diff --git a/AEDB.EM.baseline.Rmd b/AEDB.EM.baseline.Rmd
index 5cd1f53..b706ea8 100644
--- a/AEDB.EM.baseline.Rmd
+++ b/AEDB.EM.baseline.Rmd
@@ -52,7 +52,6 @@ _... and load those packages._
```{r loading_packages, message=FALSE, warning=FALSE}
source("scripts/pack01.packages.R")
-
```
_We will create a datestamp and define the Utrecht Science Park Colour Scheme_.
@@ -1011,7 +1010,7 @@ cat("CREATE BASELINE TABLE\n")
# Baseline table variables
basetable_vars = c("Hospital",
"Artery_summary",
- "Age", "Gender")
+ "Age", "Gender",
# "ORyear",
# "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
# "TC_final", "LDL_final", "HDL_final", "TG_final",
@@ -1033,7 +1032,8 @@ basetable_vars = c("Hospital",
# "IPH.bin", "VesselDensity_rankNorm",
# "Calc.bin", "Collagen.bin",
# "Fat.bin_10", "Fat.bin_40",
- # "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
+ "OverallPlaquePhenotype" )
+ # , "Plaque_Vulnerability_Index")
basetable_bin = c("Hospital",
"Artery_summary",
@@ -1183,8 +1183,8 @@ saveRDS(AEDB, file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AEDB.raw.RDS"))
------------------------------------------------------------------------------------------------------------------------
- Version: v1.0.2
- Last update: 2022-07-12
+ Version: v1.0.3
+ Last update: 2022-08-23
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to get some Athero-Express Biobank Study baseline characteristics.
Minimum requirements: R version 3.4.3 (2017-06-30) -- 'Single Candle', Mac OS X El Capitan
@@ -1200,6 +1200,7 @@ saveRDS(AEDB, file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AEDB.raw.RDS"))
_W_
**Changes log**
+ * v1.0.3 Updated baseline characteristics.
* v1.0.2 Simplified the initial script. It now outputs the relevant R-objects (as .RDS).
* v1.0.1 Update to main AEDB (there is an error in the Age-variable in the new version).
* v1.0.0 Initial version. Add 'plaque vulnerability index', Fixed baseline table, added codes, and results. Major update to WORCS system.
diff --git a/AEDB.EM.baseline.nb.html b/AEDB.EM.baseline.nb.html
index 6645fca..fc620b1 100644
--- a/AEDB.EM.baseline.nb.html
+++ b/AEDB.EM.baseline.nb.html
@@ -11,7 +11,7 @@
-
+
Baseline characteristics
@@ -2998,7 +2998,7 @@ Baseline characteristics
Accompanying ‘EntropyMasker’
-2022-08-18
+2022-08-23
@@ -3044,9 +3044,64 @@ General Setup
source("scripts/pack01.packages.R")
-* General packages...
+* General packages...
+These packages have more recent versions available.
+It is recommended to update all of them.
+Which would you like to update?
+
+1: All
+2: CRAN packages only
+3: None
+4: rlang (1.0.2 -> 1.0.4) [CRAN]
+5: pillar (1.7.0 -> 1.8.1) [CRAN]
+6: viridisLite (0.4.0 -> 0.4.1) [CRAN]
+7: farver (2.1.0 -> 2.1.1) [CRAN]
+8: tibble (3.1.7 -> 3.1.8) [CRAN]
+9: scales (1.2.0 -> 1.2.1) [CRAN]
+
+
+
+
+
+
+ checking for file ‘/private/var/folders/qr/ycksfpsx091f_xhvzv9r35x00000gq/T/RtmpxOqdin/remotes10e0f4fc624be/thomasp85-patchwork-c14c960/DESCRIPTION’ ...
+
+✔ checking for file ‘/private/var/folders/qr/ycksfpsx091f_xhvzv9r35x00000gq/T/RtmpxOqdin/remotes10e0f4fc624be/thomasp85-patchwork-c14c960/DESCRIPTION’ (442ms)
+
+
+─ preparing ‘patchwork’:
+ checking DESCRIPTION meta-information ...
+
+✔ checking DESCRIPTION meta-information
+
+
+─ checking for LF line-endings in source and make files and shell scripts
+
+
+─ checking for empty or unneeded directories
+
+
+─ building ‘patchwork_1.1.2.9000.tar.gz’
+
+
+
+
+
+* installing *source* package ‘patchwork’ ...
+** using staged installation
+** R
+** byte-compile and prepare package for lazy loading
+** help
+*** installing help indices
+*** copying figures
+** building package indices
+** installing vignettes
+** testing if installed package can be loaded from temporary location
+** testing if installed package can be loaded from final location
+** testing if installed package keeps a record of temporary installation path
+* DONE (patchwork)
@@ -3055,11 +3110,11 @@ General Setup
-
-Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
-Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
-
-source("scripts/colors.R")
+Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
+Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
+
+source("scripts/colors.R")
Load relevant samples
-EM_samples <- fread(paste0(ANALYSIS_loc, "/dataverse/EntropyMasker_image_files_used.txt"))
EM_samples <- fread(paste0(ANALYSIS_loc, "/dataverse/EntropyMasker_image_files_used.txt"))
Load data
-cat("* get Athero-Express Biobank Study Database...")
cat("* get Athero-Express Biobank Study Database...")
* get Athero-Express Biobank Study Database...
-# METHOD 1: It seems this method gives loads of errors and warnings, which all are hard to comprehend
-# or debug. We expect 3,527 samples, and 927 variables; we get 927 variables!!!
-# AEdata = as.data.table(read.spss(paste0(INP_loc,"/2017-1NEW_AtheroExpressDatabase_ScientificAE_20171306_v1.0.sav"),
-# trim.factor.names = TRUE, trim_values = TRUE, # we trim spaces in values
-# reencode = TRUE, # we re-encode to the local locale encoding
-# add.undeclared.levels = "append", # we do *not* want to convert to R-factors
-# use.value.labels = FALSE, # we do *not* convert variables with value labels into R factors
-# use.missings = TRUE, sub = "NA", # we will set every missing variable to NA
-# duplicated.value.labels = "condense", # we will condense duplicated value labels
-# to.data.frame = TRUE))
-# AEdata.labels <- as.data.table(attr(AEdata, "variable.labels"))
-# names(AEdata.labels) <- "Variable"
-
-# METHOD 2: Using library("haven") importing seems flawless; best argument being:
-# we expect 3,527 samples and 888 variables, which is what you'd get with this method
-# So for now, METHOD 2 is prefered.
-#
-require(haven)
-
-# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2022_1_NEW_AtheroExpressDatabase_ScientificAE_15-02-2022.sav")) # something wrong with Age-variable
-# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_30-09-2020.sav")) # duplicate studynumbers in it
-AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))
-
-# writing off the SPSS data to an Excel.
-# fwrite(AEdata, file = paste0(INP_loc,"/2017-1NEW_AtheroExpressDatabase_ScientificAE_20171306_v1.0.values.xlsx"),
-# sep = ";", na = "NA", dec = ".", col.names = TRUE, row.names = FALSE,
-# dateTimeAs = "ISO", showProgress = TRUE, verbose = TRUE)
-# warnings()
-
-AEDB[1:10, 1:10]
# METHOD 1: It seems this method gives loads of errors and warnings, which all are hard to comprehend
+# or debug. We expect 3,527 samples, and 927 variables; we get 927 variables!!!
+# AEdata = as.data.table(read.spss(paste0(INP_loc,"/2017-1NEW_AtheroExpressDatabase_ScientificAE_20171306_v1.0.sav"),
+# trim.factor.names = TRUE, trim_values = TRUE, # we trim spaces in values
+# reencode = TRUE, # we re-encode to the local locale encoding
+# add.undeclared.levels = "append", # we do *not* want to convert to R-factors
+# use.value.labels = FALSE, # we do *not* convert variables with value labels into R factors
+# use.missings = TRUE, sub = "NA", # we will set every missing variable to NA
+# duplicated.value.labels = "condense", # we will condense duplicated value labels
+# to.data.frame = TRUE))
+# AEdata.labels <- as.data.table(attr(AEdata, "variable.labels"))
+# names(AEdata.labels) <- "Variable"
+
+# METHOD 2: Using library("haven") importing seems flawless; best argument being:
+# we expect 3,527 samples and 888 variables, which is what you'd get with this method
+# So for now, METHOD 2 is prefered.
+#
+require(haven)
+
+# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2022_1_NEW_AtheroExpressDatabase_ScientificAE_15-02-2022.sav")) # something wrong with Age-variable
+# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_30-09-2020.sav")) # duplicate studynumbers in it
+AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))
+
+# writing off the SPSS data to an Excel.
