Here is my first question. In the Tabeleau I have two different data sources:
Tableau live data extract from which I pull date and value 1 (number of locations)
Table from SQL table with date and another value 2 (number of affected locations)
Have to make calculated field using those value 1 and value 2 (percent of affected locations) but Tableau is "forcing" me to define relation between two data sources. However, datatype of date is different and it is not possible to define relation. Any suggestion?
Thank you!
Related
I am trying to create the derived column based on the condition that met the value and trying to do the summation of multiple matching column values dynamically. So I am using reduce function in ADF derived column mapping data flow. But the column is not getting created even the transformation is correct.
Columns from source
Derived column logic
Derived column data preview without the new columns as per logic
I could see only the fields from source but not the derived column fields. If I use only the array($$) I could see the fields getting created.
Derived column data preview with logic only array($$)
How to get the derived column with the summation of all the fields matching the condition?
We are getting data of 48 weeks forecast and the data to be prepared on monthly basis.
eg: Input data
Output data:
JAN
----
506 -- This is for first record i.e. (94 + 105 + 109 + 103 + 95)
The problem is that the array($$) in the reduce function has only one element, so that the reduce function can not accumulate the content of the matching columns correctly.
You can solve this by using two derived columns and a data flow parameter as follows:
Create derived columns with pattern matching for each month-week you did it before, but put the reference $$ into the value field, instead of the reduce(...) function.
This will create derived columns like jan0, jan1, etc. containing the copy of the original values. For example Week 0 (1 Jan - 7 Jan) => 0jan with value 95.
This step gives you a predefined set of column names for each week, which you can use to summarize the values with specific column names.
Define Data Flow parameters for each month containing the month-week column names in a string array, like this:
ColNamesJan=['0jan' ,'1jan', etc.] ColNamesFeb=['0feb' ,'1feb', etc.] and so on.
You will use these column names in a reduce function to summarize the month-week columns to monthly column in the next step.
Create a derived column for each month, which will contain the monthly totals, and use the following reduce function to sum the weekly values:
reduce(array(byNames($ColNamesJan)), 0, #acc + toInteger(toString(#item)),#result)
Replace the parameter name accordingly.
I was able to summarize the columns dynamically with the above solution.
Please let me know if you need more information (e.g. screenshots) to reproduce the solution.
Update -- Here are the screenshots from my test environment.
Data source (data preview):
Derived columns with pattern matching (settings)
Derived columns with pattern matching (data preview)
Data flow parameter:
Derived column for monthly sum (settings):
Derived column for monthly sum (data preview):
I used an Apriori algorithm to view the frequent relationships in the dataset and I want to do a dashboard to better visualize this data but I don't know how to do this filter.
This is the bar chart that I created to show the support (amount of times something happend) and the confidence (probability of B happening given A) of these associations:
Apriori Chart
Next to it on the dashboard, I'll have a table with the full dataset used in this Apriori analysis where I have more information such as ID, Income, Hours Worked, etc:
Table from different data source
How can I create this relationship? The two data sources don't have a column in common that I can use for that.
I would need some way to:
Split the values in the antecedents columns by comma and filter only those columns with value equal to 1 in the other dataset
**Dataset A**
'Age Range <=30, Joblevel 1, Maritalstatus Single'
->
'Age Range <=30'
'Joblevel 1'
'Maritalstatus Single'
**Dataset B**
'Age Range <=30' == 1
'Joblevel 1' == 1
'Maritalstatus Single' == 1
Clicking this would filter the table next to it
Is there any way I can do this in Tableau?
You can download the tbwx i used in this example here https://community.tableau.com/servlet/JiveServlet/download/1083124-384949/Apriori.twbx
Thanks in advance for the help!
I am not able to check your twbx on the machine I'm using but I think you should be able to do this. The fields in the 2 data sources need to match so manipulate the data sources the make this happen.
For data source 1 there's a function SPLIT which will mean you are able to split the comma separated string to 3 fields.
Putting those 3 fields to the Detail shelf of your bar chart (or even Rows and hiding the header) will mean you can use them in an action filter.
Your second data source is a cross tab - post pivot. You should be able to pivot this data source. Highlight the measures and pivot them. This will give you the field Pivot Field Names and Pivot Field Values.
You only want to keep those with a value of 1 so create a calculated field
[Lookup1]: IF [Pivot Field Values] = 1 THEN [Pivot Field Names] END
Duplicate this field twice so you have Lookup1, Lookup2 and Lookup 3.
Then you should be able to action filter the table.
In the action filter set it up so SplitField1 = Lookup1, SplitField2 = Lookup2, etc.
Fingers crossed this works, I haven't been able to test so I am pulling it out of my head.
I am trying to filter a table visualization of all of my data by looking to see if a Study Number contains Activity A. If a Study Number contains Activity A then I want to filter for all rows containing those Study Numbers even if the Activity is not A. See mock data below. In my real data set I have ~55,000 rows.
I have created a calculated to return Study Numbers if Activity= A but I am not sure where to go from there. Thanks for any help.
If(UniqueConcatenate([Activity]) OVER ([Study Number])~="A","Y","N")
Will give you this resulting column that you can then filter on (or you can use the formula as a Data Limiting Expression:
I'm doing a break fix on a Tableau report visualization that shows the outcomes of clients by client id for a given year by showing a running sum of distinct count of client id or RUNNING_SUM(COUNTD([ID])). The X axis of the visualization is the initial date of contact with the client. Occasionally, due to errors in the data or weird behavior, there are clients that have two initial dates, listed as two separate data rows where the column Initial Date will have different values but they will share an ID.
Currently, the visualization shows such people with their chronological last Initial Date and I need it to dedup such that the visualization shows them as starting from the chronological first Initial Date.
I could create a calculated field for if there are two IDs with multiple non identical Initial Dates then use the first, but I'm not sure how to create a calculated field that can groupby or otherwise check multiple dates per ID.
In Python/psuedo code, it would be something like
For ID in IDS:
if len(groupby.IDS.ID)>1:
then Initial_Date = min(InitialDate)
But I have to do the transformation in Tableau
Keep everything the same, but create a calculated field named "Initial Contact Date" with the calculation:
{FIXED [ID]: MIN(InitialDate)}
Then replace the date field on the X axis (Columns) with this date field instead.
That LOD Expression loops through all rows given the ID, and returns only the min one.
Please See Picture of my Tableau Worksheet
I simply want to include a new column that shows the difference between 2017SU and 2016SU. Note that each of those two columns are a running sum.
I've tried doing a secondary table calculation but it does not add a new column.
Here is one solution - more work than you would think it should take, but it makes sense when you think about Tableau views your data. I can't see what the full name of you numeric field starting with Seatc ...
Define three calculated fields:
2016SU Seatc = running_sum(sum(if [Stc Term] = "2016SU" then [Seatc...] end))
2017SU Seatc = running_sum(sum(if [Stc Term] = "2017SU" then [Seatc...] end))
diff = [2017SU Seatc] - [2016SU Seatc]
You'll have to set the partitioning and addressing (aka compute using) on the table calcs appropriately.
Finally, now you can use Measure Names and Measure Values to build a table (or other chart) using these 3 measures