In a previous blog, I showed how to load a raster tiles into PowerBI data model, in theory that should solved all my issues with doing a detailed maps in PowerBI.
unfortunately, no, even if R and Python visual support up to 150K points, the reality is the implementation of R in the PoweBI service has a massive overhead and you can’t do anything about it, as it is literally a black box, all you can do is try to reduce the data passed to R visual and hope it works.
Actually, in my case, the visual did not even show up and I got an error message that resources are exceeded
I am in a situation where I can’t filter data because the whole point of the visual is to show all the data, at the same time, if the visual does not work in the service then there is no point in the whole exercise.
The trick is using wkt, I will simplify the geometry without losing any visual data, for example:
Instead of showing all the points, I will just group the points in the same order and colour as a line, as you can see from 14 rows of data, it is reduced to 5 rows, and the visual representation is the same, it is like sampling, but we keep the exact shape of the data.
Now in PowerBI, all we need to do is to automatically group those points together, turn out the solution was very easy using Rankx, keep in mind the wkt is dynamic for every update, I get a new geometry
After that I just added some calculated columns to create the wkt format
For a point, POINT (X Y)
For a line, STRINLINE (start_X start_Y,finish_X finish_Y)
Keep in mind you can create polygons too, but the DAX become more complex (maybe for another blog)
you can create the wkt file in QGIS very easily but as my data change daily, it was not practical
And here is the final result
The number or rows were reduced from 3528 to 218
That make a massive difference in PowerBI service, my real data is 58K rows and I can’t tell how much I was happy when finaly it worked in the service,not only that, but the total rows using wkt keep decreasing when I do more updates 🙂
There is a catch though, unfortunately as of Dec 2019, only R and Python script can render wkt geometry, there is a new custom visual by @james dales, but it is in a private beta and has some limitation on colors by category. ( icon map support color per category now)
This article captures 2 issues that are related and perhaps not clearly discussed or understood (not even by me): Level of Detail, and Transactional vs Account based tracking
Many cost tools, for right or wrong, are now almost entirely Control Account based. This leads to some conceptual issues where people have been use to managing data in a more transaction way.
Additionally, when we begin to establish defined control accounts, picking the right level of detail, and how changes are managed between Controls Accounts, requires a lot of creative accounting (thoughtful process mapping through all your systems & Digital Strategy)
The below is just a primer for a potential discussion. This is a post on what it means to pick a correct level of detail and what it really means to how you manage your costs.
This is my wheelhouse, the way my brain primarily works when dealing with projects. List Management. Everything is a new record.
We have contracts with detailed line items, we want to retain our budget line items, each contract line item will have various columns for Committed, Forecasts, Incurred and Paid values. We manage these detail items in this way. Below is a example of how we really manage costs (and progress and deliverable). Excel (sharepoint, or a simple flexible database) provides an ideal solution for users to manage this detail.
However, the new age of cost tools want us to view projects at a more “control account” level. In the above example, I have created a control account to a WBS and Commodity code level of detail.
In the above example, we have 4 contractors working on this scope in various capacities. Although, its way more subtle then that, we actually only have 1 contract and 3 expected contracts. We have parsed our budget into what we expect to be 4 different contracts. Thus only 1 contract has a commitment, but yet we have a forecast (and budget) for all 4. Each contract will have a full detailed list of detail items that we will manage. We will items for specified growth, perhaps contingency, maybe a few site instructions. A transactional list!
Quantity Growth – new line or modify existing?
Here again is one of the conundrums of how we manage (specifically related to progress measurement. Consider a project with some concrete and steel.
If we have a change in quantity for a foundation, where do we capture the change and what does it look like in our database. Too often, we look at this and manage it using a simple excel file – which can make the process easy. However, this is a very complex issue. If we add a line item and base our progress off committed quantities, we will have to update 2 line items with %’s. However, so many options exist to capture this.
And again, if instead this item is managed at the “control account” level, all we need is the total actual quantity, or simply the overall % for the control account. When you look at the above from a control account level, you capture all the detail, but yet for % management, you can disregard all the %’s to the details and only insert a % to the control account.
Which method is right, which method fits into your cost/progress system, which approach aligned to your specifications?
In the second method above, you loose the ability to calculate a specific % for variations. So again, we have taken just a simple issue, and created a complex nightmare. Obviously, we all solve these problems day in and day out. The issue here is again just to bring this topic to light and how the new range of cost tools may not be flexible enough to really capture what we do – nor should they! The real answer is as we all do now, some detail is managed inside excel and some abstraction ends up in the system.
