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@johnburnmurdoch
johnburnmurdoch / mourinho_clubelo.R
Created Dec 18, 2018
Scripts for downloading and visualising data from clubelo.com showing José Mourinho’s third season problem. A hand-finished version of this chart appears in this Financial Times story: https://www.ft.com/content/56acdd82-02d2-11e9-99df-6183d3002ee1
View mourinho_clubelo.R
needs(tidyverse, magrittr, scales)
jose.porto <- read_csv("http://api.clubelo.com/porto")
jose.chelsea <- read_csv("http://api.clubelo.com/chelsea")
jose.inter <- read_csv("http://api.clubelo.com/inter")
jose.real <- read_csv("http://api.clubelo.com/realmadrid")
jose.mufc <- read_csv("http://api.clubelo.com/manunited")
jose.all <- bind_rows(
jose.porto %>% filter(From >= as.Date("2002-01-22") & To <= as.Date("2004-06-30")),
View SpatioTemporal-moving.md
View gg_jitterbox.R
# function to do a dodged half-boxplot and jittered points next to each other
#
# data_in should be a data frame
# factor_col should be a bare column name (not a string)
# although it will work if that column is factor or a character type
# numeric_col is the y axis continuous variable
# offset is the width of the boxplots and jittered point cloud
#
# the basic approach is to draw a boxplot without the tails
# (e.g. only the interquartile range) and then use segments to add the
@bayesball
bayesball / broom_career_trajectory.R
Created Jul 1, 2016
Illustrating broom package using career trajectory of home run rates
View broom_career_trajectory.R
# read in Lahman batting and master files
# can also use Lahman package -- data is only through 2014 season
Batting <- read.csv("~/OneDriveBusiness/lahman-csv_2015-01-24/Batting.csv")
Master <- read.csv("~/OneDriveBusiness/lahman-csv_2015-01-24/Master.csv")
# find players with at least 500 career homes (through 2015)
library(dplyr)
View curley_seq.py
from __future__ import print_function
import copy
class permutor:
def __init__(self, infile='curley_seq.txt', max_entries=-1):
self.indata = self.read_infile(infile)
if max_entries>0:
self.indata = self.indata[0:max_entries]
View graph.R
# devtools::install_github('jalapic/engsoccerdata')
library(engsoccerdata)
library(dplyr)
library(plotly)
library(htmlwidgets)
england$Date <- as.Date(england$Date, format = "%Y-%m-%d")
#Get Data into Format Needed
df<-rbind(
View nytscatter.r
library(ggplot2) # devtools::install_github("hadley/ggplot2") or subtitles won't work
library(tidyr)
library(dplyr)
library(readr)
library(scales)
URL <- "https://static01.nyt.com/newsgraphics/2016/04/21/undervote/ad8bd3e44231c1091e75621b9f27fe31d116999f/data.tsv"
fil <- "nytimes_vote.tsv"
if (!file.exists(fil)) download.file(URL, fil)
View 00-shiny-interactive-graphics.pdf
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View all_nba_play_types_by_player.csv
We can make this file beautiful and searchable if this error is corrected: It looks like row 5 should actually have 34 columns, instead of 5. in line 4.
name.table,id.season,is.offense,name.player,team,id.player,jersey,id.position,id.team,slug.team,city.team,gp,possesions,pct.play_type,pts,fga,fgm,ppp,ppp.worse,ppp.better,possesions.per_game,pts.per_game,fga.per_game,fgm.per_game,fg.miss.per_game,rank,pct.fg,pct.efg,pct.ft_achieved,pct.to,pct.shooting_foul,pct.and_1,pct.scored,stem.table
Post-Up,2015-16,TRUE,Paul Millsap,Atlanta Hawks,200794,4,F,1610612737,ATL,Atlanta,4,13,19.69700050354,20,8,7,1.53846001625061,25,0,3.25,5,2,1.75,0.25,1,87.5,87.5,30.7692307692308,15.3846153846154,30.7692307692308,7.69230769230769,76.9230769230769,Postup
Post-Up,2015-16,TRUE,Al Horford,Atlanta Hawks,201143,15,F-C,1610612737,ATL,Atlanta,4,11,15.0684995651245,12,8,6,1.09090995788574,17,7,2.75,3,2,1.5,0.5,2,75,75,0,27.2727272727273,0,0,54.5454545454545,Postup
Post-Up,2015-16,FALSE,Kent Bazemore,Atlanta Hawks,203145,24,G,1610612737,ATL,Atlanta,4,11,22.917,7,7,1,0.636,5,4,2.75,1.75,1.75,0.25,1.5,6,14.286,14.286,27.273,9.091,27.273,0,36.364,Postup
Post-Up,2015-16,FALSE,Al Horford,A
View migrants.R
library(SmarterPoland)
library(riverplot)
library(RColorBrewer)
library(graphics)
library(reshape2)
library(plyr)
library(stringr)
library(countrycode)
# DOWNLOAD THE DATA