Tim Thomas timathomas

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:

Screen Quick Reference

Basic

Description	Command
Start a new session with session name	`screen -S <session_name>`
List running sessions / screens	`screen -ls`
Attach to a running session	`screen -x`
Attach to a running session with name	`screen -r`

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

	library(tidycensus)
	library(leaflet)
	library(sf)
	library(viridis)
	options(tigris_use_cache = TRUE)

	il1 <- get_acs(geography = "county",
	variables = c(hhincome = "B19013_001"),
	state = "IL",
	geometry = TRUE) %>%

	# ipak function: install and load multiple R packages.
	# check to see if packages are installed. Install them if they are not, then load them into the R session.

	ipak <- function(pkg){
	new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
	if (length(new.pkg))
	install.packages(new.pkg, dependencies = TRUE)
	sapply(pkg, require, character.only = TRUE)
	}