Introduction to Data Analysis for Aquatic Sciences

Fundamental aspects of data acquisition, data wrangling, and visualization

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Making Choices

Choices with Functions


The UHURU experiment in Kenya has conducted a survey of Acacia drepanolobium among each of their ungulate exclosure treatments. Data for the survey is available here in a tab delimited ("\t") format. Each of the individuals surveyed were measured for branch circumference (CIRC) and canopy width (AXIS1) and was identified for the associated ant-symbiont species present (ANT).

The following function takes a subset of the data for a given ANT symbiont and evaluates the linear regression (lm()) for a given relationship, returning the symbiont species used for the subset and the r2 of the model.

report_rsquared <- function(data, species, formula){
  subset <- dplyr::filter(data, ANT == species)
  test <- lm(formula, data = subset)
  rsquared <- round(summary(test)$r.squared, 3)
  output <- data.frame(species = species, r2 = rsquared)
  return(output)
}
  1. Execute the function using the UHURU data and specifying species = "CM" and formula = "AXIS1~CIRC".
  2. Modify the function so that it also determines if() the rsquared is significant based on a given threshold. The modified function should return() the species, rsquared and a significance value of "S" for a relationship with an rsquared > threshold or "NS" for an rsquared < threshold.
  3. Execute your modified function for species of "CM", "CS", and "TP" given a threshold = 0.667.
[click here for output]