lab9.Rmd

---
title: "STA100 Lab9"
author: "Po"
date: "5/25/2021"
output: html_document
---

```{r setup, include=FALSE}
require(pander)
options(width = 100)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE )
```

# Lab Assignment 6 (Lab projects?)

1. Go though `LabAssignment6.pdf` [LabAssignment6.pdf](LabAssignment6.pdf)

2. Download `LabAssignment6.Rmd` ([LabAssignment6.Rmd](LabAssignment6.Rmd), [egyptianCotton.csv](egyptianCotton.csv)) in RStudio and run codes inside. Things should be exactly same as `LabAssignment6.pdf`.

3. Download, open `LabAssignment6_butter.Rmd` [LabAssignment6_butter.Rmd](LabAssignment6_butter.Rmd) and save as a new Rmd file for your own Lab Assignment submission. Knit this Rmd file to see if you can make a HTML, PDF or Word. If you couldn't, just use RStudio Cloud or the Binder RStudio [![Binder](http://mybinder.org/badge_logo.svg)](http://mybinder.org/v2/gh/ucdavis-sta-100-spring-2021/polabs/main?urlpath=rstudio) set by Po.

4. Run/add/edit/delete the codes in your own copy of `LabAssignment6_butter.Rmd` and add/edit/delete some writings. Knit to see how the .pdf looks like. Tidy up the codes and writings. Knit and submit!

# Path for data/files/success

1. Set working directory.

2. Put you data [egyptianCotton.csv](egyptianCotton.csv) into a good directory. 

3. `r emo::ji("bar_chart")` Which of the following do you prefer?
   
   `r emo::ji("turkey")`
    ```{r eval=FALSE}
    ec <- read.csv("/Users/nicepo/IU_is_no1/egyptianCotton.csv")
    ```
   
   `r emo::ji("duck")`
    ```{r eval=FALSE}
    ec <- read.csv("C:\\Users\\nicepo\\IU_is_no1\\egyptianCotton.csv")
    ```

    `r emo::ji("peacock")`
    ```{r eval=FALSE}
    ec <- read.csv("egyptianCotton.csv")
    ```
  
    `r emo::ji("owl")`
    ```{r eval=FALSE}
    ec <- read.csv("data/egyptianCotton.csv")
    ```

    `r emo::ji("swan")`
    ```{r eval=FALSE}
    ec <- read.csv("https://ucdavis-sta-100-spring-2021.github.io/polabs/egyptianCotton.csv")
    ```

4. Understand your path for success!
    [https://en.wikipedia.org/wiki/Path_(computing)](https://en.wikipedia.org/wiki/Path_(computing))

# Help/Documentation

> There is no secret ingredient. 
> -- Mr. Ping `r emo::ji("noodle")`

Read the R Documentation of every functions you have seen.

```{r eval=FALSE}
?rnorm
```
```{r eval=FALSE}
?dim
```

```{r eval=FALSE}
?read.csv
```
```{r}
?head
```

```{r eval=FALSE}
?anova
```
```{r eval=FALSE}
?aov
```
```{r}
?TukeyHSD
```

```{r eval=FALSE}
?par
```
```{r eval=FALSE}
?boxplot
```
```{r eval=FALSE}
?hist
```
```{r eval=FALSE}
?plot
```


How Po knows all those built-in functions in R?

- Way 1: Google it

- Way 2: Read R Help/Documentation

# R tutorials that help you survive `r emo::ji("turkey")`

- Interactive R course lessons **swirl** https://swirlstats.com/students.html

  Install **swirl** with the following commands.
  ```{r eval=FALSE}
  # If you haven't installed the swirl package yet
  install.packages("swirl")
  library(swirl)
  install_course_github("swirldev", "R_Programming_E")
  ```

  After **swirl** is installed (you only need to do it once), you can start/resume the R tutorial with the commands:
  ```{r eval=FALSE}
  library(swirl)
  swirl()
  ```
  Schedule some time to go though all the lessons by yourself! Almost all of the R codes you need to know are here in those lessons.

- If you prefer more static readings (Po did read them to learn R!):
  - R for Beginners https://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf
  - An Introduction to R https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf

- If you like RStudio's data science things,
  - R for Data Science https://r4ds.had.co.nz/
  - RStudio Primer https://rstudio.cloud/learn/primers/