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---
title: "Derek Corcoran"
---
Website of Derek Corcoran Data Scientist
```{r, echo=FALSE}
knitr::include_graphics('GSTxF.jpg')
```
Derek was born in Punta Arenas, the southernmost city of Chile, in 1981. He studied Biological Sciences at Concepción University of Chile and obtained his bachelor degree at 2006. During 2008, he began a PhD in Ecology at Catholic University of Chile. In 2010, he came back to Punta Arenas to develop his PhD research about the [North American beaver invasion](https://cienciaustral.com/nuestro-trabajo-cientifico/research/invasion-castor-norteamericano-en-patagonia/). Studying beaver’s distribution modeling, he started to develop his quantitative skills within the R software environment.
Then in 2015, during a Postdoctoral position in the University of Missouri, he investigated the occupancy of 12 bat species in the Sierra Nevada and which variables better explained their occupancy. Both experimental design and data analysis, allowed him to implement new systems of quantitative analysis, creating an R package called [*DiversityOcupancy*](http://rpubs.com/derek_corcoran/DiversityOccupancy). At the same time, through collaborations he started his research in quantitative analysis in sport, which allowed him to create a [basketball spatial model](https://derek-corcoran-barrios.github.io/about.html) considering the pairwise comparison of teams offensive and defensive spatial configuration and performance.
Today, he is working at Catholic University of Chile in his second postdoctoral position. He is part of two projects which try to establish a model of maximization of conservation of species [considering climate change](http://www.sparc-website.org/) while minimizing the land required to incorporate to the global network of protected area using network flow optimization and collaborating with mathematicians. Equally important, he has been teaching [R courses](https://derek-corcoran-barrios.github.io/CursoR.html) for PhD students in the same University, looking forward to build up new modules for teaching R for researchers and develop an online course of R with the Postgraduate Department.
Derek is the co-founder of [Ciencia Austral](https://cienciaustral.com/), an environmental consultancy based in Punta Arenas focused in scientific advisory and education. Here, he has worked in projects about beaver eradication, sustainable harvesting of guanacos, ecosystem services of dung beetles, and data science, learning not only R, but also Python and AMPL.
<!--chapter:end:index.Rmd-->
---
title: "Project description"
---
We have created this site to share the results of our spatial analysis of the game of basketball with the public. The general concept behind this research project was to build a predictive model for the outcome of National Basketball Association (NBA) games based on the offensive and defensive matchup between teams. In order to do this, we examined around one million shots (about five years of data) by pairing up the offensive and defensive shot charts for every team in the NBA against all other opponents. From this, we have managed to build a machine learning algorithm which can be used for analysis such as:
- Predicting the spread for [every NBA game](https://derek-corcoran-barrios.github.io/Projection.html).
- Estimating the impact that trades have on expected outcome and performance for teams
- Spatially analyzing the locations where teams perform well on offense and defense using [shot charts](https://derek-corcoran-barrios.github.io/ShotCharts.html).
- Calculating our own [spatial power rankings](https://derek-corcoran-barrios.github.io/Rating.html) and [team stats](https://derek-corcoran-barrios.github.io/TeamStats.html)
- Finding key points where teams are able to improve matchups against other teams
- And much much more… From all of this, we hope to show how using spatial analysis can provide more critical insights into the nature of NBA games.
<!--chapter:end:about.Rmd-->
---
title: "BlogHome"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, cache = TRUE, warning = FALSE, message = FALSE)
```
## [Does the recent Bulls winning streak mean something?](https://derek-corcoran-barrios.github.io/Mirotic)
*December 20th, 2017*
```{r}
library(dplyr)
library(lubridate)
library(SpatialBall2)
library(gridExtra)
library(ggplot2)
Season2018 <- readRDS("Season2018.rds")
NM <- filter(Season2018, GAME_DATE >= dmy("8/12/2017"))
WONM <- filter(Season2018, GAME_DATE < dmy("8/12/2017"))
```
### Shot charts with and without Nikola Mirotic
Since Nikola, has just played 6 games, there's too few shots to make a passable shot chart of Chicago with him, so we will have to rely on team stats to figure what is going on.
```{r, fig.width=6, fig.height=12}
Without <- OffShotSeasonGraphTeam(Seasondata = WONM, team = "Chi") + ggtitle("Offensive without Mirotic")
With <- OffShotSeasonGraphTeam(Seasondata = NM, team = "Chi", quant = 0.05)+ ggtitle("Offensive with Mirotic")
grid.arrange(Without, With, ncol = 1)
```
[Read more...](https://derek-corcoran-barrios.github.io/Mirotic)
## [The first team ever to shoot more than half their shots beyond the three point line](https://derek-corcoran-barrios.github.io/Blog)
*December 2nd, 2017*
For while we`ve been wondering if there is such a thing as too many three pointers for a team. Well we can keep asking ourselves that, as today December the second of 2017, The Rockets are shooting an NBA all-time record 53% of their shots from three point range, followed by last season's Rockets with 46%. Third place is held by last year’s Cavs team, with 40%, a 13% difference. And it's not even close to the second, just compare this years Rockets shot chart with 2001 NBA Champions LA Lakers, it's a completely different sport!
```{r}
knitr::include_graphics("https://derek-corcoran-barrios.github.io/Blog_files/figure-html/unnamed-chunk-3-1.png")
```
[Read more...](https://derek-corcoran-barrios.github.io/Blog)
<!--chapter:end:BlogHome.Rmd-->
---
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE, cache = TRUE)
```
```{r load packs}
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse, DT, stringr, purrr, dplyr, ggplot2, plotly)
```
```{r getting stats, echo=FALSE}
Season2018 <- readRDS("Season2018.rds")
TeamStats2018 <- Season2018 %>% mutate(EVENT_TYPE = make.names(EVENT_TYPE), SHOT_TYPE = str_replace(SHOT_TYPE, " Field Goal", "")) %>% group_by(TEAM_NAME, SHOT_TYPE, EVENT_TYPE) %>% dplyr::summarise(N = n()) %>% spread(key = EVENT_TYPE, value = N) %>% split(.$SHOT_TYPE) %>% map(~mutate(.x, ShotPct = round(Made.Shot/(Made.Shot +Missed.Shot), 3), Total = Made.Shot + Missed.Shot))
