Alex Bresler abresler

## bridge.R
library(tidyverse)
library(e1071)
library(gganimate)

# parameters for the simulation
ntimes <- 100
nseries <- 20

# construct tibble storing simulation
tbl <- tibble(

## tidygraph.r
# SCROLL TO THE GITHUB COMMENT BELOW FOR THE FULL OUTPUT WITH IMAGES
# COPY AND PASTE THIS FOR EASY ACCESS TO REPRODUCIBLE CODE

# ------------------------------------------------------------------------------
library(tidygraph)
library(ggraph)
library(dplyr)
library(igraph)
library(tibble)

## ageingpriestly.r
library(ggplot2)
library(dplyr)

ft <- read.csv("ftpop.csv", stringsAsFactors=FALSE)

arrange(ft, start_year) %>%
  mutate(country=factor(country, levels=c(" ", rev(country), "  "))) -> ft

ft_labs <- data_frame(
  x=c(1900, 1950, 2000, 2050, 1900, 1950, 2000, 2050),

## fed_locations.R
library(purrr)
library(dplyr)
library(rvest)
library(stringr)
library(geoparser)

w_msg <- function(f) {
  force(f)
  function(...) {
    args <- list(...)

## pew.R
read_sheet <- function(file, sheet) {
  readxl::read_excel(file, sheet, skip = 6) %>% clean_data()
}

fix_names <- function(df) {
  names(df)[1] <- "location"
  names(df)[-1] <- paste(rep(c(2000, 2014), each = length(names(df)[-1]) / 2),
                         names(df)[-1], sep = "_")
  df
}

## nytimesdrugmap.r
library(jsonlite)
library(tigris)
library(ggplot2)
library(purrr)
library(tidyr)
library(dplyr)
library(sp)
library(rgeos)
library(rgdal)
library(maptools)

## question.csv

          
            belief
            y2015
            y2014

            
              Improves the security posture of my organization
              0.75
              0.71

            
              Improves the security posture of the nations critical infrastructure
              0.63
              0.64

            
              Reduces the cost of detecting and preventing cyber attacks
              0.22
              0.21

            
              Improves situational awareness
              0.60
              0.54

            
              Fosters collaboration among peers and industry groups
              0.48
              0.51

            
              Enhances the timeliness of threat data
              0.11
              0.16

            
              Makes threat data more actionable
              0.21
              0.24

## probability_of_pregnancy.R
# A Bayesian model that calculates a probability that a couple is fertile
# and pregnant. Please use this for fun only, not for any serious purpose
# like *actually* trying to figure out whether you are pregnant.

# Enter your own period onsets here:
period_onset <- as.Date(c("2014-07-02", "2014-08-02", "2014-08-29", "2014-09-25",
                          "2014-10-24", "2014-11-20", "2014-12-22", "2015-01-19"))
# If you have no dates you can just set days_between_periods to c() instead like:
# days_between_periods <- c()
days_between_periods <- as.numeric(diff(period_onset))

## migrants.R
library(SmarterPoland)
library(riverplot)
library(RColorBrewer)
library(graphics)
library(reshape2)
library(plyr)
library(stringr)
library(countrycode)

# DOWNLOAD THE DATA

## diagrammer-fixed-nodes-visnetwork.R
# Installation
#install.packages("devtools")
devtools::install_github("rich-iannone/DiagrammeR")

library(DiagrammeR)

# Create and render an empty graph
empty_graph <- create_graph()
render_graph(empty_graph, output = "visNetwork")
	library(tidyverse)
	library(e1071)
	library(gganimate)

	# parameters for the simulation
	ntimes <- 100
	nseries <- 20

	# construct tibble storing simulation
	tbl <- tibble(
	# SCROLL TO THE GITHUB COMMENT BELOW FOR THE FULL OUTPUT WITH IMAGES
	# COPY AND PASTE THIS FOR EASY ACCESS TO REPRODUCIBLE CODE

	# ------------------------------------------------------------------------------
	library(tidygraph)
	library(ggraph)
	library(dplyr)
	library(igraph)
	library(tibble)
	library(ggplot2)
	library(dplyr)

	ft <- read.csv("ftpop.csv", stringsAsFactors=FALSE)

	arrange(ft, start_year) %>%
	mutate(country=factor(country, levels=c(" ", rev(country), " "))) -> ft

	ft_labs <- data_frame(
	x=c(1900, 1950, 2000, 2050, 1900, 1950, 2000, 2050),
	library(purrr)
	library(dplyr)
	library(rvest)
	library(stringr)
	library(geoparser)

	w_msg <- function(f) {
	force(f)
	function(...) {
	args <- list(...)
	read_sheet <- function(file, sheet) {
	readxl::read_excel(file, sheet, skip = 6) %>% clean_data()
	}

	fix_names <- function(df) {
	names(df)[1] <- "location"
	names(df)[-1] <- paste(rep(c(2000, 2014), each = length(names(df)[-1]) / 2),
	names(df)[-1], sep = "_")
	df
	}
	library(jsonlite)
	library(tigris)
	library(ggplot2)
	library(purrr)
	library(tidyr)
	library(dplyr)
	library(sp)
	library(rgeos)
	library(rgdal)
	library(maptools)
belief	y2015	y2014
Improves the security posture of my organization	0.75	0.71
Improves the security posture of the nations critical infrastructure	0.63	0.64
Reduces the cost of detecting and preventing cyber attacks	0.22	0.21
Improves situational awareness	0.60	0.54
Fosters collaboration among peers and industry groups	0.48	0.51
Enhances the timeliness of threat data	0.11	0.16
Makes threat data more actionable	0.21	0.24
	# A Bayesian model that calculates a probability that a couple is fertile
	# and pregnant. Please use this for fun only, not for any serious purpose
	# like actually trying to figure out whether you are pregnant.

	# Enter your own period onsets here:
	period_onset <- as.Date(c("2014-07-02", "2014-08-02", "2014-08-29", "2014-09-25",
	"2014-10-24", "2014-11-20", "2014-12-22", "2015-01-19"))
	# If you have no dates you can just set days_between_periods to c() instead like:
	# days_between_periods <- c()
	days_between_periods <- as.numeric(diff(period_onset))
	library(SmarterPoland)
	library(riverplot)
	library(RColorBrewer)
	library(graphics)
	library(reshape2)
	library(plyr)
	library(stringr)
	library(countrycode)

	# DOWNLOAD THE DATA
	# Installation
	#install.packages("devtools")
	devtools::install_github("rich-iannone/DiagrammeR")

	library(DiagrammeR)

	# Create and render an empty graph
	empty_graph <- create_graph()
	render_graph(empty_graph, output = "visNetwork")