Pakillo

Pakillo

library(dismo)

# Get elevation data from the internet for specified coordinates
elev <- getData("SRTM", download=T, lon=-76.6, lat=18.1)

# Specify desired extent
ext <- c(-76.68, -76.65, 18.08, 18.11)
ext2 <- c(-76.675, -76.655, 18.088, 18.10) # for plotting

Pakillo

#' Retrieve number of citations of a paper in Google Scholar
#' 
#' This function retrieves the number of citations of a given paper in Google Scholar.
#' 
#' @param user character vector. The user ID in Google Scholar Citations. Obtain from author's profile website in google scholar (http://scholar.google.com/citations?user=...)
#' @param paper character vector. The paper ID in Google Scholar Citations. Copy from publication list in author's profile website.
#' @author F. Rodriguez-Sanchez
#' @examples
#' ncites_scholar(user="B7vSqZsAAAAJ", paper="d1gkVwhDpl0C")  
#' 

Pakillo

library(RefManageR)
BibOptions(check.entries=FALSE)

Read bibtex file with references:

refs <- ReadBib("C:\\Users\\FRS\\Desktop\\prueba.bib", check = "warn")

Pakillo

• Git and GitHub (Hadley Wickham): http://r-pkgs.had.co.nz/git.html

• R development using GitHub (Gabor Csardi): https://github.com/MangoTheCat/github-workshop

• Working with RStudio, Git, GitHub (STAT 545): http://stat545-ubc.github.io/git00_index.html

• Version control with git (R. Fitzjohn): http://nicercode.github.io/2014-02-13-UNSW/lessons/70-version-control/

• Version control with Git (Software Carpentry): http://software-carpentry.org/v5/novice/git/index.html

Pakillo

As far as I know Rstudio does not count words or characters at the moment, which would be useful particularly when writing Rmarkdown.

This is a quick shortcut using word_count and character_count functions from qdap package. See below for two wrapper functions that simplify their use.

library("qdap")

Just select and copy the text to the clipboard and then run in the console:

Pakillo

---
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

Pakillo

# Here are a few methods for getting text from PDF files. Do read through 
# the instructions carefully! NOte that this code is written for Windows 7,
# slight adjustments may be needed for other OSs

# Tell R what folder contains your 1000s of PDFs
dest <- "G:/somehere/with/many/PDFs"

# make a vector of PDF file names
myfiles <- list.files(path = dest, pattern = "pdf",  full.names = TRUE)

Pakillo

# Joseph R. Mihaljevic
# July 2013
# (Partial) Bayesian analysis of variance, accounting for heteroscedasticity
# Generate some artificial data:
# Normally distributed groups, but heteroscedastic
a <- rnorm(25, mean=8, sd=10)
b <- rnorm(50, mean=5, sd=2)
c <- rnorm(25, mean=3, sd=.1)
d <- rnorm(25, mean=11, sd=3)
e <- rnorm(50, mean=13, sd=2)

Pakillo

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.

Programming R curriculum

Data structures