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83 lines (72 loc) · 2.83 KB
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# See <https://gist.github.com/233296> for how to convert from page_cycler CSV
# in format:
#
# When,Site,Min,Max,Mean,StdDev,T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,...
# Before,www.example.com,101,109,102.89,0.87,...
# Before,www.wikipedia.org,100,120,108.44,4.74,...
#
# To a long data frame in this format:
# [1] "Site" "When" "variable" "value"
library('reshape')
ReadPageCyclerResults <- function(filename) {
# Load a CSV file of results from page_cycler.
#
# To generate this format with a manual run of page cycler:
# 1. Run the page_cycler
# 2. On the results page: Select all. Copy.
# 3. Paste it into Google Sheets in Column B.
# 4. Put a repeating label (for example, a revision number) in Column A.
# Multiple results can be accumulated this way in one spreadsheet.
# 5. File, Download As, CSV.
# 6. Use grep label-from-Column-A to filter out the summary data (such
# as timer lag, etc.)
#
# Args:
# filename: The name of the CSV file to load.
#
# Returns:
# A long frame with these columns:
# Site: A factor with the identifier of the page_cycler test.
# When: A factor with the label from Column A.
# variable: A factor with the iteration of the test, T1, T2, ...
# value: The result of the iteration.
csv <- read.csv(filename, header=FALSE)
num.cols <- dim(csv)[2]
fixed.col.names <- c('When', 'Site', 'Min', 'Max', 'Mean', 'Std.d')
num.results <- num.cols - length(fixed.col.names)
result.col.names <- paste('T', 1:num.results, sep='')
names(csv) <- c(fixed.col.names, result.col.names)
return(melt(csv, id=c('Site', 'When'), measure=result.col.names))
}
PageCyclerPerms <- function(before, after, f=median, n=10^5-1) {
# Computes a statistic and P-value for two sets of page_cycler data.
#
# Args:
# before: A vector of numbers "before" the point of interest.
# after: A vector of numbers "after" the point of interest.
# f: The statistic to compute. Default is median.
# n: The number of permutations to sample. Default is 10^5-1.
#
# Returns:
# A frame with one row and four columns:
# Before: The statistic for the "before" data.
# After: The statistic for the "after" data.
# Observed: The observed difference.
# P.value: The one-sided P-value for the observed difference.
sample <- numeric(n)
times <- c(before, after)
# Find the observed statistic
stat_before <- f(before)
stat_after <- f(after)
observed <- stat_after - stat_before
for (i in 1:n) {
index <- sample(length(times), size=length(times) / 2, replace=FALSE)
sample[i] <- f(times[index]) - f(times[-index])
}
p <- (sum(sample >= observed) + 1) / (n + 1)
# Construct the results
result <- data.frame()
result <- rbind(result, c(stat_before, stat_after, observed, p))
names(result) <- c('Before', 'After', 'Observed', 'P.value')
return(result)
}