Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?”. In order to be able to solve this set of exercises you should have solved the ‘part 0’ of this series, in case you haven’t you can find the solutions to run them in your machine here. […]

# Exercises

## Basic Operations Exercises

This set of exercises will help you to learn and test your skill with basic arithmetical operations and logic functions. Before proceeding, it might be helpful to look over the help pages for the **, %/%, %%, and the logical operators such as !=, ==, >=, isTRUE . Answers to the exercises are available here. […]

## Network Analysis Part 3 Exercises

This is the third set of exercises on networks in which we practice the functions for graph structure, using package igraph. The first and second part are available here: Part 1 Part 2 If you don’t have package already installed, install it using the following code: install.packages(“igraph”) and load it into the session using the […]

## Descriptive Analytics – Part 0 : data exploration

Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?”. This is the first set of exercise of a series of exercises that aims to provide a descriptive analytics solution to the ‘2008’ data set from here. Download it and save it as a csv file. This […]

## Two Way ANOVA in R Exercises

One way analysis of variance helps us understand the relationship between one continuous dependent variable and one categorical independent variable. When we have one continuous dependent variable and more than one independent categorical variable we cannot use one way ANOVA. When we have two independent categorical variable we need to use two way ANOVA. When […]

## Optimize Data Exploration With Sapply() – Exercises

The apply() functions in R are a utilization of the Split-Apply-Combine strategy for Data Analysis, and are a faster alternative to writing loops. The sapply() function applies a function to individual values of a dataframe, and simplifies the output. Structure of the sapply() function: sapply(data, function, …) The dataframe used for these exercises: dataset1 <- […]

## Creating Sample Datasets – Exercises

Creating sample data is a common task performed in many different scenarios. R has several base functions that make the sampling process quite easy and fast. Below is an explanation of the main functions used in the current set of exercices: 1. set.seed() – Although R executes a random mechanism of sample creation, set.seed() function […]

## Dates and Times – Simple and Easy with lubridate Exercises (part 3)

Welcome to the third and last part of the “lubridate” exercises. If you missed Part 1 and 2 then please refer to the links below: Part 1 Part 2 In this part, I’ll cover the following topics: 1. Durations (exact spans of time) 2. Periods (relative spans of time) 3. Rounding dates As always, in […]

## Network Analysis Part 2 Exercises

In this set of exercises we shall practice the functions for network statistics, using package igraph.If you don’t have package already installed, install it using the following code: install.packages(“igraph”) and load it into the session using the following code: library(“igraph”) before proceeding. You can find more info about the package and graphs in general here […]

## One Way Analysis of Variance Exercises

When we are interested in finding if there is a statistical difference in the mean of two groups we use the t test. When we have more than two groups we cannot use the t test, instead we have to use analysis of variance (ANOVA). In one way ANOVA we have one continuous dependent variable […]