transfer from old repo

1 PA Decline/archive/dataset.R

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+# Data
+## Import from previous work
+dta<-read.csv("/Volumes/Data/exercise/source/background.csv",na.strings = c("NA","","unknown"),colClasses = "character")
+
+## Cleaning and enhancing
+dta$pase_drop<-factor(ifelse((dta$pase_0_q=="q_2"|dta$pase_0_q=="q_3"|dta$pase_0_q=="q_4")&dta$pase_06_q=="q_1","yes","no"),levels = c("no","yes"))
+dta$pase_drop[is.na(dta$pase_6)]<-NA
+dta$pase_drop[is.na(dta$pase_0)]<-NA
+
+## Selection of data set and formatting
+library(dplyr)
+dta_f<-dta %>% filter(pase_0_q != "q_1" & !is.na(pase_drop))
+
+
+variable_names<-c("age","sex","weight","height",
+                     "bmi",
+                     "smoke_ever",
+                     "civil",
+                     "diabetes", 
+                     "hypertension",
+                     "pad", 
+                     "afli",  
+                     "ami", 
+                     "tci",
+                     "nihss_0",
+                     "thrombolysis", 
+                     "thrombechtomy",
+                     "rep_any","pase_0_q","pase_drop")
+
+
+library(daDoctoR)
+dta2<-dta_f[,variable_names]
+
+dta2<-col_num(c("age","weight","height","bmi","nihss_0"),dta2)
+dta2<-col_fact(c("sex","smoke_ever","civil","diabetes", "hypertension","pad", "afli",  "ami", "tci","thrombolysis", "thrombechtomy","rep_any","pase_0_q","pase_drop"),dta2)
+
+## Partitioning
+library(caret)
+set.seed(100)
+
+## Step 1: Get row numbers for the training data
+trainRowNumbers <- createDataPartition(dta2$pase_drop, p=0.8, list=FALSE)
+
+## Step 2: Create the training  dataset
+trainData <- dta2[trainRowNumbers,]
+
+## Step 3: Create the test dataset
+testData <- dta2[-trainRowNumbers,]
+y_test = testData[,"pase_drop"]
+
+# Store X and Y for later use.
+x = trainData %>% select(!matches("pase_drop"))
+y = trainData[,"pase_drop"]
+
+# Normalization and dummy binaries
+
+# One-Hot Encoding
+# Creating dummy variables is converting a categorical variable to as many binary variables as here are categories.
+dummies_model <- dummyVars(pase_drop ~ ., data=trainData)
+
+# Create the dummy variables using predict. The Y variable (Purchase) will not be present in trainData_mat.
+trainData_mat <- predict(dummies_model, newdata = trainData)
+
+# # Convert to dataframe
+trainData <- data.frame(trainData_mat)
+
+# # See the structure of the new dataset
+str(trainData)
+
+dummies_model <- dummyVars(pase_drop ~ ., data=testData)
+testData_mat <- predict(dummies_model, newdata = testData)
+testData <- data.frame(testData_mat)
+preProcess_range_model <- preProcess(testData, method='range')
+testData <- predict(preProcess_range_model, newdata = testData)
+testData$pase_drop<-y_test
+
+# Imputation
+
+library(RANN)  # required for knnInpute
+preProcess_missingdata_model <- preProcess(trainData, method='knnImpute')
+# preProcess_missingdata_model
+
+trainData <- predict(preProcess_missingdata_model, newdata = trainData)  # Giver fejl??
+anyNA(trainData)
+
+# skimr::skim(trainData)
+# skimr::skim(x)
+
+preProcess_range_model <- preProcess(trainData, method='range')
+trainData <- predict(preProcess_range_model, newdata = trainData)
+
+# Append the Y variable
+trainData$pase_drop <- y
+
+
+# Export
+write.csv(trainData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/trainData.csv",row.names = FALSE)
+write.csv(testData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/testData.csv",row.names = FALSE)