[ Launch: creds ] 6107792 by seanjtaylor
Sean J. Taylor seanjtaylor
seanjtaylor
/ scrape_fo.R
Created
October 10, 2016 00:52
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| library(rvest) | |
| library(dplyr) | |
| html.doc <- read_html('http://www.footballoutsiders.com/stat-analysis/2016/quarterbacks-and-progression-air-yards') | |
| # Extract table | |
| raw.table <- html.doc %>% | |
| html_table() %>% | |
| first |
seanjtaylor
/ serial_killers.R
Created
October 5, 2016 19:04
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| library(rvest) | |
| library(dplyr) | |
| library(stringr) | |
| library(ggplot2) | |
| tbls <- read_html('https://en.wikipedia.org/wiki/List_of_serial_killers_by_number_of_victims') %>% html_table() | |
| t1 <- tbls[[1]] %>% select(name = Name, country = Country, years = `Years active`, victims = `Proven victims`) %>% mutate(victims = as.character(victims)) | |
| t2 <- tbls[[2]] %>% select(name = Name, country = Country, years = `Years active`, victims = `Proven victims`) |
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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() %>% |
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(rvest) | |
| library(stringr) | |
| library(readr) | |
| library(ggplot2) | |
| library(dplyr) | |
| library(tidyr) | |
| library(broom) | |
| library(lubridate) | |
| base.url <- 'http://www.pro-football-reference.com/' |
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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 |
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()) |
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)) %>% |
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| <html> | |
| <head> | |
| <style> | |
| rect.background { | |
| fill: #EEE; | |
| } | |
| line.rule { | |
| stroke: #FFF; | |
| } | |
| text.labels { |