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View facet_labels_bottom.R
diamonds %>%
filter(str_detect(cut, "Fair|Ideal")) %>%
ggplot(aes(price, carat)) +
geom_point(color = "skyblue", alpha = 0.5) +
facet_wrap(~cut, strip.position = "bottom") +
scale_x_continuous(labels = comma) +
karlafej / plotnine_drawing.ipynb
Created Feb 1, 2019
creativity with python and math
View plotnine_drawing.ipynb
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djnavarro / bridge.R
Created Aug 24, 2018
two dimensional brownian bridge animation
View bridge.R
# parameters for the simulation
ntimes <- 100
nseries <- 20
# construct tibble storing simulation
tbl <- tibble(
whophil / jupyter.service
Last active Jan 16, 2020 — forked from doowon/jupyter_systemd
A systemd script for running a Jupyter notebook server.
View jupyter.service
# After Ubuntu 16.04, Systemd becomes the default.
# It is simpler than
Description=Jupyter Notebook
ExecStart=/home/phil/Enthought/Canopy_64bit/User/bin/jupyter-notebook --config=/home/phil/.jupyter/
conormm /
Last active Jan 13, 2020
R to Python: Data wrangling with dplyr and pandas

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 / s3.r
Created May 7, 2013
Implementation of request signing for Amazon's S3 in R.
View s3.r
s3_request <- function(verb, bucket, path = "/", query = NULL,
content = NULL, date = NULL) {
verb = verb,
bucket = bucket,
path = path,
arvearve / gist:4158578
Created Nov 28, 2012
Mathematics: What do grad students in math do all day?
View gist:4158578

Mathematics: What do grad students in math do all day?

by Yasha Berchenko-Kogan

A lot of math grad school is reading books and papers and trying to understand what's going on. The difficulty is that reading math is not like reading a mystery thriller, and it's not even like reading a history book or a New York Times article.

The main issue is that, by the time you get to the frontiers of math, the words to describe the concepts don't really exist yet. Communicating these ideas is a bit like trying to explain a vacuum cleaner to someone who has never seen one, except you're only allowed to use words that are four letters long or shorter.

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