tutorial-2-points-lines-polygons.Rmd

---
title: "Geo-spatial tutorial: points / lines / polygons"
author: "JR Ferrer-Paris"
institute: "Centre for Ecosystem Science<br/>UNSW Data Science Hub"
date: "March 2021 (updated: `r Sys.Date()`)"
output:
  learnr::tutorial:
    progressive: true
    allow_skip: true
runtime: shiny_prerendered
description: >
  Learn how to work with basic vector objects with R packages `sf` and plot with `ggplot2`.
---

```{r setup, include=FALSE}
library(learnr)
library(tidyverse)
library(sf)
library(mapview)
library(units)
require(RColorBrewer)

tutorial_options(
  exercise.timelimit = 60,
  # A simple checker function that just returns the message in the check chunk
  exercise.checker = function(check_code, ...) {
    list(
      message = eval(parse(text = check_code)),
      correct = logical(0),
      type = "info",
      location = "append"
    )
  }
)
knitr::opts_chunk$set(error = TRUE)

UBT <- st_sf(data.frame(full_name='Universität Bayreuth',
             url='https://uni-bayreuth.de/'),
             crs=st_crs("EPSG:4326"),
             geometry=st_sfc(
               st_point(c(11.585833, 49.928889))))

st_crs(franconia) <- "EPSG:4326"
st_crs(breweries) <- "EPSG:4326"
st_crs(trails) <- "EPSG:32632"

franconia_utm = franconia %>% st_transform(st_crs(trails))
UBT_utm = UBT %>% st_transform(st_crs(trails))

trails %>% mutate(length=st_length(geometry)) %>% filter(length>set_units(40000,'m')) -> long_trails

```

## Welcome

In this tutorial, you will learn some basic steps to start working with (geo)-spatial data in R. I prepared this tutorial as a intuitive "hands on" introduction, but I provide links for those interested in more background and theory.

We will cover how to:

* work with points, lines and polygons (vector data) using package `sf`
* apply basic spatial functions,
* visualise spatial data with `ggplot`

### Setup

I've preloaded the packages for this tutorial with

```{r eval = FALSE}
library(sf)
library(units)
require(mapview)
require(RColorBrewer)
```

Throughout this tutorial we will be using the  pipe operator, `%>%`. You can use the pipe to rewrite multiple operations in a way that you can read left-to-right, top-to-bottom. We'll use piping frequently because it considerably improves the readability of code. The pipe is a defining feature of the tidyverse, so we will load this set of packages as well.

```{r eval = FALSE}
library(tidyverse)
```

The tidyverse package also includes `ggplot2`, which allow us to create static maps in a similar modular fashion, but using the `+` operator instead of the pipe.

We will use the `franconia`, `breweries` and `trails` data sets from package `mapview`. These data sets are documented in `help(package=mapview)`.



```{r, eval=TRUE, child="02-points-section.Rmd"}

```

###

We learned how to plot and query spatial and non-spatial information from a `sf` object, well done!

We will come back to the questions of plotting and querying spatial objects when we have a look at other types of spatial objects. Please continue to the next section.

```{r, eval=TRUE, child="03-polygons-section.Rmd"}

```

###

We just learned to work with polygons, how to use spatial functions `st_union`, `st_intersection` and how to plot spatial objects with `ggplot`, well done!

In the next section we will work with objects in different projections.


```{r, eval=TRUE, child="04-projection-section.Rmd"}

```


### That's it from me!

In this last section we learned to work with lines, how to use spatial functions `st_length` and how to handle projections of spatial objects with ggplot` and with the function `st_transform`. Great!


I hope this has been useful and you feel more confident to start working with geo-spatial data in R.

Check the next tutorials in our [geospatial-data repository](https://github.com/UNSW-codeRs/geospatial-data-in-R)