I want to do a forecast model in excel, which automatically calculates the expected monthly values of a certain variable.
Table A (Output table):
here, I want to show the expected end of month value for a patientF.ex. I want to forecast the total value of X for Person A for the whole month of October
enter image description here
Table B (Data table):
Here, I receive a daily import from an SQL database with the relevant Person Data for that day
F.ex. on the 15.10.2021 I would receive the following:
enter image description here
In short, I would like to do the following calculation in my output table B:
Return Value of "Number of X", given that "Patient" =Person A for the MM/YYYY match (if successful, this should show 13 for person A/October in the output table)
Secondly, I want the above value to be divided by the number of days of that particular date (so 15 days in this example)
Thirdly, I want to multiply it by the total number of days for that given month (as indicated in the Output table/Date section)
I have tried different sumifs/array formulas but I really struggle with one consolidated formula. Any help/tips much appreciated!
Related
all:
I am trying to design a shared worksheet that measures salespeople performance over a period of time. In addition to calculating # of units, sales price, and profit, I am trying to calculate how many new account were sold in the month (ideally, I'd like to be able to change the timeframe so I can calculate larger time periods like quarter, year etc').
In essence, I want to find out if a customer was sold to in the 12 months before the present month, and if not, that I will see the customer number and the salesperson who sold them.
So far, I was able to calculate that by adding three columns that each calculate a part of the process (see screenshot below):
Column H (SoldLastYear) - Shows customers that were sold in the year before this current month: =IF(AND(B2>=(TODAY()-365),B2<(TODAY()-DAY(TODAY())+1)),D2,"")
Column I (SoldNow) - Shows the customers that were sold this month, and if they are NOT found in column H, show "New Cust": =IFNA(IF(B2>TODAY()-DAY(TODAY()),VLOOKUP(D2,H:H,1,FALSE),""),"New Cust")
Column J (NewCust) - If Column I shows "New Cust", show me the customer number: =IF(I2="New Cust",D2,"")
Column K (SalesName) - if Column I shows "New Cust", show me the salesperson name: =IF(I2="New Cust",C2,"")
Does anyone have an idea how I can make this more efficient? Could an array formula work here or will it be stuck in a loop since its referring to other lines in the same column?
Any help would be appreciated!!
EDIT: Here is what Im trying to achieve:
Instead of:
having Column H showing me what was sold in the 12 months before the 1st day of the current month (for today's date: 8/1/19-7/31/20);
Having Column I showing me what was sold in August 2020; and
Column I searching column H to see if that customer was sold in the timeframe specified in Column H
I want to have one column that does all three: One column that flags all sales made for the last 12 months from the beginning of the current month (so, 8/1/19 to 8/27/20), then compares sales made in current month (august) with the sales made before it, and lets me know the first time a customer shows up in current month IF it doesn't appear in the 12 months prior --> finds the new customers after a dormant period of 12 months.
Im really just trying to find a way to make the formula better and less-resource consuming. With a large dataset, the three columns (copied a few times for different timeframes) really slow down Excel...
Here is an example of the end result:
Example of final product
i want to know the percent of males in the ER (emergency room) during days that i defined as over crowded days.
i have a DF named eda with rows repesenting each entry to the ER. a certain column states if the entry occurred in an over crowded day (1 means over crowded) and a certain column states the gender of the person who entered.
so far i managed to get a series of over crowded days as index and a sub-index representing gender and the number of entries in that gender.
i used this code :
eda[eda.over_crowd==1].groupby(eda[eda.over_crowd==1].index.date).gender.value_counts()
and got the following result:
my question is, what is the most 'pandas-ian' way to get the percent of males\females in general. or, how to continue from the point i stopped?
as can be shown in the bottom of the screenshot, when i iterate over the elements, each value is the male of female consecutively. i want to iterate over dates so i could somehow write a more clean loop that will produce another column of male percentage.
i found a pretty elegant solution. i'm sure there are more, but maybe it can help someone else.
so i defined a multi-index series with all dates and counts of females and males. then used .loc to operate on each count of all dates to get percentage of males at each day. finally i just extract only the days that apply for over_crowd==1.
temp=eda.groupby(eda.index.date).gender.value_counts()
crowding['male_percent']=np.divide(100*temp.loc[:,1],temp.loc[:,2]+temp.loc[:,1])
crowding.male_percent[crowding.over_crowd==1]
I have an ssrs report that shows revenues for each month of a year for several years based on start and end date parameters the user enters. What I would like to do is add a second column that shows the revenue for that quarter (march would show q1 revenue, june q2 etc). the problem I am having is that I am able to do this for a single year but once the query starts looking at multiple years my column shows the value for all 1st quarters regardless of year. The picture shows what I am currently getting. Any recommendations on how to fix my expression to also look at the year value when summing these quarters?
Here is the code I am using to only populate the quarter column based on the month.
