I have a Tableau workbook which has 3 measures m1,m2 and m3.
Those are aggregated by year and summed, so I end up with a table like this:
| | 2017 | 2018 |
----------------------------------------
| m1 | 10 | 20 |
----------------------------------------
----------------------------------------
| m2 | 10 | 20 |
----------------------------------------
----------------------------------------
| m3 | 10 | 20 |
----------------------------------------
However, I have to apply a date filter to variable m3 so that the resulting table will look exactly like the one above, but in the background m3 data is filtered.
I would like to be able to do something like:
if metric == m3 then filter(year)
Does anyone know how to achieve this in Tableau?
Thanks
Use Level of Detail Expressions.
Create a calculated field for m1 and m2 measures, as follows:
{FIXED [Year]: SUM(m1)}
This will make all filters other than Year don't have any effect on this measure.
try this:
if metric == m3 and year=2017
then sales
end
Related
I have this table inside my postgresql database,
item_code | date | price
==============================
aaaaaa.1 |2019/12/08 | 3.04
bbbbbb.b |2019/12/08 | 19.48
261893.c |2019/12/08 | 7.15
aaaaaa.1 |2019/12/17 | 4.15
bbbbbb.2 |2019/12/17 | 20
xxxxxx.5 |2019/03/12 | 3
xxxxxx.5 |2019/03/18 | 4.5
how can i calculate the average per item, per month over the year. so i get the result something like:
item_code | month | price
==============================
aaaaaa.1 | 2019/12 | 3.59
bbbbbb.2 | 2019/12 | 19.74
261893.c | 2019/12 | 7.15
xxxxxx.5 | 2019/03 | 3.75
I have tried to look and apply many alternatives but i am still not get the point, would really appreciate your help because i am new to postgresql.
I don't see how the question relates to a moving average. It seems you just want group by:
select item_code, date_trunc('month', date) as date_month, avg(price) as price
from mytable
group by item_code, date_month
This gives date_month as a date, truncated to the first day of the month - which I find more useful that the format you suggested. But it you do want that:
to_char(date, 'YYYY/MM') as date_month
I am trying to create a "queue" system by adding an arbitrary column that creates a number based on a condition and date, to sort the importance of a row.
For example, below is the query result I pulled in Postgres:
Table: task
Result:
description | status/condition| task_created |
bla | A | 2019-12-01 07:00:00|
pikachu | A | 2019-12-01 16:32:10|
abcdef | B | 2019-12-02 18:34:22|
doremi | B | 2019-12-02 15:09:43|
lalala | A | 2019-12-03 22:10:59|
In the above, each task has a date/timestamp and status/condition applied to them. I would like to create another column that gives a number to a row where it prioritises the older tasks first, BUT if the condition is B, then we take the older task of those in B as first priority.
The expected end result (based on the example) should be:
Table1: task
description | status/condition| task_created | priority index
bla | A | 2019-12-01 07:00:00| 3
pikachu | A | 2019-12-01 16:32:10| 4
abcdef | B | 2019-12-02 18:34:22| 2
doremi | B | 2019-12-02 15:09:43| 1
lalala | A | 2019-12-03 22:10:59| 5
For priority number, 1 being most urgent to do/resolve, while 5 being the least.
How would I go about adding this additional column into the existing query? especially since there's another condition apart from just the task_created date/time.
Any help is appreciated. Many thanks!
You maybe want the Rank or Dense Rank function (depends on your needs) window functions.
If you don't need a conditional order on the status you can use this one.
SELECT *,
rank() OVER (
ORDER BY status desc, task_created
) as priority_index
FROM task
If you need a custom order based on the value of the status:
SELECT *,
rank() OVER (
ORDER BY
CASE status
WHEN 'B' THEN 1
WHEN 'A' THEN 2
WHEN 'C' THEN 3
ELSE 4
END, task_created
) as priority_index
FROM task
If you have few values this is good enough, because we can simply specify your custom order. But if you have a lot of values and the ordering information is fixed, then it should have its own table.
I will be very grateful for your advice regarding the following issue.
Given:
PostgreSQL database
Initial (basic) query
select day, Value_1, Value_2, Value_3
from table
where day=current_date
which returns a row with following columns
Day | Value_1(int) | Value_2(int) | Value 3 (int)
2019-11-14 | 10 | 10 | 14
It is needed to create a view with this starting information and add a new row every day based on the outcome of initial query executed at 22:00.
