While it might be tempting to liven up a report or presentation with a few 3D graphs, two-dimensional representation is generally better when numbers are the primary information you want to communicate. Nevertheless, on occasions when numeric values aren’t the primary focus, and you’re more interested in showing the shape of the data, adding a
Interaction Design for Data Exploration Visualizations capable of launching detail views can add value to a data analyst’s user experience. Programming in this kind of interaction automates the creation of complementary charts and increases ease of exploration by linking varied views of the data in a logical way. This tutorial offers a quick example of
Introduction In Part 1, we built an application to geographically explore the 500 Cities Project dataset from the CDC. In this post, we will demonstrate other exploratory data analysis (EDA) techniques for exploring a new dataset. The analysis will be done with R packages data.table, ggplot2 and highcharter. In this post, you will learn how
Exploratory data analysis (EDA) is generally the first step in any data science project with the goal being to summarize the main features of the dataset. It helps the analyst gain a better understanding of the available data and often can unearth powerful insights. Data visualization is the most common technique in EDA. During this
Our team recently designed a dashboard using R Shiny Leaflet allowing users to select many locations at one go on an interactive map. We created the map using the package [crayon-5da4e3282fd11535613476-i/], which enables users to draw shapes on R Shiny Leaflet maps. When combined with the package [crayon-5da4e3282fd16592765495-i/] and a function called [crayon-5da4e3282fd19214509683-i/], the [crayon-5da4e3282fd1c236351028-i/] drawing tool can