$Age <- as.integer(hosp_data$Age)mean_age <- mean(hosp_data$Age, na.rm = TRUE)
+print(mean_age)
+
+
+###Q4, 4. How many children were entertained? (Make a Bracket of Age from 1-12)  Ans == 24
+child_count <- filter(hosp_data , Age <=12 & Age >= 1)
+nrow(child_count)
+
+
+###Q5Which gender type had what kind of procedure in abundance? Dentist for both male and female
+
+hosp_data$Sex <- gsub("-", NA , hosp_data$Sex , fixed = T)
+hosp_data$Sex <- gsub("", NA , hosp_data$Sex , fixed = T)
+hosp_data$Sex <- gsub("f", "F" , hosp_data$Sex, fixed = T)
+
+sexVspecialty <- hosp_data %>% 
+  select(Sex,Specialty) %>% 
+###  group_by(Sex, Specialty) %>% 
+###  summarize(count = n())
+
+View(sexVspecialty)
+qplot(data = sexVspecialty,Sex, fill = Specialty)
+
+###6. Which Doctor is earning highest ? Doctor Alaf
+hosp_data$TotalCharges <- gsub("-", NA , hosp_data$TotalCharges , fixed = T)
+hosp_data$TotalCharges <- gsub("", NA , hosp_data$TotalCharges , fixed = T)
+hosp_data$TotalCharges <- gsub("Cancelled", "NA" , hosp_data$TotalCharges , fixed = T)
+hosp_data$TotalCharges <- as.numeric(hosp_data$TotalCharges)
+
+docVearning <- hosp_data %>%
+  select(ConsultingDoctor, TotalCharges) %>%
+  group_by(ConsultingDoctor) %>%
+  summarize( Total = sum(TotalCharges , na.rm = T))
+
+
+View(docVearning)
+qplot(data = docVearning , ConsultingDoctor , Total) + geom_bar(stat = "identity" , fill = "#FF5733")
+
+
+###7 Which procedure type earns more money? Orthodontics
+procedureVearning <- hosp_data %>%
+  select(Procedure, TotalCharges) %>%
+  group_by(Procedure) %>%
+  summarize( Total = sum(TotalCharges , na.rm = T))
+View(procedureVearning)
+qplot(data = procedureVearning , Procedure , Total) + geom_bar(stat = "identity" , fill = "#FF5733")
+
+###8 Which time of the day has highest frequency of visits by hour? 13th Hour
+hosp_data$Time <- gsub("-", NA , hosp_data$Time , fixed = T)
+hosp_data$Time <- gsub("", NA , hosp_data$Time , fixed = T)
+## hosp_data$Time <- gsub("11:00AMAM", "11:00AM" , hosp_data$Time , fixed = T)
+
+
+timebracket <- hosp_data %>% 
+  select(Time) %>%
+  mutate(Hour = hour(hm(format(strptime(Time,"%I:%M %p"),"%H:%M"))))%>%
+  group_by(Hour) %>%
+  summarize(count= n() )
+
+View(timebracket)
+
+###9. Create a bracket of time by Morning, Afternoon, Evening   :: Afternoon         
+
+timebracketfunc <- function(x){
+  print(x)
+  for (i in length(x)) {
+
+  if(x >= 6 & x < 12){
+    return("Morning")
+  }
+  else if(x >= 12 & x < 16){
+    return("Afternoon")
+  }
+  else if(x >= 16 & x < 19){
+    return("Evening")
+  }
+  else if((x >= 19 & x < 24) | (x >= 0 & x < 6)){
+    return("Night")
+  }
+  else if(is.na(x) == T){
+    return(x)
+  }
+}
+}
+
+timebracket1 <- hosp_data %>% 
+  select(Time) %>%
+  mutate(Hour = hour(hm(format(strptime(hosp_data$Time,"%I:%M %p"),"%H:%M")))) ##  %>%
+
+breaks <- hour(hm(6, 12, 16, 19, 23))
+labels <- c("Morning","Afternoon","Evening", "Night")
+
+timebracket1 <- timebracket1 %>%
+  mutate('Time_Bracket' = cut(Hour, breaks, labels)) %>%
+  group_by(Time_Bracket) %>%
+  summarize(count= n() )
+
+hosp_data <- hosp_data %>%
+  mutate('Time Bracket' = cut(Hour, breaks, labels))
+
+View(timebracket1)
+
+###10. How many patients are repeated visitors? 37
+Repeat_visitor <- hosp_data %>% 
+  select(id) %>%
+  group_by(id) %>%
+  summarize(count= n()) %>%
+  filter(count > 1)
+
+nrow(Repeat_visitor)
+
+View(Repeat_visitor)
+
+
+###11. Give us the id of repeated visitors.
