This final post about the STAN platform will specifically focus on data visualizations that can come from STAN models. In particular, we will explore these visualizations by hand with the popular shinystan package. As we already know, the STAN platform typically uses particular Markov Chain Monte Carlo (MCMC) algorithms: the Hamiltonian Monte Carlo (HMC) or […]

# data visualization

## MCMC Using STAN – Diagnostics With The Bayesplot Package: Exercises

This exercise set will continue to present the STAN platform, but with another useful tool: the bayesplot package. This package is very useful to construct diagnostics that can be used to have insights on the convergence of the MCMC sampling since the convergence of the generated chains is the main issue in most STAN models. […]

## R FOR HYDROLOGISTS – Part 3: Loading and Plotting Data: Exercises

R FOR HYDROLOGISTS LOADING AND PLOTTING THE DATA (Part 3) Creating a box plot of the data can be a good approach to inspect the historical behavior of the river level and can show us how the data spreads in different time indexing (Month/ Year). If you are not familiar with this, a boxplot is […]

## MCMC Using STAN – An Introduction With The RSTAN Package: Exercises

This blog post is the first of a set of exercises about STAN that will introduce the STAN platform and how to link it with R. STAN is a statistical modeling platform that is used as an example for MCMC computations for Bayesian inference. It is more efficient for most analysis since it is written in […]

## R FOR HYDROLOGISTS – Part 2: Loading and Plotting Data: Exercises

R FOR HYDROLOGISTS LOADING AND PLOTTING THE DATA (Part 2) In hydrology, it is very common to analyze the annual behavior of the levels in order to see if there is any recurrent patterns over the year (seasonality.) In order to observe the historical behavior of the river, we have to construct a plot with […]

## R For Hydrologists – Loading and Plotting Data Part 1: Exercises

LOADING AND PLOTTING THE DATA (Part 1) Working with hydro-meteorological data can be very time consuming and exhausting. Luckily, R can provide a framework to easily import and process data in order to implement statistical analysis and models. In these tutorials, we are going to explore and analyze data from a tropical basin in order […]

## Practice Your ggplot Skills: Exercises

ggplot2 is a great tool for complex data visualization. Let’s practice it a bit! Answers to these exercises are available here. For each exercise, please replicate the given graph. Some exercises require additional data wrangling. If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your […]

## GGplot Drilling: Exercises

Visualization is a key component to understanding and communicating your understanding to an audience. The more second nature turning your data into plots becomes, the more you can focus on the overall goals instead of being stuck on technical details. As a freelance data analyst, I know that often times between when a project […]

## Spatial Data Analysis: Introduction to Raster Processing (Part 2)

Background In the second part of this tutorial series on spatial data analysis using the raster package, we will explore new functionalities, namely: Raster algebra Cropping Reprojection and resampling We will also introduce a new type of object named RasterStack, which, in its essence, is a collection of RasterLayer objects with the same spatial extent, […]

## Graph Theory: Using iGraph Exercises (Part-2)

Following on from last time, this tutorial will focus on more advanced graph techniques and existing algorithms such as Dijkstra’s algorithm that can be used to draw real meaning from graphs. This is part 2 in the series of iGraph tutorials, for part 1, click here. When completing these tutorials be sure to read up […]