all Tutorials

1. Node.js as a HTTP Server - Building from scratch - Tutorial for Beginners

By: William Alexander : 2018-08-04

Description: This node.js tutorial is for a beginner to understand the basics of node.js from scratch. More specifically, this tutorial explores how node.js can be used as a HTTP server to handle HTTP requests and serve files. Since this assumes that you are just beginning to learn node.js, I will start from explaining what is node.js and how to install it and then continue with the step by step guide.


2. Event driven programming in node.js

By: William Alexander : 2018-08-04

Description: In olden days when there was no GUI, the programs normally were run sequentially in a blocking manner. But with GUI, the event driven programming became popular as events were generated by the user in the form of mouse clicks.


3. Send email from node.js application

By: William Alexander : 2018-08-04

Description: In this tutorial, we will learn how to send emails from node.js applications. In any application, sending out alerts and notifications via email becomes an integral part. Node.js has a package for sending mails and it is called nodemailer.


4. Using make.names() in R

By: Karthik Janar : 2018-05-22

Description: While doing data analysis, it is highly recommended to use proper naming conventions for files, variables and especially column names. This is very important for two reasons


5. Handling Date and Time in R

By: Karthik Janar : 2018-05-07

Description: R has a special way of representing dates and times, which can be helpful if you’re working with data that show how something changes over time (i.e. time-series data) or if your data contain some other temporal information, like dates of birth.


6. Functions in R - Creating your first R function

By: Karthik Janar : 2018-05-06

Description: Functions are one of the fundamental building blocks of the R language. They are small pieces of reusable code that can be treated like any other R object. Functions are usually characterized by the name of the function followed by parentheses.


7. Logical and Character Vectors in R

By: Karthik Janar : 2018-05-01

Description: The simplest and most common data structure in R is the vector. Vectors come in two different flavors: atomic vectors and lists. An atomic vector contains exactly one data type, whereas a list may contain multiple data types. Numeric vectors are one type of atomic vector. Other types of atomic vectors include logical, character, integer, and complex. In this tutorial, we’ll take a closer look at logical and character vectors.


8. Missing Values in R

By: Karthik Janar : 2018-05-01

Description: Missing values play an important role in statistics and data analysis. Often, missing values must not be ignored, but rather they should be carefully studied to see if there’s an underlying pattern or cause for their missingness.


9. Subset Vectors in R

By: Karthik Janar : 2018-05-01

Description: In this tutorial, we’ll see how to extract elements from a vector based on some conditions that we specify. For example, we may only be interested in the first 20 elements of a vector, or only the elements that are not NA, or only those that are positive or correspond to a specific variable of interest. By the end of this tutorial, you’ll know how to handle each of these scenarios.


10. Matrices and Data Frames in R

By: Karthik Janar : 2018-05-01

Description: In this tutorial, we’ll cover matrices and data frames. Both represent ‘rectangular’ data types, meaning that they are used to store tabular data, with rows and columns. The main difference, as you’ll see, is that matrices can only contain a single class of data, while data frames can consist of many different classes of data.