jilmun

'Add Microsoft Word XX.0 Object Library in Tools >> References
Public Sub FormatSelectedText()
    Dim objItem As Object
    Dim objInsp As Outlook.Inspector

    Dim objWord As Word.Application
    Dim objDoc As Word.Document
    Dim objSel As Word.Selection
    On Error Resume Next
    

jilmun

import os
from selenium import webdriver
from selenium.webdriver.common.keys import Keys

import pandas as pd
import numpy as np
import time
from itertools import groupby

def scroll(driver, timeout):

jilmun

prop_model <- function (data = c(), prior_prop = c(1, 1), n_draws = 10000, show_plot = TRUE) {
  library(tidyverse)
  library(purrr)
  library(ggjoy)
  data <- as.logical(data)
  proportion_success <- c(0, seq(0, 1, length.out = 100), 1)
  data_indices <- round(seq(0, length(data), length.out = min(length(data) + 1, 20)))
  post_curves <- map_dfr(data_indices, function(i) {
    value <- ifelse(i == 0, "Prior", ifelse(data[i], "Success", "Failure"))
    label <- paste0("n=", i)

jilmun

library(tidyverse)
library(ggrepel)
library(scales)
results <- read_csv("results.csv")

# add GDP rank, winning candidate, chart labels
results <- results %>% 
  arrange(desc(GDP)) %>%
  mutate(GDPRank = 1:51) %>%
  mutate(Win = ifelse(ClintonNum>TrumpNum, "Clinton", "Trump")) %>%

jilmun

# http://www.bnosac.be/index.php/blog/57-new-rstudio-add-in-to-schedule-r-scripts
install.packages('knitr')
install.packages('data.table')
install.packages('shiny')
install.packages('miniUI')
install.packages("taskscheduleR", repos="http://www.datatailor.be/rcube", type="source")

jilmun

# Martingale Betting System

# initialize parameters ---------------------------------------------------
init_start <- 12800
init_goal <- init_start + 1000
init_bet <- 100    # initial bet value
prob_win <- 0.47   # probability of win
n <- 500000        # simulation count

# run simulation ----------------------------------------------------------

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# create dummy data -------------------------------------------------------
set.seed(1)
d <- data.frame(col1 = sample(letters[1:3], 10, replace=T), 
                col2 = sample(letters[24:26], 10, replace=T),
                col3 = runif(10) * 10, 
                stringsAsFactors = FALSE)

d$col1 <- as.factor(d$col1)
d$col4 = d$col3 + runif(10)

jilmun

require(dplyr)
require(rvest)

options(stringsAsFactors = FALSE)

url_base <- "http://securities.stanford.edu/list-mode.html?page="
tbl.clactions <- data.frame(
  "Filing.Name" = character(0),
  "Filing.Date" = character(0),
  "District.Court" = character(0),

jilmun

require(ggplot2) 

# prepare south park data -------------------------------------------------
d2 <- data.frame(member=c(rep("Eric",5),  
                          rep("Kyle",5),  
                          rep("Stan",5),  
                          rep("Kenny",5)),  
                 shade=c("EricPants","EricShirt","Skin","EricHat","EricPomPom",  
                         "KylePants","KyleShirt","Skin","KyleHatBottom","KyleHatTop",  
                         "StanPants","StanShirt","Skin","StanBottom","StanHatTop", 

jilmun

require(ggplot2) 

# prepare simpsons data ---------------------------------------------------
# code from http://suehpro.blogspot.com/2016/03/the-simpsons-as-chart.html
d1 <- data.frame(member=c(rep("Homer",3),  
                          rep("Marge",3),  
                          rep("Bart",3),  
                          rep("Lisa",2),  
                          rep("Maggie",2)),  
                 shade=c("HomerPants","HomerShirt","Skin",