Background In the second part of this tutorial series on spatial data analysis using the raster package, we will explore new functionalities, namely: Raster algebra Cropping Reprojection and resampling We will also introduce a new type of object named RasterStack, which, in its essence, is a collection of RasterLayer objects with the same spatial extent, […]

# Tutorials

## Spatial Data Analysis: Introduction to Raster Processing (Part 1)

Background Geospatial data is becoming increasingly used to solve numerous ‘real-life’ problems (check out some examples here). In turn, R is becoming more equipped than ever to handle this type of data. Thus, providing an exceptional open-source solution to solve many problems in the Geographic Information Sciences and Remote Sensing domains. In general, two types […]

## Tensorflow – Basics Part 1

Overview In this tutorial, we will go through the basics of Tensorflow. By the end of this series, you will have the background in order to use Tensorflow for deep learning models. Tensorflow is an open source software library for numerical computation using data flow graphs. Nodes in the graph are called ops (short for […]

## How to Create Interactive Data Tables With DT

.INTRODUCTION The package “DT” offers an R “window” to JavaScript DataTables. R uses data in matrices’ or data frames’ form and 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. PACKAGE INSTALLATION & DATA […]

## Power Analysis Tutorial

Before starting any experiment, careful planning needs to take place. For instance, how many samples are required for your experiment? This question is important for two reasons. First, an experiment with too few of samples may not be able to determine real differences between, say a control and experimental group. And second, too many samples […]

## How to Display Multivariate Relationship Graphs With Lattice

INTRODUCTION The lattice package is a special visualization package, as it takes base R graphics one step further by providing improved default graphs and the ability to display multivariate relationships. PACKAGE INSTALLATION & DATA FRAME The first thing you have to do is install and load all the packages that we are going to need […]

## How to tidy up your data set with tidyr

INTRODUCTION In general data analysis includes four parts: Data collection, Data manipulation, Data visualization and Data Insights. The tidyr package is one of the most useful packages for the second category of data manipulation as tidy data is the number one factor for a succesfull analysis. Tidy data means that every column stands for a […]

## Mathematical Expressions in R Plots: Tutorial

It is quite common to want to use mathematical expressions in R. Specifically, mathematical symbols or entire equations may be needed when building plots. In this tutorial, we will examine how mathematical expressions can be included into R graphics. We will use the co2 data already found in R. The data includes the atmospheric concentrations […]

## How to use basic dplyr functions

INTRODUCTION The dplyr is an R-package that is used for transformation and summarization of tabular data with rows and columns. It includes a set of functions that filter rows, select specific columns, re-order rows, adds new columns and summarizes data. Moreover, dplyr contains a useful function to perform another common task, which is the “split-apply-combine” […]

## Regression Model Assumptions Tutorial

Regression is used to explore the relationship between one variable (often termed the response) and one or more other variables (termed explanatory). Several exercises are already available on simple linear regression or multiple regression. These are fantastic tools that are used frequently. However, each has a number of assumptions that need to be met. Unfortunately, […]