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gitlab CI pipeline

Recently gitlab platform becomes more and more popular. Not only does it provide the git version control, but also has embedded lots of useful devops related functionality. One of those is the option to build CI/CD pipelines, which is extremely useful for many projects. It allows you to automate tests and deployments. There are plenty […]

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Docker with R and keras

Having a stable environment is super useful for development. Creating one within a Docker container in many cases is a good idea. Here I show how to create a container for development with R (and RStudio instance), with installed keras and tensorflow packages. This can be particularly useful if you wish to build a forecasting […]

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Targets R package for managing workflows

Workflows help us to keep a clear structure of the flow we are building, allow for easier steps traceability and simplified maintenance. They are especially useful when dealing with data science work, where heavy computations take time to run. In R world first popular package to deal with pipelines was drake. It allows not only […]

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Memory leakage while plotting in a loop

Issue Memory leakage while generating python matplotlib plots in a loop on MacOS system. I was using python 3.9 and MacOS Catalina.   I was trying to generate lots of plots for my analysis. Idea was to create them in a loop: render plot save the output iterate further Simple example of the case:

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Spark & R – SparkR vs sparklyr

R enthusiasts can benefit from Spark using one of two available libraries – SparkR or sparklyr. They both differ in usage structure and slightly in available functionality. SparkR is an official Spark library, while sparklyr is created by the RStudio community. Due to the fact that currently Python is favourite language for Data Scientists using […]

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Spark 3 highlights

Recently Apache Spark 3.1.1 was released. Let’s take a look into some of the new features provided within Spark version 3.   HIGHLIGHTS Adaptive query execution That means allowing Spark to change the execution plan during runtime, when run statistics are being updated. In other words after some processing steps are already done and stats […]

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SparkR MLlib

When working with Spark MLlib library you may notice that there are different features available in Python and R APIs. In Python, in addition to models, we can benefit from Transformers, which represent feature transformations that can be done before the modelling. Transformers are also available in sparklyr, but are clearly missing in SparkR. Also […]

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Spark performance tuning

Spark job performance is heavily dependent on the sort of task you aim to accomplish and data you’re dealing with. Because of that there is no one magic recipe to follow when creating a job. However there are several things that impact a job execution. Those which I consider are: file format selection small data […]

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xgboost time series forecast in R

xgboost, or Extreme Gradient Boosting is a very convenient algorithm that can be used to solve regression and classification problems. You can check may previous post to learn more about it. It turns out we can also benefit from xgboost while doing time series predictions.

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