I am given a task to calculate the frequency of calls across a territory. If the rep called a physician regarding the sale of the product 5 times, then frequency is 5 and HCP count is 1....I generated frequencies from 1 to 124 in my pivot table using a calculated dimension which is working fine. But my concern is :
My manager wants frequencies till 19 in order from 1..2..3..4...5..6.....19...
And from the frequency 21-124 as 20+.
I would be grateful if someone helps me with this.....Eager for the reply....
Use the Class function in the dimension, to split into buckets:
=class(CallId,5)
And the expression:
=count(Distinct CallId)
You can then customize the output by adding parameters:
class( var,10 ) with var = 23 returns '20<=x<30'
class( var,5,'value' ) with var = 23 returns '20<= value <25'
class( var,10,'x',5 ) with var = 23 returns '15<=x<25'
I think you can do this with a calculated dimension.
If your data has one row per physician coming from the load statement below will likely work.
Dimension
- =IF(CallCount<=19,CallCount,'+20')
Expression
- =COUNT(DISTINCT Physician_ID)
Sort
- Numeric Value Ascending
If your data has to be aggregated, more than one call row per provider incoming from the load try above substituting below for the Dimension.
Dimension
- =IF(AGGR(SUM(CallCount), Physician_ID) <=19,AGGR(SUM(CallCount), Physician_ID),'+20')
Related
I'm trying to mutate a variety of summary statistics based on various groupings in my nested data. I'd like to use this strategy instead of summarize() as I want to store the summary statistics in a tibble with the original data, including other identifying variables.
Group Name
Page Name
User
Date
num
min
Area A
Page 1
user265
22-04-13
14
10
Area A
Page 1
user265
22-04-14
5
3
Area B
Page 2
user275
22-04-01
12
6
There are 8 'groups' and hundreds of 'pages' nested across those groups. Before nesting, each row represented observations by Page Name, User, and Date.
When grouping/nesting by Group Name, the stats I generate for either 'num' or 'min' match what would be expected based on the nested values.
However, when I group by Page Name, the results make no sense based on the data in the nested table. For example, the minimum value for 'num' and 'min' is 1, yet I'll get a mean of 0.10 and a min of 0 for one page. Due to the long format of the data, there are no missing values. I'm not sure why the results aren't consistent with the actual data in the nested table when grouping by Page Name.
adopt_30_nest <- adopt_30 %>%
#Select variables of interest
select(page_name, group_name, user, date, num, min) %>%
#Nest/group by grouping factor, then nest
group_by(page_name) %>%
nest() %>%
#Create a new dbl column with the max num value for each page.
mutate(max_num = map_dbl(data, ~max(.x$num)))
Any ideas for how to fix this? Thanks!
I downloaded the entire FDIC bank call reports dataset, and uploaded it to BigQuery.
The table I currently have looks like this:
What I am trying to accomplish is adding a column showing the deposit growth rate since the last quarter for each bank:
Note:The first reporting date for each bank (e.g. 19921231) will not have a "Quarterly Deposit Growth". Hence the two empty cells for the two banks.
I would like to know if a bank is increasing or decreasing its deposits each quarter/call report (viewed as a percentage).
e.g. "On their last call report (19921231)First National Bank had deposits of 456789 (in 1000's). In their next call report (19930331)First National bank had deposits of 567890 (in 1000's). What is the percentage increase (or decrease) in deposits"?
This "_%_Change_in_Deposits" column would be displayed as a new column.
