Re: Transform Data with Power Query

then we take this code instead (it will ignore all columns that are not relevant to the results):

let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WMlHSUXJ0BBJOiUXJqTn5eYlAdlhiTmpecmaiUqxOtJIpTAVcFFkxSIUZTIVvYkpRZgqQEZSfC5Eyh0mBReAqQFIW2GxGkrfEMBXVVkMDkAInfE43NIQpwel2QyOQEmesjjc0hslhut7QBCaHbDtCqymaViRbYwE=", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [ID = _t, Company = _t, Origin = _t, Destiny = _t]),
    Group = Table.Group(Source, {"Origin"}, {{"Count", each List.Count(List.Distinct(_[Destiny])), type number}, {"Destinations", each Table.FromRows({List.Distinct(_[Destiny])}), type table}}),
    AllColumnNames = Table.ColumnNames(Table.Combine(Group[Destinations])),
    #"Expanded Destinations" = Table.ExpandTableColumn(Group, "Destinations", AllColumnNames, AllColumnNames)
in
    #"Expanded Destinations"

The step "Group" will chunk the table up per "Origin" and return 2 columns: "Count" with the count of all distinct destinations and "Destinations" with a table per row containing a column with all distinct destinations.

These nested functions need to be read "inside-out" like in Excel: So for the "Count" we start with _[Destiny] which selects the column "Destiny" from the table and returns it it List-format. So we take "List.Distinct" in order to reduce it to distinct values only. Then a List.Count on it.

In order to return the list of distinct destinations in a table-format (quasi-transpose), we use Table.FromRows, as this is just made for this job. Just be aware that it could also create a table from multiple rows, so the format of the input-parameter it expects is a list of lists (therefore the additional curly brackets).

Now we have the content we need, but we need to expand the table that has been returned: If you would do that manually, a command would be created that let's you open all columns of the table in the first row and these column names would be hardcoded. So in order to avoid that, we create a list of column names that is dynamic, retrieving all column names from all tables. Therefore we combine all tables into one big one (Table.Combine) and read out the column names from it (Table.ColumnNames) in step "AllColumnNames". We use this as a variable for our last step "#"Expanded Destinations".

The key to write and read such a compact code lies in understanding the specific value types (https://msdn.microsoft.com/en-us/library/mt186367.aspx) of the necessary input- & output-parameters of the functions that you can find here: https://msdn.microsoft.com/en-us/library/mt253322.aspx?f=255&MSPPError=-2147217396