I’m finally starting to understand what people mean about getting into R. For those of you who don’t know, R is an open-source software that allows people to create their own statistical packages (and more). This is huge for multiple reasons.
Firstly, it’s free! It is very frustrating as a student to learn to use whichever statistical software that your institution decided to pay for, only to go out into the rest of the world (or even across campus) and discover that every organization and department has a different software type and your skills are not transferable. R bypasses that by being available to anyone, anywhere, anytime.
Secondly, R is incredibly powerful. If you have ever tried to use macros in Excel to calculate iterations of formulas across a big data set, you understand the limitations of many software packages. R is clean, simple, and effective for data handling, analysis and display. You can do anything in R, from running general additive models to creating dynamic light shows at an ecology conference in Bamfield….
So all this to say that I knew R was cool, important to learn, the way of the future, etc… But I still hadn’t experienced the “R high” that can only come after hours upon hours upon days of hunching over your computer, feeding lines of code into the black screen and waiting for something other than an error message to appear…
Finally, I felt that high. I was working on a figure for the Canadian Society for Ecology and Evolution 2017 meeting, and was fighting multiple battles of trying to speak the correct language for R to understand me, learning about the packages I was attempting to use, and trying to make something pretty to go with the theme of my Powerpoint presentation. After countless hours of what seemed like wasted time, I achieved this:
This may not look like much to you, but to me it was perfection. There is a particular feeling of euphoria that happens when your brain has begun to melt after hours of unsuccessful coding, and then you suddenly press command-enter and a nice looking plot pops up… It’s like you have been chasing this elusive, colourful dragon through dark corridors of code and thick cobwebs of Stack Overflow help posts and suddenly, without realizing that you had turned a corner, you are standing face to face with this glowing and magnificent creature: the R dragon. Then, with newly found courage, you attempt to shake hands with the dragon, or put a hat on it, or change the colour of its scales, and instantly it vanishes, leaving you alone and silent in your dark hallway of code and sending you trudging back to the cobwebs of internet help pages once again.
This taste though, this glimpse of the R dragon, is what motivates you to continue down the spiraling road to learning code. Now that I have tasted it, I want more! Now that I finally have felt the triumphant glow of success – I made that pretty looking plot! – I finally can understand the drive to do everything in R. Speaking with several grad students at the CSEE conference this week, I can take comfort in knowing that I am not alone in my quest. So to all you brave knights and explorers out there – I raise my carpal tunnel coding fist to you, and wish you many glimpses of the R dragon to come!
For fellow eco-nerds out there, below is the code that I used to generate my fancy figure:
Code for Chinook forklengths over time figure
#load dataset: printout of first few rows of my Chin.fl subset of data
Date Species Primary.stock forklength
1 2016-03-29 Chinook Harrison 43
2 2016-03-29 Chinook Harrison 42
22 2016-03-29 Chinook Harrison 42
23 2016-03-29 Chinook Harrison 43
24 2016-03-29 Chinook Harrison 41
25 2016-03-29 Chinook Harrison 45
#initiate plot with Chin.fl dataset
#create basic scatterplot of forklengths over time, assigning colour of points to genetic stock
p<- p + geom_point(alpha = 0.6, aes(x = Date, y = forklength, colour = Chin.fl$Primary.stock), size = 5, show.legend = TRUE, inherit.aes = TRUE)
#open external graphics window for easy plot viewing
quartz(width = 11, height = 8.5)
#rename axis titles
p<- p + ylab(“Fork length (mm)”) + xlab(“Sampling Date”)
#simple black and white theme to start
p <- p + theme_bw()
p <- p + theme(panel.grid = element_blank())
#change all the colours to have white text and elements on black background
p <- p + theme(axis.title = element_text(colour = “white”), plot.background = element_rect(fill = “black”), panel.background = element_rect(fill = “black”), axis.text = element_text(colour = “white”), axis.line = element_line(colour = “white”), axis.ticks = element_line(colour = “white”), legend.title = element_text(colour = “white”, size = 18), legend.text = element_text(colour = “white”), legend.background = element_rect(fill = “black”), legend.key = element_rect(fill = “black”))
#make all labels larger so more visible on export, push axis titles out with vjust (note opposite directionality with y vs x axis)
p <- p + theme(axis.title.y = element_text(size = 24, vjust = 1), axis.title.x = element_text(size = 24, vjust = 0), axis.text = element_text(size = 14), legend.text = element_text(size = 14))
#load scales package to edit x-axis display of dates
#take a quick look at sampling date range in data set
#create vector of selected sampling dates for custom x-axis labels
date<- (un.date[c(1, 4, 5, 10, 18, 20, 26, 32, 35, 38)])
#tell ggplot to make the breaks on the x-axis = to the values in vector ‘date’, and change display to month-day
p <- p + scale_x_date(breaks = date, labels = date_format(“%b-%d”))
#angle date labels to fit more, use hjust to increase horizontal space between axis labels and ticks, and vjust to do same vertically. Increase margins around plot to 5mm
p <- p + theme(axis.text.x = element_text(angle = 20, hjust = 0.8, vjust = 0.9), plot.margin = unit(c(5,5,5,5), “mm”))
#change colour scale to have greater contrast and highlight Harrison stock – see http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3 for more info
p <- p + scale_color_brewer(type = “qual”, palette = “Paired”, na.value = “grey80”)
#change the label of the colour legend
p$labels$colour <- “Stock Identification”
##side note on colour palettes – color brewer palette limits out at 11 distinct colours, but I have 13 variables – I am OK with this as the key stocks I was interested in stand out and contrast is nice.
#view your plot!
Resources used to make this plot:
R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009.
Hadley Wickham (2016). scales: Scale Functions for Visualization. R package version 0.4.1. http://CRAN.R-project.org/package=scales