Amy Whitehead's Research

the ecological musings of a conservation biologist


Converting shapefiles to rasters in R

I’ve been doing a lot of analyses recently that need rasters representing features in the landscape. In most cases, these data have been supplied as shapefiles, so I needed to quickly extract parts of a shapefile dataset and convert them to a raster in a standardised format. Preferably with as little repetitive coding as possible. So I created a simple and relatively flexible function to do the job for me.

The function requires two main input files: the shapefile (shp) that you want to convert and a raster that represents the background area (mask.raster), with your desired extent and resolution. The value of the background raster should be set to a constant value that will represent the absence of the data in the shapefile (I typically use zero).

The function steps through the following:

  1. Optional: If shp is not in the same projection as the mask.raster, set the current projection (proj.from) and then transform the shapefile to the new projection ( using transform=TRUE.
  2. Convert shp to a raster based on the specifications of mask.raster (i.e. same extent and resolution).
  3. Set the value of the cells of the raster that represent the polygon to the desired value.
  4. Merge the raster with mask.raster, so that the background values are equal to the value of mask.raster.
  5. Export as a tiff file in the working directory with the label specified in the function call.
  6. If desired, plot the new raster using map=TRUE.
  7. Return as an object in the global R environment.

The function is relatively quick, although is somewhat dependant on how complicated your shapefile is. The more individual polygons that need to filtered through and extracted, the longer it will take. Continue reading


Creating a presence-absence raster from point data

I’m working on generating species distribution models at the moment for a few hundred species. Which means that I’m trying to automate as many steps as possible in R to avoid having to click buttons hundreds of times in ArcView.

One of the tasks that I need to do is to convert presence-only latitude and longitude data into a presence-absence raster for each species. It seems like this would be something that relatively simple but it took me longer than it should have to figure it out. So I’m posting my code here so 1) I don’t forget how I did it; and 2) because I had someone ask me how to exactly this thing this afternoon and it took me ages to hunt through my poorly organised files to find this piece of code! So here it is:

Because I’m a function kinda girl, I wrote this as a function. It basically goes through three steps:

1. Take an existing raster of the area you are interested in mask.raster and set the background cells to zero (absences).

2. rasterize the presence points for your species and set those cells to one (presences).

3. Label the new raster by your species names raster.label and save it as a new raster.

presence.absence.raster <- function (mask.raster,,raster.label="") {

# set the background cells in the raster to 0
mask.raster[!] <- 0

#set the cells that contain points to 1
speciesRaster <- rasterize(,mask.raster,field=1)
speciesRaster <- merge(speciesRaster,mask.raster)

#label the raster
names(speciesRaster) <- raster.label

Below is an example of how the function works using data on the global distribution of foxes data from the biomod2 package.


# read in species point data and extract data for foxes
mySpecies <- read.csv(system.file("external/species/mammals_table.csv", package="biomod2"), row.names = 1)
species <- "VulpesVulpes"

# extract fox data from larger dataset and keep only the x and y coordinates <- mySpecies[,c("X_WGS84", "Y_WGS84",species)] <-[$VulpesVulpes==1,c("X_WGS84", "Y_WGS84")]

# read in a raster of the world
myRaster <- raster(system.file( "external/bioclim/current/bio3.grd",package="biomod2"))

# create presence absence raster for foxes
pa.raster <- presence.absence.raster(mask.raster=myRaster,, raster.label=species)
plot(pa.raster, main=names(pa.raster))


In this plot, the presences (1) are shown in green and the absences (0) in light grey.

Helpful things to remember (or things I learnt the hard way)

  1. Make sure your species point data and raster are in the same projection and that they actually overlap!
  2. Set your desired raster extent and resolution in the mask.raster before you get started.
  3. The species point data that you feed into the function should just be a list of x and y co-ordinates – no species names or abundances or you’ll confuse the poor beast and it won’t work!

And yes, foxes are also present in Australia where they are a pest. I guess this map shows their natural range before people started doing silly things.

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(Well it was and then things went a little awry, so I had to do some tinkering!)


Combining dataframes when the columns don’t match

Most of my work recently has involved downloading large datasets of species occurrences from online databases and attempting to smoodge1 them together to create distribution maps for parts of Australia. Online databases typically have a ridiculous number of columns with obscure names which can make the smoodging process quite difficult.

For example, I was trying to combine data from two different regions into one file, where one region had 72 columns of data and another region had 75 columns. If you try and do this using rbind, you get an error but going through and identifying non-matching columns manually would be quite tedious and error-prone.