The expected outcome tomorrow at 22:01 will be
Day | Value_1 | Value_2 | Value_3
2019-11-14 | 10 | 10 | 14
2019-11-15 | N | M | P
Many thanks in advance for your time and support.
Situation
Using Python 3, Django 1.9, Cubes 1.1, and Postgres 9.5.
These are my datatables in pictorial form:
The same in text format:
Store table
------------------------------
| id | code | address |
|-----|------|---------------|
| 1 | S1 | Kings Row |
| 2 | S2 | Queens Street |
| 3 | S3 | Jacks Place |
| 4 | S4 | Diamonds Alley|
| 5 | S5 | Hearts Road |
------------------------------
Product table
------------------------------
| id | code | name |
|-----|------|---------------|
| 1 | P1 | Saucer 12 |
| 2 | P2 | Plate 15 |
| 3 | P3 | Saucer 13 |
| 4 | P4 | Saucer 14 |
| 5 | P5 | Plate 16 |
| and many more .... |
|1000 |P1000 | Bowl 25 |
|----------------------------|
Sales table
----------------------------------------
| id | product_id | store_id | amount |
|-----|------------|----------|--------|
| 1 | 1 | 1 |7.05 |
| 2 | 1 | 2 |9.00 |
| 3 | 2 | 3 |1.00 |
| 4 | 2 | 3 |1.00 |
| 5 | 2 | 5 |1.00 |
| and many more .... |
| 1000| 20 | 4 |1.00 |
|--------------------------------------|
The relationships are:
Sales belongs to Store
Sales belongs to Product
Store has many Sales
Product has many Sales
What I want to achieve
I want to use cubes to be able to do a display by pagination in the following manner:
Given the stores S1-S3:
-------------------------
| product | S1 | S2 | S3 |
|---------|----|----|----|
|Saucer 12|7.05|9 | 0 |
|Plate 15 |0 |0 | 2 |
| and many more .... |
|------------------------|
Note the following:
Even though there were no records in sales for Saucer 12 under Store S3, I displayed 0 instead of null or none.
I want to be able to do sort by store, say descending order for, S3.
The cells indicate the SUM total of that particular product spent in that particular store.
I also want to have pagination.
What I tried
This is the configuration I used:
"cubes": [
{
"name": "sales",
"dimensions": ["product", "store"],
"joins": [
{"master":"product_id", "detail":"product.id"},
{"master":"store_id", "detail":"store.id"}
]
}
],
"dimensions": [
{ "name": "product", "attributes": ["code", "name"] },
{ "name": "store", "attributes": ["code", "address"] }
]
This is the code I used:
result = browser.aggregate(drilldown=['Store','Product'],
order=[("Product.name","asc"), ("Store.name","desc"), ("total_products_sale", "desc")])
I didn't get what I want.
I got it like this:
----------------------------------------------
| product_id | store_id | total_products_sale |
|------------|----------|---------------------|
| 1 | 1 | 7.05 |
| 1 | 2 | 9 |
| 2 | 3 | 2.00 |
| and many more .... |
|---------------------------------------------|
which is the whole table with no pagination and if the products not sold in that store it won't show up as zero.
My question
How do I get what I want?
Do I need to create another data table that aggregates everything by store and product before I use cubes to run the query?
Update
I have read more. I realised that what I want is called dicing as I needed to go across 2 dimensions. See: https://en.wikipedia.org/wiki/OLAP_cube#Operations
Cross-posted at Cubes GitHub issues to get more attention.
This is a pure SQL solution using crosstab() from the additional tablefunc module to pivot the aggregated data. It typically performs better than any client-side alternative. If you are not familiar with crosstab(), read this first:
PostgreSQL Crosstab Query
And this about the "extra" column in the crosstab() output:
Pivot on Multiple Columns using Tablefunc
SELECT product_id, product
, COALESCE(s1, 0) AS s1 -- 1. ... displayed 0 instead of null
, COALESCE(s2, 0) AS s2
, COALESCE(s3, 0) AS s3
, COALESCE(s4, 0) AS s4
, COALESCE(s5, 0) AS s5
FROM crosstab(
'SELECT s.product_id, p.name, s.store_id, s.sum_amount
FROM product p
JOIN (
SELECT product_id, store_id
, sum(amount) AS sum_amount -- 3. SUM total of product spent in store
FROM sales
GROUP BY product_id, store_id
) s ON p.id = s.product_id
ORDER BY s.product_id, s.store_id;'
, 'VALUES (1),(2),(3),(4),(5)' -- desired store_id's
) AS ct (product_id int, product text -- "extra" column
, s1 numeric, s2 numeric, s3 numeric, s4 numeric, s5 numeric)
ORDER BY s3 DESC; -- 2. ... descending order for S3
Produces your desired result exactly (plus product_id).
