Shiny modules are short (well, usually short) server and UI functions, that can be connected to each other by a common namespace, and be embedded within a regular Shiny app. You can’t run a Shiny module without a parent Shiny app. The modules can contain both inputs and outputs, and are usually centered around a […]

# Exercises (advanced)

## Specialize in Geo-Spatial Visualizations With Leaflet – Part 2: Exercises

Leaflet is a JavaScript library for interactive maps. It is widely used across many platforms and fortunately, it is also implemented as a very user-friendly R package! With leaflet, you can create amazing maps within minutes that are customized exactly to your needs and embed them within your Shiny apps, markdowns or just view them […]

## Basic Generalized Linear Modeling – Part 4: Exercises

This exercise is going to be the last exercise on Basic Generalized Linear Modeling (GLM). Please click here to find the other part of the Basic GLM Exercise that you’ve missed. In this exercise, we will discuss Logistic Regression models as one of the GLM methods. The model is used where the response data is […]

## Basic Generalized Linear Modeling – Part 3: Exercises

In this exercise, we will continue to solve problems from the last exercise about GLM here. Therefore, the exercise number will start at 9. Please make sure you read and follow the previous exercise before you continue practicing. In the last exercise, we knew that there was over-dispersion over the model. So, we tried to […]

## Basic Generalized Linear Modeling – Part 2: Exercises

In this exercise, we will try to handle the model that has been over-dispersed using the quasi-Poisson model. Over-dispersion simply means that the variance is greater than the mean. It’s important because it leads to inflation in the models and increases the possibility of Type I errors. We will use a data-set on amphibian road […]

## Basic Generalized Linear Modeling – Part 1: Exercises

A generalized linear model (GLM) is a flexible generalization of an ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function. It also allows the magnitude […]

## Sharpening the Knives in the data.table Toolbox: Exercises

If knowledge is power, then knowledge of data.table is something of a super power, at least in the realm of data manipulation in R. In this exercise set, we will use some of the more obscure functions from the data.table package. The solutions will use set(), inrange(), chmatch(), uniqueN(), tstrsplit(), rowid(), shift(), copy(), address(), setnames() […]

## Programmatically Creating Text Outputs in R: Exercises

In the age of Rmarkdown and Shiny, or when making any custom output from your data, you want your output to look consistent and neat. Also, when writing your output, you often want it to obtain a specific (decorative) format defined by the html or LaTeX engine. These exercises are an opportunity to refresh our […]

## Advanced Raster Data: Exercises

Geospatial data is becoming increasingly used to solve numerous ‘real-life’ problems (check out some examples here.) In turn, R is becoming a powerful open-source solution to handle this type of data, currently providing an exceptional range of functions and tools for GIS and Remote Sensing data analysis. In particular, raster data provides support for representing […]

## Tensorflow – Neural Network Training: Exercises

Deep learning is under active development. Papers with new approaches are being published every day. In this set of exercises we will go through some of the newer methods that boost the neural network’s performance. By the end of this post, you will be able to train neural networks with adaptive learning rates and apply […]