+# fwrite(AEdata, file = paste0(INP_loc,"/2017-1NEW_AtheroExpressDatabase_ScientificAE_20171306_v1.0.values.xlsx"),
+# sep = ";", na = "NA", dec = ".", col.names = TRUE, row.names = FALSE,
+# dateTimeAs = "ISO", showProgress = TRUE, verbose = TRUE)
+# warnings()
+
+AEDB[1:10, 1:10]
@@ -3141,7 +3196,7 @@
Load data
-
+
[1] 3791 1091
@@ -3212,41 +3267,41 @@ Symptoms
-
-# Fix symptoms
-
-attach(AEDB)
-AEDB[,"Symptoms.5G"] <- NA
-AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
-AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
-AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
-AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
-AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
-AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"
-
-
-# AsymptSympt
-AEDB[,"AsymptSympt"] <- NA
-AEDB$AsymptSympt[sympt == -999] <- NA
-AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
-AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
-AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
-
-# AsymptSympt
-AEDB[,"AsymptSympt2G"] <- NA
-AEDB$AsymptSympt2G[sympt == -999] <- NA
-AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
-AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"
-
-detach(AEDB)
-
-# table(AEDB$sympt, useNA = "ifany")
-# table(AEDB$AsymptSympt2G, useNA = "ifany")
-# table(AEDB$Symptoms.5G, useNA = "ifany")
-#
-# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
-# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
-table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
+# Fix symptoms
+
+attach(AEDB)
+AEDB[,"Symptoms.5G"] <- NA
+AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
+AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
+AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
+AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
+AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
+AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"
+
+
+# AsymptSympt
+AEDB[,"AsymptSympt"] <- NA
+AEDB$AsymptSympt[sympt == -999] <- NA
+AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
+AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
+AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
+
+# AsymptSympt
+AEDB[,"AsymptSympt2G"] <- NA
+AEDB$AsymptSympt2G[sympt == -999] <- NA
+AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
+AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"
+
+detach(AEDB)
+
+# table(AEDB$sympt, useNA = "ifany")
+# table(AEDB$AsymptSympt2G, useNA = "ifany")
+# table(AEDB$Symptoms.5G, useNA = "ifany")
+#
+# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
+# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
+table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
@@ -3256,17 +3311,17 @@ Symptoms
<NA> 0 0 0 0 0 0 1103
-# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
-# require(labelled)
-# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
-# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
-# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
-#
-# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
-#
-# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
-#
-# rm(AEDB.temp)
# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
+# require(labelled)
+# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
+# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
+# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
+#
+# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
+#
+# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
+#
+# rm(AEDB.temp)
Re-assessed symptoms
-
-AEDB$indexsymptoms_worst
+AEDB$indexsymptoms_worst
<labelled<double>[3791]>: Latest cerebrovascular symptoms (indication CEA).
@@ -3317,7 +3372,7 @@ Re-assessed symptoms
-AEDB$indexsymptoms_worst_4g
AEDB$indexsymptoms_worst_4g
<labelled<double>[3791]>: symptoms strokefenotype en symptoms.4g .
@@ -3354,7 +3409,7 @@ Re-assessed symptoms
-AEDB$indexsymptoms_latest
AEDB$indexsymptoms_latest
<labelled<double>[3791]>: Latest cerebrovascular symptoms (indication CEA).
@@ -3391,7 +3446,7 @@ Re-assessed symptoms
-AEDB$indexsymptoms_latest_4g
AEDB$indexsymptoms_latest_4g
<labelled<double>[3791]>: symptoms strokefenotype en symptoms.4g .
@@ -3433,13 +3488,13 @@ Re-assessed symptoms
-cat("New 'worst' vs 'latest' symptom categories.\n")
cat("New 'worst' vs 'latest' symptom categories.\n")
New 'worst' vs 'latest' symptom categories.
-table(as_factor(AEDB$indexsymptoms_worst_4g), as_factor(AEDB$indexsymptoms_latest_4g))
table(as_factor(AEDB$indexsymptoms_worst_4g), as_factor(AEDB$indexsymptoms_latest_4g))
@@ -3451,14 +3506,14 @@ Re-assessed symptoms
unclear 0 0 0 0 55
-cat("\nNew 'worst' symptom categories.\n")
cat("\nNew 'worst' symptom categories.\n")
New 'worst' symptom categories.
-table((AEDB$indexsymptoms_worst_4g))
table((AEDB$indexsymptoms_worst_4g))
@@ -3466,14 +3521,14 @@ Re-assessed symptoms
1000 345 723 580 55
-cat("\nNew 'latest' symptom categories.\n")
cat("\nNew 'latest' symptom categories.\n")
New 'latest' symptom categories.
-table(as_factor(AEDB$indexsymptoms_latest_4g))
table(as_factor(AEDB$indexsymptoms_latest_4g))
@@ -3486,13 +3541,13 @@ Re-assessed symptoms
-cat("New 'latest' vs original symptom 2G categories.\n")
cat("New 'latest' vs original symptom 2G categories.\n")
New 'latest' vs original symptom 2G categories.
-table((AEDB$indexsymptoms_latest_4g), AEDB$AsymptSympt2G)
table((AEDB$indexsymptoms_latest_4g), AEDB$AsymptSympt2G)
@@ -3504,14 +3559,14 @@ Re-assessed symptoms
9 7 74
-cat("\nNew 'latest' vs original symptom 5G categories.\n")
cat("\nNew 'latest' vs original symptom 5G categories.\n")
New 'latest' vs original symptom 5G categories.
-table((AEDB$indexsymptoms_latest_4g), AEDB$Symptoms.5G)
table((AEDB$indexsymptoms_latest_4g), AEDB$Symptoms.5G)
@@ -3523,7 +3578,7 @@ Re-assessed symptoms
9 7 11 10 0 13 40
-
+
@@ -3550,25 +3605,25 @@ Re-assessed symptoms
-
-# Fix symptoms
-attach(AEDB)
-
-# Symptoms.Update2G
-AEDB[,"Symptoms.Update2G"] <- NA
-AEDB$Symptoms.Update2G[indexsymptoms_latest_4g == 0] <- "Asymptomatic"
-AEDB$Symptoms.Update2G[indexsymptoms_latest_4g == 1 | indexsymptoms_latest_4g == 2 | indexsymptoms_latest_4g == 3] <- "Symptomatic"
-AEDB$Symptoms.Update2G[indexsymptoms_latest_4g == 9 ] <- NA
-
-# Symptoms.Update3G
-AEDB[,"Symptoms.Update3G"] <- NA
-AEDB$Symptoms.Update3G[indexsymptoms_latest_4g == 0] <- "Asymptomatic"
-AEDB$Symptoms.Update3G[indexsymptoms_latest_4g == 1 | indexsymptoms_latest_4g == 2 | indexsymptoms_latest_4g == 3] <- "Symptomatic"
-AEDB$Symptoms.Update3G[indexsymptoms_latest_4g == 9 ] <- "Unclear"
-
-detach(AEDB)
-
-table(AEDB$Symptoms.Update2G, AEDB$Symptoms.5G, useNA = "ifany")
+# Fix symptoms
+attach(AEDB)
+
+# Symptoms.Update2G
+AEDB[,"Symptoms.Update2G"] <- NA
+AEDB$Symptoms.Update2G[indexsymptoms_latest_4g == 0] <- "Asymptomatic"
+AEDB$Symptoms.Update2G[indexsymptoms_latest_4g == 1 | indexsymptoms_latest_4g == 2 | indexsymptoms_latest_4g == 3] <- "Symptomatic"
+AEDB$Symptoms.Update2G[indexsymptoms_latest_4g == 9 ] <- NA
+
+# Symptoms.Update3G
+AEDB[,"Symptoms.Update3G"] <- NA
+AEDB$Symptoms.Update3G[indexsymptoms_latest_4g == 0] <- "Asymptomatic"
+AEDB$Symptoms.Update3G[indexsymptoms_latest_4g == 1 | indexsymptoms_latest_4g == 2 | indexsymptoms_latest_4g == 3] <- "Symptomatic"
+AEDB$Symptoms.Update3G[indexsymptoms_latest_4g == 9 ] <- "Unclear"
+
+detach(AEDB)
+
+table(AEDB$Symptoms.Update2G, AEDB$Symptoms.5G, useNA = "ifany")
@@ -3578,7 +3633,7 @@ Re-assessed symptoms
<NA> 7 11 10 0 13 40 1088
-table(AEDB$Symptoms.Update3G, AEDB$Symptoms.5G, useNA = "ifany")
table(AEDB$Symptoms.Update3G, AEDB$Symptoms.5G, useNA = "ifany")
@@ -3597,16 +3652,16 @@ Other clinical characteristics
-
-# Fix diabetes
-attach(AEDB)
-AEDB[,"DiabetesStatus"] <- NA
-AEDB$DiabetesStatus[DM.composite == -999] <- NA
-AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
-AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
-detach(AEDB)
-
-table(AEDB$DM.composite, AEDB$DiabetesStatus)
+# Fix diabetes
+attach(AEDB)
+AEDB[,"DiabetesStatus"] <- NA
+AEDB$DiabetesStatus[DM.composite == -999] <- NA
+AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
+AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
+detach(AEDB)
+
+table(AEDB$DM.composite, AEDB$DiabetesStatus)
@@ -3615,16 +3670,16 @@ Other clinical characteristics
1 0 985
-# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
-# require(labelled)
-# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
-# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
-# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
-# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
-#
-# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
-#
-# rm(AEDB.temp)
# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
+# require(labelled)
+# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
+# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
+# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
+# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
+#
+# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
+#
+# rm(AEDB.temp)
Other clinical characteristics
-require(labelled)
-AEDB$diet801 <- to_factor(AEDB$diet801)
-AEDB$diet802 <- to_factor(AEDB$diet802)
-AEDB$diet805 <- to_factor(AEDB$diet805)
-AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
-AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
-AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)
-
-# table(AEDB$diet801)
-# table(AEDB$diet802)
-# table(AEDB$SmokingReported)
-# table(AEDB$SmokerCurrent)
-# table(AEDB$SmokingYearOR)
-# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
-#
-# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))
-
-cat("\nFixing smoking status.\n")
require(labelled)
+AEDB$diet801 <- to_factor(AEDB$diet801)
+AEDB$diet802 <- to_factor(AEDB$diet802)
+AEDB$diet805 <- to_factor(AEDB$diet805)
+AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
+AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
+AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)
+
+# table(AEDB$diet801)
+# table(AEDB$diet802)
+# table(AEDB$SmokingReported)
+# table(AEDB$SmokerCurrent)
+# table(AEDB$SmokingYearOR)
+# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
+#
+# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))
+
+cat("\nFixing smoking status.\n")
Fixing smoking status.
