Data Manipulation In R | Data Manipulation In R With dplyr | R Programming For Beginners

This video on Data Manipulation in R will help you learn how to transform and summarize your data using different packages and functions. You will use the dplyr package to select, filter, arrange, and mutate data. You will use the tidyr library to create tidy data. You will look at functions such as gather, spread, separate, and unite. Let’s begin!

#r #data-science #data-analysis #r-programming #developer

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Data Manipulation In R | Data Manipulation In R With dplyr | R Programming For Beginners

This video on Data Manipulation in R will help you learn how to transform and summarize your data using different packages and functions. You will use the dplyr package to select, filter, arrange, and mutate data. You will use the tidyr library to create tidy data. You will look at functions such as gather, spread, separate, and unite. Let’s begin!

#r #data-science #data-analysis #r-programming #developer

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Data Types In R

Data types are kept easy.

Data types of R are quite different when we compare with other programming languages. Here, we’ll outline the data types of R.

Integers

Integers are numbers without a decimal point. Unlike other programming languages, R represents all integers as a “double” data type. But the main difference is, you need to write “L” to represent integers in arithmetic operations. For example 9L.

Double

Instead of floats, R has a double data type to represent both for making arithmetic calculations simpler. And when you look for integers and floats in programming languages, you’ll see double after writing the function to specify the data type. As an example: 9 and 3,4.

Vectors

The most basic data type of R that is the lifeblood of its operations because R works with vectorized operations. When we define vectors in R, they’re a sequence of the data element of the same basic time. It only contains the element of the same data type. If not, R will try to convert it into the most dominant data type.

Complex

The complex data type is only used to represent complex (imaginary) numbers in R. Unlike most data types, the complex data type is not used commonly in R. We can give a+bi as an example.

#data-type #r-programming #r-programming-language #r #programming-languages #data science

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

A Beginner’s Guide To Cleaning Data In R — Part 1

My journey into the vast world of data has been a fun and enthralling ride. I have been glued to my courses, waiting to finish one so I can proceed to the next. After completing introductory courses, I made my way over to data cleaning. It is no secret that most of the effort in any data science project goes into cleaning the data set and tidying it up for analysis. Therefore, it is crucial to have substantial knowledge about this topic.

Firstly, to understand the need for clean data, we need to look at the workflow for a typical data science project. Data is first accessed, followed by manipulation and analysis of the data. Afterward, insights are extracted, and finally, visualized and reported.

Typical Project Workflow

Errors and mistakes in data, if present, could end up generating errors throughout the entire workflow. Ultimately, the insights generated that are used to make critical business decisions are incorrect, which may lead to monetary and business losses. Thus, if untidy data is not tackled and corrected in the first step, the compounding effect can be immense.

This guide will serve as a quick onboarding tool for data cleaning by compiling all the necessary functions and actions that should be taken. I will briefly describe three types of common data errors and then explain how these can be identified in data sets and corrected. I will also be introducing some powerful cleaning and manipulation libraries including dplyr, stringr, and assertive. These can be installed by simply writing the following code in RStudio:

install.packages("tidyverse")
install.packages("assertive")

1. Incorrect Data Type

When data is imported, a possibility exists that RStudio incorrectly interprets a data column type, or the data column was wrongly labeled during extraction. For example, a common error is when numeric data containing numbers are improperly identified and labeled as a character type.

a) Identification

Firstly, to identify incorrect data type errors, the glimpse function is used to check the data types of all columns. The glimpse function is part of the **dplyr **package which needs to be installed before glimpse can be used. Glimpse will return all the columns with their respective data types.

library(dplyr)
glimpse(dataset)

Another form of logical checks includes the is function. The is function can be used for each data type and will return with a logical output (true/false). I have only mentioned the common is functions, but it can be used for all data types. If a numeric column is an argument for the is.numeric function, the output will be true, while if a character column is an argument for the is.numeric function the output will be false.

is.numeric(column_name)
is.character(column_name)

b) Correction

After all the incorrect data type columns have been identified, they can simply be converted to the correct data type by using the as functions. For example, if a numeric data type has been incorrectly imported as a character data type, the as.numeric function will convert it to numeric data type.

as.numeric(column_name)

#data-analysis #data-scientist #data #data-cleaning #r #data analysis