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install.packages(‘dplyr’)
library(dplyr)## Warning: package 'dplyr' was built under R version 4.1.2##
## 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# import data
celebrity_patients <- read.csv("celebrity_patientsNA.csv", header=TRUE)
medications <- read.csv("medications.csv", header=TRUE)
procedures <- read.csv("procedures.csv", header=TRUE)
summary(procedures)## DATE ID ENCOUNTER CODE
## Length:1198 Length:1198 Length:1198 Min. :1.225e+06
## Class :character Class :character Class :character 1st Qu.:1.204e+08
## Mode :character Mode :character Mode :character Median :4.302e+08
## Mean :1.262e+12
## 3rd Qu.:4.302e+08
## Max. :7.560e+14
##
## Procedure BASE_COST REASONCODE REASONDESCRIPTION
## Length:1198 Min. : 261.8 Min. : 10509002 Length:1198
## Class :character 1st Qu.: 516.6 1st Qu.: 10509002 Class :character
## Mode :character Median : 683.0 Median : 72892002 Mode :character
## Mean : 3391.2 Mean :117775301
## 3rd Qu.: 3431.1 3rd Qu.:195662009
## Max. :48509.5 Max. :410429000
## NA's :942# joins the matching values
celebrity_patients$Patient_ID <- as.character(celebrity_patients$Patient_ID)
#first <- left_join(celebrity_patients, medications, by=c("Patient_ID" = "ID"))
#sec <- left_join(celebrity_patients, procedures, by=c("Patient_ID" = "ID"))
join <- inner_join(medications, procedures, by="ID")
merged_patients_df <- left_join(celebrity_patients, join, by=c("Patient_ID" = "ID"))
count(merged_patients_df)## n
## 1 26table(merged_patients_df$MARTIAL)## < table of extent 0 >table(merged_patients_df$SEX)##
## 9999 FEMALE MALE
## 1 12 13# filters martial and sex
males_only <- filter(merged_patients_df, SEX == 'MALE', MARITAL == 'MARRIED' | MARITAL == 'SEPERATED')
table(males_only$SEX, males_only$MARITAL)##
## MARRIED
## MALE 6females_only <- filter(merged_patients_df, SEX == 'FEMALE', MARITAL == 'MARRIED' | MARITAL == 'SEPERATED')
arrange(females_only, HLTHPLN1, desc(AGE))## Patient_Name Patient_ID AGE SEX MARITAL GENHLTH HLTHPLN1
## 1 Beyonce 2016001351 38 FEMALE MARRIED GOOD YES
## 2 Minaj, Nicki 2016001780 37 FEMALE MARRIED GOOD YES
## 3 Ciara 2016004483 34 FEMALE MARRIED FAIR YES
## 4 Cardi B 2016002149 27 FEMALE MARRIED VERY-GOOD YES
## CHECKUP1 EXERANY2 MAXDRNKS MARIJUANA START STOP
## 1 WITHIN PAST YEAR YES 0 YES 2/12/10 2/19/10
## 2 WITHIN PAST YEAR YES 2 YES 3/26/11 4/9/11
## 3 WITHIN PAST YEAR YES 4 YES 12/6/10 12/1/11
## 4 WITHIN PAST 2 YEARS YES 3 YES 3/26/11 4/9/11
## PAYER ENCOUNTER.x
## 1 d47b3510-2895-3b70-9897-342d681c769d ae97dfe0-c619-44c1-a687-f215d7fac984
## 2 b1c428d6-4f07-31e0-90f0-68ffa6ff8c76 86d22444-38b4-4481-bc31-8a509a56118b
## 3 b1c428d6-4f07-31e0-90f0-68ffa6ff8c76 093e8801-fc20-46d5-9520-2775ddeb8eb5
## 4 b1c428d6-4f07-31e0-90f0-68ffa6ff8c76 86d22444-38b4-4481-bc31-8a509a56118b
## CODE.x Medication BASE_COST.x PAYER_COVERAGE DISPENSES
## 1 313782 Acetaminophen 325 MG Oral Tablet 8.31 0 1
## 2 309097 Cefuroxime 250 MG Oral Tablet 163.15 0 1
## 3 831533 Errin 28 Day Pack 44.65 0 12
## 4 198405 Ibuprofen 100 MG Oral Tablet 6.13 0 1
## TOTALCOST REASONCODE.x REASONDESCRIPTION.x DATE
## 1 8.31 10509002 Acute bronchitis (disorder) 2/12/10
## 2 163.15 NA 12/14/09
## 3 535.80 NA 9/12/14
## 4 6.13 NA 12/14/09
## ENCOUNTER.y CODE.y
## 1 ae97dfe0-c619-44c1-a687-f215d7fac984 23426006
## 2 6a6caa2e-961b-432b-8b6d-6e408ef5157f 415300000
## 3 dccbc613-e1e7-4929-9855-4e19585ca350 430193006
## 4 6a6caa2e-961b-432b-8b6d-6e408ef5157f 430193006
## Procedure BASE_COST.y REASONCODE.y
## 1 Measurement of respiratory function (procedure) 516.65 10509002
## 2 Review of systems (procedure) 516.65 NA
## 3 Medication Reconciliation (procedure) 591.07 NA
## 4 Medication Reconciliation (procedure) 565.52 NA
## REASONDESCRIPTION.y
## 1 Acute bronchitis (disorder)
## 2
## 3
## 4# adds generation
males_only <- mutate(males_only, Generation = ifelse(AGE %in% 0:24, "GEN Z",
ifelse(AGE %in% 25:39, "Millennial",
ifelse(AGE %in% 40:54,"Gen X",
ifelse(AGE >= 55, "Baby Boomer",
NA)))))
table(males_only$Generation)##
## Baby Boomer Gen X Millennial
## 2 1 3write.csv(females_only, "female_patients.csv")
write.csv(males_only, "male_patients.csv")