Sam Boysel sboysel
| # Load the MNIST digit recognition dataset into R | |
| # http://yann.lecun.com/exdb/mnist/ | |
| # assume you have all 4 files and gunzip'd them | |
| # creates train$n, train$x, train$y and test$n, test$x, test$y | |
| # e.g. train$x is a 60000 x 784 matrix, each row is one digit (28x28) | |
| # call: show_digit(train$x[5,]) to see a digit. | |
| # brendan o'connor - gist.github.com/39760 - anyall.org | |
| load_mnist <- function() { | |
| load_image_file <- function(filename) { |
| #' Parse a codebook file with variable and level information. | |
| #' | |
| #' Parses a codebook file where lines starting at column zero (far left) represet | |
| #' variable information (e.g. name, description, type) and indented lines | |
| #' (i.e. lines beginning with white space, either tabs or spaces, etc.) represent factor | |
| #' levels and labels. | |
| #' | |
| #' Note that white space at the beginning and end of each line is stripped before | |
| #' processing that line. | |
| #' |
| #' Gets gender by name or email address, optionally by country or IP address. | |
| library("httr") | |
| library("XML") | |
| #' @import httr | |
| #' @import rjson | |
| #' @param name A character string containing a first name, or a character vector containing first names. One must specify name or email. | |
| #' @param email A character string containing an email address with a first name. One must specify name or email. | |
| #' @param country An optional character string containing a two-letter country name, as listed here: https://gender-api.com/en/api-docs |
Magic words:
psql -U postgresSome interesting flags (to see all, use -h or --help depending on your psql version):
-E: will describe the underlaying queries of the\commands (cool for learning!)-l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)
| theme_map <- function(base_size=9, base_family="") { | |
| require(grid) | |
| theme_bw(base_size=base_size, base_family=base_family) %+replace% | |
| theme( | |
| axis.line=element_blank(), | |
| axis.text=element_blank(), | |
| axis.ticks=element_blank(), | |
| axis.title=element_blank(), | |
| panel.background=element_blank(), | |
| panel.border=element_blank(), |
2017-08-03: Since I wrote this in 2014, the universe, specifically Kirill Müller (https://github.com/krlmlr), has provided better solutions to this problem. I now recommend that you use one of these two packages:
- rprojroot: This is the main package with functions to help you express paths in a way that will "just work" when developing interactively in an RStudio Project and when you render your file.
- here: A lightweight wrapper around rprojroot that anticipates the most likely scenario: you want to write paths relative to the top-level directory, defined as an RStudio project or Git repo. TRY THIS FIRST.
I love these packages so much I wrote an ode to here.
I use these packages now instead of what I describe below. I'll leave this gist up for historical interest. 😆
Joining multiple disparate data-sources, commonly dubbed Master-Data-Management (MDM), is usually not a fun exercise. I would like to show you how using a graph database (Neo4j) and an interesting dataset (developer-oriented collaboration sites) to put the fun back into MDM. This approach will allow you to quickly and sensibly merge data from different sources into a consistent picture and query across the data efficiently to answer your most pressing questions.
You can read the associated blog posts on my blog. The blog posts cover the hows and whys of the project, while this and other GraphGists will examine how to answer specific questions of the data.
How to download, import, and analyze San Francisco restaurant inspection data using SQLite3 and csvkit from the command-line.
A quick example of doing data wrangling from the command-line, as well as getting to know one of San Francisco's data sets: the San Francisco restaurant inspections, courtesy of the SF Department of Public Health. I don't normally do database work from the command-line, but importing bulk data into SQLite is pretty frustrating using the available GUIs or just the shell.
So thank goodness for Christopher Groskopf's csvkit, a suite of Unix-like tools that use Python to robustly handle CSV files. There's a lot of great tools in csvkit, but for this gist, I just use csvsql, which can parse a CSV and turn it into properly-flavored SQL to pass directly into your database app of choice.