+Repeat_visitor <- hosp_data %>% 
+  select(id) %>%
+  group_by(id) %>%
+  summarize(count= n()) %>%
+  filter(count > 1)
+
+View(Repeat_visitor)
+
+#12. Which patients visited again for the same problem? Ans= 24
+Repeat_visitor_same_prob <- hosp_data %>% 
+  select(id, Procedure) %>%
+  group_by(id , Procedure) %>%
+  summarize(count= n()) %>%
+  filter(count > 1)
+
+View(Repeat_visitor_same_prob)
+
+nrow(Repeat_visitor_same_prob)
+
+
+#13.What is the median age for Females and Males?  Female = 29 , male = 30
+
+hosp_data$Age <- as.numeric(hosp_data$Age)
+
+Median_age_sex <- hosp_data %>% 
+  select(Age, Sex) %>%
+  group_by(Sex)  %>%
+  summarize(Median = median(Age, na.rm = T))
+
+View(Median_age_sex)
+
+#14. What is the total amount in balance? 222500
+hosp_data$AmountBalance <- gsub("-", NA , hosp_data$AmountBalance , fixed = T)
+hosp_data$AmountBalance <- gsub("", NA , hosp_data$AmountBalance , fixed = T)
+hosp_data$AmountBalance <- gsub(",", "" , hosp_data$AmountBalance , fixed = T)
+hosp_data$AmountBalance <- as.numeric(hosp_data$AmountBalance)
+
+sum(hosp_data$AmountBalance , na.rm = T)
+
+###Q15. How much money was made by Procedure Type "Consultation" . 83950
+consulting_cash <- hosp_data %>% 
+  select(Procedure, TotalCharges) %>%
+  filter(Procedure == "Consultation")
+
+
+sum(consulting_cash$TotalCharges , na.rm = T)
+View(consulting_cash)
+
+
+#Q16.  Is there a relation between Age and Total Charges paid? Co-relation = 0.03 , no relation
+
+ageVcash <- hosp_data %>% 
+  select(Age, TotalCharges)
+
+View(ageVcash)
+cor.test(x= ageVcash$Age , y= ageVcash$TotalCharges, na.rm = T , use = "complete.obs")
+
+###Q17 . Which Age group had highest number of visits? Age 30 visits 12
+
+age_visits <- hosp_data %>% 
+  select(Age) %>%
+  group_by(Age) %>%
+  summarize(count= n()) %>%
+  filter(count > 1)
+View(age_visits)
+
+##Q18. . What is the total cost earned by Procedure Type X Ray and Scalling together 89750
+
+consulting_xray_cash <- hosp_data %>% 
+  select(Procedure, TotalCharges) %>%
+  filter(Procedure == "Consultation" | Procedure == "X Ray")
+
+
+sum(consulting_xray_cash$TotalCharges , na.rm = T)
+View(consulting_xray_cash)
+
+#####Extracting clean data
+
+write.csv(hosp_data, "C:/Users/shayan.haider/Documents/DIH/Assignment/Assignment_2/hosp_data_clean.csv")