Control Account Management
In the above, we have seen the importance of picking the right control account level of detail, but, we perhaps haven’t conceptualized what a controls account is in the first place. For “control account” management, we want to manage “SCOPE”. For this package of scope, we sum up all the detail coded to the same codes.
When we look at scope, it is much easier to compare against our estimate which was built to this level before we had to detail with specified growth, claims, site instruction and even contractor commitment. This is why we want control account management and why so many cost tools are forcing us down this path.
Whats the Problem?
In the above example, the “control account” is meaningless. We can not “manage” anything at the control account level. The control account is only a metric.
Instead we are going to manage our contracts in isolation. Each contract will have its own specifics and likely its own approvals when we modify a forecast or a commitment.
A solution to this conundrum is to split the above into 4 control accounts (or more). However, that creates a nightmare for everyone dealing with the new cost systems where creating cost accounts, loading budgets and costs is not straight forward. Doubly so as we haven’t even begun to discuss at what level we manage our time phased data.
All the new tools also allow us to manage “detail items”. Budget again, as soon as you start to push the level of detail of management into the detail items, you may as well make the detail item its own control account.
The Problem is – Whats the right Level of Detail?
You can run this problem down rabbit holes with how we manage engineering deliverables, how we manage progress items, manhours, quantity management, etc. Here I have just presented the problem related to strictly just a cost control level. But yet the dimensions for each of the items above is multiplied by each of the additional management datasets we also track.
Picking the right level of detail that goes into our cost tools, is more of an artform than it is a science.
My view is that we need easy flexible transactional capabilities from cost systems to ease the excel hell aspects. But yet at the same time, need to understand how the transactional records join up to perhaps a more formal CTR or Control Account level of detail.
In a previous blog, I showed how to use PowerBI to generate high quality print maps, with a caveat that the R script does not work in the Service unless all the packages are supported (the desktop use your local R install, so no limitation here)
I am a huge fan of ceramic, for me it shows the best of R philosophy, it does one thing and do it very well, you give it coordinated and it will give you back raster tiles.
I spent some time trying to figure out a workaround to make it works in the service and I found this trick.
Generate raster using ceramic outside PowerBI ( using Rstudio for example).
Save the raster object using saveRDS but using the option ASCII = TRUE, so it is a text file, notice you need to write version = 2, otherwise it will not work in the service.
Load the file into PowerBI using PowerQuery
The maximum data you can pass to R visual is 150K rows, which is not enough for my use case.
The trick is to group the data using concatenation, the limit is the number of rows not the number of columns :), please note the maximum number of length of a text value is 32766
Merge the table for the raster with the table of data (coordinates, attribute etc), unfortunately, you can pass only one dataframe to R script, I changed to append so the visual can be sliced
Now you have a dataframe with the coordinates and a raster data which you pass to R Visual script
Unest the raster data into a dataframe, notice the dataframe holding the raster data is 1 Million rows
Save the dataframe to a “raster.rds”
Load the “raster.rds” and it is R Magic the raster is alive
Plot the map ( it is on the PowerBI service not the Desktop)
you can filter the status and hide the tiles if you want, as it is slow to render in the service, please use query reduction option in the filter
This workflow does works with any R object, not only Raster but any binary data can be passed to PowerBI data model.
I think the main take away is you can circumvent PowerBI limitation of 150,000 rows when you pass data to R or Python, but there is a trick, the resource available in the PowerBI Pro instanced are limited and not documentation, so your mileage may vary , but it is worth the try
now, you may ask, why bother with this tedious, slow visual, the answer is very easy, in some cases you want to control the exact look of a map, and R give you just that, you can show multiple layers, text, it support more than 30 K point in a map, it is worth the pain
edit : I just noticed that PowerBI cache the visual output , if you do the exact selection again, the visual show instantly !!!
A typical situation in the construction industry the progress data is sourced from multiple system with different format, generally we get two type of reports.
Time stamp items
My preferred one, the data is tracked at a very low level (cable, spool, pre-commissioning and commissioning tracking), and you get a date when the item is completed something like this
This format is very convenient as you need to maintain only 1 file, the history is recorded in the data itself unfortunately, this kind of report is not always available for multiple reasons, the main one is, in some kind of work to finish one item it will take longer period of time, for example completing 1 drawing will take 3 weeks, no manager will wait 3 weeks to claim a progress.
This format reports the cumulative progress at a time period (daily, weekly, or whenever there is a progress) something like this
This is format is very common, it is very easy to update by the supervisor, and works with any level of details
the challenge of this format is
To get the historical data you need to keep all the previous files.
