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Hints and pseudo code for Exercise 8.23.4 (Letchford et al., 2015)

  1. Write a program that performs the test described above using all the papers published in 2010. The program should do the following: 1) read the data; 2) extract all the papers published in 2010; 3) rank the articles by citations, and by title length; 4) compute the Kendall's tau expressing the correlation between the two rankings. For this dataset, the Authors got a tau of about -0.07 with a significant p-value.

Hints:

  • the function rank can be used to rank values (e.g., title lengths, citations)
  • to perform a correlation test, use cor.test(x, y, method = "kendall", use = "pairwise")

Pseudocode:

# Read the data
l2015 <- here the code
# extract the papers in 2010
p2010 <- subset the data
# extract citations and title lengths
cor(citations, titlelength, method = "kendall", use = "pairwise")
  1. Write a function that repeats the analysis for a particular journal-year combination. Try to run the function for the top scientific publications Nature and Science, and for the top medical journals The Lancet and New Eng J Med, for all years in the data (2007-2013). Do you always find a negative, significant correlation (i.e., negative tau with low p-value)?

Pseudocode:

# subset the data using a function
compute_tau_journal_year <- function(my_data, my_journal, my_year) {
  # subset data
  # store correlation
  # run correlation test
  # store pvalue
  return(data.frame(Journal = my_journal,
                    Year = my_year,
                    tau = my_tau,
                    pvalue = my_pvalue))
}
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