Export code

.Rproj.user/DFCA1C7C/sources/prop/421CC383

@@ -1,5 +1,5 @@
 {
     "tempName": "Untitled1",
     "cursorPosition": "128,0",
-    "scrollLine": "119"
+    "scrollLine": "140"
 }

demographics.R

@@ -1,5 +1,11 @@
+library("writexl")
+
 demographics <- read.csv("oxford_hgshsa_260820_demographics.csv", sep = ";")
 
+demographics$missing <- data.export$has.na
+
+write_xlsx(demographics,"hab_taxon_demographics_2020_final_exported.xlsx")
+
 data <- read.csv("oxford_hgshsa_260820_modded.csv", sep = ";")
 
 data <- na.omit(data)

hability_taxon_data_prep.R

@@ -1,18 +1,34 @@
 library("RTaxometrics")
 library("dplyr")
+library("writexl")
 
+#Read the data
 data <- read.csv("oxford_hgshsa_260820_modded.csv", sep = ";")
 
-data <- na.omit(data)
 
+#Prepare a table for exporting for OSF
+data.export <- data
+
+#Generate the xlsx file for OSF
+data.export$has.na <- 0
+data.export$has.na[rowSums(is.na(data.export)) > 0] <- 1
+write_xlsx(data.export,"hab_taxon_2020_final_exported.xlsx")
+
+#Get the filtered data
+data <- data.export[data.export$has.na == 0,]
+
+#Subset the dataset for involuntariness scale
 invol <- select(data, INV.1:INV.12)
 
+#Classify data with base rates .15 and .10
 invol15 <- ClassifyCases(invol, p = 0.15)
 
 invol10 <- ClassifyCases(invol, p = 0.10)
 
+#Remove problematic items
 invol_purified <- select(data, INV.3:INV.10)
 
+#Classify data with base rates .15 and .10
 invol_pur15 <- ClassifyCases(invol_purified, p = 0.15)
 
 invol_pur10 <- ClassifyCases(invol_purified, p = 0.10)
@@ -25,7 +41,7 @@ invol$challenge <- invol$INV.4 + invol$INV.5 + invol$INV.6 + invol$INV.8 + invol
 invol$cogper <- invol$INV.9 + invol$INV.11
 invol$amnesia <- invol$INV.12
 
-
+#Subset subscales columns and classify
 invol_subscales15 <- select(invol, ideomotor, challenge, cogper, amnesia)
 invol_subscales15 <- ClassifyCases(invol_subscales15, p = 0.15)
 
@@ -41,7 +57,7 @@ invol_purified$ideomotor <- invol_purified$INV.3 + invol_purified$INV.7
 invol_purified$challenge <- invol_purified$INV.4 + invol_purified$INV.5 + invol_purified$INV.6 + invol_purified$INV.8 + invol_purified$INV.10
 invol_purified$cogper <- invol_purified$INV.9
 
-
+#Subset subscales columns and classify
 invol_subscales_pur15 <- select(invol_purified, ideomotor, challenge, cogper)
 invol_subscales_pur15 <- ClassifyCases(invol_subscales_pur15, p = 0.15)
 
@@ -56,6 +72,7 @@ invol_subscales_pur10 <- ClassifyCases(invol_subscales_pur10, p = 0.10)
 #Create a H. suggestibility subset
 hab <- select(data, HGSHS.A1:HGSHS.A11, HGSHS.A12)
 
+#Classify data with base rates .15 and .10
 hab15 <- ClassifyCases(hab, p = 0.15)
 
 hab10 <- ClassifyCases(hab, p = 0.10)
@@ -66,6 +83,7 @@ hab10 <- ClassifyCases(hab, p = 0.10)
 #Create a H. suggestibility subset without "bad" items
 hab_purified <- select(data,  HGSHS.A3:HGSHS.A10)
 
+#Classify data with base rates .15 and .10
 hab_pur15 <- ClassifyCases(hab_purified, p = 0.15)
 
 hab_pur10 <- ClassifyCases(hab_purified, p = 0.10)
@@ -80,7 +98,7 @@ hab$challenge <- hab$HGSHS.A4 + hab$HGSHS.A5 + hab$HGSHS.A6 + hab$HGSHS.A8 + hab
 hab$cogper <- hab$HGSHS.A9 + hab$HGSHS.A11
 hab$amnesia <- hab$HGSHS.A12
 
-
+#Subset subscales columns and classify
 hab_subsc15 <- select(hab, ideomotor, challenge, cogper, amnesia)
 hab_subsc15 <- ClassifyCases(hab_subsc15, p = 0.15)
 
@@ -96,7 +114,7 @@ hab_purified$ideomotor <- hab_purified$HGSHS.A3 + hab_purified$HGSHS.A7
 hab_purified$challenge <- hab_purified$HGSHS.A4 + hab_purified$HGSHS.A5 + hab_purified$HGSHS.A6 + hab_purified$HGSHS.A8 + hab_purified$HGSHS.A10
 hab_purified$cogper <- hab_purified$HGSHS.A9
 
-
+#Subset subscales columns and classify
 hab_subsc_pur15 <- select(hab, ideomotor, challenge, cogper)
 hab_subsc_pur15 <- ClassifyCases(hab_subsc_pur15, p = 0.15)
 
@@ -105,11 +123,7 @@ hab_subsc_pur10 <- select(hab, ideomotor, challenge, cogper)
 hab_subsc_pur10 <- ClassifyCases(hab_subsc_pur10, p = 0.10)
 
 
-# Performing the analysis
-
-## Involuntariness data: separate items
-
-### P = 0.15
+# Perform the analysis
 
 cat("invol15")
 RunTaxometrics(invol15)