I have data as follows
EmployeeID Cycle Val Group
1 1 6 A
2 1 5 A
My desired result is as follows:
EmployeeID Cycle GroupVal
1 1 5.5
2 1 5.5
I have written 2 Measures as follows:
Emp_AVG: CALCULATE(AVERAGE(EmployeeFeedback, EmployeeFeedback[Val] > 0)
Group_AVG: CALCULATE(AVERAGEX(EmployeeFeedback[Emp_AVG],EmployeeFeedback[Emp_AVG] >0)
My thought process is that the Group_AVG is averaging the avg of all employees PER GROUP however since i need the results for a SPECIFIC employee, as soon as i introduce that column, it starts slicing by the Employee and the Group avg becomes inaccurate. I guess i need to generate Group Avgs before i do any employee filtering..how?
I am running a DAX query as follows:
EVALUATE SUMMARIZECOLUMNS(
EmployeeFeedback[EmployeeID],
EmployeeFeedback[Cycle],
"Group Val", [Group_AVG]
)
I need the EmployeeID to filter it down to an employee but because of EmployeeID, the Group AVG gets screwed. Without EmployeeID, Group AVG is correct but then there is no way to filter it for a specific Employee!
Thanks!
You could try to provide the ALLEXCEPT() argument to the CALCULATE function.
Btw, I believe there's an error in your Emp_AVG measure.
Try this:
Employee Average
Emp_AVG =
CALCULATE(
AVERAGE(EmployeeFeedback[Val]),
EmployeeFeedback[Val] > 0)
Group Average
Grp_Avg =
CALCULATE(EmployeeFeedback[Emp_AVG],
ALLEXCEPT(EmployeeFeedback,EmployeeFeedback[Group]))
Result:
Related
I want to implement a running distinct count with Amazon Quicksight. Here's an example of what that would look like:
Date
ID
Amount
Running Distinct Count
1/1/1900
a
1
1
1/2/1900
a
3
1
1/2/1900
b
6
2
1/4/1900
a
3
2
1/8/1900
c
9
3
1/22/1900
d
2
4
I've tried runningSum(distinct_count, [Date ASC]), but this returns a sum of the distinct counts for each aggregated date field.
Can this be implemented in QuickSight?
You can use this workaround to get the same functionality as runningDistinctCount() as follows:
runningSum(
distinct_count(
ifelse(
datetime=minOver({Date}, [{ID}], PRE_AGG),
{ID},
NULL
)),
[{Date} ASC],
[]
)
This would give you the runningDistinctCount of ID's over the Date field. It achieves it by considering just the first time the ID appears in the dataset, counting these and finally doing a runningSum on these counts.
My goal is to create measure to get top 3 customer Names and there respective sales.
I am using the below measure to bring top 3 names along with there sales. The below measure is returning all the rows. I fail to understand why this is happening and why filtering is not happening for top 3 customers
topN = calculate(sum(Sale[Total Excluding Tax]),
TOPN(3,
values(Sale[Employee Name]),
calculate(sum(Sale[Total Excluding Tax]))
)
)
Sale[Employee Name] is calculated column and is coming from another table Employee by using Employee Name = RELATED(Employee[Employee])
The DAX is working properly and grabbing top 3 records. Order/sorting is important. You need to order your results.
Create a calculate column [Total Excluding Tax] to sum up the Total excluding tax. Then use that column in a measure; try something like:
Top Sales = TOPN ( 3, ALLSELECTED( 'Sale' ), [Total Excluding Tax]), desc)
I want to calculate tasks per day(no of tasks/no of unique users) in my dashboard.
I used the distinct count to get the unique no of employees. When I put the measure in pivot, day wise calculation is okay but at the end in MTD it is not added the day wise unique no of emp.
Tasks per day:=divide(sum(fdata[resolved]),distinctcount(fdata[sap id]))
I expect the output like second table.
Model
Date ID Resolved
12-05-2019 a 1
12-05-2019 a 1
12-05-2019 b 1
13-05-2019 a 1
13-05-2019 b 1
13-05-2019 c 1
Expected Result
Date No of emp Resolved Task Per Day
12-05-2019 2 3 1.5
13-05-2019 3 3 1
Grand Total **5** 6 1.2
This will do the trick:
Tasks per day:=
IF(
ISFILTERED( fdata[date] )
, DISTINCTCOUNT( fdata[sap id] )
, COUNTROWS(
GROUPBY(
fdata
, fdata[date]
, fdata[sap id]
)
)
)
I have a table in Clickhouse. for keep statistics and metrics.
and structure is:
datetime|metric_name|metric_value
I want to keep statistics and limit number of accesses in 1 minute, 1 hour, 1 day and so on. So I need event counts in last minute, hour or day for every metric_name and I want to prepare statistics in a chart.
