I've a calculated field "Total Revenue" which blends revenue from multiple data sources in Tableau.
Based on this field (aggregate field) on monthly basis, I would like to show only TOP 5 items with highest revenue as of latest month.
As shown in above table, items have to be filtered out (as they are TOP 5 in March) based on revenue data as of the latest month (March). How can this be achieved using RANK()? I'm not able to rank only for latest month as formulated below because it shows error as I cannot mix aggregate and non-aggregate functions.
IF DATETRUNC('month'),ReportDate)=//March 1st date given//
THEN RANK(Total Revenue)
END
My solution is a kind of workaround but seems to be working:
Create a calculated field with below formula:
IF DATEPART('month', {MAX([date])}) = DATEPART('month', [date])
and DATEPART('year', {MAX([date])}) = DATEPART('year', [date])
THEN
[revenue]
ELSE
0
END
The {MAX([date])} part on the code gets the maximum date in your data, it is fixed with { and } characters so that the value is not effected by the filters, date partitions etc. If you want the sorting month to be the one we are in then you should change that part with NOW()
Now we have a value containing only the sum of the latest month and we can sort our visual with this Measure.
You can drag your new measure (I called my measure: 'last month revenue') to the details and right click your item pill on the Rows, sort it by your new field.
And finally drag your item to the filters and go to Top tab, make filter your data according to sum of "last month revenue" measure.
Below screenshot shows the excel data and the final Tableau table:
Related
I think the question is fairly simple. I would like to have a dynamic filter on Quicksight. I have the column week which is a number (3, 4, 5 and 6) for example. The data is refreshed every week.
We want to show only the latest week by default (out of weeks 3, 4, 5 and 6, I want to filter by default on 6), but then the users can select if they want to see more weeks.
The week after, the default filter would be 7, and so on.
Is there a way to tell the filter to automatically filter by rule of MAX(week) or something like that?
It would be very helpful. I have other alternatives but they are not that great.
I would choose a date field that I understand that must exist in your data and then create a dynamic filter on that date field.
You create filters on date fields by selecting the filter conditions and date values that you want to use. There are three filter types for dates:
For your case, you will have to use
Relative – A series of date/time elements based on the current date. You can filter records based on the current date and your selected unit of measure (UOM). Date filter units include years, quarters, months, weeks, days, hours, and minutes.
Use the following procedure to create a relative filter for a date field.
On the toolbar at the left, choose Filter.
In the Filters pane that opens at left, choose the plus icon (+), and then choose a date field to filter on.
A new filter is added to the Filters pane.
In the Filters pane, choose the new filter to expand it.
For Filter type, choose Relative dates.
Choose a unit of time (week for your case).
Choose how you want the filter to relate to the time frame. For example, if you choose to report on weeks, your options are previous week, this week, week to date, last N weeks, and next N weeks.
If you choose Last n UOM, specify a number for your range—for example, last 2 weeks, or last 1 week.
Second solution.
Add Filter
Choose week
Type Top and Bottom Filter
Show Top write 1
By week ( use max aggregation)
Pasting an image from an analysis that I do the same thing for integer column year
I'm new to Tableau. I am trying to make an inventory report which tells the user how much of certain product he/she should buy in advance.
Depending on the amount of days selected on the filter, the difference in days of the complete period should be calculated. For example: If the filtered dates are from 1/03/2021 to 09/03/2021, the result should be equal to 9. The formula I used is the following: date_difference = DATEDIFF("day",MIN(DATE([Fecha])), MAX(DATE([Fecha]))) + 1
The problem comes when I try to use the value given by such date filter. My next calculation should be:
calc = Quantity Inventory / (Units sold / date_difference). Both Quantity Inventory and Units sold are calculated fields in which I have no problem. However, instead of having a fixed value of 9, date_difference changes as shown in the image, giving me incorrect results for the desired calculation.
How can I make sure that the calculated field date_difference has the value of 9 on all rows?. Actually, if I add date_difference field by itself in a different Page it does show the proper value. The problem occurs when calculating calc and trying to add it to the table.
Note: Remember that the value of date_difference will change, depending on the range of time selected on the date filter
Thanks a lot in advance.
Step-1 Use this calculation for date_difference instead
DATEDIFF('day', {min(DATE([Fecha]))}, {max(DATE([Fecha]))}) +1
Step-2 Add Fecha filter to context, by right clicking it in filters shelf.
This will solve your problem. See the GIF
My company has tasked with slicing the information on turnover and to create different graphs.
My source data looks like this: Relevant columns are: Voluntary/Involuntary, Termination Reason, Country, Production, and TermDateKey
I am trying to get counts using different filters on the data. I managed to get the basic monthly total using the formula:
Term Month Count = GROUPBY('Turnover Source','Turnover Source'[TermDateKey],"Turnover Total Count", COUNTX(CURRENTGROUP(),'Turnover Source'[TermDateKey]))
This gave me a new sheet with the counts for each month.
Table that shows TermDateKey on Column 1, and Counts on column 2
I am trying to add onto this table by adding counts but using different filters.
For example, I am trying to add another column that gives me the monthly count but filtered for 'Turnover Source'[Voluntary/Involuntary]=="Voluntary". Then another column for 'Turnover Source'[Voluntary/Involuntary]=="Involuntary" and so on. I have not found anywhere that shows me how to do this and when I add in the FILTER function it says that GROUPBY(...) can only work on CURRENTGROUP().
