I have data table with the total of health insurance coverage per state for the period of 5 years, so I created a calculated field that calculates the yearly coverage per state by using the divide statement to calculate for that in this way div(health insurance coverage,5) and I titled it Yearly coverage per state and this worked as a measure.
The next step I want to execute for my analysis is to create another calculated field using the Yearly coverage measure to divide the Health insurance coverage to give the coverage rate per state for 5 years. I tried this sum(Yearly coverage per state)/sum(Health insurance coverage), the calculation is valid but when I drag the new field to row section and drag the states field to the column section of my viz, it gives me one figure which is 0.20 for all states and that isnt what I am looking for. I tried it without the sum statement and its the same, tried both fields inside the divide statement and its giving me 0 for all states. I need it to calculate the individual health insurance coverage rate using that formula above. Please help!
here is the situation:your formula seems to be a circular reference. i mean if you perform [Yearly insurance coverage per state]/[Health Insurance Coverage Change 2010-2015] then you are just repeating the same calculation : [Health Insurance Coverage Change 2010-2015]/5, because divide by 5 will result in 20% for all the records. Is there any other formula that you are using , are the fields correct ?
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I have a Google Sheet with two tabs - one containing percentage "bands" values and the other with data in a table which includes rows for new entries and columns off to the right edge which store running totals depending on the entry type. The running totals depend on the row entry being of the same type and month period. This all works as expected.
I need to calculate a value in column I based on a row entry amount/cell (column H) which references the running total for that entry type AA:AF and month and then uses the relevant predefined percentage "bands" values (tab R1).
I had successfully got this working when a single entry would only ever cross one "band" level (the bands were previously tens of thousands apart) by using SWITCH and VLOOKUP functions.
The current formulas in column I use this method which no longer works since the percentage bands are now much closer together than they were before and a single entry could take the running total value for that entry over multiple bands (and not just the previous band, as before).
On the example sheet, cell H6 contains 9,900 as a test value since this increases the running total for that row AB6 to 16,313 from the prior running total for that type, 6,413 and spans 4 percentage bands:
Band A: 0-7,500 - 5%
Band B: 7,500-10,000 - 7.5%
Band C: 10,000-15,000 - 15%
Band D: 15,000 - 25,0000 - 17.5%
My original formula first checks the entry Type using a SWITCH, then matches the highest "band" value using a VLOOKUP and then an IF to check if the previous running total was less than the highest matched "band" value, calculating and adding the difference if needed.
I've tried to figure out how to calculate the same result when multiple bands are crossed (as in example) but I can't find a way to structure the formula so that it can apply universally down the column using the matched band rate(s), previous running total and new running total values.
Is there a mathematical way to do this or will this require multiple nested IF statements etc or would another approach work better?
I solved this by modifying the formulas on this page. Changing the layout of the bands was a good first move.
Now, column I calculates the value from the current running total (matched from type in column A) and subtracts the value calculated in the same way but using the previous running total to give the amount applicable to the newly entered value on the same row in column H. I've some more testing to do but fairly sure it works correctly. Any other ideas, feel free to suggest!
Provisionally working sheet here: https://docs.google.com/spreadsheets/d/1e2pdyOi7dz_ZA8zfNtsHxieEUb5fiZGpD_FwRvkHyYw/edit?usp=sharing
I seek your valuable support in finding a way to calculate change rate over time with tabular dataset in google data studio. Here is the link to the dataset: https://docs.google.com/spreadsheets/d/1To1n5JJA6uVkLMgwjKhghJgCJpFmtXkqNog4DzfoEbE/edit?usp=sharing
There are many rows with data stamp and have different categories and sub categories. I have created a change rate table manually based on which I want to create charts in google data studio. The charts will be from the raw tabular data not the separate change rate table that is built only for example purpose.
So the chart could be based on a main category (as in the sample) and can also be viewed as sub-category and show change rate over time between the dates.
The dates can sometimes be months or years. I am not very savy with advanced formulas or scripting but I am hopeful someone here would be able to help me out on this. I will be ever so grateful for this :)
I can only provide you with the quotient of datasets between two days. If you need different mappings between dates (day, months, years), for each the following steps have to be done:
generate a new field "yesterday" with: DATETIME_SUB(date, INTERVAL 1 day)
blend this dataset with itself, using as dimension "date" and "yesterday".
Further dimensions are your categories fields A and B.
As metric, you can use the count of the date field.
I am trying to predict the bookings of a stand-up comedian cafe. There are a lot of features I can use which have an affect on the number of sales. (e.g. day of the year, weather, average sales last month, day of the week, average sales on the specific day of the week etc.)
