steven2358

FFmpeg cheat sheet

A list of useful commands for the FFmpeg command line tool.

Download FFmpeg: https://www.ffmpeg.org/download.html

Full documentation: https://www.ffmpeg.org/ffmpeg.html

Basic conversion

ikashnitsky

################################################################################
#
# ikashnitsky.github.io 2017-12-10
# Data acquisition in R 3/4
# https://ikashnitsky.github.io/2017/data-acquisition-three
# Ilya Kashnitsky, ilya.kashnitsky@gmail.com
#
################################################################################

chrishanretty

require(tidyverse)
require(lubridate)
#Data from www.parlgov.org
dat <- read.csv("view_election.csv")
dat <- dat %>% 
	filter(election_type != "ep") %>% 
	mutate(election_date = as.Date(election_date)) %>% 
	filter(election_date > as.Date("1945-01-01")) %>% 
	distinct(country_name, election_date) %>% 
	group_by(country_name) %>% 

johngruen

/*
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;

jeroen

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()

ikashnitsky

################################################################################
#                                                    
# 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
#                                                  
################################################################################

library(tidyverse)

briatte

Keybase proof

I hereby claim:

• I am briatte on github.
• I am briatte (https://keybase.io/briatte) on keybase.
• I have a public key whose fingerprint is CF1D 937F 13FD 11F4 9060 57C7 EC0B 0E46 6A4C 4EDE

To claim this, I am signing this object:

chrishanretty

library(foreign) ## for data import
library(dplyr) ## for chaining ops together
library(ggplot2) ## for plotting
library(reshape2) ## for reshaping
library(hrbrthemes) ## for pretty pictures
library(survey) ## for... uh, survey data

party.colours <- c("#0087DC","#D50000","#FDBB30","#FFFF00","#008142","#99CC33","#70147A","#DDDDDD")

bes <- read.spss("~/Dropbox/2017-forecasting/data/individual/BES2015_W10_Panel_v0.3.sav")

conormm

R to python 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:

hadley

msg <- function(..., prob = 0.25) {
  if (runif(1) > prob) {
    return(invisible())
  }
  
  messages <- c(...)
  message(sample(messages, 1))
}

encourage <- function() {