## [1] 3
R uses vectors.
## [1] 1 4 6
## [1] 1 2 3 4 5 6 7 8 9 10
## [1] 1 5 9 13 17 21 25 29
## [1] 0 0 0 0 0 0 0 0 0 0
When you apply a function to a vector, it usually work on EACH element in the vector.
## [1] 0.0962578 1.3366320 -0.7827090 0.3168257 0.9596083 -0.6860354
## [7] 0.1484898 0.3031658 0.6301796 -0.8647272
## [1] 0.009265564 1.786585186 0.612633329 0.100378515 0.920848044
## [6] 0.470644511 0.022049233 0.091909523 0.397126298 0.747753123
## [1] 0.1925156 2.6732641 -1.5654179 0.6336514 1.9192166 -1.3720707
## [7] 0.2969797 0.6063317 1.2603592 -1.7294544
## [1] 1.1010429 3.8062027 0.4571659 1.3727633 2.6106736 0.5035686
## [7] 1.1600810 1.3541390 1.8779478 0.4211664
## [1] 0.6274894 -1.1606364 -0.8862771 -0.4122007 -1.5024925 -1.6102702
## [7] -1.7335872 0.8976902 -1.9628536 0.6891917
## [1] 10
## [1] -0.7053946
## [1] 1.189978
## [1] 1.090861
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.9629 -1.5833 -1.0235 -0.7054 0.3676 0.8977
### 1. R is case sensitive
X = 3 # This assigns 3 to object X. But you can't see it.
X # now you see what's inside X
x # "x" is not same as "X".
### 2. Vectors
X = c(1, 4, 6) # create vector
X
X = 1:10 # 1 through 10
X
X = seq(1,30, 4) # 1 to 30 in step of 2
X
X = rep(0, 10)
X
# Apply funciton to vector
X = rnorm(10) # draw random sample of size 10 from Standard Normal
X
Y = X ^ 2 # square each number
Y
Y = X * 2 # multiple 2 to each number
Y
Y = exp(X) # take exponential power of each number
Y
### 3. Descriptive statistics
X = rnorm(10) # draw random sample of n=10 from Standard Normal
X
length(X) # get length of X
mean(X) # sample mean
var(X) # variance
sd(X) # Standard Deviation
summary(X) # 5-number summary
# Correlation
X = rnorm(10) # draw random sample of n=10 from Standard Normal
Y = runif(10) # draw random sample of n=10 from Unif(0,1)
cor(X,Y) # correlation between vector X and vector Y