| { | |
| "metadata": { | |
| "name": "demo" | |
| }, | |
| "nbformat": 3, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { |
| from collections import OrderedDict | |
| import numpy as np | |
| py2ri_orig = rpy2.robjects.conversion.py2ri | |
| def conversion_pydataframe(obj): | |
| if isinstance(obj, pandas.core.frame.DataFrame): | |
| od = OrderedDict() | |
| for name, values in obj.iteritems(): | |
| if values.dtype.kind == 'O': | |
| od[name] = rpy2.robjects.vectors.StrVector(values) | |
| else: |
| # Ensure left continuity of a decreasing step function | |
| # First find discontinuities, based on minimum jump size, then make sure there | |
| # is a data point to ensure left continuity there. | |
| lcts <- function(dat, min.jump){ | |
| for(i in 2:nrow(dat)){ | |
| if(dat[i+1,2] - dat[i,2] > min.jump){ # identify discontinuity | |
| dat <- rbind(dat, c(dat[i+1,1], dat[i,2])) # generate left-cts data point |
| <!doctype HTML> | |
| <html> | |
| <head> | |
| <script src='http://polychart.com/s/third_party/polychart2.standalone.js' type='text/javascript'></script> | |
| <style> | |
| .rChart { | |
| display: block; | |
| margin-left: auto; |
| <!doctype HTML> | |
| <meta charset = 'utf-8'> | |
| <html> | |
| <head> | |
| <script src='http://polychart.com/s/third_party/polychart2.standalone.js' type='text/javascript'></script> | |
| <style> | |
| .rChart { | |
| display: block; |
| package | mostDownloads | bestRank | week | tot | rank | |
|---|---|---|---|---|---|---|
| shiny | 1018 | 70 | 2013-06-10 | 1018 | 70 | |
| RColorBrewer | 4376 | 4 | 2013-04-29 | 3408 | 4 | |
| scales | 3976 | 6 | 2012-12-10 | 1571 | 6 | |
| scales | 3976 | 6 | 2012-12-03 | 1720 | 6 | |
| reshape2 | 4330 | 6 | 2012-12-17 | 1312 | 6 | |
| reshape2 | 4330 | 6 | 2013-01-07 | 2199 | 6 | |
| reshape2 | 4330 | 6 | 2013-05-13 | 3660 | 6 | |
| proto | 3902 | 3 | 2012-12-24 | 963 | 3 | |
| ggplot2 | 5291 | 1 | 2013-03-25 | 3160 | 1 |
| xlsxToR <- function(file) { | |
| require(XML) | |
| require(plyr) | |
| suppressWarnings(file.remove(tempdir())) | |
| file.copy(file, tempdir()) | |
| new_file <- list.files(tempdir(), full.name = TRUE, pattern = basename(file)) | |
| new_file_rename <- gsub("xlsx$", "zip", new_file) | |
| file.rename(new_file, new_file_rename) |
| #'% How to format plots for publication using `ggplot2` (with some help from Inkscape) | |
| #'% Rosemary Hartman | |
| #'% 13-11-20 19:51:47 | |
| #' | |
| #' ***The following is the code from a presentation made by Rosemary Hartman to | |
| #' the [Davis R Users' Group](http://www.noamross.net/davis-r-users-group.html). | |
| #' I've run the code through the `spin` function in `knitr` to produce this post. | |
| #' Download the script to walk through [here](https://gist.github.com/noamross/7576436)*** | |
| #' | |
| #' First, make your plot. I am going to use the data already in R |
| { | |
| "scope": "source.r", | |
| "completions": | |
| [ | |
| "abline", | |
| "abs", | |
| "anova", | |
| "anova.glm", | |
| "anova.lm", |
This is a short demo of how to use brew and knitr in combination with each other to get the best of the templating facilities in brew and the literate programming functions in knitr. The main idea is to write a function brew_knit
# Preprocess template using brew and then run knit on the output
brew_knit <- function(template, params, ...){
brew::brew(template, envir = list2env(params))
input = gsub(".Rnwe", '.Rnw', template)
knitr::knit(input)
}