Rasmus Bååth rasmusab

## get_up_to_speed_with_bayes_test_script.R
# Prior to the tutorial make sure that the script below runs without error on your R installation.
# You first need to install the follwoing packages:
# install.packages(c("rstanarm", "prophet", "CausalImpact"))

library(rstanarm)
library(prophet)
library(CausalImpact)

# This will test that rstanarm works
# Don't be alarmed if you get a warning about "divergent transitions "

## analyze_gnubg.R
# A barely tested R script that takes a backgammon match in any format
# gnubg can read, analyzes the match and returns a data frame with the analysis
# It requires that gnubg is readily available on the command line.

library(tidyverse)
library(jsonlite)
library(glue)

analyze_bg_match <- function(match_fname, match_format = "auto") {
  gnubg_analysis_fname = tempfile()

## ab_test.csv

          
            date
            group
            n_purchases

            
              2017-05-01
              0
              124

            
              2017-05-02
              0
              95

            
              2017-05-03
              0
              147

            
              2017-05-04
              0
              116

            
              2017-05-05
              0
              219

            
              2017-05-06
              0
              248

            
              2017-05-07
              0
              132

            
              2017-05-08
              0
              110

            
              2017-05-09
              0
              114

## monthly_deaths.csv

          
            date
            births
            deaths

            
              1841-01-01
              254
              37

            
              1841-02-01
              239
              18

            
              1841-03-01
              277
              12

            
              1841-04-01
              255
              4

            
              1841-05-01
              255
              2

            
              1841-06-01
              200
              10

            
              1841-07-01
              190
              16

            
              1841-08-01
              222
              3

            
              1841-09-01
              213
              4

## yearly_deaths_by_clinic.csv

          
            year
            births
            deaths
            clinic

            
              1841
              3036
              237
              clinic 1

            
              1842
              3287
              518
              clinic 1

            
              1843
              3060
              274
              clinic 1

            
              1844
              3157
              260
              clinic 1

            
              1845
              3492
              241
              clinic 1

            
              1846
              4010
              459
              clinic 1

            
              1841
              2442
              86
              clinic 2

            
              1842
              2659
              202
              clinic 2

            
              1843
              2739
              164
              clinic 2

## the-probability-my-son-will-be-stung-by-a-bumblebee.R
library(tidyverse)
library(purrr)
library(rstan)

### Defining the data ###
#########################
bumblebees <- c(1, 0,  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
                0, 0, 1, 0, 0, 0, 0, 0, 0)
toddler_steps <- c(26, 16, 37, 101, 12, 122, 90, 55, 56, 39, 55, 15, 45, 8)

## fuzzycolor.R
# fuzzycolor() takes a vector of color names and matches it against the
# xkcdcolors list of color names using edit distance. fuzzycolor() always
# returns a vector of hex color strings, perhaps the ones you wanted...
fuzzycolor <- function(color_names) {
  library(xkcdcolors)
  names_distance <- adist(color_names, xcolors(), ignore.case = TRUE, partial = TRUE)
  xkcd_colors <- xcolors()[ apply(names_distance, 1, which.min) ]
  hex_colors <- name2color(xkcd_colors)
  names(hex_colors) <- xkcd_colors
  hex_colors

## pystan_test_script.R

# Prior to the tutorial make sure that the script below runs without error on your python installation.
# What you need is a working installation of Stan: http://mc-stan.org/ .
# For installation instructions, see here:
# http://mc-stan.org/interfaces/pystan.html

# After installation you should be able to run this script which should output
# some summary statistics and some pretty plots, :)

# Fitting a simple binomial model using Stan

## rstan_test_script.R
# Prior to the tutorial make sure that the script below runs without error on your R installation.
# What you need is a working installation of Stan: http://mc-stan.org/ .
# For installation instructions, see here:
# https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started

# After installation you should be able to run this script which should output
# some summary statistics and some pretty plots, :)

# Generating some fake data
set.seed(123)

## tensorflow_in_r_using_rpython.R
### An example of building a TensorFlow model from R using rPython ###
# For this script you need to
# 1. Have python 2.7 installed.
# 2. Install the rPython package in R.
# 3. Install Google's TensorFlow library as per these instructions:
#     http://www.tensorflow.org/get_started/os_setup.md#binary_installation

### Here is how to setup and run a trivial TensorFlow model ###

# Load TensorFlow (I couldn't get this to work without setting sys.argv... )