Points <- c("2Pts", "3Pts")
for(i in 1:length(TeamStats2018)){
colnames(TeamStats2018[[i]])[2:6] <- paste0(colnames(TeamStats2018[[i]])[2:6], Points[i])
}
TeamStats2018 <- TeamStats2018 %>% reduce(merge) %>% select(-SHOT_TYPE2Pts, -SHOT_TYPE3Pts, -Made.Shot2Pts, -Made.Shot3Pts, -Missed.Shot2Pts, -Missed.Shot3Pts) %>% mutate(TotalShots = Total2Pts + Total3Pts) %>% mutate(PercentageOf2s = round(Total2Pts/TotalShots,2),PercentageOf3s = round(Total3Pts/TotalShots, 2)) %>% select(-TotalShots, -Total2Pts, -Total3Pts)
Teams <- unique(Season2018$TEAM_NAME)
Prop <- data.frame(TEAM_NAME = Teams , PropShot = NA)
for(i in 1:30){
Offa <- dplyr::filter(Season2018, HTM == Teams[i] | VTM == Teams[i])
Prop$PropShot[i] <- round(nrow(dplyr::filter(Offa, TEAM_NAME == Teams[i]))/nrow(dplyr::filter(Offa,TEAM_NAME != Teams[i])),3)
}
TeamStats2018 <- full_join(TeamStats2018, Prop) %>% mutate(PPS = round((ShotPct2Pts*2*PercentageOf2s)+(ShotPct3Pts*3*PercentageOf3s),3)) %>% mutate(AdjPPS = round((PPS * PropShot),3)) %>% mutate(Season = 2018)
```
```{r MakeTable, echo=FALSE}
TeamStats2018 <- TeamStats2018
```
```{r, cache = TRUE}
pacman::p_load(tidyverse, DT, stringr, purrr, dplyr)
library(SpatialBall2)
Season2001 <- readRDS("Season2001.rds")
TeamStats2001 <- Season2001 %>% mutate(EVENT_TYPE = make.names(EVENT_TYPE), SHOT_TYPE = str_replace(SHOT_TYPE, " Field Goal", "")) %>% group_by(TEAM_NAME, SHOT_TYPE, EVENT_TYPE) %>% dplyr::summarise(N = n()) %>% spread(key = EVENT_TYPE, value = N) %>% split(.$SHOT_TYPE) %>% map(~mutate(.x, ShotPct = round(Made.Shot/(Made.Shot +Missed.Shot), 3), Total = Made.Shot + Missed.Shot))
Points <- c("2Pts", "3Pts")
for(i in 1:length(TeamStats2001)){
colnames(TeamStats2001[[i]])[2:6] <- paste0(colnames(TeamStats2001[[i]])[2:6], Points[i])
}
TeamStats2001 <- TeamStats2001 %>% reduce(merge) %>% select(-SHOT_TYPE2Pts, -SHOT_TYPE3Pts, -Made.Shot2Pts, -Made.Shot3Pts, -Missed.Shot2Pts, -Missed.Shot3Pts) %>% mutate(TotalShots = Total2Pts + Total3Pts) %>% mutate(PercentageOf2s = round(Total2Pts/TotalShots,2),PercentageOf3s = round(Total3Pts/TotalShots, 2)) %>% select(-TotalShots, -Total2Pts, -Total3Pts)
Teams <- unique(Season2001$TEAM_NAME)
Prop <- data.frame(TEAM_NAME = Teams , PropShot = NA)
for(i in 1:length(Teams)){
Offa <- dplyr::filter(Season2001, HTM == Teams[i] | VTM == Teams[i])
Prop$PropShot[i] <- round(nrow(dplyr::filter(Offa, TEAM_NAME == Teams[i]))/nrow(dplyr::filter(Offa,TEAM_NAME != Teams[i])),3)
}
TeamStats2001 <- full_join(TeamStats2001, Prop) %>% mutate(PPS = round((ShotPct2Pts*2*PercentageOf2s)+(ShotPct3Pts*3*PercentageOf3s),3)) %>% mutate(AdjPPS = round((PPS * PropShot),3)) %>% mutate(Season = 2001)
TeamStats2001 <- TeamStats2001
data("season2017")
Season2017 <- season2017
TeamStats2017 <- Season2017 %>% mutate(EVENT_TYPE = make.names(EVENT_TYPE), SHOT_TYPE = str_replace(SHOT_TYPE, " Field Goal", "")) %>% group_by(TEAM_NAME, SHOT_TYPE, EVENT_TYPE) %>% dplyr::summarise(N = n()) %>% spread(key = EVENT_TYPE, value = N) %>% split(.$SHOT_TYPE) %>% map(~mutate(.x, ShotPct = round(Made.Shot/(Made.Shot +Missed.Shot), 3), Total = Made.Shot + Missed.Shot))
Points <- c("2Pts", "3Pts")
for(i in 1:length(TeamStats2017)){
colnames(TeamStats2017[[i]])[2:6] <- paste0(colnames(TeamStats2017[[i]])[2:6], Points[i])
}
TeamStats2017 <- TeamStats2017 %>% reduce(merge) %>% select(-SHOT_TYPE2Pts, -SHOT_TYPE3Pts, -Made.Shot2Pts, -Made.Shot3Pts, -Missed.Shot2Pts, -Missed.Shot3Pts) %>% mutate(TotalShots = Total2Pts + Total3Pts) %>% mutate(PercentageOf2s = round(Total2Pts/TotalShots,2),PercentageOf3s = round(Total3Pts/TotalShots, 2)) %>% select(-TotalShots, -Total2Pts, -Total3Pts)
Teams <- unique(Season2017$TEAM_NAME)
Prop <- data.frame(TEAM_NAME = Teams , PropShot = NA)
for(i in 1:length(Teams)){
Offa <- dplyr::filter(Season2017, HTM == Teams[i] | VTM == Teams[i])
Prop$PropShot[i] <- round(nrow(dplyr::filter(Offa, TEAM_NAME == Teams[i]))/nrow(dplyr::filter(Offa,TEAM_NAME != Teams[i])),3)
}
TeamStats2017 <- full_join(TeamStats2017, Prop) %>% mutate(PPS = round((ShotPct2Pts*2*PercentageOf2s)+(ShotPct3Pts*3*PercentageOf3s),3)) %>% mutate(AdjPPS = round((PPS * PropShot),3)) %>% mutate(Season = 2017)
TeamStats2017 <- TeamStats2017
pacman::p_load(tidyverse, DT, stringr, purrr, dplyr)
Season2016 <- readRDS("shotDataTotal2016.rds")
TeamStats2016 <- Season2016 %>% mutate(EVENT_TYPE = make.names(EVENT_TYPE), SHOT_TYPE = str_replace(SHOT_TYPE, " Field Goal", "")) %>% group_by(TEAM_NAME, SHOT_TYPE, EVENT_TYPE) %>% dplyr::summarise(N = n()) %>% spread(key = EVENT_TYPE, value = N) %>% split(.$SHOT_TYPE) %>% map(~mutate(.x, ShotPct = round(Made.Shot/(Made.Shot +Missed.Shot), 3), Total = Made.Shot + Missed.Shot))
Points <- c("2Pts", "3Pts")
for(i in 1:length(TeamStats2016)){
colnames(TeamStats2016[[i]])[2:6] <- paste0(colnames(TeamStats2016[[i]])[2:6], Points[i])
}
TeamStats2016 <- TeamStats2016 %>% reduce(merge) %>% select(-SHOT_TYPE2Pts, -SHOT_TYPE3Pts, -Made.Shot2Pts, -Made.Shot3Pts, -Missed.Shot2Pts, -Missed.Shot3Pts) %>% mutate(TotalShots = Total2Pts + Total3Pts) %>% mutate(PercentageOf2s = round(Total2Pts/TotalShots,2),PercentageOf3s = round(Total3Pts/TotalShots, 2)) %>% select(-TotalShots, -Total2Pts, -Total3Pts)
Teams <- unique(Season2016$TEAM_NAME)
Prop <- data.frame(TEAM_NAME = Teams , PropShot = NA)
for(i in 1:length(Teams)){
Offa <- dplyr::filter(Season2016, HTM == Teams[i] | VTM == Teams[i])
Prop$PropShot[i] <- round(nrow(dplyr::filter(Offa, TEAM_NAME == Teams[i]))/nrow(dplyr::filter(Offa,TEAM_NAME != Teams[i])),3)
}
TeamStats2016 <- full_join(TeamStats2016, Prop) %>% mutate(PPS = round((ShotPct2Pts*2*PercentageOf2s)+(ShotPct3Pts*3*PercentageOf3s),3)) %>% mutate(AdjPPS = round((PPS * PropShot),3))%>% mutate(Season = 2016)
TeamStats2016 <- TeamStats2016
pacman::p_load(tidyverse, DT, stringr, purrr, dplyr)
Season2015 <- readRDS("shotDataTotal2015.rds")
TeamStats2015 <- Season2015 %>% mutate(EVENT_TYPE = make.names(EVENT_TYPE), SHOT_TYPE = str_replace(SHOT_TYPE, " Field Goal", "")) %>% group_by(TEAM_NAME, SHOT_TYPE, EVENT_TYPE) %>% dplyr::summarise(N = n()) %>% spread(key = EVENT_TYPE, value = N) %>% split(.$SHOT_TYPE) %>% map(~mutate(.x, ShotPct = round(Made.Shot/(Made.Shot +Missed.Shot), 3), Total = Made.Shot + Missed.Shot))
Points <- c("2Pts", "3Pts")
for(i in 1:length(TeamStats2015)){
colnames(TeamStats2015[[i]])[2:6] <- paste0(colnames(TeamStats2015[[i]])[2:6], Points[i])
}
TeamStats2015 <- TeamStats2015 %>% reduce(merge) %>% select(-SHOT_TYPE2Pts, -SHOT_TYPE3Pts, -Made.Shot2Pts, -Made.Shot3Pts, -Missed.Shot2Pts, -Missed.Shot3Pts) %>% mutate(TotalShots = Total2Pts + Total3Pts) %>% mutate(PercentageOf2s = round(Total2Pts/TotalShots,2),PercentageOf3s = round(Total3Pts/TotalShots, 2)) %>% select(-TotalShots, -Total2Pts, -Total3Pts)
