OK, so there is a bit of a circular firing squad going on in some of my R installs with ggplot2. Apparently, you can get various CRAN/Github versions out of sync and a whole host of different. Here is how it started:
R
Bayesian Models
A three-part introduction to Bayesian Data Analysis by Rasmus Bååth.
Spatial Data Sources
As part of a class in Landscape Genetics, faculty (mostly done by Melanie Murphy and Jeffrey Evans) compiled an extensive list of spatial data sources. These were made available on the course website we hosted but I wanted to make a more persistent copy of them here so they will not be lost. They are listed below the break.
Applied Environmental Statistics
This semester, I’ll be leading a graduate course in applied ecological statistics. Should be a lot of fun getting a group of people up to speed on the benefits of being an R guru!
https://sites.google.com/a/vcu.edu/applied-environmental-statistics
Collab <- Slack + R
Intalling Shiny Server on Ubuntu 14 LTS
OK, so I just ‘found‘ shiny and it has a lot of cool stuff to it. OK, I’ve known about it for a long time but have just had the opportunity to sit down and work it out and see how it can fit into the presentation and learning I’m trying to develop in my Applied Population Genetics online textbook. Here is a brief overview of how I set up the shiny server on my Ubuntu box that is hosting the book (so I can embed more interactivity in the display).
Layered Maps in R (Using GGPlot of course…)
A very cool writeup on making blow out maps.
http://urbandemographics.blogspot.co.uk/2016/04/creating-tilted-and-stacked-maps-in-r.html
RStudio Cheatsheets
Here are some very useful cheat sheets put out by RStudio. A great resource of information!
So True!
Use version control or editors in the cloud.
Volume 0 – On iBooks Store
Buffers & Convex Hulls
An analysis common to modern population genetics is that of finding ecological distances between objects on a landscape. The estimation of pairwise distance derived from spatial data is a computationally intensive thing, one that if you are not careful will bring your laptop to its knees! One way to mitigate this data problem is to use a minimal amount raster area so that the estimation of the underlying distance graph can be done on a smaller set of points. This example provides a simple solution using convex hulls. Jump below for the complete example.
Raster Plotting
A raster is essentially an image, whose pixel size correspond to a particular spatial extent and the data contained within each pixel represents a particular feature on the landscape. Common rasters are DEM’s (measuring elevation), rainfall, temperature, buildings, etc. In R, it is common to think of rasters as matrices whose values measure some feature on the landscape. In this section, we will examine how to acquire, load, manipulate, and extract data from raster objects.
Chapter 1: Learning R
Applied Population Genetics Textbook Release
I will be posting portions of all 10 chapters of my upcoming textbook, Applied Population Genetics, as early draft chapters to this website over the spring semester. Read more
Merging data frames
In R, there is often the need to merge two
data.frame objects (say one with individual samples and the other with population coordinates. The
merge() function is a pretty awesome though it may take a little getting used to.
Here are some things to remember:
- You need to have two data.frame objects to merge
- The first one in the function call will be the one merged on-to the second one is added to the first.
- Each will need a column to use as an index—it is a column that will be used to match rows of data. If they are the same column names then the function will do it automagically, if no common names are found in the names() of either data.frame objects, you can specify the columns using the optional by.x= and by.y= function arguments.
Loading in Rasters
Much of the work in my laboratory uses spatial data in some context. As such it is important to try to be able to grab and use spatial data to in an easy fashion. At present, R is probably the best way to grab, visualize, and analyze spatial data. For this example, I went to http://worldclim.org and downloaded the elevation (altitude) for tile 13 (eastern North America) as a GeoTiff. A GeoTiff is a specific type of image format that has spatial data contained within it. The tile data has a pixel resolution of 30 arc seconds which puts us in the general area of ~ 1km. First, we need to get things set up to work.
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# Set the working directory to where you want it. setwd("~/Downloads") # load in the raster library require(raster) Loading required package: raster Loading required package: sp |
Dyer RJ. 2015 Is there such a thing as landscape genetics? Molecular Ecology.