-attach(AEDB)
-AEDB[,"SmokerStatus"] <- NA
-AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
-AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
-AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
-AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
-AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
-AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
-# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
-detach(AEDB)
-
-cat("\n* Current smoking status.\n")
attach(AEDB)
+AEDB[,"SmokerStatus"] <- NA
+AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
+AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
+AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
+AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
+AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
+AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
+# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
+detach(AEDB)
+
+cat("\n* Current smoking status.\n")
* Current smoking status.
-table(AEDB$SmokerCurrent,
- useNA = "ifany",
- dnn = c("Current smoker"))
table(AEDB$SmokerCurrent,
+ useNA = "ifany",
+ dnn = c("Current smoker"))
Current smoker
@@ -3696,16 +3751,16 @@ Other clinical characteristics
0 2364 1308 119
-cat("\n* Updated smoking status.\n")
cat("\n* Updated smoking status.\n")
* Updated smoking status.
-table(AEDB$SmokerStatus,
- useNA = "ifany",
- dnn = c("Updated smoking status"))
table(AEDB$SmokerStatus,
+ useNA = "ifany",
+ dnn = c("Updated smoking status"))
Updated smoking status
@@ -3713,16 +3768,16 @@ Other clinical characteristics
1308 1814 389 280
-cat("\n* Comparing to 'SmokerCurrent'.\n")
cat("\n* Comparing to 'SmokerCurrent'.\n")
* Comparing to 'SmokerCurrent'.
-table(AEDB$SmokerStatus, AEDB$SmokerCurrent,
- useNA = "ifany",
- dnn = c("Updated smoking status", "Current smoker"))
table(AEDB$SmokerStatus, AEDB$SmokerCurrent,
+ useNA = "ifany",
+ dnn = c("Updated smoking status", "Current smoker"))
Current smoker
@@ -3733,16 +3788,16 @@ Other clinical characteristics
<NA> 0 161 0 119
-# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
-# require(labelled)
-# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
-# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
-# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
-# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
-#
-# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
-#
-# rm(AEDB.temp)
# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
+# require(labelled)
+# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
+# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
+# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
+# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
+#
+# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
+#
+# rm(AEDB.temp)
Other clinical characteristics
-
-# Fix diabetes
-attach(AEDB)
-AEDB[,"AlcoholUse"] <- NA
-AEDB$AlcoholUse[diet810 == -999] <- NA
-AEDB$AlcoholUse[diet810 == 0] <- "No"
-AEDB$AlcoholUse[diet810 == 1] <- "Yes"
-detach(AEDB)
-
-# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
-# require(labelled)
-# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
-# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
-# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
-# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
-#
-# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
-#
-# rm(AEDB.temp)
+# Fix diabetes
+attach(AEDB)
+AEDB[,"AlcoholUse"] <- NA
+AEDB$AlcoholUse[diet810 == -999] <- NA
+AEDB$AlcoholUse[diet810 == 0] <- "No"
+AEDB$AlcoholUse[diet810 == 1] <- "Yes"
+detach(AEDB)
+
+# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
+# require(labelled)
+# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
+# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
+# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
+# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
+#
+# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
+#
+# rm(AEDB.temp)
Other clinical characteristics
-
-# Fix diabetes
-attach(AEDB)
-AEDB[,"MedHx_CVD"] <- NA
-AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
-AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
-detach(AEDB)
-
-table(AEDB$CAD_history)
+# Fix diabetes
+attach(AEDB)
+AEDB[,"MedHx_CVD"] <- NA
+AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
+AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
+detach(AEDB)
+
+table(AEDB$CAD_history)
@@ -3794,7 +3849,7 @@ Other clinical characteristics
2430 1285
-table(AEDB$Stroke_history)
table(AEDB$Stroke_history)
@@ -3802,7 +3857,7 @@ Other clinical characteristics
2763 947
-table(AEDB$Peripheral.interv)
table(AEDB$Peripheral.interv)
@@ -3810,7 +3865,7 @@ Other clinical characteristics
2579 1099
-
+
@@ -3818,16 +3873,16 @@ Other clinical characteristics
1309 2475
-# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
-# require(labelled)
-# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
-# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
-# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
-# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
-#
-# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
-#
-# rm(AEDB.temp)
# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
+# require(labelled)
+# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
+# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
+# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
+# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
+#
+# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
+#
+# rm(AEDB.temp)
Plaque phenotypes
-
-# Fix plaquephenotypes
-attach(AEDB)
-AEDB[,"OverallPlaquePhenotype"] <- NA
-AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
-AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
-AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
-AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
-AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
-detach(AEDB)
-
-# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
-# require(labelled)
-# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
-# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
-# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
-#
-# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
-#
-# rm(AEDB.temp)
+# Fix plaquephenotypes
+attach(AEDB)
+AEDB[,"OverallPlaquePhenotype"] <- NA
+AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
+AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
+AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
+AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
+AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
+detach(AEDB)
+
+# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
+# require(labelled)
+# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
+# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
+# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
+#
+# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
+#
+# rm(AEDB.temp)
Plaque phenotypes
-AEDB$macmean0 <- as.numeric(AEDB$macmean0)
-AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
-AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
-AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
-AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)
-
-AEDB$MAC_rankNorm <- qnorm((rank(AEDB$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$macmean0)))
-AEDB$SMC_rankNorm <- qnorm((rank(AEDB$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$smcmean0)))
-AEDB$Neutrophils_rankNorm <- qnorm((rank(AEDB$neutrophils, na.last = "keep") - 0.5) / sum(!is.na(AEDB$neutrophils)))
-AEDB$MastCells_rankNorm <- qnorm((rank(AEDB$Mast_cells_plaque, na.last = "keep") - 0.5) / sum(!is.na(AEDB$Mast_cells_plaque)))
-AEDB$VesselDensity_rankNorm <- qnorm((rank(AEDB$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB$vessel_density_averaged)))
AEDB$macmean0 <- as.numeric(AEDB$macmean0)
+AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
+AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
+AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
+AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)
+
+AEDB$MAC_rankNorm <- qnorm((rank(AEDB$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$macmean0)))
+AEDB$SMC_rankNorm <- qnorm((rank(AEDB$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$smcmean0)))
+AEDB$Neutrophils_rankNorm <- qnorm((rank(AEDB$neutrophils, na.last = "keep") - 0.5) / sum(!is.na(AEDB$neutrophils)))
+AEDB$MastCells_rankNorm <- qnorm((rank(AEDB$Mast_cells_plaque, na.last = "keep") - 0.5) / sum(!is.na(AEDB$Mast_cells_plaque)))
+AEDB$VesselDensity_rankNorm <- qnorm((rank(AEDB$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB$vessel_density_averaged)))
library(labelled)
-AEDB$Gender <- to_factor(AEDB$Gender)
-library(patchwork)
-
-p1 <- ggpubr::gghistogram(AEDB, "macmean0",
- # y = "..count..",
- color = "white",
- fill = "Gender",
- palette = c("#1290D9", "#DB003F"),
- add = "median",
- #add_density = TRUE,
- rug = TRUE,