Here's an example of the function in use with some imaginary data. You'll note that Database One and Two have unequal number of columns (5 versus 6), a number of shared columns (species, latitude, longitude, database) and some unshared columns (method, data.source).

  species latitude longitude        method     database
1       p   -33.87     150.5   camera trap
2       a   -33.71     151.3 live trapping
3       n   -33.79     151.8   camera trap
4       w   -34.35     151.3 live trapping
5       h   -31.78     151.8   camera trap
6       q   -33.17     151.2 live trapping
      database species latitude longitude data.source accuracy
1 database.two       d   -33.95     152.7   herbarium    3.934
2 database.two       f   -32.60     150.2      museum    8.500
3 database.two       z   -32.47     150.7   herbarium    3.259
4 database.two       f   -30.67     150.6      museum    2.756
5 database.two       e   -32.73     149.4   herbarium    4.072
6 database.two       x   -33.49     153.3      museum    8.169
rbind(, database.two)
Error: numbers of columns of arguments do not match

So I created a function that can be used to combine the data from two dataframes, keeping only the columns that have the same names (I don't care about the other ones). I'm sure there are other fancier ways of doing this but here's how my function works.

The basics steps
1. Specify the input dataframes
2. Calculate which dataframe has the greatest number of columns
3. Identify which columns in the smaller dataframe match the columns in the larger dataframe
4. Create a vector of the column names that occur in both dataframes
5. Combine the data from both dataframes matching the listed column names using rbind
6. Return the combined data

rbind.match.columns <- function(input1, input2) {
    n.input1 <- ncol(input1)
    n.input2 <- ncol(input2)

    if (n.input2 < n.input1) {
        TF.names <- which(names(input2) %in% names(input1))
        column.names <- names(input2[, TF.names])
    } else {
        TF.names <- which(names(input1) %in% names(input2))
        column.names <- names(input1[, TF.names])

    return(rbind(input1[, column.names], input2[, column.names]))

rbind.match.columns(, database.two)
   species latitude longitude     database
1        p   -33.87     150.5
2        a   -33.71     151.3
3        n   -33.79     151.8
4        w   -34.35     151.3
5        h   -31.78     151.8
6        q   -33.17     151.2
7        d   -33.95     152.7 database.two
8        f   -32.60     150.2 database.two
9        z   -32.47     150.7 database.two
10       f   -30.67     150.6 database.two
11       e   -32.73     149.4 database.two
12       x   -33.49     153.3 database.two

Running the function gives us a new dataframe with the four shared columns and twelve records, reflecting the combined data. Awesome!

Edited to add:

Viri asked a good question in the comments – what if you want to keep all of the columns in both data frames? The easiest solution to this problem is to add dummy columns to each dataframe that represent the columns missing from the other data frame and then use rbind to join them together. Of course, you won't actually have any data for these additional columns, so we simply set the values to NA. I've wrapped this up into a function as well.

rbind.all.columns <- function(x, y) {

    x.diff <- setdiff(colnames(x), colnames(y))
    y.diff <- setdiff(colnames(y), colnames(x))

    x[, c(as.character(y.diff))] <- NA

    y[, c(as.character(x.diff))] <- NA

    return(rbind(x, y))
rbind.all.columns(, database.two)

And here you can see that we now have one dataframe containing all seven columns from our two sources, with NA values present where we are missing data from one of the dataframes. Nice!

   species latitude longitude        method     database data.source	accuracy
1        p   -33.87     150.5   camera trap        <NA>	NA
2        a   -33.71     151.3 live trapping        <NA>	NA
3        n   -33.79     151.8   camera trap        <NA>	NA
4        w   -34.35     151.3 live trapping        <NA>	NA	
5        h   -31.78     151.8   camera trap        <NA>	NA
6        q   -33.17     151.2 live trapping        <NA>	NA
7        d   -33.95     152.7          <NA> database.two   herbarium	3.934
8        f   -32.60     150.2          <NA> database.two      museum	8.500
9        z   -32.47     150.7          <NA> database.two   herbarium	3.259
10       f   -30.67     150.6          <NA> database.two      museum	2.756
11       e   -32.73     149.4          <NA> database.two   herbarium	4.072
12       x   -33.49     153.3          <NA> database.two      museum	8.169

Happy merging everyone!