However, one of the features that most correlates with the actual number of sales is the number of tickets already sold before the deadline. The customers are able to start making reservations 120hours (5 days) before the actual deadline of ordering (11:00 AM on the same day of the show).
I would prefer to use this data as input for my machine learning algorithm. Currently I created 120 columns in the dataframe. The columns define 120 hours before deadline untill the deadline itself. Column "hour_98" therefore shows the accumulated sales 4 days before the deadline. Column "hour_24" shows the accumulated sales 24 hours before deadline etc.
If I now would like to predict the sales 24 hours before deadline the columns "hour_24" until "hour_0" are all given "NaN" values. Since algorithms can't deal with NaN values I currently give these columns a value of 0. However, I tihnk this is too simplistic and will result in bad prediction model.
How do we deal with a changing input shape since we obtain more data if we get closer to the deadline of ordering?
Now from what I understand, you have a fixed number of columns, each representing the data from a predefined hour before the deadline. So in a sense the input data shape never changes, only the validity of some input features changes.
Provided you have a fixed input shape, with changing validity of the features (NaNs),
you can get around that issue by using a mask for each input feature.
For example a valid hour_24 can be represented as hour_24 = 20 and mask_24 = 1, and an invalid hour_24 can be represented as hour_24 = 0 (or whatever) and
mask_24 = 0.
The algorithm itself will need to learn where to ignore a given feature in respect to the related feature's mask.
This answer explains in more detail how to mask input.
I'm setting up a Google Sheet that will calculate the most effective purchase size of specific agricultural inputs (fertilizer, chemical, etc). I set up the price data in its own tab with a separate row for each input name + size.
To keep it easy for the user I'd like to require only the input name, # of gallons per acre, and acres and then have a formula spit out the total cost and most effective purchase (bulk if > X gallons, X # of 250 gallon containers + X 55 drums, etc). How can I use the input name plus a wildcard to find the appropriate purchase size?
https://docs.google.com/spreadsheets/d/1bMOPuk2qhmVuJT7vE_ni3KFxfcgKvwTwkM4p50xQF_0/edit?usp=sharing
I tried:
=ArrayFormula(iferror(INDEX('Data (Current)'!H2:H,SMALL(IF($A2&"*"='Data (Current)'!A2:A,ROW('Data (Current)'!A2:A)-1),1))))
...but it returns blank so I'm guessing the reference $A2&"*" to the input name isn't working properly. When I replace it with a string found in the 'Data (Current)' tab then it works fine.
=ArrayFormula(iferror(INDEX('Data (Current)'!H2:H,SMALL(IF($A2&"*"='Data (Current)'!A2:A,ROW('Data (Current)'!A2:A)-1),1))))
I expected the output to be the smallest value (in this case I think it's 5). Then when I change the last number to 2 or 3 it will find the next smallest value, in this case, 55 or 250. Then I can use simple formulas to interact with that and finish the spreadsheet.
Unfortunately, the actual output is nothing, or "".
Sorry if this isn't what you're looking for, as I had some trouble understanding your question.
Presuming what you want is essentially this:
I want to buy Y quantity of item.
I can buy item at cheaper prices if I buy in higher quantities, although sometimes they have a minimum order quantity.
What is the most optimal combination of the options I have to minimize the price I pay?
I'm unsure if there's a simple solution for this within Google Sheets alone. This might be treading more into Apps Script territory.
However, that's not to say that it's not impossible. I've "brute-forced" the above solution above with an iterative-like approach, for the "Chelated Calcium" product: https://docs.google.com/spreadsheets/d/1YSBiSx0IMr4T0R11Dqb-tqOhH4AOTTAWeH2yQfT4X5w
First, list the data in a standardized manner. This includes giving each same product something easy to look it up by. For example, on the Data (Current) tab, I've added 3 columns:
Product Common Name - This is used so that all items of different quantities can be found easily, without needing wildcards.
Gallons - Much easier to parse the data if it it's explicitly laid out.
Minimum Order Gallons - This is your threshold for Bulk. I've set it at an arbitrary 20,000 gallons for Chelated Calcium.
The data here is ordered least-effective first. How you do this will be up to you. In this case, I sorted by the Retail Cost Per Ounce parameter from your sheet, highest first. This eliminates any guesswork about which of the options are most effective, since you can just traverse your options in order. Note: The way I've laid out the formulas will only work IFF the same products are directly next to each other. It won't work if there are other products between them.
On the Field Level Tool tab, standardize your inputs to the Gallons unit. I do this in Total Gallons Needed column (I multiply anything with a "GAL" with 1, and "QUART" with 0.25).
