index.md

Tutorial name

Author

Tutorial Aims

1. Import data in R

2. Create a plot

3. Save the plot

You can get all of the resources for this tutorial from this GitHub repository. Clone and download the repo as a zip file, then unzip it.

1. Import data in R

load("traits.RData")
load("traits_sum.RData")

Open RStudio, create a new script by clicking on File/ New File/ R Script set the working directory and load the packages we'll need.

# Set the working directory
setwd("your_filepath")

# Load packages
library(ggplot2)
library(dplyr)
# Load libraries
library(corrplot)
library(ggplot2)

2. Create a plot

You can add more text and code, e.g.

# Add your code and comments here
# Comparing within-individual correlations
# Trait-trait correlation plot
(correlation <- corrplot(cor(traits[,2:5], use = "pairwise.complete.obs")))

# Save the plot in your working directory
png(filename = "trait_correlation.png", width = 600, height = 600)
(correlation <- corrplot(cor(data[,2:6], use = "pairwise.complete.obs")))
dev.off()

# Graph raw trait data behind mean +/- 95% CI's and save the file
(trait.plot <- ggplot()+
    geom_point(data = dlong, mapping = aes(x = SpeciesName, y = value, colour = Trait), alpha = 0.1) +
    geom_errorbar(data = dsumm, mapping = aes(x = SpeciesName, ymin = q2.5, ymax = q97.5, group = Trait), width = 0.3) +
    geom_point(data = dsumm, mapping = aes(x = SpeciesName, y = mean, group = Trait), size = 4, colour = "black") +
    geom_point(data = dsumm, mapping = aes(x = SpeciesName, y = mean, colour = Trait), size = 3) +
    facet_wrap(~Trait, scales = "free_y")+
    theme_classic() +
    scale_x_discrete(labels = c("Dryas", "Eriophorum", "Oxyria", "Salix")) +
    ylab("Trait Value") +
    xlab("Species"))

# We can save plots made using ggplot2 with ggsave, which is just one line of code
ggsave(trait.plot, filename = "traits.png", height = 5, width = 10)

3. The third section

Here you can add some more text if you wish.

# Add more code and comments

At this point it would be a good idea to include an image of what the plot is meant to look like so people can check they've done it right. Replace trait_correlation.png with your own image file:

This is the end of the tutorial. Here is a summary of what we learned:

- something

- something else

- and a third thing

We can also provide some useful links:

For more on ggplot2, read the official ggplot2 cheatsheet.