For a scientific discipline to be interdisciplinary it must satisfy two conditions; it must consist of contributions from at least two existing disciplines and it must be able to provide insights, through this interaction, that neither progenitor discipline could address. In this paper, I examine the complete body of peer-reviewed literature self-identified as landscape genetics using the statistical approaches of text mining and natural language processing. The goal here is to quantify the kinds of questions being addressed in landscape genetic studies, the ways in which questions are evaluated mechanistically, and how they are differentiated from the progenitor disciplines of landscape ecology and population genetics. I then circumscribe the main factions within published landscape genetic papers examining the extent to which emergent questions are being addressed and highlighting a deep bifurcation between existing individual- and population-based approaches. I close by providing some suggestions on where theoretical and analytical work is needed if landscape genetics is to serve as a real bridge connecting evolution and ecology sensu lato.
GStudio: An R Package for Spatial Analysis of Marker Data
This is the main package that provides data types and routines for spatial analysis of genetic marker data. The previous version is currently available on CRAN and you can install it rom within your R environtment by invoking the command
install.packages("gstudio")
If you want to keep up with the latest developments of this package, you can use the version found on GitHub. Install it from within R as:
require(devtools)
install_github("dyerlab/gstudio")
and that should get you up-to-date. You’ll need to have a fully working LaTeX install and some other stuff to build it if you fork.
The Users Manual for the package with several examples can be found here
I have started a github account for this package, you can get access to the whole codebase read about it on the wiki, and contribute to the project from its repo at https://github.com/dyerlab.
Extracting Data from Rasters
This document shows you how to extract data from rasters.
Getting The Libraries
First, I’ll load in some packages to get the ability to work with raster data and to load in the Arapatus attenuatus data set (it is part of the default gstudiopackage).
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require(raster) require(gstudio) |
Loading and Cropping Rasters
We can load in the raster, and then crop it to just the are we need. These rasters were downloaded from [http://www.worldclim.org] and are much larger than the study area. This just makes it easier on the computer to not have to deal with such large areas. After cropping it, we will load in the annual precip and temperature data as well.
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tmp <- raster("alt_22.bil") e <- extent(c(-115, -109, 22, 30)) baja_alt <- crop(tmp, e) baja_temp <- crop(raster("bio1_22.bil"), e) baja_prec <- crop(raster("bio12_22.bil"), e) |
Getting Example Data from Araptus attenuatus
Now, lets grab the Araptus data and look at the data and plot out the locations.
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data(arapat) summary(arapat) |
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## Species Cluster Population ID Latitude ## CladeA: 75 CBP-C :150 32 : 19 101_10A: 1 Min. :23.1 ## CladeB: 36 NBP-C : 84 75 : 11 101_1A : 1 1st Qu.:24.6 ## CladeC:252 SBP-C : 18 Const : 11 101_2A : 1 Median :26.2 ## SCBP-A: 75 12 : 10 101_3A : 1 Mean :26.3 ## SON-B : 36 153 : 10 101_4A : 1 3rd Qu.:27.5 ## 157 : 10 101_5A : 1 Max. :29.3 ## (Other):292 (Other):357 ## Longitude LTRS WNT EN EF ## Min. :-114 01:01:147 03:03 :108 01:01 :225 : 2 ## 1st Qu.:-113 01:02: 86 01:01 : 82 01:02 : 52 01:01:219 ## Median :-112 02:02:130 01:03 : 77 02:02 : 38 01:02: 52 ## Mean :-112 02:02 : 62 03:03 : 22 02:02: 90 ## 3rd Qu.:-110 : 11 01:03 : 7 ## Max. :-109 03:04 : 8 03:04 : 6 ## (Other): 15 (Other): 13 ## ZMP AML ATPS MP20 ## : 33 08:08 : 51 05:05 :155 05:07 : 64 ## 01:01: 46 07:07 : 42 03:03 : 69 07:07 : 53 ## 01:02: 51 07:08 : 42 09:09 : 66 18:18 : 52 ## 02:02:233 04:04 : 41 02:02 : 30 05:05 : 48 ## : 23 07:09 : 14 05:06 : 22 ## 07:09 : 22 08:08 : 9 11:11 : 12 ## (Other):142 (Other): 20 (Other):112 |
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plot(arapat, zoom = 6) |
Extracting Point Data
To elevation, temperature and precipitation from the rasters for each sampling location, we need to translate them into points first. I’ll first grab the coordinate data as a data.frame.
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coords <- StrataCoordinates(arapat) |
Then we can grab them using the normal functions in the sp library.
