I have the following table that I have loaded in Tableau (It has only one column CreatedOnDate)
+-----------------+
| CreatedOnDate |
+-----------------+
| 1/1/2016 |
| 1/2/2016 |
| 1/3/2016 |
| 1/4/2016 |
| 1/5/2016 |
| 1/6/2016 |
| 1/7/2016 |
| 1/8/2016 |
| 1/9/2016 |
| 1/10/2016 |
| 1/11/2016 |
| 1/12/2016 |
| 1/13/2016 |
| 1/14/2016 |
+-----------------+
I want to be able to find the maximum date in the table, compare it with every date in the table and get the difference in days. For the above table, the maximum date in table is 1/14/2016. Every date is compared to 1/14/2016 to find the difference.
Expected Output
+-----------------+------------+
| CreatedOnDate | Difference |
+-----------------+------------+
| 1/1/2016 | 13 |
| 1/2/2016 | 12 |
| 1/3/2016 | 11 |
| 1/4/2016 | 10 |
| 1/5/2016 | 9 |
| 1/6/2016 | 8 |
| 1/7/2016 | 7 |
| 1/8/2016 | 6 |
| 1/9/2016 | 5 |
| 1/10/2016 | 4 |
| 1/11/2016 | 3 |
| 1/12/2016 | 2 |
| 1/13/2016 | 1 |
| 1/14/2016 | 0 |
+-----------------+------------+
My goal is to create this Difference calculated field. I am struggling to find a way to do this using DATEDIFF.
And help would be appreciated!!
woodhead92, this approach would work, but means you have to use table calculations. Much more flexible approach (available since v8) is Level of Details expressions:
First, define a MAX date for the whole dataset with this calculated field called MaxDate LOD:
{FIXED : MAX(CreatedOnDate) }
This will always calculate the maximum date on table (will overwrite filters as well, if you need to reflect them, make sure you add them to context.
Then you can use pretty much the same calculated field, but no need for ATTR or Table Calculations:
DATEDIFF('day', [CreatedOnDate], [MaxDate LOD])
Hope this helps!
Related
I calculate my metrics with SQL and publish the resulting table to Tableau Server. Afterward, use this data source to create charts and dashboards.
For one analysis, I already calculated the measures per day with SQL. When I use the resulting table in Tableau, it aggregates these measures to SUM by default. However, I don't want to have SUM or AVG of the average or SUM of the Percentiles.
What I want is the result when I don't select date dimension and not GROUP BY date in SQL as attached below.
Here is the query:
SELECT
-- date,
COUNT(DISTINCT id) AS count_of_id,
AVG(timediff_in_sec) AS avg_timediff,
PERCENTILE_CONT(0.25) WITHIN GROUP(ORDER BY timediff_in_sec) AS percentile_25,
PERCENTILE_CONT(0.50) WITHIN GROUP(ORDER BY timediff_in_sec) AS percentile_50
FROM
(
--subquery
) AS t1
-- GROUP BY date
Here are the first 10 rows of the resulting table:
+------------+--------------+-------------+---------------+---------------+
| date | avg_timediff | count_of_id | percentile_25 | percentile_50 |
+------------+--------------+-------------+---------------+---------------+
| 10/06/2020 | 61,65186364 | 22 | 8,5765 | 13,3015 |
| 11/06/2020 | 127,2913333 | 3 | 15,6045 | 17,494 |
| 12/06/2020 | 306,0348214 | 28 | 12,2565 | 17,629 |
| 13/06/2020 | 13,2664 | 5 | 11,944 | 13,862 |
| 14/06/2020 | 16,728 | 7 | 14,021 | 17,187 |
| 15/06/2020 | 398,6424595 | 37 | 11,893 | 19,271 |
| 16/06/2020 | 293,6925152 | 33 | 12,527 | 17,134 |
| 17/06/2020 | 155,6554286 | 21 | 13,452 | 16,715 |
| 18/06/2020 | 383,8101429 | 7 | 266,048 | 493,722 |
+------------+--------------+-------------+---------------+---------------+
How can I achieve the desired output above?
Drag them all into the dimensions list, then they will be static dimensions. For your use you could also just drag the Date field to Rows. Aggregating 1 value, which you have for each date, returns the same value whatever the aggregation type.
In my tableau workbook, I have a calculated field - "Rolling 12 Month Sales" having the below formula and its working fine.
WINDOWS_SUM(SUM(Sales),-11,0)
Now , I am trying to achieve a Rolling 12 Month Sales % measure.
For this % measure, I am referring an existing calculation - SUM(Sales)/TOTAL(SUM(Sales)) - When this is cut on various segments, I get the percent distribution.
