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Tutorial2_Association.Rmd
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Tutorial2_Association.Rmd
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---
title: 'Tutorial 2: finding assiciations'
author: "Tian Zheng"
date: "September 20, 2016"
output:
html_notebook: default
html_document: default
pdf_document: default
---
#### Note:
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*.
## Baseball players batting performance from 2014
In this tutorial, we will look at the performance of Major League Baseball (MLB) players in the year of 2014. [source from baseballguru.com.](http://baseballguru.com/bbdata1.html)
First we load R libraries that we need for this tutorial. Basic libraries of functions are loaded every time R starts. More specialized functions need to be loaded first before they can used.
```{r}
library(dplyr)
library(readr)
library(DT)
library(RColorBrewer)
```
### Read in the data
Now let's read in the baseball 2014 batting performance data set.
```{r}
mlb2014=read_csv(file="data/mlb2014.csv")
```
Most of variables are read as characters. It is because "-" was used as an indicator of missing value. We add "-" in the string or recognized NA symbols and the issue is solved.
```{r}
mlb2014=read_csv(file="data/mlb2014.csv", na=c("", "-", "NA"))
```
Now we use `datatable` to explore the data a little bit.
```{r}
dim(mlb2014)
datatable(select(sample_n(mlb2014,50), ends_with("G")), options = list(scrollX=T, pageLength = 5))
```
### Association: categorical variables
#### Positions versus handedness
```{r}
table(mlb2014$pos1)
table(mlb2014$bats)
table(mlb2014$pos1, mlb2014$bats)
```
```{r}
col.use=brewer.pal(4, 'Set2')
plot(table(mlb2014$pos1, mlb2014$bats), col=col.use)
```
```{r}
chisq.test(table(mlb2014$pos1, mlb2014$bats))
```
### Association: categorical versus continuous
#### Slugging percentage versus positions.
From [wikipedia](https://en.wikipedia.org/wiki/Slugging_percentage): "In baseball statistics, slugging percentage (SLG) is a popular measure of the power of a hitter."
```{r}
hist(mlb2014$slg)
plot(as.factor(mlb2014$pos1), mlb2014$slg, col=col.use)
summary(lm(slg~as.factor(pos1), data=mlb2014))
anova(lm(slg~as.factor(pos1), data=mlb2014))
```
#### On base percentage versus position.
Another important baseball statistics is On Base Percentage ([OBP](https://en.wikipedia.org/wiki/On-base_percentage)).
```{r}
hist(mlb2014$obp)
plot(as.factor(mlb2014$pos1), mlb2014$obp, col=col.use)
summary(lm(obp~as.factor(pos1), data=mlb2014))
anova(lm(obp~as.factor(pos1), data=mlb2014))
```
### Association: continuous versus continuous
#### On base percentage versus slugging percentage
```{r}
cor(mlb2014$slg, mlb2014$obp)
cor(mlb2014$slg, mlb2014$obp, use="complete.obs")
plot(slg~obp, data=mlb2014)
cor.test(~slg+obp, data=mlb2014)
```
#### On base percentage versus age
```{r}
hist(mlb2014$age)
plot(obp~age, data=mlb2014)
summary(lm(obp~age, data=mlb2014))
cor.test(~age+obp, data=mlb2014)
```