• Introduction to geospatial data with R
  • Prerequisites
    • Packages
    • Data
  • 1 Introduction
    • 1.1 Getting Started
      • 1.1.1 Why R
      • 1.1.2 Why Rstudio
      • 1.1.3 Why using the Tidyverse
    • 1.2 Rmarkdown
      • 1.2.1 Basic workflow
      • 1.2.2 Rmarkdown basics
    • 1.3 Code cells
    • 1.4 R basics
      • 1.4.1 Mathematical operations
      • 1.4.2 Data types
      • 1.4.3 Comparison and logical operators
      • 1.4.4 Affectation
      • 1.4.5 not only a calculator
    • 1.5 Get help
    • 1.6 Packages management
      • 1.6.1 Install packages
      • 1.6.2 Load packages
      • 1.6.3 Exercice
      • 1.6.4 Write packages
  • 2 Data manipulation with R
    • 2.1 Get Started
    • 2.2 Data wrangling with R base
      • 2.2.1 Load data
      • 2.2.2 Get some insight about the data
      • 2.2.3 Plots
    • 2.3 Data wrangling with the Tidyverse
      • 2.3.1 First load the tidyverse package
      • 2.3.2 Dataset
      • 2.3.3 select()
      • 2.3.4 filter()
      • Exercice 1
      • 2.3.5 mutate()
      • 2.3.6 group_by()
      • 2.3.7 summarize()
      • Exercice 2
      • Exercice 3
      • 2.3.8 Commands order
  • 3 Plotting with ggplot2
    • 3.1 Adding layers to create a plot
      • 3.1.1 Create a ggplot object and add data
      • 3.1.2 Define X and Y axis
      • 3.1.3 Adding a geometry
      • 3.1.4 Add/Change labels
      • 3.1.5 More options
    • 3.2 More examples with the mpg dataset
      • 3.2.1 The mpg dataset
  • 4 Mapping with R and {sf}
    • 4.1 Simple mapping
      • 4.1.1 Needed libaries
      • 4.1.2 Loading the data
      • 4.1.3 Mapping
      • 4.1.4 Making a better map
      • 4.1.5 Adding a title and a subtitle
      • 4.1.6 Exercice
      • 4.1.7 Remarques about mapping with {ggplot2}
    • 4.2 cartography package
    • 4.3 What’s next ?
  • Resources
    • 4.4 Data science
    • 4.5 Geospatial data and mapping
  • Published with bookdown

Introduction to geospatial data with R

Resources

4.4 Data science

  • Introduction to R for non-programmers using gapminder data - Software carpentry
  • An Introduction to R - W. N. Venables, D. M. Smith, R Core Team
  • R for Data Science - Garrett Grolemund, Hadley Wickham
  • ModernDive, An Introduction to Statistical and Data Sciences via R - Chester Ismay and Albert Y. Kim
  • Hadley Wickham’s R packages book

4.5 Geospatial data and mapping

  • Geocomputation with R by Robin Lovelace, Jakub Nowosad, Jannes Muenchow
  • R Spatial by Edzer Pebesma
  • Tidy spatial data analysis (video) by Edzer Pebesma at rstudio::conf 2018
  • Introduction to mapping with {sf} & Co.  on spatial analysis with R by Sebastien Rochette