I am trying to get the exact same thing, on the Rolling 12 Month Sales % calculation. I tried the following but its not allowing to use a table calculation inside TOTAL()
WINDOWS_SUM(SUM(Sales),-11,0)/TOTAL(WINDOWS_SUM(SUM(Sales),-11,0))
Original Data
+--------+----------+----------+
| Month | Hardware | Software |
+--------+----------+----------+
| Jan-20 | 5000 | 7500 |
| Feb-20 | 6500 | 10000 |
| Mar-20 | 8000 | 10500 |
| Apr-20 | 11000 | 15000 |
| May-20 | 13500 | 21000 |
+--------+----------+----------+
Rolling 2 Months Sum Sales (This is working fine)
+--------+----------+----------+
| Month | Hardware | Software |
+--------+----------+----------+
| Jan-20 | 5000 | 7500 |
| Feb-20 | 11500 | 17500 |
| Mar-20 | 19500 | 28000 |
| Apr-20 | 25500 | 35500 |
| May-20 | 32500 | 46500 |
+--------+----------+----------+
Rolling 2 Months Sum Sales % - Below are the nos I am trying to achieve.
+--------+----------+----------+
| Month | Hardware | Software |
+--------+----------+----------+
| Jan-20 | 40.00% | 60.00% |
| Feb-20 | 39.66% | 60.34% |
| Mar-20 | 41.05% | 58.95% |
| Apr-20 | 41.80% | 58.20% |
| May-20 | 41.14% | 58.86% |
+--------+----------+----------+
Running out of options!
Best Regards
There should be no need to TOTAL a WINDOW_SUM. I suspect this could be solved with different Compute Using. But first I don't fully understand why you're taking the approach you are attempting. Any chance you could show some sample data with the results you expect along the way? For simplicity in the example it would be easier to do a rolling 2 periods rather than 12.
I am trying to understand how to pivot data within T-SQL but can't seem to get it working. I have the following table structure
+-------------------+-----------------------+
| Name | Value |
+-------------------+-----------------------+
| TaskId | 12417 |
| TaskUid | XX00044497 |
| TaskDefId | 23 |
| TaskStatusId | 4 |
| Notes | |
| TaskActivityIndex | 0 |
| ModifiedBy | Orange |
| Modified | /Date(1554540200000)/ |
| CreatedBy | Apple |
| Created | /Date(2121212100000)/ |
| TaskPriorityId | 40 |
| OId | 2 |
+-------------------+-----------------------+
I want to pivot the name column to be columns expected output
+--------+------------------------+-----------+--------------+-------+-------------------+------------+-----------------------+-----------+-----------------------+----------------+-----+
| TASKID | TASKUID | TASKDEFID | TASKSTATUSID | NOTES | TASKACTIVITYINDEX | MODIFIEDBY | MODIFIED | CREATEDBY | CREATED | TASKPRIORITYID | OID |
+--------+------------------------+-----------+--------------+-------+-------------------+------------+-----------------------+-----------+-----------------------+----------------+-----+
| | | | | | | | | | | | |
| 12417 | XX00044497 | 23 | 4 | | 0 | Orange | /Date(1554540200000)/ | Apple | /Date(2121212100000)/ | 40 | 2 |
+--------+------------------------+-----------+--------------+-------+-------------------+------------+-----------------------+-----------+-----------------------+----------------+-----+
Is there an easy way of doing it? The columns are fixed (not dynamic).
Any help appreciated
Try this:
select * from yourtable
pivot
(
min(value)
for Name in ([TaskID],[TaskUID],[TaskDefID]......)
) as pivotable
You can also use case statements.
You must use the aggregate function in the pivot table.
If you want to learn more, here is the reference:
https://learn.microsoft.com/en-us/sql/t-sql/queries/from-using-pivot-and-unpivot?view=sql-server-2017
Output (I only tried three columns):
DB<>Fiddle
From a table of "time entries" I'm trying to create a report of weekly totals for each user.
