Tag Archives: R language training in Pune

How to create Chart Templates with R Functions

R functions are used to produce chart templates to keep the look and feel of the reports intact.

 
How to create Chart Templates with R Functions
 

In this post you will come across how to create chart templates with R functions – all the R users should be accustomed to the calling functions so as to perform calculations and outline plots accurately. Remember what colors and fonts to use each time: R functions are used as a short-cut for producing customary-looking charts.

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Analyze Smartphone Sensor Data with R and the BreakoutDetection Package

Analyze-Smartphone-Sensor-Data-with-R-and-the-BreakoutDetection-Package
 

Quite interetsing. Juggling with sensor data is starkly different from economics data, document processing or social networking, but very worthwhile. In this blog, we will take a practical approach to analyze smartphone sensor data with R. We are going to use the accelerometer smartphone data that Datarella presented in its Data Fiction competition. The dataset signifies the stimulation along the three axes of the smartphone:

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How to Create Repeat Loop in R Programming

In this tutorial, we will learn to make a repeat loop with the use of R programming.
 
How to Create Repeat Loop in R Programming
 
A repeat loop is used to iterate over a block of code over several number of times.

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Debugging Magrittr Pipelines in R with Bizarro Pipe and Eager Assignment

Debugging Magrittr Pipelines in R with Bizarro Pipe and Eager Assignment

 

Pipes in R

Pipe, written as “%>%“ is basically an efficient operator, supplied by magrittr R package. The pipe operator is notably famous due to its wide range of use in dplyr and by the proficient dplyr users. The usage of pipe operator allows one to write “sin(5)” as “5 %>% sin“,  which is inspired by F#‘s pipe-forward operator “|>” and is further characterised by:

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How To Visualize Multivariate Relationships in Large Datasets in R Programming:

How To Visualize Multivariate Relationships in Large Datasets in R Programming:
 

In this post, we will discuss how to use the package nmle in R programming, which includes the dataset MathArchieve. To install the package and load it into your R programming environment, use the code mentioned below:

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ANZ uses R programming for Credit Risk Analysis

At the previous month’s “R user group meeting in Melbourne”, they had a theme going; which was “Experiences with using SAS and R in insurance and banking”. In that convention, Hong Ooi from ANZ (Australia and New Zealand Banking Group) spoke on the “experiences in credit risk analysis with R”. He gave a presentation, which has a great story told through slides about implementing R programming for fiscal analyses at a few major banks.

 
ANZ uses R programming for Credit Risk Analysis
 

In the slides he made, one can see the following:

 

How R is used to fit models for mortgage loss at ANZ

A customized model is made to assess the probability of default for individual’s loans with a heavy tailed T distribution for volatility.

One slide goes on to display how the standard lm function for regression is adapted for a non-Gaussian error distribution — one of the many benefits of having the source code available in R.

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How to Assess Clustering Tendency: Unsupervised Machine Learning

The meaning of clustering algorithms include partitioning methods (PAM, K-means, FANNY, CLARA etc) along with hierarchical clustering which are used to split the dataset into two groups or clusters of similar objects.

 
How To Assess Clustering Tendency: Unsupervised Machine Learning
 

A natural question that comes, before applying any clustering method on the dataset is:

 

Does the dataset comprise of any inherent clusters?

 

A big problem associated to this, in case of unsupervised machine learning is that clustering methods often return clusters even though the data does not include any clusters. Put in other words, if one blindly applies a clustering analysis on a dataset, it will divide the data into several clusters because that is precisely what they are supposed to do.

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Advertising Technology is Being Transformed with Big Data

Big Data is elemental these days and almost everything revolves around it, be it retail sales, technological developments or even movie-making and novels. But what about the most closely sector which has relied upon analytics since its very inception, advertising? Currently the whole advertising sector is having its commandments rewritten, driven by insights gathered from Big Data analytics.

 

However, the prevalent notion of the business coaches and market mentors are that big data can seem to be like Latin or Hebrew to the novice or companies using it for the first time. So, for the aspiring and ambitious youth it may be a good idea to learn some intuitive analytics tools and snippets to bank on this growing big data trend and make the most of the bandwagon effect that lures in all humans when it comes to making decisions.

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Python Vs R- Which You Want To Learn First

If Big Data interests you as a career choice and you are pretty much aware of the skills you need in order to be proficient in this field, in all likelihood you must be aware that R and Python are two leading languages used for analyzing data. And in case you are not really sure as to learn which of the mentioned articles first, this post will help you in making that decision.

 
Python Vs R- Which You Want To Learn First
 

In the field of analysis of data, R and Python both are free solutions that are easy to install and get started with. And it is normal for the layman to wonder which to learn first. But you may thank the heavens as both are excellent choices.

 

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New R Packages- 5 Reasons for Data Scientists to Rejoice

One of the fundamental advantages of the ecosystem related to R and the primary reason that lie behind the phenomenal growth of R is the practice and facility to contribute new packages to R. When this is added to the highly stable CRAN which happens to be the primary repository of packages of R,gives it a great advantage. The effectiveness of CRAN is further enhanced by the ability of people with sufficient technical expertise and to contribute packages through a proper system of submission.

 

5-Reasons-for-Data-Scientists-to-Rejoice

 

It is only with sufficient effort and time that one realizes the system of packages submitted through proper procedures can yield integrated software of high quality.Even those who are relatively new to R Programming the process of discovering the packages that serves as the bedrock of R language growth. Such packages add value to the language in a reliable way.

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