• Learn basic elements of the R language
  • Data types
  • Basic objects
  • Functions
  • Classes and attributes
  • Subsetting Objects
  • Loops
• Readings
  • R4DS: ch. 4
  • HOPR: ch. 1-3
  • Datacamp: online tutorial

• Information is contained in objects
  • E.g. data, variables, models, plots
• Operations are performed by functions
  • E.g. sorting data, fitting models, plotting results
  • Caveat: functions are objects as well
• Carry out analysis by applying functions to objects, creating new objects along the way

• Functions create/modify objects according to arguments
  • E.g. Generate sequence from 3 to 5, increasing by .5

seq( from = 3, to = 5, by = .5)
## [1] 3.0 3.5 4.0 4.5 5.0

• Create functions using function()

my_fun = function( x, y = 1 ){
  z = x^2
  return(z + y)
}
my_fun(x=3)
## [1] 10

• R comes with set of pre-installed functions
  • More functions can be added with packages
  • Run help(fun) or ? fun to get documentation
• Arguments can be other functions' output
  • Inner functions evaluated first

rep( x = "blah", times = 2)
## [1] "blah" "blah"
rep( x = rep("ha", 2), times = 2)
## [1] "ha" "ha" "ha" "ha"

• Create vectors with c() concatenation function
  • Name & store your objects with an assignment operator (<- or =)

c(0, 1, 2) 
## [1] 0 1 2
my_first_vector <- c("a", "b", "c")
my_first_vector
## [1] "a" "b" "c"

• class function identifies object type

class( c(0, 1, 2) )
## [1] "numeric"

• Whenever possible, R will automatically convert types, from specific to general (logical > integer > numeric > character)

class( c(TRUE, 3.14, "R") )
## [1] "character"

c(NA, NULL, Inf, Inf/Inf)
## [1]  NA Inf NaN

• R performs operations element-wise, automatically recycling smaller vectors

(1:6) + (1:2)
## [1] 2 4 4 6 6 8

• Vectors used to store categorical data: categories described by character labels but represented by integers

f <- factor( c("male", "female", "female", "male") )
class(f)
## [1] "factor"
typeof(f)  # object's storage type 
## [1] "integer"
str(f)     # object's structure
##  Factor w/ 2 levels "female","male": 2 1 1 2

• subsetting: extracting required object values using integer or logical indices in square brackets [ ]

x <- c("a", "b", "c")
x[2]
## [1] "b"
x[1:2]
## [1] "a" "b"
x[ c(T, F, T) ]
## [1] "a" "c"

• Tables of the same data type

matrix( data = 1:6, nrow = 3 )
##      [,1] [,2]
## [1,]    1    4
## [2,]    2    5
## [3,]    3    6
matrix( 1:6, ncol = 3, byrow = TRUE )
##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6

• Subset arrays using multiple indexes (one for each dimension) separated by commas
  • Empty index selects all elements

matrix(1:4, 2)
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4
matrix(1:4, 2) [1, ]
## [1] 1 3

• Object can have name attributes, also used for indexing

mat <- matrix(1:6, nrow = 3)
rownames(mat) <- paste(1:3)
colnames(mat) <- c("a", "b")
mat
##   a b
## 1 1 4
## 2 2 5
## 3 3 6
mat["1","a"]
## [1] 1

• Lists are 1D inhomogeneous objects
• Create lists with list() function

my_list <- list("a", 1:3, NULL, NA) 
class(my_list)
## [1] "list"
my_list
## [[1]]
## [1] "a"
## 
## [[2]]
## [1] 1 2 3
## 
## [[3]]
## NULL
## 
## [[4]]
## [1] NA

• Lists can have named elements
• Check list structure with str()

my_list <- list( num = 1:5, let = c("a", "b"), log = c(T, F, T) ) 
str(my_list)
## List of 3
##  $ num: int [1:5] 1 2 3 4 5
##  $ let: chr [1:2] "a" "b"
##  $ log: logi [1:3] TRUE FALSE TRUE
names(my_list)
## [1] "num" "let" "log"

• Extract sub-lists using [ ]
• Extract list elements using [[ ]], or by name with $

my_list <- list( num = 1:5, let = c("a", "b"), log = c(T, F, T) ) 
my_list[1]
## $num
## [1] 1 2 3 4 5
my_list[[1]]
## [1] 1 2 3 4 5
my_list$num
## [1] 1 2 3 4 5

• Lists of inhomogenous vectors of same length
  • Designated way of storing data tables in R

my_df <- data.frame( age = c(21, 38, 41), sex = c("M", "F", "M") ) 
str(my_df)
## 'data.frame':    3 obs. of  2 variables:
##  $ age: num  21 38 41
##  $ sex: Factor w/ 2 levels "F","M": 2 1 2
dim(my_df)
## [1] 3 2

• Execute code conditionally

if( runif(1) >= .5 ){ 
  print("Heads")
  }else{ print("Tails") }
## [1] "Heads"

• Repeat task(s) for each element of input object

for(i in c("a", "b")){ 
  print(i)
}
## [1] "a"
## [1] "b"