Ggplot is R’s premier data visualization package. Its popularity can likely be attributed to its ease of use — with just a few lines of code you are able to produce great visualizations. This is especially great for beginners who are just beginning their journey into R, as it’s very encouraging that you can create something visual with just two lines of code:
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) + geom_point()
Output of above code
In this article, I want to highlight ggplot’s flexibility and customizability. Alternatives such as Matplotlib and Seaborn (both Python) and Excel are also easy to use, but they are less customizable. In this article, I’ll walk through 8 concrete steps you can do to improve your ggplot.
In order to make sure that the advice in this article is practical, I’m going to abide by two themes:
diamondsdataset, which is included with ggplot.
Themes control all non-data display and are an easy way to change the appearance of your graph. It only takes one extra line of code in order to do this, and ggplot already comes with 8 separate themes.
ggplot(data = diamonds, aes(x = Sepal.Width, y = Sepal.Length)) + geom_point() + theme_bw()
The ggplot themes are simple. They won’t really stand out, but **they look great, are easy to read, and get the point across. **Also, if you want to use the same theme over and over, you can set a global theme with one line of code and it will apply to all ggplots.
## Set global theme theme_set(theme_bw())
For full details on the 8 themes you can visit this link.
ggplot themes, compared
Themes are also super customizable. Beyond the 8 themes that come with ggplot, you can also make your own theme but more importantly use themes that others have created already. In the few companies I’ve worked at, we’ve all had internal ggplot themes. For example, I helped create
theme_fb() at Facebook which with input from designers at the company.
#design #programming #data-science #r #data-visualization #data science
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
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
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.
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
Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.
If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?
Let’s take a look at the most important things you need to know.
#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners