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lab5.Rmd
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lab5.Rmd
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---
title: "STA100 Lab5"
author: "Po"
date: "4/27/2021"
output: html_document
---
```{r setup, include=FALSE}
options(width = 100)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE )
```
# Panda Village
1. Po visits the Panda Village. He captures **10 pandas** and marks their butts with paint.
2. A week later he returns to the village and captures **15 pandas**. **Five of these 15 pandas** have paint on their butts, indicating that they are recaptured pandas.
3. Can you guess/estimate the **total numbers of pandas** in the Panda Village?
Poll:
- $N=20$ `r emo::ji("panda")`
- $N=30$ `r emo::ji("panda")`
- $N=40$ `r emo::ji("panda")`
```{r echo=FALSE, fig.height = 6, fig.width = 10}
possibleN <- c(20, 30, 40)
panda_col <- hcl.colors(length(possibleN), palette = "TealGrn")
barplot(t(sapply(possibleN, function(N) dhyper(0:10, 10, N-10, 15))),
beside=TRUE,
main = paste0("Recapture probability of 10 marked pandas when population N = 20, 30, 40"),
ylab = "Probability",
xlab = "number of recaptured marked pandas out of 15 pandas",
col=panda_col,
names=0:10)
legend("topleft", legend = paste0("N=", possibleN), fill=panda_col)
```
```{r echo=FALSE, fig.height = 7, fig.width = 10}
possibleN <- 20:40
panda_col <- hcl.colors(length(possibleN), palette = "TealGrn")
barplot(t(sapply(possibleN, function(N) dhyper(4:6, 10, N-10, 15))),
beside=TRUE,
main = paste0("Recapture probability of 10 marked pandas when population N = 20, 21, ..., 40"),
ylab = "Probability",
xlab = "number of recaptured marked pandas out of 15 pandas",
col=panda_col,
bty='L',
names=4:6)
legend("topright", legend = paste0("N=", possibleN), fill=panda_col)
```
Based on maximum likelihoodk, what N would you pick if you recapture 4, 5 or 6 pandas?
```{r fig.height = 8, fig.width = 8}
hyperLik <- function(N,M,n,m){dhyper(m,M,N-M,n)}
possibleN <- 20:40
likelihoods <- hyperLik(possibleN, 10, 15, 5)
plot(possibleN,likelihoods,
type="l",
ylab="Likelihood",
xlab="N",
main = "Likelihood of N")
```
For more information aobut **Mark and recapture**, you may read [Wikipedia: Mark and recapture](https://en.wikipedia.org/wiki/Mark_and_recapture).
# Lab Assignment 3 (Lab projects?)
1. Go though `LabAssignment3.pdf` [LabAssignment3.pdf](LabAssignment3.pdf)
2. Download and open `LabAssignment3.Rmd` [LabAssignment3.Rmd](LabAssignment3.Rmd) in RStudio and run codes inside. Things should be exactly same as `LabAssignment3.pdf`.
3. Download, open `LabAssignment3_otg.Rmd` [LabAssignment3_jp.Rmd](LabAssignment3_jp.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 words. If you couldn't, just use RStudio Cloud or the Binder RStudio [](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 `LabAssignment3_jp.Rmd` and add/edit/delete some writings. Knit to see how the .pdf looks like. Tidy up the codes and writings. Knit and submit!
### Help/Documentation
Read the R Documentation of the functions you learn today. Enter the following codes, one by one, and read the corresponding documentation/help in the **Help** tab.
```{r eval=FALSE}
?dhyper
```
```{r eval=FALSE}
?plot
```
```{r eval=FALSE}
?which.max
```
```{r eval=FALSE}
?read.table
```
```{r eval=FALSE}
?c
```
```{r eval=FALSE}
?head
```
```{r eval=FALSE}
?mean
```
```{r eval=FALSE}
?sd
```
```{r eval=FALSE}
?summary
```
```{r eval=FALSE}
?hist
```
```{r eval=FALSE}
?table
```
```{r eval=FALSE}
?mosaicplot
```
```{r}
?boxplot
```
```{r}
?aggregate
```
How Po knows all those built-in functions in R?
- Way 1: Google it
- Way 2: Read R Help/Documentation
> There is no secret ingredient. -- Mr. Ping `r emo::ji("noodle")`
# R tutorials that help you survive in Panda Village `r emo::ji("bamboo")`
- 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/