---
title: "Football data memo"
author: "Erik Johnson"
date: "1/11/2018"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(stringr)
library(data.table)
```
# Traffic speeds
git_speed.R
Data Questions:
1. What is the difference between football_speeds_2018_2.csv and football_speed_2018.csv? (V6 and V7 in 2018_2 not in 2018.)
2. Is it possible to have these data geocoded in the SQL pull?
3. 2017 data would be useful as another potential counterfactual (same weekend of the year but away rather than home game.)
4. What is the meaning of TMC? (Unique ID for location [yes])?
5. What is ffs?
6. What is confidence?
# Quick Analysis
```{r echo=FALSE, eval=FALSE, cache=TRUE}
source('R/git_schedule_18.R')
source('R/git_speed.R')
schedule_18 <- git_schedule_18()
speed <- git_speed()
speed <- speed[, day_year:=paste0(s_day,'_', s_year)]
schedule_18 <- schedule_18[, day_year:=paste0(s_day, '_', s_year)]
game_days <- unique(schedule_18$day_year)
speed_sub <- speed[day_year %in% game_days]
TMCS <- unique(speed_sub$TMC)
for(s_tmc in TMCS){
dt_speed <- speed_sub[TMC %in% s_tmc]
title <- paste(as.character(unique(dt_speed[, .(TMC, CrossStreet, ROAD_NAME, ROAD_NUM, ROAD_DIR)])), collapse = ' ')
setkey(dt_speed, day_year)
setkey(schedule_18, day_year)
dt_speed <- schedule_18[, .(day_year, home_game)][dt_speed]
p <- ggplot2::ggplot(dt_speed, ggplot2::aes(speed, colour=as.factor(home_game))) +
ggplot2::geom_density() +
ggplot2::ggtitle(title)
ggplot2::ggsave(paste0('~/Dropbox/pkg.data/football/plots/', s_tmc, '.png'), p)
}
```