Merge pull request #47 from Mu-Sigma/develop

Corrections to vignettes

Author: naren1991

vignettes/Interoperable_Pipelines.Rmd

@@ -231,7 +231,7 @@ opWithFilter %>>% getOutputById(2)
 
 Finally, we show a case, where sequential filtering steps are performed in Spark, before visualizing in R, and running a decision tree model in Python.
 
-Note, that in this case, we register `getTargetForPyClassifcation` and `getTargetForPyClassification` as *non-data* functions. In this particular pipeline, there is no main *path* as such, as the pipeline branches into 2 paths - one in R and the other in Python. In such cases, using `outAsIn` or the `dataFunction` parameter with formula semantics is just a **question of convenience**. If the first argument of a *non-data* function is of a data frame class in R, Python (Pandas) or Spark, the package automatically performs type conversions when environments are switched (R -> Spark, Spark -> Python, and so on).
+Note, that in this case, `getTargetForPyClassifcation` and `getTargetForPyClassification` have been registered as *data* functions. Type conversions between R, Spark and Python for data functions are performed automatically by the package.
 
 ```{r}
 pipelineObj %>>% filterData_spark(condition = "Species == 'setosa' or Species == 'virginica'") %>>% 

vignettes/Streaming_pipelines_for_working_Apache_Spark_Structured_Streaming.Rmd

@@ -51,9 +51,9 @@ knitr::opts_chunk$set(
 library(analysisPipelines)
 library(SparkR)
 
-## Define these variables as per the configuration of your machine. This is just an example.
+## Define these variables as per the configuration of your machine. The below example is just illustrative.
 
-sparkHome <- "/Users/naren/softwares/spark-2.3.1-bin-hadoop2.7/"
+sparkHome <- "/path/to/spark/directory/"
 sparkMaster <- "local[1]"
 sparkPackages <- c("org.apache.spark:spark-sql-kafka-0-10_2.11:2.3.1")
 # Set spark home variable if not present
@@ -81,10 +81,10 @@ This example illustrates usage of pipelines for a streaming application. In this
 Read streaming data from Kafka.
 
 ```{r}
-## Define these variables as per the configuration of your machine. This is just an example.
+## Define these variables as per the configuration of your machine. The below example is just illustrative.
 
-kafkaBootstrapServers <- "172.25.0.144:9092,172.25.0.98:9092,172.25.0.137:9092"
-consumerTopic <- "netlogo"
+kafkaBootstrapServers <- "192.168.0.256:9092,192.168.0.257:9092,192.168.0.258:9092"
+consumerTopic <- "topic1"
 streamObj <- read.stream(source = "kafka", kafka.bootstrap.servers = kafkaBootstrapServers, subscribe = consumerTopic, startingOffsets="earliest")
 printSchema(streamObj)
 ```
@@ -95,7 +95,7 @@ Users can define their own functions and use it as a part of the pipeline. These
 
 ```{r}
 
-# Function to convert datatype json struct to colums
+# Function to convert datatype json struct to columns
 convertStructToDf <- function(streamObj) {
   streamObj <- SparkR::select(streamObj,list(getField(streamObj$`jsontostructs(value)`,"bannerId"),
                                    getField(streamObj$`jsontostructs(value)`,"mobile"),
@@ -131,7 +131,7 @@ castDfColumns <- function(streamObj) {
   return (streamObj)
 }
 
-# Function to convert datatype json struct to colums
+# Function to convert datatype json struct to columns
 convertDfToKafkaKeyValuePairs <- function (streamObj, kafkaKey) {
   streamObj <- SparkR::toJSON(streamObj)
   streamObj$key <- kafkaKey