- #add.params = list(color = "black", linetype = 2),
- title = "% of macrophages (CD68)",
- xlab = "% per region of interest",
- ggtheme = theme_minimal())
library(labelled)
+AEDB$Gender <- to_factor(AEDB$Gender)
+library(patchwork)
+
+p1 <- ggpubr::gghistogram(AEDB, "macmean0",
+ # y = "..count..",
+ color = "white",
+ fill = "Gender",
+ palette = c("#1290D9", "#DB003F"),
+ add = "median",
+ #add_density = TRUE,
+ rug = TRUE,
+ #add.params = list(color = "black", linetype = 2),
+ title = "% of macrophages (CD68)",
+ xlab = "% per region of interest",
+ ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
-p2 <- ggpubr::gghistogram(AEDB, "MAC_rankNorm",
- # y = "..count..",
- color = "white",
- fill = "Gender",
- palette = c("#1290D9", "#DB003F"),
- add = "median",
- #add_density = TRUE,
- rug = TRUE,
- #add.params = list(color = "black", linetype = 2),
- title = "% of macrophages (CD68)",
- xlab = "% per region of interest\ninverse-rank normalized number",
- ggtheme = theme_minimal())
p2 <- ggpubr::gghistogram(AEDB, "MAC_rankNorm",
+ # y = "..count..",
+ color = "white",
+ fill = "Gender",
+ palette = c("#1290D9", "#DB003F"),
+ add = "median",
+ #add_density = TRUE,
+ rug = TRUE,
+ #add.params = list(color = "black", linetype = 2),
+ title = "% of macrophages (CD68)",
+ xlab = "% per region of interest\ninverse-rank normalized number",
+ ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
-
+
-
-p1 <- ggpubr::gghistogram(AEDB, "smcmean0",
- # y = "..count..",
- color = "white",
- fill = "Gender",
- palette = c("#1290D9", "#DB003F"),
- add = "median",
- #add_density = TRUE,
- rug = TRUE,
- #add.params = list(color = "black", linetype = 2),
- title = "% of smooth muscle cells (SMA)",
- xlab = "% per region of interest",
- ggtheme = theme_minimal())
+p1 <- ggpubr::gghistogram(AEDB, "smcmean0",
+ # y = "..count..",
+ color = "white",
+ fill = "Gender",
+ palette = c("#1290D9", "#DB003F"),
+ add = "median",
+ #add_density = TRUE,
+ rug = TRUE,
+ #add.params = list(color = "black", linetype = 2),
+ title = "% of smooth muscle cells (SMA)",
+ xlab = "% per region of interest",
+ ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
-p2 <- ggpubr::gghistogram(AEDB, "SMC_rankNorm",
- # y = "..count..",
- color = "white",
- fill = "Gender",
- palette = c("#1290D9", "#DB003F"),
- add = "median",
- #add_density = TRUE,
- rug = TRUE,
- #add.params = list(color = "black", linetype = 2),
- title = "% of smooth muscle cells (SMA)",
- xlab = "% per region of interest\ninverse-rank normalized number",
- ggtheme = theme_minimal())
p2 <- ggpubr::gghistogram(AEDB, "SMC_rankNorm",
+ # y = "..count..",
+ color = "white",
+ fill = "Gender",
+ palette = c("#1290D9", "#DB003F"),
+ add = "median",
+ #add_density = TRUE,
+ rug = TRUE,
+ #add.params = list(color = "black", linetype = 2),
+ title = "% of smooth muscle cells (SMA)",
+ xlab = "% per region of interest\ninverse-rank normalized number",
+ ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
-
+
-
-
-p1 <- ggpubr::gghistogram(AEDB, "neutrophils",
- # y = "..count..",
- color = "white",
- fill = "Gender",
- palette = c("#1290D9", "#DB003F"),
- add = "median",
- #add_density = TRUE,
- rug = TRUE,
- #add.params = list(color = "black", linetype = 2),
- title = "number of neutrophils (CD66b)",
- xlab = "counts per plaque",
- ggtheme = theme_minimal())
+
+p1 <- ggpubr::gghistogram(AEDB, "neutrophils",
+ # y = "..count..",
+ color = "white",
+ fill = "Gender",
+ palette = c("#1290D9", "#DB003F"),
+ add = "median",
+ #add_density = TRUE,
+ rug = TRUE,
+ #add.params = list(color = "black", linetype = 2),
+ title = "number of neutrophils (CD66b)",
+ xlab = "counts per plaque",
+ ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
-p2 <- ggpubr::gghistogram(AEDB, "Neutrophils_rankNorm",
- # y = "..count..",
- color = "white",
- fill = "Gender",
- palette = c("#1290D9", "#DB003F"),
- add = "median",
- #add_density = TRUE,
- rug = TRUE,
- #add.params = list(color = "black", linetype = 2),
- title = "number of neutrophils (CD66b)",
- xlab = "counts per plaque\ninverse-rank normalized number",
- ggtheme = theme_minimal())
p2 <- ggpubr::gghistogram(AEDB, "Neutrophils_rankNorm",
+ # y = "..count..",
+ color = "white",
+ fill = "Gender",
+ palette = c("#1290D9", "#DB003F"),
+ add = "median",
+ #add_density = TRUE,
+ rug = TRUE,
+ #add.params = list(color = "black", linetype = 2),
+ title = "number of neutrophils (CD66b)",
+ xlab = "counts per plaque\ninverse-rank normalized number",
+ ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
-
+
-
-
-p1 <- ggpubr::gghistogram(AEDB, "Mast_cells_plaque",
- # y = "..count..",
- color = "white",
- fill = "Gender",
- palette = c("#1290D9", "#DB003F"),
- add = "median",
- #add_density = TRUE,
- rug = TRUE,
- #add.params = list(color = "black", linetype = 2),
- title = "number of mast cells",
- xlab = "counts per plaque",
- ggtheme = theme_minimal())
+
+p1 <- ggpubr::gghistogram(AEDB, "Mast_cells_plaque",
+ # y = "..count..",
+ color = "white",
+ fill = "Gender",
+ palette = c("#1290D9", "#DB003F"),
+ add = "median",
+ #add_density = TRUE,
+ rug = TRUE,
+ #add.params = list(color = "black", linetype = 2),
+ title = "number of mast cells",
+ xlab = "counts per plaque",
+ ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
-p2 <- ggpubr::gghistogram(AEDB, "MastCells_rankNorm",
- # y = "..count..",
- color = "white",
- fill = "Gender",
- palette = c("#1290D9", "#DB003F"),
- add = "median",
- #add_density = TRUE,
- rug = TRUE,
- #add.params = list(color = "black", linetype = 2),
- title = "number of mast cells",
- xlab = "counts per plaque\ninverse-rank normalized number",
- ggtheme = theme_minimal())
p2 <- ggpubr::gghistogram(AEDB, "MastCells_rankNorm",
+ # y = "..count..",
+ color = "white",
+ fill = "Gender",
+ palette = c("#1290D9", "#DB003F"),
+ add = "median",
+ #add_density = TRUE,
+ rug = TRUE,
+ #add.params = list(color = "black", linetype = 2),
+ title = "number of mast cells",
+ xlab = "counts per plaque\ninverse-rank normalized number",
+ ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
-
+
-
-
-p1 <- ggpubr::gghistogram(AEDB, "vessel_density_averaged",
- # y = "..count..",
- color = "white",
- fill = "Gender",
- palette = c("#1290D9", "#DB003F"),
- add = "median",
- #add_density = TRUE,
- rug = TRUE,
- #add.params = list(color = "black", linetype = 2),
- title = "number of intraplaque neovessels",
- xlab = "counts per 3-4 hotspots",
- ggtheme = theme_minimal())
+
+p1 <- ggpubr::gghistogram(AEDB, "vessel_density_averaged",
+ # y = "..count..",
+ color = "white",
+ fill = "Gender",
+ palette = c("#1290D9", "#DB003F"),
+ add = "median",
+ #add_density = TRUE,
+ rug = TRUE,
+ #add.params = list(color = "black", linetype = 2),
+ title = "number of intraplaque neovessels",
+ xlab = "counts per 3-4 hotspots",
+ ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
-p2 <- ggpubr::gghistogram(AEDB, "VesselDensity_rankNorm",
- # y = "..count..",
- color = "white",
- fill = "Gender",
- palette = c("#1290D9", "#DB003F"),
- add = "median",
- #add_density = TRUE,
- rug = TRUE,
- #add.params = list(color = "black", linetype = 2),
- title = "number of intraplaque neovessels",
- xlab = "counts per 3-4 hotspots\ninverse-rank normalized number",
- ggtheme = theme_minimal())
p2 <- ggpubr::gghistogram(AEDB, "VesselDensity_rankNorm",
+ # y = "..count..",
+ color = "white",
+ fill = "Gender",
+ palette = c("#1290D9", "#DB003F"),
+ add = "median",
+ #add_density = TRUE,
+ rug = TRUE,
+ #add.params = list(color = "black", linetype = 2),
+ title = "number of intraplaque neovessels",
+ xlab = "counts per 3-4 hotspots\ninverse-rank normalized number",
+ ggtheme = theme_minimal())
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
-
+
-
+
@@ -4115,17 +4170,17 @@ Plaque phenotypes
-# Plaque vulnerability
-require(labelled)
-AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
-AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
-AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
-AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
-AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
-AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
-AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
-
-table(AEDB$Macrophages.bin)
# Plaque vulnerability
+require(labelled)
+AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
+AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
+AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
+AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
+AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
+AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
+AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
+
+table(AEDB$Macrophages.bin)
@@ -4133,7 +4188,7 @@ Plaque phenotypes
1602 1215
-
+
@@ -4141,7 +4196,7 @@ Plaque phenotypes
1226 1628
-
+
@@ -4149,7 +4204,7 @@ Plaque phenotypes
540 2297
-
+
@@ -4157,7 +4212,7 @@ Plaque phenotypes
870 1962
-
+
@@ -4165,74 +4220,74 @@ Plaque phenotypes
1223 1628
-# SPSS code
-
-#
-# *** syntax- Plaque vulnerability**.
