[ Launch: creds ] 6107792 by seanjtaylor
Sean J. Taylor seanjtaylor
seanjtaylor
/ gist:2865517
Created
June 4, 2012 00:05
Most Frequent Integer 1-Grams from Google N-Grams
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| (py27)sean@leibniz:/media/datasets/n-grams/data/1gms$ zgrep "^[0-9]\+\s" vocab.gz | sort -n -r -k 2 | head -n 100 | |
| 1 2563166795 | |
| 2 1846241630 | |
| 0 1834474671 | |
| 3 1304070731 | |
| 2005 1091295264 | |
| 4 1045739277 | |
| 5 915581712 | |
| 10 698337921 | |
| 6 692314831 |
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| taylorphd-pc:julia sjt$ rm -rf ~/.julia | |
| staylorphd-pc:julia sjt$ bin/julia | |
| _ | |
| _ _ _(_)_ | A fresh approach to technical computing | |
| (_) | (_) (_) | Documentation: http://docs.julialang.org | |
| _ _ _| |_ __ _ | Type "help()" to list help topics | |
| | | | | | | |/ _` | | | |
| | | |_| | | | (_| | | Version 0.1.2+111718311.r1187 | |
| _/ |\__'_|_|_|\__'_| | Commit 1187040d02 (2013-03-08 10:00:57) | |
| |__/ | |
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| <html> | |
| <head> | |
| <style> | |
| rect.background { | |
| fill: #EEE; | |
| } | |
| line.rule { | |
| stroke: #FFF; | |
| } | |
| text.labels { |
seanjtaylor
/ weather_data.R
Created
January 24, 2015 17:16
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| library(mgcv) | |
| library(ggplot2) | |
| library(dplyr) | |
| library(XML) | |
| library(weatherData) | |
| us.airports.url <- 'http://www.world-airport-codes.com/us-top-40-airports.html' | |
| us.airports <- readHTMLTable(us.airports.url)[[1]] %>% | |
| filter(!is.na(IATA)) %>% |
seanjtaylor
/ gist:568141f04a16d518be24
Created
February 11, 2015 01:46
Reshaping a Pandas dataframe into a sparse matrix
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| import pandas as pd | |
| import scipy.sparse as sps | |
| df = pd.DataFrame({'tag1': ['sean', 'udi', 'bogdan'], 'tag2': ['sean', 'udi', 'udi'], 'freq': [1,2,3]}) | |
| # tag1 -> rows, tag2 -> columns | |
| df.set_index(['tag1', 'tag2'], inplace=True) | |
| mat = sps.coo_matrix((df.freq, (df.index.labels[0], df.index.labels[1]))) | |
| print(mat.todense()) |
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| df <- data.frame(higher = c('US', 'CA', 'MX'), | |
| lower = c('CA', 'MX', 'MX')) | |
| levels <- c('US', 'CA', 'MX') | |
| X.l <- model.matrix(~ 0 + factor(higher, levels = levels), data = df) | |
| X.r <- model.matrix(~ 0 + factor(lower, levels = levels), data = df) | |
| X <- X.l - X.r | |
| colnames(X) <- levels # makes it easier to interpret regression output |
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| library(rvest) | |
| library(stringr) | |
| library(readr) | |
| library(ggplot2) | |
| library(dplyr) | |
| library(tidyr) | |
| library(broom) | |
| library(lubridate) | |
| base.url <- 'http://www.pro-football-reference.com/' |
seanjtaylor
/ gamling_odds_implied_wins.R
Created
September 8, 2016 15:26
Analysis of Season Win O/Us
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| library(rvest) | |
| library(stringr) | |
| library(dplyr) | |
| library(ggplot2) | |
| library(tidyr) | |
| min.fun <- function(p, thresh.prob = NULL, thresh = NULL) { | |
| (thresh.prob - pbinom(thresh, 16, p))^2 | |
| } |
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| library(dplyr) | |
| library(ggplot2) | |
| library(rvest) | |
| library(tidyr) | |
| html.doc <- read_html('http://www.footballoutsiders.com/stat-analysis/2016/quarterbacks-and-progression-air-yards') | |
| # Extract table | |
| raw.table <- html.doc %>% | |
| html_table() %>% |
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