Teams <- unique(Season2015$TEAM_NAME)
Prop <- data.frame(TEAM_NAME = Teams , PropShot = NA)
for(i in 1:length(Teams)){
Offa <- dplyr::filter(Season2015, HTM == Teams[i] | VTM == Teams[i])
Prop$PropShot[i] <- round(nrow(dplyr::filter(Offa, TEAM_NAME == Teams[i]))/nrow(dplyr::filter(Offa,TEAM_NAME != Teams[i])),3)
}
TeamStats2015 <- full_join(TeamStats2015, Prop) %>% mutate(PPS = round((ShotPct2Pts*2*PercentageOf2s)+(ShotPct3Pts*3*PercentageOf3s),3)) %>% mutate(AdjPPS = round((PPS * PropShot),3))%>% mutate(Season = 2015)
TeamStats2015 <- TeamStats2015
```
```{r}
library(plotly)
TeamStats <- rbind(TeamStats2001, TeamStats2018)
TeamStats <- rbind(TeamStats, TeamStats2017)
TeamStats <- rbind(TeamStats, TeamStats2016)
TeamStats <- rbind(TeamStats, TeamStats2015)
TeamStats <- arrange(TeamStats, desc(PercentageOf3s))
p <- ggplot(TeamStats, aes(x = PercentageOf3s, y = PPS)) + geom_point() + geom_smooth(method = "lm") + ggtitle("Figure 1") + geom_text(aes(label=TEAM_NAME),hjust=0, vjust=0) + xlab("Proportion of shots that are 3pts")
```
## The first team ever to shoot more than half their shots beyond the three point line
For while we`ve been wondering if there is such a thing as too many three pointers for a team. Well we can keep asking ourselves that, as today December the second of 2017, The Rockets are shooting an NBA all-time record 53% of their shots from three point range, followed by last season's Rockets with 46%. Third place is held by last year’s Cavs team, with 40%, a 13% difference. And it's not even close to the second, just compare this years Rockets shot chart with 2001 NBA Champions LA Lakers, it's a completely different sport!
```{r}
library(SpatialBall)
library(gridExtra)
a <- SpatialBall::OffShotSeasonGraphTeam(Seasondata = Season2018, team = "Hou")
b <- SpatialBall::OffShotSeasonGraphTeam(Seasondata = Season2001, team = "Lal")
grid.arrange(a,b, ncol =2)
```
In the plot and table bellow we can see that this year the Rockets are shooting `r TeamStats$PercentageOf3s[1]*100` % of their shots from the three point range, followed by the same team of last year. The third place, is last year's Cavs team, with `r TeamStats$PercentageOf3s[3]*100` %, that is a `r TeamStats$PercentageOf3s[1]*100 -TeamStats$PercentageOf3s[3]*100`% difference!!
### Why keep jacking up threes?
Does this make any sense? As we see in figure 1, there is a positive relationship between the percentage of shots a team takes that are three pointers with their points per shot. Actually, if we don't count the Golden State Warriors teams from 2016, 2017 and this year incarnation of the team, this year Rockets team has the highest Point per shot mark in the last 5 years at `r TeamStats$PPS[1]`, while making only `r TeamStats$ShotPct3Pts[1]*100`% of them.
```{r}
ggplotly(p)
```
### What would happend if the warriors shot as many threes as the Rockets
As told above, this year warriors are the top team in the last 5 years in Points per shot making `r dplyr::filter(TeamStats, TEAM_NAME == "GSW" & Season == 2018)$PPS` points per shot. They are making an efficient `r dplyr::filter(TeamStats, TEAM_NAME == "GSW" & Season == 2018)$ShotPct3Pts*100`% of their three pointers and an outstanding `r dplyr::filter(TeamStats, TEAM_NAME == "GSW" & Season == 2018)$ShotPct2Pts*100`% of their two pointers. But what would happen if they shot they same proportion of their shots form the three point line as the Rockets did?
Well, they would change their PPS to `r round((dplyr::filter(TeamStats, TEAM_NAME == "GSW" & Season == 2018)$ShotPct3Pts*3*dplyr::filter(TeamStats, TEAM_NAME == "Hou" & Season == 2018)$PercentageOf3s) + (dplyr::filter(TeamStats, TEAM_NAME == "GSW" & Season == 2018)$ShotPct2Pts*2*dplyr::filter(TeamStats, TEAM_NAME == "Hou" & Season == 2018)$PercentageOf2s),3)` which is an increment, but no by so much.
```{r}
DT::datatable(TeamStats)
```
### Is there any downsides on shooting this many three pointers?
If we look at the proportion of shots a team takes as a proportion of the number of shots their opponent takes as shown in the formula bellow, we see in the following graph (figure 2) that the more threes you take, the less proportions of shots you take. The other team gets more second chances, but the slope of that tendency is a lot lower than the one in Figure 1. That is, usually the proportion of shots you loose by taking more threes is more than offset by the increase in points per shots.
$$PropShot =\frac{ShotsTaken_{Offense}}{ShotsAllowed_{Defense}}$$
On the other hand, it can still change the outcome of a match. If you see in this years [team stats](https://derek-corcoran-barrios.github.io/TeamStats.html), you will see that even when Golden State has the higher Points per shot in the NBA, it only takes `r dplyr::filter(TeamStats, TEAM_NAME == "GSW" & Season == 2018)$PropShot` shots per every opponent shot. That is why we added the column AdjPPS, which is basically the Points per shot you take adjusted by the proportion of shots taken in a match.
```{r}
prop <- ggplot(TeamStats, aes(x = PropShot, y = PercentageOf3s)) + geom_point() + geom_smooth(method = "lm") + ggtitle("Figure 2") + geom_text(aes(label=TEAM_NAME),hjust=0, vjust=0) + xlab("Shots taken/shots taken by opponent") + ylab("Proportion of shots that are 3pts")
ggplotly(prop)
```
<!--chapter:end:Blog.Rmd-->
---
title: "Curso de R"
author: "Derek Corcoran"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# BIO 4022. Análisis y manipulación de datos en R
### En el inicio se encuentra:
- Read me file, explicación del contenido de este repositorio
- El pdf con el temario y evaluación del curso
### En la carpeta de cada clase se encuentra:
- el archivo Rpres y HTML correspondiente a cada clase
- Los datos, figuras o links utilizados para cada clase
# Links a las Clases en html
- Antes de empezar, si no has utilizado R anteriormente por favor revisa este
[Video](https://youtu.be/w6L7Ye18yPE) y realiza las primeras 7 lecciones de
*R Programming* en Swirl
- [Clase 1](https://derek-corcoran-barrios.github.io/Clase1/Clase1TidyData)
- [Clase 2](http://rpubs.com/derek_corcoran/Clase2). También puedes ver el video de la clase.
<iframe width="560" height="315" src="https://www.youtube.com/embed/Ft6r7pD_eSs" frameborder="0" allowfullscreen></iframe>
- [Clase 3](http://rpubs.com/derek_corcoran/Clase3). También puedes ver el video de la clase.
+ Video que muestra como [realizar](https://youtu.be/lDp5OJzeG34) el ejercicio 3 de la clase.
