val dataSet = spark.createDataFrame(Seq(
  (1, "shirt for women", Seq("shirt", "women"), 19.1, "ST"),
  (1, "shirt for women", Seq("shirt", "women"), 10.1, null),
  (1, "shirt for women", Seq("shirt", "women"), 12.1, null),
  (0, "shirt group women", Seq("group", "women"), 15.1, null),
  (0, "shirt group women", Seq("group", "women"), 12.1, null),
  (3, "shirt nmn women", Seq("shirt", "women"), 16.1, "ST"),
  (3, "shirt were women", Seq("shirt", "women"), 13.1, "ST")
)).toDF("id", "raw", "filtered", "score", "types")

  +---+-----------------+--------------+-----+-----+
|id |raw              |filtered      |score|types|
+---+-----------------+--------------+-----+-----+
|1  |shirt for women  |[shirt, women]|19.1 |ST   |
|1  |shirt for women  |[shirt, women]|10.1 |null |
|1  |shirt for women  |[shirt, women]|12.1 |null |
|0  |shirt group women|[group, women]|15.1 |null |
|0  |shirt group women|[group, women]|12.1 |null |
|3  |shirt nmn women  |[shirt, women]|16.1 |ST   |
|3  |shirt were women |[shirt, women]|13.1 |ST   |
+---+-----------------+--------------+-----+-----+

+---+------------------+--------------+-----+----+
    |id |raw              |filtered      |score|types|
    +---+-----------------+--------------+-----+----+
    |1  |shirt for women  |[shirt, women]|19.1 |ST  |
    |1  |shirt for women  |[shirt, women]|10.1 |NA  |
    |1  |shirt for women  |[shirt, women]|12.1 |null|
    |0  |shirt group women[women, group] |15.1 |null|
    |0  |shirt group women|[women, group]|12.1 |NA  |
    |3  |shirt nmn women  |[shirt, women]|16.1 |ST  |
    |3  |shirt were women |[shirt, women]|13.1 |ST  |
    +---+-----------------+--------------+-----+----+

data.withColumn("max_score",
      when(col("types").isNull,
        max("score")
          .over(Window.partitionBy("id", "filtered")))
        .otherwise($"score"))
      .withColumn("type_temp",
        when(col("score") =!= col("max_score"),
          addReasonsUDF(col("type"),
            lit("NA")))
          .otherwise(col("type")))
      .drop("types", "max_score")
      .withColumnRenamed("type_temp", "types")

+---+-----------------+--------------+-----+---------+-----+
|id |raw              |filtered      |score|max_score|types|
+---+-----------------+--------------+-----+---------+-----+
|1  |shirt for women  |[shirt, women]|19.1 |19.1     |ST   |
|1  |shirt women      |[shirt, women]|10.1 |19.1     |NA   |
|1  |shirt of women   |[shirt, women]|12.1 |19.1     |NA   |
|0  |shirt group women|[group, women]|15.1 |15.1     |null |
|0  |shirt will women |[group, women]|12.1 |15.1     |NA   |
|3  |shirt nmn women  |[shirt, women]|16.1 |16.1     |ST   |
|3  |shirt were women |[shirt, women]|13.1 |13.1     |ST   |
+---+-----------------+--------------+-----+---------+-----+

def addReasons(oldreason: String, newreason: String): String = {

    if (checkIfEmpty(oldreason) && checkIfEmpty(newreason)) {
      null
    } else if (checkIfEmpty(oldreason)) {
      newreason
    } else if (checkIfEmpty(newreason)) {
      oldreason
    } else {
      Set(oldreason, newreason).mkString(",")
    }
  }

dataSet.withColumn("max_score",
      when(col("types").isNull,
        max("score").over(Window.partitionBy("id", "raw")))
        .otherwise($"score")).show(false)

+---+-----------------+--------------+-----+-----+---------+
|id |raw              |filtered      |score|types|max_score|
+---+-----------------+--------------+-----+-----+---------+
|3  |shirt nmn women  |[shirt, women]|16.1 |ST   |16.1     |
|0  |shirt group women|[group, women]|15.1 |null |15.1     |
|0  |shirt group women|[group, women]|12.1 |null |15.1     |
|3  |shirt were women |[shirt, women]|13.1 |ST   |13.1     |
|1  |shirt for women  |[shirt, women]|19.1 |ST   |19.1     |
|1  |shirt for women  |[shirt, women]|10.1 |null |19.1     |
|1  |shirt for women  |[shirt, women]|12.1 |null |19.1     |
+---+-----------------+--------------+-----+-----+---------+