Welcome to R! Here is my list of reasons why I use R:

Base R can be intimidating:

alt text

So instead many people use R Studio to write, edit and run code:

alt text

So back to my list of why I use R:

1) R is a language + A. Scripting can be scary at first but intuitive once you get going

M1<-lm(y~x, data=df); summary (M1)

2) R is Free!

3) R has a community of support

4) R graphics

5) R as GIS

What is R Markdown?

Markdown allows you to create clean AND reproducible code and analysis outputs embedded into written documents and shared in various formats (pdf, html, word)

You can integrate code in “Code Chunks”

You can integrate figures

You can also annotate your code and your analyses to share your results

more at: http://rmarkdown.rstudio.com/

We can find some examples of the types of RMarkdown Documents here: http://rpubs.com/

Getting R Markdown

Required Packages

install.packages("rmarkdown") install.packages ("knitr")

Note that if you plan on exporting files as .pdf you will also need a LaTeX editor. I used MacTeX found here: https://tug.org/mactex/mactex-download.html

once installed you can:

1) Start a new Markdown document in RStudio by: + File>New File>R Markdown alt text

  • Enter your document tilte, author and choose an output format alt text

Then simply edit your document and code using the RMarkdown formatting requirements:

Here are two key resources that will help you create your documents: The Markdown CheatSheet: https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf alt text

The RMarkdown Reference Guide: http://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf

Both of these files have been uploaded to our Basecamp account.

Now lets try an example of what an annotated script might looks like:

How does rainfall correlate with the richness of ants in the genus Pheidole?

Pheidole

I like to start my code by setting a working directory, loading the required packages and datasets

you can download the data from a shared Dropbox folder if you are interested: https://goo.gl/wZZb2T

#sets working directory 
setwd ("/Users/israel/Desktop/Teaching/R Intro/")

#load libaries 
require (raster) # a GIS library to handle spatial raster data 
## Loading required package: raster
## Loading required package: sp
require (ggplot2) # a graphics package to produce neat plots 
## Loading required package: ggplot2
#read in datasets 
sites<-read.csv ("Aussie_sites.csv", header=T)
xy<-sites[,c(2,3)] #pull out only the x and y coordinates of the sites 
ants<- read.csv ("Pheidole.csv", header=T)
bioclim = getData('worldclim', var='bio', res=10, lon=5, lat=45) #downloads climate raster data from worldclim.org
rainfall<- bioclim$bio12 #pull out only the rainfall data from the bioclim object 

#take a quick look at the data and its structure 
head (sites) # displays the first six rows of data 
##                 Site      lon       lat
## 1             Hughes 131.0869 -12.70154
## 2           Ringwood 131.1063 -13.08040
## 3       Bridge Creek 131.3134 -13.43723
## 4 North Pine Creek1  131.6766 -13.65729
## 5   South Pine Creek 131.9294 -14.00215
## 6        South Edith 132.1676 -14.37818
str (sites) #gives us information about the structure of the data
## 'data.frame':    15 obs. of  3 variables:
##  $ Site: Factor w/ 15 levels "Bridge Creek",..: 3 11 1 10 14 13 4 2 15 6 ...
##  $ lon : num  131 131 131 132 132 ...
##  $ lat : num  -12.7 -13.1 -13.4 -13.7 -14 ...

Lets first build a map to look at the range of rainfall.

# we can zoom into our study region by creating an extent object 
?extent
NT<-extent (128, 138, -20, -10)
#crop the raster to extent 
rainfall.NT<-crop (rainfall, NT)
#plot the base layer 
plot (rainfall.NT)
  # add the sites based on lonitude and latitude coordinates   
  points (sites$lon, sites$lat, pch=16, col="red", cex=.5)

You can also add text to plots, change color schemes, add polygon objects and add map insets to make it look something like this:

We can extract values to data from raster maps:

rain.data<-extract (rainfall.NT, xy)
#now simply bind your ants data with the rainfall and locality data
  ants.w.data<-cbind (xy, rain.data, ants)

Lets plot the data of ant species richness predicted by Rainfall

ggplot(ants.w.data, aes(x=rain.data, y=Richness)) +
    geom_point(shape=1) +    # Use hollow circles
    geom_smooth(method=lm)   # Add linear regression line

How much of the variance is explained by both of the regression models?

lm1<- lm (Richness ~ rain.data, data=ants.w.data)
summary (lm1)
## 
## Call:
## lm(formula = Richness ~ rain.data, data = ants.w.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2344 -0.6698  0.1426  0.8946  1.4456 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 0.1486586  0.9760332   0.152  0.88128   
## rain.data   0.0034828  0.0009956   3.498  0.00393 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.154 on 13 degrees of freedom
## Multiple R-squared:  0.4849, Adjusted R-squared:  0.4453 
## F-statistic: 12.24 on 1 and 13 DF,  p-value: 0.003928