<iframe width="560" height="315" src="https://www.youtube.com/embed/5tjCeFb2oSk" frameborder="0" allowfullscreen></iframe>
- [Clase 4](http://rpubs.com/derek_corcoran/Clase4). También puedes ver el [video](https://youtu.be/miqDWpVEMRg) de la clase.
- [Clase 5](http://rpubs.com/derek_corcoran/Clase5). También puedes ver el [video](https://youtu.be/bvzi88XRq4c) de la clase.
- [Clase 6](http://rpubs.com/derek_corcoran/Clase6). También puedes ver el [video](https://youtu.be/nGb2__ksaho) de la clase.
- [Clase 7](http://rpubs.com/derek_corcoran/Clase7). También puedes ver el [video](https://youtu.be/_deboekuWt0) de la clase.
# Evaluaciones
* Pueden ver en este [link](http://rpubs.com/derek_corcoran/NotasBIO4022_2_2017) la evaluación de la primera tarea.
* En este [link](http://rpubs.com/derek_corcoran/Eval2) pueden ver la evaluación de la segunda tarea
* Finalmente en este [link](http://rpubs.com/derek_corcoran/NotasBIO4022_2_final2017) se encuentran las notas finales.
<!--chapter:end:CursoR.Rmd-->
---
title: "Donde vivir"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
<iframe src="https://derek-corcoran.shinyapps.io/WhereShouldYouLive/?showcase=0" width="1000" height="1600px">
</iframe>
<!--chapter:end:DondeVivir.Rmd-->
---
title: "Sismos en Chile"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
knitr::opts_chunk$set(message = FALSE)
knitr::opts_chunk$set(warning=FALSE)
```
```{r, message=FALSE, echo=FALSE, warning=FALSE}
if (!require("pacman")) install.packages("pacman")
pacman::p_load(ggmap, ggplot2, dplyr, readr, leaflet, dygraphs, xts, lubridate, geojsonio, stringr)
```
```{r, echo=FALSE, warning=FALSE, cache=FALSE}
Earthquakes <- read_csv("https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/2.5_month.csv")
Earthquakes$time <- lubridate::with_tz(Earthquakes$time, tz = "America/Santiago")
Chile <- as.data.frame(dplyr::filter(Earthquakes, latitude < -17 & latitude > -60 & longitude < -65 & longitude > -75))
Last <- max(dplyr::filter(Chile, mag >= ifelse(max(Chile$mag) < 6,max(Chile$mag), 6))$time)
```
Desde el `r strftime(min(Earthquakes$time), format="%d-%m-%Y", usetz = FALSE)` al `r strftime(max(Earthquakes$time), format="%d-%m-%Y", usetz = FALSE)`, han habido `r NROW(dplyr::filter(Chile, mag >= ifelse(max(Chile$mag) < 6,max(Chile$mag), 6)))` sismos sobre magnitud `r ifelse(max(Chile$mag) < 6,max(Chile$mag), 6)` en Chile y sus cercanias. Estos han ocurrido en las localidades de `r str_replace(str_replace(dplyr::filter(Chile, mag >= ifelse(max(Chile$mag) < 6,max(Chile$mag), 6))$place, "km", " kms al"), "of", "de")`.
El temblor sobre grado `r ifelse(max(Chile$mag) < 6,max(Chile$mag), 6)`, mas reciente ocurrio en `r strftime(max(dplyr::filter(Chile, mag >= ifelse(max(Chile$mag) < 6,max(Chile$mag), 6))$time), format="%d-%m-%Y", usetz = FALSE)`, y desde esa fecha han ocurrido un total de `r nrow(dplyr::filter(Chile, time >= Last))` sismos.
En el siguiente mapa interactivo, se ven todos los temblores del último año, en este se encuentran en rojo, todos los temblores con una magnitud sobre 6 en la escala de richter y en azul bajo 6. Si te posas sobre cada circulo te mostrará la magnitud de este temblor, y si haces click sobre un circulo, aparecerá el día y hora en que este temblor ocurrio.
```{r, message=FALSE, echo=FALSE, warning=FALSE, fig.cap= "Sismos ocurridos en el mundo en los últimos 30 días con magnitud sobre 2.5 (Datos extraidos de USGS))"}
plaques <- geojsonio::geojson_read("https://raw.githubusercontent.com/fraxen/tectonicplates/master/GeoJSON/PB2002_boundaries.json", what = "sp")
MAG <- as.numeric(Earthquakes$mag)^1.3
pal <- colorFactor(c("navy", "red"), domain = c("> 6", "< 6"))
QK <- ifelse(Earthquakes$mag >= 6, "> 6", "< 6")
leaflet(data = Earthquakes) %>% addTiles() %>% addPolylines(data = plaques)%>% setView(-71.0382679, -36, zoom = 4) %>%
addCircleMarkers(~longitude, ~latitude, popup = ~as.character(time), label = ~as.character(mag), fillOpacity = 0.5, radius = MAG, color = ~pal(QK))
#%>% addLegend(position= "topright", pal = pal, values= ~QK, title = "Magnitud")
```
En la siguiente serie de tiempo, se muestran los temblores del ultimo mes, la linea punteada roja muestra el último sismo sobre `r ifelse(max(Chile$mag) < 6,max(Chile$mag), 6)` que ha ocurrido.
```{r, message=FALSE, echo=FALSE, warning=FALSE}
dyUnzoom <-function(dygraph) {
dyPlugin(
dygraph = dygraph,
name = "Unzoom",
path = system.file("plugins/unzoom.js", package = "dygraphs")
)
}
Earthquakes <- dplyr::filter(Earthquakes, latitude < -17 & latitude > -60 & longitude < -65 & longitude > -75)
Earthquakes <- as.data.frame(Earthquakes)
TimeSeries <- xts(Earthquakes[,c("mag")],Earthquakes[,"time"])
dygraph(TimeSeries, main = "Sismos ultimos 30 dias en Chile", ylab = "Magnitud") %>% dyRangeSelector() %>% dyOptions(drawPoints = TRUE, pointSize = 2) %>% dyHighlight(highlightCircleSize = 5) %>% dyLegend("follow") %>% dyUnzoom() %>% dyEvent(Last, "Ultimo evento", labelLoc = "bottom", color ="red")
```
<!--chapter:end:Earthquake.Rmd-->
---
title: "Does the recent Bulls winning streak mean something?"
author: "Derek Corcoran and Nick Watanabe"
date: "December 20, 2017"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, cache = TRUE, warning = FALSE, message = FALSE)
```
```{r}
library(dplyr)
library(lubridate)
library(SpatialBall2)
library(gridExtra)
library(ggplot2)
Season2018 <- readRDS("Season2018.rds")
NM <- filter(Season2018, GAME_DATE >= dmy("8/12/2017"))
WONM <- filter(Season2018, GAME_DATE < dmy("8/12/2017"))
```
### Shot charts with and without Nikola Mirotic
Since Nikola Mirotic has just played 6 games, there are too few shots to make a passable shot chart for the Chicago Bulls with him, so we will have to rely on team stats to figure what is going on.
```{r, fig.width=6, fig.height=12}
Without <- OffShotSeasonGraphTeam(Seasondata = WONM, team = "Chi") + ggtitle("Offensive without Mirotic")
With <- OffShotSeasonGraphTeam(Seasondata = NM, team = "Chi", quant = 0.05)+ ggtitle("Offensive with Mirotic")
grid.arrange(Without, With, ncol = 1)
```
### Stats without Nikola Mirotic
As seen in the table below, before Nikola Mirotic came back, the Bulls ranked 27th in the league in Adjusted points per shot (APPS), and then moved to a more respectable 14th after his return. The defense has not changed all that much, but they improved a tick going from being 15th to 13th in adjusted points allowed per shot.