To include products that have never been sold replace [INNER] JOIN with LEFT [OUTER] JOIN.
SQL Fiddle with base query.
The tablefunc module is not installed on sqlfiddle.
Major points
Read the basic explanation in the reference answer for crosstab().
I am including with product_id because product.name is hardly unique. This might otherwise lead to sneaky errors conflating two different products.
You don't need the store table in the query if referential integrity is guaranteed.
ORDER BY s3 DESC works, because s3 references the output column where NULL values have been replaced with COALESCE. Else we would need DESC NULLS LAST to sort NULL values last:
PostgreSQL sort by datetime asc, null first?
For building crosstab() queries dynamically consider:
Dynamic alternative to pivot with CASE and GROUP BY
I also want to have pagination.
That last item is fuzzy. Simple pagination can be had with LIMIT and OFFSET:
Displaying data in grid view page by page
I would consider a MATERIALIZED VIEW to materialize results before pagination. If you have a stable page size I would add page numbers to the MV for easy and fast results.
To optimize performance for big result sets, consider:
SQL syntax term for 'WHERE (col1, col2) < (val1, val2)'
Optimize query with OFFSET on large table
This is very similar to Cognos Calculate Variance Crosstab (Relational), but my data source is dimensional.
I have a simple crosstab such as this:
| 04-13-2013 | 04-13-2014
---------------------------------------
Sold | 75 | 50
Purchased | 10 | 15
Repaired | 33 | 44
Filter: The user selects 1 date and then we include that date plus 1 year ago.
Dimension: The date is the day level in a YQMD Hierarchy.
Measures: We are showing various measures from a Measure Dimension.
Sold
Purchased
Repaired
Here is what is looks like in report studio:
| <#Day#> | <#Day#>
---------------------------------------
<#Sold#> | <#1234#> | <#1234#>
<#Purchased#> | <#1234#> | <#1234#>
<#Repaired#> | <#1234#> | <#1234#>
I want to be able to calculate the variance as a percentage between the two time periods for each measure like this.
| 04-13-2013 | 04-13-2014 | Var. %
-----------------------------------------------
Sold | 75 | 50 | -33%
Purchased | 10 | 15 | 50%
Repaired | 33 | 44 | 33%
I added a Query Expression to the right of the <#Day#> as shown below, but I cannot get the variance calculation to work.
| <#Day#> | <#Variance#>
---------------------------------------
<#Sold#> | <#1234#> | <#1234#>
<#Purchased#> | <#1234#> | <#1234#>
<#Repaired#> | <#1234#> | <#1234#>
These are the expressions I've tried and the results that I get:
An expression that is hard coded works, but only for that 1 measure:
total(case when [date] = 2014-04-13 then [Sold] end)
/
total(case when [date] = 2013-04-13 then [Sold] end)
-1
I thought CurrentMember and PrevMember might work, but it produces blank cells:
CurrentMember( [YQMD Hierarchy] )
/
prevMember(CurrentMember([YQMD Hierarchy]))
-1
I think it is because prevMember produces blank.
prevMember(CurrentMember([YQMD Hierarchy]))
Using only CurrentMember gives a total of both columns:
CurrentMember([YQMD Hierarchy])
What expression can I use to take advantage of my dimensional model and add a column with % variance?
These are the pages I used for research:
Variance reporting in Report Studio on Cognos 8.4?
Calculations that span dimensions - PDF
IBM Cognos 10 Report Studio: Creating Consumer-Friendly Reports
I hope there is a better way to do this. I finally found a resource that describes one approach to this problem. Using the tail and head functions, we can get to the first and last periods, and thereby calculate the % variance.
item(tail(members([Day])),0)
/
item(head(members([Day])),0)
-1
This idea came from IBM Cognos BI – Using Dimensional Functions to Determine Current Period.
Example 2 – Find Current Period by Filtering on Measure Data
If the OLAP or DMR data source has been populated with time periods into the future (e.g. end of year or future years), then the calculation of current period is more complicated. However, it can still be determined by finding the latest period that has data for a given measure.
item(tail(filter(members([sales_and_marketing].[Time].[Time].[Month]),
tuple([Revenue], currentMember([sales_and_marketing].[Time].[Time]))
is not null), 1), 0)