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 […]

# Exercises

## Specalize in Geo-Spatial Visualizations With Leaflet – Part 1: 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 […]

## Harvesting Data From the Web With Rvest: Exercises

The rvest package allows for simple and convenient extraction of data from the web into R, which is often called “web scraping.” Web scraping is a basic and important skill that every data analyst should master. You’ll often see it as a job requirement. In the following exercises, you will practice your scraping skills on […]

## 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 […]

## Fighting Factors with Cats: Exercises

In this exercise set, we will practice using the forcats factor manipulation package by Hadley Wickham. In the last exercise set, we saw that it is entirely possible to deal with factors in base R, but also that things can get a bit involved and un-intuitive. Forcats simplifies many common factor manipulation tasks and […]

## 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 […]

## Facing the Facts about Factors: Exercises

Factor variables in R can be mind-boggling. Often, you can just avoid them and use characters vectors instead – just don’t forget to set stringsAsFactors=FALSE. They are, however, very useful in some circumstances, such as statistical modelling and presenting data in graphs and tables. Relying on factors but misunderstanding them has been known to “eat […]

## Basic Generalized Additive Models In Ecology: Exercises

Generalized Additive Models (GAM) are non-parametric models that add smoother to the data. In this exercise, we will look at GAMs using cubic spline using the mgcv package. Data-sets used can be downloaded here. The data-set is the experiment result of grassland richness over time in the Yellowstone National Park (Skkink et al. 2007). […]

## Melt and Cast The Shape of Your Data-Frame: Exercises

Data-sets often arrive to us in a form that is different from what we need for our modeling or visualization functions, which, in turn, don’t necessarily require the same format. Reshaping data.frames is a step that all analysts need, but many struggle with. Practicing this meta-skill will, in the long-run, result in more time to […]