lecture-09.Rmd

---
title: "Lecture 9"
---

### Lecture handout: 
chp5-handout.pdf, chp6-handout.pdf


### Lecture slides (w/ answers): 
chp5_r1.pdf, chp6.pdf

### Textbook: 
Chapter 5, Foundations for Inference, Chapter 6, Inference for Categorical Data

### R Topics:

#### User input

```{r eval=FALSE}
readline(prompt="Please, enter your sequence number: ")
```

#### Functions
```{r eval=FALSE}
readinteger <- function(){
  n <- readline(prompt="Please, enter your sequence number: ") 
  as.integer(n)
}
```
Explicit "return()" command is is optional: by default, the last line is returned.

#### Loops: 

in R, use loops sparingly b/c most functions can handle multiple/list/vector inputs (i.e. "vectorization")

``` for, while, repeat```

```for(sequence) {body}```

* loop over the elements: ```for (x in xs)```
* loop over the numeric indices: ```for (i in seq_along(xs))```
* loop over the names: ```for (nm in names(xs))```

```while(condition) {body}```

```repeat {body}```

```break, next```

For more info: https://www.datacamp.com/community/tutorials/tutorial-on-loops-in-r (Links to an external site.)

graphics parameters:
```par(mfrow = c(3, 1))```

### comments

From Jessie Zheng:
The One hot Encoder that we talked about in class was really useful in machine learning. I found a good article talks about label encoder vs. one hot encoder. Just want to share: https://medium.com/@contactsunny/label-encoder-vs-one-hot-encoder-in-machine-learning-3fc273365621