granolarr

NOTE: I have deactivated the website for this repository due to on-going issues with the Nokogiri library that was used by the website component of this repository.

A new version of these materials is currently under development as R for Geographic Data Science.

<img src="docs/assets/images/granolarr_hex.png" alt="The granolarr hex sticker" align="right" width="200" style="padding: 0 15px; float: right;"/> granolarr is a geogGRaphic dAta scieNce, reprOducibLe teAching resouRce in R

by Stefano De Sabbata

The materials included in granolarr (see the granolarr GitHub Pages) have been designed for a module focusing on the programming language R as an effective tool for data science. R is one of the most widely used programming languages, and it provides access to a vast repository of programming libraries, which cover all aspects of data science from data wrangling to statistical analysis, from machine learning to data visualisation. That includes a variety of libraries for processing spatial data, perform geographic information analysis, and create maps. As such, R is an extremely versatile, free and opensource tool in geographic information science, which combines the capabilities of traditional GIS software with the advantages of a scripting language, and an interface to a vast array of algorithms.

The materials aim to cover the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses. The first part of the module will focus on core programming techniques, data wrangling and practices for reproducible research. The second part of the module will focus on non-spatial data analysis approaches, including statistical analysis and machine learning.

The lecture slides use #EAE2DF as background colour to aviod the use of a pure white background, which can make reading more difficult and slower for people with dyslexia. For the same reason, all foreground colours have also been checked for readability using Colour Contrast Analyser. The practical sessions materials can be accessed online in their bookdwon version, where Seppia and Night themes are available and they can be downloaded in pdf or epub format from the top menu. The practical sessions materials can also be downloaded separately in pdf format for printing.

Note: This is a revised version of granolarr, currently under development to meet the University of Leicester "Ignite" approach to blended learning for the academic year 2020/2021. The first version of granolarr is still available at granolarr_v1.

Table of contents

• Materials by topic
• Reference books
• Reproducibility
• Credits and acknowledgements

Materials

All the materials are available through the lectures bookdown and practical sessions bookdown pages. Links to the lecture slides and bookdown chapters for each week are listed below.

1. R coding
   • 100 Introduction
     • 101 Lecture (slides, bookdown)
       • Introduction to R
     • 102 Lecture (slides, bookdown)
       • Core concepts
     • 103 Lecture (slides, bookdown)
       • Tidyverse
     • 104 Practical session (bookdown)
       • The R programming language
       • Interpreting values
       • Variables
       • Basic types
       • Tidyverse
       • Coding style
   • 110 R programming
     • 111 Lecture (slides, bookdown)
       • Data types (vectors, factors, matrices, arrays, lists)
     • 112 Lecture (slides, bookdown)
       • Control structures (conditional statements, loops)
     • 113 Lecutre (slides, bookdown)
       • Functions
     • 114 Practical session (bookdown)
       • Vectorss
       • Lists
       • Conditional statements
       • Loops
       • Functions
       • Scope of a variable
2. Data wrangling
   • 200 Selection and manipulation
     • 201 Lecture (slides, bookdown)
       • Data Frames
     • 202 Lecture (slides, bookdown)
       • Data selection and filtering
     • 203 Lecture (slides, bookdown)
       • Data manipulation
     • 204 Practical session (bookdown)
       • Creating R projects
       • Creating R scripts
       • Data wrangling script
   • 210 Table operations
     • 211 Lecture (slides, bookdown)
       • Join operations
     • 212 Lecture (slides, bookdown)
       • Table pivot
     • 213 Lecture (slides, bookdown)
       • Read and write data
     • 214 Practical session (bookdown)
       • Join operations
       • Data pivot
       • Read and write data
   • 220 Reproducibility
     • 221 Lecture (slides, bookdown)
       • Reproducibility
     • 222 Lecture (slides, bookdown)
       • R and Markdown
     • 223 Lecture (slides, bookdown)
       • Git
     • 224 Practical session (bookdown)
       • Reproducibile data analysis
       • RMarkdown
       • Git
3. Data analysis
   • 300 Exploratory data analysis
     • 301 Lecture (slides, bookdown)
       • Data visualisation
     • 302 Lecture (slides, bookdown)
       • Descriptive statistics
     • 303 Lecture (slides, bookdown)
       • Exploring assumptions
     • 304 Practical session (bookdown)
       • Data visualisation
       • Descriptive statistics
       • Exploring assumptions
   • 310 Comparing data
     • 311 Lecture (slides, bookdown)
       • Comparing groups
     • 312 Lecture (slides, bookdown)
       • Correlation
     • 313 Lecture (slides, bookdown)
       • Data transformations
     • 314 Practical session (to do)
       • Comparing means
       • Correlation
       • Chi-square
   • 320 Regression models
     • 321 Lecture (slides, bookdown)
       • Simple regression
     • 322 Lecture (slides, bookdown)
       • Assessing regression assumptions
     • 323 Lecture (to do) (slides, bookdown)
       • Multiple regression
     • 324 Practical session (bookdown)
       • Simple regression
       • Testing assumptions
       • Multiple regression
4. Machine learning
   • 400 Supervised
     • 401 Lecture (slides, bookdown)
       • Introduction to Machine Learning
     • 412 Lecture (to do) (slides, bookdown)
       • Artificial Neural Networks
     • 413 Lecture (to do) (slides, bookdown)
       • Support vector machines
     • *414 Practical session (to d