| qft.interp <- function(nil, tb, mitogen, tbnil.cutoff = 0.35){ | |
| # Set a tolerance to avoid floating point comparison troubles. | |
| tol <- .Machine$double.eps ^ 0.5 | |
| # Set up the results vector | |
| result <- rep(NA, times = length(nil)) | |
| # Iterate through each test. | |
| for(i in seq_along(result)){ |
| tests <- structure(list(nil = c(0.06, 0, 0.07, 0.2, 0.07, 0.15, 0.18, | |
| 0.05, 0.07, 0.06, 0.11, 9.41, 0.17, 0.09, 0.15, 0.06, 0.07, 0.1, | |
| 0.12, 0.17, 0.11, 0.13, 0.12, 0.12, 0.12, 0.13, 0.24, 0.14, 0.09, | |
| 0.17, 0.11, 0.07, 0.15, 10, 3.53, 5.48, 0.06, 0.06, 0.08, 0.1, | |
| 0.07, 0.1, 0.1, 0.08, 0.05, 0.08, 0.1, 0.07, 0.76, 0.04, 0.06, | |
| 0.07, 0.06, 0.08, 0.07, 0.09, 0.13, 0.05, 0.25, 0.12, 0.08, 0.78, | |
| 0.12, 0.11, 0.13, 0.1, 0.1, 0.05, 0.11, 0.13, 0.32, 0.09, 0.2, | |
| 0.16, 0.24, 0.29, 0.06, 0.17, 0.22, 0.1, 0.16, 0.08, 9, 0.1, | |
| 0.08, 0.15, 0.23, 0.07, 9, 0.13, 0.07, 0.06, 0.04, 0.04, 0.03, | |
| 10, 0.04, 0.05, 0.05, 0.04, 0.05, 0.04, 9, 0.06, 0.06, 0.05, |
| library(ggplot2) | |
| # Here's some data I had lying around | |
| tb <- structure(list(region = c("Africa", "Asia", "Latin America", | |
| "Other", "US-born"), ncases = c(36L, 34L, 56L, 2L, 44L)), .Names = c("region", | |
| "ncases"), row.names = c(NA, -5L), class = "data.frame") | |
| # This file is a brief exploration of the non-**ply functions in the | |
| # plyr package. It was presented (poorly) for a lightning talk, so not all of | |
| # the functions are as thoroughly explored and documented as I'd normally like - | |
| # but this should at least get you started. | |
| # The Incantation | |
| options(stringsAsFactors = FALSE) | |
| # Load plyr | |
| library(plyr) |
| REM This file converts all of the Markdown files to HTML. | |
| REM Converting in the current directory | |
| REM %%~ni returns just the filename of %%i, not its extension | |
| for %%i in (*.markdown) do pandoc -f markdown -t html5 %%~ni.markdown > html/%%~ni.html | |
| REM Converting a subdirectory - just slap it on front | |
| for %%i in (report_pages/*.markdown) do pandoc -f markdown -t html5 report_pages/%%~ni.markdown > html/report_pages/%%~ni.html |
| # Make a dataset for Channel A | |
| dat.a <- data.frame(purchase = rbinom(n = 100, size = 1, prob = .1), | |
| channel = "a") | |
| # One for Channel B | |
| dat.b <- data.frame(purchase= rbinom(n = 100, size = 1, prob = .2), | |
| channel = "b") | |
| # Add the rates | |
| dat.a$rate <- sum(dat.a$purchase) / nrow(dat.a) |
Best viewed in its own window (because I haven't set up the CSS properly yet).
This is a report to enable my clinicians to easily review clinic statistics at will. Click on rows in the table to plot that statistic; click again to remove it from the plot.
Very much a work in progress, so please feel free to heap on the feedback!
Built with the rampantly awesome D3.js.
| import os | |
| import subprocess | |
| import glob | |
| # Check for the existence of a "printed" folder | |
| # If it doesn't exist, make it | |
| if not os.path.exists(os.path.join(os.getcwd(), "printed")): | |
| os.mkdir(os.path.join(os.getcwd(), "printed")) | |
| # How do case rates vary as sensitivity changes, given | |
| # fixed population | |
| # fixed % positive | |
| # fixed number of cases? | |
| # Strings ain't factors | |
| options(stringsAsFactors = FALSE) | |
| # Strings ain't factors | |
| options(stringsAsFactors = FALSE) | |
| library(ggplot2) | |
| library(plyr) |