Using an example dataset of breeding landbirds in North America, apply your lecture notes on barracudar and functions to analyze abundance and species richness at a particular site.
From there, follow the instructions from last week’s lectures on barracudar functions to create a new project, storing the dataset in a “Original Data” folder.
Within each year’s folder, you will only be using a file from each year labeled “countdata” in its title. Using for loops, iterate through each year’s folders to gather the file names of these “countdata” .csv files.
Starting with pseudo-code, generate functions for 1) Cleaning the data for any empty/missing cases, 2) Extract the year from each file name, 3) Calculate Abundance for each year (Total number of individuals found), 4) Calculate Species Richness for each year(Number of unique species found)
Create an initial empty data frame to hold the above summary statistics-you should have 4 columns, one for the file name, one for abundance, one for species richness, and one for year.
Using a for loop, run your created functions as a batch process for each folder, changing the working directory as necessary to read in the correct files, calculating summary statistics with your created functions, and then writing them out into your summary statistics data frame.
Use the remainder of the lab period to continue work on Homework 10, if you did not complete it last week:
Use subsetting instead of a loop to rewrite the function as a single line of code.
Write a function that takes as input two integers representing the number of rows and columns in a matrix. The output is a matrix of these dimensions in which each element is the product of the row number x the column number.
Use the code from the upcoming April 2nd lecture (Randomization
Tests) to design and conduct a randomization test for some of your own
data. You will need to modify the functions that read in the data,
calculate the metric, and randomize the data. Once those are set up, the
program should run correctly calling your new functions. Also, to make
your analysis fully repeatable, make sure you set the random number seed
at the beginning (use either set.seed() in base R, or
char2seed in the TeachingDemos
package
For comparison, calculate in R the standard statistical analysis you would use with these data. How does the p-value compare for the standard test versus the p value you estimated from your randomization test? If the p values seem very different, run the program again with a different starting seed (and/or increase the number of replications in your randomization test). If there are persistent differences in the p value of the standard test versus your randomization, what do you think is responsible for this difference?
Batch
Processing Lecture Notes
Randomization
Tests Lecture Notes