Aketch  Rachel

Aketch Rachel

1623726749

Master data visualization with ggplot2: scatter and box plots

Rusers focusing on dashboards or data visualizations are inherently dependent on the ggplot2 package. It is the versatile package for plotting data based on the Grammar of Graphics. The idea behind it uses semantics like aesthetics, scales, and layers to build the visualization.

Here is a goto tutorial series for people who are looking for quick solutions with limited time on hand to polish their visualizations. This is a four-part series dealing with plotting different styled plots using ggplot2 packages and other add-on packages. These tutorials are as follows:

  1. Scatter and box plots
  2. Bar plots, Histograms, and Density plots
  3. Circular plots (pie charts, spider plots, and bar plots)
  4. theme(): create your own theme() for increased workflow

In this tutorial, we will work on creating scatter and box plots. After this tutorial, you will be able to make much better visualizations as shown below.

Dataset and packages

For this tutorial, we will be using the global temperature change recorded for the years between 1961 to 2019 for most of the countries. The dataset can be downloaded from here. For the time being, we will focus on the temperature change that occurred in India.

The packages used for this tutorial are:

tidyverse: used for all the manipulations to get the dataset in the right format for plotting purposes

ggplot2: for plotting the data

ggforce: control the width of the jitter using the density distribution of data

patchwork: combining different plots to make a single image

Once the packages are loaded and the data is processed according to the requirements, we are ready to create our first plot. We will start with the scatter plot and then move to the box plot.

#ggplot2 #r #data-visualization #data-science

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Master data visualization with ggplot2: scatter and box plots
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

Sid  Schuppe

Sid Schuppe

1617988080

How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

Aketch  Rachel

Aketch Rachel

1623726749

Master data visualization with ggplot2: scatter and box plots

Rusers focusing on dashboards or data visualizations are inherently dependent on the ggplot2 package. It is the versatile package for plotting data based on the Grammar of Graphics. The idea behind it uses semantics like aesthetics, scales, and layers to build the visualization.

Here is a goto tutorial series for people who are looking for quick solutions with limited time on hand to polish their visualizations. This is a four-part series dealing with plotting different styled plots using ggplot2 packages and other add-on packages. These tutorials are as follows:

  1. Scatter and box plots
  2. Bar plots, Histograms, and Density plots
  3. Circular plots (pie charts, spider plots, and bar plots)
  4. theme(): create your own theme() for increased workflow

In this tutorial, we will work on creating scatter and box plots. After this tutorial, you will be able to make much better visualizations as shown below.

Dataset and packages

For this tutorial, we will be using the global temperature change recorded for the years between 1961 to 2019 for most of the countries. The dataset can be downloaded from here. For the time being, we will focus on the temperature change that occurred in India.

The packages used for this tutorial are:

tidyverse: used for all the manipulations to get the dataset in the right format for plotting purposes

ggplot2: for plotting the data

ggforce: control the width of the jitter using the density distribution of data

patchwork: combining different plots to make a single image

Once the packages are loaded and the data is processed according to the requirements, we are ready to create our first plot. We will start with the scatter plot and then move to the box plot.

#ggplot2 #r #data-visualization #data-science

Gerhard  Brink

Gerhard Brink

1620629020

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).


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

Sid  Schuppe

Sid Schuppe

1618404240

Benefits of Hybrid Cloud for Data Warehouse

In today’s market reliable data is worth its weight in gold, and having a single source of truth for business-related queries is a must-have for organizations of all sizes. For decades companies have turned to data warehouses to consolidate operational and transactional information, but many existing data warehouses are no longer able to keep up with the data demands of the current business climate. They are hard to scale, inflexible, and simply incapable of handling the large volumes of data and increasingly complex queries.

These days organizations need a faster, more efficient, and modern data warehouse that is robust enough to handle large amounts of data and multiple users while simultaneously delivering real-time query results. And that is where hybrid cloud comes in. As increasing volumes of data are being generated and stored in the cloud, enterprises are rethinking their strategies for data warehousing and analytics. Hybrid cloud data warehouses allow you to utilize existing resources and architectures while streamlining your data and cloud goals.

#cloud #data analytics #business intelligence #hybrid cloud #data warehouse #data storage #data management solutions #master data management #data warehouse architecture #data warehouses