This is the code I have written so far:
select
SFRNLL.repdte, SFRNLL.cert, SFRNLL.name, SFRNLL.city, SFRNLL.county, SFRNLL.stalp, SFRNLL.specgrp AS `Loan_Specialization`, SFRNLL.lnreres as `_1_to_4_Residential_Loans`, AL.dep as `Deposits`, AL.lnlsnet as `loans_and_leases`,
IEEE_DIVIDE(SFRNLL.lnreres, AL.lnlsnet) as SFR2TotalLoanRatio
FROM usa_fdic_call_reports_1992.All_Reports_19921231_1_4_Family_Residential_Net_Loans_and_Leases as SFRNLL
JOIN usa_fdic_call_reports_1992.All_Reports_19921231_Assets_and_Liabilities as AL
ON SFRNLL.cert = AL.cert
where SFRNLL.specgrp = 4 and IEEE_DIVIDE(SFRNLL.lnreres, AL.lnlsnet) <= 0.10
UNION ALL
select
SFRNLL.repdte, SFRNLL.cert, SFRNLL.name, SFRNLL.city, SFRNLL.county, SFRNLL.stalp, SFRNLL.specgrp AS `Loan_Specialization`, SFRNLL.lnreres as `_1_to_4_Residential_Loans`, AL.dep as `Deposits`, AL.lnlsnet as `loans_and_leases`,
IEEE_DIVIDE(SFRNLL.lnreres, AL.lnlsnet) as SFR2TotalLoanRatio
FROM usa_fdic_call_reports_1993.All_Reports_19930331_1_4_Family_Residential_Net_Loans_and_Leases as SFRNLL
JOIN usa_fdic_call_reports_1993.All_Reports_19930331_Assets_and_Liabilities as AL
ON SFRNLL.cert = AL.cert
where SFRNLL.specgrp = 4 and IEEE_DIVIDE(SFRNLL.lnreres, AL.lnlsnet) <= 0.10
The table looks like this:
Additional notes:
I would also like to view the last column (SFR2TotalLoansRatio) as a percentage.
This code runs correctly, however, previously I was getting a "division by zero" error when attempting to run 50,000 rows (1992 to the present).
Addressing each of your question individually.
First) Retrieving SFR2TotalLoanRatio as percentage, I assume you want to see 9.988% instead of 0.0988 in your results. Currently, in BigQuery you can achieve this by casting the field into a STRING then, concatenating the % sign. Below there is an example with sample data:
WITH data as (
SELECT 0.0123 as percentage UNION ALL
SELECT 0.0999 as percentage UNION ALL
SELECT 0.3456 as percentage
)
SELECT CONCAT(CAST(percentage*100 as String),"%") as formatted_percentage FROM data
And the output,
Row formatted_percentage
1 1.23%
2 9.99%
3 34.56%
Second) Regarding your question about the division by zero error. I am assuming IEEE_DIVIDE(arg1,arg2) is a function to perform the division, in which arg1 is the divisor and arg2 is the dividend. Therefore, I would adivse your to explore your data in order to figured out which records have divisor equals to zero. After gathering these results, you can determine what to do with them. In case you decide to discard them you can simply add within your WHERE statement in each of your JOINs: AL.lnlsnet = 0. On the other hand, you can also modify the records where lnlsnet = 0 using a CASE WHEN or IF statements.
UPDATE:
In order to add this piece of code your query, you u have to wrap your code within a temporary table. Then, I will make two adjustments, first a temporary function in order to calculate the percentage and format it with the % sign. Second, retrieving the previous number of deposits to calculate the desired percentage. I am also assuming that cert is the individual id for each of the bank's clients. The modifications will be as follows:
#the following function MUST be the first thing within your query
CREATE TEMP FUNCTION percent(dep INT64, prev_dep INT64) AS (
Concat(Cast((dep-prev_dep)/prev_dep*100 AS STRING), "%")
);
#followed by the query you have created so far as a temporary table, notice the the comma I added after the last parentheses
WITH data AS(
#your query
),
#within this second part you need to select all the columns from data, and LAG function will be used to retrieve the previous number of deposits for each client
data_2 as (
SELECT repdte, cert, name, city, county, stalp, Loan_Specialization, _1_to_4_Residential_Loans,Deposits, loans_and_leases, SFR2TotalLoanRatio,
CASE WHEN cert = lag(cert) OVER (PARTITION BY id ORDER BY d) THEN lag(Deposits) OVER (PARTITION BY id ORDER BY id) ELSE NULL END AS prev_dep FROM data
)
SELECT repdte, cert, name, city, county, stalp, Loan_Specialization, _1_to_4_Residential_Loans,Deposits, loans_and_leases, SFR2TotalLoanRatio, percent(Deposits,prev_dep) as dept_growth_rate FROM data_2
Note that the built-in function LAG is used together with CASE WHEN in order to retrieve the previous amount of deposits per client.
I need to make some simple calculation using DAX, but I am not sure how do I achieve same result as I did in Excel's formulas.
First of all my data structure looks as on screenshot:
Measures are coming from one table, and the PactDelivered is an attribute from related table which is connected by relationship.
All I really have to do is to translate formula as below into DAX:
There is no problem when it comes to calculate B3-C3-D3, but I have a problem how do I calculate the - B4 + C4 part. Of course it should be dynamic, so if it comes to calculate result for Package number 10, then it'd take figures from number 10 and 11.