```{r}
statsWONM <- SpatialBall2::TeamStats(WONM)
```
```{r}
statsWONM[[1]]$Rank <- 1:30
statsWONM[[2]]$Rank <- 1:30
knitr::kable(dplyr::filter(statsWONM[[1]], TEAM_NAME == "Chi"))
knitr::kable(dplyr::filter(statsWONM[[2]], DefTeam == "Chi"))
```
### Stats with Nikola Mirotic
```{r}
statsNM <- SpatialBall2::TeamStats(NM)
```
```{r}
statsNM[[1]]$Rank <- 1:30
statsNM[[2]]$Rank <- 1:30
knitr::kable(dplyr::filter(statsNM[[1]], TEAM_NAME == "Chi"))
knitr::kable(dplyr::filter(statsNM[[2]], DefTeam == "Chi"))
```
Chicago has diminished the percentage of 3-point shots taken since Mirotic came back, moving from 36% of their shots being taken from beyond the arc to only 26%. Overall, the main change for them has been the shooting efficiency, as their 2-point shooting percentage rose from 45.7% to 51.5% and their 3-point pct from 34.2% to 38.2%.
```{r}
SpatialBall2::PointShotSeasonGraphPlayer(Seasondata = Season2018, player = "Nikola Mirotic", kernel = FALSE) + ggtitle("Shots taken by Mirotic")
```
In this season, Mirotic is shooting an effective field goal percentage of 63%, while having a career 51.1% eFG%. This includes him making over 50% of his 3’s, when he is a career 35.5% 3-point shooter. All this while having a 28% usage rate, the highest in his career by far, and shooting all over the court as we see above. If Mirotic can maintain this level of production and efficiency, the Bulls could certainly have a better season than predicted. However, all this points to an unsustainable efficiency, and a possible regression to the mean for the Nikola and the Bulls.
```{r}
knitr::include_graphics("https://upload.wikimedia.org/wikipedia/commons/3/34/Nikola_Mirotic_%2816240996134%29.jpg")
```
<!--chapter:end:Mirotic.Rmd-->
---
title: "El problema de los alergenos en Chile"
author: "Derek Corcoran"
output: html_document
bibliography: bibliography.bib
csl: nature.csl
---
### Resumen
Santiago de Chile, es una de las ciudades del país con mayor contaminación de aire. Pese a esto los planificadores urbanos de la zona, no han planificado el plantar vegetación urbana acorde a esta situación. Sinó que al contrario, han elegido plantar árboles exóticos con producción excesiva de polen, llegando a veces a valores de hasta 3000 particulas de polen por metro cúbico, siendo que 70, es considerado un nivel muy alto. Se comparan los niveles de alergenos para Santiago con los de Valparaiso y Talca, y se demuestra que Santiago es la ciudad que se ha planificado peor en cuanto a los árboles que se han plantado en la región, y que esto podría estar afectando tanto el bienestar de los ciudadanos de Santiago, como su economía debido a gastos en antihistamínicos.
### Introducción
Dentro de los parámetros deseados del diseño de espacios verdes urbanos tomando en cuenta las alergias, se encuentran, el plantar gran diversidad de especies para evitar picos altos de polinización de una especie única, el no plantar especies exóticas para evitar exponer a la población urbana a nuevos agentes alergenos, y en el caso de tratarse de especies dioicas, no plantar individuos masculinos para evitar el polen [@carinanos2011urban]. En la población mundial entre el 10 y el 30 porciento de la población es alergica[@berger2003overview].
Si bien en santiago se ha determinado que los árboles hen beneficiado la calidad del aire [@escobedo2008analyzing], la elección de las especies plantadas pueden tener un costo económico y social altisimo para la ciudadania, en Estados Unidos, se han estimado costos económicos directos e indirectos de 4912 dolares anuales por persona [@o2004burden]. Los costos directos concideran medicamentos, hospitalizaciones entre otras. En tanto que los costos indirectos incluyen menor aprendisaje y rendimiento, e incluso en un 25% de los alergicos, perdida de días de trabajo o escuela [@tanner1999effect], además de bajas de autoestima y/o disminución de la vida social y deportiva, llevando al sedentarismo [@fineman2002burden].
Según el Instituto de Salud Publica de Chile, tan solo entre julio 2015 y julio 2016 se vendieron 6.265.562 de antialérgicos en el país.
### Resultados
#### Santiago
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
```
En el siguiente gráfico, vemos que en los últimos 15 años (2002 al 2017), santiago ha tenido ciclos en los cuales los niveles más altos de polen se encuentran principalemnte en los meses de Septiembre y Ocutbre y destaca dentro de los volumenes de polen el Platano oriental (*Platanus orientalis*) que puede generar hasta 3000 particulas de polen por metro cúbico, siendo este considerado en niveles altos de alergia cuando llega a 70.
```{r}
if (!require("pacman")) install.packages("pacman")
pacman::p_load(rvest, lubridate, stringr, dplyr, ggplot2, tidyr, dygraphs, xts)
Alergia <- readRDS("Alergia.rds")
TimeSeries1 <- xts(Alergia[,c("platano_oriental")],Alergia[,"Fechas"])
TimeSeries2 <- xts(Alergia[,c("arboles_total")],Alergia[,"Fechas"])
TimeSeries3 <- xts(Alergia[,c("pastos")],Alergia[,"Fechas"])
TimeSeries <- cbind(TimeSeries1, TimeSeries2)
TimeSeries <- cbind(TimeSeries, TimeSeries3)
colnames(TimeSeries) <- c("Platano Oriental", "Arboles total", "Pastos")
dyUnzoom <-function(dygraph) {
dyPlugin(
dygraph = dygraph,
name = "Unzoom",
path = system.file("plugins/unzoom.js", package = "dygraphs")
)
}
dygraph(TimeSeries , ylab = "Particulas de polen/m³ de aire") %>% dyRangeSelector() %>% dyOptions(drawPoints = TRUE, pointSize = 2) %>% dyHighlight(highlightCircleSize = 5) %>% dyLegend("always") %>% dyUnzoom() %>% dyOptions(stackedGraph = TRUE)
```
```{r}
Weekly <- Alergia %>% select(Semana, platano_oriental) %>%group_by(Semana) %>% summarise_all(funs(mean, sd, max, min))
p <- ggplot(Weekly, aes(x = Semana, y = mean))+ geom_ribbon(aes(ymax = max, ymin = min, fill = "red")) + geom_ribbon(aes(ymax = mean + sd, ymin = mean - sd, fill = "blue"), alpha = 1) + geom_line() + scale_fill_manual(name = "leyenda", values = c("blue", "red"), labels = c('Error estándar','Extremos')) + ylab("polen de platano oriental /m³ de aire") + theme_classic() + theme(legend.position="bottom") + scale_x_continuous(breaks=seq(from = 2.5, to = 49.5, by = 4), labels = c("Ene", "Feb", "Mar", "Abr", "May", "Jun", "Jul", "Ago", "Sep", "Oct", "Nov", "Dic"))
```
Para el siguiente gráfico, debido a que distintas plantas generan distintos niveles de alergia, estandarizamos los niveles de polen, debido ha esto, cada vez que en el gráfico se sobrepase la linea punteada roja, estamos ante niveles altos de alergia, si el valor llega a dos, significa que tenemos a el doble un nivel ya cosiderado alto. Los valores que se muestran para santiago son en las lineas los promedios desde el 2002, y en el colores la desviación estadard de la media.