-# COMPUTE Macro_instab = -999.
-# IF macrophages.bin=2 Macro_instab=1.
-# IF macrophages.bin=1 Macro_instab=0.
-# EXECUTE.
-#
-# COMPUTE Fat10_instab = -999.
-# IF Fat.bin_10=2 Fat10_instab=1.
-# IF Fat.bin_10=1 Fat10_instab=0.
-# EXECUTE.
-#
-# COMPUTE coll_instab=-999.
-# IF Collagen.bin=2 coll_instab=0.
-# IF Collagen.bin=1 coll_instab=1.
-# EXECUTE.
-#
-#
-# COMPUTE SMC_instab=-999.
-# IF SMC.bin=2 SMC_instab=0.
-# IF SMC.bin=1 SMC_instab=1.
-# EXECUTE.
-#
-# COMPUTE IPH_instab=-999.
-# IF IPH.bin=0 IPH_instab=0.
-# IF IPH.bin=1 IPH_instab=1.
-# EXECUTE.
-#
-# COMPUTE Instability=Macro_instab + Fat10_instab + coll_instab + SMC_instab + IPH_instab.
-# EXECUTE.
-
-# Fix plaquephenotypes
-attach(AEDB)
-# mac instability
-AEDB[,"MAC_Instability"] <- NA
-AEDB$MAC_Instability[Macrophages.bin == -999] <- NA
-AEDB$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
-AEDB$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1
-
-# fat instability
-AEDB[,"FAT10_Instability"] <- NA
-AEDB$FAT10_Instability[Fat.bin_10 == -999] <- NA
-AEDB$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
-AEDB$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1
-
-# col instability
-AEDB[,"COL_Instability"] <- NA
-AEDB$COL_Instability[Collagen.bin == -999] <- NA
-AEDB$COL_Instability[Collagen.bin == "no/minor"] <- 1
-AEDB$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0
-
-# smc instability
-AEDB[,"SMC_Instability"] <- NA
-AEDB$SMC_Instability[SMC.bin == -999] <- NA
-AEDB$SMC_Instability[SMC.bin == "no/minor"] <- 1
-AEDB$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0
-
-# iph instability
-AEDB[,"IPH_Instability"] <- NA
-AEDB$IPH_Instability[IPH.bin == -999] <- NA
-AEDB$IPH_Instability[IPH.bin == "no"] <- 0
-AEDB$IPH_Instability[IPH.bin == "yes"] <- 1
-
-detach(AEDB)
-
-table(AEDB$MAC_Instability, useNA = "ifany")
# SPSS code
+
+#
+# *** syntax- Plaque vulnerability**.
+# COMPUTE Macro_instab = -999.
+# IF macrophages.bin=2 Macro_instab=1.
+# IF macrophages.bin=1 Macro_instab=0.
+# EXECUTE.
+#
+# COMPUTE Fat10_instab = -999.
+# IF Fat.bin_10=2 Fat10_instab=1.
+# IF Fat.bin_10=1 Fat10_instab=0.
+# EXECUTE.
+#
+# COMPUTE coll_instab=-999.
+# IF Collagen.bin=2 coll_instab=0.
+# IF Collagen.bin=1 coll_instab=1.
+# EXECUTE.
+#
+#
+# COMPUTE SMC_instab=-999.
+# IF SMC.bin=2 SMC_instab=0.
+# IF SMC.bin=1 SMC_instab=1.
+# EXECUTE.
+#
+# COMPUTE IPH_instab=-999.
+# IF IPH.bin=0 IPH_instab=0.
+# IF IPH.bin=1 IPH_instab=1.
+# EXECUTE.
+#
+# COMPUTE Instability=Macro_instab + Fat10_instab + coll_instab + SMC_instab + IPH_instab.
+# EXECUTE.
+
+# Fix plaquephenotypes
+attach(AEDB)
+# mac instability
+AEDB[,"MAC_Instability"] <- NA
+AEDB$MAC_Instability[Macrophages.bin == -999] <- NA
+AEDB$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
+AEDB$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1
+
+# fat instability
+AEDB[,"FAT10_Instability"] <- NA
+AEDB$FAT10_Instability[Fat.bin_10 == -999] <- NA
+AEDB$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
+AEDB$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1
+
+# col instability
+AEDB[,"COL_Instability"] <- NA
+AEDB$COL_Instability[Collagen.bin == -999] <- NA
+AEDB$COL_Instability[Collagen.bin == "no/minor"] <- 1
+AEDB$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0
+
+# smc instability
+AEDB[,"SMC_Instability"] <- NA
+AEDB$SMC_Instability[SMC.bin == -999] <- NA
+AEDB$SMC_Instability[SMC.bin == "no/minor"] <- 1
+AEDB$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0
+
+# iph instability
+AEDB[,"IPH_Instability"] <- NA
+AEDB$IPH_Instability[IPH.bin == -999] <- NA
+AEDB$IPH_Instability[IPH.bin == "no"] <- 0
+AEDB$IPH_Instability[IPH.bin == "yes"] <- 1
+
+detach(AEDB)
+
+table(AEDB$MAC_Instability, useNA = "ifany")
@@ -4240,7 +4295,7 @@ Plaque phenotypes
1602 1215 974
-table(AEDB$FAT10_Instability, useNA = "ifany")
table(AEDB$FAT10_Instability, useNA = "ifany")
@@ -4248,7 +4303,7 @@ Plaque phenotypes
1226 1628 937
-table(AEDB$COL_Instability, useNA = "ifany")
table(AEDB$COL_Instability, useNA = "ifany")
@@ -4256,7 +4311,7 @@ Plaque phenotypes
2297 540 954
-table(AEDB$SMC_Instability, useNA = "ifany")
table(AEDB$SMC_Instability, useNA = "ifany")
@@ -4264,7 +4319,7 @@ Plaque phenotypes
1962 870 959
-table(AEDB$IPH_Instability, useNA = "ifany")
table(AEDB$IPH_Instability, useNA = "ifany")
@@ -4272,11 +4327,11 @@ Plaque phenotypes
1223 1628 940
-# creating vulnerability index
-AEDB <- AEDB %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
- )
-
-table(AEDB$Plaque_Vulnerability_Index, useNA = "ifany")
# creating vulnerability index
+AEDB <- AEDB %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
+ )
+
+table(AEDB$Plaque_Vulnerability_Index, useNA = "ifany")
@@ -4284,7 +4339,7 @@ Plaque phenotypes
1324 655 728 676 298 110
-# str(AEDB$Plaque_Vulnerability_Index)
# str(AEDB$Plaque_Vulnerability_Index)
Prepare baseline summary
-cat("====================================================================================================\n")
cat("====================================================================================================\n")
====================================================================================================
-cat("SELECTION THE SHIZZLE\n")
cat("SELECTION THE SHIZZLE\n")
SELECTION THE SHIZZLE
-### Artery levels
-# AEdata$Artery_summary:
-# value label
-# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
-# USE - 1 carotid (left & right)
-# USE - 2 femoral/iliac (left, right or both sides)
-# NOT USE - 3 other carotid arteries (common, external)
-# NOT USE - 4 carotid bypass and injury (left, right or both sides)
-# NOT USE - 5 aneurysmata (carotid & femoral)
-# NOT USE - 6 aorta
-# NOT USE - 7 other arteries (renal, popliteal, vertebral)
-# NOT USE - 8 femoral bypass, angioseal and injury (left, right or both sides)
-
-### AEdata$informedconsent
-# value label
-# NOT USE - -999 missing
-# NOT USE - 0 no, died
-# USE - 1 yes
-# USE - 2 yes, health treatment when possible
-# USE - 3 yes, no health treatment
-# USE - 4 yes, no health treatment, no commercial business
-# NOT USE - 5 yes, no tissue, no commerical business
-# NOT USE - 6 yes, no tissue, no questionnaires, no medical info, no commercial business
-# USE - 7 yes, no questionnaires, no health treatment, no commercial business
-# USE - 8 yes, no questionnaires, health treatment when possible
-# NOT USE - 9 yes, no tissue, no questionnaires, no health treatment, no commerical business
-# USE - 10 yes, no health treatment, no medical info, no commercial business
-# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
-# USE - 12 yes, no questionnaires, no health treatment
-# NOT USE - 13 yes, no tissue, no health treatment
-# NOT USE - 14 yes, no tissue, no questionnaires
-# NOT USE - 15 yes, no tissue, health treatment when possible
-# NOT USE - 16 yes, no tissue
-# USE - 17 yes, no commerical business
-# USE - 18 yes, health treatment when possible, no commercial business
-# USE - 19 yes, no medical info, no commercial business
-# USE - 20 yes, no questionnaires
-# NOT USE - 21 yes, no tissue, no questionnaires, no health treatment, no medical info
-# NOT USE - 22 yes, no tissue, no questionnaires, no health treatment, no commercial business
-# USE - 23 yes, no medical info
-# USE - 24 yes, no questionnaires, no commercial business
-# USE - 25 yes, no questionnaires, no health treatment, no medical info
-# USE - 26 yes, no questionnaires, health treatment when possible, no commercial business
-# USE - 27 yes, no health treatment, no medical info
-# NOT USE - 28 no, doesn't want to
-# NOT USE - 29 no, unable to sign
-# NOT USE - 30 no, no reaction
-# NOT USE - 31 no, lost
-# NOT USE - 32 no, too old
-# NOT USE - 34 yes, no medical info, health treatment when possible
-# NOT USE - 35 no (never asked for IC because there was no tissue)
-# USE - 36 yes, no medical info, no commercial business, health treatment when possible
-# NOT USE - 37 no, endpoint
-# USE - 38 wil niets invullen, wel alles gebruiken
-# USE - 39 second informed concents: yes, no commercial business
-# NOT USE - 40 nooit geincludeerd
-
-cat("- sanity checking PRIOR to selection")
### Artery levels
+# AEdata$Artery_summary:
+# value label
+# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
+# USE - 1 carotid (left & right)
+# USE - 2 femoral/iliac (left, right or both sides)
+# NOT USE - 3 other carotid arteries (common, external)
+# NOT USE - 4 carotid bypass and injury (left, right or both sides)
+# NOT USE - 5 aneurysmata (carotid & femoral)
+# NOT USE - 6 aorta
+# NOT USE - 7 other arteries (renal, popliteal, vertebral)
+# NOT USE - 8 femoral bypass, angioseal and injury (left, right or both sides)
+
+### AEdata$informedconsent
