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Visualization Links

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

Tools

No coding required

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

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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")

Initialize client

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# 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")

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# 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

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### 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

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### 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)

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### 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)

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### 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)

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### 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)

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### 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]