For each item, determine the row numbers where the product begins and ends. This is marked by columns L (Least Efficient Index) and M (Most Efficient Index). I got these results by using the MATCH function.
Set up the iterations, from 0 to N-1. On this sheet, I've set up N=5 iterations, which means that it can traverse 5 different options of the same product only. Since Chelated Calcium only has 4 different options (5 Gal, 30 Gal, 250 Gal, Bulk), 5 is more than enough for this product. If you have products with more options, you may want to have more iterations.
The iterations are on the right side of the Field Level Tool tab.
In your case, you might want to put it on a different tab since the place I put it makes the file look very messy.
In each iteration, I perform the following steps:
To Fulfill - How many gallons still need to be purchased by this iteration?
ThisIndex - What is the row number of this iteration? This is determined by Most Efficient Index - Iteration Number. Remember that since we sorted in order of ascending efficiency, this means that the iteration starts with the most efficient option it can find first. There is a check to make sure that it only outputs a value if it is between the range [Least Efficient Index, Most Efficient Index]. Otherwise, it will be blank to avoid miscalculations by intruding into another product in the Data (Current) tab.
Retail Price, Minimum Gals, Gallons per Order - Simple data extraction for easy usage in the iteration, using INDEX (and indirectly, MATCH by virtue of ThisIndex).
Order - This formula does a couple of things, outlined below:
It checks whether there still remains a valid choice of product at this iteration. It does this by checking whether ThisIndex still exists. If the product doesn't exist, then it will be nulled. This is accomplished by using the IF function.
It will determine if there is a minimum threshold that must be met to purchase this choice. You can see in the 0th iteration, for example, that there is a minimum quantity of 20,000 gallons. If To Fulfill quantity is greater than or equal to the threshold OR there is no threshold, then a purchase is quantified by this column. The mathematics are simply to divide the To Fulfill amount by the Gallons per Order amount to determine the number of orders of this particular product choice. If there is a threshold but the To Fulfill amount doesn't meet it, then this iteration is skipped with a 0 order value.
If the item is already on its least efficient choice (ThisIndex == Least Efficient Index), it will do a CEILING function to ensure that the order is fulfilled. If not, it will do a FLOOR function instead. This is because you cannot order 3.5 units of an item, so they have to be rounded either up or down.
Expenditure - This is simply Order multiplied by the Retail Price, or how much money you spend in this iteration.
Remaining - How much of the product is left unfulfilled at the end of this iteration, to be used as To Fulfill for the next iteration.
Note: If you see formulas that are of the form =IF(ThisIndex, [calculations_here],), that is simply a check to nullify that calculation if ThisIndex is invalid.
Copy the iterations as many times as you want to the right. Something nice to do is to force the iterations to do a CEILING on the very last one to ensure that you never under-buy.
Generate a user-readable string for the purchase suggestion. You can see this on the Suggested Purchase column.
Calculate the Gallons Bought with a simple SUMPRODUCT over all the iterations.
Calculate the total expenditure with a simple SUM over all the iterations.
I hope this is what you were looking for. Regardless, it's at least a fun exercise on how much you can abuse Sheets. ;)
I need to build a data mart using power pivot for a duty free shop at Airport.
Sales manager is analying sales data using by flight number and by PAX, number of people per flight.
So, I don't know where to put PAX. In DimFlight or FactSales. It is addative, right?
Please explain me why and how should I put PAX into which table. DimFlight may includes airline, flignt_no, date, PAX. A flight may also land the airport more than once a day.
PAX is a fact describing a measureable value of a specific flight event. It should be in the fact table, not in the flight dimension. I would expect total capacity to be an attribute of the plane dimension associated to the flight event. (Flight number would likely be a degenerate dimension as it doesn't really own any attributes.) However, the PAX itself should be a measure in the fact table.
You can generate a junk dimension that has the banding mentioned by #Luis Leal to do some capacity analytics. You can even create a numbers dimension with an attribute for each group level so you can do more detailed banding. For example, an attribute for 1s, 10s, 100s, 1000s, etc. You can also calculate the filled capacity of the flight and point to the numbers dimension so you can group flights by 80% full, 90% full etc.
Nothing stops you from modeling it as both dimension and measure, so you can store it both on a dimension table and as a measure on a fact table. If you store it as a measure on the fact table, you can perform several analysis by the other possible dimensions, get insights as averages, max, min, total by x or y dimension, which would be very difficult if you store it only on the dimension table.
On the other hand,storing it in the dimension table enables additional "perspectives" of analysis, for example a common approach is to store in the dimensional table "interval" columns with values like:
from 1 to 1000 pax, from 1001 to 2000. This column calculated at ETL time depending on the value of the PAX. So why not use both?