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pts <- SpatialPoints(coords[, 2:3]) coords$elev <- extract(baja_alt, pts) coords$temp <- extract(baja_temp, pts) coords$prec <- extract(baja_prec, pts) coords |
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## Strata Longitude Latitude elev temp prec ## 1 88 -114.3 29.33 681 178 143 ## 11 9 -113.9 29.01 361 195 148 ## 20 84 -113.7 28.97 368 197 124 ## 29 175 -113.5 28.73 240 205 106 ## 36 177 -114.0 28.66 177 203 120 ## 46 173 -112.9 28.41 26 223 102 ## 56 171 -113.2 28.22 522 195 145 ## 66 89 -113.4 28.04 290 203 117 ## 76 159 -113.3 27.53 87 211 123 ## 85 SFr -113.0 27.36 305 205 107 ## 94 160 -112.5 27.40 371 212 124 ## 104 162 -112.4 27.20 248 220 119 ## 114 12 -112.7 27.18 488 202 130 ## 124 161 -113.0 27.04 29 216 91 ## 134 93 -112.0 26.95 66 230 81 ## 144 165 -112.4 26.25 NA NA NA ## 154 169 -111.4 26.21 6 238 134 ## 164 58 -111.4 26.02 12 240 129 ## 173 166 -112.1 25.91 70 224 84 ## 183 64 -111.3 25.61 397 214 245 ## 188 168 -111.2 25.56 354 217 235 ## 198 51 -111.6 25.35 80 222 140 ## 205 Const -111.7 25.02 50 217 127 ## 216 77 -110.7 24.88 52 231 193 ## 226 164 -111.5 24.75 35 213 95 ## 236 75 -110.7 24.59 21 231 166 ## 247 163 -111.0 24.21 196 219 127 ## 257 ESan -110.4 24.46 9 237 175 ## 265 153 -110.5 24.13 31 235 165 ## 275 48 -110.3 24.21 117 233 217 ## 285 156 -110.0 24.04 0 237 196 ## 291 157 -110.1 24.02 609 208 443 ## 301 73 -109.9 24.01 103 233 227 ## 311 Aqu -110.1 23.29 142 226 227 ## 319 Mat -110.1 23.09 5 234 159 ## 324 98 -109.6 23.08 75 237 233 ## 328 101 -110.6 27.91 8 249 244 ## 337 32 -109.3 26.64 18 244 337 ## 356 102 -109.1 26.38 10 245 346 |
Plotting Trend lines.
Cool, lets sort this by latitude
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coords <- coords[order(coords$Latitude), ] |
and then plot out some values to look at what is going on.
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require(ggplot2) ggplot(coords, aes(x = Latitude, y = elev)) + geom_line(color = "gray") + theme_bw() + geom_text(aes(y = elev + 10, label = Strata), color = "blue") |
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ggplot(coords, aes(x = Latitude, y = temp)) + geom_line(color = "gray") + theme_bw() + geom_text(aes(y = temp + 5, label = Strata), color = "blue") |
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ggplot(coords, aes(x = Latitude, y = prec)) + geom_line(color = "gray") + theme_bw() + geom_text(aes(y = prec + 10, label = Strata), color = "blue") |
Updating R and With Current Libraries
If you work in R for very long on mac, there comes a time when you upgrade and the framework process looses all your libraries! In some sense this is pretty lame because you now have to install all these libraries again. However, it can be a blessing if you install packages in a willy-nilly fashion as you will only reinstall the ones you use most often. At any rate, it is kind of a pain. Here is what I’ve been doing about this to automate the process. The key here is that you need to do the first part before you upgrade.
Current Library
In the old version of R, prior to updating you’ll want to save the libraries that you have installed. In R, this can be done as follows:
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pkgs <- installed.packages() pkgs <- names( is.na(pkgs[,4])) save(pkgs,file=’~/Desktop/pkgs.rda’) |
Install Updated R Version
Either download the latest package or update your svn repository and rebuild R and install it. There are a lot of options for learning more about these options elsewhere on the web.
Install Missing Packages
Now, in the new version of R, you can figure out which are installed by default and then take the differences from what you have and what the previous version had installed and then install them.
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new_pkgs <- installed.packages() new_pkgs <- names( is.na(new_pkgs[,4])) load(“~/Desktop/pkgs.rda”) to_install <- setdiff( pkgs, new_pkgs ) install.packages( to_install ) update.packages() |
And you should be done.