```{r}
Weekly2 <- Alergia %>% select(Semana, platano_oriental, arboles_total, pastos) %>% mutate(platano_oriental = platano_oriental/70, arboles_total = arboles_total/100, pastos = pastos/25) %>% gather(key = Especie, value = Polen, -Semana) %>%group_by(Semana, Especie) %>% summarise_if(is.numeric, funs(mean, sd, max, min))
Weekly2 <- as.data.frame(Weekly2)
ggplot(Weekly2, aes(x = Semana, y = mean))+ geom_ribbon(aes(ymax = mean + sd, ymin = mean - sd, fill = Especie), alpha = 0.5) + geom_line(aes(lty = Especie)) + ylab("Nivel estandarizado de polen") + xlab("Mes") + theme_classic() + theme(legend.position="bottom") + scale_x_continuous(breaks=seq(from = 2.5, to = 49.5, by = 4), labels = c("Ene", "Feb", "Mar", "Abr", "May", "Jun", "Jul", "Ago", "Sep", "Oct", "Nov", "Dic")) + geom_hline(yintercept = 1, lty=2, color = "red") + annotate("text", x = 8, y = 2, label = "Niveles altos de polen", color = "red")
```
Se observa como si bién en noviembre y diciembre, los pastos llegan a niveles altos de alregenos, mayormente no sobrepasan por mucho ese nivel, mientras que el platano oriental llega, en septiembreme, en promedio a estar 13.6 veces sobre niveles considerados altos.
#### Comparación con otras ciudades de Chile
```{r}
Valpo <- readRDS("Valpo.rds")
Alergia <- readRDS("Alergia.rds")
Talca <- readRDS("Talca.rds")
AlergiaTotal <- Alergia %>% mutate(Santiago = (arboles_total/100) + (platano_oriental/70) + (pastos/25)) %>% select(Fechas,Anno, Mes, Semana, Santiago)
ValpoTotal <- Valpo %>% mutate(Valparaiso = (arboles_total/100) + (platano_oriental/70) + (pastos/25)) %>% select(Anno, Mes, Semana, Valparaiso)
TalcaTotal <- Talca %>% mutate(Talca = (arboles_total/100) + (platano_oriental/70) + (pastos/25)) %>% select(Anno, Mes, Semana, Talca)
PolenTotal <- full_join(AlergiaTotal, ValpoTotal)
PolenTotal <- full_join(PolenTotal, TalcaTotal)
PolenTotal <- PolenTotal[complete.cases(PolenTotal),]
SUMMAR <- PolenTotal %>% select(Mes, Santiago, Valparaiso, Talca) %>%group_by(Mes) %>% dplyr::summarise_all(funs(mean))
Stgo <- xts(PolenTotal[,c("Santiago")],PolenTotal[,"Fechas"])
ValpoT <- xts(PolenTotal[,c("Valparaiso")],PolenTotal[,"Fechas"])
TalcaT <- xts(PolenTotal[,c("Talca")],PolenTotal[,"Fechas"])
PolenTotal <- cbind(Stgo, ValpoT)
PolenTotal <- cbind(PolenTotal, TalcaT)
colnames(PolenTotal)<- c("Santiago", "Valparaiso", "Talca")
dyUnzoom <-function(dygraph) {
dyPlugin(
dygraph = dygraph,
name = "Unzoom",
path = system.file("plugins/unzoom.js", package = "dygraphs")
)
}
dygraph(PolenTotal , ylab = "polen total /m³ de aire") %>% dyRangeSelector() %>% dyOptions(drawPoints = TRUE, pointSize = 2) %>% dyHighlight(highlightCircleSize = 5) %>% dyLegend("always") %>% dyUnzoom() %>% dyOptions(stackedGraph = TRUE)%>% dyLimit(1, color = "red")
```
```{r}
knitr::kable(SUMMAR)
```
## Bibliografía
<!--chapter:end:Polen.Rmd-->
---
title: "NBA Projections 2018 season"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```
These are the projected standings predicted by our spatial-based algorithm as of `r format(Sys.time(), '%d %B, %Y')`
```{r Shotscrub, echo=FALSE, message=FALSE, warning=FALSE}
if (!require("pacman")) install.packages("pacman")
pacman::p_load(rjson, grid, gridExtra, png, RCurl, ggplot2, jpeg, hexbin, sp, knitr, dplyr, lubridate, purrr)
by_team <- readRDS("by_team.rds")
Season2018 <- readRDS("Season2018.rds")
startScrape <- format((max(Season2018$GAME_DATE)), '%m/%d/%Y')
teamID <- by_team$team_id
teamName <- by_team$team_city
Season2018b <- list()
shotURLtotal <- paste("http://stats.nba.com/stats/shotchartdetail?CFID=33&CFPARAMS=2017-18&ContextFilter=&ContextMeasure=FGA&DateFrom=", startScrape ,"&DateTo=&GameID=&GameSegment=&LastNGames=0&LeagueID=00&Location=&MeasureType=Base&Month=0&OpponentTeamID=0&Outcome=&PaceAdjust=N&PerMode=PerGame&Period=0&PlayerID=0&PlusMinus=N&Position=&Rank=N&RookieYear=&Season=2017-18&SeasonSegment=&SeasonType=Regular+Season&TeamID=0&VsConference=&VsDivision=&mode=Advanced&showDetails=0&showShots=1&showZones=0&PlayerPosition=", sep = "")
# import from JSON
Season2018b <- fromJSON(file = shotURLtotal, method="C")
Names <- Season2018b$resultSets[[1]][[2]]
# unlist shot data, save into a data frame
Season2018b <- data.frame(matrix(unlist(Season2018b$resultSets[[1]][[3]]), ncol = 24, byrow = TRUE))
colnames(Season2018b) <- Names
# covert x and y coordinates into numeric
Season2018b$LOC_X <- as.numeric(as.character(Season2018b$LOC_X))
Season2018b$LOC_Y <- as.numeric(as.character(Season2018b$LOC_Y))
Season2018b$TEAM_NAME <- gsub("Detroit Pistons", "Det", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Atlanta Hawks", "Atl", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Chicago Bulls", "Chi", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Boston Celtics", "Bos", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Cleveland Cavaliers", "Cle", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("New Orleans Pelicans", "NO", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Golden State Warriors", "GSW", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Orlando Magic", "ORL", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Washington Wizards", "Was", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Philadelphia 76ers", "Phi", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Brooklyn Nets", "Bkn", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Utah Jazz", "Uta", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Miami Heat", "Mia", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Charlotte Hornets", "Cha", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Toronto Raptors", "Tor", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Indiana Pacers", "Ind", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Houston Rockets", "Hou", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Denver Nuggets", "Den", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Memphis Grizzlies", "Mem", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("New York Knicks", "NY", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Milwaukee Bucks", "Mil", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Oklahoma City Thunder", "Okc", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("San Antonio Spurs", "Sas", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Dallas Mavericks", "Dal", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Phoenix Suns", "Pho", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Portland Trail Blazers", "Por", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("LA Clippers", "Lac", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Sacramento Kings", "Sac", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Los Angeles Lakers", "Lal", Season2018b$TEAM_NAME)
