radovankavicky

radovankavicky

library(tidygraph)
library(particles)
library(jsonlite)
library(magick)

# Prepare text polygons
text <- read_json('text.json')
par_text <- text$layers[[3]]$paths
on_text <- text$layers[[2]]$paths
cran_text <- text$layers[[1]]$paths

radovankavicky

http://example.com

http://example.com

radovankavicky

Download Miniconda

• Python 2.7

  # 32 bits
  $ wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86.sh
  # 64 bits
  $ wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh

radovankavicky

EMOJI CHEAT SHEET

Emoji emoticons listed on this page are supported on Campfire, GitHub, Basecamp, Redbooth, Trac, Flowdock, Sprint.ly, Kandan, Textbox.io, Kippt, Redmine, JabbR, Trello, Hall, plug.dj, Qiita, Zendesk, Ruby China, Grove, Idobata, NodeBB Forums, Slack, Streamup, OrganisedMinds, Hackpad, Cryptbin, Kato, Reportedly, Cheerful Ghost, IRCCloud, Dashcube, MyVideoGameList, Subrosa, Sococo, Quip, And Bang, Bonusly, Discourse, Ello, and Twemoji Awesome. However some of the emoji codes are not super easy to remember, so here is a little cheat sheet. ✈ Got flash enabled? Click the emoji code and it will be copied to your clipboard.

People

😄

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

## Licence: GPL 2 or 3 <https://www.gnu.org/licenses/licenses.html#GPL>
## Author: Ioannis Kosmidis <[email protected]>
## 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 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

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

# 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!