+# value label
+# NOT USE - -999 missing
+# NOT USE - 0 no, died
+# USE - 1 yes
+# USE - 2 yes, health treatment when possible
+# USE - 3 yes, no health treatment
+# USE - 4 yes, no health treatment, no commercial business
+# NOT USE - 5 yes, no tissue, no commerical business
+# NOT USE - 6 yes, no tissue, no questionnaires, no medical info, no commercial business
+# USE - 7 yes, no questionnaires, no health treatment, no commercial business
+# USE - 8 yes, no questionnaires, health treatment when possible
+# NOT USE - 9 yes, no tissue, no questionnaires, no health treatment, no commerical business
+# USE - 10 yes, no health treatment, no medical info, no commercial business
+# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
+# USE - 12 yes, no questionnaires, no health treatment
+# NOT USE - 13 yes, no tissue, no health treatment
+# NOT USE - 14 yes, no tissue, no questionnaires
+# NOT USE - 15 yes, no tissue, health treatment when possible
+# NOT USE - 16 yes, no tissue
+# USE - 17 yes, no commerical business
+# USE - 18 yes, health treatment when possible, no commercial business
+# USE - 19 yes, no medical info, no commercial business
+# USE - 20 yes, no questionnaires
+# NOT USE - 21 yes, no tissue, no questionnaires, no health treatment, no medical info
+# NOT USE - 22 yes, no tissue, no questionnaires, no health treatment, no commercial business
+# USE - 23 yes, no medical info
+# USE - 24 yes, no questionnaires, no commercial business
+# USE - 25 yes, no questionnaires, no health treatment, no medical info
+# USE - 26 yes, no questionnaires, health treatment when possible, no commercial business
+# USE - 27 yes, no health treatment, no medical info
+# NOT USE - 28 no, doesn't want to
+# NOT USE - 29 no, unable to sign
+# NOT USE - 30 no, no reaction
+# NOT USE - 31 no, lost
+# NOT USE - 32 no, too old
+# NOT USE - 34 yes, no medical info, health treatment when possible
+# NOT USE - 35 no (never asked for IC because there was no tissue)
+# USE - 36 yes, no medical info, no commercial business, health treatment when possible
+# NOT USE - 37 no, endpoint
+# USE - 38 wil niets invullen, wel alles gebruiken
+# USE - 39 second informed concents: yes, no commercial business
+# NOT USE - 40 nooit geincludeerd
+
+cat("- sanity checking PRIOR to selection")
- sanity checking PRIOR to selection
-library(data.table)
-require(labelled)
-ae.gender <- to_factor(AEDB$Gender)
-ae.hospital <- to_factor(AEDB$Hospital)
-table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
library(data.table)
+require(labelled)
+ae.gender <- to_factor(AEDB$Gender)
+ae.hospital <- to_factor(AEDB$Hospital)
+table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
Hospital
@@ -4407,8 +4462,8 @@ Prepare baseline summary
male 1211 1420
-ae.artery <- to_factor(AEDB$Artery_summary)
-table(ae.artery, ae.gender, dnn = c("Sex", "Artery"))
ae.artery <- to_factor(AEDB$Artery_summary)
+table(ae.artery, ae.gender, dnn = c("Sex", "Artery"))
Artery
@@ -4424,109 +4479,109 @@ Prepare baseline summary
femoral bypass, angioseal and injury (left, right or both sides) 4 2
-rm(ae.gender, ae.hospital, ae.artery)
-
-# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
-# to have this 'tibble' work with 'tableone'... :-)
-
-AEDB$Age <- as.numeric(AEDB$Age)
-AEDB$diastoli <- as.numeric(AEDB$diastoli)
-AEDB$systolic <- as.numeric(AEDB$systolic)
-
-AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
-AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
-AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
-AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)
-
-AEDB$TC_final <- as.numeric(AEDB$TC_final)
-AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
-AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
-AEDB$TG_final <- as.numeric(AEDB$TG_final)
-
-AEDB$Age <- as.numeric(AEDB$Age)
-AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
-AEDB$BMI <- as.numeric(AEDB$BMI)
-AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
-AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
-AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)
-AEDB$EP_major_time <- as.numeric(AEDB$EP_major_time)
-
-require(labelled)
-AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)
-AEDB$ORyear <- to_factor(AEDB$ORyear)
-AEDB$Gender <- to_factor(AEDB$Gender)
-AEDB$Hospital <- to_factor(AEDB$Hospital)
-AEDB$KDOQI <- to_factor(AEDB$KDOQI)
-AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
-AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
-AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
-AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)
-
-AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
-AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
-AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
-AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)
-
-AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
-AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
-AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)
-
-AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
-AEDB$CAD_history <- to_factor(AEDB$CAD_history)
-AEDB$PAOD <- to_factor(AEDB$PAOD)
-AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
-
-AEDB$sympt <- to_factor(AEDB$sympt)
-AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
-AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
-AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
-AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
-AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)
-AEDB$Symptoms.Update2G <- to_factor(AEDB$Symptoms.Update2G)
-AEDB$Symptoms.Update3G <- to_factor(AEDB$Symptoms.Update3G)
-
-AEDB$restenos <- to_factor(AEDB$restenos)
-AEDB$stenose <- to_factor(AEDB$stenose)
-AEDB$EP_composite <- to_factor(AEDB$EP_composite)
-AEDB$EP_major <- to_factor(AEDB$EP_major)
-AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
-AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
-AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
-AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
-AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
-AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
-AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
-AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)
-AEDB$Plaque_Vulnerability_Index <- to_factor(AEDB$Plaque_Vulnerability_Index)
-
-AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)
-
-AEDB$informedconsent <- to_factor(AEDB$informedconsent)
-
-AEDB.full <- subset(AEDB,
- informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
- informedconsent != "no, died" &
- informedconsent != "yes, no tissue, no commerical business" &
- informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
- informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
- informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
- informedconsent != "yes, no tissue, no health treatment" &
- informedconsent != "yes, no tissue, no questionnaires" &
- informedconsent != "yes, no tissue, health treatment when possible" &
- informedconsent != "yes, no tissue" &
- informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
- informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
- informedconsent != "no, doesn't want to" &
- informedconsent != "no, unable to sign" &
- informedconsent != "no, no reaction" &
- informedconsent != "no, lost" &
- informedconsent != "no, too old" &
- informedconsent != "yes, no medical info, health treatment when possible" &
- informedconsent != "no (never asked for IC because there was no tissue)" &
- informedconsent != "no, endpoint" &
- informedconsent != "nooit geincludeerd")
-# AEDB.CEA[1:10, 1:10]
-dim(AEDB.full)
rm(ae.gender, ae.hospital, ae.artery)
+
+# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
+# to have this 'tibble' work with 'tableone'... :-)
+
+AEDB$Age <- as.numeric(AEDB$Age)
+AEDB$diastoli <- as.numeric(AEDB$diastoli)
+AEDB$systolic <- as.numeric(AEDB$systolic)
+
+AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
+AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
+AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
+AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)
+
+AEDB$TC_final <- as.numeric(AEDB$TC_final)
+AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
+AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
+AEDB$TG_final <- as.numeric(AEDB$TG_final)
+
+AEDB$Age <- as.numeric(AEDB$Age)
+AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
+AEDB$BMI <- as.numeric(AEDB$BMI)
+AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
+AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
+AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)
+AEDB$EP_major_time <- as.numeric(AEDB$EP_major_time)
