radovankavicky

import os
from subprocess import check_call

def post_save(model, os_path, contents_manager):
    """post-save hook for converting notebooks to .py and .html files."""
    if model['type'] != 'notebook':
        return # only do this for notebooks
    d, fname = os.path.split(os_path)
    check_call(['jupyter', 'nbconvert', '--to', 'script', fname], cwd=d)
    check_call(['jupyter', 'nbconvert', '--to', 'html', fname], cwd=d)

radovankavicky

library(idbr) # devtools::install_github('walkerke/idbr')
library(ggplot2)
library(animation)
library(dplyr)
library(ggthemes)

idb_api_key("Your Census API key goes here")

male <- idb1('JA', 2010:2050, sex = 'male') %>%
  mutate(POP = POP * -1,

radovankavicky

class MyStreamListener(tweepy.StreamListener):
    def __init__(self, api=None):
        super(MyStreamListener, self).__init__()
        self.num_tweets = 0
        self.file = open("tweets.txt", "w")

    def on_status(self, status):
        tweet = status._json
        self.file.write( json.dumps(tweet) + '\n' )
        self.num_tweets += 1

radovankavicky

library(httr)
library(magick)
library(hrbrthemes)
library(ggplot2)

theme_tweet_rc <- function(grid = "XY", style = c("stream", "card"), retina=FALSE) {
  
  style <- match.arg(tolower(style), c("stream", "card"))
  
  switch(

radovankavicky

# List unique values in a DataFrame column
# h/t @makmanalp for the updated syntax!
df['Column Name'].unique()

# Convert Series datatype to numeric (will error if column has non-numeric values)
# h/t @makmanalp
pd.to_numeric(df['Column Name'])

# Convert Series datatype to numeric, changing non-numeric values to NaN
# h/t @makmanalp for the updated syntax!

radovankavicky

library(ggplot2)
library(rbokeh)
library(htmlwidgets)

structure(list(wk = structure(c(16069, 16237, 16244, 16251, 16279,
16286, 16300, 16307, 16314, 16321, 16328, 16335, 16342, 16349,
16356, 16363, 16377, 16384, 16391, 16398, 16412, 16419, 16426,
16440, 16447, 16454, 16468, 16475, 16496, 16503, 16510, 16517,
16524, 16538, 16552, 16559, 16566, 16573), class = "Date"), n = c(1L,
1L, 1L, 1L, 3L, 1L, 3L, 2L, 4L, 2L, 3L, 2L, 5L, 5L, 1L, 1L, 3L,

radovankavicky

import rpy2.robjects as robjects
import pandas.rpy.common as com
import pandas as pd

## load .RData and converts to pd.DataFrame
robj = robjects.r.load('test.RData')
# iterate over datasets the file
for sets in robj:
    myRData = com.load_data(sets)
    # convert to DataFrame

radovankavicky

## Licence: GPL 2 or 3 <https://www.gnu.org/licenses/licenses.html#GPL>
## Author: Ioannis Kosmidis <i.kosmidis@ucl.ac.uk>
## Date: 21 December 2017

#' A function to create artifical snow using R base graphics and the animation package
#'
#' @param n_flakes how many flakes to throw?
#' @param fall_speed how fast should the snow fall? There will be a \code{1/fall_speed} seconds delay between moves
#' @param max_flake_size what is the maximum flake size (same as cex in \code{\link{plot}})
#' @param eps how much is the flake allowed to move to the left and to the right? (emulating wind)

radovankavicky

import turtle

screen = turtle.Screen()
screen.setup(800,600)

circle = turtle.Turtle()
circle.shape('circle')
circle.color('red')
circle.speed('fastest')
circle.up()

radovankavicky

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