RCheatSheetsAndNotes.Rmd

---
title: "R Cheat Sheets & Notes"
---

## Cheat Sheets

* [R Colors](http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf)

* [R Base Graphics Cheat Sheet](http://www.joyce-robbins.com/wp-content/uploads/2016/04/BaseGraphicsCheatsheet.pdf)

* [R Base Commands Cheat Sheet](https://www.rstudio.com/wp-content/uploads/2016/10/r-cheat-sheet-3.pdf)

* [R Markdown Cheat Sheet](https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf)

* [Keyboard Shortcuts](https://support.rstudio.com/hc/en-us/articles/200711853-Keyboard-Shortcuts)

* [GGplot Cheatsheet](https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf "Vis with GGplot")

* [GGPlot GUI Explorer](https://cran.r-project.org/web/packages/esquisse/vignettes/get-started.html)
## Notes

Add your own notes here...

```{r message=FALSE, warning=FALSE}
# Add R commands you want to remember here...
library(mosaic)
library(tidyverse)
library(pander)
library(DT)
library(ggplot2)
library(car)

```


```{r}
# Or create other R chunks to put them in like this...

```

```{r, eval=FALSE}
# Using ```{r, eval=FALSE} turns off the chunk, but still shows it.
# Useful when you want to remember code, but not run it in this file.
```




```{r, eval=FALSE}

```

T-test
```{r}

mycars <- filter(mtcars, mtcars$cyl == 4 | mtcars$cyl == 8)
test <- t.test(wt ~ cyl, data=mycars, mu = 0, alternative = "two.sided", conf.level = 0.95)
observedTestStat <- test$statistic
```

Permutation
```{r}
N <- 2000      
permutedTestStats <- rep(NA, N)
for (i in  1:N){
  permutedData <- sample(mycars$cyl)
  permutedTest <- t.test(wt ~ permutedData, data=mycars, mu = 0, alternative = "two.sided", conf.level = 0.95)
  permutedTestStats[i] <- permutedTest$statistic
}
hist(permutedTestStats)
abline(v=observedTestStat)

#Greater-Than p-value: Not the correct one in this case
sum(permutedTestStats >= observedTestStat)/N

# Less-Than p-value: This is the corret one
sum(permutedTestStats <= observedTestStat)/N

# Two-Sided p-value
2*sum(permutedTestStats <= observedTestStat)/N
```

Anova
```{r}
#View(diamonds)
myTest <- aov(price ~ clarity, data= diamonds)
observedTestStat <- summary(myTest)[[1]]$`F value`[1]
observedTestStat

```


Permutation

```{r}
N <- 100      
permutedTestStats <- rep(NA, N)
for (i in  1:N){
  permutedData <- sample(diamonds$price)
  permutedTest <- aov(permutedData ~ clarity, data= diamonds)
  permutedTestStats[i] <- summary(permutedTest)[[1]]$`F value`[1]
}
hist(permutedTestStats)
abline(v=observedTestStat)

#Step 3
sum(permutedTestStats >= observedTestStat)/N
sum(permutedTestStats <= observedTestStat)/N
```


Logistic test
```{r}
#Step 1
View(SAT)
SAT$great <- case_when(SAT$sat <= 1000 ~ 0, SAT$sat >1000 ~ 1)
myTest <- YourGlmName <- glm(great ~ expend, data = SAT,
 family=binomial)
summary(YourGlmName)
observedTestStat <- summary(myTest)[[12]][2,3]
```

Permutation

```{r}
#Step 2
N <- 100      
permutedTestStats <- rep(NA, N)
for (i in  1:N){
  permutedData <- sample(SAT$great) 
  permutedTest <- myTest <- glm(permutedData ~ expend, data = SAT,
 family=binomial)
  permutedTestStats[i] <- summary(myTest)[[12]][2,3]
}
hist(permutedTestStats)
abline(v=observedTestStat)

#Step 3
sum(permutedTestStats >= observedTestStat)/N
sum(permutedTestStats <= observedTestStat)/N

```