Documents related to Server-Side Extensions and Advanced Analytics Integration.
This document contains step-by-step exercises designed to introduce the concept of building out Qlik Sense files that call out to R via the SSEtoRserve open source connector.
In order to do these exercises you must first have R installed and configured as per this document: Installing R with Qlik Sense.pdf.
Here are the data source files for use with these exercises:
And here are the finished example QVF files for the exercises:
Thanks Dan for the post very useful.. Can we have similar example with Python?
Is there any document for Qliksense and Python integration?
Very Useful. Thanks
A python integration would be very helpful. If you guys know any tutorial about the same please point it out to me.
great post. I have a question: in the Iris example, why does the graph crash if I use as a dimension a text variable ("iris species") rather than "observation"? Clearly I've decreased the number of cluster to 1.
Here the error in the SSEtoRServe
I decided to transplant the problem in R.
Here my code:
data <- read.table("yourpath\\Iris.csv",sep=",",header=TRUE)
data <- data[,2:6]
data <- ddply(data,
sepal.length = mean(sepal.length),
sepal.width = mean(sepal.width),
petal.length = mean(petal.length),
petal.width = mean(petal.width)
# it does not work: the error is the same of the SSEtoRServe
# it works!
So it seems that R use also the iris species in the kmeans, if you do not remove it.
This imply this question: is it possible that in your script that colours the point in Qlik, the function takes also the numbers of the observation as a variable used for the kmeans? Clearly this is terrible, also because it takes it implicitly.
Also, how could you make it work?
EDIT: working on data in R without using the observation as measure (like in Qlik Sense), the result is the same.
The question is: how can I make it work with the species as dimension? I cannot understand why it does not work.
Thanks in advance.
Found this article to be very useful
Very usefull !
Hi all, First of all, great job and thanks for sharing :smileyhappy:Just a "little detail" in the R code for Air application that turns big in terms of model quality and statistical analysis...The air time series are MULTIPLICATIVE, not ADDITIVE ! Means the variability around trend expands with time... which is visually quite obvious :robotwink:
Two solutions to fix the original ts R modelling in the expressions of each measure :
I updated the *.qvf application with these two modifications to show the result in a few different ways, but I seem unable to upload it here ?
You can see in the screen below that in the multiplicative model for Air_passengers, random (residuals) stay much lower, meaning that TREND + SEASON explain the data much better.(correction : type="multi" + 2nd axe for seasonal and random)