README typos

Author: pinduzera

R/json_files.R

@@ -535,7 +535,7 @@ calculateFitStat <- function(validadedf,
 
     if (nrow(data[[i]] != 0)) {
 
-      predictions   <- ROCR::prediction(data[[i]][,2],data[[i]][,1], label.ordering = label.ordering)
+      predictions <- ROCR::prediction(data[[i]][,2],data[[i]][,1], label.ordering = label.ordering)
       accuracyRoc <- ROCR::performance(predictions, 'acc')
       tpr_fpr <- ROCR::performance(predictions,"tpr","fpr")
 

README.Rmd

@@ -232,16 +232,16 @@ diags <- diagnosticsJson(validadedf = scoreddf[scoreddf$partition == 3,],
                          path = path) ## safely ignore warning, knitr bug
 
 
-## since R sasctl can't automatically generate an score code, you can use this
-## with a sample, which has to be edited manually to the correct.
-## for this example you can get the score code sample here: https://gitlab.sas.com/edhell/mm-r-model
+## since R sasctl can't automatically generate an score code (yet), you can use
+## this function to create a sample, which has to be edited manually to work properly
+
 create_scoreSample(path, 
                    openFile = FALSE)
 
-#
+
+## writing other files
 write_in_out_json(hmeq[,-1], input = TRUE, path = path)
 
-## we don't need the partition info
 write_in_out_json(scoreddf[-4], input = FALSE, path = path)
 
 write_fileMetadata_json(scoreCodeName = "scoreCode.R",

README.md

@@ -12,10 +12,10 @@
 2.  [Installation](#installation)  
 3.  [Session](#session)  
 4.  [Examples](#examples)
-    - [PMML to SAS Example](#pmml-to-sas-example)  
-    - [A native R model example](#a-native-r-model-example)  
-    - [vPOST and vGET convenient
-      functions](#vpost-and-vget-convenient-functions)
+    -   [PMML to SAS Example](#pmml-to-sas-example)  
+    -   [A native R model example](#a-native-r-model-example)  
+    -   [vPOST and vGET convenient
+        functions](#vpost-and-vget-convenient-functions)
 5.  [Model Management helpers](#model-management-helpers)
 
 ## Overview
@@ -214,16 +214,16 @@ diags <- diagnosticsJson(validadedf = scoreddf[scoreddf$partition == 3,],
                          path = path) ## safely ignore warning, knitr bug
 
 
-## since R sasctl can't automatically generate an score code, you can use this
-## with a sample, which has to be edited manually to the correct.
-## for this example you can get the score code sample here: https://gitlab.sas.com/edhell/mm-r-model
+## since R sasctl can't automatically generate an score code (yet), you can use
+## this function to create a sample, which has to be edited manually to work properly
+
 create_scoreSample(path, 
                    openFile = FALSE)
 
-#
+
+## writing other files
 write_in_out_json(hmeq[,-1], input = TRUE, path = path)
 
-## we don't need the partition info
 write_in_out_json(scoreddf[-4], input = FALSE, path = path)
 
 write_fileMetadata_json(scoreCodeName = "scoreCode.R",