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 […]

## Graphics with Lattice: Exercise 1

In the exercises below, we will see features of the famous lattice graphics package. Please see the documentation before trying the exercises. Answers to the exercises are available here. If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that […]

## How to Use KableExtra and RMarkdown to Create Tables in PDF Documents

INTRODUCTION The goal of this tutorial is to introduce you to kableExtra, which you can use to build common complex tables and manipulate table styles. It imports the pipe %>% symbol from magrittr and verbalizes all the functions in order to permit you to add “layers” to the kable output. In combination with R Markdown, you […]

## Linear Regression in Tensorflow

Linear Regression Theory Overview In statistics, linear regression is a linear approach for modeling the relationship between a scalar dependent variable, “y”, and one or more explanatory (independent) variables. The case of one independent variable is called simple linear regression. For more than one independent variable, the process is called “multiple linear regression.” The compacted […]

## Tidy Modeling: Exercises

One of greatest things about tidiverse is the piping operator %>%, along with the fact that everything is designed to work well with it. The same applies to modeling with the modelr package that this set aims to exercise. Answers to these exercises are available here. Please do all exercises using the tidiverse package. It will involve […]

## MCMC For Bayesian Inference – Gibbs Sampling: Exercises

In the last post, we saw that the Metropolis sampler can be used in order to generate a random sample from a posterior distribution that cannot be found analytically. Following the same idea, Gibbs sampling is a popular Markov Chain Monte Carlo (MCMC) technique that is more efficient, in general, since the updates of the […]

## Groups Comparison with ANOVA: Exercises (Part 1)

As we’re aware, the growth of data science has been increased recently, and successfully applied on research for decision making or creating baseline conditions. Statistical analysis, including data visualization, exploration, and modeling are three main important elements in data science. In this exercise, we’ll learn how to analyze response and explanatory variables of data that […]

## Bonus: Build-in Numerical Functions

Welcome to the first bonus set of the new year! We just added this week’s set of bonus exercises! Bonus exercises are exercises sets, available to subscribers to our weekly newsletter. Please sign up (for free!), and receive further details by email on how to get access to the bonus exercises (and solutions, of course.) […]

## Tidy Data Reading: Exercises

Every analysis starts with data; and reading data from different sources into R can be very challenging. Multiple formats, multiple libraries, multiple interfaces, and so on. Fortunately, the authors of tidyverse created a number of packages to handle reading data in the most common formats in a simple, intuitive way. This exercise set will give you […]

## Spatial Data Analysis: Introduction to Raster Processing: Part 4

Background In the fourth part of this tutorial series on Spatial Data Analysis using the raster package, we will explore more functionalities, this time related to time-series analysis of raster data. For more information on raster data processing, see here, as well as the tutorial part-1, tutorial part-2, and, tutorial part-3, of this series. We […]