Season2018b$TEAM_NAME <- gsub("Minnesota Timberwolves", "Min", Season2018b$TEAM_NAME)
####HOME VISITOR
Season2018b$HTM <- gsub("DET", "Det", Season2018b$HTM)
Season2018b$HTM <- gsub("ATL", "Atl", Season2018b$HTM)
Season2018b$HTM <- gsub("CHI", "Chi", Season2018b$HTM)
Season2018b$HTM <- gsub("BOS", "Bos", Season2018b$HTM)
Season2018b$HTM <- gsub("CLE", "Cle", Season2018b$HTM)
Season2018b$HTM <- gsub("NOP", "NO", Season2018b$HTM)
Season2018b$HTM <- gsub("GSW", "GSW", Season2018b$HTM)
Season2018b$HTM <- gsub("ORL", "ORL", Season2018b$HTM)
Season2018b$HTM <- gsub("WAS", "Was", Season2018b$HTM)
Season2018b$HTM <- gsub("PHI", "Phi", Season2018b$HTM)
Season2018b$HTM <- gsub("BKN", "Bkn", Season2018b$HTM)
Season2018b$HTM <- gsub("UTA", "Uta", Season2018b$HTM)
Season2018b$HTM <- gsub("MIA", "Mia", Season2018b$HTM)
Season2018b$HTM <- gsub("CHA", "Cha", Season2018b$HTM)
Season2018b$HTM <- gsub("TOR", "Tor", Season2018b$HTM)
Season2018b$HTM <- gsub("IND", "Ind", Season2018b$HTM)
Season2018b$HTM <- gsub("HOU", "Hou", Season2018b$HTM)
Season2018b$HTM <- gsub("DEN", "Den", Season2018b$HTM)
Season2018b$HTM <- gsub("MEM", "Mem", Season2018b$HTM)
Season2018b$HTM <- gsub("NYK", "NY", Season2018b$HTM)
Season2018b$HTM <- gsub("MIL", "Mil", Season2018b$HTM)
Season2018b$HTM <- gsub("OKC", "Okc", Season2018b$HTM)
Season2018b$HTM <- gsub("SAS", "Sas", Season2018b$HTM)
Season2018b$HTM <- gsub("DAL", "Dal", Season2018b$HTM)
Season2018b$HTM <- gsub("PHX", "Pho", Season2018b$HTM)
Season2018b$HTM <- gsub("POR", "Por", Season2018b$HTM)
Season2018b$HTM <- gsub("LAC", "Lac", Season2018b$HTM)
Season2018b$HTM <- gsub("SAC", "Sac", Season2018b$HTM)
Season2018b$HTM <- gsub("LAL", "Lal", Season2018b$HTM)
Season2018b$HTM <- gsub("MIN", "Min", Season2018b$HTM)
###Visitor
Season2018b$VTM <- gsub("DET", "Det", Season2018b$VTM)
Season2018b$VTM <- gsub("ATL", "Atl", Season2018b$VTM)
Season2018b$VTM <- gsub("CHI", "Chi", Season2018b$VTM)
Season2018b$VTM <- gsub("BOS", "Bos", Season2018b$VTM)
Season2018b$VTM <- gsub("CLE", "Cle", Season2018b$VTM)
Season2018b$VTM <- gsub("NOP", "NO", Season2018b$VTM)
Season2018b$VTM <- gsub("GSW", "GSW", Season2018b$VTM)
Season2018b$VTM <- gsub("ORL", "ORL", Season2018b$VTM)
Season2018b$VTM <- gsub("WAS", "Was", Season2018b$VTM)
Season2018b$VTM <- gsub("PHI", "Phi", Season2018b$VTM)
Season2018b$VTM <- gsub("BKN", "Bkn", Season2018b$VTM)
Season2018b$VTM <- gsub("UTA", "Uta", Season2018b$VTM)
Season2018b$VTM <- gsub("MIA", "Mia", Season2018b$VTM)
Season2018b$VTM <- gsub("CHA", "Cha", Season2018b$VTM)
Season2018b$VTM <- gsub("TOR", "Tor", Season2018b$VTM)
Season2018b$VTM <- gsub("IND", "Ind", Season2018b$VTM)
Season2018b$VTM <- gsub("HOU", "Hou", Season2018b$VTM)
Season2018b$VTM <- gsub("DEN", "Den", Season2018b$VTM)
Season2018b$VTM <- gsub("MEM", "Mem", Season2018b$VTM)
Season2018b$VTM <- gsub("NYK", "NY", Season2018b$VTM)
Season2018b$VTM <- gsub("MIL", "Mil", Season2018b$VTM)
Season2018b$VTM <- gsub("OKC", "Okc", Season2018b$VTM)
Season2018b$VTM <- gsub("SAS", "Sas", Season2018b$VTM)
Season2018b$VTM <- gsub("DAL", "Dal", Season2018b$VTM)
Season2018b$VTM <- gsub("PHX", "Pho", Season2018b$VTM)
Season2018b$VTM <- gsub("POR", "Por", Season2018b$VTM)
Season2018b$VTM <- gsub("LAC", "Lac", Season2018b$VTM)
Season2018b$VTM <- gsub("SAC", "Sac", Season2018b$VTM)
Season2018b$VTM <- gsub("LAL", "Lal", Season2018b$VTM)
Season2018b$VTM <- gsub("MIN", "Min", Season2018b$VTM)
Season2018b$GAME_DATE <- ymd(Season2018b$GAME_DATE)
Season2018 <- full_join(Season2018, Season2018b)
```
```{r schedulescrub, echo=FALSE, message=FALSE, warning=FALSE, cache=TRUE}
pacman::p_load(XML, lubridate, rvest, dplyr)
#Gather data
Months <- c("october", "november", "december", "january", "february", "march", "april")
Years <- c(2018)
URLs <- list()
for(i in 1:length(Years)){
URLs[[i]] <- paste("http://www.basketball-reference.com/leagues/NBA_", Years[i],"_games-", Months,".html", sep = "")
}
URLs <- do.call("c", URLs)
URLs <- data.frame(URLs = URLs, Year = as.numeric(gsub("\\D", "", URLs)))
URLs$URLs <- as.character(URLs$URLs)
schedule <- list()
# import from JSON
for(i in 1:nrow(URLs)){
schedule[[i]] <- read_html(URLs$URLs[i])%>% html_table(fill=TRUE)%>% .[[1]]
}
schedule <- do.call("rbind", schedule)
schedule$Date <- mdy(as.character(schedule$Date))
schedule[,4] <- as.numeric(as.character(schedule[,4]))
schedule[,6] <- as.numeric(as.character(schedule[,6]))
schedule$Season <- 2018
schedule[,3] <- gsub("Detroit Pistons", "Det", schedule[,3])
schedule[,3] <- gsub("Atlanta Hawks", "Atl", schedule[,3])
schedule[,3] <- gsub("Chicago Bulls", "Chi", schedule[,3])
schedule[,3] <- gsub("Boston Celtics", "Bos", schedule[,3])
schedule[,3] <- gsub("Cleveland Cavaliers", "Cle", schedule[,3])
schedule[,3] <- gsub("New Orleans Pelicans", "NO", schedule[,3])
schedule[,3] <- gsub("Golden State Warriors", "GSW", schedule[,3])
schedule[,3] <- gsub("Orlando Magic", "ORL", schedule[,3])
schedule[,3] <- gsub("Washington Wizards", "Was", schedule[,3])
schedule[,3] <- gsub("Philadelphia 76ers", "Phi", schedule[,3])
schedule[,3] <- gsub("Brooklyn Nets", "Bkn", schedule[,3])
schedule[,3] <- gsub("Utah Jazz", "Uta", schedule[,3])
schedule[,3] <- gsub("Miami Heat", "Mia", schedule[,3])
schedule[,3] <- gsub("Charlotte Hornets", "Cha", schedule[,3])
schedule[,3] <- gsub("Toronto Raptors", "Tor", schedule[,3])
schedule[,3] <- gsub("Indiana Pacers", "Ind", schedule[,3])
schedule[,3] <- gsub("Houston Rockets", "Hou", schedule[,3])
schedule[,3] <- gsub("Denver Nuggets", "Den", schedule[,3])
schedule[,3] <- gsub("Memphis Grizzlies", "Mem", schedule[,3])
schedule[,3] <- gsub("New York Knicks", "NY", schedule[,3])
schedule[,3] <- gsub("Milwaukee Bucks", "Mil", schedule[,3])
schedule[,3] <- gsub("Oklahoma City Thunder", "Okc", schedule[,3])
