This gist contains the spatial layers of colleges (point patterns and polygon features) that was used for Simon Kassel's project on wealth, inequality and higher education.
This is a collection of information on PostgreSQL and PostGIS for what I tend to use most often.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# devtools::install_github("hrbrmstr/vegalite") | |
library(vegalite) | |
library(htmltools) | |
dat <- jsonlite::fromJSON('[ | |
{"a": "A","b": 28}, {"a": "B","b": 55}, {"a": "C","b": 43}, | |
{"a": "D","b": 91}, {"a": "E","b": 81}, {"a": "F","b": 53}, | |
{"a": "G","b": 19}, {"a": "H","b": 87}, {"a": "I","b": 52} | |
]') |
What follows is a technical test for this job offer at CARTO: https://boards.greenhouse.io/cartodb/jobs/705852#.WSvORxOGPUI
Build the following and make it run as fast as you possibly can using Python 3 (vanilla). The faster it runs, the more you will impress us!
Your code should:
- Download this ~2GB file: https://s3.amazonaws.com/carto-1000x/data/yellow_tripdata_2016-01.csv
- Count the lines in the file
- Calculate the average value of the tip_amount field.