1 A high technical and scientific term!

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Randomly deleting duplicate rows from a dataframe

I use R a lot in my day to day workflow, particularly for manipulating raw data files into a format that can be used for analysis. This is often a brain-taxing exercise and, sometimes, it would be totally quicker to do it in Excel. But I like to make sure that my manipulations are reproducible. 1. This helps me remember what I actually did and 2. it is hugely helpful when the raw data changes for some reason (new data are added, corrections are made, …) as I can simply rerun the code.

One of the things I battled with a few nights ago was randomly deleting duplicate records from a dataframe without using some horrendous for loop. I’m working on a dataset of Adélie penguin chick weights where we’ve measured approximately 50 chicks selected randomly at three sites once a week for the past 16 years. That’s a lot of data. But some of the chicks come from the same nest, so those data points aren’t really independent.

Two chicks from the same nest - clearly somebody has been eating all the pies!

Two chicks from the same nest – clearly somebody has been eating all the pies!

I wanted to randomly remove one chick from nests where there were two. The data look something like the data below, with nests labelled with a unique identifier and chicks within each nest labelled sequentially (a or b) in the order they were measured.

##    nest chick weight
## 1     1     a 1020.9
## 2     1     b 1042.2
## 3     2     a  844.5
## 4     2     b  829.2
## 5     3     a  871.2
## 6     4     a 1133.1
## 7     4     b 1070.6
## 8     5     a 1159.7
## 9     6     a  692.5
## 10    6     b  786.8

I’ve used the duplicated function before to remove duplicate rows but it always retains the first record. While this is appropriate in some cases, it’s possible that we have a bias towards weighing larger or smaller chicks first, so I wanted to remove rows randomly. But I kept getting stuck with using a for loop, which isn’t very efficient.

So I googled “How to randomly delete rows in R”, because this is my strategy for figuring this out in R, and I found an answer on this forum. The function duplicated.random does exactly what I wanted, so I’m reproducing it here so that I can find it again. Thanks to Magnus Torfason-2 for providing the solution (I wish I was this clever).

This function returns a logical vector, the elements of which are FALSE, unless there are duplicated values in x, in which case all but one elements are TRUE (for each set of duplicates). The only difference between this function and the duplicated() function is that rather than always returning FALSE for the first instance of a duplicated value, the choice of instance is random.

duplicated.random = function(x, incomparables = FALSE, ...) 
     if ( is.vector(x) ) 
         permutation = sample(length(x)) 
         x.perm      = x[permutation] 
         result.perm = duplicated(x.perm, incomparables, ...) 
         result      = result.perm[order(permutation)] 
     else if ( is.matrix(x) ) 
         permutation = sample(nrow(x)) 
         x.perm      = x[permutation,] 
         result.perm = duplicated(x.perm, incomparables, ...) 
         result      = result.perm[order(permutation)] 
         stop(paste("duplicated.random() only supports vectors", 
                "matrices for now.")) 

I applied this function to my nest dataset to give me a logical column indicating which chick was randomly selected for the analysis.$duplicated.chick <- duplicated.random($nest)

##    nest chick weight duplicated.chick
## 1     1     a 1020.9             TRUE
## 2     1     b 1042.2            FALSE
## 3     2     a  844.5            FALSE
## 4     2     b  829.2             TRUE
## 5     3     a  871.2            FALSE
## 6     4     a 1133.1            FALSE
## 7     4     b 1070.6             TRUE
## 8     5     a 1159.7            FALSE
## 9     6     a  692.5             TRUE
## 10    6     b  786.8            FALSE

In this case, I retain all the chicks labelled false as they either were the only chick in the nest or they have been randomly selected by the function. The chicks labelled true are the chicks that weren’t selected for analysis. Slightly counter-intuitive. But then it’s an easy step to filter out the data that I want to keep and run the analyses.

##    nest chick weight duplicated.chick
## 2     1     b 1042.2            FALSE
## 3     2     a  844.5            FALSE
## 5     3     a  871.2            FALSE
## 6     4     a 1133.1            FALSE
## 8     5     a 1159.7            FALSE
## 10    6     b  786.8            FALSE

You can see that there is now only one record per nest and, where there were two chicks, the selected chicks include a random smattering of both a and b. This seems like a useful function for all sorts of situations.

On a slight geekery aside, this is the first blog post that I’ve written using RMarkdown and knitr in Rstudio. I’m liking it. I still need to iron out some kinks in the process but expect to be bored silly with more R-oriented blog posts in the future.