| polygonizer <- function(x, outshape=NULL, gdalformat = 'ESRI Shapefile', | |
| pypath=NULL, readpoly=TRUE, quietish=TRUE) { | |
| # x: an R Raster layer, or the file path to a raster file recognised by GDAL | |
| # outshape: the path to the output shapefile (if NULL, a temporary file will be created) | |
| # gdalformat: the desired OGR vector format | |
| # pypath: the path to gdal_polygonize.py (if NULL, an attempt will be made to determine the location | |
| # readpoly: should the polygon shapefile be read back into R, and returned by this function? (logical) | |
| # quietish: should (some) messages be suppressed? (logical) | |
| if (isTRUE(readpoly)) require(rgdal) | |
| if (is.null(pypath)) { |
| swatch <- function(x) { | |
| # x: a vector of colours (hex, numeric, or string) | |
| par(mai=c(0.2, max(strwidth(x, "inch") + 0.4, na.rm = TRUE), 0.2, 0.4)) | |
| barplot(rep(1, length(x)), col=rev(x), space = 0.1, axes=FALSE, | |
| names.arg=rev(x), cex.names=0.8, horiz=T, las=1) | |
| } | |
| # Example: | |
| # swatch(colours()[1:10]) | |
| # swatch(iwanthue(5)) |
| diverge0 <- function(p, ramp) { | |
| # p: a trellis object resulting from rasterVis::levelplot | |
| # ramp: the name of an RColorBrewer palette (as character), a character | |
| # vector of colour names to interpolate, or a colorRampPalette. | |
| require(RColorBrewer) | |
| require(rasterVis) | |
| if(length(ramp)==1 && is.character(ramp) && ramp %in% | |
| row.names(brewer.pal.info)) { | |
| ramp <- suppressWarnings(colorRampPalette(brewer.pal(11, ramp))) | |
| } else if(length(ramp) > 1 && is.character(ramp) && all(ramp %in% colors())) { |
| ### Title: Back to basics: High quality plots using base R graphics | |
| ### An interactive tutorial for the Davis R Users Group meeting on April 24, 2015 | |
| ### | |
| ### Date created: 20150418 | |
| ### Last updated: 20150423 | |
| ### | |
| ### Author: Michael Koontz | |
| ### Email: mikoontz@gmail.com | |
| ### Twitter: @michaeljkoontz | |
| ### |
| --- | |
| title: "Auto-numbering and cross-referencing of figures and tables in rmarkdown" | |
| output: html_document | |
| --- | |
| TODO: check this out: https://github.com/adletaw/captioner | |
| Here's how to use: | |
| 1. Copy and paste the first two code chunks in this document to the top of your document |
| `simulate.gamm` <- function(object, nsim = 1, seed = NULL, newdata, | |
| freq = FALSE, unconditional = FALSE, ...) { | |
| if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) | |
| runif(1) | |
| if (is.null(seed)) | |
| RNGstate <- get(".Random.seed", envir = .GlobalEnv) | |
| else { | |
| R.seed <- get(".Random.seed", envir = .GlobalEnv) | |
| set.seed(seed) | |
| RNGstate <- structure(seed, kind = as.list(RNGkind())) |
| library("shiny") | |
| library("shinyjs") | |
| ui <- shinyUI(pageWithSidebar( | |
| titlePanel("", "3D Perspective Plot for Interaction Effects"), | |
| sidebarPanel( | |
| tabsetPanel( | |
| tabPanel("Data", |
| --- | |
| title: "Untitled" | |
| author: "Jeff W. Hollister" | |
| date: "2/2/2016" | |
| output: beamer_presentation | |
| --- | |
| ```{r setup, include=FALSE} | |
| knitr::opts_chunk$set(echo = FALSE) |
Notes:
I've tried to break up in to separate pieces, but it's not always possible: e.g. knowledge of data structures and subsetting are tidy intertwined.
Level of Bloom's taxonomy listed in square brackets, e.g. http://bit.ly/15gqPEx. Few categories currently assess components higher in the taxonomy.
A lot of important government documents are created and saved in Microsoft Word (*.docx). But Microsoft Word is a proprietary format, and it's not really useful for presenting documents on the web. So, I wanted to find a way to convert a .docx file into markdown.
On a mac you can use homebrew by running the command brew install pandoc.