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library(tidyverse)

#Standard normal
df = data.frame(x = seq(-3.5, 3.5, .01)) %>%
  mutate(y = dnorm(x))
p = ggplot(df, aes(x, y))
p = p + theme_bw() +
  theme(
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),

dggoldst

library(tidyverse)

df=data.frame(x=seq(-3.5,3.5,.01))
df = df %>% mutate(y=dnorm(x,sd=1))
p = ggplot(df,aes(x,y))
p = p + theme_bw() + 
  theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        panel.grid.minor.x = element_blank()) +
  labs(x="Standard Deviations")

dggoldst

library(tidyverse)
#Wind speeds from the percentile bands in 
#https://weatherspark.com/y/23912/Average-Weather-in-New-York-City-New-York-United-States-Year-Round
#https://weatherspark.com/y/14091/Average-Weather-in-Chicago-Illinois-United-States-Year-Round
#weather.gov wind chill chart at https://www.weather.gov/safety/cold-wind-chill-chart

get_wind_chill = function(temp,wind){
  35.74 + .6251*temp - 35.75 * wind^.16 + .4275 * temp * wind^.16
}

dggoldst

library(tidyverse)

CELSIUS = FALSE

heat_index16term = function(T, RH) {
  retval = 16.923 + 1.85212 * 1e-1 * T + 5.37941 * RH - 1.00254 * 1e-1 * T *
    RH +
    9.41695 * 1e-3 * T ^ 2 + 7.28898 * 1e-3 * RH ^ 2 + 3.45372 * 1e-4 *
    T ^ 2 * RH - 8.14971 * 1e-4 * T * RH ^ 2 +
    1.02102 * 1e-5 * T ^ 2 * RH ^ 2 - 3.8646 * 1e-5 * T ^ 3 + 2.91583 *

dggoldst

library(tidyverse)
setwd("C:/Dropbox/Projects/20180822_heat_index16term")

heat_index16term = function(T, RH) {
  retval = 16.923 + 1.85212 * 1e-1 * T + 5.37941 * RH - 1.00254 * 1e-1 * T *
    RH +
    9.41695 * 1e-3 * T ^ 2 + 7.28898 * 1e-3 * RH ^ 2 + 3.45372 * 1e-4 *
    T ^ 2 * RH - 8.14971 * 1e-4 * T * RH ^ 2 +
    1.02102 * 1e-5 * T ^ 2 * RH ^ 2 - 3.8646 * 1e-5 * T ^ 3 + 2.91583 *
    1e-5 * RH ^ 3 + 1.42721 * 1e-6 * T ^ 3 * RH +

dggoldst

#code by Ashton Anderson
library(tidyverse)
theme_set(theme_minimal())

mt <- read.csv(url('https://gist.githubusercontent.com/ashtonanderson/cfbf51e08747f60472ee2132b0d35efb/raw/80acd2ad7c0fba4e85c053e61e9e5457137e00ee/moveno_piecetype_counts'))

mt <- mt %>%
  group_by(move_number) %>% 
  mutate(tot = sum(count),frac = count/tot)

dggoldst

library(tidyverse)
theme_set(theme_bw())
STEPS = 251
ITER = 5000 

#Function to get the change to X and Y corresponding
#to each die roll
get_offset = function(roll)
{
  nx = ny = NA

dggoldst

library(stringr)
library(ggplot2)
library(dplyr)
library(scales)
library(markovchain)

MAXSTREAKLEN=16
#H/T https://math.stackexchange.com/questions/383704/probability-of-streaks for this soln

get_prob = function(streaklen,sequencelen)

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---
title: "Rain Per Day, Month, and Rainy Day in New York City"
author: "Dan Goldstein"
date: "August 26, 2017"
output:
  html_document: default
  pdf_document: default
  word_document: default
---
```{r setup, include=FALSE}

dggoldst

library(tidyverse)
library(weatherData)
library(viridis)
library(lubridate)
library(maps)
library(ggmap)

retList = vector('list', 10)
for (i in 2:6) {
  retList[[i]] =