Scala removing IF Else statement and write in a functional way - scala

Question

Let's say I have the following tuple
(colType, colDocV)
Where colType is a boolean and colDocV is a String
Depending on those two values, I will apply some chunk of code that applies transformations to a Dataframe.
Now, this code works. However, I am not convinced this is the proper way to write functional programming code.
I don't know which of these 3 approaches will improve the quality of the code and remove all if-if else-else :
Should I apply some kind of design pattern and which one?
Should I use some kind of pattern matching?
Should I use some anonymous function?
if (colDocV) {
val newCol = udf(UDFHashCode.udfHashCode).apply(col(columnName))
dataframe.withColumn(columnName, newCol)
} else if (colType.contains("string") || colType.contains("text")) {
val newCol = udf(Entropy.stringEntropyFunc).apply(col(columnName)).cast(DoubleType)
dataframe.withColumn(columnName, newCol)
} else if (colType.contains("date")) {
val newCol = udf(DateUtils.getTimeAsDoubleFunc).apply(col(columnName)).cast(DoubleType)
dataframe.withColumn(columnName, newCol)
} else if (colType.contains("long")) {
dataframe.withColumn(columnName, dataframe(columnName).cast(DoubleType) )
} else {
dataframe.drop(columnName) //Dropping column that cannot be processed
}

Answer 1

You can do this with a match statement and a bunch of regexps.
val str = ".*(?:string|text).*".r
val date = ".*date.*".r
val long = ".*long.*".r
def col(tuple: (Boolean, String)) = tuple match {
case (true, _) => Some(udf(...))
case (_, str()) => Some(udf(...))
case (_, date()) => Some(udf(...))
case (, long()) => Some(udf(...))
case _ => None
}
col(colType -> colDocv)
.fold(dataframe.drop(columnName)) { newCol =>
dataframe.withColumn(columnName, newCol)
}

Answer 2

According to what I understand from your question following can be a solution using match case
def callUdf(colDocV: String, colType: Boolean, dataframe: DataFrame) = (colDocV, colType) match {
case x if (x._1.contains("string") || x._1.contains("text")) => dataframe.withColumn(columnName, udf(Entropy.stringEntropyFunc).apply(col(columnName)).cast(DoubleType))
case x if (x._1.contains("date")) => dataframe.withColumn(columnName, udf(DateUtils.getTimeAsDoubleFunc).apply(col(columnName)).cast(DoubleType))
case x if (x._1.contains("long")) => dataframe.withColumn(columnName, dataframe(columnName).cast(DoubleType) )
case _ => dataframe.drop(columnName)
}