01-introduction.qmd

---
bibliography: references.bib
---

# Introduction {#introductionx}

## Use of R

::: callout-important
## REMINDER : R TIPS

1.  Comment your code ! (`# important informations on the code`)

2.  Check your R objects ! (`plot()`, `print()`, `View()` , ...)

3.  Listen to R outputs ! (Errors AND Warnings)

4.  Get help ! (`?name_of_function`, internet, other users)

5.  Keep calm and take a break !
:::

### Installation

::: callout-note
The installation part is based on "[An Introduction to R](https://intro2r.com/)" book writed by *Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau*
:::

#### R

##### Windows users

For Windows users select the '[Download R for Windows]((https://cran.r-project.org/bin/windows/))' link and then click on the 'base' link and finally the download link 'Download R 4.2.1 for Windows'. This will begin the download of the '.exe' installation file. When the download has completed double click on the R executable file and follow the on-screen instructions. Full installation instructions can be found at the [CRAN website](https://cran.r-project.org/bin/windows/).

##### Mac users

For Mac users select the '[Download R for (Mac) OS X](https://cran.r-project.org/bin/macosx/)' link. The binary can be downloaded by selecting the 'R-4.2.1.pkg'. Once downloaded, double click on the file icon and follow the on-screen instructions to guide you through the necessary steps. See the '[R for Mac OS X FAQ](https://cran.r-project.org/bin/macosx/RMacOSX-FAQ.html)' for further information on installation.

##### Linux users

For Linux users, the installation method will depend on which flavour of Linux you are using. There are reasonably comprehensive instruction [here](https://cran.r-project.org/bin/linux/) for Debian, Redhat, Suse and Ubuntu. In most cases you can just use your OS package manager to install R from the official repository. On Ubuntu fire up a shell (Terminal) and use (you will need root permission to do this):

```{bash eval=FALSE}
sudo apt update
sudo apt install r-base r-base-dev
```

which will install base R and also the development version of base R (you only need this if you want to compile R packages from source but it doesn't hurt to have it).

If you receive an error after running the code above you may need to add a 'source.list' entry to your etc/apt/sources.list file. To do this open the terminal and enter this:

```{bash eval=FALSE}
sudo apt install -y --no-install-recommends software-properties-common dirmngr
# Add keys
wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | sudo tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc

sudo add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/"
```

Once you have done this then re-run the apt commands above and you should be good to go.

Install the following packages to allow for future spatial data analysis:

```{bash eval=FALSE}
sudo apt install -y libgdal-dev libproj-dev libgeos-dev libudunits2-dev libv8-dev libnode-dev libcairo2-dev libnetcdf-dev
```

#### RStudio

Whilst its eminently possible to just use the base installation of R (many people do), we will be using a popular Integrated Development Environment (IDE) called RStudio. RStudio can be thought of as an add-on to R which provides a more user-friendly interface, incorporating the R Console, a script editor and other useful functionality (like R markdown and Git Hub integration). You can find more information about RStudio [here](https://rstudio.com/).

RStudio is freely available for Windows, Mac and Linux operating systems and can be downloaded from the [RStudio site](https://rstudio.com/products/rstudio/download). You should select the 'RStudio Desktop' version. Note: you must install R before you install RStudio.

##### Windows and Mac users

For Windows and Mac users you should be presented with the appropriate link for downloading. Click on this link and once downloaded run the installer and follow the instructions. If you don't see the link then scroll down to the 'All Installers' section and choose the link manually.

##### Linux users

For Linux users scroll down to the 'All Installers' section and choose the appropriate link to download the binary for your Linux operating system. RStudio for Ubuntu (and Debian) is available as a `*.deb` package.

To install the `*.deb` file navigate to where you downloaded the file and then enter the following command with root permission

```{bash eval=FALSE}
sudo apt install ./rstudio-2022.07.2-576-amd64.deb
```

You can then start RStudio from the Console by simply typing

```{bash eval=FALSE}
rstudio
```

or you can create a shortcut on you Desktop for easy startup.

### Help

The R help is very useful for the use of functions.

```{r eval=FALSE}
?plot #displays the help page for the plot function
help("*") #for unconventional characters
```

Calling the help opens a page (the exact behavior depends on the operating system) with information and usage examples about the documented function(s) or operators.

### Functions

The basic syntax is:

```{r}
afunction <- function(arg1, arg2){
  arg1 + arg2
}
afunction(10, 5)
```

## Spatial in R : History and evolutions

Historically, 4 packages make it possible to import, manipulate and transform spatial data:

-   The package `rgdal` [@rgdal] which is an interface between R and the [GDAL](http://www.gdal.org/) [@GDAL] and [PROJ](https://proj.org/) [@PROJ] libraries allow you to import and export spatial data (shapefiles for example) and also to manage cartographic projections\
-   The package `sp` [@sp] provides class and methods for vector spatial data in R. It allows displaying background maps, inspectiong an attribute table etc.\
-   The package `rgeos` [@rgeos] gives access to the [GEOS](http://trac.osgeo.org/geos/) spatial operations library and therefore makes classic GIS operations available: calculation of surfaces or perimeters, calculation of distances, spatial aggregations, buffer zones, intersections, etc.\
-   The package `raster` [@raster] is dedicated to the import, manipulation and modeling of raster data.

