Matt Whitaker mathzero

Wolfram Alpha API from R

The attached code file provides an easy basic interface to the Wolfram Alpha API. Inspired by the wolframalpha module available for Python.

source("wa_lib.R")

The Chartmaker directory has a nice matrix showing which tools can be used to produce many kinds of chart, with examples of how.

Two new tools allow you to interactively create bespoke chart layouts without programming

	### slightly adapted from here to save png or pdf and specify resolution etc: https://stackoverflow.com/questions/43051525/how-to-draw-pheatmap-plot-to-screen-and-also-save-to-file

	# function detects '.png' or '.pdf' in the declared filename and assigns that file type

	### pheatmap save function
	save_pheatmap <- function(x, filename, width=12, height=12){
	stopifnot(!missing(x))
	stopifnot(!missing(filename))
	if(grepl(".png",filename)){
	png(filename, width=width, height=height, units = "in", res=300)

	### prep data
	Xdata <- data %>% select(-outcome) %>% as.matrix()
	Ydata <- as.matrix(data$outcome)

	### definte stability function
	LassoSub=function(k=1, Xdata, Ydata){
	set.seed(k)
	s=sample(nrow(data), size=0.8*nrow(data))
	Xsub=Xdata[s, ]
	Ysub=Ydata[s]

	### Install packages
	list.of.packages <- c("citr", "colourpicker", "rcrossref", "jpeg","RCurl") ### add all the packages that are used in your script here
	new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])] ### check if they are installed
	if(length(new.packages)) install.packages(new.packages) ### install any that need installing

	### Load packages
	lapply(list.of.packages, require, character.only = TRUE)

	### define function for conversion to scientific notation of p-values or other numeric values
	scientific_notation <- function (values, digits = 1)
	{
	if (!is.numeric(values)) {
	stop("values must be numbers")
	}
	if (grepl("^\\d{2}$", digits)) {
	stop("digits must a one or two digit whole number")
	}
	x <- sprintf(paste0("%.", digits, "e"), values)

	### Functions for calculating Elo probabilities and scores ###

	# Uses the weighted Elo formula created by 538, with a K of 250, a shape of 0.4 and an offset of 5
	# https://fivethirtyeight.com/features/how-were-forecasting-the-2016-us-open/



	### Define a function that calculates probabilities on the basis of Elo score inputs

	#p1elo is p1's current Elo score

	# Create table 1 ------------------------------------
	library(tableone)

	myVars <- c("var1","var2") ### create list of variables to include intable

	# Make sure any categorical variables are saved as factors
	MyData$var2 <- as.factor(MyData$var2)

	# package says to pass a list of categorical variables separately but this doesn't seem to work
	# passing categorical variables in the main variable list works, so long as they are saved as factors

	# load packages
	library(ggmap)
	library(ggplot2)
	library(ggthemes)

	### set up a google API for google static maps at https://developers.google.com/maps/documentation ###

	# register key
	register_google(key = "XXXXXXXXXXXXXXX")