=IIF(Fields!Sort_Order.Value=3,
Sum(Lookup(1,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings"))
+Sum(Lookup(2,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings"))
+Sum(Lookup(3,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings")),
IIF(Fields!Sort_Order.Value=6,
Sum(Lookup(4,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings"))
+Sum(Lookup(5,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings"))
+Sum(Lookup(6,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings")),
IIF(Fields!Sort_Order.Value=9,
Sum(Lookup(7,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings"))
+Sum(Lookup(8,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings"))
+Sum(Lookup(9,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings")),
IIF(Fields!Sort_Order.Value=12,
Sum(Lookup(10,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings"))
+Sum(Lookup(11,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings"))
+Sum(Lookup(12,Fields!Sort_Order.Value,Fields!Calculated_Revenue.Value,"Bookings")),""))))
I ended up creating a quarter column within my data set that was then used as a parent group for the month rows. I then hid that column and added a total for the quarter that gave me sub totals. Not exactly what I was looking for but gave me the result I needed.
This should be simple so I think I am missing it. I have a simple line chart that shows Users per day over 28 days (X axis is date, Y axis is number of users). I am using hard-coded 28 days here just to get it to work.
I want to add a scorecard for average daily users over the 28 day time frame. I tried to use a calculated field AVG(Users) but this shows an error for re-aggregating an aggregated value. Then I tried Users/28, but the result oddly is the value of Users for today. The division seems to be completely ignored.
What is the best way to show average number of daily users over a time frame? Average daily users over 10 days, 20 day, etc.
Try to create a new metric that counts the dates eg
Count of Date = COUNT(Date) or
Count of Date = COUNT_DISTINCT(Date) in case you have duplicated dates
Then create another metric for average users
Users AVG = (Users / Count of Date)
The average depends on the timeframe you have selected. If you are selecting the last 28 days the average is for those 28 days (dates), if you filter 20 days the average is for those 20 days etc.
Hope that helps.
I have been able to do this in an extremely crude and ugly manner using Google Sheets as a means to do the calculation and serve as a data source for Data studio.
This may be useful for other people trying to do the same thing. This assumes you know how to work with GA data in Sheets and are starting with a Report Configuration. There must be a better way.
Example for Average Number of Daily Users over the last 7 days:
Edit the Report Configuration fields:
Report Name: create one report per day, in this case 7 reports. Name them (for example) Users-1 through Users-7. These are your Row 2 values. You'll have 7 columns, with the first report name in column B.
Start Date and End Date: use TODAY()-X where X is the number of days previous to define the start and end dates for each report. Each report will contain the user count for one day. Report Users-1 will use TODAY()-1 for start and end, etc.
Metrics: enter the metrics e.g. ga:users and ga:new users
Create the reports
Use 'Run reports' to have the result sheets created and populated.
Create a sheet for an interim data set you will use as the basis for the average calculation. The first column is date, the remaining columns are for the metrics, in this case Users and New Users.
Populate the interim data set with the dates and values. You will reference the Report Configuration to get the dates, and you will pull the metrics from each of the individual reports. At this stage you have a sheet with date in first columns and values in subsequent columns with a row for each day's values. Be sure to use a header.
Finally, create a sheet that averages the values in the interim data set. This sheet will have a column for each metric, with one value per column. The one value is calculated from the series in the interim data set, for example =AVG(interim_sheet_reference:range) or any other calculation you'd like to do.
At last, you can use Data Studio to connect to this data source and use the values. For counts of users such as this example, you would use Sum as the aggregation field type when you are creating the data source.
It's super ugly but it works.
I have a SSRS report I'm working on. What I would like to do is get the value of one field from its own dataset and subtract the value of another field from a different dataset. I can do this; however, the values are grouped so rather than giving me an individual value it gives me: (sum of all completed) - (sum of all completed the previous year).
Here is my expression I am using for the column "Compared to last year"
=SUM(Fields!Completed.Value, "MTDSales") - SUM(Fields!Completed.Value, "MTDminus1")
"MTDSales" and "MTDMinus1" are 2 seperate datasets. MTDSales Dataset is the current months sales outcomes grouped by company MTDMinus1 dataset is last years figure for this current month as i am comparing the 2 months separately.
I had to do this in a report where I was pulling current data from one database and older data from a data warehouse and combining. You will need to do a few things:
1. Establish a match field
This can be as simple as a single column. If you need to match on multiple fields you will need to add a calculated field to each dataset that you can match on. Assuming you need to match on company and financial year and each dataset returns one year of data, this might look something like match_id (assuming numeric values - otherwise you might need to use | or something as a separator):
`="A" & Fields!fin_year.Value & "B" & Fields!cust_id.Value`
2. Retrieve the data to the source field.
In your tablix add a column as you have to hold the looked up value:
=Lookup(Fields!matchId.Value, Fields!matchId.Value, Fields!Completed.Value, "MTDminus1")
3. Use the data
Now you can aggregate the data or do whatever further calculations you wish as if the field was part of your original dataset.