Rmarkdown_assignment_example/Example_markdown.Rmd at master · GlobalEcologyBiogeography/Rmarkdown_assignment_example · GitHub

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---
title: "Project presentations and rmarkdown example"
output:
  pdf_document:
    highlight: tango
    number_sections: yes
    toc: yes
  html_document:
    highlight: tango
    number_sections: yes
    theme: cerulean
    toc: yes
    toc_float: yes
urlcolor: blue
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Project presentations

Ignite format: 5 minutes presentations (20 slides, 15 seconds each)

You can find some advice and examples [here](http://scottberkun.com/2009/how-to-give-a-great-ignite-talk/) and [here](http://scottberkun.com/2009/how-to-give-a-great-ignite-talk/)

Presentations must include:

1. Title
2. Description of main aim or question
3. Brief methods description (Main data sources and methods)
4. A brief description of your data (e.g., table, map, plot showing the number of species, areas, etc.)
5. Primary results and a brief discussion

AVOID to include unnecessary code into the R markdown. Code for data downloading and cleaning should be incorporated into a separate R file. Start the document with the clean, processed and merge data.

# Project Rmarkdown

# Example: Life history traits and distribution of Hyraxes

```{r libraries, message=FALSE, warning=FALSE}
# Include here your libraries
library(knitr)
library(tidyverse)
library(maptools)
library(ggpubr)
```

## Introduction

- A brief background
- The main question or aim of the project

## Methods

- [Github repository link](https://github.com/GlobalEcologyBiogeography/Rmarkdown_assignment_example)
- Data sources
- Data cleaning and processing
- Describe main procedures
- Data summary (how many species? which groups? how many records?...)

### Data description

```{r}
## Data decription
Hyraxes_df<-read.csv("data/processed/Hyracoidea_traits_geo.csv")

Hyraxes_df %>%
  group_by(genus,species) %>%
  tally() %>%
  kable()


```

### Data distribution

```{r}
# Distribution
Hyraxes_df_geo<-Hyraxes_df
coordinates(Hyraxes_df_geo)<-~lon + lat

# Get map and change projection
data(wrld_simpl)
proj4string(wrld_simpl)<-proj4string(Hyraxes_df_geo)

# Colour vector
cols<-c("#00AFBB", "#E7B800", "#FC4E07","grey")
cols_sp<-cols[Hyraxes_df$Binomial]

# Plot map and species distribution
plot(wrld_simpl, xlim=c(min(Hyraxes_df$lon)-1,max(Hyraxes_df$lon)+1),
     ylim=c(min(Hyraxes_df$lat)-1,max(Hyraxes_df$lat)+1), axes=TRUE, col="light yellow")

points(Hyraxes_df_geo,col=cols_sp, pch=20)
legend('bottomright', bty='n', legend=unique(Hyraxes_df$Binomial),
cex=0.8, fill=unique(cols_sp))
```


## Results and Discussion

```{r message=FALSE, warning=FALSE}
p <- ggplot(Hyraxes_df, aes(temp, precip)) +
  geom_point(aes(color = Binomial), size = 3, alpha = 0.7) +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07","grey"))+
  xlab("Temperature C") +
  ylab("Precipitation")

# Grouped Scatter plot with marginal density plots
ggscatterhist(
  Hyraxes_df, x = "temp", y = "precip",
  color = "Binomial", size = 3, alpha = 0.6,
  palette = c("#00AFBB", "#E7B800", "#FC4E07","grey"),
  margin.plot = "boxplot",
  ggtheme = theme_bw()
  )
```