EDIT:
After modification of code from Alexis Olson I got something like below, but it's not working yet. Please note the PactDelivered is in other table than measures are. Not knowing why, but it's calculating the result on packs according to NextPack value. I tried adding another variable which was basically NextPack -1, and tried calculating the sums from return using it, but didn't work as well.
Cancellations:=
var NextPack = MAX('Order'[PactDelivered]) + 1
var NextOrders = CALCULATE(SUM('Fact DropOff'[OrdersSentOut]) + sum('Fact DropOff'[OrdersReturned]), 'Order'[PactDelivered] = NextPack)
return SUM ('Fact DropOff'[OrdersSentOut]) - SUM ('Fact DropOff'[OrdersReturned] ) - SUM('Fact DropOff'[ActiveSubscriptions]) - NextOrders
EDIT2:
Sample data from fact:
From dimension:
Relationship:
Try a measure like this:
Measure =
VAR NextPact = MAX(Orders[PactDelivered]) + 1
VAR NextOrders = CALCULATE(SUM(Orders[OurdersSentOut]) - SUM(Orders[OrdersReturned]),
Orders[PactDelivered] = NextPact)
RETURN SUM(Orders[OurdersSentOut]) - SUM(Orders[OrdersReturned]) -
SUM(Orders[ActiveSubscriptions]) - NextOrders
First, we calculate the next index by taking the current index and adding 1. Then you calculate sent out minus returns for that index. Finally, subtract that from the current row calculation.
I'm trying to rank (A,B,C) a list of customers according to their profitability , which is calculated as the amount of each sale multiplied by the product profitability (each product has a profitability value assigned). Hence, Profit = SaleAmount*ProductProfitability
To rank every customer, I have a pivot table with the customer id (CustID) as dimension and two expressions:
1)
= SaleAmount*ProductProfitability
2) = if(SaleAmount*ProductProfitability > $(vPercentile75Profit),'A', if(SaleAmount*ProductProfitability > $(vPercentil25Profit),'B','C'))
Expression 2) works correctly if I fix the values of vPercentile75Profit and vPercentile25Profit, but obviously I need this to be dynamic.
For that I've defined those variables as (same for both, just switching 0.75 with 0.25):
vPercentile75Profit =Fractile(aggr(sum({$<ProductProfitability = {'>0'} >} SaleAmount*ProductProfitability/100),CustID), 0.75)
If I understand well, this calculates a list of each customer profitability and then performs the 75 percentile of that list (which is a single value). This works great if I show the value in a Text box for example, however, if I use it in my table, it takes a different percentile for each customer (since CustID is in the dimension).
How can I bypass this? The percentiles must be the same for each customer, but I cannot find the way.
Thanks in advance, any help will be greatly appreciated!
Nothing works better to find the answer than asking your question to others. It was as simple as adding TOTAL to the variable definition:
vPercentile75Profit =Fractile(TOTAL aggr(sum({$<ProductProfitability = {'>0'} >} SaleAmount*ProductProfitability/100),CustID), 0.75)
I have a measure [Measures].[myMeasure] that I would like to create several derivatives of based on the related attribute values.
e.g. if the related [Location].[City].[City].Value = "Austin" then I want the new calculated measure to return the value of [Measures].[myMeasure], otherwise, I want the new calculated measure to return 0.
Also, I need the measure to aggregate correctly meaning sum all of the leaf level values to create a total.
The below works at the leaf level or as long as the current member is set to Austin...
Create Member CurrentCube.[Measures].[NewMeasure] as
iif(
[Location].[City].currentmember = [Location].[City].&[Austin],
[Measures].[myMeasure],
0
);
This has 2 problems.
1 - I don't always have [Location].[City] in context.
2. When multiple cities are selected this return 0.
I'm looking for a solution that would work regardless of whether the related dimension is in context and will roll up by summing the atomic values based on a formula similar to above.
To add more context consider a transaction table with an amount field. I want to convert that amount into measures such as payments, deposits, return, etc... based on the related account.
I don't know the answer but just a couple of general helpers:
1 You should use IS rather than = when comparing to a member
2 You should use null rather than 0 - 0/NULL are effecitvely the same but using 0 will slow things up a lot as the calculation will be fired many more times. (this might help with the second section of your question)
Create Member CurrentCube.[Measures].[NewMeasure] as
iif(
[Location].[City].currentmember IS [Location].[City].&[Austin],
[Measures].[myMeasure],
NULL
);