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/* rs-theme-name: Dark UI Base16 Tomorrow Night */
/* rs-theme-is-dark: TRUE */

/* Dark UI from Randy3k's Wombat (https://github.com/randy3k/dotfiles/blob/master/.R/rstudio/themes/Wombat.rstheme) */
/* "Tomorrow night" color scheme adapted from chriskempson's Base16 (https://github.com/chriskempson/base16). */

.body {
  background: #ffffff;
 }

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library(cartography)
library(sp)
# Load data
data(nuts2006)

# Get a SpatialLinesDataFrame of countries borders
nuts0.contig.spdf <- getBorders(nuts0.spdf)

# Get the GDP per capita
nuts0.df$gdpcap <- nuts0.df$gdppps2008/nuts0.df$pop2008*1000000

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R to python useful data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. 
The beauty of dplyr is that, by design, the options available are limited. 
Specifically, a set of key verbs form the core of the package. 
Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. 
Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. 
The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package). 

dplyr is organised around six key verbs

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library(tidyverse)
library(patchwork)

plot_pair <- function(data, x, y) {
  ggplot(data, aes_string(x = x, y = y, color = "Species", shape = "Species")) + 
    geom_point() +
    scale_color_brewer(palette = "Dark2") +
    theme(legend.position = "none", plot.title = element_text(size = 7)) +
    labs(x = NULL, y = NULL, title = paste0(y, " ~ ", x))

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# load package
library(ggplot2)

# write function
label_facets <- function(string){
  len <- length(string)
  string = paste('(', letters[1:len], ') ', string, sep = '')
  return(string)
}

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---
title: "World maps"
output:
  html_document:
    df_print: paged
---

```{r echo = FALSE, message = FALSE}
library(tidyverse)
library(sf)

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library(rvest)
library(lubridate)
library(tidyverse)

Truman <- read_html("http://www.presidency.ucsb.edu/data/popularity.php?pres=33")

Truman %>%
  html_table(fill=T) -> Truman

Truman[[11]] -> Truman

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library(segmented)
library(tidyverse)

dat <- structure(list(Pollster = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Poll by gfk", 
"Poll by YouGov"), class = "factor"), Fieldwork.end.date = structure(c(17539, 
17520, 17511, 17505, 17478, 17463, 17459, 17458, 17450, 17433, 
17409, 17400, 17379, 17366, 17358, 17339, 17330, 17324, 17317, 

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/*
Create a userChrome.css file by finding your Firefox profile,
making a directory called chrome and in it, a file called userChrome.css inside that. 
Pasting this CSS and restarting Firefox will kill the left drag strip
*/

@namespace url("http://www.mozilla.org/keymaster/gatekeeper/there.is.only.xul");

.titlebar-placeholder[type="pre-tabs"] {
  width: 2px !important;

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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()