I'm building a model in AnyLogic where I need to assign a value to agents that corresponds to their age. These values must be based on the population age profile that I got from national statistics (# of people with 0 to 100 years-old).
So far, I've tried to used custom distribution with ranges to define ages and the corresponding number of observation, both loaded from a table. I added a condition to match simulation year to the year in the table (i.e. for each year in the simulation there will be a different age distribution in the table, reflecting an aging population).
It seems that AnyLogic check the condition to match the year just once and use the values found during all simulation time (i.e. it carries the age distribution of first years to all years). When I run the model for a single year, it correctly use the corresponding distribution for that year.
Table with number of observations for each age per year
Custom Distribution settings
It seems that AnyLogic check the condition to match the year just once and use the values found during all simulation time
This is how it works, correct. You could instead create these distributions programmatically, one for each year:
Loop through each dbase row (using the Database query wizard) and create a custom distribution for each year entry.
Store them in a LinkedHashMap<Integer, CustomDistribution> where the key is the year and the value the distribution for that year.
Then at runtime, you can simply call myLinkedHashMap.get(getYear()) to get the distribution for that year
Related
I am an absolute Tableau beginner, so forgive my lack of proper terminology.
Context
To give some context to the problem, think of the dataset as the balances and current interest rates of two different loans for which we are trying to calculate a weighted average cost of funds at any point in time, while retaining the ability to filter on Program (specific loan).
I have a single dataset that looks like:
The Balance field is used as a running sum, i.e. to get the actual balance as of 4/30/2022, you would sum the column across all Date values on or before 4/30/2022.
The Rate field is the opposite: it represents the discrete interest rate as of the Date. Thus, it cannot be summed.
Each data point is specific to a specific loan, or Program.
So to get the interest rate of Program A as of 4/30/2022, you would simply grab the Rate value of the row where Date = 4/30/2022 and Program = A, or 5.30%. Sums are fine here, since the value of Rate is never repeated for a single Program and Date combo, but we cannot use a running sum.
On the other hand, to get the balance of Program A as of 4/30/2022, you would need to add (running sum) the Balance values for all rows where Date <= 4/30/2022 and Program = A, or 10,000 + -2500 + -2500 + -2500 = 2500.
Problem / Need
I need a report (or whatever it's called in Tableau) with the following:
Date as a column
Measures as rows
This report would NOT include Program as a row or column, but would include it as a filter.
In this report, I need a Weighted Average Cost of Funds measure.
This is effectively the weighted average Rate over/weighted by the running sum of Balance across Programs included in the filter, of course for any given Date in the columns.
In other words, by Date, latest Ratefor eachProgramtimes thePrograms running sum of Balance, divided by running sum of all Balancesfor allProgram`s included in filter.
Here's an example in Excel:
Here's an example if we were to exclude Program A:
And here's an example if we were to exclude Program B:
Finally, here's the formulas underneath everything in the Excel example:
Data granularity is per customer, per invoice date, per product type.
Generally the idea is simple:
We have a moving average calculation of the volume per week. MA based on last 12 weeks (MA Volume):
window_sum(sum([Volume]),-11,0)/window_count(count([Volume]), -11,0)
We need to see the deviation of the current week vs the MA for that week (Vol DIFF):
SUM([Volume])-[MA Calc]
We need to sum up the deviations for a fixed period of time (Year/Month)
Basically this should show us whether on average, for a given period of time, we deviate positively or negatively vs the base.
enter image description here
Unfortunately I get errors like:
"Argument to SUM (an aggregate function) is already an aggregation, and cannot be further aggregated."
Or
"Level of detail expressions cannot contain table calculations or the ATTR function"
Any ideas how I can go around this one?
Managed to solve this one. Needed to add months to the view and then just WINDOW_SUM(Vol_DIFF).
Simple as that!
Count of complaints per State by Year
I have the count of complaints per state by year in the first picture. I have another dataset which has the population of each state by year. How do I account for population and present only the ratios of count of complaints to population?
