This exercise is the last series of basic analysis of hydrological data. We will use a precipitation dataset from the previous exercise here and additional flow data at Saugeen here. At this time, we will explore seasonal analysis and visualization, including creating a hydrograph and a hydrograph with baseflow and additional plot and flow duration […]

# Exercises

## Introduction to Statistical Testing and Sampling Exercises (Part 2)

This is part 2 in a series on statistical theory using R. For part 1, go here. This tutorial concerns itself with MLE calculations and bootstrapping. Answers to the exercises are available here. Exercise 1 Set a seed to 123 and create the following dataframe: lifespans = data.frame(index = 1:200, lifespans = rgamma(200, shape = 2, […]

## Easy Web Scraping With Rvest: Exercises

The Internet is full of interesting data, there’s no doubt about it. Some sites, such as Twitter, provide users with systemized access (API) around which some neat R packages have been built. In this exercise set, we practice much more general techniques of extracting/scraping data from the web directly, using the rvest package. Note […]

## Tensorflow – Basics Part 1: Exercises

Tensorflow is an open source, software library for numerical computation using data flow graphs. Nodes in the graph are called ops (short for operations), while the graph edges represent the R multidimensional data arrays (tensors) communicated between them. An op takes zero or more Tensors, performs some computation, and produces zero or more Tensors. In […]

## Introduction to Statistical Testing and Sampling: Exercises (Part 1)

For a majority of users, the primary use of R is for statistical testing and analysis. At the heart of this, within the frequentist world, lies hypothesis testing and distribution sampling. The skill in conducting this sort of work is being able to identify an appropriate distribution on which to model the question and test […]

## Predicting Housing Prices with Linear Regression Exercises

Regression techniques are a crucial skill in any data scientist or statisticians toolkit. It is even crucial for people who are unfamiliar with regression modeling. It is a nice way to introduce yourself to the topic through a simple linear model. A linear model is an explanation of how a continuous response variable behaves, dependent […]

## Basic Bayesian Inference for MCMC techniques : Exercises (Part 1)

This post aims to introduce you to the basics of Bayesian inference. The ultimate goal of this introductory set of exercises is to get you ready for Bayesian inference using Markov Chain Monte Carlo (MCMC). Little reminder The whole Bayesian paradigm is based on the Bayesian Theorem that we all know (right ?), generally formulated […]

## Basic Time-Series Hydro-logical Data Analysis (Part 1)

Time series dataset is a well-recognized data type for modeling and forecasting purposes. However, the application in R may vary depending on the research areas. In this exercise set, we will explore basic analysis for time series hydrological data in R . We will also introduce the hydroTSM package; a useful package in hydrology. Hydro TSM is […]

## Creating interactive data tables: Exercises

INTRODUCTION The package DT offers an R “window” to JavaScript DataTables. R uses data in matrices’ or data frames’ form; with DT, they can be displayed as tables on HTML pages. The important thing is that filtering, pagination, sorting, etc. are used to transform it into a really useful tool. Before proceeding, please follow our […]

## Machine Learning With H2O Part 3: Exercises

This is the last of the exercise set on H2O’s machine learning algorithms. Please do them in sequence. This requires some additional data. I have provided the links, so please download them when it’s needed. Answers to the exercises are available here. Please check the documentation before starting this exercise set. For other parts of […]