Sample of the table:
+-----+---------+-------------------------+--------------+
| id | user_id | start_time | hours_worked |
+-----+---------+-------------------------+--------------+
| 997 | 6 | 2018-01-01 03:05:00 UTC | 1.0 |
| 996 | 6 | 2017-12-01 05:05:00 UTC | 1.0 |
| 998 | 6 | 2017-12-01 05:05:00 UTC | 1.5 |
| 999 | 20 | 2017-11-15 19:00:00 UTC | 1.0 |
| 995 | 6 | 2017-11-11 20:47:42 UTC | 0.04 |
+-----+---------+-------------------------+--------------+
Right now I can run the following and basically get what I need
SELECT COALESCE(SUM(time_entries.hours_worked),0) AS total,
time_entries.user_id,
week::date
--Using generate_series here to account for weeks with no time entries when
--doing the join
FROM generate_series( (DATE_TRUNC('week', '2017-11-01 00:00:00'::date)),
(DATE_TRUNC('week', '2017-12-31 23:59:59.999999'::date)),
interval '7 day') as week LEFT JOIN time_entries
ON DATE_TRUNC('week', time_entries.start_time) = week
GROUP BY week, time_entries.user_id
ORDER BY week
This will return
+-------+---------+------------+
| total | user_id | week |
+-------+---------+------------+
| 14.08 | 5 | 2017-10-30 |
| 21.92 | 6 | 2017-10-30 |
| 10.92 | 7 | 2017-10-30 |
| 14.26 | 8 | 2017-10-30 |
| 14.78 | 10 | 2017-10-30 |
| 14.08 | 13 | 2017-10-30 |
| 15.83 | 15 | 2017-10-30 |
| 8.75 | 5 | 2017-11-06 |
| 10.53 | 6 | 2017-11-06 |
| 13.73 | 7 | 2017-11-06 |
| 14.26 | 8 | 2017-11-06 |
| 19.45 | 10 | 2017-11-06 |
| 15.95 | 13 | 2017-11-06 |
| 14.16 | 15 | 2017-11-06 |
| 1.00 | 20 | 2017-11-13 |
| 0 | | 2017-11-20 |
| 2.50 | 6 | 2017-11-27 |
| 0 | | 2017-12-04 |
| 0 | | 2017-12-11 |
| 0 | | 2017-12-18 |
| 0 | | 2017-12-25 |
+-------+---------+------------+
However, this is difficult to parse particularly when there's no data for a week. What I would like is a pivot or crosstab table where the weeks are the columns and the rows are the users. And to include nulls from each (for instance if a user had no entries in that week or week without entries from any user).
Something like this
+---------+---------------+--------------+--------------+
| user_id | 2017-10-30 | 2017-11-06 | 2017-11-13 |
+---------+---------------+--------------+--------------+
| 6 | 4.0 | 1.0 | 0 |
| 7 | 4.0 | 1.0 | 0 |
| 8 | 4.0 | 0 | 0 |
| 9 | 0 | 1.0 | 0 |
| 10 | 4.0 | 0.04 | 0 |
+---------+---------------+--------------+--------------+
I've been looking around online and it seems that "dynamically" generating a list of columns for crosstab is difficult. I'd rather not hard code them, which seems weird to do anyway for dates. Or use something like this case with week number.
Should I look for another solution besides crosstab? If I could get the series of weeks for each user including all nulls I think that would be good enough. It just seems that right now my join strategy isn't returning that.
Personally I would use a Date Dimension table and use that table as the basis for the query. I find it far easier to use tabular data for these types of calculations as it leads to SQL that's easier to read and maintain. There's a great article on creating a Date Dimension table in PostgreSQL at https://medium.com/#duffn/creating-a-date-dimension-table-in-postgresql-af3f8e2941ac, though you could get away with a much simpler version of this table.
Ultimately what you would do is use the Date table as the base for the SELECT cols FROM table section and then join against that, or probably use Common Table Expressions, to create the calculations.
I'll write up a solution to that if you would like demonstrating how you could create such a query.
I would like to subtract promoters and detractors in Tableau by creating a new column. Thanks for all the help!
Customer Type Table (I would like to create the NPS field as shown below):
+---------+------------+----------+-----------+--------------+
| Quarter | Detractors | Passives | Promoters | NPS |
+---------+------------+----------+-----------+--------------+
| Q1 15 | 40.56 | 23.56 | 35.79 | =35.79-40.56 |
| ... | ... | ... | ... | ... |
+---------+------------+----------+-----------+--------------+
Simply create a calculated field (called NPS):
[Promoters] - [Detractors]
This will add a new field to every row of your partition called NPS.
Check out the Tableau online help on calculated fields - this is a skill well worth learning.
I understand the OPs question. The data comes in like this:
+---------+---------------+------+
| Quarter | Customer Type | Score|
+---------+------------+---------+
| Q1 15 | Detractors | 25 |
| Q1 15 | Promoters | 32 |
| Q1 15 | Passives | 45 |
| Q1 15 | Detractors | 17 |
| Q1 15 | Detractors | 28 |
| ... | ... | ... |
+---------+------------+---------+
And when brought into Tableau, the [Customer Type] field is put in the Column shelf and this arranges the data like the table below. The OP wants to calculate the [NPS] column (Promoters - Detractors).
+---------+------------+----------+-----------+--------------+
| Quarter | Detractors | Passives | Promoters | NPS |
+---------+------------+----------+-----------+--------------+
| Q1 15 | 40.56 | 23.56 | 35.79 | =35.79-40.56 |
| ... | ... | ... | ... | ... |
+---------+------------+----------+-----------+--------------+
I hope this clarifies. I am stuck with a similar situation (I want a column that shows the difference between 2015 and 2016):
+---------+-------+-------+------------+
| Measure | 2015 | 2016 | Difference |
+---------+---------------+------------+
| # Hires | 100 | 115 | 15 |
| # Terms | 9 | 6 | 3 |
+---------+---------------+------------+
I believe the steps are similar. I hope someone can help.