Can some one point me to a resource that will give me the solution I need? I am at a loss, thank you all.
It looks like you may not be aware that you don't have to calculate all possible groupings with DAX formulas.
The very nature of Power BI is that you use a column like "Termination Reason" on an X axis or in the legend of a visual. Any measure that you have created on values of another column, for e.g. a count of all rows, will then automatically be calculated to be grouped by the values in "Termination Reason", giving you a count of each of the values in the column.
You do NOT need DAX functions to calculate the grouping values for each measure for each column value combination.
Here is some simple sample data that has been grouped into dates and colours, one chart showing a count of each colour and one chart showing a sum of the Value column. No DAX was written for that.
If your scenario is different, please explain.
Please help. I don't understand why I have 2 similar data models and one works but the other does not.
I have a fact table with a Calendar Key and data as granular by day by location. I have a calendar dimension table with a Calendar key, each individual date, and month/fiscal year definitions.
Measures are Rev = SUM(FactTable[RevColumn]) then Rev PY = CALCULATE([Rev], DATEADD(CalendarTable[Day_Date], -1, YEAR))
Model 1 in Power BI, when I apply date filters, Rev calculates the total revenue & Rev PY calculates last year's rev for same range. Even if I select all dates that are YTD (which is tedious)
Model 2 in Excel 2016 Power Query, when I apply a year filter, Rev calculates total revenue for YTD in Fiscal Year & Rev PY is blank.
I don't understand what is different and why it would even fail in the first place. Calculate is supposed to override the filter context of the current filters right?
Also: the only "Date" type columns are located in the calendar table so it's not possible for me to select the wrong table.
I ultimately had to ask a professor on Udemy.com. The solution is to mark you calendar dimension table as a "Date Table." Since I'm combining the 2 tables on a whole number field, the time intelligence functions only work when filtering with date columns.
By marking the Calendar table as the Date table, the time intelligence functions know to use any attributes from that table to filter down the date ranges and get and display the appropriate answer.
In Power Pivot view, click the "Design" ribbon, then click the "Date Table" button and the "Set Default" button after that.
This resolved all issues I had.
Context:
I have a data set for the weights of truck and trailer combinations coming into my site over the span of a few years. I have organized my data by seasons as I am trying to prove that the truck:trailers in winter are noticeably heavier due to ice, snow, and mud. The theory is, if the tare weight is higher in this season (the weight of the truck after it empties its load) than its Avg tare weight (which I need to calculate from the data) it can be deduced that the truck:trailer combinations are coming in with extra weight that we pay for in part as some snow/ice/mud falls off in the trailer emptying process.
What I've done so far:
I've defined a custom date range for my seasons
I've grouped Truck:Trailer by: count to get a duplicates column and, all rows to keep all my details
I've filtered out every combination I've seen less than 50 times, as i want good representation for each truck:trailer combo so that I can better emphasize repeated patterns
I've added an index column to better keep track of the individuals before expanding the details
What I need to do:
I only want to work with truck:trailer combinations which have weighed in for all four seasons at least once
I need to find the average tare weight of the truck:trailer combinations based over the extended range for both summer and autumn (the dry time of the year) while preserving the raw tare data for all seasons, as I need to eventually compare the winter tare values to this average.
example of my data
When I'm finished I'd like the data to look something like this
Pivot Chart
query data
For your first question (all seasons) you can add a column that holds the distinct count of the values in [Season] for each [Driver:Trailer]. Then filter your table on that column, keeping only the 4's. To achieve this, add the following m-code to your script in the Advanced Editor. Change the part after in to #"DistinctCount Season"
#"DistinctCount Season" = Table.Join(#"insert name previous step","Driver:Trailer",
Table.Group(#"insert name previous step", {"Driver:Trailer"},
{{"DistinctCountSeasons", each Table.RowCount(Table.Distinct(_,"Season")),
type number}}),"Driver:Trailer")
Insert the name of your previous step where indicated.
For second question:
You can use a matrix-visual for that in you report. First create a measure:
[AverageTare] = AVERAGE(table'[Tare])
Then put [Season] on Rows and the [AverageTare] on Values. You can create a group (right-click on [Season] in the FIELDS-pain) called [DrySeason], to combine the values for Spring and Summer.
If that doesn't work for you, explore the AVERAGEX function.
EDIT
In excel you can use a pivottable. Put [Season] on Rows and the [AverageTare] on Values. Right-click a value in the pivottable. Select Value Field Setting and choose Average. Then select the Seasons you want to group, right-click and select Group.
EDIT 2
To add a column in the Power Query Editor that holds the average [Tare] for the [Season] in each row, add the following steps to your script in the Avanced Editor:
#"GroupedSeasonAvg" = Table.Group(#"Insert name previous step", {"Season"}, {{"AVG", each List.Average([Tare]), type number}}),
#"JoinOnSeason" = Table.NestedJoin(#"Insert name previous step",{"Season"},GroupedSeasonAvg,{"Season"},"AVGGrouped"),
#"ExtractSeasonAVG" = Table.ExpandTableColumn(JoinOnSeason, "AVGGrouped", {"AVG"}, {"SeasonAVG"})
It works something like this:
"GroupedSeasonAvg" : Creates a table with the avereges for each [Season]
"JoinOnSeason": Creates a new column with tables joining the [Season] value for each row to [Season] in the grouped table.
#"ExtractSeasonAVG": Expand each table and keep only [AVG].