jonocarroll

## transform the input to the output using rubiks-cube transformations and dplyr
require(dplyr)

## provided input
input <- data.frame(matrix(c("h","a","t","t","i","v","i","g","k","s","g","n","n","g","n","i"),
                           4,
                           4,
                           byrow=TRUE,
                           dimnames=list(NULL,paste0("col",1:4))), 
                    stringsAsFactors=FALSE)

jonocarroll

## libraries used to create these maps
library(maps)      ## world.cities data
library(dplyr)     ## group_by(), summarise(), and imported %>% 
library(ggplot2)   ## plotting

## the raw data
## `citation("maps")`
data("world.cities")

## limit the data to the latitude, lonitude, and population

jonocarroll

setwd("WORKINGDIRECTORY")

library(png)     ## split .png into component matrices
library(RSAGA)   ## grid.to.xyz, load before dplyr
library(dplyr)   ## group_by(), summarise(), %>% 
library(ggplot2) ## plotting
library(ggExtra) ## ggMarginal

## load the .png file
img <- png::readPNG("file10a566a2b4dc3.png")

jonocarroll

# myAPIkey <- "PlAcEyOuRkEyHeRe"

library(ggplot2)
library(ggmap)
library(ggthemes)
library(png)
library(grid)
library(RCurl)

findXnearY <- function(X          = "coffee",                               # default: coffee

jonocarroll

## 52vis challenges, week 2
## originally seen at
## https://rud.is/b/2016/04/06/52vis-week-2-2016-week-14-honing-in-on-the-homeless/
## this version blogged @ http://jcarroll.com.au/2016/04/10/52vis-week-2-challenge/
## github: github.com/jonocarroll/2016-14

## This script produces a choropleth for the USA homeless population
## as a per mille proportion of each state's population, with the
## colorscale set to white at the national median, blue at the
## lowest value, and capped at 3x the national median in red.

jonocarroll

## 52vis challenges, week 2 -- adapted for Australian statistics
## otherwise follows procedures found at
## github.com/jonocarroll/2016-14
## http://jcarroll.com.au/2016/04/10/52vis-week-2-challenge/
## this version blogged @ http://jcarroll.com.au/2016/04/12/australian-homeless/

## This script produces a chloropleth for the Australian homeless population
## as a per mille proportion of each state's population, with the
## colorscale set to the same as the USA homeless graph (white at the national
## median, blue at the lowest value, and capped at 3x the national median in red).

jonocarroll

## Inspired by http://www.milanor.net/blog/how-to-sort-a-list-of-dataframes-in-r/
## as seen on R-bloggers http://www.r-bloggers.com/how-to-sort-a-list-of-dataframes/
##
## Blogged @ http://jcarroll.com.au/2016/04/14/simpler-isnt-always-faster/
##

## input data.frame
df_list_in <- list (
  df_1 = data.frame(x = 1:5,   y = 5:1),
  df_2 = data.frame(x = 6:10,  y = 10:6),

jonocarroll

## Create a world timezone map of the #auunconf slack users' timezones
## data extracted via hrbrmstr/slackr 
## Timezone shapefile: http://efele.net/maps/tz/world/
##
## Blogged @ http://jcarroll.com.au/2016/04/14/slack-timezones/

## load relevant packages
pacman::p_load(rgdal, maptools, ggplot2)
pacman::p_load(ggthemes, albersusa, ggalt)
pacman::p_load(extrafont)

jonocarroll

## Optimising an expression subject to inequality constriaints
## as seen on 
## http://fivethirtyeight.com/features/you-have-1-billion-to-win-a-space-race-go/
## https://xianblog.wordpress.com/2016/04/21/an-integer-programming-riddle/
## http://www.r-bloggers.com/an-integer-programming-riddle/
## Blogged @ http://jcarroll.com.au/2016/04/27/solving-inequality-the-math-kind/

## The expression to optimise:
## 200a + 100b + 50c + 25d
## 

jonocarroll

# Bob Rudis
# @hrbrmstr
# Lazy cyber tweet: what prod/dev has this port combo?
# 110,111,143,21,22,23,25,3389,389,443,465,587,5900,8080,8081,8443,8888,9100,990,993,995

# install.packages("rvest")

library(rvest)
html     <- read_html("https://en.wikipedia.org/wiki/List_of_TCP_and_UDP_port_numbers")
ports1   <- html_table(html_nodes(html, "table")[5])[[1]]