Top 10 R Packages For Data Visualisation One Must Know

As per study reports, data scientists and practitioners prefer R as the language for statistical modelling after Python language. Also, R dominates the preference scale, with a combined figure of 81.9% utilisation for statistical modelling among those surveyed.

Below here, we listed the top 10 libraries in R for data visualisation one must know.

Read more: https://analyticsindiamag.com/top-10-r-packages-for-data-visualisation/

#analytics #data-science #datascientist #data #bigdata

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Top 10 R Packages For Data Visualisation One Must Know

Top 10 R Packages For Data Visualisation One Must Know

As per study reports, data scientists and practitioners prefer R as the language for statistical modelling after Python language. Also, R dominates the preference scale, with a combined figure of 81.9% utilisation for statistical modelling among those surveyed.

Below here, we listed the top 10 libraries in R for data visualisation one must know.

(The list is in alphabetical order).


1| Colourpicker

About: Colourpicker is a tool for Shiny framework and for selecting colours in plots. This tool supports various options, such as alpha opacity, custom colour palettes, and more. The most common uses of this tool include the utilisation of the colourInput() function to create a colour input in Shiny as well as the use of the plotHelper() function/RStudio Addin to select colours for a plot.

Know more here.

2| Esquisse

About: The esquisse package allows a user to interactively explore data by visualising it with the ggplot2 package. It allows a user to draw bar graphs, curves, scatter plots, histograms, export the graphs, and retrieve the code generating the graph. With the help of esquisse, one can quickly visualise the data according to their type as well as export to PNG or PowerPoint, and retrieve the code to reproduce the chart.

Know more here.

3| ggplot2

About: ggplot is a popular package that is based on the grammar of graphics. The idea behind this library is that one can build every graph from the same components, such as a dataset, a coordinate system, and more. The package provides graphics language for creating intuitive and intricate plots. It allows a user to create graphs that represent both univariate and multivariate numerical and categorical data.

Know more here.

4| ggvis

**About: **ggvis is a data visualisation package for R that allows to declaratively describe data graphics with a syntax similar in spirit to ggplot2. It allows creating rich interactive graphics locally in Rstudio or in the browser as well as leverage the infrastructure of the Shiny package to publish interactive graphics usable from any browser. The goal of ggvis is to make it easy to build interactive graphics for exploratory data analysis.

Know more here.


#developers corner #data visualisation #r libraries #r packages #data science

Siphiwe  Nair

Siphiwe Nair

1620466520

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

Ian  Robinson

Ian Robinson

1624399200

Top 10 Big Data Tools for Data Management and Analytics

Introduction to Big Data

What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.

To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

List of Big Data Tools & Frameworks

The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:

  1. Big Data Framework
  2. Data Storage Tools
  3. Data Visualization Tools
  4. Big Data Processing Tools
  5. Data Preprocessing Tools
  6. Data Wrangling Tools
  7. Big Data Testing Tools
  8. Data Governance Tools
  9. Security Management Tools
  10. Real-Time Data Streaming Tools

#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics

Gerhard  Brink

Gerhard Brink

1624696643

Top 10 Big Data Statistics You Must Know in 2021

Analytics Insight Presents the Top 10 Big Data Statistics for You to Know in 2021.

The future is bright for companies that use Big Data and analytics in this cut-throat competitive market. People are generating more than 2.5 Qn bytes of real-time data due to globalization and digital transformation in the tech-driven era. IoT is also providing data through multiple smart devices, social media accounts, and search engines. The scope of Big Data is increasing at an increasing rate that leads to more job opportunities in the field of Data Science and other disruptive technology fields. Ample Big Data software tools are available to beginners as well as professionals for effective data management to generate interactive reports for meaningful in-depth business insights. Thus, reputed companies and start-ups have started adopting Big Data by investing millions of dollars. Let’s look at the top 10 Big Data statistics to predict the nearby future of this data-driven world.

Top 10 Big Data Statistics You Must Know in 2021

#big data #latest news #top list #big data statistics #top 10 big data statistics you must know in 2021

Siphiwe  Nair

Siphiwe Nair

1624185000

Top 10 Companies Hiring Data Engineering Professionals

Analytics Insight has listed top 10 companies hiring data engineering professionals with a decent salary

Over the past few years, the usage of data has exploded drastically. More people, organizations, businesses, etc. are availing data as part of their routine mechanism. Earlier, people focused more on useful insights and analysis, but now, they have come to the sense that managing data also needs equal importance. As a result, the role of data engineer has ballooned in the technology sector. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. Data engineers are responsible for finding trends in datasets and developing algorithms to help make raw data more useful to the enterprise. The Dice 2020 Tech Job Report labeled data engineering as the fastest-growing job of 2019, with a 50% year-over-year growth in the number of openings. According to Dataquest, data engineers performs three main roles namely generalist (found in small teams or small companies), pipeline-centric (found in midsize companies) and database-centric (works in large organizations). Analytics Insight has figured top 10 companies hiring data engineering professionals with decent salary.

#big data #latest news #top 10 companies hiring data engineers #top 10 companies hiring data engineering professionals #data engineer jobs. #top companies hiring data engineering professionals