Tirgit · alkat19 · Sep 6, 2022 · Sep 6, 2022
diff --git a/.DS_Store b/.DS_Store
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diff --git a/NHANES_R/03_imputation.R b/NHANES_R/03_imputation.R
@@ -1,17 +1,30 @@
 # Set working directory
-setwd("~/GitHub/NHANES_T2D/Data")
+setwd("~/Documents/GitHub/NHANES_T2D/Data") # I need to use the Documents directory in MAC-OS
 
 # load necessary libraries
 library(mice)
+library(tidyverse)
+
 
 # load complete merged data to obtain survey number levels
 # nothing to do with this data, we only need the factor levels for the loop
 cleaned_full_df <- readRDS("cleaned_full_df.rds")
+
+# Correctly fill the missing values of now_BP_meds variable
+
+cleaned_full_df <- cleaned_full_df |> mutate(now_BP_meds = 
+                                               if_else(hypertension_ever == 'no hypertension' & is.na(now_BP_meds), 
+                                                       'no BP meds', as.character(now_BP_meds)))
+
+# Recode the now_BP_meds variable as factor
+
+cleaned_full_df$now_BP_meds <- as.factor(cleaned_full_df$now_BP_meds)
+
 cleaned_full_df_used <- cleaned_full_df[cleaned_full_df$survey_nr %in% c("1999-2000","2001-2002","2003_2004","2005_2006","2007_2008","2009_2010"),]
 
 # calculate average missingness in data
 (sum(is.na(cleaned_full_df))/prod(dim(cleaned_full_df)))*100
-# it is 13.9%, which we round up to 15% - so we will make 15 imputed copies
+# it is 11.9%, which we round up to 15% - so we will make 15 imputed copies
 
 # load data per survey (loop)
 for (i in levels(cleaned_full_df$survey_nr)) {
@@ -28,7 +41,10 @@ SDMVSTRA <- full_df$SDMVSTRA
 full_df$SDMVSTRA <- NULL
 
 # imputation: 5 copies, 5 iterations, random forest algorithm
-imputation_object <- mice(full_df, method = "rf", m = 15, maxit = 5, seed = 64370)
+
+# After changes in the mice
+# We specify in mice that rfPackage = 'randomForest', otherwise we receive error of incorrect dimensions
+imputation_object <- mice(full_df, rfPackage = 'randomForest', m = 15, maxit = 5, seed = 64370)
 
 # investigate convergence visually
 # imputation_object$method #those variables with no missing have "" as method - they are still used for imputation