As it is cumulative data, calculating the progress per time period is a bit harder, and getting something like year to date is very awkward.
we need to normalize those files to be in the same format, one approach I use with PowerQuery is
Load the cumulative files.
Calculate the reverse total cumulative using self-join
Filter only the values where there is a progress
Append to the time stamp file.
Now we have a normalize Actual Table, where quantity per period, year to date and all date calculations are very easy to calculate.
I know it is tempting to just load data and start making visual and do some complex DAX calculation, but it is not sustainable and it will make your life miserable, a simple data model will make further development much easier.
In this blog, I will present a workflow, I have been using for
the last 2 years with a rather a good feedback.
Obviously, I like interactive Dashboard, I want everyone to
login to the PowerBI service and start doing their own analysis, but to my
dismay, not everyone is interested in doing that, a lot of users want only a
report that they can print. A took me a while to understand that that there is
nothing wrong with that, and in a lot of use cases, a printed report is the
best medium to convey information.
in my case, we do a lot of maps, and users want print
quality maps, and because the data change daily, you need automation.
In previous blog, I wrote how to integrate PowerQuery with R, in the current blog, I will show how to generate multiple pdf with a customized map, by using R custom visual.
The PowerBI team has done a fantastic job, all you have to
do is add the R script visual, add the fields you need, which automatically
create a dataframe and write your code, and with one click, you can edit your
code in RStudio!!!
Rstudio Integration works by creating a temporary csv file
that hold the dataframe data
I personally prefer RStudio, but you can use any IDE
There are two caveats though
The dataframe has a maximum of 150k rows.
When you work in the desktop, it will use your R installation, all packages are supported, but when you publish to the service only the packages in this list are supported (ceramic is not supported, I think packages that downloaded external data are not supported), I found a workaround
Let’s generate some maps.
I am using the excellent package tmap for the mapping, you can customize any aspects of the map, layout, Text Size, legend, titles, it is really an amazing product and show the power of R, for tiles I am using ceramic .
you need a Mapbox token (their free tier is very generous), I will use South Australia car crash data as an example.
In this blog, I will show how to leverage Python (or R) to implement an incremental refresh in PowerBI using PowerQuery and Python, nothing is really new ( I am sure Imke and Maxim has blogged about it before).
in a previous blog, I showed how to use R & Python integration to load data to a Database
This approach make sense only when you do a lot of heavy
transformation and your data source change based on time.
As an example, in my previous job, we receive a new excel
file every Monday (300K rows), this file gets approved and corrected every Thursday.
the workflow was:
save the files in a folder, do the transformation, which was fine , but after the first year, it was around 52 files, and although technically you need only to do transformation for the last file, and as PowerBI does not support incremental refresh, twice a week we redo everything, after two years, the refresh took nearly 30 Minutes and sometimes we get out of memory errors.
in the big picture,Half an hour was not that bad (we have a desktop just for refresh), the worst was, you refresh the model and once you finish, you get a new revision and you must refresh again.
Now using Python/R script, the idea is every file get transformed only 1
time, regardless of how many times you refresh, just by exporting the
results of the transformation of every file as a csv in a staging folder.
The first run is slow, as it will process all the existing files in Source Data, but the subsequent run, will transform only new files.
Let’s say File 2 was revised, all you need to do,is to delete File2.csv and it will be transformed again, but only that file.
Ok, if you see step 4, the files are reloaded each time, I am not too much worried about that, as the batch loading of csv files from a folder using PowerQuery is relatively fast (yes, a bit slow compared to R), the bottleneck is rather the transformation.
the code for python script is here, as you can see PowerQuery integration is amazing, just add a new step and you get a dataframe, that’s all,
the script split the dataframe by the column filename, and then export each file separately, currently it is saving into a local folder, but you can easily save those files into a cloud storage
to test it, I built a quick workflow using public data, PBIX here, the source data is zip files in a public website, there is a new zip file daily, it is relatively complex transformation as you need to unzip the file split it, delete some columns etc, the first run is slow, as it is processing all the files (62 files), but the next run, will just process 1 file, you can simulate that just by deleting some csv files in the staging folder, when you refresh again, only the files deleted will be processed again.
I think the main take away is, Python and R integration are amazing tools to implement new possibilities that will not be necessary available in PowerBI, and you don’t need to be a programmer to use those integration, a serious search on stackoverflow will get you started quickly.
This article addresses to all the schedulers and project professionals who import schedules into scrubbing P6 databases, remove undesired data, export the cleaned XER, and then import to a production database or share with third-parties such as contractors or sub-contractors.
