1 - Intro to R Outline

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Your Turn• x = (4 1 3 9)T• y = (1 2 3 5)T(from examples, on previous slide)• d = √∑(x - y) 2• 2(y 1 + x 2 )• z = (1, ..., 100)T• (∑ z i) 2 - ∑(zi 2 )•pattern = (1, 7, 7, 13, 13, 13, 19, 19, 19, 19, 25, 25, 25,25, 25, 31, 31, 31, 31, 31, 31) T (don’t use c() for this)19Basic Statistical Functions• Using the basic functions we’ve learned itwouldn’t be hard <strong>to</strong> compute basic statistics.• x
Distributions• R has a lot of distributions built in.• We can typically obtain:• Density value• CDF value• Inverse CDF value• Random deviate• Normal, Chi-square, F, T, Cauchy, Poisson, Binomial, NegativeBinomial, Gamma, ..., lots more• library(help = stats)21Your Turn• Find the mean of 10, 1000, and 10000 random normalobservations from N(0,1)• Generate 1,000,000 random observations from N(0,1),square them, and find the .95 quantile of the observed data• What is the .95 quantile from a Chi-square distribution with1 df?• x = 100 random normal observations from N(5,36)• Calculate a 95% confidence interval for mu using x as yourdata [ xbar ± (t.95,99)(s) ]22
Page 1 and 2: 1 - Intro to RGood Habits and Basic
Page 3 and 4: Setting up• Open the R script in
Page 5 and 6: Learning a language• Grammar / Sy
Page 7 and 8: • x = 2 / 3• √x• a = 2(x +
Page 9: R Reference Card• Download the R
Page 13 and 14: Indexing• Accessing just a part o
Page 15 and 16: Examine Object• First few values
Page 17: Extracting parts• x$variable• x

1 - Intro to R Outline

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