Useful R functions for ecologists

Every biologist using R has some self-written functions he particularly likes and which are useful in many different analysis. Here i am sharing seven nifty functions, which have proven to be useful for me in the past. Many can be used in different contexts and their functionalities are certainly missing in the R base package.

# The function below uses the MASS package to fit provided values to given often used
# distributions. AIC and BIC information criterion values are calculated and returned in an ordered table
testdist <- function(values){
require(MASS)
distributions <- c("normal","lognormal","exponential","logistic","cauchy","gamma","geometric","weibull")
res <- data.frame(cbind(distributions)); res[,c("AIC","BIC")] <- NA
for(i in seq(1:nrow(res))){
fit <- fitdistr(values,densfun=as.character(res$distributions[i]),)
res$AIC[i] <- AIC(fit);res$BIC[i] <- BIC(fit)
}
res <- res[order(res$BIC,decreasing=F,na.last=T),]
return(res)
}
#Example output:
data(trees)
testdist(trees$Height)
#distributions AIC BIC
#weibull 204.6404 207.5083
#normal 205.7745 208.6425
#gamma 206.4929 209.3609
#lognormal 206.9348 209.8028
#logistic 207.0618 209.9298
#cauchy 218.2213 221.0893
#exponential 332.5055 333.9395
#geometric 332.5107 333.9447
# Those values shouldn't be taken for granted and you should always visually explore potential distributions
# for instance with histograms or qqplots !
# Standarderror of the mean (SEM)
stderr <- function(x) sqrt(var(x,na.rm=TRUE)/length(na.omit(x)))
#Example output:
stderr(trees$Height)
# > 1.144411
# Do segments on top of Scatter-Plot.
# Requires a transmitted data.frame with x-y values and a vector with line-length
doSegments <- function(x,y,ll,eps=0.05,...){
# Further Arguments are transmitted to segments
segments(x,y-ll,x,y+ll) # Build lines
segments(x-eps,y-ll,x+eps,y-ll) # Do the segments on top
segments(x-eps,y+ll,x+eps,y+ll) # and below
}
#Example output:
plot(trees$Girth,trees$Volume)
fit <- lm(trees$Volume~trees$Girth)
# Displays the residuals of a linear regression as errorbar
doSegments(trees$Girth,trees$Volume,ll=resid(fit) )

Rplot

# Returns a list of the elements of x that are not in y
# and the elements of y that are not in x (not the same thing...)
setdiff2 <- function(x,y) {
Xdiff = setdiff(x,y)
Ydiff = setdiff(y,x)
list(X_not_in_Y=Xdiff, Y_not_in_X=Ydiff)
}
# Example output
a <- c("A","B","C","D")
b <- c("C","D","E","F")
setdiff2(a,b)
#> $X_not_in_Y
#> [1] "A" "B"
#> $Y_not_in_X
#> [1] "E" "F"
# Remove all NA-Values from a list
remNa <- function(x) {
return(subset(x,complete.cases(x)))
}
# Example Output
a <- c("A","B",NA,"D")
remNa(a)
#> [1] "A" "B" "D"
## String manipulation
# trim white space/tabs
trim_whitespace <-function(s) gsub("^[[:space:]]+|[[:space:]]+$","",s)

# Example output
a <- "   Teststring   "
trim_whitespace(a)
#> [1] "Teststring"

# Extract numbers from string or character
numbers_from_string <- function(x) as.numeric(gsub("\\D", "", x))

# Example output
a <- "We found 13 rabbits playing on the field"
numbers_from_string(a)
#> [1] 13

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About Martin Jung

PhD researcher at the University of Sussex. Interested in nature conservation, ecology and biodiversity as well as statistics, GIS and 'big data'
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