walkerke

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) %>%

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:

jctosta

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

stevenworthington

# 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)
}

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname