139 lines
4.5 KiB
R
139 lines
4.5 KiB
R
# Data
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## Import from previous work
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# dta<-read.csv("/Volumes/Data/exercise/source/background.csv",na.strings = c("NA","","unknown"),colClasses = "character")
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library(REDCapR)
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library(lubridate)
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library(dplyr)
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library(daDoctoR)
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# source("https://raw.githubusercontent.com/agdamsbo/daDoctoR/master/R/dob_extract_cpr_function.R")
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dta <- redcap_read_oneshot(
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redcap_uri = "https://redcap.au.dk/api/",
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token = read.csv("/Users/au301842/talos_redcap_token.csv",header = FALSE)[[1]],
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fields = c("talos_basis02a", #Indlæggelsesdato
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"cpr",
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"talos_nihss16_0", #Akut NIHSS
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"basis_kon",
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"reg_hojde", #Alle fra "reg(ister/DAP)"
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"reg_vaegt",
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"reg_vaegt_anslaaet",
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"reg_rygning",
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"reg_alkohol",
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"reg_civil",
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"reg_bolig",
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"reg_diabetes",
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"reg_hyperten",
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"reg_perifer_arteriel",
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"reg_atriefli",
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"reg_ami",
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"reg_tidl_tci",
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"reg_trombolyse",
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"reg_trombektomi",
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"rtreat" #Trial treatment
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)
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)$data |>
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mutate(age=time_length(talos_basis02a-dob_extract_cpr(cpr),
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unit="year")
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select(!c("cpr"))
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## Cleaning and enhancing
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dta$pase_drop<-factor(ifelse((dta$pase_0_q=="q_2"|dta$pase_0_q=="q_3"|dta$pase_0_q=="q_4")&dta$pase_06_q=="q_1","yes","no"),levels = c("no","yes"))
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dta$pase_drop[is.na(dta$pase_6)]<-NA
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dta$pase_drop[is.na(dta$pase_0)]<-NA
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## Selection of data set and formatting
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library(dplyr)
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dta_f<-dta %>% filter(pase_0_q != "q_1" & !is.na(pase_drop))
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variable_names<-c("age","sex","weight","height",
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"bmi",
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"smoke_ever",
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"civil",
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"diabetes",
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"hypertension",
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"pad",
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"afli",
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"ami",
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"tci",
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"nihss_0",
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"thrombolysis",
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"thrombechtomy",
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"rep_any","pase_0_q","pase_drop")
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library(daDoctoR)
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dta2<-dta_f[,variable_names]
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dta2<-col_num(c("age","weight","height","bmi","nihss_0"),dta2)
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dta2<-col_fact(c("sex","smoke_ever","civil","diabetes", "hypertension","pad", "afli", "ami", "tci","thrombolysis", "thrombechtomy","rep_any","pase_0_q","pase_drop"),dta2)
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## Partitioning
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library(caret)
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set.seed(100)
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## Step 1: Get row numbers for the training data
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trainRowNumbers <- createDataPartition(dta2$pase_drop, p=0.8, list=FALSE)
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## Step 2: Create the training dataset
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trainData <- dta2[trainRowNumbers,]
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## Step 3: Create the test dataset
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testData <- dta2[-trainRowNumbers,]
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y_test = testData[,"pase_drop"]
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# Store X and Y for later use.
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x = trainData %>% select(!matches("pase_drop"))
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y = trainData[,"pase_drop"]
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# Normalization and dummy binaries
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# One-Hot Encoding
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# Creating dummy variables is converting a categorical variable to as many binary variables as here are categories.
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dummies_model <- dummyVars(pase_drop ~ ., data=trainData)
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# Create the dummy variables using predict. The Y variable (Purchase) will not be present in trainData_mat.
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trainData_mat <- predict(dummies_model, newdata = trainData)
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# # Convert to dataframe
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trainData <- data.frame(trainData_mat)
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# # See the structure of the new dataset
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str(trainData)
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dummies_model <- dummyVars(pase_drop ~ ., data=testData)
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testData_mat <- predict(dummies_model, newdata = testData)
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testData <- data.frame(testData_mat)
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preProcess_range_model <- preProcess(testData, method='range')
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testData <- predict(preProcess_range_model, newdata = testData)
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testData$pase_drop<-y_test
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# Imputation
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library(RANN) # required for knnInpute
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preProcess_missingdata_model <- preProcess(trainData, method='knnImpute')
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# preProcess_missingdata_model
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trainData <- predict(preProcess_missingdata_model, newdata = trainData) # Giver fejl??
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anyNA(trainData)
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# skimr::skim(trainData)
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# skimr::skim(x)
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preProcess_range_model <- preProcess(trainData, method='range')
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trainData <- predict(preProcess_range_model, newdata = trainData)
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# Append the Y variable
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trainData$pase_drop <- y
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# Export
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write.csv(trainData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/trainData.csv",row.names = FALSE)
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write.csv(testData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/testData.csv",row.names = FALSE)
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