Pakillo

---
title: "A few methods for making tables in rmarkdown"
output: html_document
---

Updates: 

Packages that have appeared since my original look into this, and seem great:

https://github.com/yihui/printr

Pakillo

#' Calculate total cover per species in vegetation transects
#'
#' Calculate total cover per species in each transect, layer within transect, etc.
#' Optionally, the function can also calculate length of bare ground (as ground not covered
#' by any plant species).
#'
#' @export
#' @author F Rodriguez-Sanchez
#' @importFrom plyr ddply
#' @importFrom gtools odd even

Pakillo

### 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: [email protected]
###	Twitter: @michaeljkoontz
###

Pakillo

• captioner
• fighelper
• kfigr
• Gmisc
• P. Humburg's functions
• R.M. Flight's functions

See http://stackoverflow.com/questions/13848137/figure-captions-references-using-knitr-and-markdown-to-html.

Pakillo

• knitr::kable
• xtable
• stargazer
• pander
• ztable
• moonBook
• htmlTable
• Gmisc
• hwriter

Pakillo

Some ggplot tutorials available online:

• H. Wickham: Official ggplot2 documentation
• H. Wickham: ggplot2 book
• W. Chang: R graphics cookbook and Cookbook for R
• Z. Ross: Beautiful plotting in R: A ggplot2 cheatsheet
• D. Koffman: Introduction to ggplot2
• R. Saccilotto: Tutorial: ggplot2
• R. Hartman: How to format plots for publication using ggplot2
• G. Williams: Visualising data with ggplot2

Pakillo

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))

Pakillo

## Function to use GDAL to project coordinate reference system

# See http://www.gdal.org/gdalwarp.html for additional details 
# `resampling` can be 'near' (nearest neighbour), 'bilinear', 'cubic', or
# 'lanczos' (Lanczos windowed sinc resampling). 
# `extent` should be a bbox object or a vector of c(xmin, ymin, xmax, ymax)
# `of` is the output format (use GDAL short name as given by the name field of
# gdalDrivers(), or at http://www.gdal.org/formats_list.html)
# `extension` is the output extension corresponding to the primary file
# `ot` is the output type (see http://www.gdal.org/gdal_translate.html)

Pakillo

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)) {

Pakillo

## Spatial reference systems I often use

## Source this gist from R:
# source("https://gist.githubusercontent.com/Pakillo/23212b72a02b9d524073/raw/b73b28354f61d381b5614a2254cf128b93e11c8a/projections.R")
# or using devtools:
# devtools::source_gist("https://gist.github.com/Pakillo/23212b72a02b9d524073")

# EPSG codes:
geo <- 4326   # Geographic, datum WGS84
laea.etrs89 <- 3035   # Lambert Azimuthal Equal Area - ETRS89