lecture-06.Rmd

---
title: "Lecture 6"
---

### Midterm Review Study Guide: 

https://stthomas.instructure.com/courses/26819/files/1928697/download?wrap=1


### Lecture handout: 
chp5-handout.pdf

### Textbook: 
Chapter 5, Foundations for Inference

### R topics:

#### models
```{r eval=FALSE}
plot(age ~ genhlth)
#y ~ x can be read "model y in terms of x"
plot(age ~ ., data=cdc)
plot(genhlth ~ ., data=cdc)
as.factor()
```

#### ggplot2
https://rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf 
this is one library in the "tidyverse" https://r4ds.had.co.nz
we'll be reading the Tidy Data paper after the midterm

```{r eval=FALSE}
install.packages("ggplot2")
library(ggplot2)
ggplot(kobe) + geom_bar(aes(x=basket))
ggplot(kobe) + geom_bar(aes(x=basket, fill=basket))
ggplot(kobe) + geom_bar(aes(x=basket, fill=basket)) + scale_fill_manual(values=c("#009900", "#990000"))
ggplot(cdc) + geom_histogram(binwidth=1, aes(x=age))
ggplot(cdc) + geom_histogram(binwidth=1, aes(x=age))
ggplot(cdc) + geom_histogram(binwidth=1, aes(x=age, y=..density..)) + geom_line(data=data.frame(x=1:100, y=dnorm(1:100, 45, 17)), aes(x=x, y=y))
#vs. hist(age, freq=F); lines(1:100, dnorm(1:100, 45,17))
```