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# Create labelled list of all active dataframes

my.list <- mget(ls(pattern="*_UMAC"))

# Use write.csv in a for loop, creating files for each and naming
# according to label (i.e. df name)

for (i in seq_along(my.list)){
  write.csv(my.list[i], paste(names(my.list)[i], ".csv"), col.names = TRUE)
}

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# Split column into multiple based on spaces, extra characters, etc.

df <- data.frame(x= c("crust (5)","things (3)","stuff (8)"))

library(tidyr)

df2 <- extract(df, x, into = c("pet", "sampSize"), "([^(]+)\\s+\\(([0-9]+).")

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##arrange df vars by position
# from https://stackoverflow.com/questions/5620885/how-does-one-reorder-columns-in-a-data-frame
##'vars' must be a named vector, e.g. c("var.name"=1)
# e.g.
# table <- data.frame(Time=c(1,2), In=c(2,3), Out=c(3,4), Files=c(4,5))
# arrange.vars(table, c("Out"=2))
# arrange.vars(table, c("Out"=2, "Files"=1, "Time"=4))

arrange.vars <- function(data, vars){
  ##stop if not a data.frame (but should work for matrices as well)

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#From https://stackoverflow.com/questions/1401904/painless-way-to-install-a-new-version-of-r

# Run in the old version of R (or via RStudio)
setwd("C:/Temp/") 
packages <- installed.packages()[,"Package"] 
save(packages, file="Rpackages") 

# INSTALL NEW R VERSION
if(!require(installr)) { install.packages("installr"); require(installr)} #load / install+load installr
# See here for more on installr: https://www.r-statistics.com/2013/03/updating-r-from-r-on-windows-using-the-installr-package/  

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# Read in all csv files in directory
files <- list.files(pattern = "*.csv")

# Create dfs for each csv then create list
for (i in 1:length(files)) 
  assign(files[i], 
         read.csv(files[i]))

lst <- mget(ls(pattern = "*.csv"))

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# if tweetnow is set to TRUE, sends the tweets directly from R to Twitter
# this function requires installing the rtweet package and setting up the relevant
# Twitter user credentials
# otherwise, the tweetstorm outputs to the console; the user can copy/paste to Twitter manually
#
# From https://sites.tufts.edu/emotiononthebrain/2017/08/12/time-for-a-tweetstorm/

tweetstorm <- function(s, tweetnow = FALSE) {
  
  #if you'll be tweeting directly from R, update the user name and token variables

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# E.g. qDat.corr <- as.data.frame(cor.test.p(x))

cor.test.p <- function(x){
  FUN <- function(x, y) cor.test(x, y)[["p.value"]]
  z <- outer(
    colnames(x), 
    colnames(x), 
    Vectorize(function(i,j) FUN(x[,i], x[,j]))
  )
  dimnames(z) <- list(colnames(x), colnames(x))

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# The modOverlap function calculates three metrics: 
# Shoener’s D for niche overlap
# Hellinger distance between probability distributions
# Warren's I similarity statistic
#
# This function is also found in the fuzzysim package
#
# From https://modtools.wordpress.com/2015/10/30/modoverlap/

modOverlap <- function (pred1, pred2, na.rm = TRUE) 

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# published on http://www.r-statistics.com/2010/07/visualization-of-regression-coefficients-in-r
# originally written by "<a href="http://statmath.wu.ac.at/~zeileis/">Achim Zeileis</a>"
# GPL-2
# 
# E.g. coefplot(glm, parm = -1)

coefplot <- function(object, df = NULL, level = 0.95, parm = NULL,
                     labels = TRUE, xlab = "Coefficient confidence intervals", ylab = "",
                     xlim = NULL, ylim = NULL,
                     las = 1, lwd = 1, lty = c(1, 2), pch = 19, col = 1,

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# Calculate total line length and density for a given set of polygons
#
# Intersects SpatialLine and SpatialPolygon objects, calculates individual line length,
# appends to dataframe extracted from intersect object and summarises by polygon ID.
# If density is not required, do not specify area parameter `a`.
#
# The function assumes a spatial projection system where distance is given in metres.
#
# E.g.
# sp1 = object of class SpatialLine/SpatialLineDataFrame