R functions cheat sheat

Statistics

• sum
• min/max
• length
• mean
• median
• var sample variance
• sd (sample standard deviation)
• rafalib::popsd (population standard deviation)
• cov(x, y) covariance
• cor(x, y) correlation (default Pearson)

Regression

• lm(y ~ x) Create a linear model
• summary Summary the model

Generation

• 1:10 integer range from [1, 10]
• seq(from = 1, to = 9, by = 2) 1 3 5 7 9
• rep(42, 10) repeat 42 for 10 times
• rep(1:3, times = 3) 1 2 3 1 2 3 1 2 3

Probability Distributions in R

Main: probability distributions in R

Overview

Type	Distribution	R Suffix	Comments
Continuous	Normal	-norm()
	Lognormal	-lnorm()	Normally distributed in a log scale
	Uniform	-unif()
Discrete	Binomial	-binom()
	Multinomial	-multinom()	Similar to binomial but when there are more than 2 outcomes
	Poisson	-pois()

Prefix:

• PDF or PMF (d-) e.g. dnorm(x)
• CDF (p-) e.g. pnorm(x)
• the quantile function (q-)
• The random deviate function (-r)

Link to original

Monte-Carlo Methods

• set.seed set a seed for PRNG
• sample(x, size, replace = TRUE/FALSE, prob = NULL) takes samples of the specified size with or without replacement.
• replicate replicate an experiment n times

Examples

Sample from 1-10 for 5 times without replacement

sample(x = 1:10, size = 5)
# 3 4 9 5 6

Randomly throw a die 5 times:

sample(1:6, 5, replace = T)

Do 1000 simulations of the sample sum of flipping a coin 10 times

simulation_result = replicate(1000, sum(sample(c(0, 1), 10, replace = T)))

Plot

See ggplot2