+
+require(labelled)
+AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)
+AEDB$ORyear <- to_factor(AEDB$ORyear)
+AEDB$Gender <- to_factor(AEDB$Gender)
+AEDB$Hospital <- to_factor(AEDB$Hospital)
+AEDB$KDOQI <- to_factor(AEDB$KDOQI)
+AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
+AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
+AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
+AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)
+
+AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
+AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
+AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
+AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)
+
+AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
+AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
+AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)
+
+AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
+AEDB$CAD_history <- to_factor(AEDB$CAD_history)
+AEDB$PAOD <- to_factor(AEDB$PAOD)
+AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
+
+AEDB$sympt <- to_factor(AEDB$sympt)
+AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
+AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
+AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
+AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
+AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)
+AEDB$Symptoms.Update2G <- to_factor(AEDB$Symptoms.Update2G)
+AEDB$Symptoms.Update3G <- to_factor(AEDB$Symptoms.Update3G)
+
+AEDB$restenos <- to_factor(AEDB$restenos)
+AEDB$stenose <- to_factor(AEDB$stenose)
+AEDB$EP_composite <- to_factor(AEDB$EP_composite)
+AEDB$EP_major <- to_factor(AEDB$EP_major)
+AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
+AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
+AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
+AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
+AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
+AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
+AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
+AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)
+AEDB$Plaque_Vulnerability_Index <- to_factor(AEDB$Plaque_Vulnerability_Index)
+
+AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)
+
+AEDB$informedconsent <- to_factor(AEDB$informedconsent)
+
+AEDB.full <- subset(AEDB,
+ informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
+ informedconsent != "no, died" &
+ informedconsent != "yes, no tissue, no commerical business" &
+ informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
+ informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
+ informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
+ informedconsent != "yes, no tissue, no health treatment" &
+ informedconsent != "yes, no tissue, no questionnaires" &
+ informedconsent != "yes, no tissue, health treatment when possible" &
+ informedconsent != "yes, no tissue" &
+ informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
+ informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
+ informedconsent != "no, doesn't want to" &
+ informedconsent != "no, unable to sign" &
+ informedconsent != "no, no reaction" &
+ informedconsent != "no, lost" &
+ informedconsent != "no, too old" &
+ informedconsent != "yes, no medical info, health treatment when possible" &
+ informedconsent != "no (never asked for IC because there was no tissue)" &
+ informedconsent != "no, endpoint" &
+ informedconsent != "nooit geincludeerd")
+# AEDB.CEA[1:10, 1:10]
+dim(AEDB.full)
[1] 3458 1114
@@ -4536,52 +4591,53 @@ Prepare baseline summary
-cat("===========================================================================================\n")
cat("===========================================================================================\n")
===========================================================================================
-cat("CREATE BASELINE TABLE\n")
cat("CREATE BASELINE TABLE\n")
CREATE BASELINE TABLE
-
-# Baseline table variables
-basetable_vars = c("Hospital",
- "Artery_summary",
- "Age", "Gender")
- # "ORyear",
- # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
- # "TC_final", "LDL_final", "HDL_final", "TG_final",
- # "hsCRP_plasma",
- # "systolic", "diastoli", "GFR_MDRD", "BMI",
- # "KDOQI", "BMI_WHO",
- # "SmokerStatus", "AlcoholUse",
- # "DiabetesStatus",
- # "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
- # "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
- # "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
- # "Symptoms.Update2G",
- # "Symptoms.Update3G",
- # "restenos", "stenose",
- # "CAD_history", "PAOD", "Peripheral.interv",
- # "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
- # "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
- # "Neutrophils_rankNorm", "MastCells_rankNorm",
- # "IPH.bin", "VesselDensity_rankNorm",
- # "Calc.bin", "Collagen.bin",
- # "Fat.bin_10", "Fat.bin_40",
- # "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
-
-basetable_bin = c("Hospital",
- "Artery_summary",
- "Gender")
-# basetable_bin
-
-basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
-# basetable_con
# Baseline table variables
+basetable_vars = c("Hospital",
+ "Artery_summary",
+ "Age", "Gender",
+ # "ORyear",
+ # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
+ # "TC_final", "LDL_final", "HDL_final", "TG_final",
+ # "hsCRP_plasma",
+ # "systolic", "diastoli", "GFR_MDRD", "BMI",
+ # "KDOQI", "BMI_WHO",
+ # "SmokerStatus", "AlcoholUse",
+ # "DiabetesStatus",
+ # "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
+ # "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
+ # "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
+ # "Symptoms.Update2G",
+ # "Symptoms.Update3G",
+ # "restenos", "stenose",
+ # "CAD_history", "PAOD", "Peripheral.interv",
+ # "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
+ # "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
+ # "Neutrophils_rankNorm", "MastCells_rankNorm",
+ # "IPH.bin", "VesselDensity_rankNorm",
+ # "Calc.bin", "Collagen.bin",
+ # "Fat.bin_10", "Fat.bin_40",
+ "OverallPlaquePhenotype" )
+ # , "Plaque_Vulnerability_Index")
+
+basetable_bin = c("Hospital",
+ "Artery_summary",
+ "Gender")
+# basetable_bin
+
+basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
+# basetable_con
Athero-Express Biobank Study Baseline Characteristics
-# Create baseline tables
-# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
-AEDB.tableOne = print(CreateTableOne(vars = basetable_vars,
- factorVars = basetable_bin,
- # strata = "Symptoms.4g",
- data = AEDB.full, includeNA = TRUE),
- nonnormal = c(), missing = TRUE,
- quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
- format = "pf",
- contDigits = 3)[,1:3]
# Create baseline tables
+# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
+AEDB.tableOne = print(CreateTableOne(vars = basetable_vars,
+ factorVars = basetable_bin,
+ # strata = "Symptoms.4g",
+ data = AEDB.full, includeNA = TRUE),
+ nonnormal = c(), missing = TRUE,
+ quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
+ format = "pf",
+ contDigits = 3)[,1:3]
- level Overall Missing
- n 3458
- Hospital % (freq) St. Antonius, Nieuwegein 45.3 (1567) 0.0
- UMC Utrecht 54.7 (1891)
- Artery_summary % (freq) carotid (left & right) 69.0 (2387) 0.0
- femoral/iliac (left, right or both sides) 28.8 ( 995)
- other carotid arteries (common, external) 1.3 ( 45)
- carotid bypass and injury (left, right or both sides) 0.2 ( 7)
- aneurysmata (carotid & femoral) 0.0 ( 1)
- aorta 0.2 ( 6)
- other arteries (renal, popliteal, vertebral) 0.3 ( 12)
- femoral bypass, angioseal and injury (left, right or both sides) 0.1 ( 5)
- Age (mean (SD)) 68.733 (9.214) 0.0
- Gender % (freq) female 29.7 (1026) 0.0
- male 70.3 (2432)
+
+
+ level Overall Missing
+ n 3458
+ Hospital % (freq) St. Antonius, Nieuwegein 45.3 (1567) 0.0
+ UMC Utrecht 54.7 (1891)
+ Artery_summary % (freq) carotid (left & right) 69.0 (2387) 0.0
+ femoral/iliac (left, right or both sides) 28.8 ( 995)
+ other carotid arteries (common, external) 1.3 ( 45)
+ carotid bypass and injury (left, right or both sides) 0.2 ( 7)
+ aneurysmata (carotid & femoral) 0.0 ( 1)
+ aorta 0.2 ( 6)
+ other arteries (renal, popliteal, vertebral) 0.3 ( 12)
+ femoral bypass, angioseal and injury (left, right or both sides) 0.1 ( 5)
+ Age (mean (SD)) 68.733 (9.214) 0.0
+ Gender % (freq) female 29.7 (1026) 0.0
+ male 70.3 (2432)
+ OverallPlaquePhenotype % (freq) atheromatous 14.7 ( 507) 25.9
+ fibroatheromatous 22.2 ( 769)
+ fibrous 37.2 (1285)
+ <NA> 25.9 ( 897)
@@ -4631,94 +4691,98 @@ Match samples used
-# Remove duplicate rows of the dataframe
-library(dplyr)
-temp <- EM_samples %>% select(., "Studynumber") %>%
- distinct(.)
