Gilgamath

For a number of years now, I have been using vim as a lightweight IDE. The ecosystem of vim addons is rich. There are numerous plugins for creating tags to navigate a project, browse files in directories, highlight syntax and so on. What really makes it an IDE is the ability to execute code within the context of vim. I realize this probably sounds 'charming' to disciples of that other text editor, but it might seem like an unnatural urge to my vim correligionists. The piece that glues it all together is vim-conque. The easiest way to get conque in ubuntu is via apt as follows:

sudo apt-get install vim-addon-manager vim-conque
sudo vim-addons -w install conqueterm

The skinny on using conque is that you can visual-select code that you are editing, hit <F9>, and it will be transfered to the execution window, newlines and all. So you can test out code while you are writing it. You can also work the other way, testing out code in a REPL, then, when it is working as expected, escape insert mode in the REPL, yank the working code to a register, and copy it into the file you are working on.

Dockerfile or it didn't happen!

This kind of advice is a bit abstract, so I put a working example on github and dockerhub. You can run it yourself via docker:

# this might take a little while to download
docker pull shabbychef/vim-conque
docker run --rm -it shabbychef/vim-conque

This will feel a bit odd: when you run the last command, you are in vim, but you are in vim in a docker container. When you terminate, your changes will not be saved (this is the --rm flag). Directions are given in the file on how to start conque with a screen …

I received some taster ratings from the champagne party we attended last week. I joined the raw ratings with the bottle information to create a single aggregated dataset. This is a 'non-normal' form, but simplest to distribute. Here is a taste:

library(dplyr)
library(readr)
library(knitr)
champ <- read_csv('../data/champagne_ratings.csv')
champ %>% select(winery,purchase_price_per_liter,raternum,rating) %>% 
    head(8) %>% kable(format='markdown')

Recall that the rules of the contest dictate that the average rating of each bottle was computed, then divided by 25 dollars more than the price (presumably for a 750ml bottle). Depending on whether the average ratings were compressed around the high end of the zero to ten scale, or around the low end, one would wager on either the cheapest bottles, or more moderately priced offerings. (Based on my previous analysis, I brought the Menage a Trois Prosecco, rated at 91 points, but available at Safeway for 10 dollars.) It is easy to compute the raw averages using dplyr:

avrat <- champ %>% 
    group_by(winery,bottle_num,purchase_price_per_liter) %>%
    summarize(avg_rating=mean(rating)) %>%
    ungroup() %>%
    arrange(desc(avg_rating))
avrat %>% head(8) %>% kable(format='markdown')

We have been invited to a champagne tasting party and competition. The rules of the contest are as follows: partygoers bring a bottle of champagne to share. They taste, then rate the different champagnes on offer, with ratings on a scale of 1 through 10. The average rating is computed for each bottle, then divided by the price (plus some offset) to arrive at an adjusted quality score. The champagne with the highest score nets a prize, and considerable bragging rights, for its owner. Presumably the offset is introduced to prevent small denominators from dominating the rating, and is advertised to have a value of around $25. The 'price' is, one infers, for a standard 750 ml bottle.

I decided to do my homework for a change, rather than SWAG it. I have been doing a lot of web scraping lately, so it was pretty simple to gather some data on champagnes from wine dot com. This file includes the advertised and sale prices, as well as advertised ratings from Wine Spectator (WS), Wine Enthusiast (WE), and so on. Some of the bottles are odd sizes, so I compute the cost per liter as well. (By the way, many people would consider the data collection the hard part of the problem. rvest made it pretty easy, though.) Here's a taste:

library(dplyr)
library(magrittr)
champ <- read.csv('../data/champagne.csv')
champ %>% arrange(price_per_liter) %>% head(10) %>% kable(format='markdown')