STA100 Lab7

Lab Assignment 4 (Lab projects?)

1. Go though LabAssignment4.pdf LabAssignment4.pdf

2. Download LabAssignment4.Rmd (LabAssignment4.Rmd, MendelFigure1.png, MendelFigure2.png) in RStudio and run codes inside. Things should be exactly same as LabAssignment4.pdf.

3. Download, open LabAssignment4_rev.Rmd LabAssignment4_rev.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 set by Po.

4. Run/add/edit/delete the codes in your own copy of LabAssignment4_rev.Rmd and add/edit/delete some writings. Knit to see how the .pdf looks like. Tidy up the codes and writings. Knit and submit!

Pearson’s chi-squared test

https://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test

Hardcoding in LabAssignment4.pdf

NullRatio <- c(1,1,1,1,2,2,2,2,4)
NullProportion <- NullRatio/sum(NullRatio) # converting ratios to proportions
Observed <- c(38,35,28,30,65,68,60,67,138) # from Mendel's table
Expected <-sum(Observed)*NullProportion # converting proportions to Expected counts
Discrepancy <- (Expected-Observed)^2/Expected
TestStat <- sum(Discrepancy) # chi-squared test statistic
p_value <- 1-pchisq(TestStat,9-1) # don't forget the degrees of freedom!
pander(c(TestStat=TestStat, p_value=p_value))

TestStat	p_value
2.811	0.9457

Using R built-in function chisq.test

NullRatio <- c(1,1,1,1,2,2,2,2,4)
Observed <- c(38,35,28,30,65,68,60,67,138)
pander(chisq.test(Observed, p = NullRatio, rescale.p = TRUE))

Chi-squared test for given probabilities: Observed

Test statistic	df	P value
2.811	8	0.9457

Help/Documentation

There is no secret ingredient. – Mr. Ping 🍜

Read the R Documentation of every functions you have seen.

?c

?rbind

?sum

?pchisq

?pander

?chisq.test

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 in Village 🍄

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

  Install swirl with the following commands.

  # 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:

  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/