-
-AEDB_EM <- merge(temp,
- AEDB,
- by.x = "Studynumber",
- by.y = "STUDY_NUMBER",
- sort = FALSE)
-
-AEDB_EM.full <- subset(AEDB_EM,
- informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
- informedconsent != "no, died" &
- informedconsent != "yes, no tissue, no commerical business" &
- informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
- informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
- informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
- informedconsent != "yes, no tissue, no health treatment" &
- informedconsent != "yes, no tissue, no questionnaires" &
- informedconsent != "yes, no tissue, health treatment when possible" &
- informedconsent != "yes, no tissue" &
- informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
- informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
- informedconsent != "no, doesn't want to" &
- informedconsent != "no, unable to sign" &
- informedconsent != "no, no reaction" &
- informedconsent != "no, lost" &
- informedconsent != "no, too old" &
- informedconsent != "yes, no medical info, health treatment when possible" &
- informedconsent != "no (never asked for IC because there was no tissue)" &
- informedconsent != "no, endpoint" &
- informedconsent != "nooit geincludeerd")
-# AEDB_EM.full[1:10, 1:10]
-
-rm(temp)
# Remove duplicate rows of the dataframe
+library(dplyr)
+temp <- EM_samples %>% select(., "Studynumber") %>%
+ distinct(.)
+
+AEDB_EM <- merge(temp,
+ AEDB,
+ by.x = "Studynumber",
+ by.y = "STUDY_NUMBER",
+ sort = FALSE)
+
+AEDB_EM.full <- subset(AEDB_EM,
+ informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
+ informedconsent != "no, died" &
+ informedconsent != "yes, no tissue, no commerical business" &
+ informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
+ informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
+ informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
+ informedconsent != "yes, no tissue, no health treatment" &
+ informedconsent != "yes, no tissue, no questionnaires" &
+ informedconsent != "yes, no tissue, health treatment when possible" &
+ informedconsent != "yes, no tissue" &
+ informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
+ informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
+ informedconsent != "no, doesn't want to" &
+ informedconsent != "no, unable to sign" &
+ informedconsent != "no, no reaction" &
+ informedconsent != "no, lost" &
+ informedconsent != "no, too old" &
+ informedconsent != "yes, no medical info, health treatment when possible" &
+ informedconsent != "no (never asked for IC because there was no tissue)" &
+ informedconsent != "no, endpoint" &
+ informedconsent != "nooit geincludeerd")
+# AEDB_EM.full[1:10, 1:10]
+
+rm(temp)
# Create baseline tables
-# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
-AEDB_EM.full.tableOne = print(CreateTableOne(vars = basetable_vars,
- factorVars = basetable_bin,
- # strata = "Symptoms.4g",
- data = AEDB_EM.full, includeNA = TRUE),
- nonnormal = c(), missing = TRUE,
- quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
- format = "pf",
- contDigits = 3)[,1:3]
# Create baseline tables
+# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
+AEDB_EM.full.tableOne = print(CreateTableOne(vars = basetable_vars,
+ factorVars = basetable_bin,
+ # strata = "Symptoms.4g",
+ data = AEDB_EM.full, includeNA = TRUE),
+ nonnormal = c(), missing = TRUE,
+ quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
+ format = "pf",
+ contDigits = 3)[,1:3]
- level Overall Missing
- n 56
- Hospital % (freq) St. Antonius, Nieuwegein 21.4 (12) 0.0
- UMC Utrecht 78.6 (44)
- Artery_summary % (freq) carotid (left & right) 85.7 (48) 0.0
- femoral/iliac (left, right or both sides) 12.5 ( 7)
- other arteries (renal, popliteal, vertebral) 1.8 ( 1)
- Age (mean (SD)) 69.500 (9.260) 0.0
- Gender % (freq) female 33.9 (19) 0.0
- male 66.1 (37)
+
+
+ level Overall Missing
+ n 56
+ Hospital % (freq) St. Antonius, Nieuwegein 21.4 (12) 0.0
+ UMC Utrecht 78.6 (44)
+ Artery_summary % (freq) carotid (left & right) 85.7 (48) 0.0
+ femoral/iliac (left, right or both sides) 12.5 ( 7)
+ other arteries (renal, popliteal, vertebral) 1.8 ( 1)
+ Age (mean (SD)) 69.500 (9.260) 0.0
+ Gender % (freq) female 33.9 (19) 0.0
+ male 66.1 (37)
+ OverallPlaquePhenotype % (freq) atheromatous 21.4 (12) 17.9
+ fibroatheromatous 19.6 (11)
+ fibrous 41.1 (23)
+ <NA> 17.9 (10)
-
-basetable_vars = c("Hospital",
- "Artery_summary")
-
-basetable_bin = c("Hospital",
- "Artery_summary")
-
-# Create baseline tables
-# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
-AEDB_EM.tableOne = print(CreateTableOne(vars = basetable_vars,
- factorVars = basetable_bin,
- # strata = "Symptoms.4g",
- data = AEDB_EM, includeNA = TRUE),
- nonnormal = c(), missing = TRUE,
- quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
- format = "pf",
- contDigits = 3)[,1:3]
+basetable_vars = c("Hospital",
+ "Artery_summary")
+
+basetable_bin = c("Hospital",
+ "Artery_summary")
+
+# Create baseline tables
+# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
+AEDB_EM.tableOne = print(CreateTableOne(vars = basetable_vars,
+ factorVars = basetable_bin,
+ # strata = "Symptoms.4g",
+ data = AEDB_EM, includeNA = TRUE),
+ nonnormal = c(), missing = TRUE,
+ quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
+ format = "pf",
+ contDigits = 3)[,1:3]
@@ -4739,28 +4803,32 @@ Baseline writing
Study.
-
-# Write basetable
-
-require(openxlsx)
-
-write.xlsx(as.data.frame(AEDB.tableOne),
- file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.xlsx"),
- rowNames = TRUE,
- colNames = TRUE,
- sheetName = "AE_Base", overwrite = TRUE)
-
-write.xlsx(as.data.frame(AEDB_EM.tableOne),
- file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.EM.59.BaselineTable.xlsx"),
- rowNames = TRUE,
- colNames = TRUE,
- sheetName = "AE_Base_EM_59", overwrite = TRUE)
-
-write.xlsx(as.data.frame(AEDB_EM.full.tableOne),
- file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.EM.56.BaselineTable.xlsx"),
- rowNames = TRUE,
- colNames = TRUE,
- sheetName = "AE_Base_EM_56", overwrite = TRUE)
# Write basetable
+
+require(openxlsx)
Loading required package: openxlsx
+
+
+write.xlsx(as.data.frame(AEDB.tableOne),
+ file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.xlsx"),
+ rowNames = TRUE,
+ colNames = TRUE,
+ sheetName = "AE_Base", overwrite = TRUE)
+
+write.xlsx(as.data.frame(AEDB_EM.tableOne),
+ file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.EM.59.BaselineTable.xlsx"),
+ rowNames = TRUE,
+ colNames = TRUE,
+ sheetName = "AE_Base_EM_59", overwrite = TRUE)
+
+write.xlsx(as.data.frame(AEDB_EM.full.tableOne),
+ file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.EM.56.BaselineTable.xlsx"),
+ rowNames = TRUE,
+ colNames = TRUE,
+ sheetName = "AE_Base_EM_56", overwrite = TRUE)
Baseline writing
-saveRDS(AEDB_EM.full, file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AEDB.EM.FULL.RDS"))
-saveRDS(AEDB.full, file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AEDB.FULL.RDS"))
-saveRDS(AEDB, file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AEDB.raw.RDS"))
saveRDS(AEDB_EM.full, file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AEDB.EM.FULL.RDS"))
+saveRDS(AEDB.full, file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AEDB.FULL.RDS"))
+saveRDS(AEDB, file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AEDB.raw.RDS"))
Baseline writing
-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