R Assignment with updated csv file.

aliejaz1749_khi_r_assignment2.R

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+library(dplyr)
+library(lubridate)
+library(readr)
+
+# List all objects in the workspace
+#ls()
+
+# Or remove all files from your workspace
+#rm(list = ls())
+
+# Load CSV file
+hospitaldata <- read.csv('D://diHub//Assessment2_RandPython_Marked//aliejaz1749_khi_r_assignment2//hospitaldata.csv', header = TRUE, stringsAsFactors = F)
+str(hospitaldata)
+
+#create copy of dataframe
+df <- tbl_df(hospitaldata)
+glimpse(df)
+
+# Qus1. Please remove the dots in the names, so it may become easier for you to work through it.
+names(df) <- gsub("\\.", "", names(df))
+
+# Qus2. Which day of the week is expected to have most visits?
+NameOfDays <- wday(mdy(df$Date), label = TRUE)
+which.max(summary(NameOfDays))
+
+# Qus3. What is the average age of patients?
+unique(df$Age)
+class(df$Age)
+p_age_var <- df$Age
+p_age <- as.numeric(p_age_var)
+mean(p_age, na.rm = TRUE)
+
+# Qus4. How many children were entertained? (Make a Bracket of Age from 1-12)
+p_child_age <- as.numeric(df$Age)
+p_child_age[is.na(p_child_age)] <- 0
+sum(p_child_age >= 12)
+
+# Qus5. Which gender type had what kind of procedure in abundance? i.e. Female visit mostly because of Gynae Problem
+gender_type<-df%>%
+filter(!is.na(Sex))%>%
+group_by(Procedure,Sex)%>%
+summarize(total_gender= n())%>%
+filter(total_gender==max(total_gender))
+gender_type
+
+# Qus6. Which Doctor is earning highest?
+d_high_ern <- select(df, ConsultingDoctor , AmountReceived)
+d_high_ern <- filter(df , ConsultingDoctor!='Nursing Staff' , !is.na(AmountReceived))
+grp_d_high_ern <- group_by(df, ConsultingDoctor)
+summriz_doc_ern <- summarize(grp_d_high_ern, sum(AmountReceived), na.rm = TRUE)
+summriz_doc_ern[is.na(summriz_doc_ern)] <- 0
+as.numeric(summriz_doc_ern$`sum(AmountReceived)`)
+max(summriz_doc_ern$`sum(AmountReceived)`)
+
+# Qus7. Which procedure type earns more money?
+p_proc_typ_high <- select(df, Procedure , AmountReceived)
+x <- p_proc_typ_high%>%
+ filter(!is.na(AmountReceived))%>% 
+  group_by(Procedure)%>%
+  summarize(Procedure_1 = sum(AmountReceived))%>%
+  filter(Procedure_1 == max(Procedure_1)) 
+  x
+
+# Qus8. Which time of the day has highest frequency of visits by hour?
+x <- df %>%
+  filter(!is.na(Time), Time != '-') %>%
+  group_by(Time) %>%
+  summarize(time_wise_cnt = n()) %>%
+  filter(Time != '') %>%
+  filter(time_wise_cnt == max(time_wise_cnt))
+x
+
+# Qus9. Create a bracket of time by Morning, Afternoon, Evening, Night (6am - 12pm - Morning, 12 pm- 4 pm, Afternoon, 4 pm- 7pm, Evening, 7pm - 6 am, Night).
+
+
+# Qus10. How many patients are repeated visitors?
+p_rep_patient_visit <- df %>%
+  group_by(id)%>%
+  summarize(p_count = n()) %>%
+  filter(p_count > 1) %>%
+  summarize(tot_rep_vis = n())
+p_rep_patient_visit
+
+# Qus11. Give us the id of repeated visitors.
+p_rep_vistors <- df %>%
+  group_by(id)%>%
+  summarize(p_rep_Vist = n()) %>%
+  filter(p_rep_Vist > 1) %>%
+  arrange(desc(p_rep_Vist))
+p_rep_vistors
+
+# Qus12. Which patients visited again for the same problem?
+p_p_prob_Vist <- df %>%
+  group_by(Procedure, id)%>%
+  summarize(p_prob_Vist = n()) %>%
+  filter(p_prob_Vist > 1) %>%
+  arrange(desc(p_prob_Vist))
+p_p_prob_Vist
+
+# Qus13. What is the median age for Females and Males?
+p_medi_gender <- df %>%
+  group_by(Sex)%>%
+  summarize(p_Sex = n()) %>%
+  filter(p_Sex > 1) %>%
+  arrange(desc(p_Sex))
+p_medi_gender
+
+# Qus14. What is the total amount in balance?
+p_am_blc <- df$AmountBalance
+p_am_blc <- as.numeric(parse_number(p_am_blc))
+p_am_blc <- as.numeric(p_am_blc)
+p_am_blc[which(is.na(as.numeric(as.character(p_am_blc))))]<-0
+p_am_blc = sum(p_am_blc)
+p_am_blc
+
+# Qus15. How much money was made by Procedure Type "Consultation"?
+p_consultation_max <- df %>%
+  filter( Procedure == 'Consultation', !is.na(AmountReceived), AmountReceived!= '-') %>%
+  group_by(Procedure) %>% 
+  summarize(p_consultation_max = sum(AmountReceived)) 
+p_consultation_max
+
+# Qus16. Is there a relation between Age and Total Charges paid?
+
+
+# Qus17. Which Age group had highest number of visits?
+p_max_visit <- df %>%
+  filter(Age!= '-', Age!= '', !is.na(Age)) %>%
+  group_by(Age) %>% 
+  summarize(p_max_visit = n()) %>%
+  filter(p_max_visit == max(p_max_visit))
+p_max_visit
+
+# Qus18. What is the total cost earned by Procedure Type X Ray and Scalling together?
+p_tot_cost <- df %>%
+  filter(Procedure == 'X Ray' | Procedure == 'Scalling' , Procedure!= '-', Procedure!= '', !is.na(Procedure)) %>%
+  group_by(Procedure) %>% 
+  summarize(p_tot_cost = sum(AmountReceived)) %>%
+  filter(p_tot_cost == max(p_tot_cost))
+p_tot_cost
+
+
+write.csv(df, file='D:/diHub/Assessment2_RandPython_Marked/aliejaz1749_khi_r_assignment2/updated_hospitaldata.csv')