junaidrassttwoR

junaid_raza_lhr_r_2/junaidrazalhr.R

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+#getwd()
+getwd()
+
+
+#setwd('C:/Users/junaid.raza/junaidraza_lhr_r_assignment2',header=TRUE)
+
+library(dplyr)
+library(tidyr)
+#install.packages("lubridate")
+library(lubridate)
+
+#Working with two data sets
+#Loading Data into df
+df <- read.csv('hospitaldata.csv', header = TRUE)
+genpro <- read.csv('hospitaldata.csv', header = TRUE, na.strings=c("","NA"))
+#Loading Data into test
+test <- read.csv('hospitaldata.csv', header = TRUE)
+
+#Loading Data into test
+#rlab <- read.csv('hospitaldata.csv', header = TRUE)
+
+
+#1
+#Removing the Dots 
+names(df) <- gsub("\\.", "", names(df))
+names(df)
+
+#2
+#Which week day have max visits
+#Sepate the table to extract three more columns of 
+#Day Month and Year
+test <- test %>% separate(Date,c('day','month','year'),sep=',')
+df <- df %>%  separate(Date,c('day','month','year'),sep=',')
+weekdays<-table(df$day)
+unique(weekdays)
+weekdays
+#Monday have 51 visits
+
+
+#3
+#Average age of patients
+class(df$Age)
+df$Age<-as.numeric(df$Age)
+mean(df$Age)
+#Mean os 23.33
+
+
+#4
+#Children enteratined(age 1 - 12)
+lengthofage<-length(which(df$Age<12))
+lengthofage
+#58 is length of records
+
+
+
+#5
+#Which gender have what max procedures
+class(df$Sex)
+class(df$Procedure)
+#spaces replaced with NA of all data frame
+df$Sex<-tolower(df$Sex)
+df$Sex
+genproc <- df %>%
+  group_by(Sex,Procedure) %>%
+  summarize(count=n())
+View(genproc)
+
+
+#6
+#Highest Earning
+class(df$TotalCharges)
+df$TotalCharges<- as.numeric(as.character(df$TotalCharges))
+class(df$TotalCharges)
+class(df$ConsultingDoctor)
+df$ConsultingDoctor<-as.character(df$ConsultingDoctor)
+class(df$ConsultingDoctor)
+
+docmaxpay <- df %>%
+  group_by(ConsultingDoctor) %>%
+  summarise(total = sum(TotalCharges, na.rm = TRUE))
+
+docmaxpay$ConsultingDoctor[which(docmaxpay$total==max(docmaxpay$total, na.rm = TRUE),arr.ind=TRUE)]
+#Dr Alaf Khan
+
+
+#7
+#Procedure type earn max money
+class(df$Procedure)
+df$Procedure <- as.character(df$Procedure)
+maxprocd <- df %>%
+  group_by(Procedure) %>%
+  summarise(total = sum(TotalCharges, na.rm = TRUE))
+
+maxprocd$Procedure[which(maxprocd$total==max(maxprocd$total, na.rm = TRUE),arr.ind=TRUE)]
+#Orthodontics 240,000
+
+
+#8
+#Highest freq of Visits by Hour.
+class(df$Time)
+df$Time<-as.character(df$Time)
+df$Time[df$Time=="-"]<-""
+df$Time[df$Time==""]<-NA
+unique(df$Time)
+
+get_time<-df$Time
+
+get_time<-strptime(get_time,"%I:%M %p")
+get_hours<-hour(get_time)
+
+actual_mode <- table(get_hours)
+names(actual_mode)[actual_mode == max(actual_mode)]
+#Hence the hour 13:00 is the most visited hour of the day
+
+
+
+#9
+#Create Bracket of Time.
+
+
+
+
+
+#10
+#patients are Repeated visitors
+class(df$id)
+vis_num <- df %>%
+  group_by(id) %>%
+  summarize(count=n())
+View(vis_num)
+vis_num$id[(which(vis_num$count >1))]
+
+#11
+#Info Repeated Visitor
+length(vis_num$id[(which(vis_num$count >1))])
+#37
+
+
+#12
+#Patient again visit for same problem
+vis_two <- df %>%
+  group_by(id,Procedure) %>%
+  summarize(count=n())
+vis_two
+vis_two <- vis_two$id[(which(vis_two$count >1))]
+vis_two
+
+
+
+
+
+#13
+#Median age for male and females
+fmed<-which(df$Sex=='f',arr.ind =TRUE) 
+median(as.numeric(as.character(df$Age[fmed])),na.rm = TRUE)
+#22
+mmed<-which(df$Sex=='m',arr.ind =TRUE) 
+median(as.numeric(as.character(df$Age[mmed])),na.rm = TRUE)
+#21
+
+
+
+#14
+#Total Amount Balance
+
+
+
+#15
+#Procedure consultation Money
+consult_money<- which(df$Procedure=='Consultation', arr.ind=TRUE)
+sum(df$TotalCharges[consult_money], na.rm=TRUE)
+#83950
+
+
+
+
+#16
+#Relation age and Total Charges
+class(df$Age)
+class(df$TotalCharges)
+df$TotalCharges<-as.numeric(as.character(df$TotalCharges))
+cor.test(df$Age, df$TotalCharges)
+#Value is greate then 0.05
+
+
+
+#17
+#Age with max high visits
+
+
+
+
+
+
+#18
+#Procedure X-Ray adn Scalling Total cost
+total_xray_cost<- which(df$Procedure=='X Ray' | df$Procedure=='Scalling' , arr.ind=TRUE)
+sum(df$TotalCharges[total_xray_cost], na.rm=TRUE)
+#22300
+
+
+
+
+
+
+