R Basic

url = https://sites/data.txt

"data.txt" is

***************

"x" "y" "z"

"1" 0.1 35 2030

"2" 0.2 38 182

"3" 0.3 123 23

... ... ... ...

***************

data_frame = read.table("url")

load(url("https:// .../*.Rdata"))

dim(data_frame)

names(data_frame)

data_frame$variable_name

plot(data_frame$x,data_frame$y)

plot(data_frame$x,data_frame$y,type="l")

mean(data$x)

median(data$x)

var(data$x)

sd(data$x) # standard deviation

summary(data$x) # returns min 1st-quarter median mean 3rd-quarter max

table(data$x) # count number of entries

barplot(table(data$x))

table(data$x,data$y) # 2D matrix

mosaicplot(table(data$x,data$y))

data[m,n] # mth observation and nth variable

data[i:j,] # ith-jth rows and all columns

data$x[m]

subset(data,data$x == "female")

subset(data,data$y > 20)

boxplot(data$x)

boxplot(data$x ~ data$y) # plot x as a function of y or x versus y

hist(data$x,breaks = 100) # breaks controls the number of bins

outcomes = c("heads", "tails")

sample(outcomes, size=1, replace=TRUE) # fair game by default

sample(outcomes, size=100, replace=TRUE,prob=c(0.3,0.7)) # assign probability to outcomes

sample(data$x,100) # sample 100 individuals from population data$x

pnorm(35,mean=30,sd=5) # what percentile of 35 in Norm(30,5); error function

qnorm(0.9,30,5) # what cut off value for percentile 90% in Norm(30,5)

choose(n,k) # combination number n!/k!(n-k)!

dbinom(k,n,p) # binomial distribution

sum(dbinom(k:k+m,n,p))