I have this string:
Sum (if(left(SeasonCalc,3) = '171', Fsgantal*ShoesMen))
SeasonCalc is a sales period for the year, i.e. the Season.
Fsgantal is the Number of shoes sold
ShoesMen is to only show the number of shoes sold that are Men's shoes.
The season is named like 17S1... 17S2... 18S1 etc.
What I want to do is to change the Season as the sales period changes and do that by using a Wildcard in the variable. Is this possible?
I know it's possible to use a variable to set the sales period, we do this already, but then I'd have to define *ShoesMen or *ShoesLadies etc. for every string.
Thanks!
Your question is not very clear, but following your example, I would guess that something like this could work :
Sum(if(WildMatch(SeasonCalc, '*S1'), Fsgantal*ShoesMen))
WildMatch will return true if it matches one of the a wildcard expression given as a parameter.
Wildcards can also be used in Set Analysis, also better performance :
Sum({<SeasonCalc={'*S1'}>}, Fsgantal*ShoesMen))
Related
My task is to get total inbound leads for a group of customers, leads by month for the same group of customers and conversion rate of those leads.
The dataset I'm pulling from is 20 million records so I can't query the whole thing. I have successfully done the first step (getting total lead count for each org with this:
inbound_leads <- domo_get_query('6d969e8b-fe3e-46ca-9ba2-21106452eee2',
auto_limit = TRUE,
query = "select org_id,
COUNT(*)
from table
GROUP BY org_id
ORDER BY org_id"
DOMO is the bi tool I'm pulling from and domo_get_query is an internal function from a custom library my company built. It takes a query argument which is a mysql query)and various others which aren't important right now.
sample data looks like this:
org_id, inserted_at, lead_converted_at
1 10/17/2021 2021-01-27T03:39:03
2 10/18/2021 2021-01-28T03:39:03
1 10/17/2021 2021-01-28T03:39:03
3 10/19/2021 2021-01-29T03:39:03
2 10/18/2021 2021-01-29T03:39:03
I have looked through many aggregation online tutorials but none of them seem to go over how to get data needed pre-aggregation (such as number of leads per month per org, which isn't possible once the aggregation has occurred because in the above sample the aggregation would remove the ability to see more than one instance of org_id 1 for example) from a dataset that needs to be aggregated in order to be accessed in the first place. Maybe I just don't understand this enough to know the right questions to ask. Any direction appreciated.
If you're unable to fit your data in memory, you have a few options. You could process the data in batches (i.e. one year at a time) so that it fits in memory. You could use a package like chunked to help.
But in this case I would bet the easiest way to handle your problem is to solve it entirely in your SQL query. To get leads by month, you'll need to truncate your date column and group by org_id, month.
To get conversion rate for leads in those months, you could add a column (in addition to your count column) that is something like:
sum(case when conversion_date is not null then 1 else 0) as convert_count
I have a dataset that includes dates and count of reports. I am tasked with generating matched-pairs using these guidelines:
Reports will need to be matched to the week immediately prior to or following. (For example: Jan 23, 2000 will be matched with Jan 16, 2000 and Jan 30, 2000)
Holidays must not be included in the final matched-pairs generation.
I have been able to identify the holidays within the dataset but am still stuck on how to generate the matched pairs. Any advice would be much appreciated!
Example of the Data
I am making assumptions as I could not ask for clarifications.
Assumptions I made
a> You wanted to get a formula bash
b> You wanted the date closest matching the previous week to the specific date. for example a Monday event needed to match closer to an event on Monday the previous week. As the data set you gave showed multiple reports through the week. It was not clear what pattern of the previous week you wanted to match.
Solution based on Assumptions.
1> You can mathematically turn each date to a grouping of which week they were in for the year. Then match them to one another. For example 1/1/2003 would be 1.1. A date in 14/1/2003 would be 2.1.
You can then patten match on if 1.1 = 2.1 if that hits it's a match if not it would loop until it saw an entry in the range of 2.[0-9]. You can place an if statment to check if there is a holiday on the match, if there is one it will continue the loop.
I have a csv. I want to load it into R to get the desired outcome (highlighted). For example study id 256 was taken on differing dates (ie. different vists) I want PACE01, PACE02, PACE03 or PACE04 to be added for each recurring visit respectively.
Some years have two visits so I cant just use year. I need to take into account whole date.
Hope this makes sense, I would really appreciate your help. I have 14,000+ samples to sort.
Desired Outcome
I was wondering what the most efficient way is to get the available articles for a given nntp group. The method I have implemented works as follows:
(i) Select the group:
GROUP group.name.subname
(ii) Get a list of article numbers from the group (pushed back into a vector 'codes'):
LISTGROUP
(iii) Loop over codes and grab articles (e.g. headers)
for code in codes do
HEAD code
end
However, this doesn't scale well with large groups with many article codes.
In RFC 3977, the GROUP command is indicated as also returning the 'low' and 'high' article numbers. For example,
[C] GROUP misc.test
[S] 211 1234 3000234 3002322 misc.test
where 3000234 and 2002322 are the low and high numbers. I'm therefore thinking of using these instead rather than initially pushing back all article codes. But can these numbers be relied upon? Is 3000234 definitely indicative of the first article id in the above-selected group and likewise is 3002322 definitely indicative of the last article id in the above-selected group or are they just estimates?
Many thanks,
Ben
It turns out I was thinking about this all wrong. All I need to do is
(i) set the group using GROUP
(ii) execute the NEXT command followed by HEAD for however many headers I want (up to count):
for c : count do
articleId <-- NEXT
HEAD articleID
end
EDIT: I'm sure there must be a better way but until anyone suggests otherwise I'll assume this way to be the most effective. Cheers.
Not sure if what I'm trying to do is possible or if I need to change my data model.
I have a dimension containing the different amouts a customer can loan so what I wan't to do is see the share of a certain amount compared to total sales.
Pseudo code:
member [Measures].[Share 5000] as 'count([Amount].[5000])/([Measures].[Total Sales], [Time].CurrentMember)'
I assume you want the 5000 included included in your calculation? So if 10 consumers loaned 5000 and the total sales is 100000 the share is (5000*10) / 100000 = 0,5
First you will need a 'number of customers' measure, I don't know if it exists already in your data model otherwise you will have to add it.
Then you can write your calculation something like this:
member [Measures].[Share 5000] as
'
(([Amount].[Total Amount].[5000],[Measures].[Number of Customers])*5000) /
([Amount].[Total Amount],[Measures].[Total Sales])
'
You don't need to include the Time.CurrentMember in your calculation as it does not make any difference in this case. If you put Time in the rows or columns it will be automatically included.