Snapshot of first dataset
Snapshot of second dataset
My thought was to create a calculated field to create a ratio but I'm having trouble with adding up the number of complaint counts within a certain year and then dividing by population year. How do I write the formula that only counts complaints within 2011, 2012, etc and dividing it by that population year?
Let me know if there's an easier way to do it as well, thanks for your time.
Edit 1:
Second dataset Pivoted
Population & Complaint Count
I've pivoted my second dataset and now I'm trying to graph both the counts of population and complaints. The population count across the years increases but the count of complaints stay exactly the same; its the sum of all complaints for that particular state for all the years.
Also, when I graph population count with 'Date Received' from the first dataset, I get the total population count across all the years instead of that particular year, like so:
Population per year
How do I properly 'blend' in the two date variables so that it works with both population count and complaint counts in both datasets?
Edits 2:
Blended Year
I changed the [Years] datatype in data source 2 into a date to match the date type of [Date Received] in data source 1. I also took only the 'year' parts because it would only count things on 1/1 of each year if I used [Years] in data source 2.
Now the graphs look similar except when I'm using [Years] instead of [Date Received], all the values are about several thousand off. I tried adding another relationship except this time for month again and then it only counted values for that month.
How do I account for the discrepancy and make [Years] work just like [Date Received] ?
Reshape data source 2, following these instructions.
Then you'll be able to blend the 2 data sources on State and Month and Year.
I am trying to create a trend in which visitors to an event slowly decline every year.
This is the setup: https://imgur.com/6mx3xy2
I want to ensure that for instance in year 1 there are 100,000 visitors but the next year this declines with 1%, so that next year only 99,000 visitors are present and the year after that 99.000*0.99 so in the total of those years 297.010 people have visited. (So, the Stock value of visitors being 297.010 after a simulation of 3 years)
What values/formulas should I give my NewInfoRealVisitors variable and flow equation for example? Or all the other variables for that matter
Ok, a lot of things to do here, first your structure is wrong and should be like this:
visitorsPerYear = annualVisitors it's the same variable, but defined as a flow and as a stock at the same time
annualVisitorsDecline = annualVisitors*declineRate
Now, to obtain the exact values you want (total visitors = 297010)
you need to use years as the model time units and you need to use 1 as the fixed time step:
And finally, you need to run the model in virtual time (as fast as possible) because otherwise anylogic changes your fixed time step without your control
If you don't do all this, you will just get an approximation of 297.010 based on Euler equations... close enough, but not exactly it.
I am a very basic user of tableau and I have not found an answer to my question.
I have a txt file that has historical daily data for 98% of all the stocks in the US, with their daily capitalization. Each stocks has its TICKER, Daily Market Value for every trading day of the year, and its SECTOR.
I did a simple time series that display SUM([Mktval]) (sum of all individual market values) across all stocks, on a daily daily, and where I can see that the total value as of 2016 is about 24 Trillion USD, as in the image below.
When I change the view column from DAY to YEAR, I don't see the right values, but something a lot larger. So I realized that I need to do SUM([Mktval])/252 to get the right value for a year (there are 252 trading days in a year).
If I change the view to MONTH, as in the chart below, the numbers are again wrong because 252 is not the right value to use in the division.
Is there any way that Tableau can adjust the values automatically to reflect the AVG MktVal across different time intervals?
Thanks
Replace SUM(Mktval) on the Rows shelf with the following calculated field
avg({ fixed day(Date1) : sum(Mktval) })
That solution is all in one step. It is perhaps a bit more clear to use 2 steps. First, create a calculated field called total_daily_market_value defined as
{ fixed day(Date1) : sum(Mktval) }
Then make sure that calculated field is a measure. It is an LOD calculation that you can think of as a separate table with one value for each day showing the total market value for that day.
Drag that measure to a shelf, and then change the aggregation function to AVG(), MEDIAN(), MIN(), MAX() or STDEV() as desired. Tableau will aggregate the total_daily_market_value using your chosen aggregation function for whatever values of Date1 are in your view.