briatte

library(magrittr)
library(magick)
image_read("logo:") %>%
  image_convert('png', colorspace = 'gray') %>%
  image_edge(radius = 1) %>%
  image_negate() %>%
  image_transparent('white', 10000) %>%
  image_background("white") %>%
  image_browse()

briatte

# You won't need all of these packages. I just copied it from another,related analysis:
# https://github.com/svmiller/etjc

library(car)
library(arm)
library(countrycode)
library(data.table)
library(tidyverse)
library(sqldf)
# library(pdftools)

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#!/bin/bash

cd $(echo $0 | sed 's#/[^/]*$##')

git pull > /tmp/file.tmp 2>&1

./run.sh >> /tmp/load_portes_etroites.tmp 2>&1

if git status | grep "data/" > /dev/null; then
  cat /tmp/load_portes_etroites.tmp

briatte

library(dplyr)
library(httr)
library(readr)
library(rvest)

d <- read_xml("~/Downloads/digg_reader_subscriptions.xml") %>% 
  xml_nodes("outline")
d <- data_frame(
  title = xml_attr(d, "text"),
  url = xml_attr(d, "xmlUrl")

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library(MASS)
library(rio)
library(tidyverse)

multhet <- function(y, ## = formula(1~1),
	v, ##  = formula(~1),
	data) {

	## check length of inputs
	if (class(y) != "formula") { 

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################################################################################
#                                                    
# ikashnitsky.github.io 2017-07-18
# Accessing Eurostat data using the `eurostat` R package
# Young people neither in employment nor in education and training in Europe
# Ilya Kashnitsky, ilya.kashnitsky@gmail.com
#                                                  
################################################################################

# Erase all objects in memory

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library(ggplot2)
library(readr)

d <- read_tsv("data/affiliations.tsv") %>%
  filter(j != "2015_STRC20IPSA") %>% 
  mutate(
    y = str_sub(j, 1, 4),
    e = str_extract(affiliation, "\\((.*?)\\)") %>% 
      str_replace_all("\\(|\\)", "")
  ) %>% 

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################################################################################
#                                                    
# ikashnitsky.github.io 2017-06-30
# Produce an RGB coded map of pop structures at NUTS-3 level
# Ilya Kashnitsky, ilya.kashnitsky@gmail.com
#                                                  
################################################################################

# Erase all objects in memory
rm(list = ls(all = TRUE))

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library(dplyr)
# library(igraph)
library(readr)
library(stringr)
library(tidyr)
library(networkD3)

f <- "parcours_selected.tsv"
e <- read_tsv(f, col_types = "ccccccccccccccc") %>% 
  filter(siteId %in% 3, type == "action") %>% 

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library(tidyverse)
### Make sure to download files from www.parlgov.org!

elec <- read.csv("view_election.csv")
cab <- read.csv("view_cabinet.csv")
party <- read.csv("view_party.csv")

### Date
cab$start_date <- as.Date(cab$start_date)