index.Rmd

--- 
title: "Analysis of Ecological Data with R"
author: "Marvin Ludwig"
description: |
  This is a slide and exercise collection for the ILOEK Course "Analysis of ecological data in R".
link-citations: true
github-repo: Ludwigm6/analysis_of_ecological_data
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)


library(sf)
library(tmap)
library(tidyverse)
library(stars)
library(mapview)
library(ggplot2)
library(lubridate)
library(here)

mapview::mapviewOptions(fgb = FALSE)


```


# Welcome {.unnumbered}

```{r, fig.cap = "Landscape Ecology with R", echo = FALSE}
knitr::include_graphics("assets/cover_image.png")
```


## Target audience

* You want answers to questions (in landscape ecology)!
* Primarily ecologists but the methods and skills are useful in many fields.
* No R knowledge is required (or other programming skills)
* It is helpful if you know what a spreadsheet is
* Minimum amount of statistic needed (course explains all the necessary stuff)


## Aims of this course

* Developing ideas, answering questions
* Defining steps how to solve a problem
* Translating these steps into R code
* Learning the necessary R language on the way
* A lot of data handling and manipulation
* Using statistics as a tool.

## What this course is NOT

* statistics as in mathematical equations

$$
\left( {\begin{array}{*{20}c}
   n  \\
   k  \\
\end{array}} \right) = \frac{{n!}}{{k!\left( {n - k} \right)!}}
$$

$$
\sigma ^2  = \mu _2  = \sum {\left( {x - \mu _1 } \right)^2 P\left( x \right)}
$$

* R as in computer science
* You probably won't find out anything you didn't already know about ecology


## Course Outline

* Summary and Exercises from last week
* New Input
* Exercises
* __Studienleistung:__ 10 assignments - Passed with 6 assignments > 50%