I do not know how to make a query. I get the count of metrics statistics based on the exact for example 1 minute, 1 hour, 1 day and so on.
I used to work on inflxdb:
SELECT SUM(value) FROM `TABLE` WHERE `metric_name`=`metric_value` AND time >= now() - 1h GROUP BY time(5m) fill(0)
In fact, I want to get the number of each metric per 5 minutes in the previous 1 hour.
I do not know how to use aggregations for this problem
ClickHouse has functions for generating Date/DateTime group buckets such as toStartOfWeek, toStartOfHour, toStartOfFiveMinute. You can also use intDiv function to manually divide value ranges. However the fill feature is still in the roadmap.
For example, you can rewrite the influx sql without the fill in ClickHouse like this,
SELECT SUM(value) FROM `TABLE` WHERE `metric_name`=`metric_value` AND
time >= now() - 1h GROUP BY toStartOfFiveMinute(time)
You can also refer to this discussion https://github.com/yandex/ClickHouse/issues/379
update
There is a timeSlots function that can help generating empty buckets. Here is a working example
SELECT
slot,
metric_value_sum
FROM
(
SELECT
toStartOfFiveMinute(datetime) AS slot,
SUM(metric_value) AS metric_value_sum
FROM metrics
WHERE (metric_name = 'k1') AND (datetime >= (now() - toIntervalHour(1)))
GROUP BY slot
)
ANY RIGHT JOIN
(
SELECT arrayJoin(timeSlots(now() - toIntervalHour(1), toUInt32(3600), 300)) AS slot
) USING (slot)
I'm using InfluxDB 1.4, and here's my task
1) find the closet value for each IDs.
2) Do 1) for every hour
For example,
select id, value, time from myTable where time = '2018-08-14T00:00:00Z' group by id;
select id, value, time from myTable where time = '2018-08-14T01:00:00Z' group by id;
....
select id, value, time from myTable where time = '2018-08-14T23:00:00Z' group by id;
then, some id have value at each o'clock but others don't. In this case, I want to get the closest row to the give time '2018-08-14T14:00:00Z', like as '2018-08-14T14:00:01Z' or '2018-08-14T13:59:59Z'
and I don't want to query 24 times for each hour. Can I do this task with group by time, id, or something else?
Q: I would like to select the point data closest to the hourly boundary. Is there a way I can do this without having to query 24 times for each day? Will group by time be any help on this?
A:
Will group by time be any help on this?
Unfortunately the group by time function will not be much help to you as it requires the query to have an aggregation function. What the group by time function does is that it groups all data that falls within the interval into one single record by using the aggregation function like sum, mean etc to tabulate the combined row's values.
Is there a way I can do this without having to query 24 times for each
day?
To the best of my knowledge, I don't think influxdb 1.5 has any way to build a one liner query for this task. Maybe there is something in 1.6, i'm not sure. Haven't tried that.
At the moment I think your best solution today is to build a query that uses the time filter, order by and limit functions e.g.
select * from uv where time >= '2018-08-18T14:00:00Z' and time < '2018-08-18T15:00:00Z' order by desc limit 1;
The query above means that you are selecting all the points within 2pm to 3pm and then order them by descending order but only return the first row, which is what you want.
If for some reason you can only do 1 HTTP request to influxdb for the hourly data on a particular day. You can bundle up the 24 queries into one big query using the ; seperator and retrieve the data in 1 transaction. E.g.
select * from uv where time >= '2018-08-18T14:00:00Z' and time < '2018-08-18T15:00:00Z' order by desc limit 1; select * from uv where time >= '2018-08-18T15:00:00Z' and time < '2018-08-18T16:00:00Z' order by desc limit 1; select * from uv where time >= '2018-08-18T16:00:00Z' and time < '2018-08-18T17:00:00Z' order by desc limit 1;
Output:
name: uv
time tag1 id value
---- -------- -- -----
1534603500000000000 apple uv 2
1534607100000000000 apple uv 1
1534610700000000000 apple uv 3.1