Environmental modelling
R Introduction Corellation and linear regression Basic models in R Multiple regression modelling

Introduction to R language

  • Large Data Sets
  • Modeling and simulations
  • Statistical analysis
  • Extensive computational task
  • Genomics, transcryptomycs
  • Computational evolutionary biolo
  • Structural biology
  • Image analysis
  • Quantative ecology

3 diensions of languages

  • High vs low
  • General vs targeted
  • Interpretive vs compiled

Structure of R language

  • S language
  • Functions written in R itself

Specific task

  • Multiple testing
  • MCMC
  • Bias
  • Gauss
  • Plotting
  • Sequencing
#### Check graphics system
r plot(density(rnorm(100)), col = "red")

Variables types

x = 5
x <- 5
assign("x", 5)
#### Commands and Basic Functions
function c()
r x <- c(1, 3, 6, 7)

Vectors

  • numerical
  • character
  • logical

(factors are not classic vector objects)

char <- c("8", "edge", "turn8k")
logic <- c(TRUE, FALSE, TRUE, FALSE)

Dynamic typisation

dynam <- c(8, "dds", TRUE)
dynam <- c(1, 0, 1, 1, TRUE)
dynam
## [1] 1 0 1 1 1

Priority Char->integer->Logic

Functions

x=function(arg1,arg2,…)

Invocation - function call

args:

  • tagged(named)
  • positional

Positional: log(x,base)

log(x, 10)
## [1] 0.0000000 0.4771213 0.7781513 0.8450980

Tagged: log(base=base,x)

log(base = 10, x)
## [1] 0.0000000 0.4771213 0.7781513 0.8450980
#### Logical operations
>,<,<=,>=,==
r x = 10 x < 5
## [1] FALSE
r x > 5
## [1] TRUE
r x == 10
## [1] TRUE

Math operations

+, -, *, /, ^, sin(), cos(), sqrt(), log()

x^10
## [1] 1e+10

Quiz

x = 10/(5 - 5)
x
## [1] Inf
#### Recycling Rule
r x <- c(2, 4, 5, 3, -4, 0) x * 5
## [1] 10 20 25 15 -20 0
r y <- c(5, 3) x * y
## [1] 10 12 25 9 -20 0
r z <- c(1, 2, 3, 4) x * z
## Warning in x * z: longer object length is not a multiple of shorter object ## length
## [1] 2 8 15 12 -4 0

Length function

length(x)
## [1] 6
#### More basic functions
log(), mean(), min(), max(), sum(), prod(), abs()
r x <- c(3, 4, 6, -7, 8) mean = sum(x)/length(x) mean
## [1] 2.8

Regular sequenses

x = 2:10
y = 5:1
z = -5:10
w = -5:-10
x
## [1]  2  3  4  5  6  7  8  9 10
y
## [1] 5 4 3 2 1
z
##  [1] -5 -4 -3 -2 -1  0  1  2  3  4  5  6  7  8  9 10
w
## [1]  -5  -6  -7  -8  -9 -10

Regular sequence

seq(from=,to=,by=)

z = seq(2, 5, 0.5)
x = seq(3, 9.1, 0.1)
y = seq(9, 3, -0.2)
length(x)
## [1] 62
length(y)
## [1] 31
x * y
##  [1] 27.00 27.28 27.52 27.72 27.88 28.00 28.08 28.12 28.12 28.08 28.00
## [12] 27.88 27.72 27.52 27.28 27.00 26.68 26.32 25.92 25.48 25.00 24.48
## [23] 23.92 23.32 22.68 22.00 21.28 20.52 19.72 18.88 18.00 54.90 54.56
## [34] 54.18 53.76 53.30 52.80 52.26 51.68 51.06 50.40 49.70 48.96 48.18
## [45] 47.36 46.50 45.60 44.66 43.68 42.66 41.60 40.50 39.36 38.18 36.96
## [56] 35.70 34.40 33.06 31.68 30.26 28.80 27.30
#### Regular sequence
rep(x, times=)
r x = c("A", "B", "C") rep(x, 4)
## [1] "A" "B" "C" "A" "B" "C" "A" "B" "C" "A" "B" "C"

Logical vectors

mode(x)

x <- 1:10
y <- x <= -5
mode(x)
## [1] "numeric"
mode(y)
## [1] "logical"
mode(mode(x))
## [1] "character"
#### Vector indexation
R is 1 based language
r x = 1:20 x[2]
## [1] 2
r x[0]
## integer(0)
r x[5:9]
## [1] 5 6 7 8 9
r x[c(3, 7, 8, 9, 20)]
## [1] 3 7 8 9 20
r x[-19]
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20
r x[-(1:6)]
## [1] 7 8 9 10 11 12 13 14 15 16 17 18 19 20
r x[x > 10]
## [1] 11 12 13 14 15 16 17 18 19 20
r x[TRUE]
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
r y = c(TRUE, FALSE) x[y]
## [1] 1 3 5 7 9 11 13 15 17 19

Adding to vectors

vector <- c()
v2 <- c(TRUE, FALSE, TRUE, TRUE, FALSE, TRUE)
vector = c(v2, vector)
vector = c(F, vector)
x = 1:3
x = c(5, x)
x
## [1] 5 1 2 3
#### Named vectors
r x = 1:5 names(x)
## NULL
r names(x) <- c("Fisrt", "Second", "Third", "Forth", "First") names(x)
## [1] "Fisrt" "Second" "Third" "Forth" "First"
r x["First"]
## First ## 5

Vector attributes

length() names() mode()

log(3) == log(3, base = exp(1))
## [1] TRUE
x = seq(5, 100, 0.1)
x[length(x)]
## [1] 100

Sort and order functions

x <- c(-10, 2, 78, 34, -98, 23, 0, -8)
order(x)
## [1] 5 1 8 7 2 6 4 3
sort(x)
## [1] -98 -10  -8   0   2  23  34  78
sort(x) == x[order(x)]
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE