Data analytics, statistics, and more
Data visualization is an important element of the data science process and the broader data presentation architecture discipline. This post will focus on performing some basic spatial data analysis using Bigfoot sightings in North America and the R language for statistics and visualization.
This post illustrates the use of rayshader, an R library that uses elevation data in a base R matrix and a combination of raytracing, hillshading algorithms, and overlays to generate 2D and 3D maps. A surface relief map created using digital elevation data will be rendered using rayshader and ggplot2.
For this post, we will evaluate rainfall in Australia using daily weather observations from multiple Australian weather stations. We will build a stacked ensemble classification model using the H2O machine learning platform for use in predicting if there will be rain tomorrow.
For this post, we will evaluate rainfall in Australia using daily weather observations from multiple Australian weather stations. We will build several machine learning models using the tidymodels framework for use in predicting if there will be rain tomorrow.
Shaded relief of surface elevation illustrates the shape of the terrain in a realistic fashion by showing how the three-dimensional surface would be illuminated from a point light source. This post illustrates the use of geom_relief(), a new aesthetic mapping layer, or geom (geometric object), for creating a shaded relief basemap using ggplot2.