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Data Science Articles
Page 12 of 13
How to screen for outliners and deal with them?
Introduction Data points that stand out from the bulk of other data points in a dataset are known as outliers. They can distort statistical measurements and obscure underlying trends in the data, which can have a detrimental effect on data analysis, modeling, and visualization. Therefore, before beginning any study, it is crucial to recognize and handle outliers. In this post, we'll look at different methods for dealing with outliers as well as how to check for them. Screening for Outliers We must first recognize outliers in order to deal with them. Here are a few popular techniques for identifying outliers ...
Read MoreRole of time series algorithms in Data Science
In order to recognize and predict trends in data gathered over time, time series analysis is a potent technique. Each data point in a time series represents a distinct moment in time and is gathered over time. Stock prices, weather information, and website traffic are a few examples of time series data. In a variety of disciplines, including economics, finance, and weather forecasting, time series data is often employed. The practice of employing statistical methods to comprehend and forecast the data across time is known as time series analysis. Because it enables us to spot patterns, trends, and correlations in ...
Read MoreFunctional Programming with purrr
Functional programming is a programming methodology in which we construct programs by constructing and applying functions. More specifically in programs, we apply sequential pure functions rather than statements. A pure function is a function that accepts an input and produces a consistent value as an output. Also, during this process no augment or input stream is modified. Such functions are capable of doing a single operation but for carrying out complex operations we can combine them into sequences. In this tutorial, we will discuss functional programming using purr. Nowadays, Functional programming is important to master due to its capability to ...
Read MoreDealing with Missing Data in R
In data science, one of the common tasks is dealing with missing data. If we have missing data in your dataset, there are several ways to handle it in R programming. One way is to simply remove any rows or columns that contain missing data. Another way to handle missing data is to impute the missing values using a statistical method. This means replacing the missing values with estimates based on the other values in the dataset. For example, we can replace missing values with the mean or median value of the variable in which the missing values are found. ...
Read MoreWriting Efficient R Code
Writing efficient code is very important as it makes the development time faster and leads our program to be able to understand, debug and maintain easily. We will discuss various techniques like benchmarking, vectorization and parallel programming to make our R code faster. You must learn these techniques if you are aspiring to be a data scientist. So, let’s get started − Benchmarking One of the easiest optimizations is to have the latest R version to work for. The new version cannot modify our existing code but it always comes with robust library functions that provide improved execution time. The ...
Read MoreString Manipulation in R with stringr
The stringr package is a popular R package that provides functions and tools for manipulating and processing strings in R. This package provides a consistent and convenient interface for working with strings, and it offers a wide range of functions for tasks such as searching, matching, replacing, and splitting strings. In this article, we will discuss string manipulation in R with "stringr” package. The “stringr” package provides us the following families of functions in “stringr” − Character manipulating functions: Such functions allows us to deal with the characters of a string. A family of functions to deal with whitespaces. ...
Read MoreScalable Data Processing in R
Most of the time, the R programmers encounter large data that causes problems as by default variables are stored in the memory. The R language doesn’t work well with a huge amount of data larger than 10% of the computer’s RAM. But data processing should be scalable if we want to excel in the field of data science. So, we will discuss how we can apply certain operations and use scalable data processing easily when the data is sufficiently larger than the computer’s RAM. The discussion would also be focussed on dealing with “out of core” objects. What is Scalable ...
Read MoreObject-Oriented Programming in R
Object-oriented programming focuses on data and objects rather than procedures. The object-oriented model helps us to model real-life objects. It is important to master object-oriented programming concepts in order to excel in the field of data science. Each program has special types of classes. In this tutorial, the discussion will be focused on S3 and S4 classes in R, generic functions, inheritances between classes, and polymorphism. In this tutorial, we will discuss object-oriented programming concepts in R. Object-Oriented Programming in R Object oriented programming is a programming model that revolves around classes and objects rather than functions. In R we ...
Read MoreIntroduction to the Tidyverse
The R package collection known as tidyverse was created with the goal of collaborating and handling data effectively. The Tidyverse package is open-source and constantly improved by the data science community. A data scientist must have a fundamental understanding of every package included under the tidyverse umbrella. All eight packages—purr, ggplot2, dplyr, tidyr, stringr, tibble, readr, and forcats —will be covered in depth. Tidyverse Packages Tidyverse groups several packages in R. It consists of the following packages − Package Name Usage purrr Used for function programming ggplot2 Used for creating graphics dplyr ...
Read MoreWorking with Dates and Times in R with lubridate
The dates and times appear simple and easy at first impression as we deal with them in our day-to-day life. But a lot of complexity involves when we work with dates and times objects in R. This article focuses on working with dates and times using the lubridate package in R. You can install this package locally by using the following command in the CRAN’s terminal − install.packages("lubridate") Types of data/time objects in R There are three types of data/time objects and are listed below − Date () object − Prints the date. Time () object − Prints ...
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