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kevin-smets

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Powerlevel10k

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:

jonocarroll

library(ggplot2)  ## devtools::install_github("hadley/ggplot2)
library(grid)     ## rasterGrob
library(EBImage)  ## readImage (alternatively: magick::image_read)
library(ggthemes) ## theme_minimal

## ##########
## INDEPENDENT CODE TO BE SOURCED:
## ##########
# user-level interface to the element grob

johnmackintosh

  library(ggplot2)
  library(lubridate)
  library(tidyr)
  library(viridis)
  #library(extrafont) # optional
  library(lubridate)
  library(gridExtra)
  library(ggExtra)
  library(dplyr)
  

0x4D31

Transforming Code into Beautiful, Idiomatic Python

Notes from Raymond Hettinger's talk at pycon US 2013 video, slides.

The code examples and direct quotes are all from Raymond's talk. I've reproduced them here for my own edification and the hopes that others will find them as handy as I have!

Looping over a range of numbers

for i in [0, 1, 2, 3, 4, 5]:

hrbrmstr

#!/bin/bash

TODAY=`date +%Y-%m-%d`
TODAY_MD=`date +%B\ %d,\ %Y`
YEAR=`date +%Y`

PACKAGENAME=$1

##
## CHANGE ME!!!

DavisVaughan

library(furrr)

# localhost -> AWS EC2 linux -> Docker running on that -> R

# dm_create() and dm_ip() are from an unreleased R pkg I whipped up, dockermachinery
# https://github.com/DavisVaughan/dockermachinery

# Creates 1 t2.micro EC2 instance
dm_create("amazonec2", "dockertest")

nvictus

# Notes
# -----
# * Cooler's stored bin IDs are 0-based. However, for consistency with R, this API should take 
#   1-based indexing as input for table row and matrix range queries.
# * See Ilya's implementation: https://github.com/dozmorovlab/HiCcompare/issues/9
library(hdf5r)
library(dplyr)
library(tibble)
library(purrr)
library(data.table)

andrewheiss

library(tidyverse)

# See https://stackoverflow.com/a/17313561/120898
pts <- function(x) {
  as.numeric(grid::convertUnit(grid::unit(x, "pt"), "mm"))
}

df <- tibble(x = 1:10, y = 1:10)