If it happens to you to go through such a process, then you might want to read this article and see the better way to “clean” a XER file, prevent external data from corrupting your database thus maintain security and keep schedule integrity.
Now, I want to explain how the tool works and how you can benefit from it.
How to get started with ScheduleCleaner?
ScheduleCleaner is a desktop application for Windows operating system. It’s not connected to a database, and does not require internet connection to use it.
The “cleaning” process of an XER file can be achieved in 5 steps as explained below.
Launch the software;
Add an XER File;
Select the output folder;
Click on the categories of data you want to remove;
Click “Clean” button.
As you can see, there is no manual work, no editing of a XER file in Notepad, and no scrubbing databases.
The software is intuitive, easy-to-use, and works offline as a standalone desktop application.
What’s more important, the software does not modify the original project plan. Instead it creates a copy and modifications are saved in the new file. The original project plan remain untouched.
Now, let’s see what you can accomplish with this tool in more specifics.
If it takes a lot of time to import XER file intro Primavera P6 database, POBS data might be the reason for that.
Overall, the POBS defect affect the performance of the application and users lose valuable during the import operation. According Oracle, the POBS data is not used yet:
“We do not utilize the POBS table yet we export/import the data from this table when completing XER Export/Import. The XER export/import should be written to exclude this data with XER export/import operations of P6 Professional.”
The removal of POBS data can be done manually, but the process is prone to errors and can be time consuming.
The impact of all these errors when managing global data in an enterprise, will ultimately result in a polluted database and unconscious mistakes on a project level.
So using a tool for removing POBS data is desirable.
You can see a significant difference of the file size before and after cleaning POBS which greatly affects the time needed to import XER file into a Primavera P6 database.
Imagine the time that can be saved for larger XER files.
Remove Units, Rates, Cost, Pricing, Progress
As the purpose of exporting data files in XER format is to transmit project data to another database, in many cases data should be kept private. For example, a general contractor wants to send the project to a sub-contractors, but without the cost of resources.
Another examples is related with the GDPR regulation. Namely project schedulers and managers share files that contain sensitive information such as resource names that can disrupt the guidelines of the GDPR.
To be GDPR compliant, companies need to hide/anonymize confidential information, and ScheduleCleaner is the perfect tool to easily and securely protect sensitive information.
Just by clicking checkboxes, users who want to share the XER schedule can pick certain categories of data that want to be removed from the schedule before sending to third-parties or upload to a Primavera P6 database.
Mask Project Data
Similar as removing certain categories of data, you can also mask project data.
The only difference is that with masking, you can add custom codes, labels or text for the specific categories.
Inserting prefix or suffix to different categories in the project plan, can give additional information to the person who reads the information and acts according them.
To add Prefix/Suffix, you need to select the template that will contain Prefix/Suffix, select the appropriate category, and add the terms that will be words’ prefix or suffix.
Then, you go to “Clean” ribbon and click on the “Batch” button. The end result when adding prefix/suffix are given in the image below.
The software features an option to convert Global and EPS activity codes to Project Activity codes and EPS to Global Activity Codes. The activity codes are important to schedulers and planning engineers when creating different types of work performance reports.
So here are the type of categories that can be converted with ScheduleCleaner:
Convert Global/EPS to Project Activity Codes.
Convert EPS to Global Activity Codes
Moreover, you can convert Global calendars that are used in the project plan into project and shared resource calendar. In this way, you will avoid errors when importing the XER file into P6 database.
Save time with process automation
Who doesn’t want automation? Automation saves time and gives a sense of comfort and security.
Here, it’s not actually a full automation because you still need to click on a button in order to perform an action or combination of actions. But this is quite useful when you have a set of actions that need to done on a daily basis such as sending a daily progress report to top management or uploading recent progress into a database.
Automation is ScheduleCleaner is viable through creating Templates, save them and apply to imported XER files.
“Batch Clean” is a feature that works with templates. User must create at least one template and assign it to a file in order to use the batch file cleaning.
“Quick Clean” on the other side is more suitable when user wants to modify very small number of project files, while “Batch Clean” is useful when large number of data files, usually located in different folders, need to be modified.
ScheduleCleaner enables you to quickly remove or anonymize confidential data in XER data files exported from Primavera P6, while keeping the schedule integrity.
It replaces the many work when “cleaning” XER file prior to sharing the file or import to a production database.
As the manual process of removing or anonymizing project data is time-consuming and unreliable, performing Batch Clean in combination with Templates can speed up the process.
Organizations can significantly improve their productivity, communication and security by integrating ScheduleCleaner in their working environment.