schedule[,3] <- gsub("San Antonio Spurs", "Sas", schedule[,3])
schedule[,3] <- gsub("Dallas Mavericks", "Dal", schedule[,3])
schedule[,3] <- gsub("Phoenix Suns", "Pho", schedule[,3])
schedule[,3] <- gsub("Portland Trail Blazers", "Por", schedule[,3])
schedule[,3] <- gsub("Los Angeles Clippers", "Lac", schedule[,3])
schedule[,3] <- gsub("Sacramento Kings", "Sac", schedule[,3])
schedule[,3] <- gsub("Los Angeles Lakers", "Lal", schedule[,3])
schedule[,3] <- gsub("Minnesota Timberwolves", "Min", schedule[,3])
schedule[,3] <- gsub("Charlotte Bobcats", "Cha", schedule[,3])
schedule[,3]<- gsub("New Orleans Hornets", "NO", schedule[,3])
schedule[,5] <- gsub("Detroit Pistons", "Det", schedule[,5])
schedule[,5] <- gsub("Atlanta Hawks", "Atl", schedule[,5])
schedule[,5] <- gsub("Chicago Bulls", "Chi", schedule[,5])
schedule[,5] <- gsub("Boston Celtics", "Bos", schedule[,5])
schedule[,5] <- gsub("Cleveland Cavaliers", "Cle", schedule[,5])
schedule[,5] <- gsub("New Orleans Pelicans", "NO", schedule[,5])
schedule[,5] <- gsub("Golden State Warriors", "GSW", schedule[,5])
schedule[,5] <- gsub("Orlando Magic", "ORL", schedule[,5])
schedule[,5] <- gsub("Washington Wizards", "Was", schedule[,5])
schedule[,5] <- gsub("Philadelphia 76ers", "Phi", schedule[,5])
schedule[,5] <- gsub("Brooklyn Nets", "Bkn", schedule[,5])
schedule[,5] <- gsub("Utah Jazz", "Uta", schedule[,5])
schedule[,5] <- gsub("Miami Heat", "Mia", schedule[,5])
schedule[,5] <- gsub("Charlotte Hornets", "Cha", schedule[,5])
schedule[,5] <- gsub("Toronto Raptors", "Tor", schedule[,5])
schedule[,5] <- gsub("Indiana Pacers", "Ind", schedule[,5])
schedule[,5] <- gsub("Houston Rockets", "Hou", schedule[,5])
schedule[,5] <- gsub("Denver Nuggets", "Den", schedule[,5])
schedule[,5] <- gsub("Memphis Grizzlies", "Mem", schedule[,5])
schedule[,5] <- gsub("New York Knicks", "NY", schedule[,5])
schedule[,5] <- gsub("Milwaukee Bucks", "Mil", schedule[,5])
schedule[,5] <- gsub("Oklahoma City Thunder", "Okc", schedule[,5])
schedule[,5] <- gsub("San Antonio Spurs", "Sas", schedule[,5])
schedule[,5] <- gsub("Dallas Mavericks", "Dal", schedule[,5])
schedule[,5] <- gsub("Phoenix Suns", "Pho", schedule[,5])
schedule[,5] <- gsub("Portland Trail Blazers", "Por", schedule[,5])
schedule[,5] <- gsub("Los Angeles Clippers", "Lac", schedule[,5])
schedule[,5] <- gsub("Sacramento Kings", "Sac", schedule[,5])
schedule[,5] <- gsub("Los Angeles Lakers", "Lal", schedule[,5])
schedule[,5] <- gsub("Minnesota Timberwolves", "Min", schedule[,5])
schedule[,5] <- gsub("Charlotte Bobcats", "Cha", schedule[,5])
schedule[,5]<- gsub("New Orleans Hornets", "NO", schedule[,5])
```
```{r simulatingGames, echo=FALSE, message=FALSE, warning=FALSE}
pacman::p_load(SpatialBall2, dplyr,XML, lubridate, rvest)
colnames(schedule) <- c("Date", "Start..ET.","VTM", "PTS_Visitor", "HTM", "PTS_Home", "X", "X2", "X3","Notes", "Season")
schedule <- dplyr::select(schedule, Date, VTM, PTS_Visitor, HTM, PTS_Home, Season)
future_games <- schedule[schedule$Date >= Sys.Date(),]
results <- list()
for(i in 1:nrow(future_games)){
results[[i]] <- Get_Apps(HomeTeam = future_games$HTM[i], VisitorTeam = future_games$VTM[i], Seasondata = Season2018)
message(paste("simulating", i ,"of", nrow(future_games)))
}
results <- do.call(rbind, results)
future_games <- cbind(future_games, results)
future_games$Home <- ifelse(future_games$spread < 0, "W", "L")
future_games$Visit <- ifelse(future_games$spread > 0, "W", "L")
Home <- cbind(future_games$HTM, future_games$Home)
colnames(Home) <- c("Team", "Result")
Visit <- cbind(future_games$VTM, future_games$Visit)
colnames(Visit) <- c("Team", "Result")
AddedStand <- data.frame(rbind(Home, Visit))
#Wins
AddedStand_W <- dplyr::filter(AddedStand, Result == "W")
AddedStand_W <- group_by(AddedStand_W, Team)
AddedStand_W <- dplyr::summarize(AddedStand_W, W = n())
#Loses
AddedStand_L <- dplyr::filter(AddedStand, Result == "L")
AddedStand_L <- group_by(AddedStand_L, Team)
AddedStand_L <- dplyr::summarize(AddedStand_L, L = n())
AddedStand <- merge.data.frame(AddedStand_W, AddedStand_L, all = TRUE)
#####Standing scraper
Standings <- "http://www.basketball-reference.com/leagues/NBA_2018.html"
Standings <- read_html(Standings)%>% html_table(fill=TRUE)%>% .[1:2]
Standings <- list(Western = Standings[[2]], Eastern = Standings[[1]])
Standings[[1]]$Conference <- c("West")
Standings[[2]]$Conference <- c("East")
colnames(Standings[[1]]) <- c("Team", "Current-W", "Current-L", "pct", "GB", "PS/G", "PA/G", "SRS", "Conference")
colnames(Standings[[2]]) <- c("Team", "Current-W", "Current-L", "pct", "GB", "PS/G", "PA/G", "SRS", "Conference")
Standings <- rbind(Standings[[1]], Standings[[2]])
Standings <- Standings[,c(1,2,3,9)]
Standings$Team <- gsub("76ers", "Phi", Standings$Team)
Standings$Team <- gsub("(?<=\\b[A-Z])[^A-Z]+", "", Standings$Team, perl = TRUE)
Standings$Team <- gsub("DP", "Det", Standings$Team)
Standings$Team<- gsub("AH", "Atl", Standings$Team)
Standings$Team <- gsub("CB", "Chi", Standings$Team)
Standings$Team<- gsub("BC", "Bos", Standings$Team)
Standings$Team<- gsub("CC", "Cle", Standings$Team)
Standings$Team<- gsub("NOP", "NO", Standings$Team)
Standings$Team<- gsub("OM", "ORL", Standings$Team)
Standings$Team<- gsub("WW", "Was", Standings$Team)
Standings$Team<- gsub("BN", "Bkn", Standings$Team)
Standings$Team<- gsub("UJ", "Uta", Standings$Team)
Standings$Team<- gsub("MH", "Mia", Standings$Team)
Standings$Team<- gsub("CH", "Cha", Standings$Team)
Standings$Team<- gsub("TR", "Tor", Standings$Team)
Standings$Team<- gsub("IP", "Ind", Standings$Team)
Standings$Team<- gsub("HR", "Hou", Standings$Team)
Standings$Team<- gsub("DN", "Den", Standings$Team)
Standings$Team<- gsub("MG", "Mem", Standings$Team)
Standings$Team<- gsub("NYK", "NY", Standings$Team)
Standings$Team<- gsub("MB", "Mil", Standings$Team)
Standings$Team<- gsub("OCT", "Okc", Standings$Team)
Standings$Team<- gsub("SAS", "Sas", Standings$Team)
Standings$Team<- gsub("DM", "Dal", Standings$Team)
Standings$Team<- gsub("PS", "Pho", Standings$Team)
Standings$Team<- gsub("PTB", "Por", Standings$Team)
Standings$Team<- gsub("LAC", "Lac", Standings$Team)
Standings$Team<- gsub("SK", "Sac", Standings$Team)
Standings$Team<- gsub("LAL", "Lal", Standings$Team)