Today, the main developments concerning vector data have moved away from the old 3 (`sp`, `rgdal`, `rgeos`) to rely mainly on the package `sf` ([@sf], [@pebesma2018]). In this manual we will rely exclusively on this package to manipulate vector data.

The packages `stars` [@stars] and `terra` [@terra] come to replace the package `raster` for processing raster data. We have chosen to use the package here `terra` for its proximity to the `raster`.

## The package `sf`

<img src="img/sf.gif" align="right" width="150"/> The package `sf` was released in late 2016 by Edzer Pebesma (also author of `sp`). Its goal is to combine the feature of `sp`, `rgeos` and `rgdal` in a single, more ergonomic package. This package offers simple objects (following the [*simple feature*](https://en.wikipedia.org/wiki/Simple_Features) standard) which are easier to manipulate. Particular attention has been paid to the compatibility of the package with the *pipe* syntax and the operators of the `tidyverse`.

`sf` directly uses the GDAL, GEOS and PROJ libraries.

![](img/sf_deps.png){fig-align="center" width="600"}

[From r-spatial.org](https://r-spatial.org/r/2020/03/17/wkt.html)

::: {.callout-note appearance="minimal" icon="false"}
Website of package `sf` : [Simple Features for R](https://r-spatial.github.io/sf/)
:::

Many of the spatial data available on the internet are in shapefile format, which can be opened in the following way

```{r class.output="code-out"}
library(sf)
district <- st_read("data_cambodia/district.shp")
```

::: callout-important
## Shapefile format limitations

For the multiple limitations of this format (multi-file, limited number of records...) we advise you to prefer another format such as the geopackage `*.gpkg`. All the good reasons not to use the shapefile are [here](http://switchfromshapefile.org/).
:::

A geopackage is a database, to load a layer, you must know its name

```{r class.output="code-out"}
st_layers("data_cambodia/cambodia.gpkg")
```

```{r class.output="code-out"}
road <- st_read("data_cambodia/cambodia.gpkg", layer = "road")
```

### Format of spatial objects `sf`

![](img/sf.png){fig-align="center" width="600"}

Objects`sf` are objects in `data.frame` which one of the columns contains geometries. This column is the class of sfc (*simple feature column*) and each individual of the column is a sfg *(simple feature geometry)*. This format is very practical insofa as the data and the geometries are intrinsically linked in the same object.

::: {.callout-note appearance="minimal" icon="false"}
Thumbnail describing the simple feature format: [Simple Features for R](https://r-spatial.github.io/sf/articles/sf1.html)
:::

::: callout-tip
A benchmark of vector processing libraries is available [here](https://github.com/kadyb/vector-benchmark).
:::

## Package `mapsf`

The free R software spatial ecosystem is rich, dynamic and mature and several packages allow to import, process and represent spatial data. The package [`mapsf`](https://CRAN.R-project.org/package=maps) [@mapsf] relies on this ecosystem to integrate the creation of quality thematic maps into processing chains with R.

Other packages can be used to make thematic maps. The package `ggplot2` [@ggplot2], in association with the package `ggspatial` [@ggspatial], allows for example to display spatial objects and to make simple thematic maps. The package `tmap` [@tmap] is dedicated to the creation of thematic maps, it uses a syntax close to that of `ggplot2` (sequence of instructions combined with the '+' sign). Documentation and tutorials for using these two packages are readily available on the web.

Here, we will mainly use the package `mapsf` whose functionalities are quite complete and the handling rather simple. In addition, the package is relatively light.

<img src="img/logo_mapsf.png" align="right" width="120"/>

`mapsf` allows you to create most of the types of map usually used in statistical cartography (choropleth maps, typologies, proportional or graduated symbols, etc.). For each type of map, several parameters are used to customize the cartographic representation. These parameters are the same as those found in the usual GIS or cartography software (for example, the choice of discretizations and color palettes, the modification of the size of the symbols or the customization of the legends). Associated with the data representation functions, other functions are dedicated to cartographic dressing (themes or graphic charters, legends, scales, orientation arrows, title, credits, annotations, etc.), the creation of boxes or the exporting maps.\
`mapsf` is the successor of [`cartography`](http://riatelab.github.io/cartography/docs/) [@cartography], it offers the same main functionalities while being lighter and more ergonomic.

To use this package several sources can be consulted:

-   The package documentation accessible [on the internet](http://riatelab.github.io/mapsf/) or directly in R (`?mapsf`),

-   A [*cheat sheet*](https://raw.githubusercontent.com/riatelab/mapsf/master/vignettes/web_only/img/mapsf_cheatsheet.pdf),

![](img/mapsf_cheatsheet.png){fig-align="center" width="600"}

-   The [vignettes](https://riatelab.github.io/mapsf/articles/) associated with the package show sample scripts,

-   The [R Geomatics](https://rgeomatic.hypotheses.org/) blog which provides resources and examples related to the package and more generally to the R spatial ecosystem.

## The package `terra`

<img src="img/logo_terra.png" align="right" width="150"/> The package `terra` was release in early 2020 by Robert J. Hijmans (also author of `raster`). Its objective is to propose methods of treatment and analysis of raster data. This package is very similar to the package `raster`; but it has more features, it's easier to use, and it's faster.

::: {.callout-note appearance="minimal" icon="false"}
Website of package `terra` : [Spatial Data Science with R and "terra"](https://rspatial.org/terra/)
:::

::: callout-tip
A benchmark of raster processing libraries is available [here](https://github.com/kadyb/raster-benchmark).
:::