#### Statistics with R (Part II)

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In article Statistics with R (Part I) , we walked-through the basic statistics calculation using R and also regression models incl. linear regression, multiple regression, logistic regression and Poisson regression. In this part, we will continue to explore more complicated analysis including ...

#### Statistics with R (Part I)

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Till now, we've gone through R programming basics, data types, packages and IDEs, data APIs to work with data sources and various plotting functions. Let's now dive into the most important part about statistics and modelling with R. After all, R was created for statistics. warning Due ...

#### Plotting with R (Part II)

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In Plotting with R (Part I) , I summarized the functions that can be used in R plotting. In this part, we continue the journey to plot more rich and complex charts like Pie Chart, Bar Chart, BoxPlot, Histogram, Line and Scatterplot using those functions. Pie chart can be drawn using ...

#### Plotting with R (Part I)

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For data analyst, it is critical to use charts to tell data stories clearly. R has numerous libraries to create charts and graphs. This article summarizes the high-level R plotting APIs (incl. graphical parameters) and provides examples about plotting Pie Chart, Bar ...

#### Working with Databases and Files in R

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R provides rich APIs to interact with source data such as databases and files (CSV, XML, JSON, etc.) With SparklyR, R can also be used to interact with big data platforms like Hadoop. This articles shows examples about using R to load data from relational databases and text files. The ...

#### Generating and Transforming Data with R

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In many scenarios, we need to generate data directly in memory. This article provides examples about generating regular and random sequences with R. It also shows you how to reshape or restructure data. In the preceding articles, we already used a quite a few functions to generate regular ...

#### Working with R Packages and IDE

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In this series, we've walked-through R programming basics and advanced data types . This article will focus on R packages and IDEs so that you can program efficiently with R. Let's recap these commonly mentioned R terminologies: Package : An extension of the R base system with code, data and ...

#### R Data Types Detailed Walkthrough

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R implements a number of useful data types to support complex analytics and calculations. This articles focus on String, Vector, List, Matrix, Array, Factory and Data Frame. It also shows examples about expanding data frame, for example, add or drop columns for data frames, add rows for data ...

#### R Programming Basics

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This article provides a basic introduction about programming with R incl. atomic vector, variable, operations, branching, loops and functions. info All examples can run RStudio or R Tools for Visual Studio on Windows. About these two IDEs, refer to R Introduction . We always start ...

#### R Introduction

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R is an implementation of the S programming language (Bell Labs). It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. R is named partly after the first names of the first two R authors and partly as a play on the name of S. It is currently developed by the ...