I have a data table that has a column for the fiscal quarter, a column for the net revenue made for row X's sale, and a column for the type of sale it was.
I want to use a forecasting method in R (was planning to use ARIMA, but am open to options) to predict future fiscal quarter net revenue per type of sale. For example, if the two types of sale are service and good, I want to create a model to predict future revenue for service and a model for good's future net revenue.
How would I approach this code and are there any websites you'd recommend I reference. Thank you in advance!
The websites I have found so far reference if every timestamp (i.e. every fiscal quarter) has only one row. But, my data table shows how i.e. quarter 1 can have 10 different sales and 5 can be labelled service and 5 are good.
I have transactional data for a sales team showing the transaction amount per transaction, the sales person for that transaction, his team and his salary. Every row denotes a unique transaction (please refer image). I need to make a team-level graph which shows the correlation between the salary they are paid and the revenue they generate for the company i.e. a simple stacked bar chart with salesTeam name on X axis and amounts on the y axis with every bar representing the total salary and total revenue(Amount) for a team.
In the example I've highlighted team 'Central', for which the salary paid is 25k (10k for salesperson A + 15k for salesperson B) and the revenue they make for the company is 430k. Please note that the salaries for some salespersons may be missing (eg. for E). The issue I'm facing is that sum(Salary) adds up the salaries for every row, so for salesperson A it becomes 20k instead of 10k. I tried avg(Salary) but that doesn't work as Tableau calculates the average for the entire column instead of average per salesperson. How can I solve this issue?
Thanks
Here you have a level of detail problem. Basically, Tableau will calculate a formula at the level of detail of the visualisation, so if salesperson is not in your view it will roll up the equation to calculate at the highest level. This is great when you want a dynamic calculation but that doesn't sound like what you are trying to achieve.
Your best option would be to aggregate the data into Tableau so you only have 1 line per sales person with a total of their revenue for all transactions. This would avoid the complexity of the calculated field (and make Tableau perform better).
However, if this is not possible the good news is the answer is a Level of Detail expression (i recommend doing some reading on this if you havent come across before). Basically, you tell Tableau at what level you want the calculation at.
If I understand you want to calculate the ratio of transaction amount and salary paid for each team.
So create a calculated field as follows:
{ FIXED [Team]
: sum(([Amount]))/
(sum({ FIXED [Sales person]:
AVG([Salary])}))
}
What this does is calculates for each team the ratio between the amount and the salary. The use of the second fixed equation that is nested within it (Salesperson) ensures that the salary is not summed for the number of transactions of a salesperson.
Using this I got a result of 17.2 for Central. Is this what you would expect? Do you need a way to account for salaries that are not known?
i am hoping you can improve the 'love' side of my love-hate relationship with MDX.
so, say i have a cube of sales with dimensions customer, year, month, week, product. i have calculated measure based on certain customer values. that is, the calculation is based on sales for customer X in month Y, and then other customers (same calc, maybe different month, etc), added together. basically, these are 'key' customers that have been identified as leading indicators, and the calculated measure is needed for other comparisons as a measure.
now, when i do analysis by year, month, or customer, all is great. the numbers look good and the calculation is doing what i want it to.
however, when i do analysis by week or product, this calculation presents numbers that, at first, don't look sane. i have researched the math, and I understand why it's showing what it is. not all products sell every month to all customers, or each week, etc.
so, what i would like to do in the case of say, analysis first by month, then by week or product, is show the month value as if there were no further breakout by product (or week), just the same calculation value as if the analysis "stopped" at the month level.
i have researched the mdx function reference and found some ideas, but testing has thus far provided nothing useful, so i'm not even sure i'm conceptualizing the problem correctly. i'm hoping someone can point me to the correct function or syntax, and give me an example as a starting point.
if there is any info missing, let me know and I will be happy to clarify or add to my question.
Danylo was correct. i was looking for an All() (function) when i needed the All member.
here is what worked:
sum(
(
[Time].[Week].[All],
[Product].[Product Number].[All],
[Time].[Month].[All].[Y],
[Customers].[Customers].[All].[X]
),Measures.[Sales]
)
+ ... repeat ....
Thanks for the help!!
I'm wondering how to accomplish this requirement.
I have to compare value data with the average over the selected period or over another period.
I've collected millions of records in an index. These records contains the sellout amount day by day for different vendors, products, sectors and product families.
What I'd like to do is to analyse any single value with the average of the selected period of the average of the same periodo of a previous year. I'd like to use Kibana to show data to users.
How can I accomplish it?
Thanks
I've made a SQL report that needs to do a bunch of different things, but my issue is as you can see in the picture I grouped patients, because several of them had multiple discharges and I need a count of total patients discharged. I have several other counts, but when I right click and insert summary it doesn't give me the option to select a group to do a count by. Is there a way to insert a count by the patient group?
Ryan's answer was correct. I just did a distinct count.