One of the beauties of the data analytics field is its openness to multiple tools. The field has already shown that knowledge of one single tool is a handicap. In order to survive and grow in this field, you ought to be:
Jack of all trades and master of some. — unknown
Excel is one of the prominent tools when dealing with data in the majority of businesses. I myself had the experience of working with Excel for data analysis and found few shortcomings working with them. …
Here I discuss six guiding principles that I find very effective for creating good visualizations. Some are learned from experience/observations and others by the teaching of the pioneers of data visualizations.
“Good design is a lot like clear thinking made visual.”
— Edward Tufte
This is not an exhaustive list but a great starting point in the world of data visualization. The six guidelines that will be discussed are:
Let’s get started.
When creating visuals, always strive to achieve high data density. So, what does high data…
A hands-on tutorial to get you to start creating maps with R.
Maps are one of the widely read and understood visualizations that keep getting more and more traction. Since the start of the pandemic, the maps caught my attention again after a gap of more than two decades. The internet was flooded with visualizations depicting the spread of the virus, and one such visualization or rather a dashboard attracted me was from John Hopkins University. By adding the map with the various plots, the dashboard painted a different picture. The data was more clear and informative than just looking…
Data visualizations in the form of plot and infographics are the way to convey stories. The stories are appealing to the masses if they have substance to them and are self-explanatory. The reason infographics or presentations are more popular than a mere plot because they convey a story and are self-sufficient. A plot narrates only a part of the story. To make plots more meaningful, one can either stack series of plots faceted together or stack a series of plots to create an animation. …
Excel has been the go-to data analytics tool for businesses for the last three decades. Excel provides built-in tools to conduct statistical analysis, creating budgets, forecasting, dashboards, data plotting, etc. And then there are add-ins to fill in the analytical gap or improve performance.
For me, Excel had been a great tool, but I had two issues using it: first being the occasional freezing of Excel when working with large data files (> 50MB), and second, the poor quality of default plots generated which required a lot of polishing. …
Shiny is one of the powerful tools in the hand of data analysts and data scientists to develop web-based applications and interactive data visualizations. The Shiny app consists of two important functions: UI and the server function. A key feature of Shiny is the use of reactive programming to automatically update the output when changes are made in the input.
We work on the dataset of the Food and Agriculture Organization (FAO) of the United Nations to get hands-on experience of building a Shiny app from scratch (link for the app). The article is divided into the following sections:
Apart from hosting the main pipe operator %>% used by the Tidyverse community, the magrittr package in Tidyverse holds a few other pipe operators. The %>% pipe is widely used for data manipulations and is automatically loaded with Tidyverse.
The pipe operator is used to execute multiple operations that are in sequence requiring the output of the previous operation as their input argument. So, the execution starts from the left-hand side with the data as the first argument that is passed to the function on its right and so on. …
This is the last article from the series master the data visualization using the
ggplot2 package. The complete list of tutorials are as follows:
Theme customization is key to increasing work efficiency for those who are regularly changing the default theme settings to make their visualizations more attractive. The default theme used by
ggplot2 package is
theme_gray(). So, for this tutorial, we will use the
theme_gray() function to create our own customized function,
In the third part of the data visualization series with ggplot2, we will focus on circular plots. The list of the tutorials are as follows:
So, under circular visualizations, we will be covering on how to create the following charts:
Further, we will discuss the pros and cons of using these types of visualizations.
With great power comes great responsibility, use pie and spider charts wisely.
Apart from being the birthday of Albert Einstein, March 14 has a special significance. This day also has a nerdy twist to it being written as 3.14, being the approximation of pi constant, the day is officially celebrated as π day. Often people celebrate the day by baking or eating pie.
The mathematical constant is calculated by taking the ratio of the circumference of the circle to its diameter. In 2019, Google’s employee Emma Haruka Iwao, broke the Guinness Book of World Record by calculating the value of pi to the 31 trillion digits. The precise number of digit calculated…
Passionate about Dataviz | Mathviz | Machine Learning using R. In quest to help SME’s to leverage the power of data.